The Causal Relationship between Auditor Turnover and Audit Fees
-Evidence in Japan
ABSTRACTThis study investigates the causal relationship between auditor turnover and audit fees. The aim of

this study is to accurately identify the real relationship between auditor turnover and audit fees. The

analysis in this study is based on the counterfactual model provided by Rosenbaum and Rubin(1983). The propensity score is estimated using logistic regression, which generates a matchedtreatment sample and a control (counterfactual) sample. The matched dataset containing 486

firm-years is analyzed using liner regression to identify the causal relationship between auditor

change and audit fees. The clearly significant result shows that low-balling exists in real audit

contracts. As an additional study, the sample is divided using the auditor size variable (Dummy of

Big 3 or not) into four parts. The additional results show that, after an auditor change from a

Non-Big 3 auditor to Big 3 auditor, the contract exhibits low-balling. This suggests that clients whochange auditors from a non-Big 3 firm to Big 3auditor do not receive an adequate audit because of

the auditors' cost cutting.

The coefficient of the auditor change from non-Big3 to Big 3 auditor is significant and relatively

higher than the coefficients of the other parts. Audit fees need to cover the costs of the audit practice

to maintain audit quality. The results of this study can serve as an alert for Japanese auditing practicequality issues.

1. Introduction

Generally, auditors seeking new clients are frequently discounting their fees for the

first-time audit. This price cutting phenomena is called “low-balling.” DeAngelo (1981) is the first

analytical study of this phenomenon and is observed in many aspects of economic relationships. This

first-time price cutting is not confined to auditing; as such, there is a need to expand our research.

For example, Roychowdhury (2006) examines earnings management using price cutting (“sales
manipulation”). 1 The expected future quasi-rents induce this initial price cutting by auditors.

Quasi-rents, which refer to the excess of revenues in a period over avoidable costs (including theopportunity cost of auditing), is a very complicated concept. Simply speaking the auditor will benefit

from the quasi-rents when becoming the incumbent. Lack of auditor independency will decrease theaudit fees.

A contrary theory to low-balling, where the audit fee for the first-time auditing will behigher than other periods because the auditor will manage the risk associated with taking on newclient companies, should be examined. Auditors will require high-level audit fees to cover these

additional risk adjusting activities (Simunic 1980). This opinion from Simunic also indicates that

other factors associated with these higher fees should be considered, such as auditing firm size,

auditor reputation.
1

However, the sales manipulation in real earnings management is to take the cash flow in shortterm.

This study formulates two opposing theoretical hypotheses for analyzing the state of audit

fees with a change from the incumbent auditor.

Mandatory auditor rotation has emerged as a popular accounting topic in the current

decade. Effective June 2016, the European Union adopted legislation for mandatory audit firm rotate.

In addition, in Japanese audit market, from 2004 the rules of standard audit pricing is abundant. The

new environment may give something new to the audit contract respectively.

This study confirms the effect of auditor turnover by setting up a treatment effect using

the Neyman-Rubin “Potential Outcomes Framework” to develop the empirical results. The

relationship between regression and causal effect analysis is presented in Graph 1.

The significance of the proposed research is to show the causal relationship between

auditor turnover and level of audit fees. In the previous studies, the researchers only demonstrated

the correlation of the two phenomena. This study provides evidence that auditor turnover is not only

correlated, but also causes the changes in audit fees. Theoretically, the initial audit fees reduction(low-balling) will be introduced at the first-time auditing.

DeAngelo (1981) insists that anauditor’s independency is impaired with low-balling; however, she does not show the empiricalevidence in her thesis. Deis and Giroux (1996) provide some evidence but low-balling has not been

observed in Japan until this proposed work. This study conjectures whether low-balling is observable,

and if so, under what settings.

Auditor independency is very beneficial and essential to an audit practice. However, Deis

and Giroux (1996) suggest that a low-balling (low-independence, in this audit) setting does notcorrelate to a low-quality audit. The quality of audit is maintained even if low-balling is observed.

For this research, the exact cause of the low-balling, if observable, needs to be defined. Low-ballinghas been evidenced in many industries and economic relationships.

This paper is organized as follows: First, previous works regarding audit fees andindependency are reviewed. Next, the research design is provided, followed by the data analysis

details and results. Finally, the conclusions, implications, and limitations of this study are presented.

2. Background and Previous Works

The theoretical research of DeAngelo (1981) is the first research regarding low-balling

relating to auditors. She elucidates the theoretical background of low-balling and insists that
low-balling is not connected to impairment of auditor independency. 2 The quasi-rent will beawarded to the selected auditor; therefore, auditors conduct low-balling to win the bid. The strong

independency induces the benefit between auditor and client companies. Simon and Francis (1988)

analyze the real conditions of low-balling and their relationships to auditor independence.

2

Tseng, R. P. M. M. C. (1990) is another analytical study for seeing “first audit effects (effects ofincumbency)”. They say the effects of incumbency will damage the independency of auditors.

Casterella et al. (2004) show the correlation between audit fees and auditor specialization.

Their findings reveal that premiums paid for auditor specialization only relate to smaller clients.

Carcello et al., Nagy (2005) also find the audit quality remediates for only small clientele after theclients will change their auditor from Arthur Andersen. In contrast with this research, Deis andGiroux (1996) conclude that audit quality is not changed after an auditor turnover.

Abbott et al. (2003) see a positive relationship between audit fees and audit committeecharacteristics (independence, financial expertise, and meeting frequency).

Carcello and Neal (2000) provide some evidence of a relationship between audit

committee composition and going concern opinion. They suggest that the probability of adding thegoing concern opinion will be lesser when some affiliated audit committee members are part of thecommittee. They selected 223 samples for the test (from only select financially distressedcompanies), which supported their hypothesis.

Carcello and Neal (2003) added some evidence to their previous study (Carcello and

Neal, 2000) and showed the relationship between auditor dismissal and audit committeecharacteristics. They suggest that the auditor will be protected by the audit committee even if theauditor discloses unfavorable financial situations (such as going concern issues) when audit

committee members are independent from company managers or the board of directors. Their study

shows the expected result that independent audit committee members protect the external auditor

from dismissal.

Evidence exists that shows that internal company conditions will affect the external audit

and that audit fees correlate with auditor independence. Out of the ordinary or atypical (too large or

too small) audit fees may also relate to auditor independence. Though evidence can be shown of theeffect of damaged auditor independence, this study shows the impact of the auditor-management

internal condition on the external audit.

The independence of the auditor with respect to its first audit changes relative to otherauditors. Hence, this study states two opposing hypotheses on auditor independence. The null

hypotheses are as follows,

H1. The audit fees will not be higher-level with a turnover in auditor due to the auditor’s risk

adjustment than the others.

H2. The audit fees will not be low-level with a change in auditor because of lack of independency.

3. Research Design

Data Category
This study categorizes the auditor turnover data as follows:

1． Full sample

2． Big 3 auditor to Big 3 auditor3． Big 3 auditor to non-Big 3 auditor

4． Non-Big 3 auditor to Big 3 auditor

5． Non-Big 3 auditor to non-Big 3 auditor
I just confirm the full sample case as a main study. First, I conduct the full sample analysis using

single regression. Then, following Hoshino (2009), I run a multiple regression.

Analysis policy
Causality, not just correlation, will be generated from the regression analysis. This analysis

requires the use of counterfactual data and uses propensity score matching. Why we need to see thecausality? Correlation does not prove causality; however, correlation is a part of causality. David
Hume 3 suggests that we cannot always observe causality but it exists in the real world. We have

known that we can see the causality if we reproduce the effect of treatment. This approach is basedon the Neyman-Rubin counterfactual model.

To ensure accurate results regarding the causality, this study selected the propensity

matching approach.

Data

3I refer to the Stanford Encyclopedia of Philosophy (available on

https://plato.stanford.edu/entries/hume/).

1) This study selects samples that contain data from Japanese listed companies, and exclude

financial institutions2) exclude double-listed Securities and Exchange Commission (SEC) or other stock markets inforeign country3) exclude the samples which conduct joint audit

4) include only the sample fiscal year end is March

5) include necessary data from databases

This study generates data from the Nikkei Data CD-ROM and Nikkei Kigyo Kihon Data. To exclude

the effects of internal control audit bubble, I collect data from March 2011 to March 2016. I haveobserved the 10,919 firm-year observations and those of the switching firm under 243.

Model
Most studies use the regression model and only observe correlations, which is not adequate

for investigating accurate and real conditions.

This study uses the following logistic regression model for formulating the propensity

score.

CHA =𝛼𝛼0 ＋𝛼𝛼1 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿＋𝛼𝛼2 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿＋𝛼𝛼3 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅＋𝛼𝛼4 𝐺𝐺𝐺𝐺＋𝛼𝛼5 𝐵𝐵𝐵𝐵𝐵𝐵3＋𝛼𝛼6 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷＋𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦＋𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖＋ε（equation 1）

This model consists of the determinants of auditor turnover.

The determinants shown above are the characteristics of auditees. The results are represented on

Table 2. In the model, I have identified the auditee’s size and associated risks as well as thefollowing variables:(1) Size Variable: Natural Logarithm of Total Assets

An auditee’s size is the basic characteristic. Sizable auditees incur higher audit costs andauditor charges determine auditor change. In fact, this study includes the variables and examines thecomplexity of the operation (Simunic 1980) in the model; however, the correlation between these

two factors is very high. Therefore, I have excluded the complexity variable from the analysis as thecharacteristic of the auditee.

(2) Inherent Risk: Level of Receivables, Inventory, and Leverage

Inherent risk reveals the potential risks to companies. I have selected receivables,

inventory, and leverage. Higher accounting receivables and inventory potentially expose the auditee

to more risky conditions, and high leverage instigates detrimental conditions in companies. This

study refers to the work of Simunic (1980), which is an audit pricing study focusing on auditors’

characteristics for determining audit fees.

(3) Default Risk: Going Concern Opinion Dummy Variables

The default is a basic concern for the auditor that can lead to his relinquishing his role. The

going concern opinion reveals financial distress, and therefore, this factor is not connected to thedefault directly.

(4) Auditor size: Big 3 or not dummy variables

Auditor size is very important in auditor selection. Essentially, the Big3 firms in Japan(AZSA KPMG LLP, EY Shinnihon LLP, and Deloitte Tohmatsu LLP) have strong brand power andprior studies confirm this (e.g., Oikawa 2013). I have observed the differences between the Big3 andnon-Big3 firms, and based on the results have divided the samples.

(5) Internal Control Risk: Rate of Growth of Sales

I have focused on the internal controls of high-growth companies, based on the Committeeof Sponsoring Organizations of the Treadway Commission (COSO) framework 1992 4. A company

that has an excessive growth rate poses an internal control risk. Since fiscal year 2009, internal

control reporting is mandated for all the listed and big companies. This study necessarily examinesinternal control risks. If this logic is applied, a material weakness dummy variable is needed.

However, companies exhibiting material weakness are not included in the examined sample.

4

The COSO Internal Control framework was updated on 2013. However, I see the basic concept on
1992 framework.

(Table 2 here)

While the full sample analysis did not present any problems, the divided samples did withregard to matching. Therefore, I have shown the results of the full sample with divided dummy

variables and full sample result.

4. Results

This study observes the relationship between audit fees and auditor turnover and clarifies

that auditors discount their fees for the first audit.

Descriptive statistic and correlation
Table 3 and 4 show the descriptive statistic and correlation. There are 243 matchedsamples. I matched the samples using one to one nearest neighborhood matching. See the Graph 2

and Table 2 for the details of matching

The treatment sample’s audit fees are little bit smaller than the control sample’s audit fees.

In addition, the treatment sample received Going Concern Opinion more very often and growth ofsales is bigger. For GC variables and the growth of sales variables are not able to be matchedbetween the two groups. The other variables are almost same between treatment sample and control

sample.

(Table 3 and 4 here)

Regarding the correlation, I cannot recognize significantly higher correlation number, sodo not concern about multicollinearity.

(Graph 2 here)

Main Analysis
The final examination model is as follows,
LNAF＝𝛼𝛼0 ＋𝛼𝛼１ 𝐶𝐶𝐶𝐶𝐶𝐶＋𝜀𝜀(equation 2)

LNAF = 𝛼𝛼0 ＋𝛼𝛼１ 𝐶𝐶𝐶𝐶𝐶𝐶＋𝛼𝛼2 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿＋𝛼𝛼3𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿＋𝛼𝛼4𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼＋𝛼𝛼5𝐺𝐺𝐺𝐺＋𝛼𝛼6 𝐵𝐵𝐵𝐵𝐵𝐵3＋𝛼𝛼7 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷＋ε （equation 3）
This analysis sets up auditor turnover as a treatment sample, and estimates the propensity

scores. I matched the treatment sample with the control sample using propensity scores. Hoshino(2009) states that multi-regression analysis using matched data by propensity score is the method

used by some medical studies. This research is based on the method introduced by Hoshino (2009).

This analysis is conducted using full sample. The full sample analysis shows a highly

significant causal relationship between audit fees and auditor turnover in multiple regression model.

However, single regression is not shown significant results. I think the endogeneity of the model ismodified by the control variable. The control variable is significant at appropriate level on themultiple model.

(Table 5 and 6 here)

5. Interpretation of the results

The level of audit fees is lower than control samples. This indicates the same condition as
DeAngelo. This result is important because audit cost is not covered by the audit fees completely. 5

Low-balling may have become prevalent after the introduction of the internal control

reporting institution (based on the Company Law and the Financial Instruments Exchange Law), theextent of which varies with auditor size. Had I rejected the Big3 dummy variable from the model,

the result would have changed (not tabulated). To investigate the details of the effects of the Big3

dummy variables, I have divided the sample and have analyzed it in depth.

6.

Additional analysis using categorical dummy variables

This analysis provides the details of the effects of the auditor change seeing Big3 or not 4

5

This analysis cannot provide evidence to prove whether auditor independence gets eroded. We canobserve auditor independence using methods like discretionally accruals. However, these methods

are not accurate. To examine audit quality or independence, we need to analyze the real process ofaudit, which involves substantial procedures, using real audit data.

pattern variables (b. to e.). 6
LNAF = 𝛼𝛼0 ＋𝛼𝛼１ 𝐵𝐵𝐵𝐵𝐵𝐵3𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡3＋𝛼𝛼2 𝐵𝐵𝐵𝐵𝐵𝐵3𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡3＋𝛼𝛼3 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁3𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡3＋𝛼𝛼4𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁3𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡3＋𝛼𝛼5 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿＋𝛼𝛼6 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿＋𝛼𝛼7 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼＋𝛼𝛼8 𝐺𝐺𝐺𝐺＋𝛼𝛼9 𝐵𝐵𝐵𝐵𝐵𝐵3＋𝛼𝛼10 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷＋ε(equation 4)

b. The analysis using auditor data changed from the Big 3 to Big3

Big 3 to Big3 samples suggest insignificant results. This suggests that the Big3 to Big3line does not affect audit fees. Big3 to Big3 poses some problems, and therefore, clients see noadvantage in hiring an auditor, who has to be paid some audit fees. This insignificance shows thatthe auditee needs to compromise and the auditor has the advantage of earning some audit fees.

c.

The analysis using auditor data changed from the Big3 to non-Big3

Big3 to non-Big3 data is not significant, suggesting that non-Big3 firms charge the same

audit fees from all client companies. There is no change in the results. Generally, Big 3 firm require

the higher level of audit fees and this analysis suggest the same results (See the Big3 coefficient)however non-Big 3 firm requires the same amount of fees. It suggests risk adjustment is occurred.

d.

6

The analysis using auditor data changed from the non-Big 3 to Big3

This study provides matching samples for four parts (using the Big3 dummy), but some of theirprobabilities are either 0 or 1. Therefore, I do not generate the matched samples for each category.

Non-Big3 to Big3 data is highly significant. It shows that high-growth companies have theadvantage of determining the audit contract and requesting the auditor to discount his or her fees.

This result shows the low-quality audit will be conducted in these samples. In fact, as the Big3 audit

firms have internal system rigidly, a proportion of the expenses will be covered by the non-additionalcosts (with the auditee’s rapid growth).

e.

The analysis using auditor data changed from the non- Big 3 to non- Big3

This data set shows not significant results of the relationship between the audit fees andauditor turnover. When non-Big3 sample moves to another non-big firm, client company required to

pay same fees. I conjecture that this type is not associated with the discount or risk adjustment ofaudit fees.

(Table 7.1 and 7.2 here)

7.

Conclusion

This study investigates the causal relationship between auditor turnover and audit pricing.

Specifically, the black box regarding auditor turnover has been resolved in this study. The black box

of auditor turnover either diminishes auditor independence, which decreases the level of audit fees,or it places auditors at additional risk, which increases the level of audit fees. Both these scenarios

are theoretically possible. These hypotheses have been tested using an accurate method that provides

the causal inference between auditor turnover and audit fees and produces counter-factual samples tocompare the facts (treatment samples) and analyze the relationship between the two sample groups.

This study constructs a matching database and conducts re-regression using the matched sample.

Hoshino (2009) suggests this approach is popular in some medical studies.

The results of our analysis are clear; audit fees is low level when the auditor changes.

These results make it easy to interpret real-life conditions. So, H2 is rejected.

This study only shows a causal relationship, and does not help interpret the length oftenure of auditors. Johnson et. al. (2002) show the incomplete results of the relationship between

auditors’ tenure and audit quality. We need to replicate the findings of Johnson et. al. (2002)

regarding the use of audit fees. According to the authors, a medium length of audit tenure positively

correlates with the audit quality. They denied the possibility that auditor rotation is effective inmaintaining audit quality. The European Union’s assumptions may not be the same as the research

outlook in this study or as the assumptions in Johnson et. al.’s (2002) study. The European Union’s

perception is that “mandatory rotation” will result in the achievement of high-quality audit. The

competitive condition of auditors emerged from the 2004 de-regulation of the “standard audit feesrule” in Certified Public Accountant (CPA) law. The competition between auditors may be more

intense than in the 2004–2011 fiscal-years because of the end of the internal control-bubble andsome cases of scandal-related accounting. A decline in audit fees results in low-quality audits.

Although audit fees consist of other components, a large proportion of it goes toward workers’ costs.

In addition, on the analytical policy, I do not have used adequate treated samples making

the 4-divided sample (using Big3 or not dummy variables). Moreover, I just see the low-balling inlimited samples. This kind of study must enlarge the samples and confirm more accurate audit

quality changes when the auditor turnovers.

The mandatory auditor rotation will be the big issue in the audit market from now on.

Changing auditors has many reasons in the selective change of auditor. So, the basic situation is not

same with mandatory rotation, but low-cost audit may be emerged with rotation by low-balling andlow quality audit may be occurred. The turnover issue is the big and important one in this audit

market.

Reference
Abbott, L. J., Parker, S., Peters, G. F., & Raghunandan, K. (2003). The association between audit
committee characteristics and audit fees. Auditing: A Journal of Practice & Theory, 22(2), 17-32.

Carcello, J. V., & Neal, T. L. (2000). Audit committee composition and auditor reporting. The Accounting
Review, 75(4), 453-467.

Carcello, J. V., & Neal, T. L. (2003). Audit committee independence and disclosure: Choice forfinancially distressed firms. Corporate Governance: An International Review, 11(4), 289-299.

Casterella, J. R., Francis, J. R., Lewis, B. L., & Walker, P. L. (2004). Auditor industry specialization,client bargaining power, and audit pricing. Auditing: A Journal of Practice & Theory, 23(1), 123-140.

DeAngelo, L. E. (1981). Auditor independence,‘low balling’, and disclosure regulation. Journal of
Accounting and Economics, 3(2), 113-127.

Deis, D. R., & Giroux, G. (1996). The effect of auditor changes on audit fees, audit hours, and audit
quality. Journal of Accounting and Public policy, 15(1), 55-76.

Hoshino, T.,(2009), Chousa Kansatsu Deta no Tokei Kagaku Ingasuiron / Sentakubaiyasu / De-ta
Yugo(in Japanese), Iwanami-Shoten, Publishers

Johnson, V. E., Khurana, I. K., & Reynolds, J. K. (2002). Audit‐ firm tenure and the quality of financial
reports. Contemporary accounting research, 19(4), 637-660.

Kanodia, C., & Mukherji, A. (1994). Audit pricing, lowballing and auditor turnover: A dynamic analysis.
Accounting Review, 593-615.

Menon, K., & Williams, D. D. (2008). Management turnover following auditor resignations.
Contemporary Accounting Research, 25(2), 567-604.

Nagy, A. L. (2005). Mandatory audit firm turnover, financial reporting quality, and client bargaining
power: The case of Arthur Andersen. Accounting Horizons, 19(2), 51-68.

Newton, N. J., Persellin, J. S., Wang, D., & Wilkins, M. S. (2015). Internal control opinion shopping andaudit market competition. The Accounting Review, 91(2), 603-623.

Oikawa, T. (2013) Does the Big 3 Provide Higher Audit Quality in Japan? : Evidence from Auditor Change of Former
ChuoAoyama Clients, Auditing (23), 87-95

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies
for causal effects. Biometrika, 70(1), 41-55.

Roychowdhury, S. (2006). Earnings management through real activities manipulation. Journal of
accounting and economics, 42(3), 335-370.

Simon, D. T., & Francis, J. R. (1988). The effects of auditor change on audit fees: Tests of price cutting
and price recovery. Accounting Review, 255-269.

Stanford Encyclopedia of Philosophy available on https://plato.stanford.edu/entries/hume/ (access on July
2017)

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Table and Graphs
Table 1 Definition of variables
Variables

Definition
1 in case of auditor turnover and a brand-new audit firm signs a contract with a sample client,

CHA=
otherwise 0.

LNAsset=Natural logarithm of audit fees

Leverage=

Total liability on total asset

InvRec=Inventory and accounting receivable on total asset

GC=

1 if manager declares going concern opinion, otherwise 0.

Big3

=

1 if the audit firm is AZSA KPMG LLP, EY Shinnihon LLP, or Deloitte Tohmatsu LLP, otherwise 0.

Dsales=(Salest – Salest-1) on Salest-1

Big3toBig3=

1 if the audit firm is changed from one Big3 firm to another, otherwise 0.

Big3toNonBig3=

1 if the audit firm is changed from a Big3 firm to a non-Big3 firm, otherwise 0.

NonBig3toBig3=

1 if the audit firm is changed from a non-Big3 firm to a Big3 firm, otherwise 0.

NonBig3toNonBig3=

1 if the audit firm is changed from one non-Big3 firm to another, otherwise 0.

Yeardummy=Year dummy variable

Inddummy=

Industry dummy variableTable 2
Logistic Regression Results: For estimating the propensity scores
DV=CHA［-/+］

Intercept

Coeff.

Z-value-0.7161-1.004

LNAsset--0.273-5.368***

Leverage+

0.716

2.163*

InvRec+-0.917-2.169*GC+-0.121-0.320Big3--1.788-11.517**

Dsales

+

0.096

1.538

Yeardummy

IncludedIndustrydummy

Included-Number of Obs.=

10,919Treatment sample Obs.=

243

AIC=

2102.2This table represents the result of logistic regression analysis for estimating the propensity scores. The
significance of coefficients is shown that *** is 0.1 percent level, ** is 1 percent level, * is 5 percent
level, and ‘. is 10 percent level. The definitions of variables are on the Table 1.

Table 3
Descriptive Statistics (n=486)
Minimum

Maximum

AverageStandard Deviation

Treat

Control

Treat

Control

Treat

Control

Treat

Control

LNAF

1.609

2.197

5.215

4.804

3.189

3.258

0.516

0.478

LNAsset

6.006

4.844

14.73913.945

9.835

9.783

1.555

1.490

Leverage

0.053

0.043

1.231

1.535

0.521

0.498

0.213

0.224

InvRec

0

0

0.720

0.779

0.216

0.202

0.172

0.175

GC

0

0

1

1

0.040

0.020

0.199

0.156

Big3

0

0

1

1

0.260

0.270

0.441

0.446

Dsales-0.590-0.85524.786

1.218

0.142

0.042

1.608

0.206

This table shows the descriptive statistics of the propensity matched sample. ‘Treat’ means treatment sample that auditor’s turnover ishappened, and ‘Control’ means the control samples matched using propensity scores. Regarding the definition of the variable you can see themon the Table 1.

Table 4
Correlation (n=486)
CHA

LNAF

LNAssetLeverage

InvRev

GC

Big3

Dsales

LNAF-0.0691

LNAsset

0.017

0.663

1

Leverage

0.052-0.005-0.044

1

InvRec

0.040

0.026

0.041

0.062

1

GC

0.046

-0.129-0.3020.122-0.0991

Big3-0.009

0.311

0.238

-0.0840.005-0.1111

Dsales0.044-0.059

0.054

0.083

-0.054-0.031-0.015This table represents the correlation between variables. The definitions of variables are on the Table 1.

1

Table 5
Liner Regression Result: Single regression using matched sample.

［-/+］

DV=LNAF(Intercept)?

CHA

Coeff.

Std. Error

t-value

3.258

0.032

102.113***-0.0690.045-1.527

Number of Obs.=

486

Multiple R-squared=

0.005

Adjusted R-squared =0.003

F-statistic =2.333

This table represents the result of liner regression analysis using the propensity scores matched sample.
The significance of coefficients is shown that *** is 0.1 percent level, ** is 1 percent level, * is 5 percent
level, and ‘ is 10 percent level. The definitions of variables are on the Table 1.

Table 6
Linear Regression Results: Multiple regression using matched sample
DV=LNAF［-/+］(Intercept)

Coeff.

Std. Error

t-value

1.054

0.121

8.695***

CHA?

-0.0810.033-2.459*

LNAsset

+

0.215

0.012

18.614***

Leverage

+

0.089

0.076

1.175

InvRec

+

0.008

0.095

0.080

GC

+

0.233

0.097

2.397*

Big3

+

0.185

0.038

4.868***

Dsales+-0.0390.014-2.702**

Number of Obs.=

486

Multiple R-squared=

0.487

Adjusted R-squared =0.479

F-statistic =64.68***

This table represents the result of liner regression analysis using the propensity scores matched sample. This
analysis used the same variables in the first logistic regression. The significance of coefficients is shown that*** is 0.1 percent level, ** is 1 percent level, * is 5 percent level, and ‘ is 10 percent level. The definitions ofvariables are on the Table 1.

25

Table 7.1 The number of divided samples
Big3toBig3

35

Big3toNonBig3

61

NonBig3toBig3

29

NonBig3toNonBig3

118

Total

243

26

Table 7.2 Additional Analysis
Liner Regression using the divided samples

DV=LNAF［-/+］(Intercept)

Coeff.

Std. Error

t-value

1.034

0.123

8.438 ***

Big3toBig3?

-0.0480.076-0.638

Big3toNonBig3?

-0.0670.053-1.264

NonBig3toBig3?

-0.2670.081-3.303 **NonB3toNonB3?

-0.0510.043-1.201

LNAsset

+

0.215

0.012

18.662 ***Leverage

+

0.095

0.076

1.243

InvRec

+

0.038

0.096

0.393

GC

+

0.230

0.097

2.375 *

Big3

+

0.230

0.053

4.367 ***

Dsales+-0.0400.014- 2.770 **Number of Obs.=

486

Multiple R-squared=

0.494

Adjusted R-squared =0.484

F-statistic =46.43***

This table represents the result of liner regression analysis using the propensity scores matched sample. On thisanalysis I divided the sample of CHA to the 4 parts. The definitions of variables are on the Table 1. The
significance of coefficients is shown that *** is 0.1 percent level, ** is 1 percent level, * is 5 percent level, and .
is 10 percent level.

27

Graph 1: Outline of researchCompareChanged (n=243)

Audit Fees

Audit Fees
Characteristic↓
Probability

Matched(n=243)

The above graph shows the outline of this study. The basic concept is from the causal inference
framework.

28

Graph2: Propensity Score Matching Details

The above 4 histogram shows the matching result. This “Raw Control” histogram is changed like “Matched Control”.

29