incremental information of auditor quality · 2017-08-11 · impacts of different auditors as...

Incremental Information of Auditor Quality

Baolei Qi

Xi’an Jiaotong University

Email: [email protected]

Yi Si



Gaoliang Tian



Martin G. H. Wu*

The University of Illinois at Urbana-Champaign


January 12, 2015

* Corresponding author.

The paper’s former title was “Do individual auditors affect clients’ real activities manipulation? Evidence

of learning and auditor quality.” We appreciate the helpful comments received from Andrew M. Bauer,

Timothy Bauer, Paul J. Beck, Joseph Carcello, Monica Causholli, Long Chen, Richard Crowley, Keith

Czerney, Keith Decie, Paul Demere, Laura Li, Tracey Majors, Heather Pesch, Theodore Sougiannis,

Anne Thompson, Donghui Wu, and the workshop participants at University of Illinois at Urbana-

Champaign, Xi’an Jiaotong University, Fudan University, Hunan University, the 2014 American

Accounting Association Annual Conference, and the 2014 Temple University Accounting Conference.

Gaoliang Tian acknowledges financial supports from the National Science Foundation of China (Grant

No. 71102095) and the Scientific Research Foundation of the Humanities and Social Planning Project of

the Ministry of Education of China (Grant No. 13YJA630081).

mailto:[email protected]




Incremental Information of Auditor Quality

Abstract

We investigate whether clients’ real activities manipulation (RAM) varies across individual auditors. We

predict and find significant associations of RAM with audit firms and individual auditors. We interpret

these associations as the auditors’ responses to or influences on their clients’ economics. Including

explicit control variables such as audit fees and qualified audit opinions in multiple regressions, we

document that the latter association, not the former, remains to be economically significant. Therefore,

individual auditors contribute incremental information beyond what the firm-level audit quality provides.

We also document auditors’ demographic characteristics have limited power in explaining RAM’s

variation, however. Our overall findings suggest that regulatory policies curbing “free riders” in auditing,

e.g., engagement partner signature and rotation, help strengthening auditor-client relationships and, hence,

auditor quality.

Keywords: Auditor quality, auditor identity disclosure, engagement partner rotation,

feedback and feed-forward effects, and real activities manipulation

JEL Classification: M49

Data Availability: All the data used in this study are publicly available.

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1. Introduction

Audit clients often manage earnings through accounting accruals and real activities manipulation

(RAM), and earnings quality affects audit outcome. Hence, both accruals and RAM are useful proxies for

audit quality. Using abnormal accruals and modified audit opinions (MOD) as proxies, for example, Gul,

Wu, and Yang (2013) find evidence supporting audit quality varies across different auditors. Their

evidence suggests individual auditors have distinct impacts on the audit quality measured at the firm level.

But it is unclear whether individual auditors contribute incremental information beyond what the firm-

and city/office-level audit quality provides. If they do, what is the extent to which the incremental

information goes beyond the financial reporting responsibilities shared by the auditor and the client?

RAM usually refers to opportunistic reduction in clients’ discretionary expenditures (e.g., SG&A,

R&D, quality inspection, etc.), overproduction, and price discounts to boost sales temporarily. Although it

is not likely to violate the GAAP or GAAS, RAM may work as a substitute for or complement to the

accrual method. Hence, an increase in auditors’ scrutiny constrains accrual manipulation and in turn,

increases clients’ RAM, which suggests individual auditors either respond to or influence RAM or both.

We refer to the auditors’ responses and influences as the associations between the auditors and their

clients’ economics. Since clients’ RAM is part of the clients’ operating decisions, it will capture more

information contents about the clients than the clients’ accruals for financial reporting do.

We predict and find that clients’ RAM varies across different auditors. Specifically, we document

that auditors respond to or influence clients’ economic decisions through RAM.1 In multiple regressions

of RAM, we explicitly include control variables such as audit fees, going-concern opinions (GC), MOD,

etc. These controls are traditional proxies for audit quality at the firm level. We interpret the RAM

impacts of different auditors as incremental information of auditor quality. This result suggests that the

auditor-client relationships, in China at least, are the result of clients’ economics that is complex and far-

reaching, relative to the financial reporting responsibilities shared between the auditor and client.

1 We also perform the sensitivity analyses of the fixed effects of audit firms and individual auditors on Tobin’s Q,

which represents clients’ investment opportunities and hence economics. Our findings are broadly consistent.

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Auditors unremittingly seek opportunities to improve their understanding of clients’ business

models to enhance audit quality. In a nonstrategic dynamic model, for example, Beck and Wu (2006)

analyze how auditors’ learning and selection of professional services are jointly affecting audit quality. A

large number of archival and experimental studies have documented a plethora of evidence supporting

that auditors develop audit judgments and decision-makings by specialization, leadership, and portfolio

choices (see Section 2 for the literature review). More recent studies also find that auditors react toward

clients’ RAM (Kim and Park 2014, Commerford, Hermanson, Houston, and Peters 2014a, 2014b, and

Greiner, Kohlbeck, and Smith 2013). “In particular, we expect high quality auditors to consider not only

whether the client’s accounting choices are in technical compliance with GAAP, but also how faithfully

the financial statements reflect the firm’s underlying economics,” summarize DeFond and Zhang (2014).

It is well-known that audit clients’ economic decisions affect auditors’ judgments, learning, and

selection of professional services (e.g., Wu 2006 considers oligopolistic markets for both auditing and

consulting services). It is intriguing, however, whether auditors can conversely influence clients’

economic decisions. Notice that the negative answer to the above question would cast grave doubts and

limitations on the value-generating ability of auditors, thus jeopardizing the economic demand for audits.2

But the positive confirmation has been at best the feedback effects of auditors’ approvals of accounting

policy choices on clients’ business operations. Four arguments supporting the feedback effects in the

literature are worthy of special mention.3 (1) Qualified audit opinions often carry severe consequences for

clients’ economics. GC options are frequently called “self-fulfilling prophesies” and hence precisely

pinpoint the feedback effects that accounting policy choices may have on audit clients’ economics.

2 An analogy is the debate of information disclosure in an exchange economy versus a production economy.

3 The traditional view is RAM is outside of auditors’ purview as auditors have neither incentives to affect nor means

to detect clients’ RAM. In the world’s fast-changing environments, investors start questioning the usefulness of the

traditional financial statement audits. “Regulators have rebuked three of the Big 4 firms for not fixing past problems

fast enough,” Rapoport (2013) reports, “Many investors rely on the audit report to help assure them a company’s

numbers are accurate. … Regulators and industry critics say investors need more information from auditors about

matters such as whether a company’s accounting is aggressive, and what auditors think are the most important

features of the company’s finances.” New rules for annual audit reports are currently under consideration in the U.S.

in order to enhance auditors’ feedback effects on clients’ economic decisions, not exclusively on clients’ accounting

policy choices. Furthermore, the Chinese audit market must also be significantly influenced by “Chinese relations,”

which may help to yield complex and far-reaching auditor-client relationships beyond the financial reporting

responsibilities between the auditors and clients.

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(2) Audit reports often serve as one of the principal means that shareholders use to evaluate their

company’s financial position and managerial performance. For example, shareholders almost always

include earnings, which have the virtue of being audited, in executive compensation. And compensation

contracts will influence the company’s strategic decisions and economic outcomes. (3) The Sarbanes-

Oxley Act of 2002 (SOX) requires auditors to evaluate and attest to their audit clients’ “internal controls

for external financial reporting” (COSO 2013). The SOX has unprecedentedly bound together the clients’

business operating activities and financial reporting decisions, thereby causing them interlocking and

inseparable.4 (4) Finally, auditors are an incredible source of information for audit clients. Selection of

professional services will enhance auditors’ capabilities to compete in oligopolistic markets for auditing

and consulting services (Wu 2006).5

The above arguments are compelling for the feedback effects of auditors on clients’ economics,

but there lacks empirical evidence. The goal of this study, therefore, is to document the first evidence (to

the best of our knowledge) supporting individual auditors contribute incremental information beyond

what the firm-level audit quality provides. Our study is mostly close to Gul et al. (2013) for three reasons.

First, both studies use archival data from China because two auditors (partners or senior managers) of

each audit engagement are required to sign their names on audit reports. Second, many Chinese listed

companies used both accruals and RAM in order to meet the threshold levels set by the Chinese

governments for funds and operations (Li, Zheng, and Lian 2011). Constraints on accruals could have

driven RAM. Third, the decade-long fast-growing economy in China offers a unique opportunity for

auditors to interact closely with clients, thereby knitting very intricate auditor-client relations.

4 In Section 2, we will review some recent studies of the economic (financing, investing, and operating) impacts of

disclosures of “material weaknesses of internal control over external financial reporting.” 5 For example, Rapoport (2012) reports:

Consulting and other nonaudit lines of business are growing at rates far outpacing auditing. … At

PwC, ninety percent of advisory work is for nonaudit clients, said Dana Mcilwain, PwC’s U.S.

advisory leader. The Big Four also argue that consulting provides synergies even if they don’t

consult for and audit the same companies. Offering consulting gives them expertise they can draw

upon when related issues arise at their audit clients, they say. … “We believe the services we’re

in actually help us on the front of audit quality,” said John Ferraro, Ernst &Young’s global chief

operating officer.

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More importantly, our study complements and extends Gul et al. (2013). We document the extent

to which RAM (as a complement to accruals) varies across different auditors. While accruals’ variation

shows that auditors have distinct impacts on audit quality, RAM’s variation will suggest auditors react to

or influence clients’ economic decisions differently. Therefore, the auditor-client relations are exhilarated

by more than merely the financial reporting responsibilities shared between the auditor and the client. Our

extension comes from the inclusion of explicit control variables such as audit fees, GC, MOD, etc., in

multiple regressions of RAM. As all of these control variables are proxies for audit quality at the firm

level, we refer to the association between auditors and RAM in the regressions as incremental information

of auditor quality.

The implication of our findings is straightforward. Regulatory rules (see, e.g., signature and

rotation of engagement partners) curbing free riders in auditing would help strengthen the auditor-client

relations and, hence, auditor quality. Hence, our study is a valuable addition to the list of recent studies

following the “Concept Release on Requiring the Engagement Partner to Sign the Audit Report” by

PCAOB (2009).6 These studies focus on auditor quality, rather than the firm- and city/office level of audit

quality.7 The identification of individual auditors does make the comparison between audit quality and

auditor quality possible. Since audit quality at the firm level is an average of auditor quality, identifying

and eliminating the “tail distribution” of the latter quality will naturally increase the former.

The remainder of our study proceeds as follows. In Section 2, we provide institutional

backgrounds and a literature review. We elaborate our research questions. In Sections 3 and 4, we discuss

research design, empirical findings, and regression robustness tests. Section 5 concludes the study.

6 See, e.g., Nelson and Tan (2005), DeFond and Francis (2005), Church, Davis, and McCracken (2008), Francis

(2011), Carcello and Li (2013), Knechel, Rouse, Schelleman (2013), and Gul et al. (2013). 7 Audit firm-level studies include Becker, DeFond, Jiambalvo, and Subramanyam (1998), Francis and Krishnan

(1999), and Lennox (1999), and audit office-level studies include Reynolds and Francis (2000), Krishnan (2003,

2005), Francis, Reichelt, and Wang (2005), and Reichelt and Wang (2010).

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2. Institutional Background, Literature Review, and Research Questions

2.1 Emerging Market Economy of China and Literature Review

The establishment of the Shanghai and Shenzhen stock exchanges in the early 1990s is a sign that

China has started converting its central planning economy into a market economy. In China, shareholders

largely lack the necessary knowledge about monitoring and controlling their listed companies, however.

The securities regulations are also lax relative to the counterparts of the developed markets and

economies. The Chinese regulators are practicing the philosophy called “crossing river by feeling the

stones” as they are unsure whether regulatory policies are efficient (Aharony, Lee, and Wong 2000). As a

result, the Chinese stock markets exhibit two unique characteristics (Chen, Lee, and Li 2008). One is the

quota system designed to assure business stability, and the other is the large quantities of stock

transactions regulated by the Chinese governments. Both characteristics give a rise to the multiple

threshold levels set by the Chinese governments for the Chinese listed companies to meet in order to

continue business operations. Thus, many Chinese listed companies actively manage earnings (Chen and

Yuan 2004, and Chen et al. 2008).8

Companies often manage earnings through either the accrual method or the RAM method or both.

Generally speaking, the accrual method is easier to use relative to the RAM method (Roychowdhury

2006). While accruals are subject to GAAP and GAAS, RAM is not and usually has a negative impact on

future firm performance (Ewert and Wagenhofer 2005). A large number of studies have compared the

usages of the two methods, yet it is unclear that one method dominates the other. For example, Graham,

Harvey, and Rajgopal (2005) report the majority of the 400 CFOs surveyed are eager to use RAM. Cohen,

Dey, and Lys (2008) find that companies switch from the accrual method to the RAM method after the

enactment of SOX. Bartov and Cohen (2009) find a decline in the frequency of meeting or beating

8 A delisting policy of the China Securities Regulatory Commission (CSRC, 1998) has two special provisions:

Special Treatment (ST) and Particular Transfer (PT). ST means a listed company incurring losses in two consecutive

years will receive a “detention notice.” PT means a company incurring losses in more than two consecutive years

will be explicitly identified as such. As a result, ST and PT will impose significant operating and financing hurdles

on the company going forward. For example, a company must have its Returns on Equity greater than six (ten)

percent in three successive years in order to issue a Rights Offering (Seasonal Equity Offering). In China, the two

Offerings are the primary source of additional financing for all Chinese listed companies (Chen et al. 2008).

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analysts’ earnings forecasts after SOX. Bhojraj, Hribar, Picconi, and McInnis (2009) find companies that

use both methods to beat earnings forecasts exhibit short-run stock price advantages at the expense of

worse operations and stock performances in subsequent three years. Gunny (2010) reports that companies

using RAM to meet thresholds will have operational performance advantages over those that do not.

Cohen and Zarowin (2010) find that companies using both methods during seasonal equity offerings will

use more RAM after SOX. Zang (2012) finds that executives make tradeoffs between these two methods.

Finally, Li et al. (2011) document that the Chinese listed companies use both methods in order to meet the

threshold levels set by the Chinese governments for funding and continuing operations.

Although RAM is part of clients’ business operations and hence subject to neither GAAP nor

GAAS, one cannot conclude auditors are irrelevant or unconcerned. In fact, both audit fees and qualified

audit opinions are always influenced by clients’ business operations.9 A large number of experimental and

archival studies has examined whether audit judgments and decisions are improved by specialization and

leadership in client industries (see, e.g., Simunic 1980, 1984, Craswell, Francis, and Taylor 1995,

Solomon, Shield, and Whittington 1999, Taylor 2000, Ferguson and Stokes 2002, Ferguson, Francis, and

Stokes 2003, Low 2004, and Wu 2006) and portfolio selection of clients (see, e.g., Simunic and Stein

1987, 1990, and Johnstone and Bedard 2003, 2004). GAAS, SOX, and COSO stipulate that auditors bear

undeniable responsibilities for assessing and influencing clients’ financial reporting efficiency and clients’

internal control quality (see, e.g., Nelson, Elliott, and Tarpley 2002, Nelson and Tan 2005, Knechel,

Rouse, and Schelleman 2009, and Francis 2011). More recently, studies begin examining the impacts of a

company’s financial reporting decisions (e.g., material weakness disclosures) on the company’s investing

and operating decisions. For example, Bratten, Causholli, and Myers (2014) consider whether the use of

fair value accounting by banks influences the relative costs and effectiveness of earnings management

tools over their managerial decisions. Cheng, Dhaliwal, and Zhang (2013) consider the investment

9 We had a candid conversation with more than 30 experienced partners of the international Big Four accounting

firms and many domestic auditing firms. They told us that clients’ abnormal operating decisions were a major

concern of theirs. They administered particular attentions to those clients receiving ST or PT (see the previous

footnote).

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behaviors of companies that disclosed internal control weaknesses under SOX. Feng, Li, McVay, and

Skaife (2013) investigate whether ineffective internal control over external financial reporting impacts a

company’s operations. Bauer (2012) considers the relations between tax avoidance and weak internal

controls. Audits create value because they assure the creditability of accounting information, thereby

improving the efficiency of resource allocation (DeFond and Zhang 2014).

Statement on Auditing Standards 90 requires that auditors judge “the quality, not just the

acceptability, of the accounting principles applied in the financial statements.” No. 14 requires auditors to

evaluate potential bias in corporate executives’ judgments. Furthermore, the Chinese Auditing Standards

(CAS) requires CPAs in China to undertake analytical procedures in risk evaluation at the beginning of

auditing and reviewing processes, although it is optional for substantive testing. Analytical procedures are

used to compare both financial and non-financial data with other companies’ in the same industry, as well

as annual and seasonal data of the same company. Analytical procedures consist of investigating

unexpected or inconsistent fluctuations in financial and non-financial data.

Individual auditors failing to perform the audits according to the CAS will be penalized and

sanctioned in China (Firth, Mo, and Wong 2005, 2012). In 1994, the Chinese Securities Regulatory

Committee (CSRC) issued “Provisional Rules and Regulations on Administration of Stock Issues and

Trades” (PRASIT). The Securities Law (1999, 2005) provides the legal basis for CSRC to supervise all

listed companies and market intermediaries such as audit firms, engagement auditors, securities brokers,

lawyers, etc. Article 35 of PRASIT requires “audit firm and engagement auditor should provide the fair

audit report when fulfilling their duties.” Article 37 states: "the engagement partners will be sanctioned by

CSRC if their audit report contains fraudulent, serious misleading contents or misstatements. Penalties

include warnings, fines, suspensions, disqualifications, and imprisonments.” In 1998, CSRC sanctioned

Cheng Du Shu Du for failing to report its client's (PT Hong Guang) fictitious sales and inflated inventory,

and banned the two engagement partners for life. During 1998 and 2013, CSRC penalized 128 individual

partners (fined 62 and banned or disqualified 66).

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2.2 Research Questions

Previous studies show that people, rather than organizations, make decisions. The personalities of

decision makers affect decision outcomes (Kachelmeier 2010, Bamber, Jiang, and Wang 2010, and

Dyreng, Hanlon, and Maydew 2010). In the framework of audit production (Knechel et al. 2009 and

Francis 2011), audit engagement partners put their labor into auditing processes to gather evidence,

thereby affecting audit results. Individual auditors’ effort, experience, risk preference, and incentive will

change their attitudes toward and in turn, their learning about clients’ business models. Therefore, the

identity disclosure of auditors in China provides a natural experimental setting to examine whether

individual auditors respond to and influence over clients’ economic decisions beyond they do at the

audit firm level.

We use the Chinese Stock Market and Accounting Research Database (CSMAR). In China, two

auditors of each audit engagement are required to sign their names on audit reports. We collect individual

auditors’ personal demographic characteristics such as age, gender, position in the audit firm, political

affiliation, education background, work experience, etc., see Subsection 3.3 for details. We also collect

audit fees relative to clients’ total assets (Table 1), GC, and MOD (Table 2). Finally, we compute clients’

RAM over the sample period between 2000 and 2012 and across the three categories of audit firms

(Table 3).

(Insert Tables 1, 2, 3 and Figures 1, 2A, and 2B, and 3 about here.)

Audit fees relative to clients’ total assets and qualified audit opinions are almost monotonically

decreasing (Figures 1, 2A, and 2B), but clients’ RAMs are not (Figure 3). The changes in clients’ RAM

cannot explain the declines in audit fees and qualified audit opinions. Thus, we predict that significant

changes in auditor-client relations must take place in China, which suggests that individual auditors rather

than audit firms respond to or influence clients’ economic decisions (for which clients’ RAM is a proxy).

Adopting the regression methodology of Bertrand and Schoar (2003), we assign an indicator

variable to each auditor. We investigate whether clients’ RAM varies across different auditors. More

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specifically, we consider three related issues. (i) Model (1) considers whether clients’ RAM varies across

different auditors. (ii) Model (2) studies whether audit fees and qualified audit opinions help to explain

the variation in RAM. (iii) Model (11) considers whether auditors’ personal demographic characteristics

have additional power in explaining the fixed effect that individual auditors have on clients’ RAM.

3. Empirical Design

3.1 Multivariate Regression Models

Following Gul et al. (2013) and Bertrand and Schoar (2003, Model 1), we investigate empirically

the fixed effects of audit clients, audit firms, and individual auditors on clients’ unexpected changes in

real activities (RAM). The regression model is

𝑦𝑖𝑡 = 𝛼𝑡 + 𝛾𝑖 + λ𝐴𝐹 + λ𝐼𝐴 + 𝜷𝑿𝑖𝑡 + 𝜀�̃�𝑡. (1)

The dependent variable, 𝑦𝑖𝑡, represents clients’ RAM (see its definitions in the next subsection), its

subscripts represent Client 𝑖 and Year 𝑡. The independent variables include four indicator variables:

(1) 𝛼𝑡 is the coefficient on the Year indicator variable, and captures the year fixed effect;

(2) 𝛾𝑖 is the coefficient on the Client indicator variable, and captures the client fixed effect;

(3) λ𝐴𝐹 is the coefficient on the audit firm indicator variable, and captures the fixed effect of

audit firms (subscript AF means audit firms); and

(4) λ𝐼𝐴 is the coefficient on the individual auditor indicator variable, and captures the fixed effect

of individual auditors (subscript IA means individual auditors).

𝑿𝑖𝑡 is a vector of time-varying, client- and auditor-level control variables (see Table 4 for the definition

and descriptive statistics), and 𝜀�̃�𝑡 is the error term of the regression model.

It would be impossible to estimate the fixed effect of individual auditors if auditors changed

neither audit firms nor audit clients. For example, consider an auditor who was with the same firm and

clients. Then, the fixed effect of this particular auditor would be identical to the fixed effect of her audit

firm. Fortunately, we control and limit this problem in the following three ways. First, note that there are

two significant “waves” of changes in auditors during our sample period. The first wave refers to the

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massive “influx” of auditors into China in the late 1990s, and the other refers to the “exodus” of auditors

ten years later. The majority of these auditor changes happened to the International Big 4 accounting

firms. Second, the “conversion” process from the central planning economy to a market economy in

China was a decade long. In this process, accounting firms in China also went through an unprecedented

level of mergers and acquisitions. Accounting firms changed their names almost yearly during the sample

period; individual auditors were constantly switching audit firms, branches, and clients. Third, and, most

importantly, we use the two selection criteria suggested by Gul et al. (2013) to construct our sample of

individual auditors (see details in Subsection 4.1).

In our sample, therefore, the average tenure (i.e., the number of consecutive years) of the auditor

with a client is three years and one month. The average tenure of the audit firm with a client is three years

and nine months (Panel B, Table 4). The average tenure of the audit office with the client will naturally

fall between the above two tenure numbers. As their gap is small, we assume that the audit office tenure is

not a significant explanatory variable and, hence, omit it from the regression. The city/office-level

findings of Gul et al. (2013, Panels C-E, Table 3) support this assumption. Then, we predict that audit

fees relative to clients’ total assets (RAFee) will contribute to the fixed effect of audit firms because audit

fees in China are set at the year’s beginning. We also predict that auditors’ propensity to issue qualified

audit opinions (GC and MOD) will contribute to the fixed effect of individual auditors. Thus, we extend

Model (1) by explicitly including RAFee, GC, and MOD as additional control variables,10

𝑦𝑖𝑡 = 𝛼𝑡 + 𝛾𝑖 + λ𝐴𝐹 + λ𝐼𝐴 + 𝜷𝑿𝑖𝑡 + RAFee𝑖𝑡 + 𝜀�̃�𝑡, (2a)

𝑦𝑖𝑡 = 𝛼𝑡 + 𝛾𝑖 + λ𝐴𝐹 + λ𝐼𝐴 + 𝜷𝑿𝑖𝑡 + GC𝑖𝑡 + 𝜀�̃�𝑡, (2b)

𝑦𝑖𝑡 = 𝛼𝑡 + 𝛾𝑖 + λ𝐴𝐹 + λ𝐼𝐴 + 𝜷𝑿𝑖𝑡 + MOD𝑖𝑡 + 𝜀�̃�𝑡. (2c)

10

As a robustness check, we also ran the model, 𝑦𝑖𝑡 = 𝛼𝑡 + 𝛾𝑖 + λ𝐴𝐹 + λ𝐼𝐴 + 𝜷𝑿𝑖𝑡 + RAFee𝑖𝑡 + MOD𝑖𝑡 + 𝜀�̃�𝑡. The

results are qualitatively similar. Audit fees replace the indicator variables for audit firms to improve the explanatory

power of the regression. However, neither audit fees nor qualified audit opinions can replace the indicator variable

for individual auditors. The fixed effects of individual auditors on clients’ RAM (especially, abnormal discretionary

expenditure) remain statistically and economically significant. In addition, we also used the total audit fees (audit

fees and other fees). The results are similar and hence omitted in the paper. One may argue MOD is influenced by

audit fees since it is often used to proxy for audit quality (Gul et al. 2013). In our regression model with both audit

fees and MOD, we find that RAM variation arises significantly in clients’ abnormal discretionary expenditure.

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3.2 Measures of Clients’ RAM

We consider three separate measures of a client’s RAM and a composite measure. The first

measure is the difference between a client’s actual cash flow from operations (CFO) and its standard or

predicted CFO. We refer to the difference as abnormal cash flow from operations and denote it by

AB_CFO. To compute the client’s standard CFO (see Dechow et al. 1998, Roychowdhury 2006, Cohen

and Zarowin 2010, and Zang 2012), we use the following regression model,

CFO𝑡/A𝑡−1 = 𝛼0 + 𝛼1(1/A𝑡−1) + 𝛽1/A𝑡−1 + 𝛽2ΔS𝑡/A𝑡−1 + 𝜖�̃� . (3)

Note, S𝑡 is the client’s sales in year t, ΔS𝑡 ≡ S𝑡 − S𝑡−1 is the change in sales, and A𝑡 is the total asset at

year end. Thus, AB_CFO is the residual of Model (3) (which is equivalent to −1 times the deviation of

cash flow from the predicted value, like the prior studies). The larger it is, the more the client’s RAM is.

The second measure is the difference between the client’s actual discretionary expenditure (DISE)

and normal DISE. We refer to the difference as the client’s abnormal discretionary expenditure and

denote it by AB_DISE. Following the previous studies, we estimate or predict the client’s normal DISE

by a linear function of sales and assets,

DISE𝑡/A𝑡−1 = 𝛼0 + 𝛼1(1/A𝑡−1) + 𝛽 S𝑡/A𝑡−1 + 𝜖�̃�. (4)

DISE𝑡 is the client’s discretionary expenditure in year t. Its estimation, however, gives a rise to the

problem below. Companies that manipulate sales upward to increase earnings in a particular year will

have significantly lower regression residuals, even if they do not manage earnings through discretionary

expenditures. To resolve the problem, we run a regression of lagged sales for each year and industry,

DISE𝑡/A𝑡−1 = 𝛼0 + 𝛼1(1/A𝑡−1) + 𝛽 S𝑡−1/A𝑡−1 + 𝜖�̃�. (5)

AB_DISE is the residual of Model (5) (which is −1 times the deviations of DISE from its normal or

predicted value, similar to the prior literature). Hence, the larger it is, the more the client’s RAM is.

The third measure of RAM is the difference between the client’s actual production cost (PROD)

and normal PROD. We refer to the difference as the client’s abnormal production cost and denote it by

AB_PROD. The production cost is the sum of “costs of goods sold” and “changes in inventory” for year t

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PROD𝑡 = COGS𝑡 + ΔINV𝑡. (6)

Following Dechow et al. (1998), Roychowdhury (2006), Cohen and Zarowin (2010) and Zang (2012), we

estimate or predict the client’s normal COGS and ΔINV as follows:

COGS𝑡/A𝑡−1 = 𝛼0 + 𝛼1(1/A𝑡−1) + 𝛽 S𝑡/A𝑡−1 + 𝜖�̃�, and (7)

ΔINV𝑡/A𝑡−1 = 𝛼0 + 𝛼1(1/A𝑡−1) + 𝛽1 ΔS𝑡/A𝑡−1 + 𝛽2 ΔS𝑡−1/A𝑡−1 + 𝜖�̃�. (8)

Substituting (7) and (8) into (6), we estimate the client’s normal PROD as

PROD𝑡/A𝑡−1 = 𝛼0 + 𝛼1(1/A𝑡−1) + 𝛽1 S𝑡/A𝑡−1 + 𝛽2 ΔS𝑡/A𝑡−1 + 𝛽3 ΔS𝑡−1/A𝑡−1 + 𝜖�̃�. (9)

Thus, AB_PROD is the residual of Model 9. The larger it is, the more the client’s RAM is.

In summary, for any given level of sales, companies that use RAM will likely exhibit at least one

of the three measures (Cohen and Zarowin 2010 and Zang 2012). These measures are an unusually large

cash flow from operations, an unusually large discretionary expenditure, and an unusually large

production cost. Therefore, we add these three measures to obtain a composite measure:11

COMPOSITE = AB_CFO + AB_DISE + AB_PROD. (10)

3.3 Auditors’ Demographic Characteristics As Determinants of Individual Auditor Fixed Effects

From Model (1), we notice that λ𝐼𝐴 captures the fixed effect of individual auditors. A negative

value of λ𝐼𝐴 would suggest that individual auditors help to reduce clients’ RAM. Thus, we will explore

whether the publicly disclosed demographics of individual auditors help to explain the fixed effect of

individual auditors. We hand-collect each auditor’s personal demographic characteristics (denoted by

𝐃𝐂𝑗).12

Then, we will use them as the explanatory variables for the following regression model,

λ𝐼𝐴𝑗 = 𝜃0 + 𝜽𝒋𝐃𝐂𝑗 + �̃�𝑗. (11)

11

In an earlier version of the study, we also considered two other combinations of RAM measures. The two

combinations are COMPOSITE1 = AB_CFO + AB_DISE and COMPOSITE2 = AB_DISE + AB_PROD. Their

results are qualitatively the same as counterparts of COMPOSITE and, hence, are not reported in this version. 12

We collected the auditors’ demographic characteristics from the CICPA’s website: http://www.cicpa.org.cn.

http://www.cicpa.org.cn/

~13~

Note, λ𝐼𝐴𝑗 is the fixed effect estimated by Model (1), and 𝜽𝒋 is the vector of coefficients on 𝐃𝐂𝑗. We use

the Least Trimmed Squares method (LTS) to fit the above model, similar to Rousseeuw (1984) and

Rousseeuw and Van Driessen (2006).

4. Empirical Results

4.1 Data and Sample Selection

We apply two criteria to the database of China Stock Market and Accounting Research (CSMAR)

to select the auditor sample. These selection criteria are from Gul et al. (2013). First, the auditor has

audited a client for at least three years, and there are at least three years in which the auditor does not

audit this particular client. Second the auditor must have at least two such unique clients.

We choose “three years” as the cutoff. We believe that “three years on a client” represent a

reasonable amount of time for the auditor to learn about the client’s business model. Note, the Chinese

Auditing Standards require that an auditor rotates after five years with a client. We also believe that “three

years off a client” will reasonably separate the auditor and the client. Note, the cutoff of “three years” is

close to the average tenure of the auditor with a client (see Panel B, Table 4). An increase in the cutoff

would undoubtedly strengthen both the “learning” and “cooling” effects, but it would further lose more

data than we have already had (see details below).

The original data from the CSMAR have 22,395 observations for the period between 1999 and

2012. We drop 276 observations from the financial sector, 14,049 observations that failed to meet the two

selection criteria, and 1,040 with missing variables or high correlations between auditor indicator

variables. When a pair of signing auditors becomes a “stable” team over time, the pair will likely share

the same client portfolios. Their indicator variables will likely become highly correlated. To eliminate this

collinearity problem, we drop the one of the pair of auditors, who has smaller client portfolios whenever

the pair’s correlation coefficient exceeds 0.70. This sampling procedure yields 7,030 observations in our

final sample, among which we identify 504 individual auditors, 100 audit firms, and 1,404 audit clients.

~14~

Table 4 presents the descriptive statistics of dependent and independent variables for Model (1).13

Its Panel A describes the four dependent variables, which are the regression residuals of Models (3), (5),

(9), and (10), respectively. Panel B presents the control variables used in Model (1). The distribution of

these control variables in our study is similar to that of the counterpart used in Gul et al. (2013).

We include audit fees and qualified audit opinions (GC and MOD) as explicit control variables in

Model (2) but omit a few control variables used in Gul et al. (2013). The first omitted control variable is

Loss (an indicator variable for company gains and losses). Before the enactment of China’s Bankruptcy

Law in 2006, Chinese listed companies could not declare bankruptcy. Nonetheless, the Chinese

governments imposed significant hurdles upon the listed companies with losses. These funding and

operating hurdles created strong incentives for company executives to manipulate earnings. After 2006,

the Bankruptcy Law has been effective and playing an important role of disciplining the listed companies.

The ability of these loss companies to engage in RAM relative to the profitable companies, however, may

still be severely limited due to the Chinese governments’ monitoring and scrutiny as the majority

shareholder of these companies. We do not expect a priori that differences in RAM are significant

between the groups of listed companies in China. Second, we omit Turnover (total sales divided by

average total assets) and Leverage (liability divided by total assets). The reason is below. The dependent

variable of our models is clients’ RAM, measuring the changes in the clients’ business decisions and

operational risks for which Turnovers and Leverage are most likely to capture. Third, we omit Age (the

number of years of the company as a listed company). This variable may present a major limitation

because clients’ age potentially captures survivorship bias and confounds with the auditor-client

relationships.14

Nonetheless, the adjusted R2s of our regressions are comparable with the counterparts of Gul et al.

(2013). Note, its range is between 32.53 percent to 65.09 percent in Gul et al. (2013, Panel A of Table 3).

Its ranges are between 26.61 percent and 69.14 percent in Model (1) (Panel A, Table 5), 28.71 and 70.44

13

We winsorized all continuous variables at the bottom and top one percentile to eliminate the outliers. 14

We also performed regression analyses including age as an explanatory variable. The results are complex. The

concern is that clients’ age may be a proxy for both clients’ RAM and auditors’ learning.

~15~

in Model (2a) (Panel A, Table 7), 26.64 and 69.36 in the GC regressions (Panel A, Table 8A), and 26.64

and 69.36 in the MOD regressions (Panel A, Table 8B), respectively. These wider ranges of adjusted R2s

in our models than Gul et al. (2013) suggest the variation in clients’ RAM be inherently greater than that

in clients’ discretionary accruals.15

(Insert Table 4 about here.)

4.2 Audit Firm and Individual Auditor Fixed Effects on Clients’ RAM

Table 5 presents the OLS regression results of Model 1 (Panel A). There are four regressions of

RAM, whose measures are AB_CFO, AB_DISE, AB_PROD, and COMPOSITE. Each regression model

contains four indicator variables (i.e., years, clients, audit firms, and individual auditors). The t-values are

for two-tail tests. The F-statistics show that the individual effects are jointly significant at the 0.001 level.

The range of adjusted 𝑅2s is between the lowest of 26.61 percent from the AB_DISE regression and the

largest of 69.14 percent from the AB_CFO regression. In other words, the individual effects are jointly

significant for all regressions but are significantly different in their ability to explain RAM.

Returns on the assets (ROA), client importance to the audit firm (CI𝐴𝐹), and client size (CLIENT-

SIZE) are the three control variables with significant coefficients. ROA and CLIENT-SIZE are significant

for the AB_CFO regression, CI𝐴𝐹 and CLIENT-SIZE are significant for the AB_DISE regression, and

ROA and CI𝐴𝐹 are significant for the AB_PROD regression, respectively. The coefficients on ROA are

negative across all four regressions, and the coefficients on CI𝐴𝐹 and CLIENT-SIZE are positive. These

results mean that an increase in clients’ ROA will decrease the clients’ RAM across all measures of RAM,

and an increase in CI𝐴𝐹 and CLIENT-SIZE will increase clients’ RAM. The signs and magnitudes of the

significant coefficients together imply that (i) an increase in ROA will significantly decrease clients’

AB_CFO and AB_PROD; (ii) an increase in CI𝐴𝐹 will significantly increase clients’ AB_DISE and

AB_PROD; and (iii) an increase in CLIENT_SIZE will significantly increase clients’ AB_CFO and

15

This result is consistent with the general perception that auditors have direct influences on clients’ accounting

choices and hence discretionary accruals, but less or indirect influences on the clients’ economics through RAM.

~16~

AB_DISE. For the COMPOSITE regression, both the signs and magnitudes of the coefficients on ROA,

CI𝐴𝐹, and CLIENT-SIZE are weighted sums of the counterparts from the other three regressions.


Next, we will consider whether the fixed effects of clients, audit firms, and individual auditors are

significant. Although the F-statistics show that all indicator variables (for clients, audit firms, and

individual auditors) are jointly significant at the 0.001 level (Panel A), these indicators vary substantially

across the regressions. Following Collins, Maydew, and Weiss (1997) and Gul et al. (2013), we define the

incremental adjusted 𝑅2s as follows:

Δ𝑅𝐶2 ≡ 𝑅2 − 𝑅𝑁𝐶

2 , (12a)

Δ𝑅𝐴𝐹2 ≡ 𝑅2 − 𝑅𝑁𝐴𝐹

2 , and (12b)

Δ𝑅𝐼𝐴2 ≡ 𝑅2 − 𝑅𝑁𝐼𝐴

2 . (12c)

Note, 𝑅2 represents the adjusted 𝑅2 of the model with all the indicator variables for clients, audit firms,

and individual auditors. 𝑅𝑁𝐶2 is the adjusted 𝑅2 of the model without the fixed effect of clients, and

Subscript NC stands for No Client indicator variable. Similarly, R𝑁𝐴𝐹2 and 𝑅𝑁𝐼𝐴

2 are the adjusted 𝑅2 of the

models without including the indicator variables for audit firms and individual auditors, respectively.

Δ𝑅𝐶2 , Δ𝑅𝐴𝐹

2 , and Δ𝑅𝐼𝐴2 represent the incremental explanatory power contributed by the fixed effects of

clients, audit firms, and individual auditors, respectively. Hence, we employ the Vuong’s likelihood ratio

tests or Z-statistics (Vuong 1989) to test whether the incremental 𝑅2 is significant. Finally, we calculate

the percentage changes in 𝑅2 as follows:

%Δ𝑅𝐶2 ≡ (𝑅2 − 𝑅𝑁𝐶

2 )/𝑅𝑁𝐶2 , (13a)

%Δ𝑅𝐴𝐹2 ≡ (𝑅2 − 𝑅𝑁𝐴𝐹

2 )/𝑅𝑁𝐴𝐹2 , (13b)

%Δ𝑅𝐼𝐴2 ≡ (𝑅2 − 𝑅𝑁𝐼𝐴

2 )/𝑅𝑁𝐼𝐴2 . (13c)

The F-statistics suggest all the fixed effects of clients, audit firms, and individual auditors are

highly significant at the 0.001 level across all four regressions (Panels B, C, and D). Note, we ignore the

fixed effect of years, and regard it as a control variable. The changes in the adjusted 𝑅2—representing the

~17~

incremental explanatory power of the regressions with versus without the indicator variable—are

statistically significant according to the Vuong (1989) likelihood ratio tests. In Panel B, the fixed effect of

clients is the largest increases in 𝑅2s across all four regressions (AB_CFO, AB_DISE, AB_PROD, and

COMPOSITE). Δ𝑅𝐶2 ranges between the lowest level of 26.54 percent from the AB_CFO regression and

the highest level of 49.42 percent from the AB_DISE regression, which translates into %Δ𝑅𝐶2 at the

lowest level of 124.97 percent from the AB_PROD regression and at the highest level of 180.17 percent

from the AB_DISE regression. These fixed effects suggest that clients’ RAM vary considerably across

audit clients. This result not only is consistent with, but also makes a contribution to the prior literature on

auditor specialization and leadership in client industries and portfolio choices of clients.

In Panel C, we observe that including audit firm indicator variable modestly improves the

explanatory power across all four regressions. Δ𝑅𝐴𝐹2 ranges from 0.40 percent for the AB_DISE

regression to 0.77 percent for the AB_CFO regression, which translate %Δ𝑅𝐴𝐹2 into a range between 0.52

percent and 1.74 percent. As is shown in Panel D, including individual auditor indicator variable

significantly increases the explanatory power of all the four regressions. Δ𝑅𝐼𝐴2 ranges from 2.45 percent to

3.87 percent, resulting in %Δ𝑅𝐴𝐹2 values between 3.43 percent and 9.43 percent. Therefore, both the fixed

effects of audit firms and individual auditors on clients’ RAM are statistically significant.

Now, we consider whether a small number of significant coefficients are principally responsible

for the fixed effects of audit firms and individual auditors. Similar to Gul et al. (2013), we analyze the

frequency of significant individual effects. Under the null hypothesis that audit firms or individual

auditors have no incremental effects relative to the other variables in Model (1), one would expect about

one percent (five percent, ten percent) of the audit firms or individual auditors to have coefficients that are

significant at the one percent (five percent, ten percent) significant level, respectively.


Panel A of Table 6 shows that the actual percentage of audit firms with significant coefficients

are mixed. (a) It is only about 1 percent from the AB_CFO and AB_PROD regressions across the three

~18~

significant levels, which is smaller than expected. (b) It is 4.00 percent (9.00 percent, 19.00 percent) at

the one percent (five percent, ten percent) significance level from the AB_DISE regression, respectively,

which are larger than expected. Similarly, Panel C shows that the actual percentages of individual

auditors with significant coefficients from the AB_CFO and AB_DISE regressions are substantially

larger than expected. For example, at the one percent (five percent, ten percent) significance level, the

actual percentages are 4.17 percent (17.86 percent, 37.70 percent) from the AB_CFO regression, and

30.56 percent (41.07 percent, 49.40 percent) from the AB_DISE regression, respectively. Individual

auditors significantly affect clients’ abnormal cash flow from operations and abnormal discretionary

expenditure.

Next, we will analyze the economic significance of the fixed effects of audit firms and auditors

(Panels B and D). The interquartile range and standard derivation statistic reveal that while there exist

variations in RAM across audit firms, the variations are considerably larger across individual auditors.

For example, the interquartile range from the AB_DISE regression is 0.0242 for the fixed effect of audit

firms (Panel B) and is 0.2101 for the fixed effect of auditors (Panel D), respectively. The level of

discretionary expenditures manipulated by clients for an audit firm at the 75th percentile of the AB_DISE

distribution would be 2.42 percent higher than that reported by clients for the audit firm at the 25th

percentile. This variation is economically significant if compared with the average of Returns on the

Assets (ROA) for our sample (i.e., 3.98 percent; Panel B of Table 4). Similarly, the level of discretionary

expenditures manipulated by clients for an individual auditor at the 75th percentile of the AB_DISE

distribution would be 21.01 percent higher than that reported by clients for the auditor at the 25th

percentile. This variation in the fixed effect of different auditors is also economically significant, and,

more importantly, its magnitude is nearly ten times greater than that in the fixed effect of audit firms.

For the COMPOSITE regression, the level of clients’ RAM for an audit firm at the 75th percentile

of the RAM distribution would be 14.14 percent higher than that reported by clients for the audit firm at

the 25th percentile. Similarly, the level of RAM by clients of an individual auditor at the 75

th percentile of

the RAM distribution would be 39.71 percent higher than that reported by clients for the auditor at the

~19~

25th percentile. Both the variations in the fixed effects of audit firms and individual auditors are

economically significant. The variation in the fixed effect of individual auditors is about three times

greater than that in the fixed effect of audit firms.

In summary, the fixed effects of audit firms and individual auditors on RAM are statistically and

economically significant. The economic significance of the fixed effect of auditors strictly dominates that

of the fixed effect of audit firms. In particular, for clients’ discretionary expenditures, the economic

significance of the fixed effect of individual auditors is almost ten times as much of the fixed effect of

audit firms. Overall, these findings suggest that individual auditors differ to a noticeable extent in terms of

their influences over clients’ unexpected changes in economic decisions. Thus, audit quality resides at the

level of individual auditors. To increase audit quality, one must start with auditor quality.

4.3 Audit Fees and GC/MOD Opinions as Control Variables for Individual Fixed Effects

In this subsection, we consider whether audit fees relative to clients’ total assets and auditors’

propensity to issue qualified audit opinions affect the fixed effects of audit firms and individual auditors.

Specifically, we include audit fees (denoted by RAFee) and qualified audit opinions (GC and MOD) as

contorl variables (Model 2). We summarize the regression results in Table 7, 8A, and 8B. Table 7

presents the OLS regression results of Model (2a), the t-statistics, and the F-statistics in Panel A.

The regression coefficients on RAFee are significant across all the regressions. Note, almost all

the results are comparable with the counterparts in Panel A of Table 5. (a) ROA, CI𝐴𝐹, and CLIENT-SIZE

are significant. Their coefficients have the same signs and similar magnitudes as the counterparts. (b) The

F-statistics of all the four regressions are highly significant at the 0.001 level. (c) The adjusted 𝑅2 ranges

between the lowest 28.71 percent from the AB_CFO regression and the highest 70.44 percent from the

AB_DISE regression. Note, the counterpart of Table 5 is between 26.61 percent from the AB_DISE

regression and 69.14 percent from the AB_CFO regression.

As shown in Panel B of Table 7, the fixed effect of clients is significant, little change relative to

that reported in Panels B of Table 5. In Panel D of Table 7, the fixed effect of individual auditors on

~20~

RAM reduces relative to those in Panel D of Table 5. The signs of the incremental 𝑅2s, however, remain

to be positive. The inclusion of audit fees in the regression model as an explanatory variable does not

change the fact that the fixed effects of clients and individual auditors exist on clients’ RAM.

Importantly, the significance of the fixed effect of audit firms in Panel C (Table 7) has

dramatically changed from the counterpart in Panel C (Table 5). Δ𝑅𝐴𝐹2 is negative for the each regression

of the RAM measures: AB_CFO, AB_DISE, and AB_PROD. The change in the COMPOSITE regression

is positive yet insignificant. These results imply that in the regressions with RAFee’s being an explicit

explanatory variable, the indicator variable for audit firms will reduce the explanatory power of

regressions from Model (2a). Audit fees can “replace” the indicator variable for audit firms to better

explain the variation in RAM. This finding is more direct evidence relative to the previous studies that

that uses the audit firm- and the city/office-level data to infer auditor quality. Our contribution is to

delineate the condition under which whether the fixed effect of audit firms will disappear.


Next, we consider regressions with GC and MOD as control variables. We summarize the results

in Tables 8A and 8B. We document that GC and MOD have significant coefficients, thereby suggesting

that auditors exhibit responses to or influences over clients’ RAM (Panel A, Tables 8A and 8B). Our

focus is on the fixed effects of individual auditors.16

Panel B (Table 8A and 8B) shows that Δ𝑅𝐼𝐴2 is positive for the AB_CFO and AB_DISE

regressions. This result means that the indicator variable for individual auditors increases the explanatory

power of the regressions with GC or MOD. Furthermore, the actual percentages of individual auditors

with significant coefficients are greater than expected in the AB_CFO and AB_DISE regressions.

Panel C of Table 8A shows that the percentages of the fixed effect of auditors on clients’ abnormal cash

flow from operations (abnormal discretionary expenditure, respective) at the 0.05 and 0.10 significance

levels are 11.51 percent and 26.98 percent (35.32 percent and 41.27 percent, respective). They are

16

We ignore the fixed effect of clients because it remains significant. We also ignore the fixed effect of audit firms

because it disappeared in the regressions that include the audit fees as an additional control variable.

~21~

considerably higher than the expected 5 percent and 10 percent levels, respectively. Panel D shows that

both the standard derivations and interquartile ranges of the fixed effect of auditors are large, too.

Abnormal cash flow from operations and abnormal discretionary expenditure reported by clients for an

auditor at the 75th percentile of the distribution of individual effects would be 20.97 percent higher than

the counterparts reported by clients for the auditor at the 25th percentile. These variations are

economically significant if compared to the average return on assets (ROA) for our sample (i.e., 3.98

percent; see Panel B, Table 4).

Panel C of Table 8B shows that the percentages of the fixed effect of auditors on clients’

abnormal cash flow from operations (abnormal discretionary expenditure, respective) at the 0.05 and 0.10

significance levels are 10.32 percent and 27.38 percent (36.11 percent and 42.46 percent, respective).

They are considerably larger than expected at the 5 percent and 10 percent significance levels,

respectively. Panel D shows that both the standard derivations and interquartile ranges of the fixed effect

of auditors are large, too. Abnormal cash flow from operations (abnormal discretionary expenditure,

respective) reported by clients for an auditor at the 75th percentile of the distribution of individual effects

would be 17.07 percent (20.97 percent, respective) higher than that reported by clients for the auditor at

the 25th percentile. These variations are economically significant.

In the regression analyses that include auditors’ propensity to issue GC or MOD audit opinions as

control variables, the variation in RAM remains to be significant across different auditors.17

(Insert Tables 8A and 8B about here.)

4.4 Auditors’ Demographic Characteristics As Determinants of Individual Auditor Fixed Effects

We use thirteen indicator variables and two continuous variables to represent auditors’ personal

demographic characteristics (see Table 9 for definitions and descriptive statistics). They are independent

variables of Model (11). In our sample of individual auditors, (1) about 70 percent are male, or born

17

As part of our sensitivity analyses, we also ran Model (2) by using clients’ Tobin’s Q as the dependent variable.

Note, Tobin’s Q is a proxy for clients’ economic decisions (more precisely, investment opportunities). The fixed

effects of audit firms and individual auditors are broadly consistent with the counterparts of RAM. But we did not

find the support for incremental information of individual auditors relative to audit firms.

~22~

before 1971, or have political connections;18

(2) about 50 percent are partners of their audit firms;19

(3) about 20 percent have Masters or higher degrees, and graduated from the 211 and the 985 project

universities;20

(4) less than 10 percent have work experience with the international Big 4 accounting

firms;21

(5) only 3.37 percent have received education degrees from overseas; and (6) about 7 percent

were penalized and sanctioned by the CICPA (Firth, Mo, and Wong 2005, 2012).


The dependent variables of Model (11) are the regression coefficients of the indicator variables

for individual auditors of Model (1).22

Since these dependent variables contain measurement errors, we

will use the Least Trimmed Square (LTS) method (Rousseeuw 1984) to fit the regressions. Then, we

estimate the coefficients and Chi-squares by using the final weighted least squares. We summarize the

regression results in Table 10. The explanatory power of these regressions (LTS 𝑅2) is moderate, which

ranges from 17.64 percent to 7.63 percent.

We find that auditors’ personal demographics have limited power in explaining the fixed effects

of auditors on RAM. More specifically, in terms of auditors’ ability to curb clients’ RAM, we document

(1) auditors who were penalized by CICPA (PENALTY), young (E_COHORT), or politically affiliated

(POLITICAL) are less able; (2) no significant difference exists between male and female auditors

(GENDER), or among auditors with different levels of education and work experience as a CPA

(EDUCATION, MAJOR, U_211, U_985, U_ABROAD, WELFARE, and CPA_EXP); and (3) auditors

who are partners (PARTNER) or have work experiences at the International Big 4 firms (BIG4_EXP)

exhibit higher ability. These findings overall suggest auditor-client relationships be complex.

18

Although it is controversial, gender difference exists in China. Studies found evidence supporting distinct

difference in men and women’s problem-solving ability and cognitive style (Hardies, Breesch, and Branson 2009),

risk preference (Fellner and Maciejovsky 2007), and impacts on earnings quality as board directors (Srinidhi, Gul,

and Tsui 2011). 19

Miller (1992) argues that partners are more conservative than non-partners. Trotman, Wright, and Wright (2009)

find evidence supporting that partners require more initial proposed write-downs than do non-partners. 20

People of the same generation are likely to share things in common. Bamber et al. (2010) point out that significant

events happened in one’s childhood may have a profound influence on him or her. 21

A large of number of prior studies has shown that audits of the Big Four are good (Teoh and Wong 1993, Becker

et al. 1998, Francis and Krishnan 2002). These firms have unique recruiting methods, values, goals, and quality

control mechanisms (Jeppeson 2007). 22

Alternatively, we may also use the regression coefficients of the indicator variables for auditors from Model (2a).

~23~

5. Concluding Remarks

In this study, we have documented that clients’ RAM varies across different auditors. Specifically,

the fixed effect of individual auditors on clients’ RAM (in the sense of Bertrand and Schoar 2003) are

both statistically and economically significant. We include the auditors’ propensity to issue qualified audit

opinions (GC or MOD) or the audit fees relative to clients’ total assets as control variables in our

regression analyses. We find that the fixed effect of individual auditors remains to be significant, but the

fixed effect of audit firms disappears. The percentage of auditors who respond to or influence their clients’

abnormal cash flow from operations as well as abnormal discretionary expenditures is significantly higher

than expected. Therefore, individual auditors contribute incremental information beyond the firm-level

audit quality. Our findings support regulatory policies (e.g., engagement partners’ signature and rotation)

which enforce auditors’ accountability and responsibility by identifying the “weakest link” and hence

mitigating the “free rider” problem.

There are two limitations. First, when investigating the association between the auditors’

demographics and clients’ RAM, we used the estimated fixed-effect coefficients of audit firms and

different auditors as the dependent variables. Since the regression coefficients contain measurement errors,

it is not surprising that auditors’ personal characteristics exhibit limited power in explaining the variation

in RAM. Second, we focused on clients’ RAM as the proxy for clients’ economic decisions, although the

sensitivity analyses based on clients’ Tobin’s Q provide consistent results. Future research may

incorporate the impacts of Chinese “social ties or relationships” on the strength of auditor-client

relations.23

23

See, e.g., Guan, Su, and Wu 2014, Qi, Yang, and Tian 2014, and He, Pittman, Rui, and Wu 2014.

~24~

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Table 1: Yearly Distribution of Audit Fees Relative to Clients' Total Assets (RAFee) in

China Between 2000 and 2012

YEAR

iBig 4 Audit Firms Top 10 Audit Firms Other Audit Firms

# of

Clients

RAFee_iBig

4

# of

Clients

RAFee_Top

10

# of

Clients

RAFee_Other

s

2000 110 12.83%

332 16.73%

605 11.02%

2001 80 21.40%

420 23.89%

639 27.79%

2002 126 11.19%

392 23.72%

685 26.00%

2003 123 8.77%

395 18.87%

748 21.30%

2004 113 8.38%

459 16.26%

782 19.38%

2005 115 7.38%

455 14.84%

781 16.24%

2006 113 5.27%

528 11.71%

791 12.76%

2007 128 4.31%

606 11.33%

814 9.52%

2008 123 3.72%

635 11.40%

843 10.02%

2009 130 2.42%

887 10.76%

734 8.50%

2010 151 2.34%

1097 9.60%

857 8.15%

2011 175 1.90%

1285 9.64%

881 12.25%

2012 181 1.80% 1571 11.33% 718 14.12%

Note: we classify audit firms in China into three categories: International Big 4 (iBig 4) accounting

firms, domestic top 10 audit firms (Top 10), and the other domestic audit firms (Others). The three

categories of audit firms in China do exhibit noticeably difference in their audit-fee ratios during the

sample period. Except for 2000 (the first year when Chinese listed companies began to disclose audit

fees), the audit-fee ratios of all three categories of audit firms have been almost monotonically declining.

Figure 1: Plot of Yearly Distributions of Audit Fees (RAFee)

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

RAFee_Others

RAFee_Top 10

RAFee_iBig 4

~32~

# of Clients GC_iBig 4 MOD_iBig 4 # of Clients GC_Top 10 MOD_Top 10 # of Clients GC_Others MOD_Others

2000 90 5.56% 8.89% 311 8.04% 21.54% 562 5.87% 15.48%

2001 75 2.67% 5.33% 395 5.32% 15.95% 590 5.76% 12.37%

2002 117 2.56% 7.69% 368 4.89% 12.77% 645 4.50% 12.40%

2003 115 2.61% 5.22% 377 3.71% 6.90% 710 3.24% 8.59%

2004 99 5.05% 8.08% 429 5.59% 10.02% 734 5.59% 10.49%

2005 106 3.77% 6.60% 424 5.90% 11.32% 723 6.50% 12.45%

2006 104 0.00% 4.81% 500 5.20% 10.00% 761 3.94% 9.86%

2007 120 0.83% 1.67% 556 1.80% 7.01% 782 1.66% 8.18%

2008 119 0.84% 2.52% 599 1.34% 5.68% 811 2.71% 7.89%

2009 120 0.83% 1.67% 794 1.76% 6.30% 684 2.49% 7.75%

2010 149 0.00% 2.01% 1084 1.01% 5.44% 847 1.89% 5.79%

2011 168 0.00% 1.79% 1232 0.81% 4.63% 844 1.54% 5.33%

2012 180 0.56% 2.22% 1570 0.64% 3.57% 718 0.97% 4.18%

Notes:

China's Bankruptcy Law Was Enacted in August 2006

Table 2: Yearly Distribution of Percentages of Going-Concern (GC) and Modified (MOD)

Audit Opinions Issued By Audit Firms in China Between 2000 and 2012

iBig 4 Audit Firms Top 10 Audit Firms Other Audit FirmsYEAR

1. The number of audit clients are smaller than those reported in Table 1 because we deleted audits

that do not have calendar year end. The number of going-concern (or modified) opinions is equal to the

number of clients times GC_Auditor (or MOD_Auditor).

2. According to the Chinese Auditing Standards (CAS), there were seven types of audit opinions prior

to 2003. In 2003, “adverse opinion plus explanatory paragraph” was dropped from the standards. As a

result, there are six types of audit opinions effective since July 1, 2003 (see Chapter 3, Audit Reports

of CAS): (a) unqualified opinion (meaning wholeheartedly endorsement of a financial statement), (b)

unqualified plus explanatory paragraph; (c) reservation without qualification, (d) reservation plus

explanatory paragraph; (e) rejection (meaning that the auditor is unable to express an opinion); and (f)

adverse opinion. To simplify these audit opinions and to make them comparable with those opinions

used the United States and other markets, we refer to Types (b) - (f) as modified (MOD) opinions, and

refer to Types (c) - (f) as going-concern (GC) opinions.

3. The Bankruptcy Law for Business Enterprises of the People's Republic China (Bankruptcy Law)

was enacted on August 27, 2006, and has become effective since June 1, 2007. “This law is

formulated to regulate enterprise insolvency practices and activities, to fairly liquidate claims and debts,

to protect the legitimate rights and interests of creditors and debtors, and to maintain the order of the

socialist market economy.”

4. The percentages of (or auditors’ propensity to issue) GC and MOD audit opinions among the three

categories of auditors in China seem to converge in Year 2012 (see Figures 2A and 2B below), yet their

differences remain significant statistically. More specifically, the iBig 4 audit firms issued 0.56 percent

(2.22 percent, respective) of GC (MOD, respective) opinions, the Top 10 domestic audit firms issued

0.64 percent (3.75 percent), and the other domestic audit firms issued 0.97 percent (4.18, percent).

~33~

It is visually clear from Figures 2A and 2B that all auditors of the three categories of audit firms in China are

almost monotonically decreasing their propensity to issue GC and MOD opinions during the sample period.

However, all auditors significantly increased their percentages of GC and MOD opinions in 2004, and all

domestic auditors (i.e., Top 10 and Others) further increased their percentages of GC and MOD opinions in

2005. The “spike” of GC and MOD audit opinions might have been resulted from the process of discussing

and deliberating the Bankruptcy Law for enterprises in China, which was enacted on August 27, 2006, and has

been effective since June 1, 2007. As a result, we may refer to such an increase in the percentages of GC and

MOD audit opinions as an improvement of audit quality. However, it is puzzling as of why all auditors in

China have kept reducing their propensity to issue GC and MOD audit opinions since 2006.

Figure 2B: Yearly Percentages of Modified Audit Opinions (MOD)

Figure 2A: Yearly Percentages of Going-Concern Audit Opinions (GC)

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

9.00%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

GC_Others

GC_Top 10

GC_iBig 4

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

MOD_Others

MOD_Top 10

MOD_iBig 4

~34~

Year AB_CFO AB_DISE AB_PROD COMPOSITE

2000 -7.00E-04 -4.00E-04 -8.00E-04 -1.90E-03

2001 -2.00E-04 -5.00E-04 -1.50E-03 -2.20E-03

2002 9.00E-04 -1.20E-03 -1.80E-03 -2.10E-03

2003 -1.60E-03 8.00E-04 -6.00E-04 -1.40E-03

2004 3.00E-04 -9.00E-04 4.00E-04 -2.00E-04

2005 2.00E-04 5.00E-04 3.00E-04 1.00E-03

2006 2.00E-04 -5.00E-04 3.00E-04 0.00E+00

2007 -9.00E-04 3.00E-04 -2.40E-04 -8.40E-04

2008 4.00E-04 -8.00E-04 -3.00E-04 -7.00E-04

2009 -2.00E-04 -7.00E-04 1.70E-04 -7.30E-04

2010 -4.00E-04 6.00E-04 -1.10E-03 -9.00E-04

2011 5.00E-04 7.00E-04 -7.20E-03 -6.00E-03

2012 3.00E-04 -7.00E-04 -2.00E-03 -2.40E-03

Table 3: Yearly Distribution of Clients' RAM (abnormal cash flows from

operations, abnormal discretionary expenditures, abnormal production costs,

and their composite measure)

Figure 3: Clients' RAMs Are Oscillating over the Entire Sample Period

Between 2000 and 2012

See Table 4 for the definitions of clients' RAM. All continuous variables are winsorized at

the bottom and top five percentiles to eliminate outliers' effects. All variables are from

CSMAR between 2000 and 2012.

-8.00E-03

-7.00E-03

-6.00E-03

-5.00E-03

-4.00E-03

-3.00E-03

-2.00E-03

-1.00E-03

0.00E+00

1.00E-03

2.00E-03

AB_CFO

AB_DISE

AB_PROD

COMPOSITE

~35~

Variables Mean Q1 Median Q3 Std. Dev.

AB_CFO -0.0019 -0.0496 0.0006 0.0444 0.0886

AB_DISE 0.0028 -0.0178 0.0121 0.0386 0.0632

AB_PROD -0.0014 -0.5612 0.0649 0.0648 0.1385

COMPOSITE 0.0057 -0.1029 0.0150 0.1232 0.2284

ROA 0.0398 0.0112 0.0345 0.0685 0.0742

TENUREAF 3.7650 2.0000 3.0000 5.0000 2.4756

TENUREIA 3.1084 2.0000 3.0000 4.0000 1.7144

PORT-SIZEAF 782.5314 395.8306 597.9741 953.3810 589.1020

PORT-SIZEIA 130.0412 45.2890 85.4671 130.2251 93.7844

CIAF 0.0318 0.0104 0.0234 0.0395 0.0345

CIIA 0.3445 0.1644 0.2517 0.4811 0.2688

B/M 0.4248 0.2164 0.3592 0.5809 0.2833

CLIENT-SIZE 21.4755 20.7353 21.4262 22.1511 1.0912

AB_CFO

This is consistent with Roychowdhury (2006) and Zang (2012) who multiply

deviations of a client's cash flows from predicted values by -1.

(The table is continued on the next page. )

AB_DISE Abnormal discretionary expenditures are defined as the difference between a

client's actual discretionary expenditures and its predicted values from

corresponding industry-year regression:

DISEt/At-1 = α0 + α1(1/At-1) + β(St-1/At-1) + εt.

This is consistent with Roychowdhury (2006) and Zang (2012) who multiply

deviations of a client's discretionary expenditures from predicted values by -1.

Abnormal cash flow from operations is defined as the difference between a

client's actual cash flows from operations and its predicted values from

corresponding industry-year regression:

CFOt/At-1 = α0 + α1(1/At-1) + β1(St/At-1) + β2(ΔSt/At-1) + εt.

Panel A: Dependent Variables

Panel B: Independent Variables

Table 4: Descriptive Statistics of Variables Used in Regression Models to Estimate

Individual Auditor Fixed Effects on Clients' RAM

The sample has 7,030 client-year observations (which include 504 individual auditors, 1,404 audit

clients, 100 auditor firms) between 1999 and 2012 (i.e., the total number of years is 14).

Definitions of the dependent variables:

~36~

ROA

TENUREAF

TENUREIA

PORT-SIZEAF

PORT-SIZEIA

CIAF

CIIA

B/M

CLIENT-SIZE

Client importance to an individual auditor: CIIA = Ln(TOTALASSET i) /

PSIZEIA.

The ratio of book to market value of equity (see Roychowdhury, 2006).

Natural logarithm of an audit client’s asset (see Roychowdhury, 2006).

Income before extraordinary items scaled by total assets (see Roychowdhury,

2006).

COMPOSITE

Definitions of the independent variables (also see Gul et al., 2013):

Tenure of an audit firm, measuring the number of consecutive years that an

audit firm audits a particular client.

Tenure of an individual auditor, measuring the number of consecutive years that

an auditor provides the audit to a particular client.

Client importance to an audit firm: CIAF = Ln(TOTALASSET i) / PSIZEAF.

Client portfolio size of an audit firm, measuring the audit firm's client portfolio

size in a particular year: PSIZEAF = Σn

i = 1Ln(TOTALASSET i), where

Ln(TOTALASSET i) is the natural logarithm of the total assets of a client i

(normalized in 1999 constant RMB) and n is the number of clients of the audit

firm audits in a particular year.

Portfolio size of an individual auditor, measuring the auditor’s client portfolio

size in a particular year: PSIZEIA = Σm

k=1Σli=1Ln(TOTALASSET i), where i is the

number of clients of the individual auditor k in a particular year, and m is the

number of the first auditors.

Abnormal production costs are defined as the difference between a client's actual

production costs and its predicted values from corresponding industry-year

regression (see Roychowdhury, 2006):

PRODt/At-1 = α0 + α1(1/At-1) + β1(St/At-1) + β2(ΔSt/At-1) + β3(ΔSt-1/At-1) + εt.

COMPOSITE = AB_CFO + AB_DISE + AB_PROD.

The composite is the sum of the above three measures of the client's RAM:

AB_PROD

~37~

Coeff. t_stat Coeff. t_stat Coeff. t_stat Coeff. t_stat

ROA -0.1149 -6.00*** -0.0024 -0.27 -0.2988 -11.09*** -0.4211 -10.30***

TENUREAF -0.0001 -0.08 -0.0002 -0.28 0.0033 1.98** 0.0030 1.2

TENUREIA -0.0002 -0.13 0.0003 0.52 -0.0023 -1.27 -0.0024 -0.87

PORT-SIZEAF 0.0000 -0.03 0.0000 -1.02 0.0000 -0.25 0.0000 0.01

PORT-SIZEIA 0.0000 0.51 0.0000 -1.79* 0.0000 0.42 0.0000 0.09

CIAF 0.0485 0.89 0.0939 3.82*** 0.2006 2.62*** 0.3342 2.87***

CIIA 0.0093 1.85* -0.0012 -0.54 -0.0010 -0.14 0.0062 0.58

B/M -0.0007 -0.12 0.0026 1.02 -0.0016 -0.2 -0.0008 -0.07

CLIENT-SIZE 0.0087 2.92*** 0.0083 6.12*** 0.0034 0.81 0.0187 2.93***

Indicator variables for

year, client, audit

firm, and individual

auditor

F -statistics [p- value]

Adj. R2

Included Included

Panel A: Regression Results

Included Included

Table 5: Estimating Client, Audit Firm, and Individual Auditor Fixed Effects on Clients' RAM (Model 1)

VariablesAB_CFO AB_DISE AB_PROD COMPOSITE

9.97 [<0.001] 2.45 [<0.001] 3.52 [<0.001] 4.64 [<0.001]

(The table is continued on the next page. )

69.14% 26.61% 38.65% 47.59%

~38~

AB_CFO AB_DISE AB_PROD COMPOSITE

F -statistics [p- value] 2.95 [<0.001] 12.42 [<0.001] 4.53 [<0.001] 5.99 [<0.001]

ΔR2C [Vuong Z] 26.54% [21.81***] 49.42% [33.60***] 29.98% [22.36***] 35.11% [27.40***]

%ΔR2

C 144.32% 180.17% 124.97% 137.36%


ΔR2

AF [Vuong Z] 0.77% [4.49***] 0.40% [2.90***] 0.58% [3.99***] 0.54% [4.29***]

%ΔR2

AF 1.74% 0.52% 1.09% 0.90%


ΔR2IA [Vuong Z] 3.87% [7.80***] 2.55% [7.64***] 2.45% [7.46***] 2.51% [8.41***]

%ΔR2

IA 9.43% 3.43% 4.76% 4.32%

*, **, and *** represent the three conventional significant levels of 0.10, 0.05, and 0.01 with two-tailed tests.

R2 is the adjusted R-square of the model that includes all the fixed effects;

R2

NC, R2NAF, and R

2NIA are the adjusted R -squares of the model that excludes client, audit firm, and individual auditor, respectively;

Table 5 (continued)

Panel B: Testing the Significance of Client Fixed Effects

Panel C: Testing the Significance of Audit Firm Fixed Effects

Panel D: Testing the Significance of Individual Auditor Fixed Effects

See Table 4 for the definitions of all variables. The adjusted R2 is based on SAS's GLM procedures that absorb time-invariant client

characteristics by demeaning each variable for each client. The F -statistics from the F -tests examine the joint significance of fixed effect

coefficients. The Vuong (1989) Z -statistics examine whether changes in model R2 after the inclusion of fixed effects are statistically

significant. In Panels B, C, and D, the changes and the percentage changes in adjusted R2s are calculated as follows:

ΔR2

C = R2 - R

2NC

ΔR2AF = R

2 - R

2NAF

ΔR2

IA = R2 - R

2NIA

%ΔR2C = (R

2 - R

2NC) / R

2NC

%ΔR2AF = (R

2 - R

2NAF) / R

2NAF

%ΔR2

IA = (R2 - R

2NIA) / R

2NIA

~39~


% significance at 1% level 1.00% 4.00% 1.00% 25.00%



Mean -0.0855 0.0532 0.1153 0.9492

Interquartile range 0.0807 0.0242 0.0833 0.1414

Standard deviation 0.1395 0.0791 0.0933 0.2125




Mean -0.1992 -0.1138 -0.0285 -0.2019

Interquartile range 0.1801 0.2101 0.2032 0.3971

Standard deviation 0.1455 0.1323 0.1640 0.3030

Panel A: The Percentages of Significant Estimated Audit Firm Fixed Effects

Panel B: Distribution of Estimated Audit Firm Fixed Effects

Table 6: Testing the Significance of Audit Firm and Individual Auditor Fixed Effects on

Clients' RAM

Panel C: The Percentages of Significant Estimated Individual Auditor Fixed Effects

Panel D: Distribution of Estimated Individual Auditor Fixed Effects

~40~


ROA -0.1446 -6.87*** -0.0066 -0.69 -0.3362 -11.43*** -0.4891 -11.00***

TENUREAF 0.0003 0.21 -0.0006 -1.14 0.0024 1.42 0.0020 0.78

TENUREIA -0.0006 -0.45 0.0006 1.79* -0.0016 -0.85 -0.0011 -0.38

PORT-SIZEAF 0.0000 -0.4 0.0000 -0.87 0.0000 -0.37 0.0000 -0.3

PORT-SIZEIA 0.0000 0.61 0.0000 -1.86* 0.0000 -0.83 0.0000 -0.77

CIAF 0.0329 0.56 0.0936 3.54*** 0.2226 2.73*** 0.3484 2.82***

CIIA 0.0082 1.54* -0.0013 -0.55 0.0000 0 0.0052 0.46

B/M -0.0025 -0.43 0.0031 1.14 0.0000 0 0.0007 0.06

CLIENT-SIZE 0.0086 2.67*** 0.0104 7.12*** 0.0027 0.6 0.0194 2.86***

RAFee -1.3667 -1.80* 2.8177 8.21*** -2.7252 -2.57*** -1.9017 -1.19

Indicator variables for year,

client, audit firm, and

individual auditor


Adj. R2

Table 7: Estimating Audit Firm Fixed Effects in Regressions Including Audit Fees As an Explanatory Variable (Model 2a)



28.71% 70.44% 40.80% 50.09%

(The table is continued on the next page.)

Included

2.47 [<0.001] 9.73 [<0.001] 3.52 [<0.001] 4.68 [<0.001]

Included Included Included

~41~

F -statistics [p -value]

ΔR2AF [Vuong Z]

%ΔR2AF


ΔR2AF [Vuong Z] -0.05% [1.49**] -0.03% [0.96] -0.03% [0.96] 0.03% [0.96]

%ΔR2AF


ΔR2AF [Vuong Z]

%ΔR2AF 0.60%

5.68 [<0.001]

0.05% [0.83] 0.79% [2.55***] 0.94% [2.97***] 0.30% [1.85**]

4.30 [<0.001]

0.17% 1.12% 2.30%

Panel B: Testing the Significance of Client Fixed Effects

Table 7: (continued)

See Tables 2 and 4 for definitions of variables.

30.30% [26.97***]

153.11%221.06%

3.25 [<0.001]

22.95% [21.73***]

128.57%

3.55 [<0.001]

17.27% [22.45***]

3.91 [<0.001]

48.50% [31.62***]

Panel D: Testing the Significance of Individual Auditort Fixed Effects

0.06%

2.56 [<0.001] 10.13 [<0.001] 3.67 [<0.001]

2.34 [<0.001]

Panel C: Testing the Significance of Audit Firm Fixed Effects

2.86 [<0.001] 11.57 [<0.001]


4.84 [<0.001]

150.83%

-0.17% -0.04% -0.07%

~42~


ROA -0.1249 -6.28*** -0.0175 -1.96** -0.3362 -11.59*** -0.4684 -11.04***

TENUREAF -0.0001 -0.1 -0.0002 -0.34 0.0024 1.95** 0.0029 1.16

TENUREIA -0.0002 -0.13 0.0003 0.54 -0.0016 -1.26 -0.0024 -0.86

PORT-SIZEAF 0.0000 -0.05 0.0000 -1.1 0.0000 -0.3 0.0000 -0.04

PORT-SIZEIA 0.0000 0.54 0.0000 -1.70* 0.0000 -0.47 0.0000 0.15

CIAF 0.0507 0.93 -0.0973 3.97*** 0.2225 2.69*** 0.3447 2.97***

CIIA 0.0092 1.83* -0.0014 -0.6 0.0000 -0.17 0.0058 0.55

B/M -0.0009 -0.15 0.0023 0.91 0.0000 -0.26 -0.0017 -0.14

CLIENT-SIZE 0.0089 2.98*** 0.0085 6.35*** 0.0027 0.93 0.0195 3.07***

GC -0.0158 -1.84* -0.0240 -3.67*** -0.0406 -3.36*** -0.0749 -4.09***

Indicator variables for

year, client, audit firm,

and individual auditor


Adj. R2

Table 8A: Estimating Individual Auditor Fixed Effects in Regressions Including Going-Concern Audit Opinions As an

Explanatory Variable (Model 2b)



Included Included

2.45 [<0.001] 10.06 [<0.001] 3.53 [<0.001] 4.66 [<0.001]

Included Included


38.77% 47.74%26.64% 69.36%

~43~


ΔR2IA [Vuong Z] -0.86% [1.59**] -0.22% [0.98]

%ΔR2IA

Mean

Interquartile range

Standard deviation

% significance at 5% level


Table 8A: (continued)


Panel B: Testing the Significance of Individual Auditor Fixed Effects

2.82 [<0.001] 11.84 [<0.001] 4.31 [<0.001] 5.65 [0.001]

0.12% [0.83] 1.11% [2.24**]

0.45% 1.63% -0.86% -0.22%


1.39% 26.59% 0.20% 1.19%% significance at 1% level

11.51% 35.32% 0.99% 1.98%

26.98% 41.27% 1.59% 5.56%


-0.1815 -0.1021 -0.0014 -0.1522


0.2097 0.2097 0.1867 0.2980

0.1358 0.1316 0.1417 0.2768

~44~


ROA -0.1288 -6.50*** -0.0175 -1.88* -0.3362 -11.61*** -0.4716 -11.15***

TENUREAF -0.0002 -0.13 -0.0002 -0.4 0.0024 1.92** 0.0028 1.11

TENUREIA 0.0000 -0.1 0.0003 0.59 -0.0016 -1.23 -0.0023 -0.82

PORT-SIZEAF 0.0000 -0.06 0.0000 -1.08 0.0000 -0.29 0.0000 -0.04

PORT-SIZEIA 0.0000 0.51 0.0000 -1.81* 0.0000 0.41 0.0000 0.08

CIAF 0.0498 0.92 -0.0973 3.88*** 0.2225 2.65*** 0.3387 2.92***

CIIA 0.0091 1.82* -0.0014 -0.61 0.0000 -0.17 0.0057 0.54

B/M -0.0009 -0.17 0.0023 0.92 0.0000 -0.26 -0.0017 -0.14

CLIENT-SIZE 0.0091 3.06*** 0.0085 6.46*** 0.0027 1 0.0202 3.17***

MOD -0.0201 -2.67*** -0.0209 -6.14*** -0.0406 -3.45*** -0.0733 -4.56***

Indicator variable for

year, client, audit firm,

and individual auditor


Adj. R2

Table 8B: Estimating Individual Auditor Fixed Effects in Regressions Including Modified Audit Opinions As an

Explanatory Variable (Model 2c)




Included Included Included Included

2.45 [<0.001] 10.06 [<0.001] 3.53 [<0.001] 4.66 [<0.001]

26.64% 69.36% 38.77% 47.78%

~45~


ΔR2IA [Vuong Z] -0.89% [1.03*] -0.24% [0.85]

%ΔR2IA

Mean

Interquartile range

Standard deviation



Table 8B: (continued)


Panel B: Testing the Significance of Individual Auditor Fixed Effects

2.83 [<0.001] 11.83 [<0.001] 4.31 [<0.001] 5.66 [<0.001]

0.12% [0.67] 1.14% [2.31**]

0.12% 1.67% -0.89% -0.24%


1.19% 27.38% 0.20% 1.59%% significance at 1% level

10.32% 36.11% 0.99% 2.18%

27.38% 42.46% 1.79% 6.94%


-0.1778 -0.1011 -0.0275 -0.1462


0.1707 0.2097 0.1793 0.3542

0.1336 0.1332 0.1420 0.2760

~46~

Demographic

Characteristics Dummy variables Frequency Percentage

0 (Male) 361 71.63%

1 (Female) 143 28.37%

0 (Without Position) 259 51.39%

1 (Position) 245 48.61%

0 (Non-CCP Party Members) 149 29.56%

1 (CCP Party Members) 355 70.44%

0 (Junior College Degree or Lower) 119 23.61%

1 (Bachelor Degree) 288 57.14%

2 (Master Degree or PhD) 97 19.26%

0 (Non-Accounting Major) 203 40.28%

1 (Accounting Major) 301 59.72%

0 (Non-211 Project Graduates ) 393 77.98%

1 (211 Project Graduates ) 111 22.02%

0 (Non-985 Project Graduates ) 406 80.56%

1 (985 Project Graduates ) 98 19.44%

0 (No Abroad Study experience) 487 96.63%

1 (Study Abroad) 17 3.37%

0 (Non-partner) 233 46.23%

1 (Partner) 271 53.77%

0 (No Welfare Activities) 478 94.84%

1 (Welfare Activities) 26 5.16%

0 (Working in Domestic Audit Firms) 456 90.48%

1 (Working in Big4 Audit Firms) 48 9.52%

0 (Born before 1971) 373 74.01%

1 (Born after 1971) 131 25.99%

0 (No Penalties) 468 92.86%

1 (penalties or sanctions from CICPA) 36 7.14%

E_COHORT

PENALTY

Table 9: Individual Auditors' Personal Demographic Characteristics


U_211

U_985

U_ABROAD

PARTNER

WELFARE

BIG4_EXP

Panel A: Dummy Variables

GENDER

POSITION

POLITICAL

EDUCATION

MAJOR

~47~

Demographic

Characteristics Mean Q1 Median Q3 Std.Dev.

YEAR_BIRTH 1965.9070 1963 1967 1971 7.3041

CPA_EXP 15.5318 14 16 18 3.6885

GENDER

POSITION

POLITICAL

EDUCATION

MAJOR

U_211

U_985

U_ABROAD

PARTNER

PENALTY

WELFARE

BIG4_EXP

E_COHORT

YEAR_BIRTH

CPA_EXP Years of working experience after receiving CPA from CICPA.

An indicator variable for an auditor's education degrees.

An indicator variable for an auditor's major of study in accounting or auditing

at university.

An indicator variable for auditor's alma mater being a 211 project university.

Welfare equals one when an individual auditor has taken part in public

welfare activities.

An indicator variable for an auditor having work experience with an

international Big4 accounting firms.

Panel B: Continuous Variables

Table 9: (Continued)

An indicator variable of an auditor's gender.

Year of birth of an auditor.

An indicator variable for an auditor's position in the audit firm.

An indicator variable for an auditor being a member of the Chinese

Communist Party.

Definition of the demographic characteristics:

An indicator variable for an auditor's alma mater being a 985 project

university.

An indicator variable is equal to one if an auditor gets a degree from abroad

or Hong Kong or Macao special administrative regions and zero otherwise.

An indicator variable for an auditor being a partner at the audit firm.

An indicator variable for an auditor receiving penalties or sanctions from

CICPA .

Education cohort equals one when au individual auditor was born in or after

1971 and zero otherwise.

~48~

Coeff. Chi-square Coeff. Chi-square Coeff. Chi-square Coeff. Chi-square

GENDER -0.0414 0.18 0.1296 1.74 0.1523 2.58* 0.0864 0.79

POSITION 0.1267 2.49* 0.0875 0.89 0.1459 2.69* 0.1336 2.14*

POLITICAL -0.1962 3.08** -0.0323 0.1 -0.0207 0.05 -0.0958 0.91

EDUCATION -0.0881 0.56 -0.1727 2.03 0.0952 0.68 -0.0684 0.33

MAJOR -0.1181 1.71 -0.0194 0.04 -0.0594 0.45 -0.0281 0.09

U_211 0.0092 0.01 0.0617 0.3 0.1194 1.23 0.0915 0.68

U_985 -0.1648 3.27* -0.0199 0.03 0.0169 0.02 -0.0571 0.24

U_ABROAD 0.0952 0.14 0.3588 1.84 -0.1933 0.59 0.1122 0.19

PARTNER 0.1572 3.09* 0.1922 4.39** -0.0319 0.13 0.1115 1.52

WELFARE -0.0305 0.02 0.0593 0.08 -0.1174 0.36 -0.1463 0.53

BIG4_EXP -0.1309 0.74 0.3468 4.90** 0.5997 16.21*** 0.4427 8.23***

E_COHORT -0.1242 0.95 -0.2626 4.00** -0.0625 0.25 -0.1568 1.47

PENALTY 0.2709 2.55* -0.2000 1.31 -0.2404 2.14* -0.1423 0.7

YEAR_BIRTH 0.0196 6.13*** 0.0140 3.17* 0.0008 0.01 0.0109 1.85

CPA_EXP 0.0111 0.62 -0.0143 0.98 -0.0088 0.4 -0.0053 0.14

LTS R2

The dependent variables are the coefficients on indicator variables for individual auditors in Regression Model (1), representing the

individual auditor fixed effects on clients' RAM. We standardized all these coefficients so that they have unit variance and zero mean.

The independent variables are auditors' personal demographic characteristics (see the definitions in Table 9). The regressions are

analyzed by using the Least Trimmed Square (LTS) method (Rousseeuw, 1984). The coefficients and Chi-squares are estimated by

using the final weighted least squares after we detected and deleted some outliers.

Table 10: Explaining Individual Auditor Fixed Effects By Auditors' Demographic Characteristics (Model 11)

VARIABLES

14.02% 17.64% 12.34% 7.63%


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