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Incremental Information of Auditor Quality
Baolei Qi
Xi’an Jiaotong University
Email: [email protected]
Yi Si
Xi’an Jiaotong University
Email: [email protected]
Gaoliang Tian
Xi’an Jiaotong University
Email: [email protected]
Martin G. H. Wu*
The University of Illinois at Urbana-Champaign
Email: [email protected]
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).
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.
~1~
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.
~2~
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.
~3~
(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.
~4~
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).
~5~
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).
~6~
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).
~7~
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).
~8~
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
~9~
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
~10~
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.
~11~
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.
~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.
(Insert Table 5 about here.)
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.
(Insert Table 6 about here.)
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.
(Insert Table 7 about here.)
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).
(Insert Table 9 about here.)
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~
Reference
Aharony, J., C. J. Lee, and T. J. Wong. 2000. “Financial Packaging of IPO Firms in China,” Journal of
Accounting Research 38 (1): 103-126.
Bamber, L. S., J. Jiang, and I. Y. Wang. 2010. “What’s My Style? The Influence of Top Managers on
Voluntary Corporate Financial Disclosure,” The Accounting Review 85 (4): 1131-1162.
Bartov, E., and D. A. Cohen. 2009. “The ‘ Numbers Game’ in the pre-and post-Sarbanes-Oxley eras,”
Journal of Accounting, Auditing & Finance 24 (4): 505-534.
Bauer, A. M. 2012. “Tax Avoidance and the Implications of Weak Internal Controls,” working paper,
University of Illinois at Urbana-Champaign.
Bazerman, M. H., K. P. Morgan, and G. F. Loewenstein. 1997. “The Impossibility of Auditor
Independence,” Sloan Management Review 88.
Beck, P. J. and M. G. H. Wu. 2006. “Learning by Doing and Audit Quality,” Contemporary Accounting
Research 23 (1): 1-30.
Becker, C. L., M. L. DeFond, J. Jiambalvo, and K. R. Subramanyam. 1998. “The Effect of Audit Quality
on Earnings Management,” Contemporary Accounting Research 15 (1): 1-24.
Bertrand, M., and A. Schoar. 2003. “Managing with style: The effect of managers on firm policies,” The
Quarterly Journal of Economics 118 (4): 1169-1208.
Bhojraj, S., P. Hribar, M. Picconi, and J. McInnis. 2009. “Making Sense of Cents: An Examination of
Firms That Marginally Miss or Beat Analyst Forecasts,” The Journal of Finance 64 (5): 2361-2388.
Bratten, B., M. Causholli, and L. A. Myers. 2014. “Fair Value Accounting, Auditor Specialization, and
Earnings Management: Evidence from the Banking Industry,” working paper, University of Kentucky,
University of Kentucky, and University of Arkansas.
Carcello, J. V. and C. Li. 2013. “Costs and Benefits of Requiring an Engagement Partner Signature:
Recent Experience in the United Kingdom,” The Accounting Review 88 (5): 1511-1546.
Carey, P., and R. Simnett. 2006. “Audit Partner Tenure and Audit Quality,” The Accounting Review 81
(3): 653-676.
Causholli, M., and W. R. Knechel. 2012. “An Examination of the Credence Attributes of an Audit,”
Accounting Horizon 26 (4): 631-656.
Chen, K.C.W., and H. Yuan. 2004. “Earnings Management and Capital Resource Allocation: Evidence
from China’s Accounting-Based Regulation of Rights Issues,” The Accounting Review 79 (3): 645-
665.
Chen, X., C. J. Lee, and J. Li. 2008. “Government assisted earnings management in China,” Journal of
Accounting and Public Policy 27 (3): 262-274.
~25~
Chen, C. Y., C. J. Lin and Y. C. Lin. 2008. “Audit Partner Tenure, Audit Firm Tenure, and Discretionary
Accruals: Does Long Auditor Tenure Impair Earnings Quality?” Contemporary Accounting Research
25 (2): 415-445.
Chen, S., Sunny Y. J. Sun, and D. Wu. 2010. “Client Importance, Institutional Improvements, and Audit
Quality in China: An Office and Individual Auditor Level Analysis,” The Accounting Review 85 (1):
127-158.
Cheng, M., D. S. Dhaliwal, and Y. Zhang. 2013. “Does Investment Efficiency Improve after the
Disclosure of Material Weaknesses in Internal Control over Financial Reporting?” The Journal of
Accounting & Economics (forthcoming).
Chi, W., L. L. Lisic, and M. Pevzner. 2011. “Is Enhanced Audit Quality Associated with Greater Real
Earnings Management?” Accounting Horizons 25 (2): 315-335.
Chin, C., and H.Y. Chi. 2008. “Gender Differences in Audit Quality,” 2008 American Accounting
Association annual meeting.
Church, B. K., S. M. Davis, and S. A. McCracken. 2008. “The Auditor’s Reporting Model: A Literature
Overview and Research Synthesis,” Accounting Horizons 22 (1): 69-90.
Cohen, D. A., A. Dey, and T. Z. Lys. 2008. “Real and Accrual-Based Earnings Management in the Pre-
and Post-Sarbanes-Oxley Periods,” The Accounting Review 83 (3): 757-787.
Cohen, D. A., and P. Zarowin. 2010. “Accrual-Based and Real Earnings Management Activities Around
Seasoned Equity Offerings,” Journal of Accounting & Economics 50 (1): 2-19.
Collins, D. W., E. L. Maydew, and I. S. Weiss. 1997. “Changes in the Value-Relevance of Earnings and
Book Values over the Past Forty Years,” Journal of Accounting & Economics 24 (1): 39-67.
Commerford, B., D. Hermanson, R. Houston, and M. Peters. 2014a. “Auditor Sensitivity to Real Earnings
Management: An Experimental Investigation,” working paper, University of Alabama, Kennesaw
State University, University of Alabama, and Villanova University.
Commerford, B., D. Hermanson, R. Houston, and M. Peters. 2014b. “Real Earnings Management:
The Auditor’s Perspective,” working paper, University of Alabama, Kennesaw State University,
University of Alabama, and Villanova University.
Committee of Sponsoring Organizations of the Treadway Commission (COSO). May, 2013.
Craswell, A. T., J. R. Francis, and S. L. Taylor. 1995. “Auditor Brand Name Reputations and Industry
Specializations,” Journal of Accounting & Economics 20 (3): 297-322.
DeAngelo, L. E. 1981. “Auditor Size and Audit Quality,” Journal of Accounting & Economics 3 (3): 183-
199.
Dechow, P. M., S. P. Kothari, and R. L. Watts. 1998. “The Relation between Earnings and Cash Flows,”
Journal of Accounting & Economics 25 (2): 133-168.
Dechow, P. M., and D. J. Skinner. 2000. “Earnings Management: Reconciling the Views of Accounting
Academics, Practitioners, and Regulators,” Accounting Horizons 14 (2): 235-250.
~26~
DeFond, M. L., T. J. Wong, and S. Li. 1999. “The Impact of Improved Auditor Independence on Audit
Market Concentration in China,” Journal of Accounting & Economics 28 (3): 269-305.
DeFond, M. L., and J.R. Francis. 2005. “Audit Research after Sarbanes-Oxley,” Auditing: A Journal of
Practice & Theory 24 (s-1): 5-30.
DeFond, M. L., and J. Zhang. 2014. “A Review of Archival Auditing Research,” Journal of Accounting
and Economics, working paper.
Dyreng, S. D., M. Hanlon, and E. L. Maydew. 2010. “The Effects of Executives on Corporate Tax
Avoidance,” The Accounting Review 85 (4): 1163-1189.
Ewert R. and A. Wangenhofer. 2005. “Economic Effect of Tightening Accounting Standards to Restrict
Earnings Management,” The Accounting Review 80: 1102-1124.
Fellner, G., and B. Maciejovsky. 2007. “Risk Attitude and Market Behavior: Evidence from Experimental
Asset Markets,” Journal of Economic Psychology 28 (3): 338-350.
Feng, M., C. Li, S. E. McVay, and H. Ashbaugh-Skaife. 2013. “Ineffective Internal Control over
Financial Reporting and Firm Operations,” working paper, University of Pittsburgh, University of
Pittsburgh, University of Washington, and University of California at Davis.
Ferguson, A. and D. J. Stokes. 2002. “Brand Name Audit Pricing, Industry Specialization, and Leadership
Premiums post-Big 8 and Big 6 Mergers,” Contemporary Accounting Research 19 (1): 77-110.
Ferguson, A., J. R. Francis, and D. J. Stokes. 2003. “The Effects on Firm-Wide and Office-Level Industry
Expertise on Audit Pricing,” Accounting Review 78 (2): 429-48.
Firth, M., P. L. L. Mo, and R. M. K. Wong. 2005. “Financial Statement Frauds and Auditor Sanctions:
An Analysis of Enforcement Actions in China, Journal of Business Ethics 62: 367-381.
Firth, M., P. L. L Mo, and R. M. K. Wong. 2012. “Auditors’ Organizational Form, Legal Liability, and
Reporting Conservatism: Evidence from China,” Contemporary Accounting Research 29 (1): 57-93.
Fletcher, G. 2013. SEC & PCAOB Updates, at the 12th Annual SEC Financial Reporting Conference,
(September 20).
Francis, J. R. and E. R. Wilson. 1988. “Auditor changes: A joint test of Theories Relating to Agency
Costs and Auditor Differentiation,” The Accounting Review: 663-682.
Francis, J. R. and J. Krishnan. 1999. “Accounting Accruals and Auditor Reporting Conservatism,”
Contemporary Accounting Research 16 (1): 135-165.
Francis, J. R. 2004. “What Do We Know About Audit Quality?” The British Accounting Review 36 (4):
345-368.
Francis, J. R., K. Reichelt, and D. Wang. 2005. “The Pricing of National and City-Specific Reputations
for Industry Expertise in the U.S. Audit Market,” The Accounting Review 80 (1): 113-136.
Francis, J. R. 2011. “A Framework for Understanding and Researching Audit Quality,” Auditing: A
Journal of Practice & Theory 30 (2): 125-152.
~27~
Fudenberg, D. and J. Tirole. 1995. “A Theory of Income and Dividend Smoothing Based on Incumbency
Rents,” Journal of Political Economy: 75-93.
Ge, W., D. Matsumoto, and J. L. Zhang. 2011. “Do CFOs Have Style? An Empirical Investigation of the
Effect of Individual CFOs on Accounting Practices,” Contemporary Accounting Research 28 (4):
1141-1179.
Graham, J. R., C. R. Harvey, and S. Rajgopal. 2005. “The Economic Implications of Corporate Financial
Reporting,” Journal of Accounting & Economics 40 (1): 3-73.
Greiner, A. J., M. J. Kohlbeck, and T. J. Smith. 2013. “Do Auditors Perceive Real Earnings
Management as a Business Risk?” working paper.
Guan, Y., L. N. Su, D. H. Wu. 2014. “Do School Ties between Auditors and Client Executives Influence
Audit Quality,” working paper at City University of Hong Kong, The Hong Kong Polytechnic
University, and The Chinese University of Hong Kong.
Gul, F. A., D. Wu, and Z. Yang. 2013. “Do Individual Auditors Affect Audit Quality? Evidence from
Archival Data,” The Accounting Review 88 (6): 1993-2023.
Gunny, K. A. 2010. “The Relation between Earnings Management Using Real Activities Manipulation
and Future Performance: Evidence from Meeting Earnings Benchmarks,” Contemporary Accounting
Research 27 (3): 855-888.
Hardies, K., D. Breesch, and J. Branson. 2009. “Are Female Auditors Still Women? Analyzing the Sex
Differences Affecting Audit Quality,” working paper.
He, X. J., J. Pittman, O. M. Rui, and D. W. Wu. 2014. “Do Social Ties between External Auditors and
Audit Committee Members Affect Audit Quality,” working paper at Shanghai University of Finance
and Economics, Memorial University of Newfoundland, China Europe International Business School,
and The Chinese University of Hong Kong.
Healy, P. M., and J. M. Wahlen. 1999. “A Review of the Earnings Management Literature and Its
Implications for Standard Setting,” Accounting Horizons 13 (4): 365-383.
Hung, C. K., and D. Wu. 2011. “Aggregate Quasi Rents and Auditor Independence: Evidence from Audit
Firm Mergers in China,” Contemporary Accounting Research 28 (1): 175-213.
Hurtt, R. K. 2010. “Development of a Scale to Measure Professional Skepticism,” Auditing: A Journal of
Practice & Theory 29 (1): 149-171.
Ittonen, K., E. Vahamaa, and S. Vahamaa. 2013. “ Female Auditors and Accruals Quality,” Accounting
Horizons 27 (2): 205-228.
Jeppesen, K. K. 2007. “Organizational Risk in Large Audit Firms,” Managerial Auditing Journal 22 (6):
590-603.
Johnstone, K. M. and J. C. Bedard. 2003. “Risk Management in Client Acceptance Decisions,” The
Accounting Review 78 (4): 1003-1025.
~28~
Johnstone, K. M. and J. C. Bedard. 2004. “Audit Firm Portfolio Management Decisions,” Journal of
Accounting Research 42 (4): 659-690.
Kachelmeier, S. J., and M. G. Williamson. 2010. “Attracting Creativity: The Initial and Aggregate Effects
of Contract Selection on Creativity-Weighted Productivity,” The Accounting Review 85 (5): 1669-
1691.
Kachelmeier, S. J. 2010. “Introduction to a Forum on Individual Differences in Accounting Behavior,”
The Accounting Review 85 (4): 1127-1128.
Kim, Y. and M. S. Park. 2013. “Real Activities Manipulation and Auditors’ Client-Retention Decisions,”
The Accounting Review 89 (1): 367-401.
Knechel, W. R., P. Rouse, and C. Schelleman. 2009. “A Modified Audit Production Framework:
Evaluating the Relative Efficiency of Audit Engagements,” The Accounting Review 84 (5): 1607-
1638.
Knechel, W. R., A. Vanstraelen, and M. Zerni. 2013. “Does the Identity of Engagement Partners Matter?
An Analysis of Audit Partner Reporting Decisions,” Contemporary Accounting Research
(forthcoming).
Krishnan, G. V. 2003. “Audit Quality and the Pricing of Discretionary Accruals,” Auditing: A Journal of
Practice & Theory 22 (1): 109-126.
Krishnan, J. “Audit Committee Quality and Internal Control: An Empirical Analysis,” The Accounting
Review 80.2 (2005): 649-675.
Lennox, C. S. 1999. “Audit Quality and Auditor Size: An Evaluation of Reputation and Deep Pockets
Hypotheses,” Journal of Business Finance & Accounting 26 (7‐8): 779-805.
Li, D. D. 1998. “Changing Incentives of the Chinese Bureaucracy,” The American Economic Review 88
(2): 393-397.
Li Z., Y. Zheng and Y. Lian. 2011. “Equity Refinancing, Earnings Management and the Performance
Decline of China Listed Companies: Viewpoint from Accruals and Real Activities Manipulation,”
China Journal of Management Science 19 (2): 49-56.
Miller, T. 1992. “Do We Need to Consider the Individual Auditor When Discussing Auditor
Independence?” Accounting, Auditing & Accountability Journal 5 (2): 74-84.
Myers, J. N., L. A. Myers, and T. C. Omer. 2003. “Exploring the Term of the Auditor-Client Relationship
and the Quality of Earnings: A Case for Mandatory Auditor Rotation?” The Accounting Review 78
(3): 779-799.
Nelson, M. W., J. A. Elliott, and R. L. Tarpley. 2002. “Evidence from Auditors about Managers’ and
Auditors’ Earnings Management Decisions,” The Accounting Review 77 (s-1): 175-202.
Nelson, M., and H. Tan. 2005. “Judgment and Decision Making Research in Auditing: A Task, Person,
and Interpersonal Interaction Perspective,” Auditing: A Journal of Practice & Theory 24 (s-1): 41-71.
~29~
Newey, W. K., and K. D. West. 1986. “A Simple, Positive Semi-Definite, Heteroskedasticity and
Autocorrelation Consistent Covariance Matrix.”
Norris, F. 2013. “High & Low Finance: Accounting World, Still Resisting Sunlight,” The New York
Times (October 24).
Prawitt, D. F., J. L. Smith, and D. A. Wood. 2009. “Internal Audit Quality and Earnings Management,”
The Accounting Review 84 (4): 1255-1280.
Rapoport, M. 2012. “Eyebrows Go Up as Auditors Branch Out,” The Wall Street Journal (December 6;
B1).
Rapoport, M. 2013. “New Rules Expected for Annual Audit Reports,” The Wall Street Journal (August
13; C1).
Qi, B. L., R. Yang, and G. L. Tian. 2014. “Do Social Ties between Individual Auditors and Client
Executives Matter to Audit Quality,” working paper at Xi’an Jiaotong University, Rochester Institute
of Technology, and Xi’an Jiaotong University.
Reichelt, K. J. and D. Wang. 2010. “National and Office‐Specific Measures of Auditor Industry Expertise
and Effects on Audit Quality,” Journal of Accounting Research 48 (3): 647-686.
Reynolds, J. K. and J. R. Francis. 2000. “Does Size Matter? The Influence of Large Clients on Office-
Level Auditor Reporting Decisions,” Journal of Accounting & Economics 30 (3): 375-400.
Rousseeuw, P. J. and K. V. Driessen. 2006. “Computing LTS Regression for Large Data Sets,” Data
Mining and Knowledge Discovery 12 (1): 29-45.
Roychowdhury, S. 2006. “Earnings Management through Real Activities Manipulation,” Journal of
Accounting & Economics 42 (3): 335-370.
Simunic, D. A. 1980. “The Pricing of Audit Services,” Journal of Accounting Research 18 (1): 161-190.
Simunic, D. A. 1984. “Auditing, Consulting, and Auditor Independence,” Journal of Accounting
Research 22 (2): 679-702.
Simunic, D. A. and M. T. Stein. 1987. Product Differentiation in Auditing: Auditor Choice in the Market
for Unseasoned New Issues. Vancouver, B.C.: The Canadian Certified General Accountants’
Research Foundation (Research Monograph Number 13).
Simunic, D. A. and M. T. Stein. 1990. “Audit Risk in a Client Portfolio Context,” Contemporary
Accounting Research 6 (2): 329-343.
Solomon, I., M. Shield, and R. Whittington. 1999. “What Do Industry Auditors Know?” Journal of
Accounting Research 37 (1): 191-208.
Srinidhi, B., F. A. Gul, and J. Tsui. 2011. “Female Directors and Earnings Quality,” Contemporary
Accounting Research 28 (5): 1610-1644.
Sterman, J. 2000. Business Dynamics: Systems Thinking and Modelling for a Complex World. McGraw-
Hill/Irwin.
~30~
Taylor, M. H. 2000. “The Effects of Industry Specialization on Auditors’ Inherent Risk Assessments and
Confidence Judgments,” Contemporary Accounting Research 17 (4): 693-712.
Teoh, S. H. and T. J. Wong. 1993. “Perceived Auditor Quality and the Earnings Response Coefficient,”
The Accounting Review: 346-366.
Trotman, K. T., A. M. Wright, and S. Wright. 2009. “An Examination of the Effects of Auditor Rank on
Pre-Negotiation Judgments,” Auditing: A Journal of Practice & Theory 28 (1): 191-203.
Vuong, Q. H. 1989. “Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses,”
Econometrica: 307-333.
Wang, Q., T. Wong, and L. Xia. 2008. “State Ownership, the Institutional Environment, and Auditor
Choice: Evidence from China,” Journal of Accounting & Economics 46 (1): 112-134.
Weber, J. and M. Willenborg. 2003. “Do Expert Informational Intermediaries Add Value? Evidence from
Auditors in Microcap Initial Public Offerings,” Journal of Accounting Research 41 (4): 681-720.
Willenborg, M. 1999. “Empirical Analysis of the Economic Demand for Auditing in the Initial Public
Offerings Market,” Journal of Accounting Research 37 (1): 225-238.
Wu, M. G. H. 2006 “An Economic Analysis of Audit and Non-Audit Services: The Tradeoff between
Competition Crossovers and Knowledge Spillovers,” Contemporary Accounting Research, 23 (2):
527-554.
Yang, Z. 2012. “Do Political Connections Add Value to Audit Firms? Evidence from IPO Audits in
China,” Contemporary Accounting Research 30 (3): 891-921.
Yongtae K. and M. S. Park. 2014. “Real Activities Manipulation and Auditors’ Client Retention
Decisions,” The Accounting Review (forthcoming).
Zang, A. Y. 2012. “Evidence on the Trade-off between Real Activities Manipulation and Accrual-Based
Earnings Management,” The Accounting Review 87 (2): 675-703.
~31~
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%
F -statistics [p- value] 4.84 [<0.001] 5.51 [<0.001] 9.12 [<0.001] 9.44 [<0.001]
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%
F -statistics [p- value] 2.59 [<0.001] 4.55 [<0.001] 3.65 [<0.001] 4.01 [<0.001]
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~
AB_CFO AB_DISE AB_PROD COMPOSITE
% significance at 1% level 1.00% 4.00% 1.00% 25.00%
% significance at 5% level 1.00% 9.00% 1.00% 83.00%
% significance at 10% level 1.00% 16.00% 1.00% 89.00%
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
% significance at 1% level 4.17% 30.56% 0.40% 2.18%
% significance at 5% level 17.86% 41.07% 2.98% 5.36%
% significance at 10% level 37.70% 49.40% 8.73% 15.67%
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~
Coeff. t_stat Coeff. t_stat Coeff. t_stat Coeff. t_stat
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
F -statistics [p- value]
Adj. R2
Table 7: Estimating Audit Firm Fixed Effects in Regressions Including Audit Fees As an Explanatory Variable (Model 2a)
VariablesAB_CFO AB_DISE AB_PROD COMPOSITE
Panel A: Regression Results
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
F -statistics [p -value]
ΔR2AF [Vuong Z] -0.05% [1.49**] -0.03% [0.96] -0.03% [0.96] 0.03% [0.96]
%ΔR2AF
F -statistics [p -value]
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]
AB_CFO AB_DISE AB_PROD COMPOSITE
4.84 [<0.001]
150.83%
-0.17% -0.04% -0.07%
~42~
Coeff. t_stat Coeff. t_stat Coeff. t_stat Coeff. t_stat
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
F -statistics [p- value]
Adj. R2
Table 8A: Estimating Individual Auditor Fixed Effects in Regressions Including Going-Concern Audit Opinions As an
Explanatory Variable (Model 2b)
VariablesAB_CFO AB_DISE AB_PROD COMPOSITE
Panel A: Regression Results
Included Included
2.45 [<0.001] 10.06 [<0.001] 3.53 [<0.001] 4.66 [<0.001]
Included Included
(The table is continued on the next page.)
38.77% 47.74%26.64% 69.36%
~43~
F -statistics [p -value]
ΔR2IA [Vuong Z] -0.86% [1.59**] -0.22% [0.98]
%ΔR2IA
Mean
Interquartile range
Standard deviation
% significance at 5% level
% significance at 10% level
Table 8A: (continued)
AB_CFO AB_DISE AB_PROD COMPOSITE
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%
Panel C: The Percentages of Significant Estimated Individual Auditor Fixed Effects
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%
Panel D: Distribution of Estimated Individual Auditor Fixed Effects
-0.1815 -0.1021 -0.0014 -0.1522
See Tables 2 and 4 for definitions of variables.
0.2097 0.2097 0.1867 0.2980
0.1358 0.1316 0.1417 0.2768
~44~
Coeff. t_stat Coeff. t_stat Coeff. t_stat Coeff. t_stat
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
F -statistics [p- value]
Adj. R2
Table 8B: Estimating Individual Auditor Fixed Effects in Regressions Including Modified Audit Opinions As an
Explanatory Variable (Model 2c)
VariablesAB_CFO AB_DISE AB_PROD COMPOSITE
Panel A: Regression Results
(The table is continued on the next page.)
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~
F -statistics [p -value]
ΔR2IA [Vuong Z] -0.89% [1.03*] -0.24% [0.85]
%ΔR2IA
Mean
Interquartile range
Standard deviation
% significance at 5% level
% significance at 10% level
Table 8B: (continued)
AB_CFO AB_DISE AB_PROD COMPOSITE
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%
Panel C: The Percentages of Significant Estimated Individual Auditor Fixed Effects
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%
Panel D: Distribution of Estimated Individual Auditor Fixed Effects
-0.1778 -0.1011 -0.0275 -0.1462
See Tables 2 and 4 for definitions of variables.
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
(The table is continued on the next page.)
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%
AB_CFO AB_DISE AB_PROD COMPOSITE