the effects of mandated ifrs adoption on the cost of the

The effects of mandated IFRS adoption on the

cost of the audit function

Emmanuel De George*, Colin Ferguson* and Nasser Spear*

Working Paper

January 2007

This paper seeks to provide the first empirical evidence into the costs, namely audit fees, associated with the recent mandatory switch in reporting regimes. Australia, inline with the European Union, announced full adoption of International Financial Reporting Standards (IFRS) for all reporting periods beginning on or after 1 January 2005. Accordingly, the recent reporting season represents the first time that Australian companies are required to present IFRS-compliant financial statements, presenting a unique opportunity for an examination of the realised costs of this major reporting regime switch on the audit function. The objective of this paper is twofold: first, we investigate the effect of mandated IFRS adoption on audit fees, specifically addressing the issues surrounding the current concerns of smaller companies of the rigours of IFRS adoption. The results of our analysis of 438 Australian listed companies suggest a 23 percent audit fee premium in the year of adoption, these results remain robust across industries. Further analysis suggests no significant difference between the audit fee premium incurred by small and large companies, evidence that is inconsistent with emerging literature arguing a correlation between IFRS effects and company size, perhaps indicating opportunistic behaviour in audit pricing at the lower end of the market. Second, this study discusses specific differences between IFRS and previous AGAAP, we obtain expert opinions regarding the effect of IFRS on the costs associated with the audit function, these expectations are then empirically tested. We find that reporting requirements for share-based incentives, financial instruments, and impairment of goodwill and other intangible balances are posing the greatest issues for auditors with companies required to apply these standards incurring greater audit costs than all other companies in the year of IFRS adoption. The findings from our research have several global implications for regulators, the accounting profession and companies alike, in particular those involved in current convergence projects, such as the FASB. Key Words: IFRS, audit fees, reporting regime switch.

*The University of Melbourne, Victoria, Australia 3010.

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1. Introduction

The past decade has seen significant changes to the global financial reporting environment, in

particular the recent push for international harmonization through the adoption of International

Financial Reporting Standards (IFRS) given the worldwide integration of the global capital

markets. With close to 100 countries mandating the use IFRS or country-specific equivalents,

including the European Union and Australia, and the recent convergence projects being

undertaken by the world’s leading economies (e.g. the US and Japan), the IASB has achieved

great success in the development of an adhered-to global accounting framework. The recent

2006 Australian reporting season represents first-time compliance with AIFRS/IFRS, presenting

a unique opportunity for an empirical examination of the impacts of a mandatory regime change

on costs of compliance, specifically audit costs observed through fees paid in relation to the

completion of the statutory audit, hence providing the motivation for this study. 1

The advocating of a single global set of accounting standards has sparked intense debate

concerning the perceived economic costs and benefits, both globally and within local adopting

jurisdictions, from academics, professional interests and the financial press alike (see for

example: Ball, 2007; Barth et al., 2004; Daske, 2006; Nobes, 2001; Pasias, 2006; Webb, 2006;

Jones et al., 2004; Schipper, 2005; Submissions to the PJCCFS 2, 2005). However, to date there

is no robust, directly-relevant body of evidence within the academic literature that exclusively

addresses the costs of IFRS implementation and thus little resolved theory to build a proper

assessment of the advantages and disadvantages of mandatory adoption (Ball, 2007).

Conjectural arguments both in support and opposition of mandated IFRS adoption rely on a cost

versus benefit analysis as the key premise when rationalizing IFRS in favour of localised GAAP

(Daske, 2006). Consequently, for the most part, the extant literature has provided only

1 In Australia, the AASB has developed the current Accounting Standards ased on International Standards released by the IASB, these are known as Australian equivalents to International Accounting Standards (AIFRS), however we note that the difference between IFRS as issued by the IASB and Australian equivalents is, in most cases, minimal. For instance, specific differences may arise in equity accounting treatments of associates or controlled entities, or in terms of tax consolidation criteria in Australia. That said, while the current study is concerned with the effects of the adoption of International standards in Australia in Australia, whether or not a company applies AIFRS or IFRS is irrelevant given that both are based on the premise of fair value measurement and encompass significant changes from previous local GAAP. Accordingly, we use the terms AIFRS and IFRS interchangeably throughout this paper. 2The Parliamentary Joint Committee on Corporations and Financial Services (PJCCFS) inquiry into the Australian Accounting Standards was began via resolution on 2 December 2004, with a scope of inquiry, essentially regarding the proposed standards’ consistency with the objectives of current corporations legislation, with discussion welcome regarding any other related matter. A total of 25 submissions were received from auditors and professional bodies regarding their views on the implementation of IFRS and potential issues arising from adoption, with findings

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anecdotal and limited empirical evidence addressing the economic consequences of

international harmonization of IFRS, primarily focusing on issues surrounding perceived capital

market benefits and financial statement effects of IFRS adoption (or US-GAAP) over local

GAAP (eg. Brown & Clinch, 1998; Daske, 2006; Barth et al., 2004; Goodwin & Ahmed,

2006b). However, there has been little research concerning the costs of international

harmonization, in particular realised costs incurred by companies for the audit of fully

compliant IFRS statements given this switch in reporting regimes.

Moreover, the current IFRS debates are dominated by the conjecture of the onerousness and

costliness of mandated adoption on smaller companies, with some in the financial press quick to

call into question the merit of IFRS adoption on the smaller end of the market. It has been

widely reported that the smaller end of the market, in particular Small and Medium entities

(SME’s) have found the increased disclosure, workload and subsequent professional fees to be

an unwelcome burden (Pasais, 2006; Webb, 2006). Whereas larger firms are said to have more

adequate internal resources to cope with the increased pressures of a reporting regime change

and any present complaints should “disappear in time”, smaller companies lack the technical

knowledge within their internal resources and have “found it more challenging” (Kitney &

Buffini, 2006). To date, only a very small body of literature has empirically addressed this

issue, with initial results suggesting a correlation between company size and IFRS adjustments

in the financial statements (Goodwin & Ahmed, 2006b). While such studies provide evidence

on the size debate through investigation of financial statement adjustments and disclosures, the

realised costs associated with mandated IFRS adoption have yet to be investigated. The current

study position itself within this gap.

The aim of this research is to provide empirical evidence on the realised costs to companies of a

switch in reporting regimes, by specifically addressing the impact of IFRS adoption on audit

fees within the Australian reporting jurisdiction. In particular, this study empirically addresses

conjecture concerning the onerousness of IFRS on smaller companies and discusses key

differences between IFRS and AGAAP which are said to have a major impact on and provides

evidence as to the effect of these changes on professional fees paid to auditors.

Through cross-sectional analysis of pooled time-series data of 438 Australian listed companies,

initial results suggest that average audit fees were 23 percent higher in the year of IFRS

adoption than the preceding three years, after controlling for other relevant audit fee

presented to parliament on 8 February 2005. A copy of the committee’s report and a full listing of submissions is available at the Parliamentary Website: http://www.aph.gov.au/senate/committee/corporations_ctte/aas/index.htm

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determinants, a result that is intuitively appealing and not surprising. 3 However, results

addressing the main aim of this paper in terms of the current conjecture surrounding the

onerousness of IFRS, suggest a rather surprising outcome. We find evidence inconsistent with

the arguments and conclusions of prior literature concerning the existence of a correlation

between IFRS impacts and company size. Our results suggest no significant difference between

the audit fee premium incurred by small and large companies in the initial year of IFRS

adoption. We provide two contradictory explanations for these findings: either larger firms

were able to mitigate the extent of costly IFRS implementation and associated audit costs given

the vast amount on in-house resources available to them, or there has been some extent of

opportunistic pricing practices by audit firms in their smaller client segment, given that

intuitively we would expect larger firms to be more complex and exhibit greater risk, thus incur

a much higher audit fee premium.

Finally, by drawing on the extant research releases by the professional bodies and audit firms

regarding implementation issues of specific standards, recent Australian studies, and analysis of

obtained expert opinions, we hypothesize as to which of the newly adopted reporting standards

are proving the most costly. Consistent with our arguments, we find that companies that either

(1) make share-based incentive payments, and/or (2) undertake complex financial instruments

transactions, and/or (3) are subject to the impairment of goodwill and other intangible balances,

under the requirements of AIFRS/IFRS exhibit higher audit fees, relative to other companies,

after controlling for other factors. In particular, these results provide initial empirical evidence

on the specific aspects of IFRS/AIFRS that are imposing the most complexity and risk to

auditors, and therefore proving the most costly to comply with, from an auditing perspective.

Despite the focus of the empirical investigation being on the Australian experience of IFRS

transition, our study also addresses fundamental issues relevant to the global debate. The

contributions of this paper are twofold: first, we provide the first empirical insights into the cost

effects of IFRS adoption on fees paid to auditors. In particular, this study addresses the current

conjecture concerning the issue of the effects of IFRS on smaller companies, and the purported

costly burden that IFRS represents, thus enriching the current debate and allowing for a more

productive analysis of the true cost/benefit trade-off within adopting jurisdictions. Furthermore,

the results of this paper also draw into question the decision of the Australian regulators not to

limit the mandatory application of IFRS to listed companies, a move which the FRC is now

3 Time series data consisted of 4 consecutive years of auditor and financial data for the 438 listed Australian firms,

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rethinking. Given the results support the conjecture that IFRS is proving a costly burden to

smaller companies, this may also proxy for the experience of unlisted SME’s during the

transitional process. Second, the discussion and empirical evidence presented in this paper may

have several implications for the international convergence projects undertaken throughout the

world, such as the FASB. With the SEC reaffirming its support of a unified set of global

standards, a timeline is now in place for the joint US GAAP and IFRS convergence project to be

completed by 2009. To this end, the results and discussion presented in this study may provide

a blueprint per se, allowing companies and regulators currently in the process of convergence to

better tailor their transition programmes in those areas identified as “costly” in order to mitigate

the potential cost burden. Similarly, auditors will also be able to better prepare and tailor their

internal resource allocations, in terms of technical knowledge building and budgeting processes,

towards those areas that were identified as proving the most complex and time consuming, thus

leading to more efficient contracting between auditors and clients.

The rest of this paper is organised as follows: Section 2 provides a brief discussion of the issues

of IFRS adoption and provides the arguments and results of expert opinions obtained that form

the basis of the hypotheses, Section 3 discusses the research design, Section 4 presents the

empirical results and econometric issues identified, Section 5 presents the conclusions and

implications of this study, while discussing the limitations and highlighting areas for future

research.

2.0 Background and Theoretical development

2.1 Background of IFRS adoption

In Australia there has been much debate regarding the potential impacts and uncertainties of the

proposed reporting regime change following the adoption announcement by the Financial

Reporting Council (FRC) on 3rd July 2002. Despite being hailed as “a real push towards

international convergence within the global economy” by Sir David Tweedie, IASB chairman

(IASB, 2002),4 the economic consequences on the Australian corporate landscape continues to

be a divisive issue inviting mixed opinions from regulators, the accounting profession and

businesses alike. In particular, criticisms levelled at the local adoption of IFRS have raised

concerns surrounding the existence of potentially significant costs and onerousness of adoption

being the year of IFRS adoption, and the preceding 3 years. 4 IASB press release 3/07/02 “IASB welcomes Australian decision” Sourced from Deloitte IAS-Plus homepage at

http://www.iasplus.com/pressrel/2002pr11.pdf.

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on smaller companies (e.g. AICD, 2004; Lynch & Fabro, 2005). In particular, opponents of

AIFRS/IFRS argue that the costs of mandatory adoption will be far greater than the posited

benefits for a significant number of companies. For instance, the submission by the Australian

Institute of Company Directors (AICD) stated that they had “grave concerns” over the impact of

the transition to IFRS on Australian companies, specifically sighting the smaller listed

companies as being the most ill-prepared and therefore predicting heavy transitional costs to be

incurred. Contrary to these criticisms, the Australian Accounting Standards Board (AASB),

back by the FRC, affirmed their position via a press release in 2003, stating that:

“Complying with these requirements is unlikely to impose significant burdens and costs on

entities when compared with the benefits of relevant and reliable information to users of

financial statements.” (Ahmed & Goodwin, 2006a: p.3.)

While receiving overwhelming support from the accounting industry (e.g. KPMG, Ernst and

Young, PriceWaterhouseCoopers), auditors were forced to concede that where implementation

issues have been identified, “considerable effort” will have to be expended by both preparers

and auditors of financial statements (see PWC submission to PJCCFS committee, 2005),

eluding to the potential costly burden of IFRS transition on companies and their auditors 5.

Moreover, the switch in reporting regimes means that the transition to IFRS/AIFRS is not just

an accounting exercise for Australian companies, and will affect a broad range of areas within

firms with companies and auditors alike required to assess the impact of each new standard on

the firm and any additional disclosure required in the financial statements (NSW Audit office,

2002). Consequently, although not previously addressed in any great detail in the extant

literature, it is clear that the mandatory adoption of IFRS/AIFRS represents substantial changes

to the auditing landscape. This sentiment is echoed in the emerging empirical research with the

findings of Higgins and Jones (2006) suggesting that companies perceived the most significant

impact of the transition to IFRS was on the responsibilities of their external auditors.

The current study builds on these arguments, we hypothesize an increase in litigation risk

because of the reporting regime switch, resulting in an increase in audit fees charged in the

5 Accounting firms articulated their support of mandatory IFRS adoption through invited submissions to the Parliamentary Joint Committee on Corporations and Financial Services (PJCCFS) in early 2005. The PJCCFSI inquiry into the Australian Accounting Standards was began via resolution on 2 December 2004, with a scope of inquiry, essentially regarding the proposed standards’ consistency with the objectives of current corporations legislation, with discussion welcome regarding any other related matter. A total of 25 submissions were received from auditors and professional bodies regarding their views on the implementation of IFRS and potential issues arising from adoption, with findings presented to parliament on 8 February 2005. A copy of the committee’s report

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adopting jurisdiction. We then further investigate the existence and magnitude of this increase

in audit fees on small and large companies in order to provide empirical evidence to the recent

conjecture surrounding the onerousness of IFRS on smaller companies.

Motivated in part by the current debates surrounding onerousness of IFRS on smaller companies

and potential adverse effects of IFRS adoption, coupled with the lack of empirical evidence in

support of any such conjecture, recent Australian studies have examined the impact of IFRS

adoption on financial statements through examination of the magnitude and frequency of

adjustments required from previous AGAAP (see Goodwin & Ahmed, 2006b; Ahmed &

Goodwin, 2006a; Jubb, 2005). Recent evidence from this emerging stream of literature find

inconsistent evidence with the conjecture of small companies, instead, results suggest a

correlation between company size and IFRS effects. In particular Goodwin & Ahmed (2006b)

find that as company size increases so does the frequency and magnitude of AIFRS adjustments

reported in their financial statements, with a number of the small companies reporting no

AIFRS adjustments at all, thus concluding that the conjecture surrounding onerousness of IFRS

adoption on smaller companies, in reality, may be unfounded.

We enrich the current debate and provide for further analysis by providing empirical evidence

into an aspect of realised costs associated with the mandated adoption of IFRS, namely the costs

of obtaining a statutory audit. Such investigation will allow for more in-depth analysis of the

cost/benefit trade-off, providing initial evidence as the effect of reporting regime change on the

audit function and the differing costs faced by small and large companies.

2.2 Audit pricing and IFRS adoption

We position the adoption of IFRS within the framework of previous audit pricing literature

concerning the effects of risk on the audit function, developing arguments consistent with the

notion that the mandated switch in reporting regimes imposes a shift in the perceived litigation

risks faced by auditors given the increased complexity and disclosure requirements. Litigation

risk is defined as the probability of incurring liability payments and/or loss of reputation capital

of the quality of audit services, and previous Australian and US literature has empirically

established the relationship between litigation risk and audit fees(e.g. Lyon & Maher, 2005;

Francis, 1984; Simunic and Stein, 1996; Francis & Simon, 1987).

and a full listing of submissions is available at the Parliamentry Website: http://www.aph.gov.au/senate/committee/corporations_ctte/aas/index.htm

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The audit pricing function is concerned with the minimisation of legal liability loss, hence, the

level of audit effort expended and subsequent fees charged are dictated by the perceived levels

of litigation risk. Under these conditions, an increased perception of risk equates to increase in

the perceived potential liability, consequently auditors are required to take defensive action, by

either (1) charging an insurance premium to cover future expected losses or (2) expend more

effort in the audit engagement in order to rationally manage the increase in expected loss

liability (Simunic and Stein, 1996; Seetharaman, Gul, and Lynn, 2002; Lyon and Maher, 2005).

In applying the above rationale to the current situation of mandated IFRS adoption, we define

the switch in reporting regimes from AGAAP to IFRS as a condition that increases the

perceived risks faced by the auditor, thus resulting in an increase in audit effort expended,

manifesting itself through an increase in audit fees. The mandated adoption of IFRS is likely to

affect auditors risk assessments for the following reasons:

First, the switch in emphasis from historical cost to fair value as a measurement attribute on a

number of key account balances has raised concerns as to the reliability of financial statements,

Schipper (2005) highlights the potential lack of organised and liquid markets for a number of

assets and obligations, potential bias and intrinsic error within fair value estimation models and

a lack of expertise among accountants as areas of key concern. Additionally, Ball (2007)

foresees the IASB’s “fascination with fair value accounting” as the source of potential

implementation and uniformity issues. Given the above conjecture, there exists greater potential

for auditors to issue an inappropriate opinion, and therefore will expend more audit effort in

order to protect their reputation capital. KPMG national audit partner articulated the increased

audit effort required with his comments in the financial press, in which he pressed the “need to

manage additional complexity in the audit”, an argument that is further evidenced by the

development of in-house IFRS-accreditation programs for audit staff within a number of audit

firms (Lynch & Fabro, 2005: p.47).

Moreover, in addressing the perceptions of companies in the AIFRS transitional process, Jones

& Higgins (2006) found that companies perceived the transition to IFRS to have the most

impact on the responsibilities of their external auditors. However, a recent report released by

the Association of Chartered Certified Accountants (ACCA) claimed that auditors were “still

struggling with the new regime” (Chong, 2006: p. 32). Following this, a number of recent

studies have raised concerns about the need for further guidance within IFRS (e.g. review

papers such as Schipper, 2005), thus pointing to an increased uncertainty in the current reporting

requirements. Clarkson, Ferguson and Hall (2003) suggest that uncertainty over the reporting

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environment increases the litigation risk faced by auditors and consequently induces auditors to

pursue measures to protect their reputation capital. Given this increased reliance on the

external audit function coupled with an increased level of uncertainty over IFRS requirements,

there exists an increased likelihood of incurring costly litigation and regulator intervention given

the potential ex-post scrutiny over IFRS-compliant financial statements.

Therefore, individually or taken together, the above arguments lead to the following initial

hypothesis:

H1: As a result of IFRS implementation in Australia, audit fees have increased, all other things

equal.

2.3 Onerousness of AIFRS adoption on smaller companies

Emerging literature has reported firm size as an important discriminate variable when gauging

the effects of AIFRS adoption, with findings suggesting the financial statement effects of the

AIFRS are correlated with firm size (Ahmed & Goodwin, 2006a). In particular, Ahmed and

Goodwin (2006a) find results suggesting that the number and magnitude of adjustments to net

income and equity reported in the financial statements increases with firms size, with over half

of the sample of small firms reporting no change in net income or equity from the transition to

AIFRS. Such a finding appears to contradict the current conjecture in the financial press of the

onerousness of IFRS implementation on smaller entities.

Many of the concerns voiced in the financial press from professional bodies, such as the

Australian Institute of Company Directors, and financial commentators articulate the view that

the additional costs of preparing audited IFRS-compliant financial statements will be “heavy on

smaller companies”. This conjecture is centred around the premise that IFRS is more

burdensome on smaller sized entities as they relatively under-prepared and less able to access

the requisite accounting skills (Ahmed and Goodwin, 2006a). Furthermore, early academic

literature by Kinnunen et al. (2000) suggests that the IFRS improves information content for

foreign investors but not domestic investors. Given that the majority of smaller companies do

not actively seek sustained foreign investment they are effectively receiving none of the

purported benefits but are burdened with transition costs. This is especially pertinent in the

Australian situation, as the adoption of IFRS is not just limited to listed entities but extends to

all entities covered by the Corporations Act 1994, including smaller unlisted companies.

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Moreover, a number of companies themselves are reporting that the information they report in

their annual reports has now doubled under IFRS, with their accounts now up to 50% bigger,

referring to the IFRS mandate as “overkill” (Chong, 2006). However, the AASB has hit back at

these suggestions through comments in the financial press, in which David Boymal, chairman of

the AASB, asserted that smaller listed companies and SME’s alike “are in for a pleasant

surprise, that it [IFRS] was not such an onerous thing as they were first led to believe”

(Andrews, 2005), notwithstanding, given the recent escalation in such criticisms both in

Australia and globally, the IASB has been prompted to take action. In 2006 the IASB launched

a project to develop an IFRS for Small and Medium sized entities with the ultimate goal of the

project to relieve the costly burden of adoption to small and medium sized companies. To date

there has been no empirical evidence to support the conjecture of either side concerning the

realised costs associated with IFRS adoption for small or large companies. Therefore, we

address these issues by partitioning our sample based on size and empirically examining the

audit costs incurred by small and large companies.

2.4 Identification of “costly” International Accounting Standards

The secondary objective of this paper is to further disaggregate the effect of IFRS adoption on

audit fees, to identify specific standards and requirements now mandated under AIFRS that are

proving the most costly to implement.

Professional bodies and accounting firms have undertaken a number of studies in which they

attempt to gauge the effects of IFRS adoption on Australian companies. For instance, a survey

commissioned by the Institute of Chartered Accounting of Australia (ICAA) of over 200

Australian businesses raised concerns in relation to the onerous IFRS requirements surrounding

the application of income taxes, financial instruments and impairment. Similarly, the TCG

Report released by Ernst & Young in 2005 reports similar findings, suggesting that share-based

incentives, income taxes, defined benefit plans and financial instruments are having the most

significant impact on the financial statement disclosures of proposed IFRS/AIFRS adjustments

of Australian companies (Ernst & Young, 2005).

We may reconcile these findings to prior empirical evidence presented in the emerging

Australian academic literature on IFRS adoption. Jubb (2005) provides evidence based on her

empirical investigations of the anticipated IFRS adjustments disclosed by 808 companies in

their “expected impacts of transition” disclosures, as required by AASB 1047 in the preceding

year of full adoption. Her results consistently identified five key accounting policy differences

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arising from transition to IFRS, namely: income tax, asset impairment, share-based

remuneration, financial instruments and goodwill on consolidation.

Following Jubb (2005), Goodwin and Ahmed (2006b) empirically examine the impact of

AIFRS on the accounts of small, medium and large-sized firms, utilizing sample data sourced

from AGAAP to AIFRS reconciliations as required by the disclosure requirements of AASB 1,

in first AIFRS-compliant half-year reports. In their study of 135 Australian listed companies,

Goodwin and Ahmed (2006b) investigate the changes from previous AGAAP reporting in major

balance sheet and income statement elements as a result of the transition to AIFRS. More

recently Ahmed and Goodwin (2006a) further examine the financial statement effects and

quality of earnings under AIFRS via an examination of half-yearly and annual reports of 1,387

firms listed on the ASX. While their study primarily focuses on issues surrounding accounting

quality and the value relevance of AIFRS compared to previous AGAAP, they also provide

evidence as to the significant AIFRS adjustments reported in financial statements. The findings

of both studies are consistent, suggesting the greatest impacts on Australian financial statements

were in the areas of income taxes, share-based payments, financial instruments, impairment on

goodwill and intangibles.

Reconciling the above findings, we determine IFRS/AIFRS reporting requirements over income

taxes, share based payments, financial instruments, impairment, goodwill and employee benefits

as encompassing the greatest change from previous local GAAP, and thus posing the greatest

risks to auditors in providing assurance over IFRS/AIFRS-compliant financial statements.

2.4.1 Differences between IFRS and AGAAP

AASB 2: Share based incentives

Under AIFRS (AASB 2), companies are required to recognize the expense relating to share-

based payments on the face of the financial statements at fair value. Companies are required to

estimate the fair value of employee stock options as at grant date and then expense the value

over the vesting period. However, in certain circumstances the standard requires the use of

subjective option pricing models in determining fair values and becomes problematic and

complex, thus increasing the likelihood of material misrepresentations and misstatements. To

this end, the audit effort and expertise required to provide the adequate assurance over such

calculations and greater disclosure has increased. Recent academic literature has found that

adjustments stemming from the current and retrospective implementation of AASB 2 affect

approximately 45% of Australian companies (Ahmed and Goodwin, 2006a).

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AASB 112: Income taxes

Under the requirements of AIFRS, AASB 112 mandates the use of the “balance sheet” method

for tax effect accounting, where temporary differences are identified for each asset and liability,

accordingly, deferred tax balances are recognised on the balance sheet when differences arise

between the carrying value of assets and liabilities, and their tax base. 6 That said, revaluations

due to fair value adjustments require the recognition of the attached tax effects, thus the inherent

fair value measurements of assets and liailities directly affects the amount of deferred taxes

recognised on the face of the financial statements. Additionally, the criterion concerning the

recognition of deferred tax losses has been relaxed under the new IFRS, becoming more

subjective. Prior literature suggests that more than 50 percent of the top-100 listed companies

were significantly affected by the mandatory application of AIFRS requirements (Ernst &

Young Australia, 2005), while Ahmed and Goodwin (2006a) find that 33% of ASX listed

companies reported material AIFRS adjustments due to the implementation of AASB 112.

AASB 119: Employee Benefits

Whilst previous AGAAP required companies that maintained employer sponsored defined

benefit plans to expense the payment of contributions, the newly adopted IFRS (AASB 119)

requires the additional recognition of any net surplus or deficit of the plan funds as an asset or

liability. Specifically, AIFRS requires that companies obtain independent actuarial valuations

of the “fair value” of plan assets and liabilities to determine the extent of a net surplus or deficit.

While prior literature has demonstrated significant financial statement impacts of the application

of IFRS over employee benefits (AASB 119) on a number of Australian companies (Ernst &

Young Australia, 2005; Ahmed & Goodwin, 2006a). Whilst the effect of these new AIFRS

requirements on the audit function may be onerous, the reliance on independent actuarial

valuations may seek to mitigate any increased risk of misstatement or misrepresentation

stemming from management biases and/or lack of expertise.

6 Whereas previous AGAAP permitted a choice between the “balance sheet” and “profit loss” method of tax –effect

accounting, the newly adopted IFRS (AASB 112) has the force of limiting the recognition and measurement

requirements of tax balances to that of the “balance sheet” method only. The profit and loss approach to tax effect

accounting allowed companies to simply account for the effects of timing and permanent differences between taxable

income and accounting profit. Prior evidence suggests that the majority of Australian companies previously

undertook this method of tax effect accounting under previous AGAAP.

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AASB 136: Impairment

AIFRS (AASB 136) now requires that assets subject to impairment, primarily indefinite-life

intangibles such as goodwill, to be held at recoverable amount on the face of the financial

statements, where the recoverable amount is defined as the higher of fair value less costs

involved with sale or value in use, determined by discounted cash flow methods. Effectively,

this standard introduces the mandatory requirement to discount future cash flows in the

measurement of recoverable amounts, reducing the policy choices available under previous

AGAAP and increasing the likelihood of material misrepresentations or misstatements.

AASB 138: Intangibles

The recognition requirements under AIFRS (AASB 138) do not extend to internally generated

intangibles, thus companies are forced to de-recognise any internally generated intangibles

previously recognised under local GAAP, moreover recognised intangibles are now subject to

impairment testing. Additionally, the new AIFRS requirements do not permit research

expenditure to be capitalised, and the capitalization of development costs is now subject to

principles-based recognition criteria, e.g. the ability to demonstrate the technical feasibility of

developing an asset available for use or sale and the probability of generating future economic

benefits. Accordingly, auditors will be under increased pressure from clients given that

companies will seldom want to write-off significant asset balances, thus increasing the

likelihood of material misstatements or misrepresentations from deliberate management bias.

AASB 132/139: Financial Instruments

The changes to requirements embodied within AASB 132/139 from previous AGAAP are

substantial. Measurement of financial instruments is now predominantly fair value based,

requiring complex and subjective estimates in the absence of active and liquid financial markets,

for certain assets and liabilities. Additionally, IFRS requirements over hedged instruments are

also proving significant, with companies now required to perform extensive tests over hedge

effectiveness and ensure adequate documentation of relevant hedged relationships.

Accordingly, auditors are now required to review all such documentation as part of audit

procedures, and provide assurance over perceived hedge effectiveness and fair value

calculations in light of the potential lack of management expertise and misstatement.

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Professional literature has reported the application of AASB 132/139 is proving the most

difficult and onerous for companies (ICAA, 2006).

2.4.3 Expert opinions regarding significant IFRS impacts

In order to gain a deeper understanding of the impacts of IFRS adoption on audit pricing and

validate our a priori expectations regarding the theorised high impact standards above, we

obtain expert opinions. We distributed a questionnaire to 45 professional auditors from a Big4

accounting firm, inviting their opinions on AIFRS discussed above. Experts were asked to

assign a rating from 1 through to 10, and rank, each of the AIFRS’s previously discussed, on the

additional audit effort required to satisfy the requirements, relative to previous AGAAP, in

order to capture the incremental effects on the audit function given the change from AGAAP to

IFRS7.

[Insert Table 1 here]

Results obtained from the expert opinion questionnaires are summarized in table 1. Our final

sample of 31 experts consisted of the following: 12 Assistant Managers (average of 3.75 years

experience), 7 Managers (6.07 years experience), 7 Senior Managers (10 years experience) and

5 Partners (18 years experience) within a Big4 audit firm. All respondents were members of

professional bodies (CA or CPA) and had attended in-house IFRS training, validating their

standing as experts for the purposes of this paper.

Table 1, Panel B indicates that AASB 139, relating to the recognition and measurement of

financial instruments, resulted in the greatest additional audit effort being expended, with a

mean rating of 8.3 (out of maximum of 10). Coupled with a mean rating of 7.3 on AASB 132,

results imply that, overall, auditors are expending the greatest level of audit effort in complying

with the new IFRS requirements in relation to financial instruments. Moreover, 68 percent of

experts ranked AASB 132/139 as having the greatest impact on audit effort. AASB 2

concerning share based incentives also had a significant effect on incremental audit effort

required, behind financial instruments, with a mean rating 7.7 and 23 percent of experts ranking

it as the most significant impact on audit effort and expertise required.

Impairment was also determined to have significant impact on auditors, with AASB 136 given a

mean rating of 6.8, behind financial instruments and share based incentives. Panel B of Table 1

also indicates that in all three instances related to the above AIFRS, less experienced staff rated

7 The completed research instruments are available from the authors upon request.

- 14 -

the financial instruments, share based incentives and impairment as having significantly less

impact than more experience staff (t-statistics of 2.94, 2.02 & 1.53, respectively). 8 One

possible explanation for this disparity may be that greater audit expertise is needed in addressing

the more complex and high risk areas of AIFRS, thus further validating the hypothesized

relationship between mandatory IFRS adoption and the need for greater auditor expertise

needed given the risk perceived by auditors.

Intangibles and income tax were also perceived by auditors as having a significant impact on the

audit function, with mean rating of 6.3 and 6.5 respectively, however both of these standards

were consistently ranked lower to middle range in comparison to previously discussed IFRS.

Furthermore, there existed no significant difference between the ratings of more experienced

and less experienced staff, this implies that IFRS requirements over intangibles and income

taxes, while posing an increased risk requiring additional audit effort, do not require the

additional auditor expertise of high-level staff required by the more complex financial

instruments, share based payments requirements and impairment.

The results suggest AASB 119 as having only a low to moderate impact on audit effort, with a

mean rating of only 4.9 and consistently ranked as having the least impact on auditors (45% of

experts). This finding is consistent with the notion that risks faced by the auditor is somewhat

mitigated by the requirement of companies to obtain independent actuarial valuations,

consequently auditors are able to rely on 3rd party valuations and expend less audit effort.

Accordingly, AASB 119 on employee benefits is not perceived as encompassing a significant

impact on the audit function and therefore not empirically tested in the following subsections.

Overall, expert opinions imply that the requirements of the newly adopted IFRS in relation to

financial instruments, share based payment and impairment are having the most significant

impact on the audit function, requiring the greatest audit effort and auditor expertise to be

expended by more experienced auditors, consistent with our understanding. While income taxes

and intangibles are perceived as requiring additional audit effort, results suggest that greater

auditor experience is not required in order to ensure adequate compliance with AIFRS/IFRS.

8 Less experienced staff consisted of Assistant Managers (12) with the least number of years of audit experience (mean: 3.75 years). Senior level staff consisted of all other staff, being Managers (7), Senior Managers (7) and Partners (5), this subset of experts had an average of 11 years of audit experience.

- 15 -

In line with the above expectations and analysis of expert opinions, we form the following

hypotheses:

H2a: Companies that undertake complex financial instruments transactions under AASB

132/139 will exhibit greater audit fees relative to other companies, all other things equal.

H2b: Companies that must apply AASB 2 will exhibit greater audit fees relative to other

companies, all other things equal.

H2c: Companies that must apply AASB 136 will exhibit greater audit fees relative to other


H2d: Companies that must apply AASB 138 will exhibit greater audit fees relative to other


H2e: Companies affected by the application of AASB 112 will exhibit greater audit fees relative

to other companies, all other things equal.

3. Research Method

3.1. Effect of IFRS adoption on audit fees – time series

In order to test H1, we utilise pooled time-series audit fee data in an OLS regression. We use a

cross-sectional audit fee regression model adapted from the well established prior audit fee

research (see for example Simunic, 1980; Ferguson, Francis and Stokes (2003); Francis, 1984;

Craswell, Francis & Taylor, 1995). The model controls for systematic differences in client size,

audit complexity and auditor-client risk sharing, and previous literature has shown these models

to have demonstrated good explanatory power (R 2 of 0.70 or above) and produced robust results

across a myriad of samples, time periods, countries and sensitivity analyses regarding model

misspecification (Craswell et al., 1995; Francis & Simon, 1987).

In order to test the effects of a mandated IFRS adoption on audit fees, we perform an OLS

cross-sectional regression using time-series data, on a sample of 438 Australian companies

across 4 years leading up to and including the adoption of IFRS. We utilize an adaptation of the

established audit fee model adding an experimental variable indicating the year of full IFRS

adoption is added to the model. The inclusion of an experimental indicator variable in our audit

fee model, will determine if there is a significant shift in the intercept of the fitted regression

model, enabling us to test for a difference in mean audit fees between the preceding years and

the year of mandated adoption (Craswell et al., 1995). We hypothesize that the coefficient on

the experimental variable will be significantly positive, indicating a significant increase in mean

- 16 -

audit fees for the year of full IFRS adoption, relative to the preceding years. To provide some

insight into the debate surrounding the costs incurred by smaller companies, we partition the

sample into small and large firms, based on total assets, and statistically test for differences

between sub-samples. The sample is partitioned on median total assets, and then is further

partitioned based on the highest and lowest quartile, thus allowing us to test for differences

between the most extreme observations of large and small companies.

The OLS regression model to be estimated is as follows9:

=iLogAF

)1(

Re

1110987

6543210

iiiiii

iiiiii

IFRSAuditorOpinionYELoss

ROIDEInvcCurrentLogAsset

εββββββββββββ

++++++++++++

Where 0β to 11β are regression coefficients, and iε is assumed to have the normal OLS

regression properties. Econometric and estimate issues are examined in Section 4.3. The

variables are defined as follows:

Dependent Variable LogAF = Natural log of total audit fees paid to external auditors Control Variables LogAsset = Natural log of total assets Current = Ratio of current assets to current liabilities Rec = Ratio of total receivables to total assets Inv = Ratio of total inventory to total assets DE = Ratio of long term debt to total assets ROI = Ratio of NPAT to total assets Opinion = Indicator variable: assigned a value of 1 if the firm was issued with a

modified audit opinion, otherwise assigned a value of zero for all other instances.

YE = Indicator variable: assigned a value of 1 for firms with a 30 June year end, otherwise assigned a value of zero for all other year end dates.

Loss = Indicator variable: assigned a value of 1 if firm reported a loss in Net Profit after Tax and Abnormal items in any of the last 3 years, otherwise assigned a value of zero in all other instances.

Auditor = Indicator variable: assigned a value of 1 if the firm employs a Big4 auditor, otherwise assigned a value of zero in all other circumstances

Experimental Variable

IFRS = Indicator variable: assigned a value of 1 in the year of full IFRS adoption, otherwise assigned a value of 0 for all preceding periods.

9 Please note, we are currently in the process of gathering subsidiary data for our sample of companies in order to

include the number of subsidiaries as a control variable. This variable is an additional control for company size and

audit complexity, given a higher number of subsidiaries requires greater audit effort and time.

- 17 -

3.1.2 Control Variables

The control variables used in the above model have been established in the prior literature as

determinants of audit fees, controlling for auditee size (LogAsset), audit complexity (Rec, Inv),

loss exposure (Current, DE), and the likelihood that losses will be borne by the auditor (Opin,

ROI, Loss). Prior literature has established a Big-N auditor premium (see Francis, 1984; Francis

and Simon, 1987; Craswell et al., 1995), additionally prior literature suggests an increase in the

Big-N auditor premium in times of reporting regime changes (Asthana, Balsim and Kim, 2004).

To control for any confounding effects of auditor reputation premiums an indicator variable,

Auditor, has been included in the model. Prior studies have also evidenced lower audit fees for

non-peak-season audits, accordingly we include a control variable, YE.

3.1.3 Experimental Variable

The experimental variable is an indicator variable, IFRS, which is assigned a value of 1 in the

year of full IFRS adoption and zero in all other circumstances. Following the announcement by

the FRC, companies were required to prepare AIFRS/IFRS-compliant financial standards for

reporting periods beginning on or after 1 January 2005. Accordingly, we have defined the year

of IFRS adoption as the first reporting period subject to full IFRS/AIFRS compliance, being

reporting periods ending 31 December 2005 and after.

Following Craswell et al. (1995) the percentage shift in audit fees in the current fitted regression

model is estimated to infer the magnitude of changes in audit prices attributable to the adoption

of IFRS. In addition to the tests of statistical significance of the parameter estimate on IFRS,

the magnitude of the intercept shift can be calculated using the procedure widely utilized in

prior audit fee studies (e.g. Francis & Simon, 1988; Craswell et al., 1995).

3.2 Effect of specific AIFRS/IFRS on audit fees

In testing the second set of hypotheses we employ the same cross-sectional audit fee regression

model as above, controlling for cross-sectional differences in client size, audit complexity, and

auditor-client risk sharing. Regression model (2) is limited to estimation in the year of IFRS

adoption only, not across time. In order to test the significance of the hypothesized “costly”

AIFRS/IFRS as discussed in section 2, the following explanatory variables were added to the

model: Hedge, Fininst, SBI, Gwill, Intan, Tax. The experimental variables are discussed in

detail in section 3.2.2.

- 18 -

The cross-sectional model estimated is as follows:

=iLogAF

iiiii

iiiiiii

iiiiii

InGwillTaxSBI

FininstHedgeAuditorLossYEOpinion


εββββββββββ

βββββββ

+++++

++++++

++++++

tan

Re

16151413

121110987

6543210

(2)

Where 0β to 16β are regression coefficients, and iε is assumed to have the normal OLS

regression properties. The variables are defined as follows:

Dependent Variable LogAF = Natural log of total audit fees paid to external auditors Control Variables LogAsset = Natural log of total assets Current = Ratio of current assets to current liabilities Rec = Ratio of total receivables to total assets Inv = Ratio of total inventory to total assets DE = Ratio of long term debt to total assets; ROI = Ratio of NPAT to total assets; Opin = Indicator variable: assigned a value of 1 if the firm was issued with a

modified audit opinion, otherwise assigned a value of zero for all other instances;

YE = Indicator variable: assigned a value of 1 for firms with a 30 June year end, otherwise assigned a value of zero for all other year end dates;

Loss = Indicator variable: assigned a value of 1 if firm reported a loss in Net Profit after Tax and Abnormal items in any of the past 3 years, otherwise assigned a value of zero in all other instances;

Auditor = Indicator variable: assigned a value of 1 if the firm employs a Big4 auditor, otherwise assigned a value of zero in all other circumstances;

Experimental Variables Hedge = indicator variable, equals 1 if company applied hedge accounting

under AASB 139, and 0 otherwise; Fininst = length of financial instruments note (in pages);

SBI = indicator variable, equals 1 if company applied AASB 2, and 0 otherwise;

Tax = indicator variable, equals 1 if company recorded AIFRS adjustments in relation adoption of AASB 112, and 0 otherwise;

Gwill = indicator variable, equals 1 if company had recorded goodwill balance, and 0 otherwise;

Intan = indicator variable, equals 1 if company had recognised intangible assets in prior year under previous AGAAP, and 0 otherwise;

- 19 -

3.2.2 Measurement of experimental variables

AASB 132/139: Financial Instruments

AIFRS requirements governing financial instruments are covered by two accounting standards:

AASB 132 concerning presentation and disclosure, and AASB 139 which details the

recognition and measurement criteria, it is the combined effects of these standards that will be

examined in the current study. Whilst financial instruments criteria captures key account

balances such trade receivables and payables, by and large, all companies will be required to

adhere to at least the basic requirements of fair value measurement and disclosure under AASB

132/139, therefore a simple indicator variable indicating the application of these standards as

insufficient for the purposes of this study.

Accordingly, in measuring the greater audit risk and complexity related to AASB 132/139, we

took a much broader approach, measuring the impact of financial instrument requirements in

two ways, (1) length of the financial instruments note in terms of number of pages (Fininst), a

method that loosely follows the rationale of Wiseman (1982) in measuring the complexity of

disclosures, and (2) whether or not the company applied hedge accounting (Hedge).

Measuring the length of the financial instruments note adequately captures the increased risk

and audit effort required, given that the greater number of financial instruments held or complex

transactions undertaken, the more detailed measurement and disclosure required under IFRS.

The second measure captures the increased risk and audit effort required over hedge accounting

requirements of AIFRS. Given the requirements governing hedge accounting are recognised as

encompassing significant changes from previous local GAAP, namely more stringent

requirements over hedge effectiveness and designation of hedged relationships. If a company is

applying hedge accounting as governed by AASB 132/139, they are required to disclose

information to that effect in the accounting policies described in the annual report (Note 1), thus

we were able to objectively ascertain which companies are undertaking hedge accounting.

AASB 2: Share-based incentives

When companies enter into share-based payments they are required to apply AASB 2

disclosures within the notes, therefore a review of the notes to the financial statements allowed

us to ascertain which companies undertook share-based payments in the current or prior period.

We note that the prior period is also of relevance in the measurement of this variable, as AASB

1 requires companies to restate comparatives and disclose AIFRS to AGAAP reconciliations for

the previous period. Therefore, where a company applied AASB 2 in the current period or

retrospecively, we assigned a value of 1 to this variable, and 0 in all other circumstances (SBI).

- 20 -

AASB 112: Income taxes

In measuring the impact of AASB 112, we reviewed AIFRS/IFRS to AGAAP reconciliations

within the notes of financial statements, scrutinizing for any adjustments to previous periods

equity resulting from the application of AASB 112 in order to ascertain which companies were

affected by the implementation of AASB 112. Therefore, where it was determined that an

adjustment had been in relation to AASB 112, we assigned this variable a value of 1, and 0 in

all other circumstances (Tax).

AASB 136: Impairment

Prior literature (Ahmed and Goodwin, 2006; Jubb, 2005), coupled with additional comments

obtained from experts in section 2.3.3 indicate that the new IFRS on impairment primarily

applies to indefinite life intangibles, which, by and large are goodwill balances. Consequently,

the risk posed by this standard are primarily in relation to impairment tests of goodwill balances.

Therefore, if a company reported a goodwill balance in the year of IFRS transition, we assigned

this variable a value of 1, and 0 in all other circumstances (Gwill).

AASB 138: Intangibles

AASB 138 requires the de-recognition of self-generated intangible assets and any previous

capitalised R&D expenditure that does not meet the current requirements under IFRS. To

capture all companies affected by the implementation of AASB 138, we reviewed annual

reports in the current and preceding year of IFRS adoption, in order to determine which

companies held intangible assets other than goodwill. Companies that held intangible assets

other than goodwill on their balance sheets were assigned a value of 1, and 0 in all other

circumstances (Intan).

3.3 Data and Sample Selection

Our sample consists of ASX listed companies followed on Connect4 Annual Report Database 10

that had released their annual reports prepared in full accordance with AIFRS/IFRS at the time

of data collection, along with annual reports for the preceding 3 years11. Data in relation to

audit fees, auditor information and audit opinion was hand-collected from annual reports held

10 Connect4 Annual Reports Collection provides complete annual reports for approximately 1,300 companies listed on the ASX (as at 30/06/06 they were approximately 1,700 listed companies). 11 Companies listed on the ASX were required to prepare annual reports in accordance with IFRS/AIFRS for reporting periods beginning on or after 1 January 2005. The relevant year-ends for the purposes of this study are 31 December 2005 and after.

- 21 -

on Connect4 for reporting periods 2001/2002 to 2005/2006, being the year of first time AIFRS-

compliance and the preceding three annual reporting periods. Additional data in relation to the

experimental variables indicating application of specific AIFRS was hand-collected from

2005/2006 AIFRS-compliant annual reports prepared in accordance with AIFRS/IFRS, sourced

from Connect4. Financial information in relation to the control variables employed in the audit

fee regression models was sourced from Aspect Huntley FinAnalysis database for the relevant

years required. The audit fee data was then matched to financial information, resulting in a final

sample of 1,752 firm-year observations (438 firms across 4 years) with all relevant auditor and

financial information, after deleting firms that were not previously adopting AGAAP. 12 We

note our sample does not include banks. Our final sample accounts for approximately 52% of

the current total ASX market capitalisation.

From this sample, we then matched IFRS variable data to complete audit fee and financial data

in the year of IFRS adoption to obtain our sample for the cross-sectional analysis utilizing

model (2). This resulted to reduced sample of 300 companies with all relevant variable data

required for the cross-sectional regression analysis of model (2).


Table 2 reports the distribution of the full sample of 438 and reduced sample of 300 companies

across GICS Industry codes, in Panels A and B, respectively. As shown, the sample contains a

larger number of companies in the financial services (402030, 402010) and metals & mining

(151040) industry segments than in other industries. Following the approach adopted in a

number of prior studies (e.g. Francis & Wang, 2005; Lyon & Maher, 2005), we control for the

effects of these industries in two ways: (1) by including indicator variables for companies in

those industries (Fin and Mine), and (2) by omitting these firms from the sample 13, in order to

ensure these industries are not driving the results.

12 Prior evidence on the Australian experience suggests that auditor switches result in auditors charging lower fees in the early years of an engagement (see Simon & Francis, 1988; Craswell & Francis, 1999). Moreover, prior analysis on the pricing of initial engagements has shown that price cutting is not uniform across all categories of audit switches and is more likely to occur for switches from non-Big6 to Big6 auditors (Craswell & Francis, 1999). Consequently, this may bias down the underlying effect of the adoption of IFRS on the fees charged by these new auditors. In running our regressions we removed 30 companies that had switched auditors between Big-N and non Big-N auditors within our sample period. We note that results were not significantly different from results obtained on the full sample as reported in Table 5 and 6. Accordingly, these companies were left in the sample and results reported on the full sample. 13 Firms within GICS of Financial Institutions, Metals and Mining, and Utilities were omitted from the sample. This amounted to 132 firms being omitted from the full sample, leaving 306 firms in the trimmed sample.

- 22 -

4. Empirical Results


Table 3 presents the descriptive statistics for the pooled sample of 1,752 observations (438

companies across 4 years) and each individual year. For each variable we present the mean,

standard deviation, and count for binary variables. From table 3, we note no noticeable

anomalies with means and standard deviations behaving as expected across time. Significant

variation is noted in total dollar value of audit fees and total assets, however the log

transformation collapses and normalises the distribution of these variables. It is evident that

audit fees have steadily risen in the preceding three years, however the largest increase occurs in

the year of IFRS adoption, which, prima facie, lends credibility to the arguments presented in

this paper. Furthermore, we note an increasing trend of modified opinions, this appears

consistent with the notion of auditors undertaking “defensive action” in times of uncertainty,

further validating our arguments of the uncertainty surrounding mandated IFRS adoption.

Overall, all variables appear to be behaving fairly consistent from year to year and the data

suggest no significant anomalies biasing the sample.


The correlation matrices for each model and relevant sample periods are reported in table 4.

Panel A reports the correlation between dependent and independent variables employed in

model (1) for the full sample. Panel B reports the correlation coefficients between the

dependent and independent variables employed in model (2) for the cross-sectional analysis.

[Insert table 5 and 6 here]

Table 5 presents the results for the test of hypothesis 1. Note that the full regression equation in

panel A has an adjusted R2 of 78%; the modified regression in panel B, which controls for

possible industry effects via the inclusion of indicator variables, has an adjusted R 2 of 79%; and

the modified regression in panel C which omits financial service companies and utilities, and

metals and mining companies, has an adjusted R 2 of 80%. These statistics are in the range of

those reported in the extant literature on audit fee estimates (e.g. Craswell et al., 1995; Lyon &

Maher, 2005; Simunic, 1980). Furthermore, the reported signs and significance levels on the

coefficients of the control variables are in line with expectations and prior literature findings.

For the experimental variable, IFRS; the full regression in panel A reports a coefficient of 0.21

and is statistically significant at p < 0.025. The modified regressions shown in panels B and C

- 23 -

report coefficients on IFRS of 0.22 and 0.23, respectively, both statistically significant at p <

0.025. These findings are consistent with the initial arguments and conjecture of increased audit

fees as a result of a mandated reporting regime change, suggesting a significantly positive shift

in mean audit fees in the year of IFRS adoption, above and beyond the average audit fees for the

preceding years, after controlling for relevant factors. The parameter value reported is

interpreted in an economic sense in section 4.2.

Table 8 reports the results from the regression on the subset of “smaller” firms, in panel A

(adjusted R2 of 59%), and reports the results from the regression on the subset of “larger” firms,

(adjusted R 2 of 70%). Panel B reports the results from the regression of the subsample of the

highest and lowest quartiles, based on median total assets, with the highest quartile reporting an

adjusted R 2 of 67%, and the lowest quartile of reporting an adjusted R 2 of 56%. The results

appear consistent across both partitions of small and large firms, with the coefficient estimate of

IFRS remaining significant at p < 0.025 (one-tailed) in both regressions. The parameter value

reported is interpreted in an economic sense in section 4.2.

[Insert table 7 here]

Table 7 presents the results of the cross-sectional analysis of specific accounting standards in

the year of IFRS adoption testing the second set of hypotheses. The full regression in panel A

has an adjusted R 2 of 83%, a statistic that is the top-end of the range of those reported in the

extant literature. The reported coefficients on Hedge, Tax, are positive but non-significant at an

appropriate level (at p < 0.025) providing no support for the H2d and casting doubt on H2a.

Coefficients on the experimental variables, Fininst, SBI, Gwill and Intan are positive and

significant at p < 0.025, providing support for H4b, H4c, H4e and partial support for H4a.

These results imply that companies that; (1) undertake share-based payments, and/or (2) are

subject to impairment testing over Goodwill and other intangible balances, and/or (3) undertake

complex financial instrument transactions exhibit higher audit fees, relative to other companies.

Conversely, hedge accounting under AASB 132/139, and adjustments required under AASB

112 do not appear to be significant determinants of audit fees in the year of IFRS adoption. The

parameter values reported are interpreted in an economic sense in section 4.2

The modified regression model in panel B, which omits financial institutions, mining and

insurance companies, has an adjusted R2 of 82%. However, only SBI and Gwill retain their

- 24 -

significance in the modified regression (t-statistics of 2.81 and 4.06, respectively), while all

other hypothesized variables were found to be insignificant. Therefore, the previous results of

significance attached to financial instruments and intangibles in panel A, were potentially driven

by industry effects. These findings appears intuitively appealing, and consistent with our

understanding of business operations across industries, for instance: financial institutions and

insurance companies are more likely to engage in complex financial instruments transactions

(e.g. derivatives trading, foreign currency transactions, borrowings) given the nature of their

operations, relative to companies within other industries. Furthermore, mining companies are

also more likely to undertake more complex financial instruments transactions, for example:

extensive borrowings to fund explorations and hedging the prices of inputs and outputs.

Furthermore, mining companies are also expected to hold a number of intangible assets relating

to the licenses over mining sites and explorative research and development.

4.2 Economic significance and discussion of results

Consistent with prior literature, (see Simon and Francis, 1988; Craswell et al., 1995;

Seetharaman, Gul and Lynn, 2002; and Lyon and Maher, 2005) we are able to compute the

audit premium associated with an indicator variable as e a – 1, where a represents the coefficient

on the experimental indicator variable. In the current study, we use this formula where a

represents the parameter value of the IFRS variable reported in table 5, this will allows for a

computation of the audit fee premium associated with the adoption of IFRS. We obtain an audit

fee premium attached with the adoption of IFRS of 23%, indicating that auditors charged, on

average, 23% higher audit fees in the year of IFRS adoption. This result is consistent with the

fundamental arguments put forward in the current paper that implies the mandatory adoption of

IFRS has lead to an increase in audit fees.

The more intriguing finding of the paper however is the economic significance attached to

results obtained from the partitioned sample in table 6, panels A and B. The results of the

simple partition on median total assets results suggest that smaller companies incur a premium

of approximately 26% in the year of IFRS adoption, while large companies incur a premium of

21%, these results are not significantly different (chi-square = 1.06). The results of the more

extreme partition, based on upper and lower quartiles are also consistent those presented in

panel A, suggesting an audit fee premium of 29% and 20%, respectively to the smallest and

largest companies, however these results are not significantly different (chi-square = 1.38).

Whilst results are not significantly different, this study finds evidence in support of the

conjecture in the financial press regarding the onerousness of IFRS related to the increased costs

- 25 -

enforced on smaller companies. More specifically, when reconciled with previous literature,

these results appear somewhat contradictory. Prior research (Goodwin and Ahmed, 2006b) has

indicated that smaller firms are less likely to be affected by the transition to AIFRS/IFRS.

Through their examination of the magnitude and frequency of IFRS adjustments reported by

Australian companies, Goodwin & Ahmed (2006b) suggest that the conjecture regarding the

onerousness of IFRS adoption on smaller companies is unfounded. Therefore, given the results

of Goodwin & Ahmed (2006b) alone, it is intuitively appealing to conclude that smaller

companies should have exhibited significantly lower audit premium than larger companies.

However, results presented the current paper show an audit fee premium of 20-29% arising

from the implementation of IFRS, consistent across both small and large companies.

We put forward two possible explanations for this disconnect: (1) Auditors may be exhibiting

some degree of opportunism in pricing smaller audit engagements, given the level of uncertainty

and potential lack of understanding within their smaller client market of the effects of IFRS, or

(2) the effect of IFRS implementation on larger firms is actually understated, given that larger

firms have greater access to resources and expertise, larger firms may have implemented very

effective IFRS transitional programs, in order to mitigate potential implementation issues

arising, and thus decreasing the risk perceived by auditors.

Applying the above formula to the parameter estimates obtained in the cross-sectional analysis

presented in table 7, we are able to quantify the effects of specific IFRS. Results from panel A

of table 7 suggest significant audit fee premia of approximately 40%, 56% and 35% attached to

companies that undertake share-based payments, are subject to impairment requirements over

goodwill and hold other intangible assets on their balance sheets, respectively. It must

acknowledged that these premiums appear quite substantial, however are not completely out of

line with prior audit fee research that has found audit fee premia in excess of 40% (e.g. Lyon &

Maher, 2005; Ireland & Lennox, 2002).

The interpretation on Fininst is relatively limited, given this is a continuous variable

representing the length of the financial instruments note the above formula is not appropriate in

attaching economic significance to the parameter estimate. Accordingly, quantifying the effect

of this variable on audit fees in any meaningful way becomes problematic. Hence for the

purposes of this discussion, we may only draw the following conclusion based on reported

results: companies that undertake more complex financial instrument transactions will exhibit

significantly higher audit fees relative to other companies.

- 26 -

When financial institutions, insurance firms and mining companies are omitted from the

sample, the only experimental variables to retain their significance are SBI and Gwill exhibiting

audit fee premiums of 35% and 52%, consistent with the findings of the full sample in panel A.

4.3 Econometric Issues

Prior audit fee studies have shown that audit fee regression models may be subject to issues of

heteroskedasticity and collinearity. In order to ensure that the current regression results are not

biased by such econometric issues we perform the following diagnostics.

The control variables utilized in the widely accepted audit fee model are accounting-based

measures and as previously established, are susceptible to potentially high levels of collinearity,

given their nature (Lyon and Maher, 2005). Consequently, if explanatory variables are

correlated the interpretation of the magnitude of the these coefficients becomes increasingly

difficult to support. In the current study we are concerned with isolating an interpretable

coefficient on the experimental variables of interest, consequently if the model contains a

significant measure of correlation the interpretation of parameter magnitudes is then difficult to

support. Following the approach of Lyon & Maher (2005) we obtain variance inflation factors

(VIF) for all variables within the model. VIFs measure how much the variance of an estimated

regression coefficient is inflated in the presence of correlation between your explanatory

variables. A VIF of 1 for a regression coefficient indicates that it is orthogonal to other

variables and exhibits very minimal collinearity. Judge et al (1987) and Neter, Wasserman &

Kutner (1983) indicate that a VIF values in excess of 5 indicate severe collinearity and may be

unduly influencing the least squares estimation (Lyon & Maher, 2005). All variables in the

current study exhibit VIF scores at or below 2.5. Therefore, while the model exhibits some

level of collinearity, it is relatively low when compared with the benchmarks provided by

econometric literature (e.g. Judge et al., 1987; Neter, Wasserman & Kutner, 1983), to this end

we conclude that our interpretations of the coefficients remain robust to issues of collinearity.

In order to address concerns of the models vulnerability to heteroskedasticity the reported

standard errors and associated t-statistics are computed using White’s (1980) heteroskedastic-

consistent standard errors. To this end, we conclude that the conclusions regarding the

magnitude and significance of the experimental variables are unlikely to be affected by

collinearity or heteroskedasticity. We further note that results reported in Table 5 remain robust

- 27 -

to concerns of serial dependence of observations, given our use of pooled time-series data in

cross-sectional regression analysis of model (1)14.

In order to determine the effect of outliers on our results, we removed the top and bottom 1

percent of observations across all continuous variables, this resulted in a trimmed sample of 401

companies. In re-estimating the regression results presented in Tables 5 and 6 using the

trimmed sample, we find no significant difference in the reported results, and conclude that the

results obtained on the full sample are not subject to outlier biases.

5. Summary and Conclusions

The proposed switch to IFRS has sparked intense debate concerning the proposed costs and

benefits of a mandatory regime change, however empirical evidence addressing the potential

costs of IFRS adoptionq is lacking. Therefore, the purpose of this paper is to provide evidence

on the effects of IFRS adoption on the audit function and the subsequent costs faced by

Australian companies.

The results in this study provides initial insight into effects of IFRS on audit pricing, finding a

23 percent audit premium in the year of IFRS adoption, relative to previous years. This result is

consistent across industries and, both small and large companies, implying that overall, the

mandatory adoption of IFRS has cost Australian companies an extra 23% in audit fees.

To conduct tests of the relation between the adoption of IFRS and audit fees, we control for the

typical size, complexity and risk factors established in previous literature. We infer that the

14 The reported t-tests in Table 5 and Table 6 reflect the pooling of time series observations of audit fee observations for 438 companies. Prior academic literature has argued econometric shortcomings in using pooled time-series data in a cross-sectional regression analysis, citing the potential lack of independence of observations across time. If we are to assume serial dependence between subsequent firm specific observations, it would represent a violation of the classical assumptions of OLS, and may overstate t-statistics along with the model’s explanatory power (see Taylor, 1992). While in present studies we assume independence both in time series and cross-section, it must conceded that these assumptions can be “violated by real world counter examples” (Anderson & Zimmer, 1992: pp 57). However, from a theoretical viewpoint, the temporal independence of audit fee data is consistent with the theoretical underpinnings audit pricing theory (established in Simunic, 1980) given that due to potential changes in operating and financial structures, and subsequent changes in risk profiles of clients from one year to the next, auditors will re-perform risk assessments at the beginning of each engagement, determining the appropriate level of audit effort required to minimise “loss liability”. Notwithstanding, To address these concerns we apply the correction suggested in Taylor (1992) to the t-statistics reported in Tables 5 and 6. Taylor (1992) suggests the following adjustment for non-independence based on Christie (1990):

tnt ×= where:

t = original t-statistic reported n = number of repeated observations which form the time series component

t = average t-statistic from a regression with no time series component The adjusted t-value reported above represents the potential “lower bound” of the possible corrected t-values from our four year pooled sample, according to Taylor (1992) this is the “worst possible scenario” assuming near perfect serial dependence. Applying the above formula to the t-statistic on the experimental variable, IFRS, obtained from cross-sectional time series regression reported in panel A, Table 5 yields an adjusted t-statistic of 2.75 (p < 0.025 one-tailed test). While adjusting the t-statistics reported on the IFRS variable time-series regression yields adjusted t-statistics of 2.65 (p < 0.025 one-tailed test) and 1.54 (p < 0.05 one-tailed test).

- 28 -

adoption of IFRS within Australia has caused audit firms to increase their fees in order to reflect

insurance premiums, or increased audit effort expended to cover potential future losses arising

from misstatements of misrepresentations given the shift in reporting regimes. The results in

this paper imply that auditors have viewed the transition to IFRS as encompassing a significant

level of risk and have priced their expected costs in audit fees charged.

The results of the partitioned sub-samples, indicate that this premium is robust across small and

large companies, a finding that is somewhat contradictory when reconciled with prior studies

that examine the onerousness of IFRS on smaller companies. For instance, Goodwin & Ahmed

(2006b) find that smaller companies exhibit significantly smaller and less frequent IFRS

adjustments that larger companies, intuitively this would lead us to believe that audit fees in

relation to smaller companies should not significantly effected as they are less risky. However,

our findings suggest that mean audit fees have risen significantly in the year of IFRS adoption,

across both and small companies, thus indicating potential opportunistic behaviour by audit

firms within their smaller client segment. These results present the first empirical evidence of

the costs faced by Australian companies as a result of IFRS adoption, and further shed empirical

light on the conjectures of the onerousness of IFRS on smaller companies.

Our secondary tests find evidence suggesting that new IFRS covering share-based incentives,

impairment in relation to Goodwill and other intangible balances, and financial instruments are

perceived by auditors to contain the greatest risk, and require the greater auditor effort and

auditor expertise in order to ensure compliance. Consequently, auditors pass on the increased

costs associated of auditing these particular transactions onto clients, hence the implementation

of IFRS has proven costly for companies that are subject to these requirements, relative to other

companies.

These results imply that auditors assess the risk of IFRS adoption on an individual client level

as well as on a broad jurisdictional level and pass on expected costs to clients through charging

higher fees. This study highlights the significant costs associated with a mandatory regime

change, specifically the global push of international harmonization through the adoption of

IFRS. The results imply that IFRS adoption with Australia is costly to both auditors and

companies alike, contributing to the continuing debate surrounding the perceived costs and

benefits of harmonization. The discussion and results presented in this study will have several

implications for international convergence projects undertaken throughout the world, providing

evidence as to the significant costs associated with adoption of IFRS on the institutional

environments.

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5.2 Limitations and direction of future research

We acknowledge two primary limitations of the current study: first, the inherent inability to

preclude other explanations for the results. While the arguments made in this paper focus on a

perceived increase in risk stemming from a reporting regime change as the driver of increased

audit fees, we acknowledge that this increase in fees could simply be driven by increased

transaction costs faced by the auditors in dealing with such a significant switch in reporting

regimes. For example, it is intuitively appealing to assume that audit firms would have incurred

costs in relation to: development and training of human capital; amendments to specific audit

manuals and procedural guidelines; and the updating of financial statement templates. While

these costs are not directly attributable to any one client, nor do they relate the risk arguments

put forward in this paper, they represent costs arising from the switch to IFRS that presumably,

may be priced across individual audit engagements and passed onto clients. Consequently, the

costs incurred in relation to these one-off transactions, while not directly related to the

arguments presented in this paper may be a significant factor in the increase in audit fees

reported. Therefore, the results of this paper should be viewed with some scepticism and future

research may seek to further distinguish between these effects.

A secondary limitation lies in the inherent drawback with the cross-sectional research design

employed in this study, being the potential effect of serial-correlation between observations

when employing time series data. For the most part, auditing studies are unable to utilise formal

time-series regression, due to the sheer number of years needed to maintain statistical validity.

In auditing, this is both impractical and would lead to issues regarding statistical validity, in that

the change within accounting standards and auditing standards, have fundamental effect on the

audit function. This represents an inherent limitation that exists in all studies that utilise pooled

time-series data in an cross-sectional regression (Anderson & Zimmer, 1992).

On a final note, while the year of transition to IFRS presents a unique opportunity in examining

the costs associated with switch in reporting regimes, which is of primary interest to the current

study. The results in this paper, while representing the effect of IFRS adoption on audit pricing

may not be generalized to future years, given that the increased risk assessments argued in this

paper may only hold in the year of transition. Therefore, in future years, auditors and

companies alike may increase expertise and become more skilled in the application of IFRS,

mitigating the initial risk assessments. Accordingly, research could revisit this issue in future,

in order to gauge the effects on audit prices in the years after adoption.

- 30 -

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TABLE 1 Summary of Results of Expert Opinions

Panel A: Biographical information of experts Count Mean Mean

No. of responses % Yrs of experience no. of IFRS training

Assistant Managers 12 39 3.75 4 Managers 7 23 6.07 4-5 Senior Managers 7 23 10.10 >5 Partners 5 16 18.00 >5

31 100 Panel B: Mean (standard deviations) of audit effort/complexity ratings assigned by professionals

Full sample Low level

staff Senior level

staff t-

statistic

(n = 22) (n = 6) (n = 16) AASB 2 7.7 6.3 8.3 2.02**

(1.6) (2.2) (1.0)

(n = 31) (n = 12) (n = 19) AASB 119 4.9 3.4 5.8 3.01**

(2.4) (2.2) (2.1) AASB 112 6.5 6.2 6.6 0.76

(1.7) (1.6) (1.7) AASB 132 7.3 7.1 7.5 0.54

(1.9) (2.3) (1.6)

AASB 139 8.3 7.2 9.1 2.94**

(1.7) (1.8) (1.1)

AASB 136 6.8 6.2 7.2 1.53*

(1.6) (1.8) (1.5)

AASB 138 6.3 5.8 6.7 1.30

(2.0) (2.0) (1.8)

Panel C: Top three rankings of new IFRS with regards to audit effort

No. of experts

Primary (1) % Secondary (2) % Secondary (3) %

AASB 2 (n = 22) 5 23% 5 23% 7 32%

AASB 119 (n = 31) 0 0% 3 10% 1 3%

AASB 112 (n = 31) 1 3% 4 13% 4 13%

AASB 132 (n = 31) 4 13% 9 29% 5 16%

AASB 139 (n = 31) 17 55% 6 20% 4 13%

AASB 136 (n = 31) 2 6% 2 6% 5 16%

AASB 138 (n = 31) 2 6% 2 6% 5 16%

**p < 0.025 (one-tailed test).

*p < 0.050 (one-tailed test). It is noted that where audit staff had not yet had any experience in applying new IFRS’s it was assigned as N/A, and given neither a rating or rating. 8 staff had yet to apply the new requirements under AASB 2, and therefore were unable to rate this standard, thus n=22 for mean ratings assigned to this standard. Panel A: This panel reports biographical information of expert respondents, such as their years of audit experience and number of IFRS training sessions attended, based on their level within the organisation. Panel B: This panel reports the mean and standard deviation of expert ratings of the incremental audit effort/risk associated with new AIFRS/IFRS requirements within the relevant accounting standards: AASB 2, AASB 119, AASB 112, AASB 132, AASB 139, AASB 136 and AASB 138. The rating ranges from 0 to 10 with 0 indicating no perceived impact on audit effort/complexity and 10 indicating an extremely high impact. We further report the mean ratings of lower level staff (Assistant Managers with average of 3.75 years of audit experience) and mean ratings of higher level staff (Managers and above, with average of 11 years of audit experience. The t-statistics reported represent the statistical difference between the ratings assigned by lower level audit staff and upper level audit staff. Panel C: This panel reports the frequency (percentages) of experts that ranked relevant standards as: (1) primary, indicating the relevant standard as encompassing the most significant impacts on auditors, (2) secondary, indicating this standard as having the second most significant impact on auditors, (3) secondary, indicating the standard is perceived as the third most significant impact on auditors.

- 36 -

TABLE 2 Distribution of Sample Firms across Industries The table lists all industries represented in the sample data, based on GIC industry group.

GICS Industry Groups Full sample: (438) Number of Companies

Reduced sample: (300) Number of Companies

Auto Components 7 4

Capital Goods 30 18

Commercial Services & Supplied 18 11

Consumer Durables & Apparel 12 10

Consumer Services 10 7

Financial Services 38 31

Energy 33 21

Food, Beverage & Tobacco 16 12

Health Care Equipment & Supplies 20 17

Insurance 4 4

Materials – Metals and Mining 91 52

Materials – Paper and Forestry 4 -

Media 20 16

Pharmaceuticals & Biotechnology 25 16

Real Estate Management & Development\ 32 21

Retailing 11 8

Software and Services 29 21

Technology Hardware and Equipment 8 6

Telecommunication 6 5

Transportation 11 9

Other (no more than three companies in any one industry) 13 11

Total 438 300

- 37 -

TABLE 3 Descriptive Statistics

The table provides the mean and standard deviation (in parentheses) statistics and count data for all variables across the pooled sample of all years, and the sub samples of each individual year, being the year of IFRS adoption and the preceding three years.

Total Sample (n = 1752)

Year -3 (n = 438)

Year -2 (n = 438)

Year -1 (n = 183)

Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Audit fees paid to auditors $399,248 $1,192,627 $327,590 $1,011,709 $374,381 $1,261,439 $405,997 $1,138,979

LogAF $11.55 $1.48 $11.32 $1.48 $11.42 $1.49 $11.61 $1.46

Total assets $1,300m $6,500m $1,290m $8,460m $1,130m $5,110m $1,290m $5,510m

LogAsset $18.08 $2.41 $17.80 $2.47 $18.01 $2.36 $18.16 $2.39

Quick 5.54 15.65 6.73 25.06 5.26 10.40 4.50 7.20

Rec 0.15 0.16 0.15 0.15 0.15 0.16 0.14 0.15

Inv 0.08 0.13 0.08 0.14 0.08 0.13 0.08 0.13

DE 0.12 0.17 0.11 0.15 0.11 0.15 0.13 0.19

ROI -0.15 0.79 -0.23 0.83 -0.10 0.59 -0.15 0.95

Loss 977 273 256 231

YE 1,512 378 378 378

Opin 86 11 13 24

Auditor 1,124 279 277 281

Hedges - - - -

Fininst - - - -

SBI - - - -

Tax - - - -

Gwill - - - -

Intan - - - -

The variables are defined as follows: LogAF = natural log of total audit fees; LogAsset = natural log of total asset; Current = ratio of current assets to current liabilities;Rec = ratio of total receivables to total assets; Inv = ratio of total inventory to total assets; DE = ratio of long-term debt to total assets; ROI = ratio of net income after interest and tax;

Loss = indicator variable, equals 1 if loss reported in any of the prior three years, and 0 otherwise; YE = indicator variable, equals 1 if company has 30 June year-end, and 0 otherwise; Opin = indicator variable, equals 1 if a modified/qualified opinion was issued in any of the prior three years, and 0 otherwise; Auditor = indicator variable, equals 1 if company employed Big4 auditor, and 0 otherwise; Hedge = indicator variable, equals 1 if company applied hedge accounting under AASB 139, and 0 otherwise; Fininst = length of financial instruments note (pages); SBI = indicator variable, equals 1 if company applied AASB 2, and 0 otherwise; Tax = indicator variable, equals 1 if company recorded AIFRS adjustments in relation adoption of AASB 112, and 0 otherwise; Gwill = indicator variable, equals 1 if company had recorded goodwill balance, and 0 otherwise Intan = indicator variable, equals 1 if company had recognised intangible assets other than goodwill in prior year balance sheet, and 0 otherwise

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TABLE 4 Correlation Matrices

Panel A: Correlation matrix for model (1): pooled sample n = 1752 (438 companies across 4 years)

LogAsset Current Rec Inv DE ROI Loss YE Opin Auditor IFRS

LogAF 0.84 -0.24 0.19 0.20 0.35 0.24 -0.44 -0.27 -0.12 0.46 0.11

LogAsset -0.13 0.02 0.15 0.38 0.38 -0.55 -0.31 -0.20 0.43 0.07

Current -0.12 -0.13 -0.16 0.00 0.09 0.05 -0.01 -0.05 0.00

Rec 0.16 -0.03 0.08 -0.14 0.01 -0.03 -0.06 -0.01

Inv 0.11 0.11 -0.23 -0.12 -0.03 0.04 -0.01

DE -0.03 -0.15 -0.05 -0.05 0.14 0.03

ROI -0.28 -0.09 -0.21 0.15 0.02

Loss 0.20 0.17 -0.21 -0.07

YE 0.05 -0.17 0.01

Opin -0.10 0.14

Auditor 0.02 The variables are defined as follows: LogAF = natural log of total audit fees; LogAsset = natural log of total asset; Current = ratio of current assets to current liabilities; Rec = ratio of total receivables to total assets; Inv = ratio of total inventory to total assets; DE = ratio of long-term debt to total assets; ROI = ratio of net income after interest and tax; Loss = indicator variable, equals 1 if loss reported in any of the prior three years, and 0 otherwise; YE = indicator variable, equals 1 if company has 30 June year-end, and 0 otherwise; Opin = indicator variable, equals 1 if a modified/qualified opinion was issued in any of the prior three years, and 0 otherwise; Auditor = indicator variable, equals 1 if company employed Big4 auditor, and 0 otherwise; Experimental indicator variables IFRS = indicator variable, equals 1 for the year company prepared annual statements in accordance with IFRS/AIFRS, and 0 otherwise.

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TABLE 4 Correlation Matrices (continued)

Panel B: Correlation Matrix for model (2): Cross sectional analysis on IFRS year. n = 300 companies

LogAsset Current Rec Inv DE ROI Loss YE Opin Auditor Hedge Fininst SBI

LogAF 0.83 -0.24 0.11 0.19 0.33 0.27 -0.42 -0.27 -0.20 0.48 0.29 0.62 0.27

LogAsset -0.13 -0.06 0.13 0.38 0.43 -0.55 -0.25 -0.37 0.45 0.26 0.60 0.08

Current -0.12 -0.14 -0.17 -0.01 0.09 0.10 -0.06 -0.08 -0.23 -0.14 -0.13

Rec 0.16 -0.07 0.02 -0.14 -0.01 -0.02 -0.07 0.01 -0.02 0.22

Inv 0.07 0.08 -0.22 -0.12 0.00 0.04 0.13 0.12 0.00

DE 0.13 -0.14 -0.06 -0.15 0.17 0.19 0.24 -0.04

ROI -0.34 -0.10 -0.53 0.19 0.03 0.12 0.00

Loss 0.20 0.35 -0.21 -0.11 -0.35 0.01

YE 0.08 -0.13 -0.10 -0.22 -0.04

Opin -0.18 0.09 -0.16 -0.03

Auditor 0.09 0.30 0.10

Hedge 0.28 0.09

Fininst 0.08

SBI

tax

Gwill The variables are defined as follows: LogAF = natural log of total audit fees; LogAsset = natural log of total asset; Current = ratio of current assets to current liabilities; Rec = ratio of total receivables to total assets; Inv = ratio of total inventory to total assets; DE = ratio of long-term debt to total assets; ROI = ratio of net income after interest and tax; Loss = indicator variable, equals 1 if loss reported in any of the prior three years, and 0 otherwise; YE = indicator variable, equals 1 if company has 30 June year-end, and 0 otherwise; Opin = indicator variable, equals 1 if a modified/qualified opinion was issued in any of the prior three years, and 0 otherwise; Auditor = indicator variable, equals 1 if company employed Big4 auditor, and 0 otherwise; Experimental indicator variables Hedge = indicator variable, equals 1 if company applied hedge accounting under AASB 139, and 0 otherwise; Fininst = length of financial instruments note (pages); SBI = indicator variable, equals 1 if company applied AASB 2, and 0 otherwise; Tax = indicator variable, equals 1 if company recorded AIFRS adjustments in relation adoption of AASB 112, and 0 otherwise; Gwill = indicator variable, equals 1 if company had recorded goodwill balance, and 0 otherwise Intan = indicator variable, equals 1 if company had recognised intangible assets other than goodwill in prior year balance sheet, and 0 otherwise.

- 40 -

TABLE 5

Coefficient Estimates for the Audit Fee Regression Model (1)

The table provides coefficient estimates (t-statistics in parentheses) for the following regression model:

=iLogAF

iiiiii

iiiiii

IFRSAuditorLossYEOpinion

ROIDEInvcQuickLogAsset


++++++++++++

1110987

6543210 Re(1)

The variable definitions are given below the table. Panel A shows the results for the full sample of industries in table 2. Panel B shows the results after controlling for all financial institutions and mining companies from the data set by the inclusion of indicator variables. While Panel C shows the results after deleting all financial institutions, mining companies and insurance firms from the data set. The sample drops from 1752 observations (438 companies) in panels A and B, to 1224 observations (306 companies) in panel C. All t-statistics reported are computed using White’s heteroskedasticity consistent standard errors to address concerns of heteroskedasticity.

Control Variables

Intercept LogAsset Current Rec Inv DE ROI Loss YE Opinion Auditor Fin

Panel A: Results for the full sample

1.68 0.51 -0.01 1.62 0.59 0.05 -0.16 0.20 -0.03 0.24 0.41 -

(6.60)** (39.42)** (-3.99)** (12.41)** (4.66)** (0.39) (-7.24)** (4.69)** (0.63) (2.83)** (10.22)** -

Panel B: Results for the full sample - controlling for industry effects 1.89 0.51 -0.01 1.46 0.30 -0.08 -0.16 0.21 -0.01 0.18 0.38 -0.53

(7.60)** (40.39)** (-3.74)** (11.49)** (2.53)** (-0.71) (-7.17)** (5.24)** (-0.17) (2.31)** (10.53)** (-6.79)**

Panel C: Results after deleting financial institutions, mining and utilities 1.51 0.53 -0.01 1.42 0.14 -0.24 -0.15 0.25 -0.02 0.24 0.30 -

(5.85)** (40.78)** (-3.83)** (10.13)** (1.22) (-1.98)** (-5.35)** (5.61)** (-0.54) (2.87)** (7.54)** - The variables are defined as follows: LogAF = natural log of total audit fees; LogAsset = natural log of total asset; Quick = ratio of current assets to current liabilities; Rec = ratio of total receivables to total assets; Inv = ratio of total inventory to total assets; DE = ratio of long-term debt to total assets; ROI = ratio of net income after interest and tax; Loss = indicator variable, equals 1 if loss reported in any of the prior three years, and 0 otherwise; YE = indicator variable, equals 1 if company has 30 June year-end, and 0 otherwise; Opin = indicator variable, equals 1 if a modified/qualified opinion was issued in any of the prior three years, and 0 otherwise; Auditor = indicator variable, equals 1 if company employed Big4 auditor, and 0 otherwise; Fin = indicator variable, equals 1 if company is a financial services and utilities (GICs 402030, 402010, 5510) firm, and 0 otherwise; Mine = indicator variable, equals 1 if company is a mining (GIC 151040) firms, and 0 otherwise; IFRS = indicator variable, equals 1 for the year company prepared annual statements in accordance with IFRS/AIFRS, and 0 otherwise. **p < 0.025 (one-tailed test).

- 41 -

TABLE 6

Coefficient Estimates for the Audit Fee Regression Model


=iLogAF

iiiiii

iiiiii

IFRSAuditorLossYEOpinion



++++++++++++

1110987

6543210 Re(1)

The variable definitions are given below the table. Panel A shows the results for the subset of smaller firms, determined by median total assets ($81.26m). The sample consists of the smaller half of firms, 876 observations (219 companies). Panel B shows the results for the subset of larger firms, determined by median total assets. The sub sample consists of the larger half of firms, 876 observations (219 companies). Panel C and D show the results of the lowest and highest quartiles (respectively), based on total assets, 440 observations (110 companies). All t-statistics reported are computed using White’s heteroskedasticity consistent standard errors to address concerns of possible heteroskedasticity.

Experimental

Control Variables Variable Adjusted

Intercept LogAsset Current Rec Inv DE ROI Loss YE Opinion Auditor IFRS R-squared

(n=219)

Panel A: “smaller” firms 4.33 0.37 -0.01 1.27 0.56 0.17 -0.07 0.03 -0.22 0.28 0.42 0.23

(10.69)** (16.57)** (-6.05)** (8.64)** (3.90)** (1.23) (-3.82)** (0.46) (-1.99)** (3.98)** (10.46)** (5.27)** “larger” firms

-0.83 0.63 -0.01 2.41 0.64 0.06 -0.03 0.26 0.01 -0.50 0.49 0.19

(-2.42)** (36.55)** (-4.50)** (12.48)** (3.22)** (0.35) (-0.15) (4.14)** (0.12) (-2.24)** (5.80)** (3.08)**

(n=110) Panel B: Lowest quartile (Smallest) of firms

5.62 0.29 -0.01 0.87 0.54 0.19 -0.03 0.10 -0.21 0.36 0.35 0.25

(11.00)** (10.37)** (-6.76)** (5.32)** (2.16)** (1.03) (-1.61) (0.93) (-1.31) (4.36) (6.29)** (4.08)**

Highest quartile (Largest) of firms -3.23 0.72 -0.02 4.03 0.12 -0.13 1.12 0.25 0.06 -0.58 0.48 0.18

(-5.30)** (26.85)** (-3.99)** (11.32)** (0.41) (-0.51) (1.92)* (2.74)** (0.65) (-0.78) (3.32)** (2.21)**

The variables are defined as follows: LogAF = natural log of total audit fees; LogAsset = natural log of total assets; Current = ratio of current assets to current liabilities; Rec = ratio of total receivables to total assets; Inv = ratio of total inventory to total assets; DE = ratio of long-term debt to total assets; ROI = ratio of net income after interest and tax;

Loss = indicator variable, equals 1 if loss reported in any of the prior three years, and 0 otherwise; YE = indicator variable, equals 1 if company has 30 June year-end, and 0 otherwise; Opin = indicator variable, equals 1 if a modified/qualified opinion was issued in any of the prior three years, and 0 otherwise; Auditor = indicator variable, equals 1 if company employed Big4 auditor, and 0 otherwise; IFRS = indicator variable, equals 1 for the year company prepared annual statements in accordance with IFRS/AIFRS, and 0 otherwise. **p < 0.025 (one-tailed test).

- 42 -

TABLE 7

Coefficient Estimates for the Audit Fee Regression Model (2)


=iLogAF

iiiiiii

iiiiiiii

InGwillTaxSBIFininstHedgeAuditorLoss

YEOpinionROIDEInvcCurrentLogAsset

βββββββββββββββββ

++++++++++++++++

tan

Re

161514131211109

876543210

The variable definitions are given below the table. Panel A shows the results for the full sample of industries in Table 2, Panel B with 300 companies. Panel B shows the results after deleting all financial institutions, mining companies and insurance firms from the data set. The sample drops from 300 company observations in panels A, to 215 company observations in panel B. All t-statistics reported are calculated using White’s heteroskedasticity consistent standard errors in order to control for heteroskedasticity

Control Variables Experimental variables

Intercept LogAsset Current Rec Inv DE ROI Loss YE Opinion Auditor Hedge Fininst SBI

Panel A: Results for the full sample

2.95 0.41 -0.01 0.78 0.48 0.00 -0.09 0.20 -0.12 0.34 0.37 -0.03 0.11 0.34

(6.03)** (14.17)** (-3.60)** (3.00)** (2.05)** (0.00) (-1.19) (2.27) (-1.25) (1.77) (3.70)** (-0.44) (3.75)** (3.90)** Panel B: Results after deleting financial institutions, mining and insurance firms

2.39 0.47 -0.01 0.60 0.11 -0.22 -0.06 0.27 -0.09 0.53 0.21 -0.09 0.06 0.30

(4.32) (14.47)** (-0.66) (1.70) (0.48) (-0.96) (-0.84) (2.52)** (-1.05) (2.54)** (1.85) (-0.89) (1.90) (2.81)** The variables are defined as follows: LogAF = natural log of total audit fees; LogAsset = natural log of total asset; Current = ratio of current assets to current liabilities; Rec = ratio of total receivables to total assets; Inv = ratio of total inventory to total assets; DE = ratio of long-term debt to total assets; ROI = ratio of net income after interest and tax; Loss = indicator variable, equals 1 if loss reported in any of the prior three years, and 0 otherwise; YE = indicator variable, equals 1 if company has 30 June year-end, and 0 otherwise; Opin = indicator variable, equals 1 if a modified/qualified opinion was issued in any of the prior three years, and 0 otherwise; Auditor = indicator variable, equals 1 if company employed Big4 auditor, and 0 otherwise; Hedge = indicator variable, equals 1 if company applied hedge accounting under AASB 139, and 0 otherwise; Fininst = length of financial instruments note (pages); Gwill = indicator variable, equals 1 if company had recorded goodwill balance, and 0 otherwise; Intan = indicator variable, equals 1 if company had recognised intangible assets in prior year balance sheet, and 0 otherwise; SBI = indicator variable, equals 1 if company applied AASB 2, and 0 otherwise; Tax = indicator variable, equals 1 if company recorded AIFRS adjustments in relation adoption of AASB 112, and 0 otherwise; **p < 0.025 (one-tailed test).

the effects of mandated ifrs adoption on the cost of the

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