note on audit fee premiums to client size and industry specialization

Accounting and Finance 47 (2007) 423446

The AuthorsJournal compilation

2007 AFAANZ

Blackwell Publishing LtdOxford, UKACFIAccounting and Finance0810-5391 The AuthorsJournal compilation 2007 AFAANZXXX

ORIGINAL ARTICLES

E. Carson and N. Fargher/Accounting and Finance 47 (2007) xxxxxxE. Carson and N. Fargher/Accounting and Finance 47 (2007) xxxxxx

Note on audit fee premiums to client size and industry specialization

Elizabeth Carson

a

, Neil Fargher

b

a

School of Accounting, University of New South Wales, Sydney, 2052, Australia

b

Department of Accounting & Finance, Macquarie University, Sydney, 2109, Australia

Abstract

This research note examines the impact of client size on the estimation of audit feepremiums in the Australian market for audit services. Previous research suggeststhat higher audit fees are expected for both larger clients and for industryspecialization. We find that in the Australian market for audit services, the feepremium attributed to industry specialist audit firms is concentrated in the auditfees paid by the largest clients in each industry. One reason for higher fees paidby larger clients is the demand for additional audit services. We find higher feesfor companies cross-listed on US exchanges. We also find that fee premiums toauditors that are city-industry leaders are strongly related to client size.

Key words

: Audit fees; Auditing; Industry specialization

JEL classification

: M42

doi

: 10.1111/j.1467-629x.2007.00213.x

1. Introduction

This research note examines the association between client size and audit feepremiums. This topic is important in helping to understand the extent to which feepremiums attributable to auditor characteristics, such as industry specialization,

We gratefully acknowledge the use of data provided by the Centre for Audit and AssuranceResearch at the University of New South Wales, the Securities Industry Research Centre ofAsia-Pacific (SIRCA) from Aspect Financial, the Faculty of Economics and Business,University of Sydney, and Allen Craswell. We appreciate the helpful suggestions fromAsher Curtis, Chris Hogan, Rajib Doogar, Brian Mayhew, Renee Radich, Peter Roebuck,Roger Simnett, Mike Stein, Stephen Taylor, Stuart Turley, Arnie Wright, Mike Wilkins,participants at the American Accounting Association (AAA) Auditing Section meeting 2004,the International Symposium on Audit Research 2004, the AAA Annual meeting 2004, andat workshops at the University of Connecticut, Boston College and Northeastern University.

Received 21 December 2005; accepted 29 September 2006 by Gary Monroe (Deputy Editor).

424 E. Carson and N. Fargher/Accounting and Finance 47 (2007) 423446


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can be distinguished from fee premiums attributable to client characteristics,such as client size.

Prior studies of industry specialization have observed that the results fortests of industry specialization are sensitive to test specification with respect toclient size. Craswell

et al.

(1995) report that the industry specialist premiumonly occurs for auditees in the top half of the sample based on auditee totalassets. Ferguson and Stokes (2002) and Ferguson

et al.

(2003) report that thepremium for industry specialists increases as client size increases. As clientsize can proxy for client characteristics influencing the scope and complexity ofan audit, the present study examines client size as a determinant of audit feepremiums. Specifically, we investigate whether specialist auditors obtain a feepremium for clients of all sizes. As large clients pay higher fees, and auditorsof larger clients are designated as the industry specialist, the research questionof interest is whether specialist auditors earn fee premiums for audits of small-and medium-size clients.

We find that the national industry specialist premium documented by Ferguson

et al.

(2003) is found only in the highest quintile of client size. The fee premiumto industry-specialist auditors is not significantly different from zero for thelower and middle quintiles of clients based on total assets. The fee premium tocity-industry lead auditors (Ferguson

et al.

, 2003) is also strongly related toclient size.

The present study contributes to the prior literature by documenting the sensitivityof premiums to industry specialization with respect to client size. Although thissensitivity is noted in the seminal research, it is frequently not cited by subsequentpapers assuming a positive association between specialization and fees. Inexamining this sensitivity, we also document three potentially omitted variablesin fee models using Australian data: cross-listing on a US exchange, location ofthe client head office and client bargaining power. Of particular importance isthe need to control for larger client demand for assurance services related to UScross-listing when estimating fee models using Australian data.

2. Previous research

The general audit fee model common to most of the studies in the literaturerepresents audit fees as a function of client size, client complexity, client andauditor risk, and audit quality (e.g. Craswell and Francis, 1999). Early researchin this paradigm (e.g. Simunic, 1980; Francis, 1984; Francis and Stokes, 1986;Palmrose, 1986; Francis and Simon, 1987) makes it clear that client size is asignificant issue with respect to the overall fee model being estimated. Speci-fically, the fee premium to auditors is expected to be different depending on themarket segment in which the auditor is competing, where market segment isbased on client size.

Recent studies that have considered the role of client size include Reynoldsand Francis (2001), who find no evidence that Big Five auditors report more

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favourably for larger clients, and Chung and Kallapur (2003), who find nosupport for the contention that important clients have higher abnormal accruals.Sullivan (2000) finds that following the Big Eight to Big Six mergers there isevidence of reduced marginal costs of auditing large clients as measured bypatterns of client switches following the mergers. DeFond

et al.

(2000) find thatthe fee premium to Big Six industry specialist is robust across the spectrum ofcompany size in the Hong Kong market.

Prior studies of industry specialization using Australian data have observedthat the results for tests of industry specialization are sensitive to test specifica-tion with respect to client size. Craswell

et al.

(1995) report that the industryspecialist premium only occurs for larger auditees, where large is defined asbeing in the top half of the sample based on auditee total assets . Craswell

et al.

(1995) argue that larger-sized companies in general have greater agency prob-lems and, hence, are more likely to benefit from the additional audit qualityof a Big Eight industry specialist. Ferguson

et al.

(2003) also report that theindustry specialist premium increases as client size increases. Recent researchalso shows evidence consistent with demand for industry specialist auditorsfrom Australian companies with characteristics that are likely to be associatedwith client size. Chen

et al.

(2005) find that companies with an audit committeeand companies with a higher proportion of non-executive directors are morelikely to be audited by an industry specialist.

Ferguson

et al.

(2003, 2006) provide evidence that national rankings aredriven by city-industry leaders. In a small market like Australia with few largecities, there is the distinct potential that conditioning on industry and city isidentifying the largest, more complex audit clients. Using data from the largerUS market, Francis

et al.

(2005) find evidence of audit fee premiums beingassociated with joint national and city specialist auditors. Francis

et al.

(2005)argue that the results are generally consistent for the upper and lower halves oftheir sample by client size; however, they also note that the estimated premiumfor joint nationalcity leadership is 22 per cent for larger clients and only 7 percent for smaller clients (Francis

et al.

, 2005). Therefore, in our study, we invest-igate the concentration of the observed fee premiums with respect to client sizefor both national-level and city-level industry specialists.

3. Client size, industry specialization and fee premiums

The client size and auditor specialization explanations for audit fees bothpredict a fee premium for the industry specialist on the largest audits in theindustry. However, the difference is with respect to whether fee premiums areearned by the specialist auditor for audits other than the largest clients thatdominate the industry. If the audit fee premium is attached to the industryspecialist auditor, then we would expect the premium to attach to all auditsundertaken by that specialist auditor in that industry. If these premiums areattached only to audits of the largest clients in an industry, then we would not



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expect to observe significant fee premiums for audits beyond the premiumsearned for audits of the largest clients.

To the extent that the largest clients endogenously choose the auditor bestsuited to their business, client size and industry specialist are inherently relatedand cannot be separated. However, empirical evidence can provide a basis forunderstanding the extent of specialist audit fee premiums across clients ofvarying size and the ability of the specialist to earn a fee premium beyond thepremium earned from the largest clients.

There is evidence that differences in client size are related to audit fees.Mayhew and Wilkins (2003) find that audit firms that possess significantlyhigher market share than their competitors earn higher audit fees. Audit firmswith higher market share also typically have a higher proportion of the largerclients. In contrast, Casterella

et al.

(2004) examine client bargaining powerwhere power is measured as each clients sales relative to the sales to allclients in the industry audited by the companys auditor. They find that auditfees are lower as the client size increases relative to their auditors industryclientele.

Consistent with prior studies, we do not have sufficient data to directly exam-ine the quantity or nature of assurance services demanded by specific clients.However, we can attempt to carefully model the relation between the clientattributes and audit fees, and then consider the incremental fees attributable toindustry specialization for clients of differing size. Overall, in considering therelation between client size and industry specialization, there are at least threeaspects of client size that can be considered: absolute client size, client sizewithin industry and client size relative to other clients.

With respect to absolute client size, we consider two aspects of the associ-ation between client size and audit fees. The audit fee model is generally wellspecified. However, it is known from prior research that the coefficient on logof assets varies with client size (Bell

et al.

, 1994). Therefore, we consider non-linearity in the relation between audit fees and client size. Palmrose (1986)observes that if larger clients require more audit services than smaller clients,then we would expect that these large clients pay relatively higher fees per dol-lar of size relative to smaller clients. To examine possible sources of increaseddemand for audit services from larger clients, we identified companies cross-listed on a US exchange as a client characteristic correlated with client size andlikely to increase the scope and complexity of audit procedures required bylarge clients.

1

With respect to size within industry we focus on the premium to industryspecialization to clients of varying size. We empirically examine the extent to

1

From an econometric perspective, cross-listing could be considered an omitted variablefrom a typical audit fee model, but only to the extent that size and other variables do notfully capture the variation in fees attributable to the need to provide assurance on thereconciliation to US GAAP and other issues relating to dual listing status.



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which the audit fee premium attached to the industry specialist auditor isearned across clients of varying size.

Casterella

et al.

(2004) argue that relative client size within industry resultsin clients having greater bargaining power and that, ceteris paribus, this willresult in lower audit fees. In contrast, Mayhew and Wilkins (2003) argue that theclient can only bargain for a lower fee where the auditor has not successfullydifferentiated itself from competitors. They find evidence that audit firms thathave successfully differentiated themselves earn a fee premium. This evidenceis consistent with the successfully differentiated audit firm retaining a strongerbargaining position with their clients. To the extent that larger clients havelarger reputational capital at risk, and tend to select a differentiated leadingauditor in the industry, we would expect higher fee premiums (Mayhew andWilkins, 2003). We include a measure for client bargaining power as measuredby Casterella

et al.

(2004); however, based on the prior research using US datathe premium predicted is ambiguous.

We specifically consider the following aspects of client size in audit feemodels: (i) additional variables to capture non-linearity in the client size to feerelation; (ii) an additional variable to capture variation in fees arising fromlarge client demand for additional audit services associated with size (cross-listing on a US stock exchange); (iii) a variable to control for client bargainingpower (POWER); and (iv) interaction variables to capture the variation in feepremium to industry specialization across clients of varying size.

4. Sample

4.1. Sample selection

For comparability to Ferguson

et al.

(2003), the sample comprises auditengagements in Australia for the fiscal year 1998. It should be noted that the Pricewaterhouse Coopers and Lybrand merger occurred in late 1997 and potentiallyimpacts the industry reputation and pricing during this period. However, to addressthe issue of the sensitivity of the results to the year selected, we have also estimatedthe models for 1999 and 2004, and reported the results as sensitivity analysis.

The 1998 and 1999 samples are drawn from the Who Audits Australia data-base that contains audit fees and non-audit services fees data for the populationof Australian listed companies. The 2004 sample is drawn from the Universityof New South Wales audit fee database. The audit fee data were then matchedto the Aspect database of financial statement information. The data werereviewed for reasonableness and identified errors corrected by reference toannual reports. Only observations with all available data, matching total assetson both databases, and two prior years of financial statement information wereincluded. We further excluded companies with financial periods of other than12 months (including one large company WMC), companies reporting in aforeign currency and audits where the audit office is outside Australia. All



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monetary units are in Australian dollars. For the city-level analysis, audit feesare examined for companies headquartered in the five largest Australian cities:Adelaide, Brisbane, Melbourne, Perth and Sydney.

We refer to the Big Six auditors because of the use of fiscal year end 1998data in which the firm Coopers and Lybrand was noted to be still issuing auditopinions. We have not redefined five Coopers and Lybrand audits as Price water-house Coopers because of the problematic issue of treating a lower market shareauditor as an industry leader by arbitrary reclassification.

2

Between 1999 and 2004 the primary industry code used by the AustralianStock Exchange (ASX) was redefined. This creates an interesting problem forresearchers. We have used the new Global Industry Classification Standard(GICS) industry codes for 2004 as any reputation effect based on industrywould arguably be better measured by the industry codes actually in use. For2004, industry is measured as the four digit GICS code. Two industries withfewer than 10 companies audited by Big Four auditors were included in largerindustry groupings at the three digit level (GICS 3030 is included with 3020,and GICS 4530 is included with 4520). This resulted in 18 distinct non-financialindustry groupings. Despite the lack of direct comparability between the 1998and 2004 industry groupings, the results for 2004 are generally consistent withthose for 1998.

5. Empirical model

We use as a basis for our analysis the audit fee model from Ferguson

et al.

(2003).We then extend the basic model to capture variations in the association betweenclient characteristics and fee premium. We specifically consider the followingaspects of client size: (i) allow the coefficient on client size to vary by quintile oftotal assets; (ii) include an additional variable to capture variation in fees arisingfrom larger clients cross-listing on US markets; (iii) a variable to control forclient bargaining power (

POWER

); and (iv) interaction variables to capture thevariation in fee premium to industry specialization across clients of varying size.

5.1. Industry specialist auditors

For comparability to prior research, we follow the methodology of Ferguson

et al.

(2003) in designating the first and second ranked auditors, based on theproportion of industry audit fees, as the specialist auditors (

SPECIALIST

). Weextend the analysis to consider alternate measures of specialization in auditorreputation, such as the auditor that is the leader in both the industry nationallyand the city office (Ferguson

et al.

, 2003; Francis

et al.

, 2005). The city-industry

2

Ferguson

et al.

(2003) refer to Big Five audits and treatment of these observations isnot reported.



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leader indicator variable takes the value of 1 when the auditor is the numberone auditor, based on proportion of audit fees, in the city and 1 of the top 2firms nationally (

CITYIL

).

5.2. Non-linearity in the client size to audit fee relation

Prior research indicates that the coefficient on log of assets varies with clientsize (Bell

et al.

, 1994). We model this aspect by inclusion of indicator variables foreach quintile by total assets to partially capture the effect of client size on auditfees. We interact an indicator variable for the quintile of total assets with the log oftotal assets. This allows the coefficient on log of total assets to vary with client size.

3

5.3. Large client demand for assurance services: US cross-listing

To examine possible sources of increased demand for audit services from largerclients, we identified companies required to complete reconciliations betweenUS Generally Accepted Accounting Principles (GAAP) and Australian GAAP. Toexamine this possible source of relatively higher fees for large clients, we includean indicator variable (

ADR

) for companies with Level II or Level III AmericanDepository Receipts (ADR). An ADR is a dollar-denominated negotiablecertificate that represents ownership of shares in a non-US company. ADRswere identified from the JPMorgan database with an effective date prior to theend of the 1999 financial year. Level II and Level III ADRs are required to com-plete reconciliations between US GAAP and Australian GAAP, and file auditedfinancial statements with the US Securities and Exchange Commission. For the1998 sample, 13 of the 24 largest clients in the 24 industry categories have ADRs.

5.4. Specialist premiums earned on clients of differing size

Finally, we partition the specialist premium by clients of different size. Wereport the results using quintiles of total assets. We calculate the quintile of firmsize within the sample of Big Six clients and interact an indicator variable forthe largest quintile, medium quintiles and small quintile with the indicatorvariable for audit by a specialist auditor. Similar results are obtained usingdeciles with slightly stronger reductions in the specialist premium when decilesare used. When size within industry is used, the empirical relations suggest thatthe association is stronger between fee premiums and size within industry thanbetween absolute size and the fee premium.

3

We also allowed both intercept and slope to vary with the quintile of total assets. Theresults are generally consistent with those reported, although the combinations of interceptand slope for each quintile varied less systematically. The inferences for the industryspecialist premium are not altered.



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5.5. Relative client size

Relative client size is measured following Casterella

et al.

(2004).

POWER

ismeasured for each company as the natural log of company sales divided by thesum of logged sales for all firms in the industry audited by the companysauditor. Because of missing sales data available for 2004 from Aspect, totalrevenue is used for sales in 2004.

5.6. Client size and measures of office level industry expertise

Ferguson

et al.

(2003) find that the premium to industry expertise in Australiais driven by industry leadership at a city level. In the city-level analysis, weinclude an additional control for variation in city-level costs. As audit practicesin major cities (Sydney and Melbourne) are more likely to have higher over-heads than practices in smaller cities (Adelaide and Perth), it is necessary tocontrol for the higher cost of audit production in these cities. As the major auditcost driver is labour, we use the annual salary cost for an experienced auditorin each of the major cities from Hays Personnel Services for the relevant year.Higher salaries are reported for Sydney and Melbourne relative to the othercapital cities. Audits outside the major capital cities are allocated the salarycost for Adelaide. Although we recognize that an audit production cost wouldnot be expected to enter linearly into a production cost function, we use thelinear approximation as a parsimonious model allowing comparison to priorresearch.

5.7. Model

The resulting ordinary least squares regression model for examining theimpact of size not conditioned on industry is specified as follows (ignoring thecompany subscript):

LAF

=

+

q

=

1,5

1,

q

(

q

*

LTA

)

+ 2LSUB + 3CATA + 4QUICK + 5DE + 6ROI + 7FOREIGN + 8OPINION + 9YE + 10LOSS + 11ADR + 12POWER + 13SPECIALIST*LARGE + 14SPECIALIST*MEDIUM + 15SPECIALIST*SMALL + , (1)

where LAF is the natural log of total audit fees paid to the auditor ($A thousands);q is an indicator variable for each quintile of client size to allow the slope tovary with client size; LTA is the natural log of total assets ($A millions);LSUB is the natural log of the number of subsidiaries; CATA is the ratio ofcurrent assets to total assets; QUICK is the ratio of current assets less inventoriesto current liabilities; DE is the ratio of long-term debt to total assets; ROIis the ratio of earnings before interest and tax to total assets; FOREIGN is theproportion of subsidiaries that represent foreign operations; OPINION is an


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indicator variable with a value of 1 for a qualified or modified opinion; YE isan indicator variable with a value of 1 for non-June fiscal year end; LOSS is anindicator variable with a value of 1 for a loss in any of the last 3 years; ADR isan indicator variable taking the value 1 where the client is cross-listed on aUS exchange as a Level II or Level III ADR; POWER is a measure of clientbargaining power relative to the auditors total clientele in the industry asmeasured by the natural log of company sales, divided by the sum of loggedsales for all firms in the industry audited by the companys auditor; SPECIALISTis an indicator variable with a value of 1 for the first and second rankedauditor in the industry based on proportion of industry audit fees; SPECIALIST*LARGE is an indicator variable for the two specialist auditors in the industryinteracted with a value of 1 if the client is in the largest quintile of clientsbased on total assets and 0 otherwise; SPECIALIST*MEDIUM is an indicatorvariable for the two specialist auditors in the industry interacted with a value of1 if the client is in quintile 2, 3 or 4 of clients based on total assets and 0otherwise; SPECIALIST*SMALL is an indicator variable for the two specialistauditors in the industry interacted with a value of 1 if the client is in thesmallest quintile of clients based on total assets and 0 otherwise; and = anerror term.

5.8. Descriptive statistics

Table 1 reports descriptive statistics for the sample of firms with Big Sixauditors. As in Craswell et al. (1995) and Ferguson et al. (2003), to avoid theconfounding effect of brand name on audit fees, the regression models areestimated only for the firms having Big Six auditors in fiscal year 1998. Wealso exclude the financial services and property development industries(ASX industry codes 16, 17, 19 and 20; GICS codes starting with 40)because of their dissimilar nature relative to other industries (Fields et al.,2004).

As is common in studies using Australian data, there is a broad variation inclient size. Consistent with previous research the distributions of the client size,subsidiaries and audit fee variables are highly skewed. To reduce the impact ofoutliers on the residuals, the natural log of size (total assets), log of subsidiariesand the natural log of audit fees are used in the subsequent analysis. To reducethe impact of outliers on the distribution of the regression residuals, theQUICK, DE and ROI variables are winsorized (consistent with Ferguson et al.,2003) at the 5 per cent level.

Table 2 reports the Spearman correlations between audit fees, client size,auditor and client characteristics. Indicator variables used for industry specialist(SPECIALIST) and city-industry specialist (CITYIL) are all significantly positivelycorrelated with both audit fees and client size. Of interest to this study iswhether industry specialist auditors still earn higher fees after controlling forany higher fees earned on the largest audits.



Table 1Descriptive statistics for sample of Big Six audits of non-financial Australian companies

Sample Operating companies with Big Six Auditors

1998 1999 2004N = 558 N = 543 N = 611

Variable Mean SD Mean Mean

Audit fees ($A thousand) 241.21 736.40 273.04 318.182Log of audit fees (LAF) 4.13 1.46 4.24 4.58Total assets ($A million) 658.85 3359.70 708.68 773.09Log of total assets (LTA) 10.80 2.25 10.93 10.91Number of subsidiaries 20.28 53.27 21.75 21.86Log of subsidiaries (LSUB) 0.51 4.54 0.52 0.75CATA 0.38 0.25 0.40 0.45QUICK 4.11 8.41 3.41 4.44DE 0.16 0.16 0.16 0.13ROI 0.11 0.32 0.07 0.09FOREIGN 0.21 0.27 0.20 0.23CITYCOST 46.47 4.53 46.54 60.58POWER 0.17 0.18 0.12 0.11Categorical variables

OPINION 13% 12% 13%YE 23% 23% 22%LOSS 66% 64% 61%ADR 9% 9% 7%NATIONAL #1 or #2 (SPECIALIST) 57% 57% 61%CITY #1 AUDITOR AND 28% 29% 31%INDUSTRY #1 or 2 AUDITOR (CITYIL)

Sample excludes banks, financial and property segments (industry codes 16, 17, 19, and 20 for1998 and 1999; GICS code 40 for 2004). LAF is log of total audit fees paid to the auditor ($Athousands); LTA is log of total assets ($A thousands); LSUB is the log of the number of subsidiaries;CATA is the ratio of current assets to total assets; QUICK is the ratio of current assets less inventoriesto total assets; DE is the ratio of long-term debt to total assets; ROI is the ratio of earnings beforeinterest and tax to total assets; FOREIGN is the proportion of subsidiaries that represent foreignoperations; OPINION is an indicator variable with a value of 1 for a modified orqualified opinion; YEis an indicator variable with a value of 1 for non-June fiscal year end; LOSS is an indicator variablewith a value of 1 for a loss in any of the last 3 years; CITYCOST is the use the annual salary cost foran experienced auditor in each of the major cities from Hays Personnel Services; ADR is an indicatorvariable taking the value 1 if the firm is cross-listed as an American Depository Receipt; POWER is ameasure of client bargaining power relative to the auditors total clientele in the industry as measuredby the natural log of company sales, divided by the sum of logged sales for all firms in the industryaudited by the companys auditor; SPECIALIST is an indicator variable with 1 if the client is audited bythe specialist auditor in the industry, where specialist auditor is identified as the two auditors with thelargest market share based on the highest proportion of industry audit fees; and CITYIL is an indicatorvariable with 1 if the company is audited by the number one auditor in the industry and the city, andnumber one or two in the industry nationwide based on total audit fees. SD, standard deviation.

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Table 2Non-parametric correlations between variables

Variable SPECIALIST LAF LTA LSUB CATA QUICK DE ROI FOREIGN OPIN LOSS ADR POWER CITYCOST

SPECIALIST 1.00LAF 0.13 1.00LTA 0.12 0.86 1.00LSUB 0.14 0.80 0.69 1.00CATA 0.02 0.10 0.04 0.08 1.00QUICK 0.01 0.34 0.29 0.26 0.25 1.00DE 0.06 0.59 0.62 0.48 0.13 0.30 1.00ROI 0.07 0.51 0.57 0.33 0.15 0.21 0.37 1.00FOREIGN 0.07 0.42 0.29 0.53 0.06 0.07 0.16 0.09 1.00OPINION 0.11 0.19 0.26 0.11 0.17 0.11 0.18 0.35 0.03 1.00LOSS 0.09 0.50 0.52 0.41 0.09 0.21 0.35 0.60 0.17 0.26 1.00ADR 0.05 0.21 0.22 0.22 0.08 0.02 0.12 0.02 0.15 0.01 0.05 1.00POWER 0.09 0.52 0.53 0.41 0.13 0.25 0.39 0.43 0.13 0.15 0.40 0.07 1.00CITYCOST 0.05 0.33 0.25 0.19 0.08 0.08 0.12 0.17 0.06 0.09 0.07 0.02 0.20 1.00CITYIL 0.55 0.25 0.24 0.22 0.06 0.05 0.14 0.15 0.07 0.13 0.16 0.17 0.04 0.11

All correlations greater than 0.10 are significant at the 0.01 level. LAF is log of total audit fees paid to the auditor ($A thousands); LTA is log of total assets($A thousands); LSUB is the log of the number of subsidiaries; CATA is the ratio of current assets to total assets; QUICK is the ratio of current assets less inventoriesto total assets; DE is the ratio of long-term debt to total assets; ROI is the ratio of earnings before interest and tax to total assets; FOREIGN is the proportionof subsidiaries that represent foreign operations; OPINION is an indicator variable with a value of 1 for a modified or qualified opinion; LOSS is an indicatorvariable with a value of 1 for a loss in any of the last 3 years; ADR is an indicator variable taking the value 1 if the firm is cross-listed as an American DepositoryReceipt; POWER is a measure of client bargaining power relative to the auditors total clientele in the industry as measured by the natural log of company sales,divided by the sum of logged sales for all firms in the industry audited by the companys auditor; CITYCOST is the use the annual salary cost for an experiencedauditor in each of the major cities from Hays Personnel Services; and CITYIL is an indicator variable with 1 if the company is audited by the number one auditorin the industry and the city, and number one or two in the industry nationwide based upon total audit fees.



6. Results

6.1. Client size and audit fees

We initially document specification issues regarding the association betweenclient size and audit fees in Australian audit fee data. Table 3 documents theinfluence of scale on the Studentized residuals for a typical fee model. Weestimate typical fee models, excluding the industry specialist variable, for eachquintile of client size and calculate the Studentized residual for each observa-tion. As the Studentized residual is expected to be normally distributed (Eastonand Sommers, 2003), we count the number of observations with the absolutevalue of Studentized residuals greater than 1.96 and compare the frequency ofStudentized residuals across size quintiles.

In Panel A of Table 3, the median absolute value of the Studentized residualranges from 0.538 for quintile 3 of client size to 0.832 for quintile 1. The meanand median absolute value for the largest client quintile, quintile 1, is thelargest with a value of 0.973. The proportion of observations greater than 1.96is less than 5 per cent of the sample indicating that the audit fee model isgenerally well specified. However, the extreme observations are clustered in thelargest and smallest client quintiles. All the observations with Studentizedresiduals greater than 1.96 are in the extreme quintiles. The extreme observa-tions represent 3.6 and 5.4 per cent in the small and large client quintiles,respectively. There are no extreme observations in quintiles 2 through 4.

Furthermore, while less than 5 per cent of the observations are potentiallyinfluential, 16 per cent of the largest clients in each industry and 17 per cent ofthe second largest clients in each industry have Studentized residuals greaterthan 1.96. (For 1999, not reported, 20 per cent of the largest clients and 17 percent of the second largest clients have Studentized residuals greater than 1.96.)The large clients in each industry are overrepresented in the potentially influentialobservations.

After deleting any potentially influential observations (with absolute value ofthe Studentized residuals greater than 1.96), we consider the pattern in the coeffi-cient on log of total assets when audit fee models are estimated on subsamplesof clients by size quintile. The results are reported in Panel B of Table 3. Thecoefficients on log of total assets increase with client size. Large audit clientspay more per dollar of assets than small audit clients. The coefficient on size isno longer monotonically increasing across size quintiles, but varies dependingon the size of the intercept estimated for the model. Panel B of Table 3 alsoreports the same data reported sorted by industry-size quintile; that is, thequintile of size of total assets within industry. In this case, the coefficient onsize is monotonically increasing, suggesting that size within industry is a deter-minant of audit fees.

To provide further evidence of the difference between audit fee modelcoefficients for small and large client segments, we divided the sample into



Table 3Client size effects on residuals and coefficients in standard audit fee models

Panel A: Client size effects in Studentized residuals: 1998 Dataa

Variable

Mean of total assets ($A million)

Mean of absolute value of Studentized residuals

Median of absolute value of Studentized residuals

Proportion extreme observations (5% level)b %

Median of Studentized residuals

Size quintile 1 3078.420 0.973 0.832 3.604 0.387Size quintile 2 169.798 0.695 0.612 0 0.084Size quintile 3 44.530 0.702 0.538 0 0.287Size quintile 4 13.755 0.757 0.667 0 0.145Size quintile 5 3.501 0.812 0.667 5.405 0.302

Panel B: The association between client size and audit fees by quintile of client size after removing potentially influential observationsc

VariableMean of total assets ($A million) Intercept

Coefficient on log of total assets: 1

(1) Rank on client total assetsSize quintile 1 3010.941 3.737 0.646Size quintile 2 162.991 1.841 0.478Size quintile 3 44.337 5.363 0.835Size quintile 4 13.703 0.879 0.436Size quintile 5 3.635 1.386 0.362

(2) Rank on size within industrySize quintile 1 2298.216 3.888 0.671Size quintile 2 644.629 2.970 0.596Size quintile 3 320.023 2.638 0.591Size quintile 4 71.428 1.863 0.533Size quintile 5 13.922 1.602 0.469

aStudentized residuals from the basic audit fee model excluding industry specialist auditor. The sampleexcludes audits of financial and property trusts (industry codes 16, 17, 19, and 20). Model: LAF = + 1LTA + 2LSUB + 3CATA + 4QUICK + 5DE + 6ROI + 7FOREIGN + 8OPINION+ 9YE + 10LOSS + .LAF is log of total audit fees paid to the auditor ($A thousands); LTA is log of total assets ($Athousand); LSUB is the log of the number of subsidiaries; CATA is the ratio of current assets to totalassets; QUICK is the ratio of current assets less inventories to total assets; DE is the ratio of long-termdebt to total assets; ROI is the ratio of earnings before interest and tax to total assets; FOREIGN is theproportion of subsidiaries that represent foreign operations; OPINION is an indicator variable with avalue of 1 for a modified or qualified opinion; YE is an indicator variable with a value of 1 for non-Junefiscal year end; and LOSS is an indicator variable with a value of 1 for a loss in any of the last 3 years.bProportion of extreme observations is proportion of quintile observations with the absolute value ofStudentized residuals greater than 1.96. cThis table reports coefficients for fee models estimated onsubsamples partitioned by client size excluding all potentially influential observations identified asobservations with the absolute value of Studentized residuals greater than 1.96.



client segments below and above the median size (not reported). We can rejectthe hypotheses that the coefficients on total assets, number of subsidiaries,current ratio, quick ratio, and number of foreign subsidiaries in the large clientsubsample are equal to the value of the coefficients in the small client subsample.The coefficient on the log of total assets is 51 per cent higher for the large clientsample than for the small client sample.

Overall, the results so far suggest that the audit fee model is reasonably wellspecified; however, there are distinct patterns in the association between clientsize and audit fees in the extreme client size quintiles. The observations thatpotentially have significant influence are clustered in the quintiles for the smallestand largest companies. As prior research does not exclude these observations,and exclusion would remove the largest clients in each industry from the sample,we use the full sample in the subsequent analysis but we allow the interceptsand slopes to vary with client size.

6.2. The impact of client size on industry specialist fee premiums

Table 4 estimates fee models with variables capturing the various aspects ofclient size predicted to impact fees. Column 3 provides results for a basic feemodel with an indicator variable for the two largest industry specialist auditorsbased on market share for comparison with prior research. The model is wellspecified with an adjusted R2 of 83.2 per cent. The coefficient on specialist ispositive and significant (coefficient 0.12, t = 2.26).

Column 4 of Table 4 includes the additional variables allowing the coefficienton log of total assets to vary with client size. Consistent with the results fromTable 3, there is a U-shaped pattern in the coefficients with slightly highercoefficients for the largest and smallest clients. The differences between coeffi-cients are not statistically significant; however, even small differences whenmultiplied by log of total assets predict significantly different fees in theextreme quintiles. The indicator variable for cross-listing in the USA is positiveand significant (coefficient 0.22, t = 2.27). With respect to potential clientbargaining power, the coefficient is positive and significant (coefficient 0.33,t = 2.78). This is consistent with the view that clients that are relatively large intheir industry tend to choose an auditor that has successfully differentiatedthemselves from their competitors and, hence, retains strong bargaining powerwith the client. We do not find any evidence of the clients that are relativelylarger in the industry obtaining any fee discount. The coefficient on specialistremains positive and significant (coefficient 0.12, t = 2.30) similar to the initialmodel estimate.

Column 5 of Table 4 partitions the specialist premium by quintile of clientsize. The premium to specialization for the largest quintile of clients is 0.28compared to 0.08 for the middle quintiles and 0.11 for the smallest quintiles.The coefficient on specialization is significant for the largest clients but notsignificant for the majority of clients. A hypothesis of equal coefficients for

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Table 4Audit fee premiums and client size

Year: (2)

Premiums to industry specialist (3)

Expanded model to allow fees to vary with size (4)

Expanded model to allow specialist premium to vary by client size 1998 (5)

Expanded model 1999sample (6)

Expanded model 2004 sample revised GICS industries (7)

INTERCEPT (.) 1.86 (8.88) 1.22 (3.08) 1.15 (2.90) 1.04 (2.50) 0.34 (0.91)LTA (+) 0.51 (28.98)***LTA*QUINTILE 1 (large) 0.46 (15.78)*** 0.45 (14.72)*** 0.44 (14.13)*** 0.42 (15.26)***LTA*QUINTILE 2 0.44 (13.21)*** 0.44 (13.08)*** 0.42 (12.01)*** 0.39 (12.68)***LTA*QUINTILE 3 0.43 (11.61)*** 0.42 (11.46)*** 0.40 (10.60)*** 0.39 (11.53)***LTA*QUINTILE 4 0.43 (10.49)*** 0.42 (10.31)*** 0.40 (9.49)*** 0.38 (10.11)***LTA*QUINTILE 5 (small) 0.45 (9.53)*** 0.45 (9.22)*** 0.41 (8.43)*** 0.40 (9.33)***LSUB (+) 0.04 (5.24)*** 0.04 (5.64)*** 0.04 (5.67)*** 0.03 (4.47)*** 0.03 (5.27)***CATA (+) 0.80 (7.25)*** 0.82 (7.44)*** 0.82 (7.38)*** 0.89 (8.50)*** 0.91 (9.14)***QUICK () 0.02 (4.76)*** 0.01 (5.40)*** 0.02 (5.40)*** 0.02 (5.46)*** 0.03 (9.76)***DE (+) 0.36 (1.86) 0.32 (1.72) 0.33 (1.75)** 0.58 (3.29)*** 0.54 (2.71)***ROI () 0.52 (5.10)*** 0.36 (3.49)*** 0.35 (3.38)*** 0.19 (1.79)** 0.27 (2.35)***FOREIGN (+) 0.60 (5.82)*** 0.56 (5.64)*** 0.55 (5.54)*** 0.76 (7.75)*** 0.11 (2.19)**OPINION (+) 0.07 (0.83) 0.09 (1.12) 0.09 (1.08) 0.13 (1.64) 0.09 (1.11)YE () 0.11 (1.82) 0.09 (1.55) 0.09 (1.44) 0.03 (0.32) 0.07 (1.30)LOSS () 0.18 (2.70)*** 0.15 (2.32)*** 0.14 (2.24)*** 0.02 (0.30) 0.01 (0.18)ADR (+) 0.22 (2.27)** 0.21 (2.18)** 0.25 (2.74)*** 0.26 (2.64)***POWER (+/) 0.44 (3.05)*** 0.45 (3.05)*** 0.41 (3.01)*** 0.24 (1.03)SPECIALIST (+) 0.12 (2.26)** 0.12 (2.30)**SPECIALIST and LARGE

SIZE QUINTILE 1(+) 0.28 (2.18)**

(n = 77)0.23 (1.84)**

(n = 75)0.31 (2.66)***

(n = 86)SPECIALIST and MEDIUM

SIZE QUINTILES 24(+) 0.08 (1.26)

(n = 179)0.16 (2.55)***

(n = 178)0.12 (1.92)**

(n = 217)

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SIZE QUINTILE 5(+) 0.11 (0.94)

(n = 62)0.12 (1.10)

(n = 57)0.02 (0.19)

(n = 72)N 558 558 558 543 611Adjusted R2 (%) 83.2 84.3 84.3 84.9 82.5

*** and ** indicate significance at the 1 and 5 per cent levels (one-tailed), respectively. LTA is log of total assets ($A thousand); LSUB is the log of the numberof subsidiaries; CATA is the ratio of current assets to total assets; QUICK is the ratio of current assets less inventories to total assets; DE is the ratio of long-term debt to total assets; ROI is the ratio of earnings before interest and tax to total assets; FOREIGN is the proportion of subsidiaries that represent foreignoperations; OPINION is an indicator variable with a value of 1 for a qualified opinion; YE is an indicator variable with a value of 1 for non-June fiscal year end;LOSS is an indicator variable with a value of 1 for a loss in any of the last 3 years; ADR is an indicator variable taking the value 1 if the firm is cross-listed asan American Depository Receipt; POWER is a measure of client bargaining power relative to the auditors total clientele in the industry as measured by thenatural log of company sales, divided by the sum of logged sales for all firms in the industry audited by the companys auditor; and SPECIALIST is an indicatorvariable with 1 if the client is audited by the specialist auditor in the industry, where specialist auditor is identified as the two auditors with the largest marketshare based on the highest proportion of industry audit fees.

Year: (2)

Premiums to industry specialist (3)

Expanded model to allow fees to vary with size (4)

Expanded model to allow specialist premium to vary by client size 1998 (5)

Expanded model 1999sample (6)

Expanded model 2004 sample revised GICS industries (7)

Table 4 (continued)



the largest size quintiles of clients and for size quintiles 24 cannot, however,be rejected at an alpha level of 1 per cent (F = 1.81, p = 0.18) due to largestandard errors for this test.

Overall, the results suggest that the typical audit fee model using Australiandata needs to include an indicator variable for cross-listing to capture thedemand for increased audit services by this group of large clients. Furthermore,the premium to specialization appears to be clustered in the fees paid by thelargest clients. This confirms the seminal research in the area but also documentsthe extent to which fee premiums to specialization are conditional on client size.

6.3. Client size and measures of office level industry expertise

Table 5 provides a similar fee analysis using an audit fee model with anindicator variable for the city-industry leader (Ferguson et al., 2003). Onlyaudits in the five largest metropolitan areas are included in this analysis. Thecity-industry leader variable takes the value of 1 when the auditor is the numberone auditor in the city and one of the top two firms nationally (CITYIL). Threepartitions of the city-industry lead auditor variable are also included in theanalysis partitioning the specialization measures by client size quintiles. Theindicator variables take the value of 1 if the auditor is a city-industry leader(CITYIL) and the client is in the highest quintile of the full sample by totalassets, quintiles 2 to 4, or quintile 5, respectively.

Column 3 of Table 5 reports the coefficients for a basic fee model. Thecoefficient on city cost is positive and significant. Audits where the head officeof the client is in a higher-cost city have higher fees than audits of clients withhead offices in lower-cost cities. The premium on the city-industry leader in is0.16, which is a little lower than Ferguson et al. (2003) but still a very largepremium. Column 4 allows the coefficient on total assets to vary with clientsize, includes the indicator variable for cross-listing in the USA and allowsthe city-industry premium to vary with client size. Consistent with theindustry-level analysis the coefficient on cross-listing in the USA (ADR) islarge and significant (coefficient 0.19, t = 2.06). The coefficient on thecity-industry leader indicator variable remains positive and significant(coefficient 0.33, t = 2.78). As for industry the coefficient on industry special-ization for clients in quintiles 24 is not significantly different from 0. Asdiscussed below, the coefficient on the medium quintiles is, however, signific-antly greater than 0 for 1999 and 2004. The pattern of coefficients and theresulting inferences are generally consistent with those reported for nationalindustry-level analysis.

7. Sensitivity analysis and further discussion

In this section, we consider the sensitivity of our results to the period exam-ined and alternate definitions of industry specialist (Gramling and Stone, 2001).

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Table 5Fee premiums to city-industry leaders

Year: (2)

Premiums to city-industry specialist (3)

Expanded model to allow specialist premiumto vary by client size 1998 (4)

Expanded model 1999 (5)

Expanded model2004 revised GICS industries(6)

INTERCEPT (.) 3.41 (10.75) 2.86 (6.11) 2.28 (4.65) 1.02 (2.03)LTA (Average coefficient in columns 4, 5, 6) (+) 0.48 (27.32)*** 0.43 (11.84)*** 0.39 (10.56)*** 0.37 (10.84)***LSUB (+) 0.04 (5.32)*** 0.04 (5.73)*** 0.02 (4.66)*** 0.03 (5.04)***CATA (+) 0.74 (6.83)*** 0.77 (7.10)*** 0.81 (7.72)*** 0.88 (8.72)***QUICK () 0.02 (4.99)*** 0.02 (5.58)*** 0.02 (5.11)*** 0.03 (9.55)***DE (+) 0.42 (2.21)** 0.40 (2.16)** 0.63 (3.60)*** 0.54 (2.71)***ROI () 0.57 (5.70)*** 0.40 (3.98)*** 0.23 (2.14)*** 0.24 (2.15)**FOREIGN (+) 0.62 (6.16)*** 0.58 (5.94)*** 0.76 (7.75)*** 0.09 (1.78)**OPINION (+) 0.05 (0.65) 0.08 (0.94) 0.16 (1.99)** 0.09 (1.13)YE () 0.06 (0.98) 0.04 (0.67) 0.03 (0.63) 0.09 (1.47)LOSS () 0.22 (3.42)*** 0.20 (3.11)*** 0.04 (0.70) 0.06 (0.85)CITYCOST (+) 0.04 (6.71)*** 0.04 (6.75)*** 0.03 (5.31)*** 0.02 (2.95)***

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ADR (+) 0.19 (2.06)** 0.22 (2.41)*** 0.25 (2.56)***POWER (+/) 0.37 (2.53)*** 0.36 (2.60)*** 0.08 (0.33)CITYIL (+) 0.16 (2.48)***CITYIL and LARGE

SIZE QUINTILE 1(+) 0.33 (2.78)***

(n = 46)0.40 (3.33)***

(n = 46)0.52 (4.57)***

(n = 58)CITYIL and MEDIUM

SIZE QUINTILES 24(.) 0.12 (1.51)

(n = 88)0.22 (2.76)***

(n = 80)0.16 (2.19)**

(n = 99)CITYIL and SMALL SIZE QUINTILE 5 (.) 0.05 (0.33)

(n = 18)0.17 (1.24)

(n = 20)0.18 (1.47)

(n = 30)NATIONAL 1 or 2 but not CITY 1 0.08 (1.29) 0.08 (1.34) 0.10 (1.59) 0.09 (1.45)CITY 1 not NATIONAL 1 or 2 0.08 (0.77) 0.08 (0.84) 0.06 (0.69) 0.26 (2.13)**N 546 546 526 594Adjusted R2 (%) 84.3 85.3 85.6 83.1

*** and ** denote significance at the 1 and 5 per cent levels (one tailed), respectively. LTA is log of total assets ($A thousand); LSUB is the log of the numberof subsidiaries; CATA is the ratio of current assets to total assets; QUICK is the ratio of current assets less inventories to total assets; DE is the ratio of long-term debt to total assets; ROI is the ratio of earnings before interest and tax to total assets; FOREIGN is the proportion of subsidiaries that represent foreignoperations; OPINION is an indicator variable with a value of 1 for a qualified opinion; YE is an indicator variable with a value of 1 for non-June fiscal year end;LOSS is an indicator variable with a value of 1 for a loss in any of the last 3 years; CITYCOST is the use the annual salary cost for an experienced auditor ineach of the major cities from Hays Personnel Services; ADR is an indicator variable taking the value 1 if the firm is cross-listed as an American DepositoryReceipt; POWER is a measure of client bargaining power relative to the auditors total clientele in the industry as measured by the natural log of company sales,divided by the sum of logged sales for all firms in the industry audited by the companys auditor; and CITYIL is an indicator variable with 1 if the company isaudited by the number one auditor in the industry and the city, and number one or two in the industry nationwide based on total audit fees.

Year: (2)

Premiums to city-industry specialist (3)

Expanded model to allow specialist premiumto vary by client size 1998 (4)

Expanded model 1999 (5)

Expanded model2004 revised GICS industries(6)



We specifically expect that definitions of industry specialist will affect the estima-tion of relations between client size, identification of the industry specialist,and the resulting measures of fee premiums.

7.1. Sensitivity to period examined

We chose the 1998 year to facilitate comparison with research by Fergusonet al. (2003). To examine the robustness of the results to the period examined,we re-estimated the results using equivalent data for 1999 and for 2004. In1999, for the base model, the coefficient on specialist is positive and significant(coefficient 0.17, t = 3.16). Consistent with the results for 1998, there is a U-shaped pattern in the coefficients when the coefficient is allowed to vary byquintile of total assets. For the complete model (reported in column 6 ofTable 4), the coefficient on the indicator variable for cross-listing in the USA ispositive and significant (coefficient 0.25, t = 2.74). The premium to specializa-tion for the largest quintile is 0.23 compared to 0.16 for the middle quintilesand 0.12 for the smallest quintile. The coefficient for the interaction betweenspecialist premium and medium size of client is significantly greater than 0;however, there is a consistent decline in premium between the largest clientsand the medium size of client. Overall, the results are consistent with thosereported using the 1998 data.

With the demise of Arthur Andersen, there has been a further concentrationof suppliers of audit services. To establish whether our results are generalizableto this period, we re-estimate our models for 2004. For the base model, reportedin column 7 of Table 4, the ADR variable continues to be positive and signi-ficant (coefficient of 0.26, t = 2.64). We also find evidence of premiums tonational specialist for the largest quintile of clients (0.31, t = 2.66), whichdecline across the medium and small quintiles. In column 6 of Table 5, wereport a similar pattern of results for city-industry leaders. Overall, we find evid-ence that the pattern of premiums to specialization for large firms is robust totime period despite the significant change in industry designations adopted bythe ASX before 2004. However, in the post-Arthur Andersen market, we no longerfind support for a higher fee paid by relatively large clients in an industry as wefound in earlier periods. For 2004, we do find a premium to the city, but notnational leader (0.26, t = 2.13) that appears to clustered in industry groupingsfor GICS code 1510 (materials) and GICS code 3020 (food) where larger industrygroupings than previously result in some large companies employing city leadingbut not industry leading auditors. However, we have not partitioned this groupby client size because of the small number of companies involved.

7.2. Number of clients in the industry

A concern in use of the market share measure of specialization in prior liter-ature is the intuition that an industry must have a minimum number of clients



in an industry for an auditor to be designated a specialist in that industry.Although Ferguson et al. (2003) assign specialists in all industries, earlierresearch by Craswell and Taylor (1991) and Craswell et al. (1995) were carefulin only identifying specialists in industries with greater than 30 observationsand only identifying auditors as specialists where the auditor has a threshold of10 per cent of market share on either the number of clients or the percentage oftotal audit fees. Replicating our models on the nine industries that previousresearch has identified as having industry premiums (0104, 07, 09, 11, 13 and19), we find similar results to those reported. Influential observations areclustered in the largest and smallest quintiles by asset size. For the full model(column 5 of Table 4), fee premiums to specialization are found for ADR(coefficient 0.21, t = 2.18). The premiums to specialization for the large, mediumand small clients were 0.28, 0.08 and 0.11, respectively.

Replicating our models on the 7 industries that previous research has identifiedas having industry premiums and that have 30 clients in our sample (1, 2, 4, 9,11, 13 and 19), we again find results consistent with those reported above.

7.3. Number of auditors in the industry and identifying a specialist

Seminal research on specialization (e.g. Craswell et al., 1995) examines amarket with eight large international audit firms. Given the small number ofauditors in the current market we consider the ability of the typical methodologyto distinguish between alternate explanations for specialist fee premiums. Todetermine whether the fee premium attributed to specialization can also beexplained by client attributes, we examine the observed fee premium whenclients are assigned to five auditors at random. That is, we randomize clientswithin industry with respect to the reputation of the auditor selected. We ini-tially allocated all clients to five auditors using a random draw from a uniformdistribution. The industry specialist auditor is then identified based on the high-est share of audit fees in an industry and the basic fee model estimated (as percolumn 3 of Table 4). We repeated this procedure 1000 times. The mean coeffi-cient on the industry specialist auditor is 0.12 with a range of 0.06 to 0.19.The largest clients in each industry pay fee premiums in the Australian market,and whichever auditor has these clients is designated the industry specialist andreceives an apparent fee premium. Fee premiums of the magnitude typicallyobserved for industry specialist auditor can be explained by the fee premiumspaid by the largest clients, and whether this premium is due to industry special-ization or other client attributes requires additional information not typicallyavailable in this type of study.

As a further sensitivity, we also re-estimated the regressions reported asTable 4 removing clients from one auditor from each estimation. For 1998, onlythe test of the significance of the coefficient on the ADR variable is not signific-ant when KPMG is excluded (KPMG audits 13 of 49 clients with ADRs in1998). The coefficient on the SPECIALIST and LARGE SIZE QUINTILE 1



interaction is also not significant when Price waterhouse Coopers clients areexcluded (166 clients are excluded from the sample). However, the coefficientsare consistently in the direction of those reported and most statistical inferencesare consistent with those reported.

8. Conclusion

Previous research suggests that industry specialist auditors earn higher fees.Previous research has also found that, in the Australian environment, higherfees are concentrated in clients of larger size. This research attempts to providefurther insight into the association between the client characteristic of size andaudit fee premiums.

Premiums to national industry specialists are concentrated in the fees paid bythe largest clients in each industry. The premium to industry specialist auditorsis not significantly different from 0 for the middle and smaller quintiles of clientsbased on total assets. The findings are consistent with auditor specializationonly benefiting large clients. Another possible explanation is that the clientcharacteristics of large clients impact audit fees and the auditors of the largestclients are designated the industry specialist. Given the differences in apparentpremiums to large and medium clients it appears important to partition thespecialist premium by client size, or to report results by subsamples partitionedby client size (e.g. above and below the median). Given the strong relationshipbetween client size and audit fees, and that audit fees do not vary strictly linearlywith client size, we suggest that it is important to examine the robustness of anyobserved fee premium across clients of varying size.

We document a variable that can be considered an omitted variable in typicalaudit fee models. Companies that are cross-listed on US exchanges pay higheraudit fees. As these large companies have more complex filing requirements itis to be expected that they pay higher audit fees. The premium to this factor islarge relative to premiums attributed to industry specialization and should beconsidered in audit fee models using Australian data.

We also document three smaller effects. There is non-linearity in the asso-ciation between client size and audit fees. For the period examined, both smallerand larger clients tend to pay higher audit fees. We also observe that audit costsvary by the city of the client head office. Clients located in higher-cost citiespay higher audit fees. We also find evidence that clients that are large relativeto their industry paid higher fees in the earlier years of our sample period. Wefind no evidence consistent with larger clients being able to negotiate loweraudit fees.

The issue of fee premiums paid by large clients is of particular concernbecause of the possible explanation that there is a potentially less competi-tive segment for large auditees that is dominated by a few large audit firms(Simunic, 1980; Francis and Stokes, 1986). In the USA, the SarbanesOxley Actexplicitly refers to the reduction in the number of firms capable of providing



audit services to large businesses (SarbanesOxley Act, US Congress, 2002,section 701). This paper highlights the higher fees received from larger clientsin each industry with indirect implications for the importance of these clients tothe auditor and auditor independence. However, we find evidence consistentwith a higher demand for more complex accounting and auditing issues fromthese clients that needs to be considered before attributing fee premiums to a lackof competitive pressure. Because of the changes observed in the audit market,future research could also consider these issues in a dynamic market settingwith a declining pool of large auditors available to audit large companies.

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