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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

[email protected]

Wuchun Chi

Department of Accounting

College of Commerce

National Chengchi University

Taipei, Taiwan

[email protected]

Wan-Ying Lin

Department of Accounting

College of Commerce

National Chengchi University

Taipei, Taiwan

[email protected]

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

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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.

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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

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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.

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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.

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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.

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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.

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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

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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

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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

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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.


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.


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.


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.


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.


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).


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.


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.


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.


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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36

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.

auditors’ brand-name reputation, audit office size and the ......impairs audit quality holds only...

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