Hitotsubashi University
Institute of Innovation Research

Personal Knowledge of Audit Partners andOrganizational Knowledge of Audit Firms and the
Impact on Audit Fees

Hironori Fukukawa
Masaru Karube
IIR Working Paper WP#13-06

Mar. 2013

Institute of Innovation Research
Hitotsubashi University
Tokyo, Japan
http://www.iir.hit-u.ac.jp

Personal Knowledge of Audit Partners and Organizational Knowledge of Audit
Firms and the Impact on Audit Fees

Hironori Fukukawa
Graduate School of Commerce and Management,
Hitotsubashi University, Japanh.fukukawa@r.hit-u.ac.jp
Masaru Karube
Institute of Innovation Research,
Hitotsubashi University, Japankarube@iir.hit-u.ac.jp

January 2013

Personal Knowledge of Audit Partners and Organizational Knowledge of Audit
Firms and the Impact on Audit Fees

Abstract: This study develops a conceptual framework for auditor knowledge comprisingboth the personal knowledge of auditors and the organizational knowledge of audit firms. We
use this to examine how three measures of the personal knowledge held by engagement audit
partners—the depth and width of knowledge and industry expertise—impact upon audit fees.
We find that an engagement partner with deeper knowledge provides audit services more
efficiently. In addition, audit fees are negatively associated with partners’ wider knowledge
and positively with partners’ industry expertise, but only for audits by Big 4 audit firms.
Keywords: Personal knowledge, Organizational knowledge, Industry expertise,Engagement partners
Data Availability: The data used are from publicly available sources.

Personal Knowledge of Audit Partners and Organizational Knowledge of Audit
Firms and the Impact on Audit Fees

INTRODUCTION
The purpose of this study is to develop a conceptual framework for auditor knowledge(or expertise), propose several proxies for measuring auditor knowledge at the personal level,and preliminarily examine whether these proxies affect the level of audit fees. Althoughsome experimental studies directly measure or manipulate the content or structure of
personal knowledge and examine its effects on auditors’ judgment and decision making (e.g.,
Nelson et al., 1995; Thibodeau, 2003), almost all archival studies that explore the industry
expertise of auditors adopt a measure based on market share at the firm or office level.
However, given that the audit market is currently dominated by the Big 4 audit firms, it isdoubtful that this approach appropriately captures auditor industry expertise.
To address this, this study develops a conceptual framework for auditor knowledge
comprising personal- and organizational-level knowledge. The former type of knowledge isunique to each auditor and is obtained through such means as audit experience, in-housetraining, quality control at the firm level, and the supervision and direction in an audit team.
The latter form of knowledge accumulates in an audit firm through the review of audit
processes and results in each audit team and the exercise of quality control, and it is explicitly
reflected in firm documentation, including audit-related manuals.
It would appear that in practice, at least in the Japanese context, emphasis has
traditionally been placed on personal knowledge over organizational knowledge. This isevidenced by the fact that the large audit firms in Japan were formed by mergers and1acquisitions in the 1980s or earlier (Karube and Fukukawa, 2012), and that most certified
public accountants (CPAs) who worked for a now-defunct Big 4 firm (ChuoAoyama) moved
to another audit firm and continued to provide audit services following the firm’s 2006
demise (Fukukawa, 2011). If organizational knowledge were crucial to the conduct of aquality audit, many audit firms would have encountered difficulties after the wave of mergers
and acquisitions and many CPAs could not have moved to another firm from ChuoAoyama
following its demise. However, with the implementation of mandatory audit partner rotationin many developed countries (e.g., in Japan, every five years for large firms and seven years
for other firms), organizational knowledge has become increasingly more important in recent
years.
Prior archival research on auditor knowledge or expertise has generally failed to
distinguish the personal knowledge of auditors from the organizational knowledge of theiraudit firm. Using our proposed conceptual framework for auditor knowledge, we focus on
the personal knowledge of engagement partners, which we attempt to measure using archival
data. Specifically, in Japanese practice, each engagement partner signs his/her name on theauditor’s report. By using the information contained in this unique dataset, we can measure
the depth of knowledge (i.e., how many years an engagement partner has continuously been
involved with a particular client), the width of knowledge (i.e., how many clients the partner
has served as an engagement partner in the current fiscal year), and industry expertise (i.e., towhat extent the engagement partner has focused on a particular industry). We then measurefirm-level knowledge using each firm’s market share, and compare these knowledge aspects,
both personal and organizational, across audit firms.

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Finally, in a preliminary analysis, we examine whether personal knowledge impacts
upon the level of audit fees. We find that the depth of the knowledge held by auditors
negatively affect audit fees. We also find that audit fees are negatively affected by the widthof the knowledge and positively by industry expertise, but only for audit partners in Big 4
audit firms. In addition, market-share-based organizational knowledge exerts a positive
impact on audit fees, but only for non-Big 4 audit firms. Taken together, these findings
suggest that an audit partner with deeper and wider knowledge provides audit services more
efficiently, that partners with greater industry expertise charge a fee premium, and that Big 4
firms with sufficient industry expertise provide high-quality audits in any industry and are
therefore unable to earn additional fees for incremental industry market share.
The remainder of the paper is organized as follows. The second section discusses the
background to this study and describes the Japanese audit market. In the third section, wedevelop our conceptual framework for auditor knowledge and propose the three measures of
personal knowledge held by auditors. The fourth section describes a preliminary analysis thatinvolves regressing audit fees on these knowledge variables and presents the results of thisanalysis. The final section summarizes our conclusions and discusses some directions for
future research.

BACKGROUND AND THE JAPANESE AUDIT MARKET
The importance of auditor knowledge (or expertise) for audit quality cannot be
overemphasized. As a result, much research has already been devoted to investigating theeffects of auditor knowledge on judgment and decision making (for a comprehensive reviewof the experimental research, see Bonner, 2008, Ch. 3). Most archival research has
3considered the industry expertise of auditors based on the industry market shares of audit
firms and its effect on audit fees. For example, Craswell et al. (1995) define industry
specialist Big 8 audit firms as those with at least a 10% market share in the industry and findthat audit fees for these firms are generally higher than those for non-specialist Big 8 firms.
Moreover, many studies find that the fee premium resulting from industry specialization ishigher for larger clients (Ferguson and Stokes, 2002; Ferguson et al., 2003; Carson andFargher, 2007). In particular, Ferguson et al. (2003) conclude that the fee premium is
attributable more to office-level specialization than to firm-level specialization. Elsewhere,
Casterella et al. (2004) investigate the relationship between auditor industry specializationand client bargaining power and find no evidence of an audit fee premium for industry
specialization when a client has strong bargaining power. Conversely, Mayhew and Wilkins(2003) find evidence of a negative association between audit fees and audit firm market shareand a positive association between audit fees and audit firm differentiation (i.e., whether anaudit firm has both the largest market share in the industry and a large market share lead overits closest competitor).
Importantly, although these studies use firm- or office-level market share as a measureof auditor industry specialization and generally find evidence of a positive relationship withaudit fees, it is not clear whether market share remains valid as a proxy for the auditor
industry expertise. To help clarify this point, we consider the current audit market in Japan as
an example.
[TABLES 1 AND 2 ABOUT HERE]

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Tables 1 and 2 provide information on the market shares of the Big 4, namely,
ShinNihon (Ernst & Young), Tohmatsu (Deloitte), Azsa (KPMG), and Aarata(PricewaterhouseCoopers, PwC), and other audit firms in Japan in 2010 in terms of thenumber of clients and the level of audit fees, respectively. As shown, the audit market in
Japan is clearly dominated by the Big 4 firms (i.e., 73.1% in terms of the number of clientsand 86.8% in terms of audit fees).1 It is also notable that the market shares of three of the Big
4 firms (ShinNihon, Azsa, and Aarata) are higher in terms of audit fees than in the number ofclients. This implies that these particular firms generally have larger clients than do otherfirms.
The market share data in Tables 1 and 2 cast some doubt on the validity of amarket-share-based measure of auditor industry specialization (or expertise). For example,
by looking at the fiber product industry, which is a relatively small industry, we can see that
ShinNihon has 13 clients (a client market share of 22.4%) and earns 973 million yen (a fee
market share of 31.0%). Conversely, in the case of the trading industry, which includes 394
companies, Azsa has 73 clients and earns 5.7 billion yen in fees, implying a market share inthis industry of 18.5% and 24.2%, respectively. Based on the market-share-based measure ofindustry specialization, we would consider that ShinNihon is more specialized in terms of the
conduct of audit services in the fiber product industry than is Azsa in the trading industry,even though Azsa has many more clients and earns substantially more fees in that industry.
The question is why 73 clients in a fairly large industry (in terms of the number of
1

Aarata (PwC) had only 78 listed company clients in 2010 (or 2.2% of the total). ChuoAoyama,
previously one of the Big 4 firms and formerly part of the PwC alliance, was dissolved in 2006
because of its involvement in a major accounting scandal. Following its demise, some former
ChuoAoyama partners and PwC established Aarata, but it is still only about one-third the size of theother Big 4 firms. For more details about the demise of ChuoAoyama, see Fukukawa (2011).

5

companies) would be insufficient for Azsa to establish appropriate industry-specialized
expertise. A similar question is whether having 108 clients in the same industry, as forTohmatsu, necessarily leads to more specialized expertise than is possible for Azsa to achieve.
Given the highly concentrated nature of the Japanese audit market, it would be reasonable toargue that market share is not an appropriate measure of either auditor specialization or
expertise.
In addition, prior archival research has not distinguished between auditors’ personal
knowledge and firms’ organizational knowledge, nor attempted to directly measure personal
knowledge. For the most part, the market-share-based industry specialization measure usedin extant studies is considered to adequately capture at least some aspects of audit firmorganizational knowledge. However, as this is clearly unrelated to personal knowledge, we
focus on the personal knowledge of auditors.

CONCEPTUAL FRAMEWORK AND KNOWLEDGE MEASURES
Figure 1 depicts the conceptual framework for auditor knowledge adopted in thisstudy. As shown, auditor knowledge comprises the personal knowledge of auditors and theorganizational knowledge of audit firms. Personal knowledge is gained through audit
experience, along with in-house training such as continuous professional education, effective
quality control by the firm, and the supervision/direction of a superior in an audit team(Arrow [1]). In contrast, as indicated by the direction of arrows [2] and [6], organizational
knowledge is accumulated through feedback from the firm’s constituents.2

2

See, for example, Spender (1996) and Tsoukas and Vladimirou (2001).

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In turn, there are two layers in the personal knowledge of auditors: (1) knowledge of aparticular client and (2) knowledge of other companies and a particular industry. The firstlayer relates to the depth of knowledge, obtained through direct experience in auditing theclient’s financial statements. Generally, the longer an auditor is involved with a client, thegreater the knowledge of the client gained. The second layer relates to knowledge obtained
through auditing other companies and by concentrating on a smaller range of industries. Suchknowledge can be useful to the auditor in auditing the client, as shown by arrow [3] in Figure
1, because it provides additional information for use in comparing the client with othercompanies in the same industry. As a rule, the more clients an auditor is involved with, thegreater the knowledge of other companies obtained (width of knowledge). Likewise, if anauditor focuses on clients in fewer industries, he/she can develop deeper expertise in thoseindustries.
Next, we examine how to measure the personal knowledge of auditors (i.e., depth ofknowledge, width of knowledge, and industry expertise). As discussed, in Japan eachengagement partner signs his or her name on the auditor’s report. We collected data on all
engagement partners for all listed companies during the period from 2004 to 2010 from theauditor reports included in the company annual reports, complemented by the 2012 Reporton Auditor and Audit Fees of Publicly Listed Companies (Study Group on Auditor and Audit
Fee Issues, 2011).
Using this large dataset, we measure the personal knowledge of engagement partners
and describe in detail whether and how such personal knowledge differs among Big 4 audit
firms and between Big 4 and other audit firms. First, we can operationalize the depth ofknowledge with the number of years an engagement partner has been involved with a7particular client. Table 3 details how many years the engagement partners in the 2010 audits
have continuously provided audit services to the same client. In 2010, 2,397 engagement
partners signed their name on 8,066 auditor’s reports for a total of 3,585 listed companies.
This means that on average each auditor’s report was signed by 2.25 engagement partners.
[TABLE 3 ABOUT HERE]
On average, each engagement partner is involved with a particular client for 3.31
years, with a range of one to seven years.3 To examine whether there are any significant
differences in the mean number of years for client involvement among audit firms, we
conducted a multiple comparison test (Tukey test). The test result shows that the mean for
Aarata (3.02 years) is significantly lower than those for ShinNihon (3.42 years) andTohmatsu (3.48 years) (at the 10% and 5% level, respectively), while the mean for Others(2.99 years) is significantly lower than those for ShinNihon (3.42 years), Tohmatsu (3.48
years), and Azsa (3.38 years) (all at the 1% level).
To operationalize the width of knowledge, we use the number of clients with which apartner is involved in 2010. Table 4 presents the number of partners who provided audit
services to a particular number of clients. For example, 82 partners in ShinNihon are
involved with only one client. In total, about 60% of partners serve as an engagement partner
for three or fewer clients and more than 90% of partners serve as an engagement partner forseven or fewer clients. Surprisingly, some partners provide audit services to more than 10
clients.

3

As the data period is 2004–2010, engagement partners who have been providing audit services to thesame client for more than seven years are included in the seven-year category.

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[TABLE 4 ABOUT HERE]We also conduct a multiple comparison (Tukey test) to examine the differences
between audit firms in the mean number of clients with which a partner is involved. We findthat the mean number of clients for Tohmatsu (4.37 clients) is significantly higher than forthe other Big 4 firms (at either the 1% or 5% level). Furthermore, the mean number of clientsfor Others (3.37 clients) is significantly lower than for all Big 4 firms (at the 1% level). This
result indicates that in 2010 Big 4 partners were generally involved with more clients thanpartners in non-Big 4 firms and that Tohmatsu’s partners usually serve more clients as anengagement partner than do partners in other Big 4 firms.
Table 5 details the numbers of industries with which partners were involved inproviding audit services in 2010. We calculated these numbers by classifying the clients thepartner serves as an engagement partner into the industry categories presented in Tables 1
and 2. Of the 2,397 partners, more than 50% are involved with only one or two industries. On
average, a partner is involved with 2.74 industries, with a range from 1 to 12 industries.
[TABLE 5 ABOUT HERE]
The results of a multiple comparison test (Tukey test) show that the mean number ofindustries for ShinNihon (3.20) is significantly higher than those for Aarata (2.43) (at the 5%
level) and Others (2.00) (at the 1% level). Likewise, the mean for Tohmatsu (3.36) issignificantly higher than those for Azsa (3.06), Aarata (2.43), and Others (2.00) (at the 10%,
1% and 1% level, respectively) and the mean for Azsa is significantly higher than that for
Others (at the 1% level). This result indicates that non-Big 4 partners are generally involved
with fewer industries than are Big 4 auditors and that there are also some differences between
9

Big 4 firms. We consider that this reflects each firm’s policy on partner assignment and thedevelopment of industry expertise.
Next, we define industry expertise as the number of clients a partner serves as anengagement partner minus the number of industries in which the clients operate plus one. In
general, this measure of partner industry expertise takes a larger value when a partner serves
more clients as an engagement partner and is involved with fewer industries. As shown in
Table 6, the mean industry expertise score is 1.62 with a range of 1 to 17.
[TABLE 6 ABOUT HERE]
A comparison of the audit firms’ mean scores (Tukey test) reveals that the mean forTohmatsu (2.01) is significantly higher than those for ShinNihon (1.76), Azsa (1.62), andOthers (1.33) (at the 1% level), while the mean for Others (1.33) is significantly lower thanthose for ShinNihon (1.76) and Azsa (1.62) (both at the 1% level). This indicates that Big 4
partners generally have greater industry expertise than non-Big 4 partners and that the levelof industry expertise among Tohmatsu’s partners is higher than that of other Big 4 firms.
In summary, our data indicate that Big 4 partners provide audit services to a particularclient over a longer period, serve more clients as an engagement partner, are involved withmore industries, and have a higher level of industry expertise than non-Big 4 partners. There
are also some significant differences among the Big 4 firms in the personal knowledge ofpartners. Generally, Tohmatsu’s partners provide audit services to a particular client over alonger period, have more clients, are involved with more industries, and have a higher levelof industry expertise than partners in the other Big 4 firms. We consider that such differencesamong Big 4 firms reflect differences in their approaches to developing the personal

10

knowledge of their partners, the organizational knowledge of the firm, and the knowledge
relationships between the partners and the firm.
The measures of personal knowledge detailed, i.e., (1) the depth of knowledge,
defined as the number of years an engagement partner has consecutively served a particularclient, (2) the width of knowledge, defined as the number of clients with which a partner isinvolved, and (3) industry expertise, calculated as the combination of the width of knowledge
and the number of industries with which a partner is involved, capture important components
in our conceptual framework described earlier (Figure 1), which is one of the main
contributions of this study. It is also interesting to see the extent to which these aspects ofauditor personal knowledge vary across audit firms.

PRELIMINARY ANALYSIS
Overview
Although we have proposed new measures of the personal knowledge of auditors thatappear to be useful in capturing important aspects of knowledge and have provided detailed
descriptive statistics of these same measures, it is unclear whether these measures areassociated with audit quality overall or the quality of auditor judgment and decision making.
Consequently, in a preliminary analysis, we examine whether the depth and width ofknowledge and industry expertise as defined affect audit pricing. More specifically, we
address the following two research questions in our analysis.

RQ1: How do the measures of the personal knowledge of engagement partners affectaudit fees?

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RQ2: Does the effect of the personal knowledge of engagement partners differ
depending on whether they are auditors in Big 4 firms?

The first research question relates to the role of knowledge or expertise in determining
audit fees. If a knowledgeable engagement partner provides a higher-quality audit to a client,
audit fees will be higher. On the other hand, if the quality of audits is determined andcontrolled by the audit firm and is similar for all the firm’s partners, audit fees for audits
conducted by a knowledgeable partner will be lower because the partner can produce thegiven level of audit service more efficiently. However, it is not clear whether aknowledgeable partner provides a higher level of audit service or a predetermined level ofaudit service more efficiently. We leave this to empirical testing. The second researchquestion concerns whether the effects of personal knowledge, if any, are moderated bywhether a partner belongs to a Big 4 firm. In other words, it relates to whether the
relationship between auditor personal knowledge and firm organizational knowledge in Big
4 firms differs from the relationship in other firms.
As discussed, we found significant differences in the three measures of personal
knowledge between Big 4 and non-Big 4 partners. Moreover, prior studies generally find thatBig 4 firms provide higher-quality audits than non-Big 4 firms in terms of discretionary
accruals (Krishnan, 2003), the ex ante cost of capital (Khurana and Raman, 2004), andanalyst forecast accuracy (Behn et al., 2008). Furthermore, existing studies generally
conclude that Big 4 firms are able to charge higher audit fees than non-Big 4 firms (theso-called Big 4 premium) (e.g., Choi et al., 2008; Hay et al., 2006).

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Sample and Data
Our sample selection begins with all Japanese listed companies in 2010 (n = 3,583;
see also Table 1). We first excluded 12 companies for which two or more audit firms jointly
conducted the audit or whose fiscal year in 2010 was not for a full 365 days. We thenremoved companies in the finance-related industries (i.e., Banks, Securities, Insurance, andOther Financial Businesses) (n = 178). Finally, we excluded 594 companies for which thenecessary financial and other data are unavailable. Our final sample comprises 2,799
companies, of which 2,040 companies are Big 4 clients and 759 companies are non-Big 4
clients. We decided to include only 2010 audits in the analysis because one of the variables
we are interested in is the depth of auditor personal knowledge (defined as the number ofyears an engagement partner has been consecutively involved with a particular client), forwhich we have data on engagement partners for the seven years from 2004 to 2010.
We hand-collected data on the engagement partners (name and firm affiliation) fromthe auditor’s reports included in the company annual reports. We collected audit fee datafrom the 2012 Report on Auditor and Audit Fees of Publicly Listed Companies (Study Groupon Auditor and Audit Fee Issues, 2011) and each company’s annual report. We obtained theremaining financial data from the Corporate Financial Data Bank developed by the Japan
Economic Research Institute, Inc.

Variables and Model Specification
Following earlier studies, we specify the following regression model.

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lnFEE=0 + 1 LEAD_YEARS + 2 LEAD_CLIENTS + 3 LEAD_EXPERTISE+4 SHARE + 5 NO_PARTNERS + 6 lnAssets + 7 SUBS + 8 CATA
+ 9 QUICK + 10 ROA + 11 LEV + 12 LOSS + 13 SEC+  industry dummies + 

The dependent variable, lnFEE, is the natural log of audit fees paid to the audit firm.
In this analysis, we are most interested in four variables: LEAD_YEARS, LEAD_CLIENTS,
LEAD_EXPERTISE, and SHARE. LEAD_YEARS relates to the depth of auditors’ personal
knowledge and is defined as the number of years a lead engagement partner (an engagement
partner who first signed an auditor’s report) has provided audit services to a particular client.
As our dataset covers the period from 2004 to 2010, this variable ranges from 1 to 7. As avariable to measure the width of personal knowledge, we include LEAD_CLIENTS, which isthe number of clients the lead engagement partner served as an engagement partner in 2010.
LEAD_EXPERTISE is a variable used to measure the level of industry expertise of a lead
engagement partner and is defined as (LEAD_CLIENTS – the number of industries withwhich the partner is involved in 2010 + 1).4 As the variable indicating the organizational
knowledge of audit firms, we include SHARE, which represents the audit fees paid by clientsof the firm compared with the total audit fees paid by companies in the industry (i.e.,
fee-based market share in the industry). In addition, to control for the total effort ofengagement partners, we include the number of engagement partners (NO_PARTNERS) in
the regression.

4

As discussed, an auditor’s report in Japan is generally signed by two or more engagement partners.
As a robustness check, we repeated the regression analysis using the mean or maximum values of theengagement partners, rather than the values of the lead engagement partners. The results obtained
were not qualitatively different from those reported in the main results section.

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We select control variables that have been commonly found in prior studies toinfluence audit fees (Hay et al., 2006), particularly in Japan (Fukukawa, 2011; Kim andFukukawa, 2013). To control for client size, we specify the natural log of the client’s total
assets (lnAssets). To proxy client complexity, we specify SUBS and CATA, where SUBS is
the square root of the number of consolidated subsidiaries and CATA is the ratio of current
assets to total assets. The size and complexity variables are expected to positively affect audit
fees.
Four variables (QUICK, ROA, LEV, and LOSS) are included to control for client risk.
QUICK is the ratio of quick assets to current liabilities. ROA is income before interest and tax
divided by total assets. LEV is the ratio of long-term debt to total assets. We winsorized thesethree variables at the 99th percentile as in some existing studies (e.g., Carson and Fargher,
2007; Fukukawa, 2011). LOSS is an indicator variable that equals one if the client reported aloss in the fiscal year, and zero otherwise. We expect that the higher the client risk, the higher
the audit fees. As a final client attribute, we include SEC, which is an indicator variable that isequal to one if the client is a US Securities and Exchange Commission (SEC) registrant;
studies have found that audit fees for SEC registrants are generally much higher than thosefor other companies (Fukukawa, 2011; Kim and Fukukawa, 2013). Finally, we include
industry dummy variables to control for differences in audit fees by industry.

Descriptive Statistics
Table 7 provides descriptive statistics of the variables included in the regression
model. To examine the differences between Big 4 and non-Big 4 firms, we conduct t-tests
and chi-squared tests.
15

[TABLE 7 ABOUT HERE]
Some differences between Big 4 and non-Big 4 firms are noteworthy. First, audit fees
are significantly higher for Big 4 firms. Consistent with this result, the clients of Big 4 firms
are typically larger (lnAssets), more complex (SUBS), and less risky (ROA and LOSS) thanthe clients of non-Big 4 firms. As for the auditor knowledge variables, the lead engagement
partners of Big 4 firms usually have deeper (LEAD_YEARS) and wider (LEAD_CLIENTS)
knowledge of a client. However, there is no significant difference in the industry expertise oflead partners (LEAD_EXPERTISE). Organizational industry expertise, as measured in termsof fee-based market share, is also higher for Big 4 firms.
Table 8 includes the Pearson (product moment) correlation matrix. As shown, lnFEE
is significantly correlated with all independent variables (at the 5% level), although thedirection of some of the correlations lies opposite to our expectations (i.e., CATA, ROA, LEV,and LOSS). The correlation between LEAD_YEARS and lnFEE (0.048) is significantly
positive, and lnFEE is negatively correlated with LEAD_CLIENTS and LEAD_EXPERTISE(–0.055 and –0.075, respectively). In addition, SHARE is positively correlated with lnFEE(0.339). These results warrant the use of multivariate analysis to further investigate therelationships between audit fees and knowledge-related and other variables. Furthermore,some of the correlations between the personal knowledge variables are significantly positive.
In particular, the correlation between LEAD_CLIENTS and LEAD_EXPERTISE is high(0.844). Upon closer examination, the correlation turns out to be very high for the non-Big 4
sample (0.950). To avoid any potential problem with multicollinearity, we include thesevariables sequentially in the regression analysis for the non-Big 4 audit firms.

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[TABLE 8 ABOUT HERE]

Results
Table 9 presents the regression results. As shown, the explanatory power of the modelin terms of adjusted R-squared is higher for the Big 4 sample (0.83) than the non-Big 4
sample (0.60).5[TABLE 9 ABOUT HERE]
In terms of the estimated coefficients, LEAD_YEARS has a significant negative
impact on audit fees (at the 5% level for the Big 4 sample and the 10% level for the non-Big
4 sample). This means that the deeper a lead engagement partner’s knowledge of a client, thelower the audit fee, implying that a knowledgeable engagement partner is able to provide
audit services more efficiently. Second, LEAD_CLIENTS exerts a significant negative
impact on audit fees, but only for the sample of Big 4 clients. Third, LEAD_EXPERTISE
influences audit fees positively, but also only for the Big 4 sample. That is, if a lead
engagement partner from a Big 4 firm serves more clients, the audit fee is lower. In addition,a Big 4 lead partner’s higher industry expertise leads to higher audit fees. However, suchsignificant effects for LEAD_CLIENTS and LEAD_EXPERTISE are not found among
non-Big 4 partners.
Conversely, SHARE affects audit fees positively only for the non-Big 4 sample. Thismeans that the larger the industry market share of a non-Big 4 firm, the higher the fees thatthe firm can charge in that industry. However, this does not hold true for Big 4 firms. These5

As all variance inflation factors are smaller than 3.9, multicollinearity is not too severe a problem.

17

results may be related to the fact that the market shares of non-Big 4 firms are much smaller
than those of Big 4 firms, which we can interpret as follows. When the market share in anindustry is below the threshold level necessary to establish greater industry expertise (as inmany cases in non-Big 4 audit firms), a higher share brings greater expertise and thus higher
fees. However, an audit firm with a market share above the threshold level (as for Big 4
firms) already has high expertise and cannot further increase its industry expertise byincreasing market share. The positive association between the number of engagement
partners (NO_PARTNERS) and audit fees is significant at the 1% level for the Big 4 firm
sample and the 10% level for the non-Big 4 firm sample, which implies that increased total
effort by engagement partners leads to higher fees.
The remaining control variables generally influence audit fees as hypothesized. Client
size (lnAssets) and complexity (SUBS) are positively associated with audit fees. For the Big 4
firm sample, audit fees are affected positively by CATA, LEV, and LOSS and negatively by
QUICK. For the non-Big 4 firm sample, QUICK and ROA have a negative relationship withaudit fees. These differences in audit fee determinants between Big 4 and non-Big 4 firms
may reflect variation in audit pricing behavior or client characteristics. Finally, audit firms
charge higher fees for SEC registrants.
In summary, for Big 4 audits, an engagement partner with deeper and wider
knowledge provides audit services more efficiently while a partner with greater industry
expertise can earn higher fees. Conversely, firm organizational knowledge, as measured byindustry market share, does not affect the level of audit fees. In contrast, for non-Big 4 audits,
when a firm has more organizational knowledge, it can earn higher fees. However, thepersonal knowledge of audit partners appears to have little effect on audit fees, with only the18depth of knowledge marginally influencing audit fees. These differences in the effects of theauditor knowledge variables across Big 4 and non-Big 4 firms may result from differences in
the amount and content of the personal knowledge of partners, firm organizational
knowledge, and their relationship.

CONCLUSIONSIn this study, we developed a conceptual framework for auditor knowledge, wheresuch knowledge is categorized into personal knowledge and firm organizational knowledge.
In turn, in our framework, auditor personal knowledge is comprised of three importantelements: the depth of knowledge of a particular client, the width of knowledge obtained
through audit experience with other clients, and industry expertise cultivated by providing
audit services to more clients in fewer industries.
In an attempt to measure the three components of personal knowledge using archival
data, we proposed three variables, defined as the number of years an engagement partner
consecutively serves a particular client (the depth of knowledge), the number of clients withwhich the partner is involved as an engagement partner in the fiscal year (the width ofknowledge), and the number of clients a partner serves as an engagement partner less thenumber of industries in which the clients operate plus one, respectively. Using a uniquedataset on the individual identity of engagement partners, we provided detailed descriptive
statistics for these variables. Overall, we found some differences in auditor personal
knowledge between Big 4 and non-Big 4 partners and among the partners in Big 4 firms.
In addition, we preliminarily examined whether and how these knowledge-related
variables influenced audit fees. We found that the depth of knowledge has a negative impact
19

on audit fees. Similarly, the width of knowledge is negatively associated with audit fees for
the Big 4 audit firms. These results indicate that these components are useful for conducting
efficient audits. We also found that Big 4 partners with greater industry expertise earn higher
fees. In contrast, we found that audit firm organizational knowledge has a positive effect on
audit fees, but only for non-Big 4 firms. We argue that these differences between Big 4 andnon-Big 4 firms reflect variation in the amount and content of personal knowledge andorganizational knowledge and the relationship between them.
This work could be extended in several important ways. First, the relationshipbetween auditor personal knowledge and firm organizational knowledge could be examined
more closely. In particular, this study addressed only one aspect of firm organizational
knowledge. Supplementary data on firm organizational knowledge gathered throughinterview or questionnaires would enable us to better discern how firm organizational
knowledge accumulates and how it interacts with the personal knowledge of the firm’s
partners. Second, this study only preliminarily examined the effects of knowledge-related
variables on audit fees. Further investigation is then necessary to ascertain whether thesevariables also impact upon audit quality. Finally, it would be interesting to examine thenature of the personal relationships and networks between partners constructed in a particular
audit firm. Whether we can group partners across a number of criteria (e.g., premerger audit
firms to which partners belonged) and how such groups, if any, function in the firm areimportant research questions that presently remain outstanding.

20

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forecasts. The Accounting Review 83(2): 327–359.
Bonner, S. E. 2008. Judgment and Decision Making in Accounting. Pearson Prentice Hall.
Carson, E., and N. Fargher. 2007. Note on audit fee premiums to client size and industry
specialization. Accounting and Finance 47(3): 423–446.
Casterella, J. R., J. R. Francis, B. L. Lewis, and P. L. Walker. 2004. Auditor industry
specialization, client bargaining power, and audit pricing. Auditing: A Journal of Practice& Theory 23(1): 123–140.
Choi, J.-H., J.-B. Kim, X. Liu, and D. A. Simunic. 2008. Audit pricing, legal liability regimes,
and Big 4 premiums: Theory and cross-country evidence. Contemporary Accounting
Research 25(1): 55–99.
Craswell, A. T., J. R. Francis, and S. L. Taylor. 1995. Auditor brand name reputations andindustry specializations. Journal of Accounting and Economics 20(3): 297–322.
Ferguson, A., and D. Stokes. 2002. Brand name audit pricing, industry specialization, andleadership premiums post-Big 8 and Big 6 mergers. Contemporary Accounting Research19(1): 77–110.
Ferguson, A., J. R. Francis, and D. J. Stokes. 2003. The effects of firm-wide and office-levelindustry expertise on audit pricing. The Accounting Review 78(2): 429–448.
Fukukawa, H. 2011. Audit pricing and cost strategies of Japanese Big 3 firms. International
Journal of Auditing 15(2): 109–126.
Hay, D. C., W. R. Knechel, and N. Wong. 2006. Audit fees: A meta-analysis of the effect of
supply and demand attributes. Contemporary Accounting Research 23(1): 141–191.
Karube, M., and H. Fukukawa. 2012. Integrating personal expertise: A history of Japanese audit
firms, 1965–2010. Paper presented at the 16th Annual Conference of the European
Business History Association (Paris, France).
Khurana, I., and K. Raman. 2004. Litigation risk and the financial reporting credibility of Big 4
versus non-Big 4 audits: Evidence from Anglo-American countries. The Accounting
Review 79(2): 473–495.

21

Kim, H., and H. Fukukawa. 2013. Japan’s Big 3 firms’ response to clients’ business risk: Greater
audit effort or higher audit fees? International Journal of Auditing(DOI: 10.1111/j.1099-1123.2012.00464.x).
Krishnan, G. 2003. Audit quality and the pricing of discretionary accruals. Auditing: A Journalof Practice & Theory 22(1): 109–126.
Mayhew, B. W., and M. S. Wilkins. 2003. Audit firm industry specialization as a differentiation
strategy: Evidence from fees charged to firms going public. Auditing: A Journal ofPractice & Theory 22(2): 33–52.
Nelson, M. W., R. Libby, and S. E. Bonner. 1995. Knowledge structure and the estimation of
conditional probabilities in audit planning. The Accounting Review 70(1): 27–47.
Spender, J.-C. 1996. Organizational knowledge, learning and memory: Three concepts in searchof a theory. Journal of Organizational Change Management 9(1): 63–78.
Study Group on Auditor and Audit Fee Issues (Kansanin Kansahoushu Mondai Kenkyukai).
2011. 2012 Report on Auditor and Audit Fees of Publicly Listed Companies (Joujoukigyo
Kansanin Kansahoushu Hakusho). Nihon Kounin Kaikeishi Kyoukai Shuppan Kyoku (inJapanese).
Thibodeau, J. C. 2003. The development and transferability of task knowledge. Auditing: A
Journal of Practice & Theory 22(1): 47–67.
Tsoukas, H., and E. Vladimirou. 2001. What is organizational knowledge? Journal ofManagement Studies 37(7): 973–993.

22

Figure 1: Conceptual Framework for Auditor Knowledge

Knowledgeof a particularclient (Depth)

Auditor personal knowledge

[3]
[1]

Knowledge of other companies(Width)Knowledge of a particularindustry (Expertise)[5]

Audit firm organizational knowledge

[4]

[6]

Knowledge accumulated by an audit firmthat is reflected in: Audit manuals
 Quality control
 CPE
 Review of working papers (supervision)

23

[2]

Table 1: Numbers of Clients and Market Shares
Industry

ShinNihon

Tohmatsu

Azsa

AarataOthers

Total

Food

35 (26.1)

41 (30.6)

21 (15.7)

1 (0.7)36 (26.9)

134 (3.7)

Fiber Products

13 (22.4)

11 (19.0)

12 (20.7)

2 (3.4)20 (34.5)

58 (1.6)

Pulp/Paper

7 (30.4)

8 (34.8)

3 (13.0)0 (0.0)5 (21.7)

23 (0.6)

Chemicals

64 (33.0)

37 (19.1)

37 (19.1)

2 (1.0)54 (27.8)

194 (5.4)

Pharmaceuticals

13 (23.2)

20 (35.7)

15 (26.8)

1 (1.8)7 (12.5)

56 (1.6)Oil

3 (27.3)

1 (9.1)

3 (27.3)

3 (27.3)

1 (9.1)

11 (0.3)

Rubber Products

8 (34.8)

3 (13.0)

5 (21.7)

0 (0.0)7 (30.4)

23 (0.6)

Ceramic Products

12 (19.7)

20 (32.8)

13 (21.3)

0 (0.0)16 (26.2)

61 (1.7)Steel

15 (28.3)

7 (13.2)

17 (32.1)

3 (5.7)

11 (20.8)53 (1.5)

Nonferrous Metals

29 (22.5)

31 (24.0)

19 (14.7)

3 (2.3)47 (36.4)

129 (3.6)

Machinery

61 (26.5)

57 (24.8)

38 (16.5)

7 (3.0)57 (29.1)

230 (6.4)

Electric Appliances

86 (28.9)

76 (25.5)

52 (17.4)

7 (2.3)77 (25.8)

298 (8.3)

Shipbuilding

2 (28.6)

1 (14.3)

4 (57.1)

0 (0.0)0 (0.0)7 (0.2)

Automobile

27 (33.3)

17 (21.0)

17 (21.0)

10 (12.3)

10 (12.3)

81 (2.3)

Transportation Equipment

2 (12.5)

3 (18.8)

3 (18.8)

0 (0.0)8 (50.0)

16 (0.4)

Precision Equipment

10 (20.0)

17 (34.0)

10 (20.0)

0 (0.0)13 (26.0)

50 (1.4)

Other Manufacturing

27 (21.4)

24 (19.0)

27 (21.4)

3 (2.4)45 (35.7)

126 (3.5)

Fisheries

4 (36.4)

2 (18.2)

2 (18.2)

0 (0.0)3 (27.3)

11 (0.3)

Mining5 (50.0)

1 (10.0)

3 (30.0)0 (0.0)1 (10.0)

10 (0.3)

Construction

49 (28.2)

33 (19.0)

39 (22.4)

0 (0.0)53 (30.5)

174 (4.9)

Trading81 (20.6)

108 (27.4)

73 (18.5)

13 (3.3)

119 (30.2)

394 (11.0)

Retail58 (22.8)

87 (34.3)

44 (17.3)

3 (1.2)62 (24.4)

254 (7.1)

Banks

37 (40.2)

25 (27.2)

26 (28.3)

1 (1.1)3 (3.3)92 (2.6)

Securities

6 (27.3)

4 (18.2)

6 (27.3)

1 (4.5)5 (22.7)

22 (0.6)

Insurance

4 (66.7)

0 (0.0)0 (0.0)2 (33.3)

0 (0.0)6 (0.2)Other Financial Businesses

16 (27.6)

14 (24.1)

10 (17.2)

1 (1.7)17 (29.3)

58 (1.6)

Real Estate

30 (28.0)

14 (13.1)

15 (14.0)

0 (0.0)48 (44.9)

107 (3.0)

Railroad/Bus

13 (46.4)

2 (7.1)

10 (35.7)

0 (0.0)3 (10.7)

28 (0.8)

Land Transportation

11 (31.4)

10 (28.6)

10 (28.6)

0 (0.0)4 (11.4)

35 (1.0)

Marine Transportation

10 (58.8)

2 (11.8)

2 (11.8)

0 (0.0)3 (17.6)

17 (0.5)

Air Transportation

2 (40.0)0 (0.0)2 (40.0)0 (0.0)1 (20.0)5 (0.1)

Warehouse

14 (35.0)

5 (12.5)

10 (25.0)

0 (0.0)11 (27.5)

40 (1.1)

Communication

3 (8.6)

11 (31.4)

11 (31.4)

0 (0.0)10 (28.6)

35 (1.0)

Electricity

4 (35.4)

4 (36.4)

2 (18.2)

0 (0.0)1 (9.1)

11 (0.3)Gas

2 (15.4)

2 (15.4)

5 (38.5)

0 (0.0)4 (30.8)

13 (0.4)

Services

187 (25.9)

201 (27.8)

129 (17.8)

15 (2.1)

191 (26.4)

723 (20.2)

950 (26.5)

899 (25.1)

695 (19.4)

78 (2.2)

963 (26.9)

3585 (100.0)

Total

24

Table 2: Audit Fees and Market Shares
Industry

ShinNihon

Tohmatsu

Azsa

AarataOthers

Total

Food

2480 (34.5)

2175 (30.3 )1449 (20.2 )

33 (0.5 )1043 (14.5 )7180 (3.2 )Fiber Products

973 (31.0 )

630 (20.1 )

798 (25.4 )

131 (4.2 )609 (19.4)

3141 (1.4)

Pulp/Paper

769 (53.0)

237 (16.3)

349 (24.1)

0 (0.0)95 (6.6)1450 (0.7)

Chemicals

4886 (43.3 )1913 (16.9)

2666 (23.6)

329 (2.9)

1493 (13.2)

11287 (5.1)

Pharmaceuticals

827 (25.9)1180 (36.9)

964 (30.2)

72 (2.3)

154 (4.8)

3197 (1.4)

Oil1157 (66.2)

28 (1.6)317 (18.1)

223 (12.8)

24 (1.4)

1749 (0.8)

Rubber Products348 (27.1)

346 (26.9)

416 (32.3)

0 (0.0)176 (13.7)

1286 (0.6)

Ceramic Products

845 (31.9)

698 (26.3)

727 (27.4)

0 (0.0)3802650 (1.2)

Steel

977 (27.2)

598 (16.7)

1546 (43.1)

130 (3.6)

337 (9.4)3588 (1.6)

Nonferrous Metals

1583 (24.2)

1567 (23.9)

1719 (26.3)

233 (3.6)

1441 (22.0)6543 (3.0)

Machinery

3056 (28.3)

2422 (22.5)

2685 (24.9)

528 (4.9)

2096 (19.4)

10787 (4.9)

Electric Appliances

10252 (37.7)

3941 (14.5)

8208 (30.1)

1783 (6.5)

3045 (11.2)

27229 (12.3)

Shipbuilding

467 (44.4)

99 (9.4)

485 (46.1)

0 (0.0)0 (0.0)1051 (0.5)

Automobile

2714 (33.4)

678 (8.3)

1663 (20.5)

2665 (32.8)

404 (5.0)8124 (3.7)

Transportation Equipment

88 (16.6)

119 (22.5)

101 (19.1)

0 (0.0)221 (41.8)

529 (0.2)

Precision Equipment

598 (20.8)

919 (31.9)

936 (32.5)

0 (0.0)427 (14.8)2880 (1.3)

Other Manufacturing1102 (21.2)

1048 (20.1)

1557 (29.9)

109 (2.1)

1389 (26.7)5205 (2.3)

Fisheries

206 (29.6)

45 (6.5)360 (51.7)

0 (0.0)85 (12.2)

696 (0.3)

Mining456 (67.8)

51 (7.6)

138 (20.5)

0 (0.0)28 (4.2)

673 (0.3)

Construction

3010 (36.5)

1507 (18.3)

2087 (25.3)

0 (0.0)1644 (19.9)

8248 (3.7)

Trading

4210 (17.8)9365 (39.5)

5738 (24.2)

1182 (5.0)

3222 (13.6)

23717 (10.7)

Retail2641 (22.1)

4555 (38.1)

2888 (24.1)

127 (1.1)

1751 (14.6)

11962 (5.4)

Banks6394 (31.6)

8458 (41.8)

5198 (25.7)

72 (0.4)

100 (0.5)20222 (9.1)

Securities

1422 (57.4)

170 (6.9)

614 (24.8)

35 (1.4)

236 (9.5)

2477 (1.1)

Insurance

899 (46.1)

0 (0.0)0 (0.0)1049 (53.9)

0 (0.0)1948 (0.9)

Other Financial Businesses

1039 (22.7)

1559 (34.0)1310 (28.6)

60 (1.3)

619 (13.5)

4587 (2.1)

Real Estate1893 (38.9)510 (10.5)

1067 (22.0)

0 (0.0)1391 (28.6)

4861 (2.2)

Railroad/Bus

1289 (30.9)

526 (12.6)

2267 (54.3)

0 (0.0)94 (2.3)

4176 (1.9)

Land Transportation

621 (35.6)

468 (26.9)

530 (30.4)

0 (0.0)124 (7.1)

1743 (0.8)

Marine Transportation

434 (38.4)

338 (29.9)

298 (26.4)

0 (0.0)59 (5.2)1129 (0.5)

Air Transportation

209 (59.4)

0 (0.0)121 (34.4)

0 (0.0)22 (6.3)352 (0.2)

Warehouse

444 (33.2)

220 (16.4)

453 (33.8)

0 (0.0)222 (16.6)

1339 (0.6)

Communication

213 (3.1)

550 (7.9)5179 (74.5)

0 (0.0)1009 (14.5)

6951 (3.1)

Electricity

585 (41.8)

436 (31.2)

327 (23.4)

0 (0.0)50 (3.6)

1398 (0.6)

Gas

57 (6.8)

113 (13.5)

574 (68.8)

0 (0.0)90 (10.8)834 (0.4)7218 (27.3)

7482 (28.3)

5985 (22.7)

643 (2.4)5064 (19.2)

26392 (11.9)

66362 (29.9)

54951 (24.8)

61720 (27.9)9404 (4.2)

29144 (13.2)

221581 (100.0)

Services
Total

25

Table 3: Length of Partners’ Continuous Involvement with a Particular Client (Years)
Number of Years

1

2

3

4

5

6

7

Total

453

344

312

366

522

169

70

2236

(20.7)(15.4)(14.0)(16.4)(23.3)

(7.6)

(3.1)

(27.7)

333

340

304

351

312

213

81

1934

(17.2)(17.6)(15.7)(18.1)(16.1)(11.0)(4.2)(24.0)

296

304

317

275

265

130

83

1670

(17.7)(18.2)(19.0)(16.5)(15.9)

(7.8)

(5.0)

(20.7)

29

31

19

30

17

4

4

134

(21.6)(23.1)(14.2)(22.4)(12.7)

(3.0)

(3.0)

(1.7)

442

527

358

355

255

93

62

2092

(21.1)(25.2)(17.1)(17.0)(12.2)

(4.4)

(3.0)

(25.9)

1553

1546

1310

1377

1371

609

300

8066

(19.3)(19.2)(16.2)(17.1)(17.0)

(7.6)

(3.7)

(100.0)ShinNihon

Meana
3.42

Tohmatsu

3.48

Azsa

3.38

Aarata3.02

Others

2.99

Total

3.31

a

: Tukey test result indicates that the mean for Aarata is significantly lower than those for ShinNihon andTohmatsu (at the 10% and 5% level, respectively) and the mean for Others is significantly lower thanthose for ShinNihon, Tohmatsu and Azsa (all at the 1% level).

26

Table 4: Engagement Partners: Number of Clients with which Partners are Involved
Number of Clients

1

2

3

4

5

6

7

8

9

10

11

12

13

14

17

19

20

24

Total

Meana

ShinNihon

82(14.4)

94(16.5)

103(18.1)

86(15.1)

68(12.0)

52
(9.2)

33
(5.8)

25
(4.6)

10
(1.8)

10
(1.8)

3
(0.5)

0
(0.0)

1
(0.2)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

568
(23.7)

3.96

Tohmatsu

74(16.7)

70(15.8)

60(13.5)

60(13.5)

45(10.2)

34
(7.7)

34
(7.7)

25
(5.6)

15
(3.4)

11
(2.5)

6
(1.4)

4
(0.9)

1
(0.2)

2
(0.5)

1
(0.2)

1
(0.2)

0
(0.0)

0
(0.0)

443
(18.5)

4.37

Azsa

76
(16.8)

88(19.4)

76(16.8)

68(15.0)

57(12.6)

36
(7.9)

29
(6.4)

9
(2.0)

9
(2.0)

1
(0.2)

0
(0.0)

4
(0.9)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

453
(18.9)

3.68

Aarata14(33.3)

6(14.3)

7(16.7)

6(14.3)

1
(2.4)

3
(7.1)

3
(7.1)

0
(0.0)

2
(4.8)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

42
(1.8)

3.17

Others397(44.6)

205(23.0)

120(13.5)

71
(8.0)

44
(4.9)

28
(3.1)

11
(1.2)

8
(0.8)

2
(0.2)

1
(0.1)

1
(0.1)

2
(0.2)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

1
(0.1)

1
(0.1)

891
(37.2)

2.33

643
463
366
291
215
153
110
67
38
23
10
10
2
2
1
1
1
1
2397
3.37
(26.8) (19.3) (15.3) (12.1)
(9.0)
(6.4)
(4.6)
(2.8)
(1.6)
(1.0)
(0.4)
(0.4)
(0.1)
(0.1)
(0.0)
(0.0)
(0.0)
(0.0) (100.0)a: Tukey test result indicates that the mean for Tohmatsu is significantly higher than that for other Big 4 firms (at either the 1% or 5% level) and the mean for Others is significantly lower than that for other firms(at the 1% level).
Total

27

Table 5: Engagement Partners: Number of Industries with which Partners are Involved
Number of Industries

1

2

3

4

5

6

7

8

9

10

11

12

Total

Meana

ShinNihon

100(17.6)

131(23.1)

126(22.2)

84(14.8)

65(11.4)

32
(5.6)

19
(3.3)

9
(1.6)

2
(0.4)

0
(0.0)

0
(0.0)

0
(0.0)

568
(23.7)

3.20

Tohmatsu

81(18.3)

88(19.9)

84(19.0)

78(17.6)

52(11.7)

34
(7.7)

16
(3.6)

4
(0.9)

3
(0.7)

1
(0.2)

1
(0.2)

1
(0.2)

443
(18.5)

3.36

Azsa

86
(19.0)

109(24.1)

108(23.8)

66(14.6)

43
(9.5)

18
(4.0)

14
(3.1)

8
(1.8)

1
(0.2)

0
(0.0)

0
(0.0)

0
(0.0)

453
(18.9)

3.06

Aarata16(38.1)

11(26.2)

6(14.3)

4
(9.5)

1
(2.4)

3
(7.1)

1
(2.4)

0
(0.0)

0
(0.2)

0
(0.0)

0
(0.0)

0
(0.0)

42
(1.8)

2.43

Others397(47.9)

205(25.7)

120(13.6)

72
(7.0)

44
(4.3)

28
(0.8)

11
(0.3)

8
(0.4)

2
(0.0)

1
(0.0)

1
(0.0)

2
(0.0)

891
(37.2)

2.00

709
568
445
294
199
94
53
25
6
1
1
1
2397
2.74
(29.6) (23.7) (18.6) (12.3)
(8.3)
(3.9)
(2.2)
(1.0)
(0.3)
(0.0)
(0.0)
(0.0) (100.0)a: Tukey test result indicates that the mean for ShinNihon is significantly higher than those for Aarata and Others (at the 5% and 1% level, respectively), themean for Tohmatsu is significantly higher than those for Azsa, Aarata, and Others (at the 10%, 1%, and 1% level, respectively), and the mean for Azsa issignificantly higher than that for Others (at the 1% level).
Total

28

Industry Expertisea

Table 6: Industry Expertise of Partners
5
6
7
8
9
7
5
2
0
0
(1.2)
(0.9)
(0.4)
(0.0)
(0.0)

1
306
(53.9)

2
153(26.9)

3
69(12.1)

426(4.6)

Tohmatsu

233(52.6)

101(22.8)

50(11.3)

28
(6.3)

15
(3.4)

7
(1.6)

4
(0.9)

1
(0.2)

Azsa

275
(60.7)

110(24.3)

42
(9.3)

19
(4.2)

5
(1.1)

2
(0.4)

0
(0.0)

Aarata26(61.9)

6(14.3)

7(16.7)

1
(2.4)

2
(4.8)

0
(0.0)

Others713(80.0)

118(13.2)

34
(3.8)

15
(1.7)

8
(0.9)

0
(0.0)

ShinNihon

Meanb

10
0
(0.0)

13
0
(0.0)

17
0
(0.0)

Total
568
(23.7)

2
(0.5)

1
(0.2)

1
(0.2)

0
(0.0)

443
(18.5)

2.01

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

453
(18.9)

1.62

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

0
(0.0)

42
(1.8)

1.74

1
(0.1)

0
(0.0)

0
(0.0)

0
(0.0)

1
(0.0)

1
(0.0)

891
(37.2)

1.33

1.76

1553
488
202
89
37
14
7
1
2
1
2
1
2397
1.62
(64.8) (18.7)
(8.4)
(3.7)
(1.5)
(0.6)
(0.3)
(0.0)
(0.1)
(0.0)
(0.0)
(0.0) (100.0)a: Partner industry expertise is defined as the number of clients with which a partner is involved minus the number of industries in which the clients
operate plus one.
b
: Tukey test result indicates that the mean for Tohmatsu is significantly higher than those for ShinNihon, Azsa, and Others (all at the 1% level) and themean for Others is significantly lower than those for ShinNihon and Azsa (both at the 1% level).
Total

29

Table 7: Descriptive Statistics
Pooled Sample(n = 2,799)

Big 4 Sample(n = 2,040)

Non-Big 4 Sample(n = 759)

t-test or
chi-squared test

Variable

Mean

S.D.

Mean

S.D.

Mean

S.D.

lnFEE

3.70

0.75

3.85

0.76

3.31

0.52

***

LEAD_YEARS

3.62

1.73

3.71

1.74

3.39

1.68

***

LEAD_CLIENTS

5.95

3.63

6.40

3.02

4.74

4.72

***

LEAD_EXPERTISE

2.57

2.26

2.62

1.78

2.44

3.22

SHARE

20.50

14.60

27.70

10.10

1.10

1.40

***

NO_PARTNERS

2.26

0.49

2.29

0.51

2.18

0.40

***

lnAssets

17.47

1.67

17.75

1.65

16.72

1.50

***

SUBS

3.32

2.82

3.61

3.10

2.54

1.61

***

CATA

55.10

19.00

55.00

18.80

55.40

19.80

QUICK

166.30139.60168.20137.80161.30144.20

ROA

1.70

6.10

2.40

4.50

–0.20

8.70

LEV

16.60

13.20

16.40

13.00

17.10

13.60

LOSS

0.15

0.36

0.12

0.32

0.24

0.43

***

SEC

0.01

0.10

0.01

0.12

0.00

0.04

***

*** p < 0.01

30

***

Table 8: Pearson Correlation Matrix
1

2

3

4

5

6

7

8

9

10

11

12

13

1. lnFEE

1.000

2. LEAD_YEARS

0.048*

1.000

3. LEAD_CLIENTS–0.055*

0.035

1.000

4. LEAD_EXPERTISE–0.075*0.005

0.844*

1.000

5. SHARE

0.339*

0.078*

0.152*

0.021

1.000

6. NO_PARTNERS

0.483*

0.044*–0.038*–0.041*0.197*

1.000

7. lnAssets

0.822*

0.091*–0.068*–0.114*

0.296*

0.449*

1.000

8. SUBS

0.803*

0.063*–0.061*–0.076*0.202*

0.449*

0.730*

1.000

9. CATA–0.131*–0.0240.057*

0.073*–0.057*–0.098*–0.244*–0.129*1.000

10. QUICK–0.156*–0.0150.041*

0.051*–0.009–0.112*–0.180*–0.133*0.333*

1.000

11. ROA

0.109*

0.065*

0.055*

0.030

0.146*

0.056*

0.236*

0.076*

0.059*

0.089*

1.000

12. LEV

0.205*

0.019–0.058*–0.060*0.037*

0.146*

0.233*

0.234*–0.550*–0.398*–0.139*1.000

13. LOSS–0.114*–0.069*–0.044*–0.008–0.133*–0.049*–0.242*–0.095*–0.023–0.033–0.679*0.086*

1.000

14. SEC

0.364*

0.006–0.051*–0.0370.086*

0.178*

0.250*

0.420*–0.034–0.0090.033

0.041*–0.033* p < 0.05

31

14

1.000

Table 9: Regression Results
Pooled Sample

Big 4 Sample

Non-Big 4 Sample[1]

Non-Big 4 Sample[2]

Constant–1.2186[–10.73]***–1.5287[–11.42]***0.3102[1.42]

0.3195[1.46]

LEAD_YEARS–0.0103[–2.83]***–0.0079[–1.97]**–0.014[–1.89]*–0.014[–1.90]*

LEAD_CLIENTS–0.0054[–1.62]–0.0193[–5.30]***0.0014[0.51]

LEAD_EXPERTISE

0.0057[1.07]

0.0161[2.58]**

SHARE

0.5797[12.21]***0.0607[0.82]

8.7476[7.52]***

8.8264[7.64]***

NO_PARTNERS

0.0951[6.46]***

0.1001[6.18]***

0.0556[1.75]*

0.0565[1.78]*

lnAssets

0.2503[37.44]***0.2764[36.17]***0.1621[12.19]***0.1616[12.18]***SUBS

0.0931[24.57]***0.0827[20.79]***0.109[9.95]***

0.1089[9.94]***

CATA

0.2956[6.56]***

0.4131[7.81]***–0.0398[–0.50]–0.0392[–0.50]QUICK–0.0204[–3.93]***–0.0239[–4.05]***–0.0167[–1.73]*–0.0165[–1.71]*

ROA–0.5125[–3.58]***–0.2481[–1.17]–0.5444[–2.71]***–0.5409[–2.70]***LEV

0.0937[1.47]

0.1862[2.47]**–0.0963[–0.88]–0.0955[–0.87]LOSS

0.0604[2.51]**

0.0674[2.29]**

0.0605[1.50]

0.0601[1.49]

SEC

0.4247[6.18]***

0.4326[6.45]***

0.5842[1.70]*

0.5818[1.69]*

R-squared

0.80980.83770.6216

0.6215

Adj. R-squared

0.80680.83410.60000.5999

N

2,799

2,040

759

759

* p < 0.1, ** p < 0.05, *** p < 0.01.
Figures in square brackets are t-statistics.

32

0.0006[0.17]