Auditors’ Brand-Name Reputation, Audit Office Size and
the Impact of Client Importance on Audit Quality at the Office Level: Evidence from China
Bo Chen*
School of Accounting and Finance
Zhongnan University of Economics and Law
Wuhan, China
Wuchun Chi
Department of Accounting
College of Commerce
National Chengchi University
Taipei, Taiwan
Wan-Ying Lin
Department of Accounting
College of Commerce
National Chengchi University
Taipei, Taiwan
December 2012
* Corresponding author.
Auditors’ Brand-name Reputation, Audit Office Size and the Impact
of Client Importance on Audit Quality at the Office Level:
Evidence from China
Abstract
This study examines whether an audit office’s high degree of economic dependence
on a client impairs audit quality in China, a developing country characterized by weak
investor protection and a low risk of litigation for auditors. Using performance-
matched discretional accruals as a proxy for audit quality, we find evidence that client
importance at the audit-office level has a negative impact on audit quality, especially
for those vital few clients of an audit office. However, such a conclusion holds only
for small offices of non-Big4 accounting firms. Our results indicate that auditors’
brand-name reputation and the size of an audit office play essential roles in mitigating
the negative impact of client importance on audit quality at the office level. Our
findings suggest that investors and regulators should pay more attention to the audit
quality of small audit offices of non-Big4 auditors when they provide audit services to
their vital few clients.
Keywords: client importance, audit quality, brand-name reputation, audit office size
1
1. Introduction
In this study, we examine the impact of client importance on audit quality at the
audit office level, in an institutional environment characterized by weak investor
protection and a low risk of litigation for auditors, and the role of auditors’ brand-
name reputation and the size of an audit office in maintaining auditors’ independence
from economically significant clients. The unique institutional environment in China
enables us to disentangle the effect of auditors’ economic dependence on their
important clients from auditors’ incentives to avoid the greater loss of reputation
associated with their larger clients’ audit failures. Specifically, the great majority of
listed companies in China are audited by non-Big4 local accounting firms whose
incentives to protect reputation are relatively weak, and the potential negative impact
of client importance on audit quality is probably more salient. Furthermore, we also
test the effect of audit office size on the relationship between client importance and
audit quality at the office level, a research issue that has not been explored thoroughly
by existing studies.
It is asserted by many researchers that the audit office is a more appropriate unit
of analysis for audit research (e.g., Francis, et al., 1999; Reynolds and Francis, 2001),
especially for studies of client importance, because clients that are unimportant from
the viewpoint of the accounting firm as a whole are perhaps the main sources of
revenues for the audit office conducting the audits (Francis, 2004), and are therefore
very important from the viewpoint of the audit office. Due to the much thinner
clientele, audit offices are more likely to be economically dependent on larger clients.
However, studies on client importance conducted at the audit-office level have
generally failed to find consistent evidence that client importance has a negative
impact on audit quality. On the contrary, some studies in this field have found
evidence that auditors in practice offices of Big4 accounting firms are more prudent
with their important audit clients, perhaps due to stronger incentives to protect their
reputation or to avoid higher risk of litigation (Reynolds and Francis, 2001; Li, 2009).
Such results suggest that investors and regulators need not worry that auditors’
economic dependence on important clients might impair audit quality. However, it is
questionable whether conclusions based on data from developed countries (such as
the U.S., the U.K., and Australia) with better investor protection mechanisms and a
high risk of litigation for auditors can be generalized (Chen et al., 2010). In fact,
countries around the world differ greatly in their legal systems and the degree of
investor protection (La Porta et al., 1997; La Porta et al., 1998; Leuz et al., 2003). It is
therefore worthwhile to investigate the relationship between client importance and
audit quality using data from countries with different institutional environments.
2
Based on data on Chinese listed companies from 2007 to 2010, we find evidence
that a high degree of client importance at the office level impairs audit quality, as
proxied by absolute and signed performance-matched discretional accruals. More
specifically, auditors at the office level show greater tolerance of aggressive earnings
management (especially income-increasing earnings management) for their highly
important clients, defined as the clients ranked in the top 25% in order of economic
importance to the engagement office. However, such a negative effect could be
mitigated by auditors’ incentives to protect their brand-name reputation and by the
large size of an audit office. Our conclusion that client importance at the office level
impairs audit quality holds only for small offices of non-Big4 audit firms, which audit
more than 50% of all A-share listed companies in China. This qualification of our
conclusion suggests that Big4 auditors have a stronger incentive to protect their
brand-name reputation and thus maintain independence from their economically
important clients, even in countries where investor protection is weaker and the risk of
litigation for auditors is lower. It also suggests that large audit offices are more likely
to withstand pressures from important clients, due either to reputation protection
concerns or to stronger quality control from their national headquarters. Our
additional analysis indicates that client importance has no significant influence on
audit quality, either at the audit-firm level or at the engagement-partner level. This
finding lends some support for the notion that the audit office is the most appropriate
unit of analysis in the research on client importance(Reynolds and Francis, 2001;
Francis,2004). Our results are robust to a series of sensitivity tests, including a control
for the endogenous audit choice, and alternative measurements of audit quality and
client importance.
Our study differs from prior research in several significant ways. First, we use
data from China, a country with distinctive institutional environment, to examine the
relationship between client importance and audit quality at the office level. To the best
of our knowledge, only Chen et al. (2010) deal with a similar research issue in China.
Second, we not only examine the potential negative impact of client importance on
audit quality, but also explore the roles of auditors’ brand-name reputation and of the
size of an audit office in mitigating such a negative impact. Third, we adopt a
relatively new measurement of client importance based on the management decision
theory. According to the widely used 80-20 rule―for many events a few (about 20
percent) are vital and many (about 80 percent) are trivial―the decision maker’s
attention is generally focused on the vital few (Craft and Leake, 2002; Sanders, 1988)1.
1 The 80-20 rule is also known as the Pareto principle, named after the Italian economist Vilfredo Pareto who discovered that 80% of Italian land was owned by 20% of the population. As a common business rule-of-thumb, the number 20% (80%) is not used as a mathematically precise criteria, but as a rough guideline. Actually, the specific quantitative criteria depends on the issue of interest, and any number between 0 and 50% might be
3
Some anecdotal evidence shows that accounting firms in China manage their client
portfolio based on a similar rule2. We believe that only the vital few clients of an audit
office will be treated more favorably than other clients, or in other words, only a high
level of client importance will impair audit quality.
Our study makes several contributions to the literature. First, we find evidence
that the high degree of economic dependence of an audit office on its important
clients impairs audit quality in a country where investor protection is weak and the
risk of litigation for auditors is low. This finding suggests that the relationship
between client importance and audit quality will not be the same in different
institutional contexts. Second, we provide evidence that even in developing countries
like China, audit offices of Big4 accounting firms can withstand improper pressures
from their important clients, and the audit quality for their important clients will not
be lower than for other relatively less important clients. This result suggests that
auditors’ brand-name reputation is valuable in maintaining auditors’ independence
from economically important clients, a conclusion that is consistent with the findings
of some prior studies (Reynolds and Francis, 2001; Li, 2009). Third, we demonstrate
that the large size of an audit office plays an essential role in maintaining auditors’
independence from economically important clients, perhaps due to concerns of
reputation protection or to tighter quality control from the national headquarters. This
finding is consistent with those of Francis and Yu (2009) and Choi et al. (2010), who
find that larger audit offices of Big4 accounting firms are associated with higher audit
quality. Overall, our findings imply that investors and regulators should pay more
attention to the potential problems in audit quality when small offices of non-Big4
accounting firms are providing audit services to their vital few clients.
The remainder of this paper is arranged as follows. Section 2 reviews related
studies, discusses the relevant institutional background in China, and develops the
research hypotheses. Section 3 describes the research design, including the sources of
data and the selection of the sample, the measurements of audit quality and client
importance, and the research models. Section 4 reports the empirical results, section 5
provides additional and sensitivity analyses, and section 6 concludes.
2. Literature Review, Institutional Backgrounds, and Hypotheses Development
2.1 Literature Review
appropriate to define the vital few. 2 Interviews with some audit partners in large accounting firms confirm our conjecture. For example, one partner tells us that the clients in his firm are categorized as A, B, C and D in accordance with their economic importance and risk; the A-type clients account for about 25% of all the clients, but generate more than 70% of the total revenue. The input from practitioners inspire us to define the vital few as the top 25% large clients of a particular audit office. In the sensitivity test, we also define the vital few as the top 20% large clients, and find similar results.
4
It has long been suspected that auditors’ economic dependence will impair audit
quality, and a great deal of research has been done on this issue. Even though some
researchers have found evidence that client importance is negatively related to
perceived audit quality, proxied by earnings response coefficients (Krishnan et al.,
2005 ; Francis and Ke, 2006), or cost of capital (Khurana and Raman,2006), findings
concerning the relationship between client importance and actual audit quality are
much more uncertain, with most studies failing to find a statistically significant
relationship between client importance and the surrogate measures of actual audit
quality, such as qualified audit reports (Craswell et al., 2002), going-concern audit
opinions (DeFond et al., 2002), or discretional accruals (Chung and Kallapur, 2003).
Early studies on client importance were conducted at the level of the audit firm.
In the last decade, however, more and more studies have been using the audit office as
a unique and relevant unit of analysis, based on the fact that it is not the national
headquarters, but the office-based engagement partners or audit teams who actually
administer the individual audit engagement contracts, including the delivery of audit
services and the issuance of an audit opinion (Choi et al.,2010; Ferguson et al., 2003;
Francis et al., 1999; Wallman, 1996). It is believed that the audit office is an
especially suitable unit of analysis in the research on client importance (Francis,
2004), because using the accounting firm as the unit of analysis tends to under-
estimate the economic importance of a given client to the audit office responsible for
the engagement. Beginning with Francis et al. (1999), quite a few studies have
followed this line of research, exploring audit issues at the audit-office level.
Generally speaking, studies of client importance at the office level fail to provide
consistent evidence that economic dependence on important clients impairs audit
quality. Some studies have failed to find a statistically significant relationship between
client importance at the office level and audit quality (e.g., Craswell et al., 2002;
Chung and Kallapur, 2003). However, more studies provide evidence that client
importance at the office level is positively related to audit quality. Reynolds and
Francis (2001) find that for Big4 auditors3, client importance at the office level is
negatively associated with total accruals and absolute discretional accruals, and
auditors at those offices are more likely to issue going-concern audit opinions for
economically important clients when they are in financial distress. Gaver and Paterson
(2007) find that financially weaker insurers are less likely to understate reserves when
they are economically important to the audit office. Li (2009) finds that financially
distressed companies are more likely to receive going-concern audit opinions in the
3 Big4 accounting firms’ predecessors were called Big8, Big6 or Big5 in different historical periods. For simplicity, we only use the abbreviation “Big4”, instead of the various terms used before.
5
post-SOX era when they are economically significant clients of the audit office. As
perhaps the only exception, Chen et al. (2010) examine this issue in China and find
that client importance measured at the office level is negatively related to auditors’
propensity to issue modified audit opinions (MAOs) from 1995 to 2000. However,
their findings are sensitive to their model specification and sample composition. Even
more significantly, the result disappears from 2001 to 2004, a period when the
institutional environment became more investor-friendly.
Although quite a bit of work has been done on the issue of client importance at
the office level, there is still a lot of room for further study. First, most current studies
on this issue have been conducted in developed counties with strong investor
protection and a high risk of litigation for auditors. As a result of these conditions, the
lack of evidence that client importance at the office level negatively influences audit
quality could largely be explained as the avoidance of risk on the part of the auditors.
It is still worth exploring whether auditors are more prudent with their economically
important clients in countries with different institutional environments. Second, most
current studies have focused on Big4 auditors, which dominate the audit market. Big4
auditors have greater brand-name reputation to protect and thus act more
conservatively in many situations. The fact that Big4 auditors will be more prudent
with their economically important clients at the office level does not mean that non-
Big4 auditors will act in the same way. The behaviors of non-Big4 auditors when they
are facing important clients are thus worth exploring. Third, since audit office size has
been proved to be positively associated with audit quality (Francis and Yu, 2009; Choi
et al., 2010), it is worth examining whether large and small offices of the same
accounting firm will treat economically important clients differently, or whether the
large size of some audit offices has a positive role in maintaining auditor
independence from economically important clients at the office level. This study tries
to fill these gaps.
Although this study is somewhat similar in theme to that of Chen et al. (2010),
there are also significant differences between their study and ours. First of all, in their
sample period (i.e., from 1995 to 2004), those accounting firms qualified to audit
listed companies have no more than two practice offices on average, a factor that
makes office-level research less meaningful, since client importance at the office level
and that at the firm level can hardly be distinguished4. In our sample period, the
average number of practice offices has increased substantially, and the audit office
thus becomes a much more relevant unit of analysis. Second, our proxy for audit 4 Chen et al. (2010) find that, in 1995-1999, the correlation coefficient between firm-level and office-level client
importance was as high as 0.9. The coefficient fell to 0.6 in 2000-2004, but still very high. In our sample period, the coefficient is only 0.3.
6
quality and our measurement of client important differ from theirs; i.e., we use
discretional accruals as proxy for audit quality instead of MAOs5, and we define client
importance as a dichotomous variable rather than as a continuous variable based on
the 80-20 decision rule. We believe our definition of client importance can better
capture the audit quality differentiations caused by apparent differences in client
importance at the office level. Third, Chen et al. (2010) fail to provide strong and
consistent evidence concerning the negative impact of client importance on audit
quality in their whole sample period, but we find such evidence in a more recent
sample period, using a somewhat new research design. It is noteworthy that Chen et al.
(2010) find a negative impact of client importance on audit quality based on 1995-
2000 data, a finding that is similar to ours. However, this statistically significant
relationship disappears from their sample for the period from 2001 to 2004. This
disappearance probably occurs because auditors became more prudent than in normal
times due to the tightening regulation shortly after some high-profile accounting
scandals were discovered in 2000 and 2001. During our sample period, the regulatory
environment had largely returned to normal, providing a better context for us to test
this relationship without the disturbance of a changing environment.6 Last but not
least, we formally test the roles of auditors’ brand-name reputation and audit office
size in mitigating the negative impact of client importance on audit quality, and reach
the conclusion that the negative impact of client importance on audit quality occurs
only in those companies audited by small offices of non-Big4 auditors. However,
these issues are largely untouched in their study. To sum up, our study sheds new light
on the issue explored by Chen et al. (2010) and investigates some important issues not
covered by their study.
2.2 Institutional Backgrounds
As a country still undergoing economic transition, China has an institutional
environment distinct from those of developed countries such as the U.S. and the U.K.
China's capital markets were set up only at the beginning of the 1990s, and thus have
a relatively short history7. It has been widely acknowledged that investor protection
mechanisms are generally weak in China (Chen et al., 2010; Chen et al., 2011). First,
China has a civil law system similar to those of Germany and Japan, but the judicature
is not independent from the administrative branch of the government, and law
5 We also use MAOs as alternative measures for audit quality in our sensitivity tests and find similar results as the main tests. 6 Wu (2007) finds that 88.2% auditors in audit failure cases were punished by regulatory agencies in the period from 1999 to 2002, but only 23.6% auditors in such cases were punished in the 2003-2006 period. He concludes
that regulators were less harsh toward auditors in 2003-2006. Wu’s (2007) conclusion supports our speculation
that Chen et al. (2010)’s finding may be driven by the tightening regulation in 2000-2001. 7 There are two main stock exchanges in China: Shanghai Stock Exchange and Shenzhen Stock Exchange, established in 1990 and 1991 respectively.
7
enforcement is relatively inefficient and ineffective. According to the findings of La
Porta et al. (1998), the characteristics of the legal system in China indicate that the
degree of investor protection will be much weaker than that in common law countries
and other civil law countries with independent judiciaries and efficient law
enforcement. For example, MacNeil (2002) finds that the minority shareholders in
China are entitled to only two of the six fundamental rights defined by La Porta et al.
(1998). Second, the capital markets are tightly controlled by the government and
dominated by State Owned Enterprises (SOEs), which enjoy more privileges than
non-SOEs in access to the resources of capital markets. Since the government as a
whole acts both as a regulator and as a controlling shareholder of SOEs, the minority
shareholders in SOEs can hardly enjoy the same level of protection from the
government; neither can shareholders of non-SOEs enjoy the same rights as those of
SOEs. Third, there is still no class-action system for lawsuits in capital markets in
China, a fact that makes legal costs prohibitively high for individual investors who
want to sue top managers and auditors of listed companies. Due to this weak investor
protection, it is believed that there is not sufficient demand for high quality audits in
China (DeFond et al., 2000), and the incentives for auditors in China to provide high-
quality audit services may not be as strong as for their counterparts in countries like
the U.S. and U.K.
The risk of litigation faced by auditors is one of most important factors that
influence auditors’ incentives. It is even more important than auditors’ brand-name
reputation in determining the actual as well as perceived audit quality (Lennox, 1999;
Khurana and Ramen, 2006). The positive association observed in the U.S. between
client importance at the office level and audit quality could be attributed largely to the
higher risk of litigation faced by Big4 auditors. However, the risk of litigation for
auditors is negligible in China. Even though auditors’ liabilities to interested parties
have long been defined by the Securities Law, Corporate Law and Certified Public
Accountant Law, and the Supreme Court in China mandated in 2002 that lawsuits
against auditors could be accepted by civil courts in China from then on, investors are
still confronted with significant obstacles to putting in a claim for their losses due to
auditors’ fraudulent or negligent conduct. For example, an administrative penalty
notice from regulatory agencies is a required prerequisite to filing a lawsuit against
wrongdoers in the capital markets. Thus the legal rights of investors to sue top
managers or auditors of listed companies are largely constrained by the authority of
regulators, especially the China Securities Regulatory Commission (CSRC). Due to
the lack of a class-action system, the legal costs are not affordable to individual
investors in most cases. What makes things even worse is that the great majority of
8
accounting firms in China are incorporated as limited liability companies8, which is a
legitimate choice for accounting firms so far in China (Firth et al., 2012). This means
that auditors’ liability will not exceed their investments in the accounting firms, and
their personal assets will be exempted from damages. Even if investors win a lawsuit
against an auditor or his/her firm, it is almost impossible for the amount awarded in
damages to compensate for their losses. For the reasons listed above, lawsuits against
auditors have been rarely accepted by courts in China, let alone finally won by
investors. Up to now, litigation risk has imposed only a potential threat on auditors in
China, but has not been a matter of genuine concern for them. In other words,
auditors in China so far need not worry about lawsuits against them; what they really
fear is investigation from regulatory agencies and the resulting penalties, which might
include open reprimand, fines, revocation of licenses, and even bans against access to
capital markets. Auditors in China will try their best to avoid these penalties, which
threaten the survival and development of accounting firms.
Another important factor that has a profound influence on auditors’ incentive is
the structure of the audit market. The audit market in China has features that
distinguish it from that of the U.S. First of all, Big4 auditors in China as a group have
much smaller market share than their counterparts in the U.S. From 2002 to 2010, the
Big4 market share by number of clients was no more than 10% of the audit market of
A-share listed companies. In the same period, the total revenues of Big4 accounting
firms accounted for no more than 65% of the sum of total revenues of all the
accounting firms qualified to audit listed companies. Panel A of Table 1 shows the
relevant statistics. These numbers show that the concentration of the national audit
market is much lower in China, and competition among auditors thus much more
intense. In such a market, it is harder for auditors to maintain their independence from
important clients. Second, with the support from the government, the so-called local
top-10 accounting firms are growing very quickly through mergers and acquisitions.
However, they are still smaller than the Big4, and their brand-names have not yet
been widely recognized or valued by the capital markets. Hence, the role of brand-
name reputation in maintaining auditors’ independence is fairly limited for big local
accounting firms, not to mention small and medium-sized local accounting firms.
Third, since 2007, the operation of big accounting firms in China, including Big4 and
local top-10, has become more geographically dispersed and organizationally
decentralized, with a fast-growing number of audit offices and the delegation of more
and more decision rights to local audit partners. As shown in Panel B of Table 1, there
8 According to statistics from the Ministry of Finance, 64% of the 6892 accounting firms in China are incorporated as limited liability companies up to July 1, 2010. By the end of 2010, all the 53 accounting firms qualified to audit listed companies adopt the legal form of limited liability companies.
9
were no more than 2 audit offices on average before 2005, but the average number of
audit offices had soared to 8 by the end of 2010. This means that the audit office is a
more meaningful unit of analysis in our 2007-2010 sample period.
[Insert Table 1 Here]
2.3 Hypotheses Development
There are two competing hypotheses concerning the impact of client importance
on audit quality, i.e., the economic dependence hypothesis and the reputation
protection hypothesis (Reynolds and Francis, 2001). The first hypothesis is supported
by those who believe that auditors will compromise their independence with respect
to economically important clients in order to maximize their profits, or to avoid the
heavy losses of large clients switching to competitors. This hypothesis is consistent
with DeAngelo’s (1981) argument that an auditor will sacrifice his or her
independence with respect to a client when the quasi-rent earned from that client
accounts for a substantial part of the total quasi-rents from the entire client portfolio
of that auditor. The second hypothesis is based on the reasoning that economically
important clients generally pose greater audit risk to an auditor (Reynolds and Francis,
2001), because large clients are usually high profile and thus more likely to be
targeted and sued, and auditors’ litigation cost and loss of reputation associated with
audit failures of large clients will be greater, which will force auditors to be more
prudent with large clients in order to protect their reputation. It is evident that
economic dependence and reputation protection incentives of auditors are not
mutually exclusive, but co-exist. When the economic dependence incentive outweighs
the reputation protection incentive, auditors will compromise their audit quality with
respect to economically important clients; conversely, auditors will become more
prudent when the reputation protection incentive outweighs the economic dependence
incentive. In different countries, auditors’ incentives may differ due to the different
institutional environments. In the U.S, the risk of litigations for auditors is very high,
so auditors’ reputation protection incentives usually outweigh economic dependence
incentives; it is likely for this reason that prior research has failed to find evidence
that client importance has a negative impact on audit quality. However, this may not
be the case in other countries, especially in countries with institutional environments
like China's. Thus, it is worthwhile to re-examine the relationship between client
importance and audit quality using data from China.
The above analysis applies not only to the audit firm, but also to audit offices.
The two hypotheses could also be used to explain the association between client
importance and audit quality at the office level. However, the difference between the
10
firm-level analysis and the office-level analysis must be clarified and emphasized. It
should be recognized that audit offices are not independent legal entities, but branches
of accounting firms. The audit offices are the primary beneficiaries of the client
revenues they generate, while the full cost of litigation and loss of reputation is borne
by the entire firm, and the asymmetry in gains and losses can be exploited by a risk-
seeking opportunistic partner (Reynolds and Francis, 2001). Thus, economic
dependence could outweigh concerns for reputation protection in local offices and
lead to favorable reporting for large clients. The audit quality problems associated
with economically important clients could therefore be very serious at the office level.
When the risk of litigation is very high and auditors’ brand-name capital is highly
valuable, the headquarters of accounting firms will have a strong incentive to
maintain a homogeneous level of service quality across different offices (Choi, et al.,
2010) and an effective internal quality control system will be established and
maintained. When the effective internal quality control system is in place, the
negative impact of client importance at the office level will be largely mitigated and
thus no longer be a matter of concern.
As described in Section 2.2, in China’s capital market, investor protection is
weak and the risk of litigation for auditors is low, but the competition for clients
among auditors is intense. What makes things even worse is that most local
accounting firms have no substantial brand-name capital to distinguish them from
competitors. Even though the local top-10 accounting firms have been growing very
quickly with government support since 2007, they have become larger mainly through
mergers and acquisitions. A lot of local small accounting firms have been absorbed or
acquired and become audit offices of the local top-10. However, most of the time, the
newly absorbed or acquired audit offices of big local accounting firms are only
loosely connected with each other, and the national headquarters have only very weak
control over the operation of their local offices. In such a situation, the incentive of
partners in audit offices to bid for high-risk clients and garner the private benefit at
the cost of the entire firm will be boosted, and economic dependence on important
clients will outweigh the rather weak incentive to protect brand-name reputation,
leading to lenient attitudes of local engagement partners toward their larger clients.
Based on the above analysis, we put forward the first hypothesis as follows:
H1: Certeris paribus, client importance at the office level has a significant
negative impact on audit quality in China.
It remains as an interesting research question whether the member firms of Big4
accounting firms in China will act in the same way as their local counterparts, or can
live up to their international reputation as high-quality auditors. There are several
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reasons to believe that audit offices of Big4 accounting firms could be in a stronger
position to withstand the pressure from their important clients. First of all, the
member firms of the Big4 in China belong to their respective global networks, which
have a strong incentive to protect their international brand-name reputation and to
maintain uniform audit quality across member firms in different countries. The
member firms of the Big4 generally must comply with the same quality standards,
abide by the same personnel recruitment and training policies, share industry expertise
with other member firms, and take part in internal programs that inspect audit quality.
If a member firm fails to fulfill its obligations, it may be subject to disciplinary
actions imposed by the international headquarters, including even the revocation of
the right to use the brand name of the franchise. Second, even though litigation risk
has not been a real threat to auditors in China, the potential risk of being defendants in
court has existed since the 2002 mandate of the Supreme Court that permits lawsuits
against auditors. Once the legal obstacles are removed (e.g., the class-action system is
established), auditors will inevitably be targeted and sued when audit failures are
revealed, and the deep pockets of the member firms of the Big4 in China will force
them to be more prudent than local accounting firms. Third, most of the Big4’s clients
in China are the biggest companies in various industries, and thus are critical to the
national economy and subject to tight supervision from the government. For example,
the four biggest commercial banks in China are all clients of the Big4. Many of the
Big4’s clients are cross-listed in different capital markets (such as the NYSE, LSE, or
HKSE) and must comply with more stringent issuing and listing rules. Hence, the
Big4 in China have to be more cautious in order to avoid being targeted by regulators.
Last but not least, since 2006 the Big4 in China have been implicitly barred from
merging with or acquiring local accounting firms due to policy concerns of the
government, such as national information security. Even before 2006, mergers
between Big4 and local accounting firms were scarce. The Big4 in China thus have to
set up offices by themselves, rather than grow through mergers or acquisitions.
Although the speed of expansion for the Big4 is thus constrained, it also enables them
to form an important advantage over local competitors: they can tightly control their
audit offices and maintain uniform audit quality across the entire firm. Not
surprisingly, the opportunistic risk-taking behaviors in audit offices of the Big4 in
China are far less severe than those of local accounting firms. Based on the reasoning
above, we develop the second hypothesis as follows:
H2: At the office level, certeris paribus, the negative impact of client importance
on audit quality is significantly weaker for Big4 auditors than for non-Big4 auditors.
Another research question yet to be answered is whether audit offices of different
12
sizes differ in their incentives involving important clients. According to the findings
of Francis and Yu (2009) and Choi, et al.(2010), there are systematic quality
differences across various audit offices of the same accounting firm, and larger office
size is associated with higher audit quality. Francis and Yu (2009) attribute the
superior quality of large audit office to greater in-house experience, and Choi, et al.
(2010) explain it as the result of less economic dependence on a particular client. We
believe that the large size of an audit office could mitigate the negative impact of
client importance on audit quality at the office level. First, according to DeAngelo
(1981), the quasi-rent from a particular client creates economic dependence on that
client, but the larger the size of an accounting firm, the less the economic dependence
of the firm on that client. This reasoning also applies to the analysis of client
importance at the office level, i.e., the large size of an audit office will reduce the
economic dependence on a particular client. More specifically, a client that is
unimportant from the viewpoint of a large office could reasonably be an important
client to a small office, but an important client from the viewpoint of a small office is
not necessary important to a large office. Second, since the profits of a particular audit
office are generally shared among all the partners in that office, the income of each
local partner in a small office is highly sensitive to the gain or loss of a large client,
thus leading to the overall compromise of independence in that office. However, when
the office is large enough, even the loss of a very big client is not likely to result in an
unbearable decrease of income for partners in that office. In other words, large audit
offices have a better risk-sharing function, in the sense of Gilson and Mnookin (1985),
and with the decrease of idiosyncratic risk of a local audit partner, he or she will be in
a better position to resist improper pressure from an important client. Third, due to the
deeper clientele of large offices (Choi et al., 2010), it is very likely that large offices,
compared with small offices, are more important to the entire firm and will be subject
to tighter quality control from national headquarters. As mentioned above, when
effective quality control is in place, the incentive of a local audit partner to treat his or
her important client more leniently will be weakened. Hence, it is more likely for
audit partners in a large office to maintain independence from economically important
clients due to stronger quality control from national headquarters. Based on the above
analysis, we present the third hypothesis as follows:
H3: Certeris paribus, the negative impact of client importance on audit quality is
significantly weaker for large offices than for small offices.
3. Research Design
3.1 Data and Sample
13
In office-level audit research, it is critical to identify the local office primarily
responsible for a particular audit engagement (hereafter the engagement office). In the
U.S., the engagement partner issues the final audit report on the engagement office
letterhead. Researchers can thus identify the engagement office through the signed
audit report (Reynolds and Francis, 2001; Francis and Yu, 2009). However, this is not
the case in China as the audit report for a listed company is issued in the name of the
accounting firm, and no information with respect to the engagement office is
disclosed. Fortunately, laws and regulations in China require that at least two CPAs
sign a listed company’s audit reports9, including the engagement partner. Their names
can be found in the audit report, which makes it possible for us to identify the
engagement office. In China, the Chinese Institute of Certified Public Accountants
(CICPA) and its branches in various provinces must inspect the qualifications of all
practicing CPAs annually, and results of the annual inspection, containing information
regarding a CPA’s practice office, are released to the public. We collect all the public
announcements on annual inspection released by the CICPA and its branches from
2007 to 2010, and follow two steps to identify engagement offices. First, we identify
the practice office of each signing CPA. Second, we infer the engagement office from
the combination of two signing CPAs’ practice offices10. We check the reliability of
these data by comparing them with data from other sources11, such as those obtained
from the websites established by the Ministry of Finance (http://www.acc.gove.cn)
and by CSRC (http://assdata.csrc.gov.cn) for regulatory purposes.
We choose the years from 2007 to 2010 as the sample period for two reasons.
First, the complete set of announcements on annual inspections of practicing CPAs
before 2007 is not publicly available. Second, beginning from January 1, 2007, China
adopted a new system of accounting standards that completely converged with the
IFRS, making the accounting information of listed companies after 2007 largely
incomparable to that before 2007. Except for the information regarding engagement
office and mergers between accounting firms, which have been manually collected,
the other data needed for this study are drawn from the CSMAR Database12. We
obtain 7,101 firm-year observations from CSMAR, and exclude 148 observations
with missing information about signing CPAs’ identities. In order to eliminate a
9 There are rare cases that an audit report is signed by more than two CPAs. We only consider the first two signing CPAs in determining the engagement office when more than two CPAs sign an audit report. 10 If the two signing CPAs are practicing in the same audit office, that very office is identified as the engagement office. If two signing CPAs are from different audit offices and one of the CPAs is practicing in the audit office located in the same province as the client, the audit office having the same location as its client is identified as the engagement office. If two CPAs are from different offices and none of the offices is located in the same province as the client, the first signing CPA’s audit office is identified as the engagement office because it is generally the first signing CPA who is in charge of the engagement. 11 These sources only provide information in one recent year and are not updated in time, thus are not complete. 12 The CSMAR database is the most widely used database in capital market research in China, provided by the GTA Co. Ltd.
14
potential estimation bias from observations with distinctive features, we delete 90
observations of B-Share companies that issue shares only to foreign investors, 121
observations of companies in financial industry that follow different accounting
standards and regulatory rules, 633 observations of IPO companies that underwent
drastic financial changes, and 686 observations of so-called ST companies with a
higher risk of delisting and tighter supervision from regulators. The deletion leaves
5,423 firm-year observations. In order to distinguish office-level analysis from firm-
level research, and rule out outliers with extremely high values of client importance,
we make the following exclusions: 825 observations audited by accounting firms
with only one practice office, and 128 observations audited by audit offices with only
one publicly-listed client. After deleting observations with missing data to calculate
discretional accruals or to estimate the regression models, we finally get a sample
comprised of 3,864 firm-year observations. Table 2 details the sample selection
process and yearly sample distribution.
In the final sample, the 3,864 observations are audited by 218 unique audit
offices of 54 accounting firms qualified to audit listed companies. The 54 firms have
an average of 4 audit offices and a range of 1-18 audit offices per firm. Each office
may appear up to four times (2007, 2008, 2009, and 2010), and there are in total 535
office-year observations in the final sample. Of the 218 unique offices, 16 belong to
Big4 accounting firms in China, which are distributed as follows: Pricewaterhouse
Coopers (6), Deloitte (3), KPMG (4), and Ernst &Young (3). Compared with the Big4
in the U.S. (Reynolds and Francis, 2001; Francis and Yu, 2009), the Big4 in China
have significantly fewer audit offices. In the final sample, the 218 offices have an
average of 20.6 publicly-listed clients per office, and a range of 1-73 clients per office,
with great variations across different offices.
[Insert Table 2 Here]
3.2 Measurement of audit quality and client importance
3.2.1 Measurement of audit quality
We use discretional accruals as our primary proxy for audit quality. Prior studies
show that lower discretional accruals suggest higher audit quality (Becker et al., 1998;
Reynolds and Francis, 2001; Frankel et al., 2002). In the Chinese setting, several
studies also use discretional accruals as a proxy for audit quality (e.g., Gul et al., 2009;
Chen et al., 2011).
Specifically, we run the following regression model for each industry-year group
15
with at least 10 available observations13, and take the residuals as our measures of
discretional accruals:
it
itit
itit
itit
it
ASSET
PPE
ASSET
RECREV
ASSETASSET
TA
1
3
1
2
1
1
1
1 (1)
In the equation (1); TA is total accruals, calculated as operating income less cash
flow from operations; ASSET is total assets, △REV is change in revenue from the
prior year to the current year; △REC is change in accounts receivable from the prior
year to the current year; and PPE is the gross property, plant and equipment.
Following Kothari et al. (2005), we match each observation with another from
the same year and industry, requiring the matched pair to have closest ROA, i.e. net
income divided by beginning-of-year total assets. The performance-matched abnormal
accrual (hereafter DA) of an observation is its discretional accruals minus that of its
matched pair in the same year and industry. We use both unsigned and signed DA as
our proxies for audit quality, with DA_ABS, DA_POS and DA_NEG denoting the
absolute value of DA, positive DA, and negative DA, respectively.
3.2.2 Measurement of client importance
Client importance is generally measured as the ratio of audit fee (or non-audit fee,
total fee) paid by a particular client to the total audit fees (or total non-audit fees, total
fees, respectively) earned by an audit office (Li, 2009). When the fee data are not
publicly available, researchers often use surrogates for various fee ratios, e.g., the
proportion of a client’s sales to the total sales audited by the report-issuing office
(Reynolds and Francis, 2001), or the proportion of a client’s assets to the total assets
audited by the audit office (Chen et al., 2010). Data on audit fees paid by listed
companies has been publicly available in China since 2001, but we do not use fee
ratios as a measure of client importance for several reasons. First, there are a lot of
missing values in the audit fee data. Of the 6863 observations of A-Share companies
with necessary data about auditors’ identities, 1051 (or 15.3%) include no audit fee
data. Second, there exist serious incomparability problems in the audit fee data. For
example, some listed companies disclose audit fees on a cash basis, while others
disclose on an accrual basis, i.e., the audit fees disclosed might be pre-paid fees or
payables. Also, the audit fees disclosed by some companies include both semi-annual
and annual audit fees, while those disclosed by other companies include only annual
audit fees. Because of the low quality of audit fee data, measurements of client
importance based on disclosed audit fee are subject to serious errors.
13 Our industry classification is based on the authoritative guideline issued by CSRC in 2001. Our sample is divided into 20 industry groups (9 manufacturing industries and 11 non-manufacturing industries).
16
In China, the total assets of a client are the most critical determinant of audit fees
(Chen et al., 2010); total assets are also highly correlated with audit fees14. It is thus
reasonable for us to measure the ratio of a client’s total assets to the sum of total
assets audited by the audit office, consistent with Chen et al. (2010). Similar to Chen
et al. (2010), we use the logarithm of total assets rather than original asset value as the
bases to calculate client importance. In doing so, we capture the non-linear
relationship between total assets and audit fees, and make the distribution of the client
importance variable closer to normal distribution. The continuous client importance
variable is thus calculated using the following formula:
)(
)(
1
k
iij
ij
ij
ASSETLn
ASSETLnCI_OFFICE (2)
In the above formula, CI_OFFICEij denotes client j’s economic importance to
audit office i. The numerator is the natural logarithm of client j’s total assets. The
denominator is the sum of the natural logarithm of total assets of the k clients audited
by audit office i in a given year. However, we don’t use CI_OFFICE directly in our
empirical tests. We construct a new dichotomous variable IMPOR_OFFICE based on
CI_OFFICE, which is coded as 1 when a client belongs to the vital few that are most
likely to be treated favorably by the engagement office. The variable is coded as 0
when the client belongs to the trivial many, a group that auditors have no strong
incentives to treat favorably. We have several reasons for doing so. First, the
distribution of CI_OFFICE is not actually continuous between 0 and 1, with most of
the observations falling into the range of 0-0.1 and the others dispersed between 0.1
and 1. This shows that there are two groups in our sample, the first with a large
number of observations and relatively low level of economic importance, and the
other with a small number of observations but relatively high degree of economic
importance. The distribution of the data suggests that it is probably inappropriate to
define client importance as a continuous regressor in our regressions. Second, the 80-
20 rule is widely used in management decisions (Craft and Leake, 2002; Sanders,
1988), which means that managers’ attention is generally focused on the vital few
rather than the trivial many. Anecdotal evidence shows that auditors in China also use
a similar rule to manage their client portfolios, and they will not treat a client more
favorably than others unless the client belongs to the vital few. In other words, client
importance will not impair audit quality unless it exceeds a certain threshold; only a
high degree of client importance might impair audit quality.
14 In our sample, the correlation coefficient between total assets of a client and the audit fees is as high as 0.86, and significant as 1% level.
17
There are no explicit criteria as to what clients constitute the vital few for a given
audit office. We define IMPOR_OFFICE as follows. We first rank all the clients of
the audit office in a given year in order of their CI_OFFICE. Then, we categorize
those clients ranked in the top 25% in their CI_OFFICE as the vital few.
IMPOR_OFFICE is then coded as 1 for the vital few, and as 0 for the others. There
are two reasons why we use the above criteria to identify the vital few. First, in our
full sample used to calculate CI_OFFICE (6,863 observations of A-Share Companies
with necessary data about auditors’ identities), the aggregate total assets of the top 25%
of an audit office’s clients— according to their CI_OFFICE —on average account for
more than 70% of the sum of total assets audited by that office. Hence, the loss of any
one of these clients would certainly represent a heavy economic loss to that audit
office. Second, in accounting literature, the third quartile is frequently used to
distinguish one subsample from the other, according to their differences in underlying
variables. For example, Coles et al. (2008) define firms as R&D intensive firms if
their R&D ratio exceeds the third quartile of a full sample’s R&D ratios in a given
year.
3.3 Model specification
Following prior studies (e.g., Becker et al., 1998; Reynols and Francis, 2001;
Chen et al., 2011), we use discretional accruals as a proxy for audit quality. We
predict a positive correlation between unsigned discretional accruals and client
importance at the office level. Additionally, we predict that the negative impact of
client importance on audit quality at the office level is weaker for Big4 auditors and
also weaker for large audit offices. The following multivariate regression model is
used to test our hypotheses (the firm and time subscript are omitted for simplicity):
εINDθYEARηMERGERβLOCALβSHORTβ
SWITCHβGROWTHβBMβLOSSβROEβCFOβLEVβ
SIZEβBHSHAREβSOEβLagTAβLARGECEIMPOR_OFFIβ
BIGCEIMPOR_OFFIβLARGEβBIGβCEIMPOR_OFFIββABSDA
jjtt
191817
16151413121110
98765
43210
*
4*4_
(3)
In equation (3), DA_ABS is performance-matched discretional accruals in
absolute value; IMPOR_OFFICE is a dummy variable of client importance at the
office level; BIG4 is an indicator variable of auditor type (i.e., Big4 vs. non-Big4),
and LARGE is a dummy variable of audit office size, which is coded as 1 when the
size of an audit office exceeds the median size of all audit offices in a given year, and
as 0 otherwise15. We predict that is positive, and and are negative, to reflect
that client importance negatively impacts audit quality at the office level, and that
15 The audit office size is measured as the sum of total assets audited by the audit office in the given year.
18
audit quality are positively related to auditors’ brand-name reputation and audit office
size. We further test whether auditors’ brand-name reputation and audit office size
mitigate the negative impact of client importance on audit quality at the office level.
To test this mitigating effect, we put two interaction terms in equation (3), i.e.,
IMPOR_OFFICE*BIG4 and IMPOR_OFFICE*LARGE, and predict the coefficients
and to be negative. Three dichotomous variables, i.e., IMPOR_OFFICE, BIG4
and LARGE, divide the sample into eight sub-samples. The coefficients from to ,
and various combinations thereof, can be used to reflect differences in audit quality
among the eight sub-samples, as described in Table 3.
[Insert Table 3 Here]
Table 3 shows the economic meanings of the coefficients of interests in this study:
is the DA_ABS of the reference group, i.e., unimportant clients audited by small
offices of non-Big4 auditors; reflects the incremental effect of client importance on
DA_ABS (at the office level) for clients audited by small offices of non-Big4 auditors;
represents the net effect of auditor’ brand-name reputation on DA_ABS for
unimportant clients audited by small audit offices; denotes the net effect of audit
office size on DA_ABS for unimportant clients audited by non-Big4 auditors; and
and refer to the respective difference-in-differences effects on audit quality of
auditors’ brand-name reputation and audit office size, respectively.
Consistent with previous studies (e.g., Becker et al., 1998; Reynols and Francis,
2001; Choi et al., 2010; Chen et al., 2011), we include some commonly used control
variables to capture the impacts of other factors on the level of discretional accruals,
including client size (SIZE), financial risk (LEV), operating cash flow (CFO),
profitability (ROE and LOSS), and firm growth (BM and GROWTH). Following Choi
et al. (2010), we control for the reversal effect of total accruals on discretional
accruals in the subsequent accounting period (LagTA). According to DeFond and
Subramanyam (1998), auditor changes may have a significant effect on discretional
accruals, so we put a dummy variable for auditor change (SWITCH) into our model.
Following Francis and Yu (2009), we control for the effect of auditor tenure (SHORT)
on discretional accruals. We also control for some factors which are considered
important in China-related studies, including ownership type (SOE) (Chen et al.,
2011), cross-listing status (BHSHARE) (Chen et al., 2011), and audit office location
(LOCAL) (Chan, et al., 2006).Considering the large number of mergers among
accounting firms during the sample period, we add another variable MERGER into the
model to control for possible effects of accounting firm mergers on discretional
accruals. We also control for the year (YEAR) and industry (IND) effects on the
19
regression results.
Based on the theory and previous research results, we predict that the following
control variables are positively associated with absolute discretional accruals: LEV,
GROWTH and LOCAL. We also predict that SIZE, BHSHARE, CFO, ROE, and BM
will be negatively related to absolute discretional accruals. Due to the lack of a
theoretical foundation or consistent empirical findings, we do not predict the signs of
coefficients on other control variables. Appendix 1 summarizes the definition of
variables.
[Insert Appendix 1 Here]
We predict the same signs on the independent variables in the DA_POS
regression as in the DA_ABS regression, and opposite signs in the DA_NEG
regression.
4. Empirical Analysis and Results
4.1 Descriptive statistics
We provide detailed descriptive statistics in Table 4. All continuous variables in
equation (3) are winsorized at the 1% and 99% percentiles of their annual
distributions in order to reduce the influence of outliers. Panel A of Table 4 reports
descriptive statistics for the full sample. The mean (median) value of DA_ABS is 0.10
(0.07), which is similar to the value found by Chen et al. (2011). The sample is
composed of 1,901 firm-year observations with positive discretional accruals and
1,963 firm-year observations with negative discretional accruals. The mean (median)
value of CI_OFFICE is 0.10 (0.06), indicating that the degree of client importance is
generally higher at the office level than at the audit firm level, due to the relatively
thinner audit office clientele16. The mean value of our dichotomous client importance
variable IMPOR_OFFICE indicates that about 33% of the observations in our sample
are classified as the vital few clients of audit offices17.
[Insert Table 4 Here]
With respect to the mean values of control variables, Panel A of Table 4 indicates
that about 65% of the observations in our sample are state-owned (SOE), and 9%
issue both A-shares and B-shares or are cross-listed in mainland China and Hong
16 The untabulated mean (median) value of client importance at audit firm level (CI_FIRM ) is 0.03(0.02) in our sample. 17 By definition, the mean value of IMPOR_OFFICE should be close to 25%. Due to the sample selection process and the fact that about 11% of the observations in our sample are audited by audit offices with less than 5 listed clients, the actual mean value of IMPOR_OFFICE is larger.
20
Kong (BHSHARE). About 8% of our observations change their auditors (SWITCH)
and 25% have auditors with tenures of no more than 3 years (SHORT). In addition, 63%
of the observations are located in the same province as their engagement offices, and
22% are audited by accounting firms undergoing mergers with other accounting firms.
The mean values of variables LEV, CFO, ROE, BM and GROWTH indicate that the
listed companies in China were generally financially healthy in 2007-2010.
To present possible differences between Big4 clients and non-Big4 clients, we
report descriptive statistics by auditor type in Panel B of Table 4, together with the t
test and Mann-Whitney test results of the differences in means and medians between
the two sub-samples, respectively. There are only 294 observations audited by Big4
(constituting about 7.6% of the full sample), showing that Big4 auditors in China as a
group have much less market share compared with their counterparts in the U.S. Panel
B of Table 4 provides some preliminary evidence that Big 4 auditors offer higher audit
quality, as reflected by the significantly less absolute and income-increasing
discretional accruals (DA_ABS and DA_POS) in their clients’ financial statements. It
is noteworthy that CI_OFFICE is on average significantly higher for Big4 auditors,
probably due to the larger size of their clients. It seems that higher economic
dependence on clients does not lead to lower audit quality at the office level for Big4
auditors, without considering other confounding factors. As shown in Panel B of
Table 4, Big4 and non-Big4 clients differ significantly in almost all control variables
except for LOSS and SHORT. The Big4 have more SOE and BHSHARE clients, and
their clients are generally larger (SIZE) and more profitable (ROE), and have higher
financial leverage (LEV), a stronger ability to generate operating cash flow (CFO),
and a lower growth rate (BM and Growth). Meanwhile, the clients of Big4 auditors
have less location overlap (LOCAL) with the engagement offices, and Big4
accounting firms undergo fewer mergers (MERGER) during our sample period.
Overall, Panel B of Table 4 shows the systematic differences between Big4 and non-
Big4 clients and the necessity to control for the potential bias caused by endogenous
auditor choice. We deal with the potential endogeneity problems in Section 5.
Panel C of Table 4 reports the descriptive statistics by audit office size. No
significant differences exist between the two groups of audit offices for DA_ABS,
DA_POS and DA_NEG. It seems that size of the audit office has no impact on audit
quality without considering the effects of other factors. The mean value of
CI_OFFICE is much lower for large audit offices (0.04) than for small offices (0.15),
showing that large size can reduce an aduit office’s economic dependence on a
particular client. As shown in Panel C, large audit offices have more SOE and
BHSHARE clients, and their clients are generally larger (SIZE) and more profitable
21
(ROE), and have lower growth potential (BM). However, large audit offices have less
location overlap (LOCAL) with their clients. Generally speaking, Panel C indicates
that there are some noteworthy differences between clients of large audit offices and
those of small offices.
4.2 Univariate correlation analysis
In Table 5 we report Pearson (below the diagonal) and Spearman (above the
diagonal) pairwise correlations among the variables of interest for the full sample. As
predicted, absolute discretional accruals (DA_ABS) and the dummy variable of client
importance at the office level (IMPOR_OFFICE) are significantly positively
correlated. However, the continuous variable of client importance (CI_OFFICE) is
not significantly correlated with DA_ABS, and has a sign contrary to our expectation.
One explanation is that audit offices treat only the vital few clients more favorably,
and small differences in client importance do not change auditors’ incentive to
provide high-quality audits. Both of the dummy variables BIG4 and LARGE are
negatively correlated with DA_ABS as expected, but only the correlation between
BIG4 and DA_ABS is statistically significant. As shown in Table 5, SOE and
BHSHARE companies have lower DA_ABS, perhaps due to the distinct financial
reporting incentives associated with their different ownership structure (Chen et al.,
2011). As expected, companies with more operating cash flows (CFO) and lower
growth rate (BM and GROWTH) tend to have lower DA_ABS, and companies with
higher financial leverage (LEV) tend to have higher DA_ABS. Companies that change
their auditors in the current year (SWITCH) generally have higher DA_ABS, providing
some preliminary evidence for the opinion shopping hypothesis of audit switching.
[Insert Table 5 Here]
Most of the correlations among variables in Table 5 are below the value of 0.10.
Only three pairs of variables have correlations that approach or exceed 0.50:
CI_OFFICE and LARGE, IMPOR_OFFICE and SIZE, and ROE and LOSS. These
relatively high correlations probably come from the intrinsic connections between the
measurement of each of these three pairs of variables. Generally speaking, Table 5
shows there will be no serious multicollinearity problems in our regression results.
The Variance Inflation Factor (VIF) analysis provides us with more comfort, since no
single independent variable in equation (3) has a value of VIF exceeding 10, and the
average value of all variables’ VIFs is less than 2.
4.3 Empirical Results
We report the OLS regression results in Table 6 and Table 7. The dependent
22
variables are unsigned and signed discretional accruals in Table 6 and Table 7,
respectively. In order to control for the auto-correlations problems inherent in panel
data and the potential heteroscedasticity problems, we calculate t-statistics based on
the robust standard errors clustered by each company in our sample. We report the
results of four specifications of our regression model in Table 6, with the evidence
generally in support of our three hypotheses.
[Insert Table 6 Here]
In model specification (1), we constrain the coefficient on IMPOR_OFFICE (β1
in equation (3)) to be the same for Big4 and non-Big4 auditors, and for large audit
offices and small audit offices; i.e., we assume that coefficients β2, β3, β4 and β5 in
equation (3) are all equal to 0. The estimated coefficient β1 is significantly positive (β1
= 0.009, t = 2.20) as expected, and thereby supports our first hypothesis that a high
degree of client importance at the office level decreases audit quality.
In model specification (2), we assume the relationship between DA_ABS and
IMPOR_OFFICE is affected by auditors’ brand-name reputation, but not by audit
office size (i.e., β3 =β5 = 0). The coefficient β1 is still significantly positive (β1 = 0.008,
t = 1.90), which means that non-Big4 auditors’ important clients have more unsigned
discretional accruals than their relatively unimportant clients. The signs for
coefficients on BIG4 and its interaction term with IMPOR_OFFICE are both negative
as expected, although not significant (β2 = -0.002, t = -0.21; β4 = -0.007, t =-
0.64). The difference in DA_ABS between important and unimportant clients audited
by Big4 auditors is indicated by (β1+β4) as shown in Table 3, which is positive but not
significant (β1+β4 = 0.001, t = 0.11). This suggests that Big4 auditors do not treat their
highly important clients more favorably at the office level. Generally speaking, the
regression results of model (2) support our second hypothesis.
In model specification (3), we assume that client importance at the office level
affects audit quality contingent upon audit office size, but not upon auditors’ brand-
name reputation (i.e., β2=β4=0). The estimated coefficient β1 is still significantly
positive (β1=0.013, t= 2.58), which means that client importance is positively related
to unsigned discretional accruals for clients of small audit offices. The coefficient on
LARGE is positive (β3=0.003), contrary to expectation, but not statistically
significant. The coefficient on the interaction term is negative (β5= -0.010, t = -
1.63), consistent with our prediction, but not statistically significant. For large audit
offices, the incremental effect of IMPOR_OFFICE on DA_ABS is indicated by
(β1+β5) as shown in Table 3. Since (β1+β5) is positive but not statistically significant
(β1+β5 = 0.003, t= 0.49), there is no evidence that large audit offices treat their
23
highly important clients more favorably. In general, the regression results in model
(3) support our third hypothesis.
Specification (4) is the complete form of our model, which simultaneously
considers the effects of auditors’ brand-name reputation and audit office size.
Consistent with the results in models (1), (2), and (3), β1 is still significantly positive
(β1= 0.012, t= 2.37), suggesting that IMPOR_OFFCE is positively associated with
DA_ABS for small offices of non-Big4 auditors. The coefficients β2, β4 and β5 are all
negative, consistent with our prediction, but not significant. The coefficient β3 is
positive, contrary to expectation but not significant. For small offices of Big4 auditors,
the incremental effect of IMPOR_OFFICE on DA_ABS is indicated by (β1+β4), which
is positive but not significant (β1+β4= 0.010, t= 0.83). The incremental effect of
IMPOR_OFFICE on DA_ABS for large offices of non-Big4 auditors is indicated by
(β1+β5), which is also positive but not significant (β1+β5= 0.002, t= 0.25). The
incremental effect of IMPOR_OFFICE on DA_ABS for large offices of Big4 auditors
is indicated by (β1+β4+β5), which is negative but not significant (β1+β4+β5= -0.001,
t= -0.07). The above results, taken together, suggest that the negative impact of
client importance on audit quality occurs only in audits by small audit offices of non-
Big4 auditors. Further, auditors’ brand-name reputation and large audit office size
both have a mitigating effect on such a negative impact. It is noteworthy that the
coefficient on IMPOR_OFFICE (β1) is not only statistically significant but also
economically important, because the observations audited by small offices of non-
Big4 auditors account for 56.7% of our full sample. Among these observations, on
average, the unsigned discretional accruals of a highly important client will be higher
by an amount of about 1.2% of total assets than those of relatively unimportant clients.
It is also noteworthy in Table 6 that DA_ABS increases with GROWTH and SWITCH,
and decreases with BM. Signs of coefficients on other control variables are generally
congruent with our expectation, but not statistically significant.
Table 7 reports the regression results of signed discretional accruals. Following
prior studies (e.g., Becker, et al., 1998; Reynolds and Francis, 2001; Chen et al., 2011),
we partition the full sample into two subsamples: those with positive (income
increasing) discretional accruals (DA_POS) and those with negative (income
decreasing) discretional accruals (DA_NEG). The full model (specification (4)) in
Table 6 is then re-estimated for each subsample. We find that the conclusions based
on full sample regressions still hold for the DA_POS subsample, but not for the
DA_NEG subsample. Specifically, we find that for clients audited by small offices of
non-Big4 auditors, client importance at the office level is positively related to positive
discretional accruals and negatively related to negative discretional accruals,
24
consistent with our expectations. However, only the relationship between client
importance and positive discretional accruals is statistically significant. This suggests
that the negative impact of client importance on audit quality at the office level is
primarily evidenced by auditors’ greater tolerance of income increasing earnings
management by their highly important clients. Our finding is consistent with prior
studies which find that auditors have asymmetric incentives in constraining clients’
income-increasing and income-decreasing earnings management. Generally speaking,
they are more sensitive to clients’ income-increasing earnings management (e.g.,
Becker, et al., 1998; Nelson et al., 2002).
[Insert Table 7 Here]
5. Additional analyses and sensitivity tests
5.1 Additional analyses
As mentioned in Section 2.1, most prior studies on client importance are
conducted at the audit firm level. A few studies are conducted at engagement partner
level in countries or regions where the information of engagement partners is
available, such as mainland China and Taiwan (e.g., Chen et al., 2010; Chi et al. 2012).
It is an open argument which level of analysis is most appropriate for research on
client importance. To shed some light on this issue, we also analyze the impact of
client importance on audit quality at the audit-firm level and the engagement-partner
level, using similar research design as our audit office level analysis. Since our third
hypothesis is pertinent only to the office level analysis, we only test the effect of
client importance on audit quality and the role of auditors’ brand-name reputation in
maintaining auditors’ independence from important clients. The regression results are
reported in Table 8.
[Insert Table 8 Here]
Consistent with our office level analysis, we define client importance at the audit
firm level and the engagement partner level as dichotomous variables, i.e.,
IMPOR_FIRM and IMPOR_PARTNER, respectively. IMPOR_FIRM is coded as 1
when a particular client belongs to the vital few among all clients of the accounting
firm, i.e., the top 25% of clients ranked in order of their economic importance.
Similarly, IMPOR_PARTNER is coded as 1 when a particular client is one of the vital
few clients of the engagement partner. Since there is more than one signing CPA in an
audit report in China and the first signing CPA is typically the engagement partner,
IMPOR_PARTNER is calculated based on the clientele of the first signing CPA. In
order to exclude the influence of outliers, we conduct the sample screening process
25
similar to our office level analysis without considering the number of practice offices
for an accounting firm and the number of listed clients for a given audit office, and
require that the engagement partner have at least two listed clients in a given year. We
obtain 4,712 observations in the final sample for firm-level analysis and 3,644
observations for engagement partner analysis.
We use the modified version of equation (3) to conduct our firm level and
engagement partner level analyses, i.e., we drop the LARGE variable and its
interaction term with the dichotomous client importance variable, and replace
IMPOR_OFFICE with IMPOR_FIRM or IMPOR_PARTNER. It is interesting that we
find that client important has no significant impact on audit quality, either at the audit
firm level or at the engagement partner level, as shown in Table 8. The coefficients on
IMPOR_FIRM and its interaction term with BIG4 are 0.003 and 0.001, respectively,
but neither is statistically significant. The coefficients on IMPOR_PARTNER and its
interaction term with BIG4 are -0.001 and -0.015, respectively, but again neither is
statistically significant. These results suggest that auditors do not treat their highly
important clients more favorably than the relatively unimportant clients, either at the
audit firm level or at the engagement partner level. In other words, only client
importance at the office level will impair audit quality.
In order to explain our findings, we should reconsider the theoretical bases for
using different units of analysis in research on client importance. In our opinion, the
appropriateness of various units of analysis largely depends on the degree of internal
integration of an accounting firm. In an extreme situation where an accounting firm is
highly integrated (e.g., the headquarter office can exert significant influences on all
critical decisions made by engagement offices or engagement partners, and all
revenues generated by engagement offices and engagement partners are shared by the
firm as a whole), the audit firm is probably the most appropriate unit of analysis. In
the opposite extreme, where each engagement partner is fully responsible for his or
her engagements and does not share any profit or risk with other partners, the audit
partner is probably the most suitable unit of analysis. However, neither of above
extreme situations exists in the real world. Actually, just as some researchers claim
(e.g., Reynolds and Francis, 2001; Francis, 2004), the practice office is probably the
most appropriate unit of analysis in research on client importance, because most of the
critical decisions concerning an audit engagements are made at the audit-office level,
and engagement partners work with each other closely in an audit office and share
profits and risks almost equally within the audit office. Our additional analysis
indicates that when investors and regulators are considering the effect of client
importance on audit quality, they should shift their attention from accounting firms to
26
audit offices, but not necessarily to the engagement partner.
5.2 Sensitivity tests
5.2.1 Controlling for the effect of endogenous auditor choice
Our conclusion that auditors’ brand-name reputation can mitigate the negative
impact of client importance on audit quality may not be robust due to the potential
self-selection problems in auditor choice. The association of Big4 auditors with higher
earnings quality of their clients could be simply explained away as companies with
higher earnings quality being more likely to hire Big4 auditors. Following Lennox et
al. (2012), we use the two-stage treatment effect model to prove that our result is not
driven by endogenous auditor choice. The first stage auditor choice model is specified
as follows:
GROWTHBM
LOSSROECFOLEVSIZEγBHSHAREγSOEγ
OWNERγINDIRγMKINDEXγREGIONγREGIONγγ)Probit(BIG
1413
1211109876
543210 2114
(4)
In equation (4), we include REGION1, REGION2, MKINDEX, INDIR and
OWNER in our first stage regression model and exclude them from our second-stage
regression model. According to common sense or the findings of previous relevant
studies (Wang et al., 2008; Chen, et al., 2011), these variables as defined below satisfy
the restriction exclusion condition of the two-stage selection models (Lennox et al.,
2012), i.e., they are important determinants of the first-stage dependent variable
(auditor choice), but unlikely to have a direct and significant influence on the second-
stage dependent variable (discretional accruals). Since the headquarters of Big4
accounting firms in China are located either in Beijing or Shanghai18, the two dummy
variables REGION1 and REGION2 are included to reflect the effect of auditors’
location on the clients’ choice of auditors. REGION1 (REGION2) is coded as 1 when
the client is located in Beijing (Shanghai), and as 0 otherwise. We predict that for cost
efficiency considerations, client companies are more likely to hire Big4 auditors when
REGION1 (REGION2) equals 1. MKINDEX is the marketization index constructed by
Fan and Wang (2011)19, which is used to capture institutional heterogeneity across
different provinces (Wang et al., 2008; Chen, et al., 2011). Consistent with Chen et al.
(2011), we predict client companies located in provinces with higher MKINDEX are
18 The Chinese member firms of KPMG and Ernst & Young are located in Beijing, and the Chinese member firms of PricewaterhouseCoopers and Deloitte are located in Shanghai. 19 Fan and Wang provide their index by year, and we use their latest version, which updates data up to 2009. Since no data are available for the year 2010, we use the 2009 data of marketization index for both the 2009 and 2010 observations.
27
more likely to choose Big4 auditors20. INDIR and OWNER indicate the percentage of
independent directors on the board and the percentage of ownership held by the
ultimate share-holder as defined by Chen et al. (2011), respectively, which are
included to capture the influence of corporate governance on auditor choice. We don’t
predict the signs of INDIR and OWNER because it is unclear whether internal
corporate governance mechanisms are complements or substitutes to external auditors’
monitoring. We also include in equation (4) the control variables in equation (3),
unless the variables are obviously unrelated to auditor choice. The control variables
not included in equation (4) are SWITCH, SHORT, LOCAL and MERGER.
We start by running the first-stage probit model and report the results in Panel A
of Table 9. Consistent with prior studies (Wang et al., 2006; Chen, et al., 2011), we
find that companies more likely to choose Big4 auditors are located in more market-
oriented regions (MKINDEX), larger in size (SIZE), and cross-listed (BHSHARE),
with lower financial leverage (LEV) and growth rate (GROWTH), but more cash flow
from operations (CFO) in hand. However, companies with more independent boards
are less likely to hire Big4 auditors, possibly because the strengthened internal
monitoring mechanisms reduce the demand for high-quality external auditors. Based
on the first-stage model, we calculate the Inverse Mills’ Ratio (IMR) and include it as
an additional control variable in the second-stage regression. The regression results of
the second-stage model are reported in Panel B of Table 9. The coefficient on IMR is
significantly negative at the 10% level, indicating that the regression results are
potentially biased without a control for endogenous auditor choice, even though the
potential bias may not be serious. However, the correction for endogeneity bias does
not change our conclusions drawn from Section 4; i.e., a high degree of client
importance impairs audit quality, but only for clients audited by small offices of non-
Big4 auditors.
[Insert Table 9 Here]
5.2.2 Alternative measure of audit quality
Many China-related audit researches use MAOs as a proxy for audit quality
(DeFond et al., 2000; Chen et al., 2001; Chan et al., 2006; Chen et al., 2010; Firth et
al., 2012). In order to check the sensitivity of our empirical results to different audit
quality measures, we also use MAOs as an alternative proxy for audit quality.
However, there exist certain limitations to the use of this audit quality measure in our
research setting. First, only 2.57% observations in our sample receive MAOs, and
20 The higher value of MKINDEX indicates less government intervention, better legal environment, and better credit market development (Chen et al., 2011). Companies in regions with higher MKINDEX are deemed to have higher demand for high quality auditors.
28
most companies receive MAOs when they are suffering serious operational and
financial problems, or under investigations by regulatory agencies. Actually, in most
cases, the problems of those companies are so obvious that it is not difficult for
auditors to issue a MAO report. Furthermore, in many cases the problematic
companies receive MAO reports in several consecutive years for basically the same
reasons. Second, those companies defined as highly important clients are generally
significantly larger and in better financial conditions, and are thus less likely to
receive MAOs. If we use MAOs as a measure of audit quality, the potential self-
selection problems could be more serious. Third, in our final sample, those companies
defined as highly important clients of Big4 auditors receive no MAO reports in our
sample period, which makes it impossible for us to use MAO as an audit quality
measure to test our second hypothesis. Generally speaking, MAO is not a very good
measure of audit quality in our research.
Following prior studies (e.g., Chen et al., 2010), we define MAO in two ways.
First, MAO is defined as OP1, a dummy variable coded as 1 when the company
receives an unqualified audit opinion with explanatory paragraph or a qualified audit
opinion, and coded as 0 otherwise. Second, MAO is defined as OP2, an ordinal
variable coded as 0 when the company receives a clean opinion, coded as 1 when it
receives an unqualified opinion with explanatory paragraph, coded as 2 when it
receives a qualified opinion, and coded as 3 when it receives an adverse opinion or
disclaimer. The MAO model is specified as follows:
jjtt
i
i
INDYEARTURNOVERQUICK
LOSSROAINVRECCFOLEVSIZE
LagOPLARGECEIMPOR_OFFIBIGCEIMPOR_OFFI
LARGEBIGCEIMPOR_OFFIOPProbit
1514
13121110987
654
3210
*4*
4)1(
(5)
In equation (5), OPi (i=1, 2) is a binary or an ordinal audit opinion variable, and
LagOPi (i=1, 2) is the audit opinion of the previous accounting period to control for
the persistence of audit opinions (Chen et al., 2010). REC and INV are the net
accounts receivable and net inventory (both deflated by total assets), respectively.
QUICK is the quick ratio measured as quick assets (current assets minus inventory)
divided by current liabilities. TURNOVER is the asset turnover ratio calculated as
total revenue divided by total assets. Other variables are defined as before. Table 10
reports the regression results of the probit model for OP1, and of the multivariate
ordered probit model for OP2. As shown in Table 10, the regression results for OP1
and OP2 are qualitatively the same, and they generally support our prediction that
economically important clients are significantly less likely to receive MAOs from
small offices of non-Big4 auditors, and the large size of an audit office can greatly
29
mitigate the negative impact of client importance on audit quality. Overall, our
conclusions are robust to alternative measures of audit quality.
[Insert Table 10 Here]
5.2.3 Alternative measures of client importance
In most prior studies, client importance is measured as a continuous variable. In
order to test whether our conclusions still hold when we use a continuous variable for
client importance, we substitute IMPOR_OFFICE with CI_OFFICE in all the
regressions run in Section 4. The untabulated results indicate that there is a positive
but not significant relationship between CI_OFFICE and DA_ABS, which means that
client importance at the office level has no significantly negative impact on audit
quality if we measure it as a continuous variable. We also fail to find that CI_OFFICE
has any significant impact on signed discretional accruals (DA_POS and DA_NEG).
However, the significantly negative coefficient on the interaction term
CI_OFFICE*BIG4 in the DA_ABS model (the coefficient is -0.104 and significant
at 5% level) indicate that client importance at the office level has a much weaker
impact on audit quality for Big4 auditors than for non-Big4 auditors. These findings
further confirm our prediction that only a high degree of client importance might
impair audit quality; in other words, small differences in client importance are not
strongly associated with audit quality differentiations.
As mentioned in Section3, there is no established criterion as to which clients
constitute the vital few clients of a particular audit office. In our primary tests, we
adopt the third quartile threshold to define the vital few. In order to test the sensitivity
of our results to different client importance thresholds, we use two other cut-off points
to calculate our alternative client importance dummies. First, consistent with the
definition of high-influence and low-influence clients devised by Reynolds and
Francis (2001), we use the median value of CI_OFFICE for a particular audit office
as the cut-off point, and define those clients with CI_OFFICE above the median value
as the vital few (IMPOR_OFFICE_P50=1). Second, we take the fourth quintile (top
20%) as the cut-off point and define those clients with an importance level exceeding
the fourth quintile as the vital few (IMPOR_OFFICE_P80=1). We use
IMPOR_OFFICE_P50 and IMPOR_OFFICE_P80 as our alternative client
importance dummy variables. Untabulated results indicate that our conclusions are
basically unchanged when we use IMPOR_OFFICE_P80 as the predictor variable,
but we fail to find a significant relationship between IMPOR_OFFICE_P50 and
unsigned (or signed) discretional accruals, even though the sign of coefficient on
IMPOR_OFFICE_P50 is consistent with our expectation. These findings support our
30
speculation that only a few (far less than 50%) highly important clients might be
treated favorably by their auditors.
5.2.4 Other sensitivity tests
In order to further distinguish the firm level and the office level analyses, we
impose more stringent requirements on the number of practice offices for a given
accounting firm than those used in our primary tests, i.e., we require that each
accounting firm have at least three practice offices including the national headquarters
office. The firm-year observations in our final sample are then reduced to 3,414. We
re-run the regressions, and the untabulated results indicate that our conclusions are not
changed.
Since the audit offices on average have smaller clientele in our sample, it is
possible that our results are driven by a few audit offices whose client number is very
small and the client importance level is extremely high. To rule out this possibility, we
require that audit offices have at least three listed clients in our sample, which reduces
the firm-year observations in our final sample to 3,714. We re-run the regressions,
and the untabulated results are not different from those we obtain from the primary
tests.
In the primary tests, we have already adopted a strict sample selection process to
rule out companies with distinct characteristics, and winsorize all continuous
variables at the 1% and 99% percentile of their annual distributions, which has largely
reduced the influence of possible outliers. To further ensure that our results are not
driven by outliers, we define those observations with absolute standard residuals
exceeding 3 as outliers, and 673 outliers are generated. We re-run the regressions
without these outliers and the results (untabulated) are qualitatively the same.
6. Conclusions
Theoretical analysis indicates that auditors are more likely to be economically
dependent on their large clients at the audit office level. Most prior studies, however,
fail to find evidence that client importance at the office level negatively impacts audit
quality. The distinct features of the institutional environment in China, including weak
investor protection and a low risk of litigation for auditors, enables us to disentangle
the effect of economic dependence on important clients from auditors’ incentives to
protect their reputation. Using data on A-share listed companies in China from 2007
to 2010, we find that a high level of client importance is negatively associated with
audit quality at the audit-office level, as proxied by unsigned and signed performance-
matched discretional accruals. We also find that auditors’ brand-name reputation and
31
the large size of an audit office can largely mitigate such a negative impact. The
empirical results are robust to a series of sensitivity tests. Our findings suggest that,
absent an institutional environment that enhances auditors’ incentive to protect their
reputation, audit quality may be unacceptably low when small audit offices of non-
Big4 auditors provide audit services to their vital few clients. According to Reynolds
and Francis (2001), Australia used to have an audit standard that explicitly cautioned
auditors to avoid situations in which an “office regularly depends on one audit client
or group of connected clients for a significant portion of its total fees” and suggests 15%
as a rule-of-thumb limit on the portion of revenues from a single client. Our results
lend some support to such an action taken by regulators or standard-setters21.
Our study makes several contributions to the literature. First, we find evidence
that client importance at the office level could impair audit quality in an institutional
context characterized by weak investor protection and a low risk of litigation for
auditors. Second, we find that auditors’ brand-name reputation and the large size of an
audit office are critical in maintaining auditors’ independence from their economically
important clients at the audit-office level. It is noteworthy that it is not client
importance in general but a high level of client importance that could impair audit
quality, and the negative impact of client importance on audit quality occurs only for
clients audited by small offices of non-Big4 auditors. We define client importance
based on the widely used 80-20 rule in decision making, and our definition may be
useful for other researchers in auditing. We do not find evidence that client
importance negatively impacts audit quality at the firm level or the engagement-
partner level, which raises the question as to which unit of analysis is most
appropriate in research on client importance. Although we believe that the audit office
is the most suitable unit of analysis, this question is definitely worth further
exploration.
There are several limitations worth mentioning in our study. First, our measure
of client importance is based on a client’s total assets (in natural logarithm form)
rather than on audit or non-audit fees paid by the clients. This is because the
information on fees paid to auditors by listed companies in China is not complete or
consistent. Even though clients’ total assets are highly correlated with audit fees, the
appropriateness of our measure of client importance still depends on how well audit
fees are surrogated by clients’ total assets. Second, the number of practice offices for
an average accounting firm is far fewer in China than in the U.S., which obscures the
boundary between the firm level and the office level analyses. When the number of
21 In our sample, the total assets(in natural logarithm form) of each vital few client averagely accounts for 12% of the sum of total assets (in natural logarithm form) audited by the engagement office.
32
audit offices is small, national headquarters are in a stronger position to impose tighter
quality control on local offices, which makes it less reasonable to treat the audit office
as an independent unit of analysis. Third, the appropriateness of the office level
analysis depends largely on the degree of internal integration in an accounting firm.
When the accounting firm is highly integrated (i.e., the national headquarters make
key decisions and profits are shared firm-wide), it is not appropriate to conduct audit
research at the audit-office level. However, in our empirical analyses we can hardly
obtain information on, and subsequently control for, the degree of internal integration
of an accounting firm. Despite the above limitations, our study sheds some new light
on the impact of client importance on audit quality at the office level.
33
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Appendix 1 : Definition of variables
Variables Definition
Dependent variables:
DA_ABS performance-matched absolute discretionary accruals computed based on
modified Jone’s model
DA_POS positive discretionary accruals
DA_NEG negative discretionary accruals
Predictor variables (predicted sign):
CI_OFFICE (+) client importance at the local office level, measured as a ratio: the natural
logarithm of the total assets of client j to the sum of the total assets (in natural
logarithm form) of k clients audited by audit office i in a given year
IMPOR_OFFICE (+) dummy variable for client importance at the local office level, coded as 1 if a
client belongs to the top 25% of clients of a particular audit office according to
their rank of CI_OFFICE, and as 0 otherwise.
BIG4 () dummy variable coded as 1 if the client is audited by Big4 auditors and as 0
otherwise
LARGE () dummy variable coded as 1 if the size of an audit office exceed the median
size of all audit offices in a given year, and as 0 otherwise
Control variables:
LagTA (?) lag total accruals divided by its beginning-of-year total assets
SOE (?) dummy variable coded as 1 if the company is state-owned, and as 0 otherwise
BHSHARE () dummy variable coded as 1 if the company issues A-Share and B-Share
simultaneously, or is cross-listed both in main-land China and Hong Kong, as
0 otherwise
SIZE () natural logarithm of total assets
LEV (+) financial leverage ratio, measured as total liability divided by total assets
CFO () operating cash flows divided by total assets
ROE () net income divided by net assets
BM () book-to-market ratio, measured as ratio of book value to the market value of
equity
GROWTH (+) revenue growth rate
LOSS (?) dummy variable coded as 1 if the company reports net loss in the current year,
and as 0 otherwise
SWITCH (?) dummy variable coded as 1 if the company changes its auditor, and as 0
otherwise
SHORT (?) dummy variable coded as 1 if the company is audited by an auditor for less
than 3 consecutive years, and as 0 otherwise
LOCAL (+) dummy variable coded as 1 if the engagement office is located in the same
province as its client
MERGER (?) dummy variable coded as 1 if the accounting firm undergoes mergers with
other accounting firms, and as 0 otherwise
37
Table 1: Some statistics on the audit market in China
Panel A: Changes in the China audit market, 2002 -2010
Year
No. of accounting firm
with qualification to audit
listed companies
Percentage of A-share
companies audited by
Big4
Audit market share
measured in terms of total
revenues earned by Big4
2002 68 9.136 43.107
2003 68 8.353 45.784
2004 68 7.006 52.436
2005 66 7.249 55.738
2006 63 6.899 59.407
2007 62 7.360 60.708
2008 58 6.862 58.097
2009 54 6.393 48.872
2010 53 6.122 45.212
Total 7.245 52.151
Panel B: Change in number of audit offices, 2003-2010
Year No. of accounting firm
with qualification to audit listed companies
No. of audit offices
Mean Min. Max.
2003 68 2 0 7
2004 68 2 0 8
2005 66 2 0 8
2006 63 3 0 13
2007 62 4 0 22
2008 58 5 0 18
2009 54 7 0 27
2010 53 8 0 30
Total 4 0 30
38
Table 2: The sample selection and yearly sample distribution
Panel A: Sample Selection
Total number of firm-year observations in 2007-2010 7101
Less:
Observations with missing information about signing CPA’s identity (148)
B-Share companies (90)
Companies in the financial industry (121)
IPO companies (633)
ST companies (686)
Observations audited by accounting firms with less than one local office (825)
Observations audited by audit offices with only one listed client (128)
Observations with other necessary data missing (606)
Observations in the final sample 3864
Panel B: Yearly sample distribution
Year 2007 2008 2009 2010 Total
Observations 759 928 1069 1108 3864
39
Table 3: The meanings of coefficients in equation (3)
BIG4 IMPOR_OFFICE LARGE
0 1
0 0
1
1 0
1
40
Table 4: Descriptive statistics
Panel A:Full sample Variable N Mean Median Std.Dev. Min Max DA_ABS 3864 0.10 0.07 0.09 0.00 0.45 DA_POS 1901 0.10 0.08 0.09 0.00 0.44 DA_NEG 1963 0.10 0.07 0.09 0.45 0.00 CI_OFFICE 3864 0.10 0.06 0.11 0.01 0.54 IMPOR_ OFFICE 3864 0.33 0.00 0.47 0.00 1.00 LagTA 3864 0.01 0.02 0.11 0.64 0.50 SOE 3864 0.65 1.00 0.48 0.00 1.00 BHSHARE 3864 0.09 0.00 0.29 0.00 1.00 SIZE 3864 21.85 21.67 1.24 19.02 26.76 LEV 3864 0.50 0.52 0.19 0.03 1.26 CFO 3864 0.06 0.05 0.09 0.28 0.38 ROE 3864 0.08 0.08 0.25 8.20 1.58 BM 3864 0.31 0.25 0.22 0.70 1.28 GROWTH 3864 0.56 0.09 2.24 0.99 23.42 LOSS 3864 0.09 0.00 0.29 0.00 1.00 SWITCH 3864 0.08 0.00 0.27 0.00 1.00 SHORT 3864 0.25 0.00 0.44 0.00 1.00 LOCAL 3864 0.63 1.00 0.48 0.00 1.00 MERGER 3864 0.22 0.00 0.41 0.00 1.00
Panel B: Descriptive statistics by auditor type: Big4 vs. non-Big4 auditors
Big4: (N = 294) Non-Big4 (N = 3,570) Differences Variables Mean Median S.D. Mean Median S.D. Mean Median DA_ABS 0.09 0.06 0.08 0.10 0.07 0.09 0.01** 0.01**
DA_POS 0.08 0.05 0.09 0.11 0.08 0.09 0.02*** 0.03***
DA_NEG 0.09 0.08 0.08 0.10 0.07 0.09 0.01 0.01
CI_OFFICE 0.13 0.09 0.10 0.10 0.05 0.11 0.03*** 0.04***
IMPOR_ OFFICE 0.21 0.00 0.41 0.34 0.00 0.47 0.13*** 0.00***
LagTA 0.03 -0.03 0.10 0.01 0.02 0.11 0.02*** 0.02***
SOE 0.82 1.00 0.39 0.63 1.00 0.48 0.19*** 0.00***
BHSHARE 0.53 1.00 0.50 0.05 0.00 0.22 0.48*** 1.00***
SIZE 23.64 23.48 1.41 21.70 21.58 1.11 1.94*** 1.89***
LEV 0.53 0.52 0.18 0.50 0.51 0.19 0.03*** 0.01**
CFO 0.08 0.08 0.08 0.05 0.05 0.09 0.03*** 0.03***
ROE 0.12 0.12 0.12 0.07 0.08 0.26 0.04*** 0.04***
LOSS 0.08 0.00 0.27 0.09 0.00 0.29 0.02 0.00
BM 0.40 0.34 0.24 0.31 0.24 0.22 0.09*** 0.09***
GROWTH 0.18 0.02 0.84 0.59 0.10 2.32 0.41*** 0.08***
SWITCH 0.11 0.00 0.31 0.08 0.00 0.26 0.03** 0.00**
SHORT 0.29 0.00 0.45 0.25 0.00 0.43 0.03 0.00
LOCAL 0.53 1.00 0.50 0.64 1.00 0.48 0.11*** 0.00***
MERGER 0.06 0.00 0.25 0.23 0.00 0.42 0.17*** 0.00***
41
Panel C: Descriptive statistics by audit office size: Large vs. small audit offices
Large Office: (N =1,888) Small Office (N = 1,976) Differences Variables Mean Median S.D. Mean Median S.D. Mean Median DA_ABS 0.10 0.07 0.09 0.10 0.07 0.09 0.00 0.00
DA_POS 0.10 0.07 0.09 0.11 0.08 0.09 0.00 0.00
DA_NEG 0.10 0.07 0.09 0.10 0.07 0.09 0.00 0.00
CI_OFFICE 0.04 0.03 0.05 0.15 0.12 0.12 0.11*** 0.09***
IMPOR_ OFFICE 0.30 0.00 0.46 0.36 0.00 0.48 0.07*** 0.00***
LagTA 0.01 0.02 0.11 0.01 0.02 0.11 0.00 0.00
SOE 0.69 1.00 0.46 0.61 1.00 0.49 0.08*** 0.00***
BHSHARE 0.14 0.00 0.35 0.04 0.00 0.20 0.10*** 0.00***
SIZE 22.14 21.92 1.39 21.57 21.50 1.01 0.58*** 0.42***
LEV 0.50 0.51 0.19 0.51 0.52 0.18 0.00 0.01
CFO 0.06 0.05 0.09 0.05 0.05 0.08 0.00 0.00
ROE 0.08 0.09 0.32 0.07 0.07 0.17 0.00 0.02***
LOSS 0.09 0.00 0.28 0.10 0.00 0.30 0.01 0.00
BM 0.32 0.26 0.23 0.30 0.24 0.22 0.02** 0.01***
GROWTH 0.53 0.08 2.22 0.58 0.10 2.26 0.05 0.02
SWITCH 0.08 0.00 0.27 0.08 0.00 0.27 0.00 0.00
SHORT 0.26 0.00 0.44 0.25 0.00 0.43 0.01 0.00
LOCAL 0.56 1.00 0.50 0.70 1.00 0.46 0.14*** 0.00***
MERGER 0.22 0.00 0.41 0.22 0.00 0.41 0.00 0.00
Differences in means (medians) are based on t-tests (Mann-Whitney tests).
*, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.
See Appendix1 for variable definitions.
42
Table 5: Correlation matrix
Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
DA_ABS (1) 1.000 0.003 0.036** 0.038** 0.011 0.032** 0.038** 0.029* 0.003 0.051***
CI_OFFICE (2) 0.007 1.000 0.095*** 0.169*** 0.687*** 0.031* 0.057*** 0.038** 0.037** 0.053***
IMPOR_ OFFICE (3) 0.044*** 0.126*** 1.000 0.076*** 0.071*** 0.045*** 0.112*** 0.001 0.612*** 0.308***
BIG4 (4) 0.039** 0.083*** 0.075*** 1.000 0.260*** 0.059*** 0.103*** 0.443*** 0.332*** 0.035**
LARGE (5) 0.007 0.503*** 0.071*** 0.260*** 1.000 0.019 0.081*** 0.170*** 0.196*** 0.006
LagTA (6) 0.063*** 0.003 0.056*** 0.045*** 0.018 1.000 0.083*** 0.032** 0.026 0.115***
SOE (7) 0.038** 0.028* 0.112*** 0.103*** 0.081*** 0.090*** 1.000 0.104*** 0.249*** 0.113***
BHSHARE (8) 0.035** 0.024 0.001 0.443*** 0.170*** 0.025 0.104*** 1.000 0.218*** 0.049***
SIZE (9) 0.006 0.019 0.567*** 0.413*** 0.231*** 0.031* 0.258*** 0.286*** 1.000 0.370***
LEV (10) 0.056*** 0.043*** 0.297*** 0.042*** 0.002 0.069*** 0.110*** 0.058*** 0.355*** 1.000
CFO (11) 0.068*** 0.012 0.051*** 0.084*** 0.019 0.124*** 0.031* 0.013 0.008 0.183***
ROE (12) 0.004 0.013 0.083*** 0.047*** 0.010 0.084*** 0.040** 0.012 0.125*** 0.160***
LOSS (13) 0.006 0.020 0.070*** 0.015 0.026 0.063*** 0.026 0.031* 0.128*** 0.149***
BM (14) 0.076*** 0.013 0.166*** 0.107*** 0.039** 0.052*** 0.149*** 0.053*** 0.261*** 0.015
GROWTH (15) 0.127*** 0.002 0.005 0.048*** 0.011 0.035** 0.060*** 0.061*** 0.032** 0.056***
SWITCH (16) 0.043*** 0.083*** 0.016 0.033** 0.006 0.003 0.068*** 0.014 0.003 0.036**
SHORT (17) 0.017 0.140*** 0.027* 0.021 0.010 0.021 0.107*** 0.017 0.041** 0.065***
LOCAL (18) 0.005 0.091*** 0.008 0.062*** 0.147*** 0.009 0.042*** 0.003 0.031* 0.027*
MERGER (19) 0.002 0.034** 0.008 0.107*** 0.000 0.031* 0.029* 0.076*** 0.062*** 0.002
43
Table 5 (continued)
Variable (11) (12) (13) (14) (15) (16) (17) (18) (19)
DA_ABS (1) 0.033** 0.063*** 0.012 0.080*** 0.059*** 0.038** 0.009 0.005 0.001
CI_OFFICE (2) 0.034** 0.02 0.025 0.014 0.020 0.067*** 0.124*** 0.103*** 0.048***
IMPOR_ OFFICE (3) 0.036** 0.157*** 0.070*** 0.182*** 0.013 0.016 0.027* 0.009 0.008
BIG4 (4) 0.099*** 0.098*** 0.015 0.111*** 0.058*** 0.033** 0.021 0.062*** 0.107***
LARGE (5) 0.019 0.082*** 0.026 0.045*** 0.011 0.006 0.010 0.147*** 0.000
LagTA (6) 0.146*** 0.129*** 0.074*** 0.025 0.019 0.008 0.02 0.015 0.027*
SOE (7) 0.021 0.064*** 0.027 0.161*** 0.044*** 0.068*** 0.107*** 0.042** 0.029*
BHSHARE (8) 0.017 0.017 0.031* 0.049*** 0.077*** 0.014 0.017 0.003 0.076***
SIZE (9) 0.014 0.274*** 0.145*** 0.294*** 0.034** 0.004 0.030* 0.030* 0.054***
LEV (10) 0.175*** 0.037** 0.141*** 0.007 0.069*** 0.034** 0.066*** 0.028* 0.000
CFO (11) 1.000 0.291*** 0.141*** 0.084*** 0.102*** 0.019 0.065*** 0.016 0.005
ROE (12) 0.147*** 1.000 0.499*** 0.277*** 0.044*** 0.014 0.029* 0.042*** 0.048***
LOSS (13) 0.114*** 0.455*** 1.000 0.034** 0.055*** 0.023 0.041** 0.038** 0.060***
BM (14) 0.082*** 0.073*** 0.060*** 1.000 0.170*** 0.034** 0.026 0.018 0.025
GROWTH (15) 0.079*** 0.024 0.019 0.084*** 1.000 0.005 0.034** 0.017 0.003
SWITCH (16) 0.020 0.026 0.023 0.017 0.057*** 1.000 0.489*** 0.080*** 0.015
SHORT (17) 0.058*** 0.042*** 0.041** 0.004 0.065*** 0.489*** 1.000 0.163*** 0.016
LOCAL (18) 0.007 0.020 0.038** 0.028* 0.011 0.080*** 0.163*** 1.000 0.023
MERGER (19) 0.002 0.024 0.060*** 0.045*** 0.037** 0.015 0.016 0.023 1.000
Notes: *, **, *** indicate two-tailed statistical significance at the 10%, 5%, and 1% level, respectively. Above the diagonal is Spearman pairwise correlations and blow the diagonal is Pearson
pairwise correlations.
44
Table 6: Empirical results of unsigned discretional accruals model
Variable Pred.
sign
Specification (1) Specification (2) Specification (3) Specification (4)
Coeff. t-stat. Coeff. t-stat Coeff. t-stat. Coeff. t-stat
Intercept ? 0.100 2.46** 0.092 2.00** 0.093 2.19** 0.081 1.70*
IMPOR_OFFICE + 0.009 2.20** 0.008 1.90* 0.013 2.58** 0.012 2.37**
BIG4 0.002 0.21 0.004 0.55
IMPOR_OFFICE*BIG4 0.007 0.64 0.002 0.19
LARGE 0.003 0.78 0.003 0.83
IMPOR_OFFICE*LARGE 0.010 1.63 0.011 1.62
LagTA ? 0.020 1.35 0.020 1.32 0.020 1.33 0.020 1.29
SOE ? 0.002 0.50 0.002 0.53 0.002 0.52 0.002 0.55
BHSHARE 0.006 1.12 0.005 0.93 0.006 1.17 0.005 0.93
SIZE 0.001 0.59 0.002 0.68 0.001 0.68 0.002 0.85
LEV + 0.007 0.66 0.006 0.62 0.007 0.72 0.007 0.67
CFO 0.026 0.92 0.025 0.90 0.026 0.93 0.026 0.91
ROE 0.008 1.09 0.008 1.09 0.008 1.10 0.008 1.10
LOSS ? 0.001 0.25 0.001 0.21 0.001 0.23 0.001 0.20
BM 0.032 3.39*** 0.033 3.40*** 0.032 3.36*** 0.032 3.36***
GROWTH + 0.003 2.58** 0.003 2.59** 0.003 2.59** 0.003 2.59**
SWITCH ? 0.015 2.21** 0.015 2.21** 0.015 2.20** 0.015 2.21**
SHORT ? 0.002 0.59 0.002 0.59 0.002 0.60 0.002 0.61
LOCAL + 0.002 0.57 0.002 0.59 0.002 0.60 0.002 0.63
MERGER ? 0.000 0.11 0.001 0.15 0.000 0.09 0.001 0.14
YEAR (controlled) (controlled) (controlled) (controlled)
IND (controlled) (controlled) (controlled) (controlled)
No. of Obs. 3864 3864 3864 3864
F 7.58*** 7.17*** 7.24*** 6.86***
Adj R2 6.15% 6.11% 6.17% 6.13%
Notes: t-values are calculated based on cluster-robust standard errors. *, **, *** indicate two-tailed statistical significance at the 10%, 5%, and 1% level, respectively.
45
Table 7 : Empirical results of signed discretional accruals models
Variables
DA_POS DA_NEG
Pred.
sign Coeff. t-stat
Pred.
sign Coeff. t-stat
Intercept ? 0.269 4.33*** ? 0.077 1.24
IMPOR_OFFICE + 0.013 2.07** 0.007 1.12
BIG4 0.008 0.79 + 0.013 1.26
IMPOR_OFFICE*BIG4 0.017 0.81 + 0.005 0.30
LARGE 0.004 0.78 + 0.002 0.32
IMPOR_OFFICE*LARGE 0.004 0.49 + 0.014 1.58
LagTA ? 0.013 0.70 ? 0.025 1.28
SOE ? 0.002 0.47 ? 0.006 1.45
BHSHARE 0.001 0.11 + 0.002 0.22
SIZE 0.006 1.98** + 0.006 1.95*
LEV + 0.031 2.60*** 0.034 2.47**
CFO 0.518 16.51*** + 0.438 12.94***
ROE 0.180 6.17*** + 0.028 3.69***
LOSS 0.010 1.04 + 0.025 4.20***
BM + 0.021 1.58 0.004 0.34
GROWTH ? 0.003 2.00** ? 0.001 0.38
SWITCH ? 0.027 3.32*** ? 0.003 0.29
SHORT ? 0.011 2.19** ? 0.006 1.15
LOCAL + 0.001 0.19 0.000 0.11
MERGER ? 0.002 0.41 ? 0.002 0.39
YEAR (controlled) (controlled)
IND (controlled) (controlled)
No. of Obs. 1901 1963
F 12.52*** 9.93***
Adj R2 22.96% 17.42%
Notes: t-values are calculated based on cluster-robust standard errors. *, **, *** indicate two-tailed statistical significance at the 10%, 5%, and 1% level, respectively.
46
Table 8: Empirical results of firm-level and partner-level analysis
Variable Pred.
Sign
Firm-level analysis Partner-level analysis
Coefficient t-stat. Coefficient t-stat.
IMPOR_FIRM
/IMPOR_PARTNER + 0.003 0.68 0.001 0.28
BIG4 0.003 0.46 0.001 0.10
IMPOR_FIRM*BIG4
/IMPOR_PARTNER*BIG4 0.001 0.05 0.015 1.33
No. of obs. 4712 3644
F 8.794*** 7.955***
Adj. R2 6.4% 6.9%
Notes: t-values are calculated based on cluster-robust standard errors. *** indicate the two-tailed statistical significance at 1% level. For brevity, we don’t report the regression results for control variables.
47
Table 9: Empirical results of the two-stage selection model
Panel A: Empirical results of first-stage regression
Variable Pred.Sign Coefficient z-stat.
Intercept ? 13.979 15.69***
REGION1 + 0.189 1.25
REGION2 + 0.089 0.74
MKINDEX + 0.090 3.51***
INDIR ? 0.789 2.14**
OWNER ? 0.410 1.48
SOE ? 0.005 0.05
BHSHARE + 1.164 11.84***
SIZE + 0.534 12.92***
LEV 0.887 3.43***
CFO + 1.429 2.55**
ROE + 0.534 1.21
LOSS 0.279 1.44
BM ? 0.272 1.43
GROWTH ? 0.080 1.72*
No. of obs. 3556
Wald χ2 768.08***
Pseudo R2 40.51%
Panel B: Empirical results of second-stage regression
Variable Pred.Sign Coefficient z-stat
IMR ? 0.018 1.71*
IMPOR_OFFICE + 0.014 2.59**
BIG4 0.030 1.44
IMPOR_OFFICE*BIG4 0.011 0.74
LARGE 0.003 0.68
IMPOR_OFFICE*LARGE 0.011 1.66*
No. of obs. 3556
Wald χ2 739.70***
Notes: *, **, *** indicate two-tailed statistical significance at the 10%, 5%, and 1% level, respectively. For brevity, we don’t report the regression results for control variables. However, the regression results for controls variables are qualitatively the same as those reported in Table 7.
48
Table 10: Regression results of the MAO model
Variable Pred. Sign
OP1 OP2 Coefficient z-stat Coefficient z-stat
IMPOR_OFFICE 0.375 1.83 * 0.319 1.65* BIG4 + 0.020 0.07 0.070 0.29 LARGE + 0.066 0.54 0.099 0.88 IMPOR_OFFICE*LARGE + 0.157 0.58 0.172 0.66 LagOP1/ LagOP2 + 1.973 11.17*** 1.258 10.13*** SIZE 0.129 1.56 0.139 1.77* LEV + 0.738 2.06** 0.719 2.18** CFO 0.261 0.35 0.344 0.45 REC + 0.600 0.78 0.754 0.96 INV + 0.911 1.79* 1.059 2.29** ROA 6.081 5.21*** 6.461 5.65*** LOSS + 0.184 0.93 0.129 0.67 QUICK 0.009 0.26 0.002 0.05 TURNOVER 0.204 1.41 0.224 1.58 YEAR (controlled) (controlled) IND (controlled) (controlled) No. of obs. 4325 4325 Wald χ2 2475.29*** 2786.59*** Pseudo R2 41.25% 35.67% Notes: *, **, *** indicate two-tailed statistical significance at the 10%, 5%, and 1% level, respectively. The interaction term IMPOR_OFFICE*BIG4 is omitted because none of the companies defined as highly important clients of Big4 auditors receive MAO report in our sample.