ECONOMIC RESEARCH CENTERDISCUSSION PAPER
E-Series

No.E14-7

Auditor Size as a Measure for Audit Quality:
A Japanese Studyby
HU DanKATO Ryo

April 2014

ECONOMIC RESEARCH CENTERGRADUATE SCHOOL OF ECONOMICSNAGOYA UNIVERSITY

Auditor Size as a Measure for Audit Quality: A Japanese Study

HU, Dan, Nagoya University
KATO, Ryo, Nagoya University

SUMMARY

This study examines the Japanese audit market from 2001 to 2011 to demonstrate that audit quality does
not differ with the size of audit firms in Japan. There has been a growing concern worldwide regarding
the audit quality of large audit firms in Japan due to scandals such as the Kanebo (2005) and Olympus(2011). Thus, using inverse probability weighting (IPW) and five proxy variables for audit quality, weshow that irrespective of their size, all audit companies in Japan provide the same quality of service,when controlled for client characteristics. We also find that small- to medium-sized audit firms in Japan
provide going-concern audit opinions to their clients more readily. Finally, our results suggest that
although only three major audit firms remain in the audit market after the dissolution ofPricewaterhouseCooper’s member firm in 2007, the audit quality offered by Big N has remained
unchanged.

Key words: audit quality, auditor size, inverse probability weighting (IPW), propensity score, Japan1

INTRODUCTION

The purpose of a financial audit is to ensure that the financial statements published by companies
are a fair representation of their financial position and performance. The interested parties, including
regulators, use the auditor verified financial statement information to make investment decisions andconduct economic activities. Thus, the level of audit quality is a concern for various interested parties,
and the maintenance of audit quality is imperative to ensure the smooth performance of economic
activities and economic development. This is the reason that issues related to the determination of thelevel of audit quality and retention of high quality in auditing have attracted the attention of multiple
interested parties and are discussed widely by researchers and policy makers.
Many existing studies evaluate the relationship between audit quality and auditor size to identify
the level of audit quality. In case the relationship between auditor size and audit quality is consistent,
researchers can identify or predict the level of audit quality by observing the size of an audit firm.
Studies on the U.S. market reveal that, generally, large audit firms with international brand names
provide better audit quality than do other firms (e.g., Becker et al. 1998; Francis et al. 1999; Behn et al.
2008). The office size of audit practices is also positively related to audit quality (e.g., Francis and Yu
2009; Choi et al. 2010). In contrast, Louis (2005) suggests that smaller audit firms provide better
acquisitions advice to their clients. However, Lawrence et al. (2011) suggest that there is no difference
between the audit quality of large and small- to medium-sized audit firms when their client
characteristics are controlled. Thus, the relationship between audit quality and auditor size is still acontentious point in the empirical research within the field of accounting and auditing.
Moreover, very few studies (e.g., Ajward 2010) have examined the relationship between audit
quality and auditor size in Japan. Japan’s role in global economy is quiet significant. In 2012, Japan
recorded the third highest gross domestic product (GDP) in the world, and until 2011, it’s stock market
was the second largest in the world. In addition, the Japanese audit market is quite large, and in 2011
alone, it serviced around 3731 clients (also see Table 1). However, with the involvement of two of the2

largest Japanese firms, ChuoAoyama (PricewaterhouseCoopers) and Azsa (KPMG), in the 2005 Kanebo
scandal and the 2011 Olympus scandal (see Table 2 and Figure 2), respectively, the audit quality of thelarge audit firms in Japan has come under the scrutiny of investors worldwide. Besides, the Japanese
audit market offers a unique data setting with low litigation risk and high volumes of work per auditor;
in fact, one Big 4 international audit firm in Japan even lowered its scale and became a non-Big N audit
firm. Thus, this study seeks to address this void in the literature by conducting a comprehensive study on
all the listed companies in the Japanese stock market from 2001 to 2011.
This study uses various audit quality proxy variables to reveal that the audit quality of big andsmall- to medium-sized audit firms is essentially the same after controlling for the effect of clientscompanies’ characteristics. This suggests that the positive relationship between audit quality and auditor
size is due to the characteristics of audit firms’ clients. More interestingly, this study finds that small- to
medium-sized audit firms are more able, or willing, to identify and report going-concern issues,compared to large audit firms. Finally, we find that the audit quality of audit firms remains unaffectedeven when one of the “Big 4” audit firms reduces in scale.
This study contributes to the accounting and auditing literature in several ways. First, with anextensive review of studies on audit quality, this study sets five proxy variables for audit quality:
discretionary accruals (Francis and Yu 2009; Choi et al. 2010; Lawrence et al. 2011), benchmark
earnings targets (Francis and Yu 2009), going-concern audit reports (Francis and Yu 2009), ex ante costof capital (Lawrence et al. 2011), and analyst forecast accuracy (Lawrence et al. 2011). In contrast,
previous studies have only used one, two, or three variables to measure audit quality. Thus, this analysis
tries to address the following research question more comprehensively: What is the relationship between
audit quality and auditor size in Japan?
Second, this study attempts to add to the literature on the Japanese audit market, especially audit
quality in Japan. Despite Japan’s economic significance to global investors and its possession of one of
the largest stock market, very few studies focus on the Japanese audit market. A search for abstracts3

using the keywords “audit” and “Japan” in the English EconLit/EBSCO database returns eight papers,
whereas a search using “audit quality” and “Japan” as keywords does not yield any results.
Third, this study uses the Japanese audit market’s unique date setting to address the following
research question: How is audit quality influenced in an audit market where one of the big audit firms
reduces in scale and the power relationship between audit firms and client companies changes?
Fourth, this study uses Rubin’s (1985) inverse probability weighting (IPW) method, a kind of
propensity score weighting used in medical and social science research, to verify the relationshipbetween audit quality and auditor size. Compared to the propensity score matching model of Rosenbaumand Rubin (1983), which is used in auditing or accounting studies (e.g., Lawrence et al. 2011), Rubin’s(1985) methodology decreases the bias that occurs during the process of reducing the sample in theapplication of the propensity score matching model. Therefore, this study seeks to utilize an improved
method from the medical and social literature.
The remainder of this paper is structured as follows: Section 2 provides a background of theJapanese audit market and discusses its characteristics. Section 3 builds the hypothesis for this study anddescribes the five proxy variables for audit quality. Section 4 describes the methodology used in thispaper, while section 5 describes the sample and presents the descriptive statistics. Section 6 and 7
provide the empirical and robustness results, respectively, before the conclusion of the study in section 8.

THE JAPANESE AUDIT MARKET

Table 1 compares the Japanese audit market with other major markets in the world. In 2012, Japan’s
stock market was the third largest in the world and the number of auditors per company was extremely
low compared to that in other countries.
Insert Table 1 here
In addition, Figure 1 presents the historical situation of the Japanese audit market. The figure showsthat with the increase in market capitalization and the number of listed companies, especially during the41970s, the number of auditors also increased in Japan. Consistent with Table 1, the number of auditors
per company is low, suggesting that the personal and professional ability of every auditor is importantwhen audit quality is considered.
Insert Figure 1 here
Insert Figure 2 here
Further, Figure 2 shows the transition history of audit firms in Japan. Since the late 1960s, severaltransitions have occurred in Japan and many small- to medium-sized audit firms have merged into larger
audit firms. As of 2012, the four major Japanese audit companies with international affiliations were
Tohmatsu (Deloitte Touche Tohmatsu), Aarata (PricewaterhouseCoopers [PwC]), ShinNihon (Ernst &Young), and Azsa (KPMG).
Additionally, Table 2 presents the market shares of the Big Four international audit firms in Japan.
As indicated, in 2011, the market share of the PwC’s member firm, Aarata, is only 2.41%, based on thenumber of clients in the entire audit market. Thus, there are only three large audit firms in the Japanese
audit market according to the market shares based on client numbers.
Insert Table 2 here

LITERATURE REVIEW, HYPOTHESIS, AND MEASURES OF AUDIT QUALITY
Literature Review and Hypothesis

Previous studies generally concur that the audit quality of large audit firms (offices) withinternational brand names is better than that of small audit firms (offices) (DeAngelo 1981; Becker et al.
1998; Francis et al. 1999; Behn et al. 2008; Francis and Yu 2009; Choi et al. 2010). Some studies suggestthat the difference in the audit quality of large and small audit firms is a result of their different
client-industry specialty, independence, and accuracy levels. Dopuch and Simunic (1980) mention thatlarge audit firms have abundant client-industry-specific knowledge and experience and spend significant
amounts of money to educate auditors under their employment. DeAngelo (1981) also points out that5

larger audit firms provide more independent audits in an attempt to protect their brand name reputation,
as they have “more to lose” if their reputation is tarnished. She further argues that audit quality of larger
audit firms is also higher in general. Moreover, Lennox (1999) reasons that large audit firms have manyclients and depend on all their clients’ audit fees, regardless of the actual value of the amounts. They also
have deeper pockets and therefore face higher litigation risks compared to small audit firms, which isanother incentive to issue accurate reports.
Nonetheless, the Japanese audit market is significantly different from that of the U.S. Skinner andSrinivasan (2012, 1743) point out that “litigation against auditors [...] is virtually nonexistent in Japan,”showing that the litigation risk for Japanese audit firms is low and therefore need not be considered.
Moreover, by using the propensity score matching model (an increasingly used model in accounting),
Lawrence et al. (2011) find that after controlling for the clients’ characteristics, the differences between
large and small audit firms disappear. This suggests that the differences between small- to medium-sized
and large audit firms are due to the auditors’ clients and the market environment. Subsequently, thispaper tests the following null hypothesis:
Hypothesis: There is no difference between the audit quality of large and small- to medium-sized
audit firms in the Japanese audit market.

Measures of Audit Quality

A comprehensive review of the literature reveals that audit quality concept is difficult to define,unclear, and perceived differently by various stakeholders (e.g., Knechel et al. 2013; IAASB 2013). Thus,
Francis (2011) and Knechel et al. (2013) summarize academic articles on audit quality and offer insights
into the measurement of audit quality.
This study seeks to gather the knowledge from existing literature and use the following five proxyvariables to capture audit quality: discretionary accruals, benchmark earnings targets, going-concern
audit reports, ex ante cost of capital, and analyst forecast accuracy. Discretionary accruals are calculated6

by subtracting non-discretionary accruals from total accruals and are not affected by management
interference (Jones 1991). If the audit quality of financial statements is high, the discretionary accruals
managed by the management are expected to decrease. This study uses discretionary accruals to measureaudit quality using Dechow et al.’s (1995) model, which is the modified Jones model that has been usedby Lawrence et al. (2011). This study also uses Kasznik’s (1999), Dechow and Dichev’s (2002), andKothari et al.’s (2005) models to conduct additional analyses.
Furthermore, according to Burgstahler and Dichev (1997), a phenomenon unique to the U.S. market
is the existence of many companies with a small net income and few companies with small negative net
income. They reason that, in the U.S., the management manages earnings and tries to avoid recording
losses. In contrast, earnings management is more conspicuous in Japan (Shuto 2000, 137). There are
currently many companies with a small net income and few companies with a small negative net income.
Thus, this study uses the the dummy variable of small positive earnings (net income deflated by the lastterm end; total assets between 0 and 1 percent) to measure audit quality and applies other several
percentages of small positive earnings to recapture audit quality to conduct additional analyses.
Additionally, DeFond et al. (2002) argue that audit independence is a necessity for auditors toevaluate a company’s financial position and report going-concern audit opinions. Thus, they use
auditors’ propensity to issue going-concern audit opinions as a substitute for auditors’ independence.
Since the audit independence level is considered for the assessment of the level of audit quality, previous
studies (e.g., Francis and Yu 2009) use the information of going-concern audit opinions as a surrogate toaudit quality. Therefore, this study uses the dummy variable of going-concern audit opinions as a proxy
for audit quality. As the audit for going-concern systems in Japan began only in January 2002, thesample of this analysis part in which audit quality’s proxy is the dummy variable of going-concern issmaller than that of other parts of this study.
Moreover, Khurana and Raman (2004) use ex ante cost of capital as the proxy variable for financial
reporting credibility and find that the lower the ex ante cost of capital, the higher the audit quality in the7

U.S. market. Given the results of Khurana and Raman (2004), Lawrence et al. (2011) also use ex ante
cost of capital as the surrogate variable for audit quality. Thus, this study uses ex ante cost of capital as a
substitute for audit quality to evaluate if the lower ex ante cost of capital is received positively by themarket participants against the audit report.
Finally, Behn et al. (2008) and Lawrence et al. (2011) use analyst forecast accuracy as the proxyvariable for audit quality. Lawrence et al. (2011) argue that analyst forecast accuracy could capture thepost-audit financial reporting reliability, and might become a proxy variable for audit quality.
Accordingly, this study uses analyst forecast accuracy as the fifth measure for audit quality.

RESEARCH DESIGN

We adopt the IPW methodology, developed by Rubin (1985), for this analysis. First, we estimate
the propensity score ( ei ), which is defined as the estimated probability of receiving a treatment, that is(in our case), the probability of a company to select a Big N as its auditor. We use a logistic regression
model to estimate the propensity score (Big N’s estimate in equation (1)) as follows (see the definitionsof the variables in Table 3):
BigN i ,t   0  1 ln ASSETi ,t   2 LOSS i ,t   3 LIABi ,t   4 ATURN i ,t   5CURRi ,t   6 ROAi ,t  7 DOCFi ,t   8 lg ACCRi ,t   9 BETAi ,t  10 CASH i ,t  11VOLATILITYi ,t  12 lg LOSS i ,t (1) 13 SALESVOLAT ILITYi ,t  14 SALESGROWT H i ,t  15CFOVOLATIL ITYi ,t 16 SAF 2002 i ,t  17 CONSOLi ,t  iD  yD   i ,tAfter the estimation of the propensity score ( ei , Big N’s estimate in equation (1)), we weigh thetwo groups of samples (Big N vs. Non-Big N) by using the estimated inverse of probability ( iei ),calculated by the following equation:

iei 

zi

ei

N1
z
i1 eiiN1  zi
1  eiN2
N 1 zi1 1  eii(2)

where, iei denotes the sampling weights, z denotes the treatment group (Big N group) and the8control group (Non-Big N group) (1, if it is in the Big N group, otherwise 0), ei denotes the predicted
propensity score, N1 denotes the number of Big N samples, N 2 denotes the number of Non-Big N
samples, and N = N1 + N 2 .
Insert Table 3 here
Then, we include only Big N as an independent variable to estimate the following equation after
employing propensity score weighting, to test our hypothesis:
ADAi ,t

PROBIT[ BENCHMARK  1]
PROBIT[GCREPORT 1]

  0  1 BigN i ,t   i ,t(3)

RPEG i ,t

ACCYi ,t
ADA, BENCHMARK, GCREPORT, RPEG, and ACCY represent the five proxy variables that
capture audit quality, that is, discretionary accruals, benchmark earnings targets, going-concern audit
reports, ex ante cost of capital, and analyst forecast accuracy (see Table 3 for details). PROBIT refers to
the probit model. If 1 in this model is not significant, the hypothesis is proved, ceteris paribus.
Finally, we split the sample into two parts—before and after the dissolution of PwC’s member firm,
Chuo-Aoyama, in 2007—to verify if 1 in equation (3) will change. If 1, pre is different from1, post , it means that audit quality in Japan has somewhat changed after the dissolution of
Chuo-Aoyama.

SAMPLE AND DESCRIPTIVE STATISTICS

We used Nikkei NEEDS Data-Base to collect data for this study. The data consist of all thelisted companies in the Japanese stock market from 2001 to 2011 that closed their yearly accounts in
9March, excluding financial institutions such as banks. In sum, the sample used in this paper includes
14,985 firm years (See Table 4).
Insert Table 4 here
Insert Table 5 here
Table 5 compares the test results for client companies of Big Ns and Non-Big Ns. As can be seen,
the client companies of Big Ns and Non-Big Ns have many differences when analyzed from various
perspectives. This suggests that a propensity score to control for the differences in clients’ characteristics
is necessary.
Table 6 presents the correlations among variables used in this study. We could not find any potential
multicollinearity problem in this case.
Insert Table 6 here
RESULTS

Insert Table 7 here
Table 7 shows the results of the regression for estimating propensity score. As can be seen, thelogistic model results indicate eight variables that are significant to: lnASSET(+), ATURN(+), CASH(-),
LAGLOSS(-), SALESVOLATILITY(-), CFOVOLATILITY(+), SAF2002(+), and CONSOL(+). In
other words, it shows that the companies that have larger total assets, greater turnover of assets, a lower
percentage of cash/ the total amount of assets, a positive net income in the last term, less volatile sales,
highly volatile operating cash flow, a lower possibility of going bankrupt, and more complex
organization have the propensity to choose larger audit firms for auditing activities. These results clearlyjustify the necessity to control for the clients’ characteristics of Big N and Non-Big N audit firms.
Insert Table 8 here
Table 8 presents the results of audit quality in terms of auditor size using normal regression and
IPW, respectively. When the absolute value of discretionary accruals, ADA, is used as the proxy variable
of audit quality in normal regression, model 1, Big N is significantly negative, while in IPW regression,

10

model 2, Big N is not significant at all. In other words, after controlling for the characteristics of audit
firms’ clients, the discretionary accruals of Big N’s clients are not different from that of the Non-Big N’s.
Thus, in the Japanese audit market, after controlling for various factors by propensity score, larger andsmaller audit firms have the same restraining power on earnings management and the hypothesis of thispaper is confirmed from this perspective.
When benchmark earnings targets, BENCHMARK (net income limited to the total amount of assets
is 0~1%), is used as the proxy variable for audit quality, both model 1 and model 2 show that Big N are
insignificant. It shows that both large and small- to medium-sized audit firms could restrain the
management’s accounting management actions to avoid a loss or to receive a small positive net income.
This evidence once again supports our hypothesis.
We obtained quite an interesting result in the case of going-concern audit reports, GCREPORT (SeeTable 8). The Big N’s coefficients in both model 1 and 2 are negative and significant, suggesting thatlarge audit firms are less likely to report going-concern audit opinions. In other words, in the Japanese
audit market, small- to medium-sized audit firms are more open to the idea of suggesting going-concern
audit opinions.
Regarding the fourth proxy variable of audit quality, ex ante cost of capital (RPEG), the results ofboth model 1 and 2 show that there is no difference between large and small- to medium-sized audit
firms. These results indicate that investors equally value the audit reports of large and small- to
medium-sized audit firms.
Lastly, when the fifth proxy variable for audit quality, analyst forecast accuracy, ACCY, is used fornormal regression, that is, in model 1, Big N is significantly negative, while in case of IPW regression,that is, model 2, Big N is not at all significant. These results suggest that for investors (analysts), theliability of getting reports audited by large and small- to medium-sized audit firms is the same.
Finally, the untabulated results indicate no difference in the 1 , the coefficient of Big N, of thesamples from before and after the dissolution of PwC’s member firm, Chuo-Aoyama, in 2007. The
11

z-values of ADA, BENCHMARK, GCREPORT, RPEG, and ACCY to test the difference before and after
Chuo-Aoyama’s dissolution are 0.276, 0.8226, -0.548, -0.917, and 1.017, respectively.

SENSITIVITY AND ROBUSTNESS TESTS

To calculate the first proxy variable for audit quality, the absolute value of discretionary accruals,
we use the modified Jones model presented in section 6. To test the robustness of our results, we use
Kasznik’s (1999), Dechow and Dichev’s (2002), and Kothari et al.’s (2005) models to calculate
discretionary accruals individually. We find that the results do not change with the application ofdifferent models.
Moreover, to set the benchmark of earning targets, the second proxy variable, we use 0%~1% of the
net income from the total assets (16% of the entire data set) as the benchmark. When we change thebenchmark to 0~0.5% (6.5% of the entire data set), or to 0~1.5% (25% of the entire data set), the results
do not change.
Carey and Simnett (2006) use going-concern reports as the surrogate variable for audit quality;
however, they just include those company years in the sample where there are net losses or negative cash
flows from operating activity. In our sample, a look at going-concern reports indicates that 89% of thecompanies suffered a net loss or had negative cash flows; nonetheless, when we use only this fraction foranalysis, similar to Carey and Simnett (2006), the results do not change compared to that in section 6.
In addition, the regulations regarding the going-concern report changed in 2009. Inokuma (2013)states that after 2009, there has been an increase in propensity of audit firms to disclose theirgoing-concern reports. When we split our sample into the years before and after 2009 and apply thesame methodology, the results remain the same.
Finally, as discussed in section 2, we do not treat Aarata (PwC’s member firm) as a large audit firm
for our analysis in section 6 due to its smaller market share (see Table 2). To conduct the robustness test,
we remove Aarata’s data from the complete sample or consider it a part of the larger audit firm (PwC’s)

12

for re-examination. In both cases, the results do not change.

CONCLUSION

Both the Kanebo scandal in 2005 and the Olympus scandal in 2011 involved large Japanese audit
firms. This made investors worldwide concerned about the audit quality of large audit firms in Japan.
Thus, this study attempts to verify the relationship between audit quality and auditor size by
investigating all the listed companies (audit firms’ clients) in the Japanese stock market from 2001 to
2011. We use IPW to investigate five proxy variables for audit quality to determine the relationshipbetween audit quality and auditor size and confirm our hypothesis, which states that there is nodifference between the audit quality of large and small- to medium-sized audit firms in the Japanese
audit market.
Using discretionary accruals, benchmark earnings targets, ex ante cost of capital, and analyst
forecast accuracy as the proxy variables for audit quality, we provide evidence in support of ourhypothesis. In other words, in terms of the degree of restraint on accounting management, the market
evaluation of audit reports and the reliability of financial statements, as evaluated by analysts (investors),
are the same for large and small- to medium-sized audit firms after controlling for the clients’characteristics.
However, when going-concern audit reports are used as the proxy variable for audit quality, we findthat small- to medium-sized audit firms tend to give their clients more positive going-concern audit
opinions compared to large audit firms in Japan. There are two possible reasons for this result. First,
there is a possibility that, in Japan, the majority of companies with going-concern problems are auditedby small- to medium-sized audit firms instead of large audit firms. Azuma (2007) suggests that in Japan,
large audit firms usually turn down clients if they anticipate a going-concern problem, and thus, the next
audit firm to audit that client company would typically be a small- to medium-sized audit firm. Second,
there is a possibility that small- to medium-sized audit firms follow a more appropriate procedure for the13issuance of a going-concern report. Louis (2005) points out that small audit firms are more informed ofthe local market and have closer relationships with their clients, when compared to the large audit firms.
Accordingly, small- to medium-sized audit firms might are more conversant at assessing the possibilityof a going-concern problem at their client company. In addition to these facts, there is also room fordiscussion regarding the validity of going-concern audit opinions as a proxy variable of audit quality.
Lennox (1999) uses going-concern reports to identify the differences between large and small- to
medium-sized audit firms and uses going-concern reports as a measure of the appreciation of audit
opinion but not audit quality itself. Contrary to Francis and Yu’s (2009) study in the U.S. market, whichshows large audit firms tend to issue going-concern reports more readily than do small- to medium-sized
audit firms, our findings indicate that the same is not true in case of the Japanese market.
Finally, our results do not indicate any change in the audit quality of Big N firms before and after
the dissolution of PwC’s member firm, Chuo-Aoyama, in 2007, suggesting that the audit quality offeredby Big N has remained unaffected despite the reduction in scale of one of the Big 4 players.

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Shoten. (Japanese)
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15

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16

Table 1 The Japanese Audit Market Compared to Other Markets of the World in 2012
Market capitalization(Dollar)USA
18,668,333,210,000
China (exluding Hong Kong) 3,697,376,039,677
Japan
3,680,982,116,116
UK
3,019,467,050,240
French
1,823,339,266,082
Hong Kong
1,108,127,258,370

Domestic market capitalization The number of The number of
The number ofof world market capitalization listed companies
auditors
auditors per company

35.11%
6.95%
6.92%
5.68%
3.43%
2.08%

4,102
2,494
3,470
2,179
862
1,459

352,297
100,000
24,733
118,758
18,50033,901

85.88
40.10
7.13
54.50
21.46
23.24

Source: The World Bank (http://data.worldbank.org/) and the homepages of each country’s Institute of Certified Public
Accountants.

17

Table 2 The Market Shares of the Big Four International Audit Firms in Japan

BigN (% ) (based on client number)
Year
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011

S hinnihon

Tohmatsu (Deloitte Chuo-Aoyama(Ernst & Young's
m em ber firm )Touche Tohm atsu's
m em ber firm )

22.03
21.61
21.57
20.70
20.58
20.97
23.15
26.59
26.97
26.19
26.70

19.46
20.37
21.10
21.61
22.01
21.71
21.86
24.95
24.66
24.72
24.82

Azsa

(PricewaterhouseCoop (KPMG's m em ber
ers's m em ber firm )firm )

19.70
20.37
20.82
20.90
21.37
20.13
11.14

14.89
15.09
15.29
16.15
16.50
17.29
18.31
20.73
20.18
19.42
19.27

Note: Chuo Aoyama dissolved in 2007 due to the Kanebo scandal.
Source: Nikkei NEEDS Data-Base.

18

Arata
(PricewaterhouseCoo
pers's m em ber firm )

0.46
2.07
2.28
2.29
2.34
2.41

Non-BigN (% )number)ClientNumber

23.91
22.57
21.22
20.63
19.55
19.90
25.54
27.72
28.19
29.66
29.21

3,659
3,957
4,015
4,086
4,189
4,302
4,397
4,076
3,930
3,810
3,731

(based on clientTable 3 Variables’ Definition and Measurement
V a ri a bl es

D ef i ni ti on

TATotal accruals=income from continuing operations after tax cash flow from operation activity

lnASSETROA

Natural logarithm of total assets of t-1 year end
The increase of the sales: sales of this term-sals of last term
The increase of receivables: receivables of this term end-receivables of last term end
Property Plant, and Equipment
1 if the client has a BigN auditor, and 0 otherwise
1 if the firm recorded net loss for year t, and 0 otherwise(Total liability for t-1 year end)/(total assets for t-1 year end)
(Sales for year t)/(average total assets for t-1 year end)
(Current assets for t-1 year end)/(current liabilities for t-1 year end)
(Net income for year t)/(average total assets for year t)

DOCF(Operating cash flows for year t– operating cash flows for year t-1)/(total assets for t-1 year end)

⊿REV
⊿REC
PPE
BigN
LOSS
LIAB
ATURN
CURR

Total accruals for year t-1/total assets for t-1 year end
BETA
By CAPM model using past 36 months' return to estimate
CASH
Sum of a client’s total cash deflated by total assets for t-1 year end
VOLATILITY
Stock volatility and is the standard deviation of past 12 monthly stock returns
lgLOSS
1 if the firm recorded net loss for year t-1, and 0 otherwise
SALESVOLATILITY Standard deviation of sales revenue of the last three years
SALESGROWTH (Sales for year t/sales for year t-1)-1
CFOVOLATILITY Standard deviation of CFO of the last three years
SAF2002
SAF2002 value (Shirata 2003), which measures the probability of bankruptcy in JapanCONSOL
Number of the firm's consolidated companies
ADA
Absolute of discretionary accruals
BENCHMARK
1 if net income/total assets for t-1 year end is 0-2%, otherwise 0
GCREPORT
1 if the note about going concern is disclosed in audit report, and 0 otherwise
RPEG
Ex ante cost of equity capital estimated by using Easton's (2004) approach
ACCY
Absolute number of earnings per stock minus estimate earnings per stock by analyst
iD
Industry dummy classified by Tokyo Stock ExchangeｙDYear dummy from 2001 to 2011.
lgACCR

19

Table 4 Details of the Sample, Classified by Industry
Classified by Industry
Construction
Foods
Textiles & Apparels
Chemicals
Pharmaceutical
Glass & Ceramics Products
Iron & Steel
Nonferrous Metals
Metal Products
Machinery
Electric Appliances
Transportation Equipments
Precision Instruments
Other Products
Land Transportation
Warehousing & Harbor Transportation Services
Information & Communication
Wholesale Trade
Retail Trade
Real Estate
Services
Total

20

Firm-year
1,195
542
403
1,541
303
390
466
323
515
1,460
1,677
904
322
482
580
350
674
1,417
525
317
599
14,985

BigN %
81.42%
75.83%
78.66%
76.31%
84.49%
89.49%
68.03%
87.62%
66.41%
72.47%
74.78%
75.22%
82.61%
70.95%
89.14%
80.86%
83.23%
76.64%
77.52%
66.88%
78.80%
77.16%

Table 5 Comparison between Big Ns and Non-Big Ns’ Client Companies

BigN

lnASSET

LOSS

LIAB

ATURN

CURR

ROA

DOCF

lgACCRBETA

CASH VOLATILITY LAGLOSS SALESVOLATILITY SALESGROWTH CFOVOLATILITY SAF2002 CONSOL

firm-years 11,562 11,562 11,562 11,562 11,562 11,562 11,562 11,562 11,562 11,562 11,562
BigN's clientcompanies

Non-BigN'sclientcompanies

11,562

11,562

11,562

11,562

11,562

11,562 11,562

1
0
1
1
1

11.275
1.399
9.693
11.07313.2050.172 2.754 1.099 2.028 0.019 0.002 -0.007
0.377 21.776 0.603 18.478 0.046 0.068 0.013
00.370 0.571 0.808 -0.021 -0.061 -0.020
01.314 0.964 1.425 0.019 0.001 -0.007
1
4.747 1.758 3.110 0.062 0.068 0.007

0.838
2.031
0.149
0.757
1.581

0.125
0.099
0.036
0.101
0.242

0.102
0.078
0.047
0.088
0.166

0.180
0.384
0
0
1

0.088
0.104
0.016
0.059
0.186

0.033
0.202
-0.125
0.021
0.174

0.034
0.035
0.008
0.025
0.069

0.933
0.360
0.524
0.936
1.364

10.86330.1330
0
28

firm-years 3,423
Mean0
Std. Dev.
010%0Median
0
90%
0

3,423
10.8091.327
9.206
10.771
12.486

3,423
0.219
0.414
0
0
1

3,423
5.198
13.6080015Mean
Std. Dev.
10%
Median90%

firm-years 14,985
Mean 0.7716
All samples (AllStd. Dev. 0.4198
client10%0companies)
Median1
90%
1t-valuez-value

3,423
2.391
6.476
0.390
1.264
4.493

3,423
1.073
0.600
0.542
0.931
1.733

3,423 3,423 3,423 3,423
1.868 0.008 0.001 -0.007
2.827 0.097 0.078 0.022
0.815 -0.033 -0.070 -0.021
1.491 0.016 -0.002 -0.007
3.057 0.056 0.071 0.007

3,423
0.861
0.813
0.143
0.772
1.629

3,423
0.137
0.103
0.041
0.117
0.252

3,423
0.114
0.138
0.049
0.091
0.180

3,423
0.234
0.424
0
0
1

3,423
0.099
0.156
0.017
0.064
0.203

3,423
0.025
0.288
-0.163
0.011
0.173

3,423
0.038
0.042
0.008
0.028
0.074

3,423
0.832
0.820
0.450
0.906
1.317

14,985 14,985 14,985
11.169 0.183 2.671
1.397 0.386 19.377
9.564
0
0.374
11.0060
1.304
13.0671
4.661

14,985
1.093
0.602
0.564
0.958
1.753

14,985 14,985 14,985 14,985
1.991 0.016 0.002 -0.007
16.287 0.062 0.071 0.016
0.810 -0.024 -0.063 -0.020
1.439 0.019 0.001 -0.007
3.091 0.061 0.069 0.007

14,985 14,985
0.843 0.128
1.826 0.100
0.148 0.037
0.760 0.105
1.594 0.244

14,985
0.104
0.095
0.047
0.088
0.169

14,985
0.192
0.394
0
0
1

14,985
0.090
0.118
0.016
0.060
0.189

14,9850.031
0.224-0.131
0.019
0.173

14,985
0.035
0.037
0.008
0.026
0.070

14,985 14,985
0.910 9.569
0.505 27.359
0.505
0
0.928
0
1.354
24

16.21

-5.96

2.23

0.89

-0.99

-6.20

-4.89

-6.73

-3.99

1.39

-4.68

6.96

***

***

***

***

***

***

***

***

***

15.30

-6.26

1.95

3.27

-2.45

7.52

-1.87

-8.98

-4.39

-7.08

-4.03

5.33

-4.65

5.40

10.02

***

***

**

***

**

***

*

***

***

***

***

***

***

***

***

1.57

**

6.39

0.79

***

1.77
*

1.62

0.93

21

15.56

Table 6 Correlation Coefficient of Pearson and Spearman

BigN

lnASSET

LOSS

LIAB

ATURN

CURR

ROA

DOCF

lgACCRBETA

CASH

VOLATILITYLAGLOSS

1
lnASSET
0.140
LOSS-0.051
LIAB
0.008
ATURN
0.018
CURR
0.004
ROA
0.074
DOCF
0.007
lgACCR
0.019
BETA-0.004CASH-0.052VOLATILITY-0.053LAGLOSS-0.058SALESVOLATILITY -0.040
SALESGROWTH
0.014
CFOVOLATILITY -0.042
SAF2002
0.083
CONSOL
0.087

0.125
1
-0.1130.027-0.1190.010
0.113-0.011-0.0170.028-0.184-0.104-0.111-0.1230.012-0.2190.093
0.410-0.051-0.1131
0.025-0.072-0.021-0.533-0.068-0.0210.029-0.074
0.132
0.276
0.042
-0.1700.062-0.397-0.007

0.016
0.120
0.111
1
0.011
-0.007-0.0500.005-0.012-0.026-0.041
0.032
0.068
0.022
-0.0150.001-0.0820.032

0.027-0.112-0.1040.171
1-0.020
0.066
0.034
0.054
-0.021-0.001-0.016-0.059
0.400
0.094
0.147
0.089
-0.047-0.020-0.072-0.162-0.764-0.108
1
0.024
0.007
-0.018-0.0010.040-0.0120.005-0.004-0.002-0.0080.032-0.0080.061
0.058-0.669-0.298
0.155
0.340
1
0.056
0.114
-0.0360.161-0.157-0.246-0.0200.191-0.061
0.795-0.001

0.013
0.001-0.0930.022
0.034-0.001
0.107
1
0.451
-0.028
0.119
0.027
0.080
0.064
0.154
0.093
-0.0020.006

0.008-0.041-0.013
0.010
0.022
0.068
0.001
0.471
1
0.008
0.001
-0.048-0.0700.009
0.060-0.0590.115-0.021

0.009
0.027
0.051
0.010
-0.021-0.014-0.067-0.055
0.022
1
0.019
0.090
0.011
0.052
0.018
0.049
-0.037-0.012-0.073-0.192-0.070-0.345
0.039
0.490
0.222
0.062
0.012
0.011
1
0.057
-0.045
0.191
0.144
0.236
0.206
-0.102-0.036-0.0690.181
0.186-0.008-0.095-0.1070.020-0.016
0.348
0.049
1
0.174
0.142
-0.0190.152-0.256-0.027-0.058-0.1100.276
0.176-0.086-0.142-0.3320.097-0.0400.013-0.041
0.208
1
0.050
-0.0290.081-0.294
0.012

BigN

Note: The lower left is Pearson and the upper right is Spearman correlation coefficient.

22

SALESVOLATILITY SALESGROWTH CFOVOLATILITY-0.033-0.137
0.055
0.079
0.436
0.008
0.069
0.046
0.021
0.212
0.135
0.240
0.064
1
0.370
0.333
-0.063-0.0640.044
0.056-0.287-0.055
0.167
0.071
0.408
0.130
0.017
0.078
0.056
-0.068-0.120
0.147
1
0.068
0.116
0.029

-0.038-0.214
0.071
0.014
0.181
0.098
0.013
0.035
0.024
0.137
0.176
0.213
0.085
0.345
-0.0371-0.175-0.102

SAF2002

CONSOL

0.044
0.019-0.468-0.653
0.170
0.530
0.688
0.038
-0.021-0.1090.299-0.266-0.3560.000
0.210-0.0551-0.048

0.082
0.191
0.008
0.140
-0.072-0.126-0.0480.023
0.029-0.179-0.0920.011
0.039-0.1500.116-0.146-0.1231

Table 7 Logistic Model Results for the Estimation of Propensity Score [Formula (1)]coefficient Std. Dev. z-vaule-1.2220.254-4.81 ***
0.198
0.019
10.67 ***
0.075
0.065
1.14
0.001
0.001
0.87
0.136
0.046
2.94 ***
0.001
0.002
0.25
0.301
0.613
0.49
0.180
0.342
0.53
0.722
1.590
0.45
-0.0020.010-0.15-1.0900.224-4.86 ***
-0.3160.217-1.45-0.1070.056-1.93 *
-0.5090.208-2.45 **
0.091
0.104
0.87
1.436
0.629
2.28 **
0.370
0.081
4.57 ***
0.008
0.002
4.45 ***
included
Percent Correctly Predicted
77.50%
14,985
No. Obs
Intercept
lnASSET
LOSS
LIAB
ATURN
CURR
ROA
DOCF
lgACCRBETA
CASH
VOLATILITYLAGLOSSSALESVOLATILITY
SALESGROWTHCFOVOLATILITY
SAF2002
CONSOLiD & yD23

Table 8 Results of audit quality in terms of auditor size using normal regression and IPW [Formula (3)]model 1: without weight (normal regression)

ADA
Intercept
BigN
No. Obs

coefficient
0.0346-0.0031

Std. Dev. t-value
0.000657.40
0.0007-4.45
14,985BENCHMARKIntercept
BigN
No. Obs-0.9845-0.0060

0.0257-38.370.0292-0.21
14,985

GCREPORTIntercept
BigN
No. Obs-1.8998-0.4784

RPEG
Intercept
BigN
No. Obs
ACCY
Intercept
BigN
No. Obsmodel 2: with weight (using estimating Bign from logistic model)

coefficient
0.0312-0.0002

Std. Dev.
0.00060.0007
14,985t-value
52.93 ***
-0.26

***

-0.9889-0.00100.02770.031014,985-35.76-0.03***

0.0473-40.130.0619-7.73
12,661******-2.1757-0.13770.04580.0617
12,661-47.46
-2.23

***
**

0.1545-0.00650.00490.0055
6,190

31.77
-1.18

***

0.1531-0.00220.01460.0147
6,190

10.50
-0.15

***

-0.1033
0.03660.0061-16.840.00695.28
11,956******-0.0952
0.02360.01680.017011,956

-5.65
1.39

***

***
***

24

Figure 1 The Historical Situation of the Japanese Audit Market

company/person
25000

The number of listed companies
The number of auditors per company

The number of auditors
Market capitalization (billion Yen)billion Yen
70000060000020000
50000015000

40000030000010000

200000
5000
10000001949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 20110Year

Note: The following companies listed in the Japanese stock exchange are included: Tokyo, Osaka, Nagoya, Sapporo,
Niigata, Kyoto, Hiroshima, and Fukuoka. A double listed company is counted as one. The stocks of emerging
markets in Japan, such as Mothers, Hercules, and the JASDAQ, are excluded.
Source: Tokyo Stock Exchange Fact Book 2012.

25

Figure 2 History of the Transition of Audit Firms in Japan
Tohmatsu-Aoki(1968.5)

Chuo(1968.12)

Sanwa-TohmatsuAoki(1986.10)

Chuo-Shinko(1988.7)(1988.4)

Asashi(1963.7)
Shinwa(1968.12)

Musashi(1971.9)

Ota-Showa(1985.10)

Asahi-Shinwa(1985.7)

Century(1986.1)

Heiwa(1989.4)

Eiko(1967.5)

Nisshin(1970.11)

Showa(1969.12)

Shinko(1978.4)

Sanwa(1971.6)

Daiichi(1969.3)

OtaTetsuzou(1967.1)

SapporoChuo

Marunouchi(1987.4)(1989.10)

Chuo(1993.7)(1990.7)

Minato(1992.7)

Yoko

Nishikata(1988.10)

NagoyaDaiichi

Mita(1990.2)

YokohamaKannai(2006..7)(2000.4)

Aoyama(2001.1)

ItoChuoChuo-Aoyama(1993.10)

OtaShowa-Century(2000.4)

TKAiizuka

InoueSaitoEiwa

Takachiho(2001.7)

Asahi(KPMG)
ShinNihon(2003.3)

Tohmatsu(Deloitte Touche Tohmatsu)

Aarata(PwC)

Misuzu(liquidated)

ShinNihon(Ernst & Young)

Azusa(KPMG)Azusa(KPMG)(2007.7)(2004.1)

26