www.asb.unsw.edu.au

Last updated: 9/05/14 CRICOS Code: 00098G

School of Accounting Seminar Series Semester 1, 2014

How does mandatory IFRS adoption 

affect the audit service market? 

Chen Chen 

University of Auckland 

Date:   Friday 16th May 2014 

Time:   3.00pm – 4.30pm 

Venue:  ASB 216 

Australian School of Business

Accounting

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How Does Mandatory IFRS Adoption Affect the Audit Service Market?

Chen Chen

University of Auckland

Zili Zhuang

The Chinese University of Hong Kong

Abstract

Using data from 17 European countries, we examine how mandatory IFRS adoption affects

the audit market and competition among auditors with different IFRS expertise. We argue

that Big Four auditors have higher IFRS expertise than non-Big Four auditors. Further, within

the Big Four (non-Big Four) audit market, PWC has (BDO and Grant Thornton have) higher

expertise based on their relative market shares in the voluntary IFRS adopter market. We find

that after IFRS adoption (i) audit quality of lower IFRS expertise auditors deteriorates while

that of higher expertise auditors does not change; (ii) Lower IFRS expertise auditors increase

audit fees more than higher expertise auditors; (iii) Higher IFRS expertise auditors gain

market shares in countries with greater GAAP changes. The results hold for the overall audit

market and within the non-Big Four market, but not within the Big Four market, suggesting

the latter are perceived as having similar IFRS expertise.

Key words: IFRS adoption, audit quality, audit fees, audit expertise, Big Four auditors.

Data Availability: Audit firm identities are primarily based on a dataset provided by

Professor Luzi Hail, supplemented with our own hand collection. Other data are publicly

available from sources identified in the paper.

We would like to thank Steven Cahan, Liz Carson, Hans Christensen, Tony Kang, Xiaohong Liu, Gilad Livne,

Gerald Lobo, Linda Myers, Suresh Radhakrishnan, Liu Zheng, conference participants at the 2011 AAA

International Accounting Section Midyear Meeting, the 2013 EAA Annual Congress, and workshop participants

at The Chinese University of Hong Kong, Monash University, Renmin University of China, Tsinghua

University, and The University of Hong Kong for their helpful comments. We are grateful to Luzi Hail for

sharing his auditor data with us. All errors are our own.

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How Does Mandatory IFRS Adoption Affect the Audit Service Market?

1. Introduction

There is a growing body of literature examining the economic consequences of the

mandatory adoption of the International Financial Reporting Standards (IFRS), such as

liquidity (Daske et al. [2008]), foreign mutual fund holdings (e.g., DeFond et al. [2011]),

analysts’ forecasts (Byard, Li, and Yu [2011]), and investment efficiency (Chen, Young, and

Zhuang [2013]). However, little research has directly examined the effects of IFRS adoption

on the market for audit services and auditor competition. For example, we lack knowledge on

whether audit quality increases, decreases, or remains constant from the pre-adoption period

to the post-adoption period. It is important to understand the impacts of accounting regime

changes, such as IFRS adoption, on the audit market, since such changes could affect the

service quality of auditors and thus the quality of financial information supplied to users.

Furthermore, audit firms differ in their expertise in IFRS. It is unclear how the effects of

mandatory IFRS adoption on the quality, fees of audit services as well as markets shares

differ across auditors with varying levels of IFRS expertise. We examine these issues in this

paper because first, institutions and regulatory changes are expected to differentially affect

different auditors (e.g., Choi et al. [2008], Francis and Wang [2008], DeFond and Lennox

[2011]); and second, audit fee changes representing auditor revenue changes that help to

inform the potential wealth redistribution effect among large and small auditors associated

with IFRS adoption (Hail, Leuz, and Wysocki [2010]).

IFRS are principles-based and fair value oriented (e.g., Ahmed, Neel, and Wang

2013). Auditors, who evaluate managers’ compliance with these standards, now have the

difficult task of assessing whether the discretion exercised by managers, including managers’

fair value estimates, are in compliance with IFRS. While the audit quality is likely to be

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negatively affected for all auditors due to the increased audit complexity associated with

IFRS adoption, we argue that such an effect differs for auditors with different levels of

expertise in IFRS. We define IFRS expertise for two levels of audit markets: the overall audit

market, the Big Four audit market where clients employ Big Four auditors both before and

after IFRS adoption, and the non-Big Four audit market where clients employ non-Big Four

auditors both before and after IFRS adoption.

For the overall audit market, we define the Big Four auditors as having higher IFRS

expertise and the non-Big Four auditors as having lower IFRS expertise. Big Four auditors

are relatively more capable than non-Big Four auditors of dealing with the increased audit

complexity brought about by mandatory IFRS adoption for the following reasons. First, Big

Four auditors have a greater capacity to provide high-quality professional judgments. They

are also supported in the judgment process by a worldwide network of branches that tend to

have more experience, and employ more advanced techniques (Carson [2009]). These in turn

help them to obtain higher IFRS expertise more quickly than non-Big Four auditors. Second,

IFRS may be geared towards Big Four audit firms to some extent as they are likely to have

influenced the specific standards by being involved in the standard-setting process (e.g.,

having representatives on the IFRS interpretation committee or writing comment letters).

This gives Big Four auditors a further competitive edge in auditing IFRS-based financial

reports. Third, Big Four auditors may already have experience of IFRS through serving

clients who voluntarily adopted IFRS before 2005. In contrast, non-Big Four auditors lack the

competence in making professional judgments (Carcello, Vanstraelen, and Willenborg

[2009]), and need to expend more effort than Big Four auditors in dealing with the audit

complexity associated with IFRS.

Within the Big Four audit market, IFRS expertise can also differ among the four audit

firms. While Big Four auditors all have worldwide network of branches and all were involved

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in the IFRS standard setting process, they differ in their experience in auditing IFRS financial

reports. Specifically, their markets shares in the voluntary IFRS adopter market are not one-

fourth each. A significantly higher market share than the peers could signify a higher level of

expertise in a market segment such as an industry (Reichelt and Wang [2010]). Our data

reveal that PWC has a 35% market share in the voluntary IFRS adopter market, higher than

the other three. And we define PWC as having higher perceived IFRS expertise among Big

Four auditors. Similarly, Within the non-Big Four audit market, BDO and Grant Thornton

have significantly higher market shares in the voluntary IFRS adopter market. In addition,

these two auditors, like the Big Four auditors, were involved in the IFRS standard setting

process. Therefore, we define BDO and Grant Thornton as having higher IFRS expertise

among non-Big Four auditors.

Auditors with higher IFRS expertise are better prepared to cope with the audit

complexity brought about by mandatory IFRS adoption than those with lower IFRS expertise.

As such, we predict that the audit quality gap between these two types of auditors is greater

after IFRS adoption. Compared with high IFRS expertise audit firms, the lack of IFRS

expertise of low IFRS expertise auditors implies that they need to expend more effort after

IFRS adoption to be competent in auditing IFRS financial reports. To the extent that audit

fees reflect audit efforts, we predict that auditors with lower IFRS expertise increase audit

fees more than those with higher IFRS expertise. Note that this does not necessarily imply

that the former increase profits more than the latter as audit fees are their revenues and we do

not have information in changes in their costs. The increased audit complexity is high in

countries whose pre-adoption local GAAP are very different from IFRS than in countries

where IFRS adoption involves fewer changes in GAAP. Therefore, we further expect that the

above patterns in audit quality and audit fees are limited to or more pronounced in countries

with greater GAAP changes. Relatedly, in countries where IFRS adoption means greater

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changes in GAAP, auditors with higher IFRS expertise are expected to gain market shares

from other auditors because of their relatively superior quality and a smaller increase in audit

fees.

Our sample contains mandatory IFRS adopters in 17 European countries. Using

accrual quality and the issuance of going concern opinions as measures of audit quality, we

find that compared to the pre-adoption period, the audit quality gap between Big Four (i.e.,

higher IFRS expertise) auditors and non-Big Four (i.e., lower IFRS expertise) auditors is

higher after IFRS adoption. Specifically, the audit quality of non-Big Four auditors decreases

from the pre-adoption to the post-adoption period, while that of Big Four auditors does not

change. Therefore, the audit quality gap increases after IFRS adoption. We also find that non-

Big Four auditors increase audit fees more than Big Four auditors. The two findings are more

pronounced in countries where the difference between the pre-adoption domestic accounting

standards and IFRS is larger. We also find that Big Four auditors’ market shares increase in

countries with greater GAAP changes. When we examine IFRS expertise within Big Four

audit firms and within non-Big Four audit firms, our whole sample results hold for the non-

Big Four audit market subsample, but not the Big Four audit market subsample. Overall, our

evidence is consistent with the prediction that the audit quality gap between auditors with

higher IFRS expertise and those with lower IFRS expertise increases after IFRS adoption, the

former increases audit fees less and gains market shares from the latter in countries with

greater GAAP changes. It also suggests that clients view Big Four audit firms as having about

the same level of IFRS expertise but they do view non-Big Four auditors as having different

levels of IFRS expertise.

In additional analysis, we find that our audit quality results are stronger in countries

with strong institutions, but our audit fee result are not affected by the strength of a country’s

legal enforcement. Our results are not likely to have been driven by concurrent non-IFRS

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regulatory changes in Europe (Christensen, Hail, and Leuz [2013]). Our results are also

robust to the difference-in-differences design where we use matched firms in the U.S and

Japan as the control sample.1 Finally, we repeat our analysis for each individual country in

our sample, and find that our results hold in multiple countries and are not driven by the few

largest countries in our sample.

This study contributes to the broad line of research examining the economic

consequences of regulations in general and of mandatory IFRS adoption in particular.

Regulations such as the Sarbanes-Oxley Act are found to have significant impacts on various

aspects of auditing (e.g., DeFond and Lennox [2011]). Despite the mandatory adoption of

IFRS by over 3,000 E.U. firms, there is very limited research on how this change in standards

affects audits. While Kim, Liu, and Zheng [2012] and De George, Ferguson, and Spear [2013]

document higher audit fees after IFRS adoption in the EU and Australia respectively, whether

the increased audit fees merely represent a shock to audit costs or are associated with

improved audit quality remains an open question. This study makes a distinctive contribution

by showing that audit quality is not necessarily uniformly higher or lower after mandatory

IFRS adoption, but depends on how IFRS affect the ability of enforcement by different types

of auditors as they have different level of IFRS expertise. By showing that audit quality and

audit fees for large and small auditors are affected differently by the new accounting

standards, we provide important evidence on the effects of IFRS regulation on audits.

We also contribute to the audit literature relating to auditors expertise. The literature

has argued the industry specialists presumably have a greater knowledge of their client’s

industry and are better able to evaluate the client’s financial statement. Our paper enriches the

literature by documenting the audit specialists can also have greater knowledge of IFRS

1 We do not tabulate the difference-in-differences results because while this approach controls for

contemporaneous changes in the audit market (e.g., the time-series of audit quality gap between difference

auditors) unrelated to IFRS, we note that local (non-Big Four) auditors in the U.S. and Japan may not be

comparable to local auditors in European countries. As such, the control sample may not be a control sample.

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adoption and are better able to utilize the advantage in auditing the mandatory IFRS adopters.

However, our results also indicate that market share, which are commonly used in the audit

industry expertise literature may not always be the best measure of audit expertise, at least in

the Big Four IFRS setting.

In addition, we show that the cross-country institutional features, such as the pre-

adoption difference between local GAAP and IFRS and legal environments, affect the audit

quality and audit fee changes of Big Four and non-Big Four auditors associated with IFRS

adoption. Francis and Wang [2008] cast doubt on the effects of harmonizing auditing and

financial reporting standards around the world. They argue that institutional factors shape

auditors’ behavior around the world, and that mandating uniform audit and accounting

standards alone may not guarantee improved audit quality. Our study provides direct answers

to the question raised by Francis and Wang [2008]. Choi et al. [2008] find that Big Four audit

fee premium is affected by country institutions and their results are opposite to Francis and

Wang [2008]. Therefore, how institutions affect the two types of auditors differently is

unresolved. We add to the audit literature by finding that the increase in audit quality gap

between Big Four auditors and non-Big Four auditors after mandatory IFRS adoption is more

pronounced in countries with strong legal regimes, consistent with Francis and Wang [2008].

Our finding highlights the importance of institutional arrangements, rather than accounting

standards alone, in shaping the outcome of financial reporting and auditing convergence.

2. Hypothesis Development

2.1. IFRS Adoption and Audit Complexity

IFRS are a set of new accounting standards that are more principles-based and fair

value oriented than many of the local accounting standards. We note that IFRS adoption

increases audit complexity. Such increased audit complexity affect the three aspects of the

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audit service market and auditor competition that we investigate: audit quality, audit fees, and

audit market shares.

With the mandatory adoption of IFRS, auditors now have the difficult task of

assessing managers’ compliance with the new accounting standards including judgment on

things like managers’ fair value estimates. Because audit firms have different expertise and

experience with IFRS, the degrees of audit complexity increase differ among them.

Consequently, their audit quality, audit fees, and audit market shares are likely to be affected

differently by mandatory IFRS adoption. Audit quality is likely to be affected less for

auditors that are more knowledgeable on IFRS and have more experience in auditing IFRS-

based financial reports than for other auditors. Auditors with less IFRS expertise need to

expand more effort in auditing IFRS-based financial reports than those with more IFRS

expertise. To the extent that audit fees reflect audit efforts, the audit fee change related to

IFRS adoption likely differs across the two types of auditors. Regarding market shares,

auditors with higher IFRS expertise are likely to gain market shares from other auditors,

especially in countries whose pre-IFRS GAAP is very different from IFRS (i.e., higher audit

complexity).

2.2. IFRS Expertise and Audit Quality

When IFRS adoption is mandated, many technical verification issues, such as fair

value measurements, related party disclosures, intangible asset verifications, and accounting

for retirement benefits, must be addressed to certify IFRS compliance. This change requires

auditors to have a greater ability to deal with principles-based compliance issues and to make

professional judgments. Moreover, IFRS’s introduction of more fair value accounting

requires that auditors master more sophisticated valuation techniques and develop a deeper

understanding of financial markets. Therefore, auditors need to acquire more expertise and

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expend more effort to cope with mandatory adoption of IFRS (Kim, Liu, and Zheng [2012]).

The service quality of different audit firms is affected differentially because they differ in

IFRS knowledge and experience at the time of mandatory IFRS adoption. For example, in

comparison with non-Big Four auditors, Big Four auditors tend to have a greater capacity to

provide high-quality professional judgments, as they have more resources to offer specialist

training to their staff and are supported in the judgment process by a worldwide network of

branches (Vera-Munoz, Ho, and Chow [2006]).2 Dopuch and Simunic [1980] suggest that

auditors in large audit firms are more competent than those in small firms. Large audit firms

are better able to recruit graduates from leading universities, hire reputable and experienced

specialists from the labor market, and offer specialized training to their staff. In contrast,

small local auditors likely have neither the same ability nor the same degree of technical

expertise when engaging in the professional decision-making process.

Furthermore, some audit firms have more experience and a better understanding of

IFRS than others (Hail, Leuz, and Wysocki [2010]). One reason for such differences is that

some auditors are involved in the standard-setting process which could potentially make

IFRS geared towards their expertise and preference. For example, all Big Four audit firms

plus BDO and Grant Thornton have representatives in the IFRS Advisory Council, which is

the formal advisory body to the IASB;3 and all six have issued comment letters on IASB-

proposed standard changes. 4 In contrast, small local auditors do not have significant

involvement in the IFRS standard-setting process. Consequently, they are likely to have a

2 Big Four auditors tend to have specialized audit knowledge stored in centralized databases for use and retrieval

by offices in different countries. Examples are KPMG’s KWorld and Ernst & Young’s Knowledge Web (Vera-

Munoz, Ho, and Chow [2006]). 3 The IFRS Advisory Council is comprised of representatives from user groups, preparers, auditors, analysts,

academics, investor groups, etc. The following link provides the details: http://www.ifrs.org/The-

organisation/Advisory-bodies/IFRS-Advisory-Council/IFRS-Advisory-Council-membership/Pages/IFRS-

Advisory-Council-membership.aspx 4 In addition, Big Four auditors, but not non-Big Four auditors, have representatives in the IFRS Interpretation

Committee. Also, 25% of previous and incumbent IASB board members worked for Big Four firms before they

joined IASB, yet that percentage is zero for non-Big Four auditors. Details are at

http://www.iasplus.com/en/resources/resource3/#omalley

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disadvantage in terms of IFRS expertise.

The differences in IFRS expertise can also result from different levels of experience

in auditing IFRS reporting prior to 2005. For example, the Big Four were more likely to serve

as auditors for European firms that voluntary adopted IFRS before 2005, and the spillovers of

their accumulated knowledge help other offices in IFRS auditing.5 As such, Big Four auditors

have higher IFRS expertise than non-Big Four auditors. However, even within Big Four (or

within non-Big Four) audit firms, the market shares of all voluntary adopters are not divided

evenly. A specific audit firm may audit a higher percentage of those voluntary IFRS adopters

than others. Market shares are viewed as an indication of auditor expertise in a particular

segment of the audit market (e.g., Mayhew and Wilkins [2003]; Reichelt and Wang [2010]).

In our case, a higher market share of the voluntary adopters market before 2005 signifies

higher IFRS expertise. While the market share argument implies that Big Four have more

IFRS knowledge than non-Big Four auditors, it also implies that within Big Four (or within

non-Big Four) audit firms, those with higher markets shares in the voluntary IFRS adopter

market are perceived to have higher IFRS expertise than their peers.

IFRS adoption also increases managers’ flexibility in accounting choices. When

managers are offered more flexibility in the financial reporting process, they may have

greater incentives to use this flexibility to their own advantage. Given the potential for

managers to engage in opportunistic behavior, auditors have to negotiate with them to discuss

their subjective estimates or choices in financial reports. A principles-based system arguably

requires auditors to use their judgment and stand up to their clients to a much greater extent

than they would under a rules-based system (e.g., Lambert [2010]). Ceteris paribus, auditors

that have more specialized expertise in IFRS are more able to judge the appropriateness of

5 We find that 88% of voluntary IFRS adopter firms are audited by Big Four auditors.

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clients’ use of IFRS-allowed flexibility in financial reporting.6

The larger the difference between the pre-adoption local accounting standards and

IFRS, the more complex the auditor’s job is likely to become after IFRS adoption. Our

arguments imply that the larger the difference between local GAAP and IFRS, the larger the

advantage that auditors with IFRS expertise have over other auditors in competence upon the

transition to IFRS. Based on the discussion, we expect the following:

H1: The audit quality difference between auditors with higher and lower IFRS expertise

increases after mandatory IFRS adoption. Such an increase is more pronounced in or limited

to countries with greater GAAP changes.

2.3. IFRS Expertise and Audit Fees

Kim, Liu, and Zheng [2012] argue that two factors predict the change in audit fees

after IFRS adoption in opposite directions. First, if IFRS adoption increases financial

reporting quality, then the likelihood of financial misstatement is lower. Consequently, audit

risk and audit fees would be lower. Alternatively, IFRS require auditors to make more

complex estimates and use greater professional judgment, since IFRS are comprehensive, fair

value oriented, and principles based (KPMG [2007], Deloitte [2008]). IFRS involve more

footnote disclosure, requiring the auditor to certify financial information of a different nature

than was previously reported under domestic GAAP (Webb [2006]). This implies that audit

fees would be higher after IFRS adoption. Empirically, Kim, Liu, and Zheng [2012] and De

George, Ferguson, Spear [2013] find that audit fees are higher after IFRS adoption,

suggesting that the increase in audit complexity dominates the financial reporting quality

factor in affecting audit fees. Furthermore, greater flexibility in managers’ financial reporting

6 Auditor independence is also important factor for audit quality. DeAngelo [1981] argues that auditors’

commitment to independence is positively related to audit firm size because larger auditors have higher quasi-

rents. Our argument implies that larger auditors tend to have higher IFRS expertise. Following DeAngelo

[1981], they also tend to be more independent.

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process allowed under IFRS increases audit risk, in turn also increasing audit fees.

As discussed earlier, auditors with higher IFRS expertise have greater capabilities for

coping with audit complexity than those with lower IFRS expertise. This implies that the

incremental costs incurred by these auditors during the switch from local accounting

standards to IFRS are lower than those for other auditors. Correspondingly, we expect that

auditors with lower IFRS expertise increase fees more than auditors with higher expertise do.

When the difference between local GAAP and IFRS is large, auditors with lower

IFRS expertise have to expend more effort to deal with the increased audit complexity than

when the difference between local GAAP and IFRS is small. Therefore, the higher increase in

audit fees by auditors with lower IFRS expertise in the post-adoption period is more

pronounced when the GAAP difference is larger. In other words, we expect the following:

H2: Audit fees increase more after mandatory IFRS adoption for auditors with lower IFRS

expertise than for auditors with higher IFRS expertise. Such an audit fee change pattern is

more pronounced in or limited to countries with greater GAAP changes.

2.4. IFRS Expertise and Audit Market Shares

To provide a comprehensive examination of the impacts of IFRS adoption on the

audit market and auditor competition, we also investigate market share changes resulting

from auditor switches when transitioning to IFRS. We first note that a larger GAAP change

can reduce the incumbent auditor’s competitive advantage due to its lack of IFRS expertise,

while the impact is much reduced when the transitioning to IFRS results in a smaller GAAP

change. Therefore, we expect auditor switch and thus market share changes to mainly take

place in countries with greater GAAP changes.

Furthermore, if the increased quality gap between auditors with higher IFRS expertise

and those with lower expertise, and that the latter increases audit fees more than the former,

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then auditors with higher IFRS expertise will are expected to gain market shares from other

auditors. We have the following hypothesis:

H3: Clients of auditors with lower IFRS expertise switch to auditors with higher IFRS

expertise. Such a change of market share is limited to countries with greater GAAP changes

We categorize auditors with higher or lower IFRS expertise in three different settings.

The first is based on the whole audit market, where Big Four auditors are perceived to have

higher IFRS expertise and non-Big Four auditors lower IFRS expertise. The second is based

on the audit market within Big Four auditors, where the Big Four auditor(s) with distinctively

higher market shares of voluntary IFRS adopters before 2005 are perceived to have more

IFRS expertise. The third is based on the audit market within non-Big Four auditors, where

again the non-Big Four auditor(s) with significantly higher market shares of voluntary IFRS

adopters are perceived to have more IFRS expertise.

3. Research Design

3.1. Audit Firm IFRS Expertise and Audit Complexity

We use three samples to test our hypotheses. The first sample is the whole audit

market that consists of the Big Four market and the non-Big Four market. In this setting, we

use the Big Four status to define higher IFRS expertise, because they have more network

support and experience with IFRS (e.g., through involvement in standard setting and auditing

voluntary IFRS adopters before 2005) in comparison to non-Big Four auditors as discussed in

Section 2. The second sample is the Big Four audit market subsample consisting of firms that

employ Big Four auditors in both the pre- and post-IFRS periods. The third sample is the

non-Big Four audit market subsample consisting of firms that employ non-Big Four auditors

in both the pre- and the post-IFRS periods. For these two subsamples, we follow the literature

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on industry expertise (Mayhew and Wilkins [2003]; Reichelt and Wang [2010]) and classify

a particular auditor’s level of IFRS expertise according to its market share in the voluntary

IFRS adopter market.

Specifically, in the Big Four audit market for voluntary IFRS adopters, PWC has a 35%

market share in 2004. Ernst & Young and KPMG have a 25% market share each, whereas

Deloitte has a 15% market share. In the non-Big Four market for voluntary IFRS adopters,

BDO has a 39% market share and Grant Thornton has a 37% market share. The third highest

market share for the voluntary adopter market for a non-Big Four auditor is 11% and the

audit firm is Moore Stephens. Following Reichelt and Wang [2010], we use market shares to

define the IFRS expertise in two ways. The first is that an auditor has high IFRS expertise if

the auditor has the largest market share in the voluntary IFRS adopter sample in 2004 and the

different between its market share and the second largest market share is at least 10%. The

second is that an auditor has high IFRS expertise if the auditor has a market share greater

than 30% in the voluntary IFRS adopter sample in 2004. Satisfying both definitions, PWC is

classified as having high IFRS expertise among the Big Four auditors. BDO and Grant

Thornton are classified as the IFRS expertise in among the non-Big Four auditors, again

satisfying both definitions.

Following Kim, Liu, and Zheng [2012], we use the Absence score and Divergence

score from Ding et al. [2007] to construct the audit complexity measure. Ding et al. [2007]

classify the difference between local GAAP and IFRS along two dimensions: the Absence

score is based on the number of accounting rules regarding particular accounting issues that

are missing in the (pre-IFRS) local GAAP, but that are explicitly in IFRS; the Divergence

score is based on the number of accounting rules regarding the same accounting issue that

differ between local GAAP and IFRS. We define an indicator variable, ∆COMPLEX, that

equals one if the change in audit complexity measured by the sum of the Absence score and

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the Divergence score from Ding et al. [2007] is above the sample median, and zero otherwise.

Countries with ∆COMPLEX equal to one have greater GAAP changes after IFRS adoption,

and thus greater increased audit complexity.

3.2. Research Design for Audit Quality

The literature assumes that audit quality can be inferred by examining clients’

earnings properties and implied earnings management behavior (e.g., Becker et al. [1998]).

Following prior audit research, we use the following audit quality measures: (1) accrual

quality developed by Dechow and Dichev [2002] (e.g., Francis and Yu 2009);7 and (2) going

concern opinions (e.g., Francis and Yu 2009). Specifically, to test H1, we estimate the

following three models, each using a different audit quality measure.8 The first regression

model uses accrual quality to measure audit quality:

AQ = α0 + α1POST + α2EXP + α3POST*EXP + ∑βkCONTVk + Country Effects + Industry

Effects + ε (1)

where AQ is the accrual quality measure based on Dechow and Dichev [2002]. AQ for year t

is the standard deviation of the residuals for a firm calculated over the past five years (i.e.,

year t-4 through year t). 9 Consistent with prior studies (e.g., Francis et al. [2005]), we run the

Dechow and Dichev [2002] model for each two-digit SIC industry-year group with at least 20

observations in the group. 10 POST is an indicator variable that equals one for the post-IFRS

period (2005/2006–2009), and zero for the pre-IFRS period (2000–2004/2005). The adoption

year is labeled 2005 for firms with a December fiscal year end, and 2006 for firms with a

7 Using discretionary accruals and small positive net income to proxy for audit quality does not alter our

inferences. 8 For notational simplicity, we omit the firm-year subscripts i,t in the equations and tables. 9 We only use AQ values in 2004 or 2005 and in 2009 to avoid estimating AQ with numbers generated with both

local accounting standards and IFRS (e.g., AQ for 2007 involves accrual residuals for years 2003–2007). 10 In robustness tests, we also use the Fama and French’s [1997] industry classification. The inferences remain

unchanged.

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non-December fiscal year end. EXP is an indicator variable that equals one if the firm is

audited an auditor with higher IFRS expertise, and zero if it audited by an auditor with lower

IFRS expertise. 11 In the full sample consisting of both the Big Four audit market and the non-

Big Four audit market, EXP equals one if the auditor is a Big Four auditor, and zero

otherwise. In the Big Four audit market subsample, EXP equals one if the auditor is PWC,

and zero otherwise. In the non-Big Four subsample, EXP equals one is the auditor is BDO or

Grant Thornton, and zero otherwise. Since higher standard deviations of residual accruals

imply lower audit quality, we predicts that α3 is negative.

In addition to country and industry effects, we include five control variables (CONTVk)

following Dechow and Dichev [2002] and Francis et al. [2005]. TA is the natural logarithm of

year-end total assets in millions of U.S. dollars. OC is the natural logarithm of a firm’s

operating cycle, where operating cycle equals the sum of turnover days for accounts

receivables and inventories. NEGEARN is the incidence of negative earnings over the past ten

years. (CFO) is the standard deviation of a firm’s cash flows from operations, calculated

over the past ten years.(SALE) is the standard deviation of a firm’s sales, calculated over

the past ten years. Consistent with Francis et al. [2005], we require at least five observations

in each rolling 10-year window to calculate (CFO) ands (SALE).

Our second measure of audit quality is the likelihood of issuing first-time going

concern opinions for financially distressed firms.12 We run the following logistic regression.

Logit(Pr(GC=1)) = α0 + α1POST +α2EXP + α3POST*BIG4 + ∑βkCONTVk + Country

Effects + Industry Effects + ε (2)

where GC is an indicator variable that equals one if the firm receives a first-time going

concern opinion, and zero otherwise. The going-concern model follows Francis and Yu

11 For French firms audited by two auditors simultaneously, we define the EXP indicator as equal to one if one

of the auditors is a Big Four auditor, and zero otherwise. This applies to our whole sample tests and the within

Big Four sample tests. 12 Following Francis and Yu [2009], we define a firm-year observation as financially distressed if the firm has a

negative net income or negative operating cash flows in the year.

17

[2009]. In addition to country and industry effects, we include the following control variables,

as in Francis and Yu [2009]. TA is the natural logarithm of total assets. AGE is the natural

logarithm of the number of years that the client firm appears in Worldscope. LEV is the year-

end total liabilities divided by year-end total assets. LOSS is an indicator variable that equals

one for observations with annual net income less than 0, and zero otherwise. CLEV is the

change of LEV. RET is the firm’s cumulative stock return over the current year. CFO is the

operating cash flow scaled by total assets for the current year. FINANCE is an indicator

variable that equals one if the client has a new issuance of equity or debt over the subsequent

fiscal year (i.e., positive EISSUE or positive DISSUE), and zero otherwise. ZSCORE is

Altman’s [1968] Z-score. VOL is the standard deviation of monthly stock returns over the

current year. INVEST is cash and cash equivalents scaled by total assets. We predict that α3 is

positive.

We also add the three-way interaction term POST*EXP*∆COMPLEX, as well as the

relevant two-way interaction terms and main effect involving ∆COMPLEX to equations (1)

and (2). We call these equations augmented equations (1) to (2). Our hypothesis predicts that

the coefficient on POST*EXP*∆COMPLEX is negative in augmented equations (1) and

positive in augmented equation (2).

3.3. Research Design for Audit Fees

To conduct the audit fee test, we run the following regression:

AUDFEE = α0 + α1POST + α2EXP + α3POST*EXP + ∑βkCONTVk + Country Effects +

Industry Effects + ε (3)

where AUDITFEE is the natural logarithm of audit fees in thousands of U.S. dollars. In

addition to country and industry effects, the following control variables are included, as in

Kim, Liu, and Zheng [2012]. TA is the natural logarithm of total assets. INVREC is the sum

of inventories and receivables divided by total assets. NBS is the natural logarithm of 1 plus

18

the number of business segments. NGS is the natural logarithm of 1 plus the number of

geographical segments. LEV is the year-end total liabilities divided by year-end total assets.

LOSS is an indicator variable that equals one for observations with annual net income less

than 0, and zero otherwise. QUICK is the quick ratio, equal to quick assets divided by current

liabilities. MTB is the ratio of the firm’s market value to the book value of its common equity.

SPECIAL is an indicator variable that equals one if the firm reports special items, and zero

otherwise. QUALIFIED is an indicator variable that equals one if the firm receives qualified

opinion, and zero otherwise. MERGE is an indicator variable that equals one if the firm is

engaged in a merger or acquisition, and zero otherwise. FINANCE is an indicator variable

that equals one if the client has a new issuance of equity or debt over the subsequent fiscal

year (i.e., positive EISSUE or positive DISSUE), and zero otherwise. XLIST is an indicator

variable that equals one if the firm is cross listed on any U.S. stock exchanges, and zero

otherwise. We predict that α3 is negative.

We add the three-way interaction term POST*EXP*∆COMPLEX, as well as the

relevant two-way interaction terms and main effect involving ∆COMPLEX to equation (3)

and call it augmented equation (3). Our hypothesis H2 predicts that the coefficient on POST*

EXP*∆COMPLEX is negative in augmented equation (3).

3.4. Research Design for Market Shares

To test the market share changes around IFRS, we run the following regression:

EXP = α0 + α1POST + α2POST*ΔCOMPLEX + ∑βkCONTVk + Country Effects + Industry

Effects + ε (4)

It is worth mentioning that in our market share test we require that the firm is present

in all the five years between 2003 and 2007 so that we can measure whether auditors lose

existing clients to each other. The dependent variable is the EXP indicator variable. POST is

19

an indicator variable that equals one for observations in the adoption period (2005/2006–

2007), and zero for the pre-adoption period (2003–2004/2005). Following the literature (e.g.,

Landsman, Nelson and Rountree [2009]), we include the following control variables. EMV is

the natural logarithm of year-end market value of equity in millions of U.S. dollars. TURN is

the sales divided by year-end total assets. LEV is the year-end total liabilities divided by year-

end total assets. LOSS is an indicator variable that equals one for observations with annual net

income less than 0, and zero otherwise. MTB is the ratio of the firm’s market value to the

book value of its common equity. FINANCE is an indicator variable that equals one if the

client has a new issuance of equity or debt over the subsequent fiscal year (i.e., positive

EISSUE or positive DISSUE), and zero otherwise. MERGE is an indicator variable that

equals one if the firm is engaged in a merger or acquisition, and zero otherwise. ROA is the

net income divided by total assets. XLIST is an indicator variable that equals one if the firm is

cross listed on any U.S. stock exchanges, and zero otherwise. GROWTH is the annual

percentage change in sales. ANALYST is the natural logarithm of 1 plus the number of

analysts following the firm (from I/B/E/S). NEWLISTING is an indicator variable that equals

one for firm-year observations of firms that were first listed in the years 2000–2003, and zero

otherwise. Our hypothesis H3 predicts that α2 is positive.

Throughout the paper, we report test statistics based on robust standard errors

clustered at the firm level.13

4. Sample and Empirical Results

4.1. Sample and Descriptive Statistics

We obtain an initial sample of 24,112 firm-year observations from Worldscope. It

contains all mandatory IFRS adopters in the 17 European countries of Austria, Belgium,

13 The inferences do not change if we use standard errors clustered by country.

20

Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands,

Norway, Portugal, Spain, Sweden, Switzerland, and the United Kingdom (U.K.). The sample

covers the period 2000–2009. We exclude firms in the banking, insurance, or other financial

services industries. We further require firms to have non-missing values on our test and

control variables. Because of missing values associated with different variables, the samples

used in different regressions differ. The final treatment sample contains 3,268 firm-year

observations for the accrual quality test, 3,662 firm-year observations for the going concern

test, and 10,514 firm-year observations for the audit fee test and 9,380 firm-year observations

for market share test.14

Worldscope only provides the identities of incumbent auditors for the most recent

year (i.e., “current year”), and does not have historical audit firm identities. To get accurate

information on which auditors a firm employed in each year in the past, we use hand-

collected audit firm identities for each firm in each year.15 To mitigate the undue influence of

outliers, all the continuous variables are winsorized at the 1st and 99th percentiles.

[Insert Table 1 about here]

Table 1 provides the summary of sample observations by country. Most of the IFRS

mandatory adopters are from U.K.. Table 2 provides univariate tests for dependent variables

used in our regression analysis. We report the mean value of difference of variables between

IFRS expertise and non IFRS expertise, for the pre-adoption period (pre-IFRS) and post-

adoption period (post-IFRS) in our three different settings.

14 Untabulated results show that our findings remain unchanged if we require a common sample in all the four

tests. 15 We thank Luzi Hail for sharing his auditor identity dataset with us. We supplemented that dataset with our

own hand collection of audit firm identities.

21

Panel A of Table 2 shows the univarite test for our full sample where we compare the

difference of audit quality, audit fee and market share between Big Four auditor and non-Big

Four auditors. Column (1) in Panel A shows the audit quality measured by clients’ standard

deviation of accrual residuals (i.e., the Dechow and Dichev [2002] model) is higher for Big

Four auditors than non-Big Four auditors in our sample and in both the pre-IFRS period and

post-IFRS period as indicated by the significantly negative values in difference. However, the

audit quality gap increases significantly from the pre-IFRS period to the post-IFRS period

(the gap widens from -0.018 to -0.045). Untabulated t test indicates that the Big Four audit

quality does not change from pre-IFRS period to post-IFRS period, but the non-Big Four

audit quality significantly decreases from the pre-IFRS period (0.057) to the post-IFRS

period (0.081) with a p-value of 0.00.

Using clients’ likelihood of receiving a first-time going concern audit opinion as a

measure of audit quality, Column (2) shows again that the audit quality gap between Big Four

and non-Big Four auditors increases significantly in the post-IFRS period. In particular, non-

Big Four auditors reduce the likelihood of issuing first-time going concern audit opinion

significantly after IFRS adoption. In summary, the univariate statistics in Column (1) and

Column (2) suggest increased audit quality gap between Big Four and non-Big Four auditors

for IFRS mandatory adopters in the post-adoption period and it is mainly due to the worsened

audit quality provided by non-Big Four auditors.

Column (3) of Panel A displays the audit fee pattern. It is easy to identify that the

audit fee gap between Big Four and non-Big Four auditors becomes smaller in the post-IFRS

period for the treatment firms (with a decrease of 0.111).

Column (4) of Panel A shows the Big Four auditors’ market share across the pre-IFRS

period and post-IFRS period. It is found that Big Four auditors’ market share doesn’t change

significantly in our event period.

22

Panel B of Table 2 displays the comparison of difference in audit quality, audit fee

and market share between PWC and other three Big Four auditors for our Big Four auditor

sample. In fact, we don’t find any significant change for the difference in the Pre and Post

IFRS period.

Panel C of Table 2 displays the comparison of difference in audit quality, audit fee

and market share between BDO/Grant Thornton and other non-Big Four auditors for our non-

Big Four auditor sample. We find the audit quality gap becomes widen after IFRS adoption

and the audit fee gap has the similar pattern. The last but not least, we also find the market

share of BDO/Grant Thornton increase significantly for our non-Big Four Sample.

[Insert Table 2 about here]

4.2. Audit Quality Test

Table 3 reports results for testing H1, which is concerned with the differential impacts

on audit quality of IFRS expertise and non-IFRS expertise brought about by mandatory IFRS

adoption. Panel A shows the results when Big Four auditor is used to proxy for IFRS

expertise in our full sample. As shown in column (1) of Panel A, the coefficient on POST is

positive and significant (coefficient = 0.025, t = 2.20), suggesting that the audit quality of

non-Big Four auditors in our mandatory IFRS adopters decreases from the pre-adoption to

the post-adoption period. Our interested coefficient, the coefficient on POST *BIG4 is

negative and significant (coefficient = -0.027, t = -2.34), suggesting that Big Four auditors’

audit quality increases relative to non-Big Four auditors in the post-adoption period. The

combination of coefficients POST+POST*BIG4 is insignificant (p-value = 0.555), indicating

that Big Four audit quality of mandatory IFRS adopters does not change from the pre-

adoption to the post-adoption period. In summary: first, the audit quality of non-Big Four

23

auditors in our sample deteriorates after IFRS adoption; second, the audit quality of Big Four

auditors remains unchanged from the pre-adoption to the post-adoption period; and third,

after IFRS adoption, the audit quality gap between Big Four and non-Big Four auditors

increases, consistent with H1.

Column (2) of Panel A reports the test result when the three-way interaction term

POST* BIG4*∆COMPLEX as well as the relevant two-way interaction terms are added into

equations (1). Our interested coefficient, the coefficient on POST*BIG4*∆COMPLEX is

negative and significant at the 0.05 level (coefficient = -0.015, t =-2.25), and the coefficient

on POST *BIG4 is still negative and significant at the 0.10 level (coefficient = -0.020, t =-

1.74). These results indicate that in the countries with greater IFRS changes, the increased

audit quality gap between Big Four and non-Big Four auditors becomes larger, which is also

consistent with our H1.

Column (3) of Panel A presents the test result using the likelihood of issuing a first

time going-concern opinion as a measure of audit quality. The coefficients on POST is

negatively significant, suggesting that non-Big Four auditors are less likely to issue first time

going-concern audit opinions in the post IFRS adoption period. Our interested coefficient, the

coefficient on POST*BIG4 is positive and significant at the 0.05 level (coefficient = 0.419, χ2

= 4.35), suggesting that Big Four auditors are more likely to issue first time going-concern

audit opinions to their clients in the post IFRS period compared to their non-Big Four

counterparts. The combination of coefficients POST+POST*EXP is insignificant (p-value =

0.716), indicating that Big Four auditors’ tendency of issuing going-concern audit opinion

doesn’t change from the pre-adoption to the post-adoption period. To summarize, the audit

quality of non-Big Four auditors decreases in the post-adoption period whereas the audit

quality of Big Four auditors remain unchanged. Therefore, the audit quality gap between Big

Four and non-Big Four auditors becomes larger as a result of IFRS adoption.

24

Column (4) of Panel A reports the test result when the three-way interaction term

POST* BIG4*∆COMPLEX as well as the relevant two-way interaction terms are added into

equations (2). Our interested coefficient, the coefficient on POST*BIG4*∆COMPLEX is

positive and significant at the 0.05 level (coefficient = 0.128, χ2 = 4.20), and the coefficient

on POST *BIG4 is still positive and significant at the 0.10 level. These results indicate that in

the countries with greater IFRS related changes, the increased audit quality gap between Big

Four and non-Big Four auditors becomes larger, which is consistent with our H1.

Panel B of Table 3 reports the results for audit quality test in the Big Four sample only.

In this sample, PWC is defined as IFRS expertise. Our interested coefficients, the coefficients

of POST*EXP in column (1) and column (3) are found to be insignificant, suggesting that

PWC as the IFRS expertise in Big Four auditors, doesn’t have any role in the audit quality of

mandatory IFRS adopters. Similarly, the coefficients of POST*EXP*∆COMPLEX in column

(2) and column (4) are also found to be insignificant, suggesting the IFRS expertise in Big

Four auditors doesn’t play any role in audit quality in the countries with greater changes of

IFRS.

Panel C of Table 3 reports the results for audit quality test in the non-Big Four sample

only. In this sample, BDO and Grant Thornton is defined as IFRS expertise. Our interested

coefficients, the coefficient of POST*EXP in column (1) is found to be significantly negative,

suggesting that BDO and Grant Thornton as the IFRS expertise in non-Big Four auditors,

provide better audit quality in terms of accrual quality to the mandatory IFRS adopters in the

post IFRS period. Similarly, the coefficient of POST*EXP in column (3) is found to be

significantly positive, indicating the IFRS expertise in non-Big Four auditor group provide

better audit quality in terms of issuing more first time going-concern audit opinion to the

mandatory IFRS adopters in the post IFRS period. Turning to the coefficients of

POST*EXP*∆COMPLEX in column (2) and column (4), they are also found to be

25

significantly negative and positive respectively, suggesting the audit quality gap between

IFRS expertise in non-Big Four auditors and other non-Big Four auditors in the post IFRS

period are more pronounced in the countries with greater IFRS changes.

[Insert Table 3 about here]

Taken together, using two different measures of audit quality, our empirical tests

show that after IFRS adoption, the audit quality gap between IFRS expertise and non-IFRS

expertise increases for mandatory IFRS adopting firms and these enlarged gap is more

pronounced in the countries where the adoption of IFRS brings more changes when we define

IFRS expertise as Big Four auditors in the full sample and BDO/Grant Thornton in the non-

Big Four sample. However, we don’t find any significant difference for the IFRS expertise in

affecting audit quality for the Big Four sample.

4.3. Audit Fee Test

Table 4 reports the result of the audit fee test. Panel A shows the results when Big

Four auditor is used to proxy for IFRS expertise in our full sample. The coefficient on POST

in Column (1) of Panel A is positive and significant (coefficient = 0.416, t = 3.32), indicating

that, non-Big Four audit fees increase after IFRS adoption. We are concerned with whether

audit fee changes for Big Four and non-Big Four auditors differ systematically. The

coefficient on POST*EXP is negative and significant (coefficient = -0.112, t = -2.06),

suggesting that Big Four auditors do not increase audit fees as much as non-Big Four auditors

after IFRS adoption. The combination of coefficients, POST+POST*EXP, is also significant

at the 0.05 level, suggesting the Big Four auditors also increase their audit fees significantly

for the mandatory IFRS adopters. As a result, the Big Four audit fee premium decreases in

26

the post-adoption period, consistent with H2.16 Column (2) shows the interaction effect of

audit complexity is added into the model. Our interested coefficient, the coefficient on

POST*BIG4*∆COMPLEX is negative and significant at the 0.05 level (coefficient = -0.134,

t= -2.06), suggesting that in the countries with greater IFRS related changes, the decreased

audit fee gap between Big Four and non-Big Four auditors becomes more pronounced, which

is consistent with our H2.

Panel B presents the results for audit fee test when we focus on the Big Four sample

only and when PWC is defined as the IFRS expertise. Again, the coefficient of POST*EXP in

column (1) and the coefficient of POST*EXP*∆COMPLEX in column (2) are found to be

insignificant, indicating that the IFRS expertise in Big Four auditors doesn’t charge any

premium or discount compared to other Big Four auditors for the IFRS adoption on the

mandatory IFRS adopters.

Results of audit fee test for non-Big Four sample are shown in Panel C of Table 4. The

coefficient of POST*EXP in column (1) and the coefficient of POST*EXP*∆COMPLEX in

column (2) are found to be significantly negative, suggesting that the IFRS expertise in non-

Big Four auditors increases less audit fees on the mandatory IFRS adopters compared to other

non-Big Four auditors after IFRS adoption and this result is more pronounced in the countries

with more complicated IFRS changes.

[Insert Table 4 about here]

16 We also conduct the analysis to check whether the decreased audit fee gap is a one-time change happened in

the transition year of 2005/2006 or it’s a constant change. Untabulated results indicate the decreased audit fee

gap is constant in the post IFRS period and it is still significant until 2009.

27

4.4 Market Share Test

Table 5 presents the logistic regression results for our market share test. Panel A

presents the results for our full sample and our dependent variable for IFRS expertise equals

one if auditors are Big Four auditors and zero otherwise. The coefficient on POST is

insignificant in column (1) of Panel A. This indicates that overall market shares of Big Four

and non-Big Four auditors do not change during the transition to IFRS. The coefficient on

POST is negative and significant in column (2) (coefficient = -0.273, χ2 = 4.19), suggesting

that in countries where the GAAP difference between (pre-adoption) domestic standards and

IFRS is smaller, firms are likely to switch down from Big Four auditors to non-Big Four

auditors. The coefficient on POST*∆COMPLEX in column (2) is positive and significant

(coefficient = 0.395, χ2 = 4.38). The untabulated Wald test on POST +POST*∆COMPLEX

indicates that the sum of the coefficient is statistically significant with a p-value of 0.050.

This suggests that Big Four auditors’ market share increases in countries with greater GAAP

changes compared to the control sample. Even though non-Big Four auditors gain market

share from Big Four auditors in countries with fewer GAAP changes (mostly in the U.K.),

they lose market share to Big Four auditors in countries with greater GAAP changes. As a

result, the overall market share of Big Four auditors does not change significantly, as

evidenced by the insignificant coefficient estimate on POST in the regression results of

column (1). Overall, this result is consistent with our H3.

Panel B displays the results of market share test for our Big Four sample only. The

dependent variable EXP equals to one if auditors are PWC and zero for the other three Big

Four auditors. Consistent with the previous results, we find insignificant for the coefficient of

POST and the coefficient of POST*∆COMPLEX, suggesting the IFRS expertise in the Big

Four auditors doesn’t gain or lose market shares in the mandatory IFRS adopters.

Panel C displays the results of market share test for our non-Big Four sample. The

28

dependent variable EXP equals to one if auditors are BDO/Grant Thornton and zero for the

other non-Big Four auditors. The coefficient of POST in column (1) and the coefficient of

POST*∆COMPLEX in column (2) are found to be significantly positive, meaning that the

IFRS expertise in the non-Big Four auditors gains market share from their non-Big Four

peers in the post IFRS period and this result is more pronounced in the countries with greater

IFRS changes.

[Insert Table 5 about here]

5. Additional Analysis

5.1. Difference in Difference Test

We also use difference-in-difference approach to rerun all the tests. In particular, we

use one-to-one matched firms based on firms’ size and Book-to-Market ratio from Japan and

the United States (U.S.), which do not mandate IFRS, to form the control sample. The

untabulated results show that all our findings remain qualitatively unchanged.

5.2. The Impact of Country Enforcement Institutions

Country-level institutions can affect audits. For example, when IFRS fair value rules

are introduced in a jurisdiction where the factor market is less developed, it is difficult all

auditors to establish whether values provided by managers are fair, as the market for the

underlying asset may be illiquid or nonexistent. Following the literature, countries are viewed

as having strong institutions if they have greater investor protection and effective legal

enforcement (e.g., La Porta et al. [1998]).

We define an indicator variable, ENFORCE, which equals one if the enforcement

score calculated based on La Porta et al. [1998] is above the sample median, and zero

otherwise. Countries that have ENFORCE equal to one are deemed to have strict legal

29

enforcement and strong institutions. We include the three-way interaction term POST*

EXP*ENFORCE, as well as the relevant two-way interaction terms and main effect involving

ENFORCE in equations (1) to (4).

Table 6 reports the results. Panel A shows the results for our full sample. We find that

the coefficient on POST*EXP*ENFORCE is negative and significant in column (1) of Panel

A, positive and significant in column (2), and insignificant in column (3) and column (4).

These results suggest that the increase in audit quality gap between Big Four and non-Big

Four auditors after IFRS adoption are more pronounced in countries with stronger institutions.

This appears to be consistent with the argument in Francis and Wang [2008] whereby Big

Four auditors have more reputation capital at risk than non-Big Four auditors given the higher

litigation risk in countries with stronger legal enforcement. As far as audit fee is concerned,

the result in column (3) of Table 8 suggests that country institutions have no impact on the

decrease in Big Four audit fee premium after IFRS adoption, as the coefficient on

POST*EXP*ENFORCE is statistically insignificant. For the market share test, we don’t find

the country institutions have any effects in Big Four auditors’ market share change around

IFRS adoption either. Panel B reports the results when the Big Four auditor sample is used.

Again, we don’t find any significant results for our interested three-way interaction. Panel C

reports the results for the non-Big Four sample. We find weak results (significance is in 10%

level) for the audit quality test and market share test, suggesting the IFRS expertise play a

more important role in increase audit quality and decreasing audit fee gap in the countries

with strong institutional factors in the non-Big four sample.

[Insert Table 6 about here]

30

5.3. Concurrent Regulatory Enforcement Changes

Christensen, Hail, and Leuz [2013] find that across all countries mandatory IFRS

reporting has little impact on liquidity, and the liquidity effects around IFRS adoption are

concentrated in the five E.U. countries that concurrently made substantive changes in

reporting enforcement: Finland, Germany, the Netherlands, Norway, and the U.K. 17 We

perform analyses to test whether our audit results are driven by concurrent regulatory changes

in Europe instead of IFRS adoption. In particular, we repeat all of our tests after deleting

observations from the above-mentioned five countries. Untabulated results from both

analyses indicate that the inferences drawn from Table 3 through Table 6 do not change.

5.3. Within-Country Analysis

We repeat our analysis within each individual country in our sample. The first reason

for conducting this additional analysis is that UK firms account for almost 50% of the

observations and we would like to know whether our results hold after omitting the UK.

Country-level analysis will inform whether our results are driven by a few large countries in

the sample. The second reason is that it is possible that in some countries the institutions and

incentives in place result in less difference between qualities/fees of large and small auditors

(such as between Big Four and non-Big Four auditors). This will shed some light on the

sources of “big auditor advantage”. We find that our results on audit quality and audit fee are

significant in Austria, UK, Germany, France, Spain, Switzerland and Netherlands. Tests on

Market share is 10% significant in Austria, Germany, France, Greece, Italy and Switzerland.

Our results imply that it is not a single country or a few countries that drive our findings.

17 Another reason to exclude U.K. is that more than 40% of our sample is from U.K. and we want to make sure

our results are not driven by one particular country.

31

6. Conclusion

We argue that auditor with higher IFRS expertise (such as being a Big Four auditor or

having more prior experience in auditing IFRS financial reports of voluntary IFRS adopters)

and those with lower IFRS expertise are affected differently by mandatory IFRS adoptions.

Auditors with higher IFRS expertise have advantages over other auditors in terms of

competence when dealing with the audit complexity associated with principles-based and fair

value-oriented accounting standards, such as IFRS.

In the overall audit market, we find that the audit quality of lower IFRS expertise (i.e.,

non-Big Four) auditors declines after IFRS adoption, while that of higher IFRS expertise (i.e.,

Big Four) auditors does not change from the pre-adoption period to the post-adoption period.

Therefore, the audit quality gap between these auditors increases after IFRS adoption. We

also find that lower IFRS expertise (i.e., non-Big Four) auditors increase audit fees more than

higher IFRS expertise (i.e., Big Four) auditors after IFRS adoption, lowering the Big Four

audit fee premium in the post-adoption period. In addition, we find Big Four auditors gain

market shares from non-Big Four auditors after IFRS adoption in countries with greater

GAAP changes resulting from IFRS adoption. Within the Big Four audit market, we do not

find any of the above patterns in audit quality, fees, and auditor switch. This suggests that

clients do not perceive Big Four auditors as having different levels of IFRS expertise. Within

the non-Big Four audit market, we find the same results as in the overall audit market.

Specifically, the audit quality gap between higher IFRS expertise auditors (i.e., BDO and

Grant Thornton) and lower IFRS expertise (i.e., other non-Big Four auditors) increase after

IFRS adoption. The lower IFRS expertise non-Big Four auditors increase audit fees more

than the higher IFRS expertise auditors. And that BDO and Grant Thornton gain market

32

shares from other non-Big Four auditors in countries with greater GAAP changes after IFRS

adoption.

Our study provides evidence on the differential impacts that changes in accounting

standards have on audits, highlighting the importance of audit expertise and enhancing our

understanding of how regulations on accounting standards affect accounting service providers.

33

References

AHMED, S., M. NEEL, and D. WANG. ‘Does Mandatory Adoption of IFRS improve

Accounting Quality? Preliminary Evidence.’ Contemporary Accounting Research

30 (2013): 1344-1372.

ALTMAN, E. ‘Financial Ratios, Discriminant Analysis and the Prediction of Corporate

Bankruptcy.’ Journal of Finance 23 (1968): 589-609.

BECKER, C. L., M. L. DEFOND, J. JIAMBALVO, and K. R. SUBRAMANYAM. ‘The

Effect of Audit Quality on Earnings Management.’ Contemporary Accounting

Research 15 (1998):1-24.

BYARD, D., Y. LI, and Y. YU. ‘The Effect of Mandatory IFRS Adoption on Financial

Analysts’ Information Environment.’ Journal of Accounting Research 49 (2011):

69-96.

CARCELLO, J. V., A. VANSTRAELEN, and M. WILLENBORG. ‘Rules Rather than

Discretion in Audit Standards: Going-Concern Opinions in Belgium.’ The

Accounting Review 84 (2009): 1395-1428.

CARSON, E. ‘Industry Specialization by Global Audit Firm Networks.’ The Accounting

Review 84 (2009): 355-382.

CHEN, C., D. YOUNG, and Z. ZHUANG. ‘Externalities of Mandatory IFRS Adoption:

Evidence from Cross-Border Spillover Effects of Financial Information on

Investment Efficiency.’ The Accounting Review 88 (2013) 881-914.

CHOI, J., J. B. KIM, X. H. LIU, and D. SIMUNIC. ‘Audit Pricing, Legal Liability

Regimes, and Big 4 Premiums: Theory and Cross-Country Evidence.’

Contemporary Accounting Research 25 (2008): 55-99.

CHRISTENSEN, H., L. HAIL, and C. LEUZ. ‘Mandatory IFRS Reporting and Changes

in Enforcement.’ Journal of Accounting and Economics 56 (2013): 147-177.

DASKE, H., L. HAIL, C. LEUZ, and R. VERDI. ‘Mandatory IFRS Reporting around the

World: Early Evidence on the Economic Consequences.’ Journal of Accounting

Research 46 (2008): 1085-1142.

DE GEORGE, E., C. FERGUSON, and N. SPEAR. ‘How Much Does IFRS Cost? IFRS

Adoption and Audit Fees.’ The Accounting Review 88 (2013): 429-462.

DEANGELO, L. ‘Auditor Size and Audit Quality.’ Journal of Accounting and Economics

3 (1981): 183-199.

DECHOW, P., and I. DICHEV. ‘The Quality of Accruals and Earnings: The Role of

Accrual Estimation Errors.’ The Accounting Review 77 (2002): 35-59.

DEFOND, M., X. HU, M. HUNG, and S. LI. ‘The Impact of IFRS Adoption on Foreign

Mutual Fund Ownership: The Role of Comparability.’ Journal of Accounting and

Economics 51 (2011): 240-258.

DEFOND, M., and C. LENNOX. ‘The Effect of SOX on Small Auditor Exits and Audit

34

Quality.’ Journal of Accounting and Economics 52 (2011): 21-40.

DELOITTE. 2008 IFRS Survey: Where Are We Today? London: Deloitte & Touche LLP,

2008.

DING, Y., O.-K. HOPE, T. JEANJEAN, and H. STOLOWY. ‘Differences between

Domestic Accounting Standards and IAS: Measurement, Determinants and

Implications.’ Journal of Accounting and Public Policy 26 (2007): 1-38.

DOPUCH, N., and D. SIMUNIC. ‘The Nature of Competition in the Auditing Profession:

A Descriptive and Normative View,’ in Regulation and the Accounting Profession,

edited by W. Buckley and P. Weston. Belmont, CA: Lifetime Learning

Publications, 1980: 77-94.

FAMA, E., and K. FRENCH. ‘Industry Costs of Equity.’ Journal of Financial Economics

43 (1997): 153-194.

FRANCIS, J., R. LAFOND, P. OLSSON, and K. SCHIPPER. ‘The Market Pricing of

Accruals Quality.’ Journal of Accounting and Economics 39 (2005): 295-327.

FRANCIS, J., and D. WANG. ‘The Joint Effect of Investor Protection and Big 4 Audits

on Earnings Quality around the World.’ Contemporary Accounting Research 25

(2008): 157-191.

FRANCIS, J. and M. YU. ‘The Effect of Big Four Office Size on Audit Quality.’ The

Accounting Review 84 (2009): 1521-1552.

HAIL, L., C. LEUZ, and P. WYSOCKI. ‘Global Accounting Convergence and the

Potential Adoption of IFRS by the U.S. (Part I): Conceptual Underpinnings and

Economic Analysis.’ Accounting Horizons 24 (2010): 355-394.

KIM, J., X. LIU, and L. ZHENG. ‘The Impact of Mandatory IFRS Adoption on Audit

Fees: Theory and Evidence.’ The Accounting Review 87 (2012): 2061-2094.

KPMG. International Financial Reporting Standards: The Quest for a Global Language.

London: KPMG LLP (U.K.), 2007.

LA PORTA, R., F. LOPEZ-DE-SILANES, A. SHLEIFER, and R. VISHNY. ‘Law and

Finance.’ Journal of Political Economy 106 (1998): 1113-1155.

LAMBERT, R. ‘Discussion of “Implications for GAAP from an Analysis of Positive

Research in Accounting”.’ Journal of Accounting and Economics 50 (2010): 287-

295.

LANDSMAN W., K. NELSON, and B. ROUNTREE. ‘Auditor switches in the pre- and

post-Enron eras: risk or realignment?’ The Accounting Review 84 (2010): 531–

558.

MAYHEW, B. W., AND M. S. WILKINS. ‘Audit Firm Industry Specialization as a

Differentiation Strategy: Evidence from Fees Charged to Firms Going Public.’

Auditing: A Journal of Practice and Theory 22 (2003): 33–52.

REICHELT,K., and D.WANG. ‘National and Office-Specific Measures of Auditor

Industry Expertise and Effects on Audit Quality.’ Journal of Accounting Research

35

48 (2010): 647-686.

VERA-MUNOZ, S., J. HO, and C. CHOW. ‘Enhancing Knowledge Sharing in Public

Accounting Firms.’ Accounting Horizons 20 (2006): 133-155.

WEBB, R. ‘Brace Yourselves: IFRS will be Bumpy.’ Financial Review 25 (2006): 26.

36

Table 1 Sample Distribution

Country No. of Obs. in

Accrual Quality

Analysis

No. of Obs. in

Going Concern

Analysis

No. of Obs. in

Audit Fee

Analysis

No. of Obs. in

Market Share

Analysis

Treatment Sample

Austria 4 4 3 10

Belgium 54 38 117 105

Denmark 194 152 530 535

Finland 94 80 309 240

France 480 391 1,342 1045

Germany 270 186 334 930

Greece 38 30 20 65

Ireland 28 24 206 45

Italy 246 205 533 780

Luxembourg 2 2 0 5

Netherlands 100 44 166 210

Norway 70 119 368 180

Portugal 36 28 119 85

Spain 12 188 589 35

Sweden 188 284 920 540

Switzerland 450 353 881 1315

United Kingdom 1002 1,534 4,077 3255

This table presents sample distribution by country. The treatment sample, obtained from Worldscope,

consists of mandatory IFRS adopters in Austria, Belgium, Denmark, Finland, France, Germany, Greece,

Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the United

Kingdom (U.K.).

37

Table 2 Univariate Tests

Panel A: Full Sample

Variable Pre-IFRS Post-IFRS Change from Post to Pre

AQ -0.018** -0.045*** -0.027***

GC 0.006*** 0.016** 0.010***

AUDITFEE 0.588*** 0.477*** -0.111***

MARKETSHARE 0.762 0.764 0.002

Panel B: Big Four Sample

Variable Pre-IFRS Post-IFRS Change from Post to Pre

AQ -0.003 -0.002 0.001

GC 0.004 0.003 -0.001

AUDITFEE 0.034 0.007 -0.027

MARKETSHARE 0.296 0.302 0.006

Panel C: Non Big Four Sample

Variable Pre-IFRS Post-IFRS Change from Post to Pre

AQ -0.016** -0.047** -0.031***

GC 0.009*** 0.017*** 0.008***

AUDITFEE 0.336** 0.227** -0.109***

MARKETSHARE 0.712 0.779 0.067***

This table presents univariate tests of key dependent variables used in our regression analysis. The tests

compare the means of the difference in the main interested variables across pre-IFRS adoption and post-IFRS

adoption periods in three different samples. Our sample consists of mandatory IFRS adopters in Austria,

Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Norway,

Portugal, Spain, Sweden, Switzerland, and the United Kingdom (U.K.). Pre-IFRS indicates pre-IFRS period and

includes the years 2000–2004 (2005) for firms with December (non-December) fiscal year end. Post-IFRS

indicates post-IFRS period and includes the year(s) 2005 (2006–2009) for firms with December (non-

December) fiscal year end. For firms in the control sample, we assign the pseudo-adoption year 2005 (2006) for

firms with a December (non-December) fiscal year end to be consistent with the adoption pattern in the

treatment sample. Difference in AQ between IFRS Expertise and Non IFRS expertise is calculated using AQ,

which for year t is the standard deviation of the residuals for a firm calculated over the past five years (i.e., year

t-4 through year t). We run the Dechow and Dichev [2002] model for each two-digit SIC industry-year group

with at least 20 observations in the group. Difference in GC between IFRS Expertise and Non IFRS expertise is

calculated using GC, which is an indicator variable that equals one if the firm receives a first-time going-

concern opinion, and zero otherwise. Difference in AUDITFEE between IFRS Expertise and Non IFRS

expertise is calculated using AUDITFEE, which is the natural logarithm of audit fees in thousands of U.S.

dollars. MARKETSHARE is the percentage ratio of market share for Big Four auditors in Panel A, market

share of PWC in Panel B and market share of BDO and Grant Thornton in Panel C. We identify IFRS expertise

in our full sample in Panel A/Big Four sample in Panel B/Non Big Four sample in Panel C if auditors belong to

Big Four auditor/PWC/BDO and Grant Thornton respectively. *, **, *** represent significance at 0.10, 0.05,

and 0.01 level, respectively, of the t-tests comparing the two means.

38

Table 3 Audit Quality Test

Panel A:Full Sample

Variable (1)

Accrual Quality

(2)

Accrual Quality with

Audit Complexity

(3)

Going Concern

(4)

Going Concern with

Audit Complexity

Coeff (t-stat) Coeff (t-stat) Coeff (χ2-stat) Coeff (χ2-stat)

Intercept 0.050*** (9.52) 0.086** (10.15) -1.285** (4.15) -1.131** (4.02)

EXP -0.018* (-1.75) -0.015 (-1.51) 0.522* (3.62) 0.489* (3.43)

POST 0.025** (2.20) 0.022* (1.68) -0.403** (5.02) -0.326* (3.60)

POST*EXP -0.027** (-2.34) -0.020* (-1.74) 0.419** (4.35) 0.359* (3.18)

POST*EXP*∆COMPLEX -0.015** (-2.25) 0.128** (4.20)

BIG4*∆COMPLEX -0.007** (-1.96) 0.065 (2.15)

POST*∆COMPLEX 0.009** (2.01) -0.167 (1.95)

TA -0.007*** (-6.21) -0.008*** (-6.49) -0.452*** (15.02) -0.424*** (13.37)

OC 0.024*** (3.83) 0.022*** (3.07)

NEGEARN 0.011*** (9.64) 0.010*** (8.82)

(CFO) 0.014** (2.34) 0.013** (2.18)

(SALE) 0.035* (1.67) 0.037* (1.70)

LEV 0.611** (4.84) 0.603** (4.56)

CFO -1.848** (3.97) -1.812* (3.56)

LOSS 1.175*** (10.42) 1.088*** (9.94)

AGE -0.221* (2.78) -0.206* (2.72)

CLEV 0.209 (0.47) 0.197 (0.42)

RET -0.331*** (9.11) -0.326*** (8.85)

VOL 0.244*** (7.15) 0.236*** (7.03)

FINANCE -0.551** (4.51) -0.578** (4.72)

ZSCORE -0.071** (5.76) -0.068** (5.34)

INVEST -3.239*** (11.15) -3.204*** (10.81)

Country Effects Yes No Yes No

Industry Effects Yes Yes Yes Yes

R2 0.237 0.236

Pseudo R2 0.725 0.727

N 3,268 3,268 3,662 3,662

POST+POST*EXP F-stat = 0.589

(p-value = 0.555)

χ2-stat = 0.667

(p-value = 0.716)

Panel B: Big Four Sample

Variable (1)

Accrual Quality

(2)

Accrual Quality with

Audit Complexity

(3)

Going Concern

(4)

Going Concern with

Audit Complexity

Coeff (t-stat) Coeff (t-stat) Coeff (χ2-stat) Coeff (χ2-stat)

POST*EXP 0.001 (0.41) 0.002 (0.07) -0.019 (0.15) -0.025 (1.89)

POST*EXP*∆COMPLEX -0.003 (-0.15) 0.007 (0.50)

Control Variables Yes Yes Yes Yes

Country Effects Yes No Yes No

Industry Effects Yes Yes Yes Yes

R2 0.198 0.815

Pseudo R2 0.196 0.811

N 2,312 2,312 2,419 2,419

39

Panel C: Non Big Four Sample

Variable (1)

Accrual Quality

(2)

Accrual Quality with

Audit Complexity

(3)

Going Concern

(4)

Going Concern with

Audit Complexity

Coeff (t-stat) Coeff (t-stat) Coeff (χ2-stat) Coeff (χ2-stat)

POST*EXP -0.031** (-2.34) -0.024* (-1.80) 0.370** (3.99) 0.309* (3.08)

POST*EXP*∆COMPLEX -0.017* (-1.72) 0.186* (2.84)

Control Variables Yes Yes Yes Yes

Country Effects Yes No Yes No

Industry Effects Yes Yes Yes Yes

R2 0.265 0.263

Pseudo R2 0.844 0.840

N 2,312 2,312 2,419 2,419

This table presents the OLS result for accruals quality analysis and the logistic results for going concern opinion analysis. t-

statistics and χ2-statistics are two-tailed, and based on robust standard errors clustered by firm. Coefficients on country effects and

industry effects are not reported for brevity. *, **, *** represent significance at 0.10, 0.05, and 0.01 level, respectively. POST is an

indicator variable that equals one for observations in the adoption period (2005/2006–2009), and zero for the pre-IFRS period

(2000–2004/2005). We identify IFRS expertise (EXP) in our full sample in Panel A/Big Four sample in Panel B/Non Big Four

sample in Panel C if auditor belongs to Big Four auditor/PWC/BDO and Grant Thornton respectively.

For OLS result in Column (1) and Column (2), AQ for year t is the standard deviation of the residuals for a firm calculated over the

past five years (i.e., year t-4 through year t). We run the Dechow and Dichev [2002] model for each two-digit SIC industry-year

group with at least 20 observations in the group. TA is the natural logarithm of end-of-year total assets in millions of U.S. dollars.

OC is the natural logarithm of firm’s operating cycle, where operating cycle equals the sum of turnover days for accounts

receivables and inventories. NEGEARN is the incidence of negative earnings over the past ten years. (CFO) is the standard

deviation of a firm’s cash flows from operations, calculated over the past ten years. (SALE) is the standard deviation of a firm’s

sales, calculated over the past ten years. Consistent with Francis et al. [2005], we require at least five observations in each rolling

ten-year window in order to calculate (CFO) and (SALE).

For logistic result in Column (3) and Column (4), GC is an indicator variable that equals one if the firm receives a first-time going-

concern opinion, and zero otherwise. TA is the natural logarithm of total assets. AGE is the natural logarithm of the number of

years of data for the client firm since the coverage in Worldscope/Compustat. LEV is the year-end total liabilities divided by year-

end total assets. LOSS is an indicator variable that equals one for observations with annual net income less than 0, and zero

otherwise. CLEV is the change of LEV. RET is the firm’s cumulative stock return over the current year. CFO is the operating cash

flow scaled by total assets for the current year. FINANCE is an indicator variable equal to one if the client has a new issuance of

equity or debt over the subsequent fiscal year (i.e., positive EISSUE or positive DISSUE), and zero otherwise. ZSCORE is

Altman’s [1968] Z-score. VOL is the standard deviation of monthly stock returns over the current year. INVESTMENT is cash and

cash equivalents scaled by total assets.

40

Table 4 Audit Fees Test

Panel A: Full Sample

Variable (1)

Audit Fee

(2)

Audit Fee with Audit

Complexity

Coeff (t-stat) Coeff (t-stat)

Intercept 1.537*** (11.18) 1.432*** (10.25)

POST 0.416*** (3.32) 0.308** (2.28)

EXP 0.587** (2.41) 0.503** (2.04)

POST*EXP -0.112** (-2.06) -0.051* (-1.72)

POST*EXP*∆COMPLEX -0.134** (-2.06)

EXP*∆COMPLEX 0.168* (1.74)

POST*∆COMPLEX 0.219** (2.34)

TA 0.529*** (25.26) 0.515*** (24.18)

INVREC 0.329*** (8.71) 0.314*** (7.54)

NBS 0.051*** (20.52) 0.050*** (19.92)

NGS 0.076*** (29.95) 0.072*** (27.70)

LOSS 0.224*** (22.30) 0.215*** (21.17)

LEV 0.048*** (6.52) 0.047*** (6.49)

MTB 0.012*** (2.69) 0.013*** (2.67)

QUICK -0.038*** (-19.42) -0.035*** (-17.75)

FINANCE 0.045*** (4.35) 0.047*** (4.56)

SPECIAL 0.104*** (4.50) 0.116*** (4.29)

QUALIFIED 0.277*** (11.55) 0.268*** (10.82)

MERGE 0.225* (1.73) 0.216* (1.68)

XLIST 0.072*** (3.85) 0.079*** (4.10)

Country Effects Yes No

Industry Effects Yes Yes

R2 0.802 0.801

N 10,514 10,514

POST+ POST*EXP

F-stat = 5.242

(p-value = 0.022)

Panel B: Big Four Sample

Variable (1)

Audit Fee

(2)

Audit Fee with Audit

Complexity

Coeff (t-stat) Coeff (t-stat)

POST*EXP -0.027 (-0.52) 0.006 (0.08)

POST*EXP*∆COMPLEX -0.036 (-0.69)

Control Variables Yes Yes

Country Effects Yes No

Industry Effects Yes Yes

R2 0.771 0.769

N 7,716 7,716

41

Panel C: Non Big Four Sample

Variable (1)

Audit Fee

(2)

Audit Fee with Audit

Complexity

Coeff (t-stat) Coeff (t-stat)

POST*EXP -0.109** (-2.06) -0.064* (-1.78)

POST*EXP*∆COMPLEX -0.097* (-1.69)

Control Variables Yes Yes

Country Effects Yes No

Industry Effects Yes Yes

R2 0.849 0.846

N 2,571 2,571

This table presents the OLS results of audit fee analysis. t-statistics are two-tailed, and based on robust standard

errors clustered by firm. Coefficients on country effects and industry effects are not reported for brevity. *, **, ***

represent significance at 0.10, 0.05, and 0.01 level, respectively. POST is an indicator variable that equals one for

observations in the adoption period (2005/2006–2009), and zero for the pre-IFRS period (2000–2004/2005). We

identify IFRS expertise (EXP) in our full sample in Panel A/Big Four sample in Panel B/Non Big Four sample in

Panel C if auditor belongs to Big Four auditor/PWC/BDO and Grant Thornton respectively. AUDITFEE is the

natural logarithm of audit fees in thousands of U.S. dollars. TA is the natural logarithm of total assets in millions of

U.S. dollars. INVREC is the sum of inventories and receivables divided by total assets. NBS is the natural logarithm

of 1 plus the number of business segments. NGS is the natural logarithm of 1 plus the number of geographical

segments. LEV is the year-end total liabilities divided by year-end total assets. LOSS is an indicator variable that

equals one for observations with annual net income less than 0, and zero otherwise. QUICK is quick ratio, equal to

quick assets divided by current liabilities. MTB is the ratio of the firm’s market value to the book value of its

common equity. SPECIAL is an indicator variable that equals one if the firm reports special items, and zero

otherwise. QUALIFIED is an indicator variable that equals one if the firm receives qualified opinion, and zero

otherwise. MERGE is an indicator variable that equals one if the firm is engaged in a merger or acquisition, and zero

otherwise. FINANCE is an indicator variable that equals one if the client has a new issuance of equity or debt over

the subsequent fiscal year (i.e., positive EISSUE or positive DISSUE), and zero otherwise. XLIST is an indicator

variable that equals one if the firm is cross listed on any U.S. stock exchanges, and zero otherwise.

42

Table 5 Market Shares Test

Panel A: Full Sample

Variable (1)

Market Share

(2)

Market Share with Audit

Complexity

Coefficient (χ2-stat) Coefficient (χ2-stat)

Intercept 1.056*** (34.21) 0.051*** (33.08)

POST 0.016 (0.77) -0.273** (4.19)

∆COMPLEX -0.145* (3.49)

POST*∆COMPLEX 0.395** (4.38)

EMV 0.456*** (11.29) 0.398*** (9.45)

TURN 0.322** (4.43) 0.317** (4.61)

LOSS -0.621* (3.39) -0.607* (3.46)

LEV 0.279** (4.13) 0.256** (4.04)

MTB -0.101 (2.01) -0.096 (2.07)

FINANCE 0.319* (3.35) 0.031* (1.71)

MERGE 1.788* (3.09) 1.643* (3.20)

ROA 0.240** (4.52) 0.249** (4.47)

XLIST 1.419*** (17.49) 1.519*** (15.28)

GROWTH -0.215* (3.05) -0.232* (3.24)

ANALYST 0.056 (0.71) 0.047 (0.78)

NEWLISTING -0.409 (1.36) -0.420 (1.49)

Country Effects Yes No

Industry Effects Yes Yes

Pseudo R2 0.184 0.183

N 9,380 9,380

Panel B: Big Four Sample

Variable (1)

Market Share

(2)

Market Share with Audit

Complexity

Coefficient (χ2-stat) Coefficient (χ2-stat)

POST 0.117 (0.25) 0.086 (0.09)

POST*∆COMPLEX 0.073 (1.03)

Control Variables Yes Yes

Country Effects Yes No

Industry Effects Yes Yes

Pseudo R2 0.164 0.162

N 6,820 6,820

Panel C: Non Big Four Sample

Variable (1)

Market Share

(2)

Market Share with Audit

Complexity

Coefficient (χ2-stat) Coefficient (χ2-stat)

POST 0.559** (4.48) 0.334* (3.02)

POST*∆COMPLEX 0.229** (4.13)

Control Variables Yes Yes

Country Effects Yes No

Industry Effects Yes Yes

Pseudo R2 0.285 0.281

N 2,280 2,280

43

This table presents the logistic result of market share analysis for the overall audit market. The dependent

variable EXP is an indicator variable that equals one if the firm is audited by a Big Four auditor/PWC/BDO

and Grant Thornton in in our full sample in Panel A/Big Four sample in Panel B/Non Big Four sample in

Panel C, and zero otherwise. χ2-statistics are two-tailed, and based on robust standard errors clustered by firm.

POST is an indicator variable that equals one for observations in the adoption period (2005/2006–2007), and

zero for the pre-IFRS period (2003–2004/2005). ROA is the net income divided by total assets. ANALYST is

the natural logarithm of 1 plus the number of analysts following the firm (from I/B/E/S). NEWLISTING is an

indicator variable which equals to one for firm-year observations of firms that were first listed in the years

2000–2003. All other variables are as defined in the footnotes of Tables 3 to 6. Coefficients on country effects

and industry effects are not reported for brevity. *, **, *** represent significance at 0.10, 0.05, and 0.01 level,

respectively.

44

Table 6 Effects of Legal Enforcement

Panel A: Full Sample

Variable (1)

Accrual Quality

(2)

Going Concern

(3)

Audit Fee

(4)

Market Share

Coeff (t-stat) Coeff (χ2-stat) Coeff (t-stat) Coeff (χ2-stat)

POST*ENFORCE 0.004 (0.19) 0.021 (0.36) 0.032 (0.54) 0.072 (0.84)

POST*EXP*ENFORCE -0.011** (-2.20) 0.109** (4.08) -0.019 (-0.74)

Control Variables Yes Yes Yes Yes

Country Effects No No No No

Industry Effects Yes Yes Yes Yes

R2 0.236 0.721 0.798 0.180

Pseudo R2

N 3,268 3,662 10,514 9,380

Panel B: Big Four Sample

Variable (1)

Accrual Quality

(2)

Going Concern

(3)

Audit Fee

(4)

Market Share

Coeff (t-stat) Coeff (χ2-stat) Coeff (t-stat) Coeff (χ2-stat)

POST*ENFORCE 0.000 (0.02) 0.016 (0.74) 0.011 (0.26) -0.024 (0.41)

POST*EXP*ENFORCE 0.000 (0.14) -0.034 (0.62) -0.027 (-0.45)

Control Variables Yes Yes Yes Yes

Country Effects No No No No

Industry Effects Yes Yes Yes Yes

R2 0.196 0.810 0.770 0.162

Pseudo R2

N 2,312 2,419 10,514 9,380

Panel C: Non Big Four Sample

Variable (1)

Accrual Quality

(2)

Going Concern

(3)

Audit Fee

(4)

Market Share

Coeff (t-stat) Coeff (χ2-stat) Coeff (t-stat) Coeff (χ2-stat)

POST*ENFORCE -0.006 (-0.11) 0.043 (0.54) 0.008 (0.53) 0.115* (3.37)

POST*EXP*ENFORCE -0.019* (-1.74) 0.176* (3.28) -0.016 (-0.34)

Control Variables Yes Yes Yes Yes

Country Effects No No No No

Industry Effects Yes Yes Yes Yes

R2 0.264 0.840 0.849 0.280

Pseudo R2

N 776 629 2,571 2,280

This table presents OLS and logistic results of adding interaction terms involving legal enforcement strength ENFORCE to

models in Table 3, Table 4 and Table 5. t-statistics and χ2-statistics are two-tailed, and based on robust standard errors clustered

by firm. All variables are as defined in the footnote of Table 3, Table 4 and Table 5. Coefficients on control variables, and

industry effects are not reported for brevity. *, **, *** represent significance at 0.10, 0.05, and 0.01 level, respectively. POST

is an indicator variable that equals one for observations in the adoption period (2005/2006–2009), and zero for the pre-IFRS

period (2000–2004/2005). EXP in Panel A is an indicator variable that equals one if the firm is audited by a Big Four auditor,

and zero if it is audited by a non-Big Four auditor. EXP in panel B is an indicator variable that equals one if the firm is audited

by PWC, and zero if it is audited by the other three Big Four auditors. EXP in Panel C is an indicator variable that equals one if

the firm is audited by BDO or Grant Thornton, and zero if it is audited by other non-Big Four auditors. ENFORCE is an

indicator variable that equals one if the enforcement score, which is calculated based on La Porta et al. [1998] is above the

sample median, and zero otherwise.