Knowledge spillovers in audit-related non-audit

Knowledge spillovers in audit-related non-audit services and industry specialist auditors*

Nicolas Gambetta KPMG, Uruguay

Jane Hamilton La Trobe University

Donald Stokes University of Technology, Sydney February 2007

Corresponding author: Donald Stokes School of Accounting University of Technology, Sydney PO Box 123 Broadway 2007 AUSTRALIA Ph: +61 2 409304035 Fax: +61 2 95143669 Email: [email protected]

*This paper is developed from Nicolas Gambetta’s Masters by Research thesis at UTS. The paper has benefited from comments by Keith Houghton, Gary Monroe, Terry O’Keefe, Ken Trotman and other participants at the 2006 ANCAAR audit research forum and from colleagues in seminars at UTS.

Knowledge spillovers in audit-related non-audit services and industry specialist auditors

Abstract This study examines the relationship between auditor industry specialization and the non-audit services (NAS) they provide to their clients. Industry specialist auditors use their industry-specific audit information to create knowledge spillovers for clients demanding NAS that are more audit-related. The benefits of the knowledge spillovers are reflected in a greater range of such services and lower fees for those services for clients of industry specialist auditors than for clients of non-specialists. Based on NAS disclosures by a sample of Australian companies for the period 2002-2004, the results show that, relative to non-industry specialist auditors, industry specialist auditors supply between 60% and 152% more of the “more audit-related NAS” to their clients than nonindustry specialist auditors. The results also show that industry specialist auditors, relative to non-industry specialist auditors, charge between 8.15%-15.72% lower fees for more audit-related NAS to their audit clients. The results imply that knowledge spillover economies derived by the clients from the joint supply of audit and NAS are contingent on the audit supplier and the type of service demanded.

1.

Introduction This paper studies the joint supply of audit and NAS by auditors focussing on the

effects of knowledge spillovers from the supply of NAS when the audit is conducted by an industry specialist auditor. The study provides evidence from the Australian audit market on the relationship between auditor industry specialization in the supply of audit services and: (1) the range of more audit related NAS provided to the client from their specialist auditor and (2) the fees charged for these NAS. The empirical evidence to date on the existence of knowledge spillovers between audit and NAS is mixed (see e.g., Simunic 1984, Palmrose 1986, Abdel-khalik 1990, O’Keefe et al. 1994, Ezzamel et al. 2002, Craswell et al. 2002, Whisenant et al. 2003) and has not considered the value of industry specialist auditors in the joint supply together with the type of NAS supplied. In particular, the studies have not considered whether the benefits of auditor industry specialization in NAS supply are related to the proximity of the NAS to the audit function. We take advantage of the Australian audit market setting in which enhanced NAS disclosure requirements provide atomistic data on the different types of NAS provided to clients by incumbent auditors. This study contributes to the literature by arguing that the propensity to generate knowledge spillover economies depends upon the supplier of the NAS (whether they are an industry specialist auditor or not) and the types of NAS (whether the NAS are more audit related or not). The study is also motivated by the introduction of strict regulations - e.g., the Sarbanes-Oxley Act in the U.S.1 and CLERP 9 in Australia2 - and other ethical rules 1

The Sarbanes-Oxley Act requires US companies and Australian companies with US parents to apply strict independence rules limiting the NAS their auditors can provide. Whilst Australian legislation and guidelines are not as prescriptive as in the US, corporate governance best practice is increasingly moving towards the US model. More and more ASX listed companies are restricting the level of NAS provided by their external auditors. Following recent corporate failures and scandals the growing perception is that the role of the external auditor in a large public company should not extend beyond the external audit. Source: BDO Australia website: http://www1.bdo.com.au 2

The Corporate Law Economic Reform Program (Audit Reform and Corporate Disclosure) Bill (“the CLERP 9 Bill”) was passed on 25 June 2004. Whilst the CLERP 9 Bill does not go so far as to ban auditors from providing NAS to their audit clients, it has introduced new disclosure requirements in relation to NAS. For financial years beginning on or after 1 July 2004 the ‘NAS’ section of the directors’ report must include: Details of the amount paid to the auditor for NAS split by each of the NAS provided; a statement whether the directors are satisfied that the provision of NAS by the auditor is compatible with the general standards of independence for auditors; and a statement of the directors’ reasons for being satisfied that the auditor’s independence was not compromised. The Board can only make these statements on the independence of their auditor providing NAS if: such advice has been provided by the company’s audit committee; or if the company does not have an audit committee, then following a resolution of the directors

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around the world that limit auditors’ ability to provide certain NAS to audit clients. The new regulations followed a period where the provision of NAS by large auditors to their clients had expanded (e.g., in Australia Barkess et al. 1994, Carson et al. 2002 and in the US Antle et al. 2006) and corporate collapses such as Enron and WorldCom in the US and HIH Insurance Ltd in Australia, attracted the attention of investors and regulators to the level and types of NAS provided by auditors to their audit clients and their effects on auditor independence. If the demand for NAS from the incumbent auditor is not homogeneous across companies, regulatory restrictions could impose economic costs on some companies. To the extent this study demonstrates that there are benefits to companies taking NAS from their industry specialist auditors, this adds to wider calls in the literature (e.g. Culvenor, Stokes and Taylor 2002) questioning “one-size fits all” regulatory solutions and calling for a balancing of these benefits in joint supply against the costs of joint supply attached to concerns of lack of auditor independence. A further motivation for this study is that several recent studies [e.g. Antle et al (2006) and Ruddock, Sherwood and Taylor (2004)] examine the extent to which high levels of NAS, provided by the incumbent auditor, impair audit quality and accounting numbers quality. The basic rationale of these studies is that high levels of NAS provided by the incumbent auditor result in a reduction of auditor independence which leads to a reduction in the quality of the audit and therefore, lower quality accounting numbers. The results from these studies are somewhat mixed and leave open the question of what determines the underlying demand and supply for NAS. The theory in this study draws on the important role that industry specialist auditors play in client risk assessments and in the audit process. Prior research outlined in section 2, suggests performance gains in audits are related to the use of industry specialists, who serve to moderate audit risk. The specific knowledge that specialists have on the audit supply side can be used to create opportunities for the provision of NAS to the client. Industry specialist auditors have industry-specific audit information not available to non-industry specialist auditors and so can create knowledge spillovers for clients demanding NAS that are more audit-related. The benefits of the knowledge spillovers are reflected in a greater range of such services provided by industry specialist auditors to their clients and lower fees for those services, than non-specialists. to that effect. These new requirements place an additional responsibility on the members of the audit committee and on directors in general. Source: BDO Australia website: http://www1.bdo.com.au

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The study tests these predictions on Australian data for the period 2002-2004 using a demand side model of the audit-related NAS controlling for client risk, size and complexity factors as well as controlling for the underlying reasons for selecting the industry specialist auditor in the first place (specialists’ self-selection). The results suggest that industry specialist auditors provide a greater range (between 60% and 152% more) of more audit-related NAS than non-specialists and they charge lower fees (between 8.15% - 15.72% lower) than non-specialists for these services over this period. The remainder of this paper is organized as follows. Section 2 outlines the theory and hypotheses to be tested. The research design and details of the sample selection and some descriptive statistics are provided in section 3. Section 4 reports the results of the tests and the sensitivity tests, and section 5 explains the study’s conclusions and identifies some future research issues. 2.

Theory development Industry specialist auditors play an important role in client risk assessment and

during the audit process and are defined as personnel with relatively high industry expertise, functional expertise and/or high risk client experience (Johnstone and Bedard, 2003). Deep knowledge of the business environment and industry allows an auditor to anticipate the management assertions contained in the accounts that are most likely to contain misstatements and concentrate audit work on these areas. This knowledge also allows them to better assess industry-specific and client-specific inherent risks and control risks than non-specialists. They can also influence control risk in the long term by making better recommendations to the management by drawing on this deeper knowledge of industry best practice not held by the non-specialists. Empirical evidence supports the argument that there are performance gains related to the use of industry specialists that serve to moderate audit risk. For example, in their study of the use of specialist personnel, Johnstone and Bedard (2003) find that the intent to use specialist personnel moderates the negative relationship between audit risk (fraud and error risk) and client acceptance likelihood.3 Bedard and Biggs (1991) report that auditors with greater experience with manufacturing clients holding inventory were more 3

They also found that charging higher billing rates moderates the negative relationship between both going-concern risk and public trading status and client acceptance likelihood, but does not moderate the negative relationship between audit risk and client acceptance likelihood (the use of specialist personnel does it).

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likely to correctly identify errors using analytical procedures. Johnson et al. (1991) report that industry experience is of greater value in identifying the presence of a material fraud than is general audit experience. In the joint supply of audit and NAS, industry specialist auditors can contribute to generating economies of scope for the audit firm by sharing know-how input (“knowledge spillovers”) across the services. The empirical research has not considered the value of industry specialist auditors in joint supply, and the evidence on the existence and effect on fees of knowledge spillovers in general is mixed. Simunic (1984) examines the effect of total management advisory services (MAS) fees on audit fees. He finds that audit fees for clients that purchase NAS from their auditors are higher than those for clients that did not do so and this observed increase is interpreted as arising from a beneficial knowledge spillover between services. Palmrose (1986) provides a more detailed classification of NAS arguing that this approach will be useful to investigate whether the proximity of the service to accounting influences the existence and magnitude of any beneficial effects from joint supply. Palmrose (1986) categorizes NAS as follows: 1) Tax services, 2) Accounting-related MAS, 3) Non-accounting MAS. Palmrose finds a positive relationship between fees for audit and NAS, with the strongest result for accounting-related MAS. The study also reports for a small sub-sample, higher than expected audit fees when NAS were provided by non-incumbent auditors. Abdel-khalik (1990) examines the effect of knowledge spillovers by estimating the cost to the clients for self-selecting into a regime of jointly acquiring NAS and audit from the same auditor. The study shows that audit fees do not differ by the choice of sourcing NAS (acquiring NAS from the incumbent auditor or from another auditor) suggesting the absence of any “spillover” effect in the joint provision of audit and NAS by the incumbent auditor. O’Keefe et al. (1994) categorize NAS as: 1) Management consulting services and 2) Tax services, to test the existence of knowledge spillovers and the effect on audit fees. They too find no evidence of lower audit fees due to knowledge spillovers from management consulting and/or tax work to audit services, consistent with results in Davis, Ricchiute and Trompeter (1993). Ezzamel et al. (2002) study the relationship between fees paid for categories of NAS and audit fees. They breakdown categories of NAS: 1) Accounting-related NAS, 2) Finance-related services, 3) Tax-related services, 4) Management consultancy services (advisory) and 5) Other NAS provided by audit firms. They find a significant positive relationship between taxation services fees and audit fees [consistent with Palmrose -4-

(1986) and Davis et al. (1993)], a significant positive relationship between the residual category containing other NAS fees, the new category containing finance advice fees and audit fees, no significant association between management services fees and audit fees [consistent with O’Keefe et al. (1994)] and no significant association between accounting-related fees and audit fees. Ezzamel et al (2002) conclude that there is little evidence of any cost savings from the joint provision of NAS and audit services being passed on to the client and that this could call into question the importance of spillovers. Notably these studies rely heavily upon survey data for fees paid for audit and non-audit services whereas the current study utilizes reported fee data that in turn has been audited. Using reported audit and non-audit fee data from the U.K. for the period 19942000, Antle et al. (2006) find evidence consistent with knowledge spillovers (or economies of scope) from auditing to NAS and from NAS to auditing. The paper does not identify categories of NAS to study the spillover effect. Craswell et al. (2002) use a sample of 136 Australian firms and find that the higher audit fees obtained by suppliers of NAS result from two influences: an increase in demand for audit services and an increase in economic rents. Consequently, any downward impact on the unit price of audit services from knowledge spillovers is more than offset by rent-seeking behaviour of auditors. Craswell’s Australian study does not investigate NAS fees. This study contributes to this literature by arguing that the propensity to generate economies from joint supply depends upon both the supplier of the NAS (whether they are an industry specialist auditor) and the types of NAS (whether the NAS are more audit related). None of the studies cited above investigate the effect of auditor specialization on either the type of NAS purchased or NAS fees. Rather the focus is mainly on audit fee effects of joint supply of different types of NAS ignoring characteristics of the audit supplier. In addition, several of the studies are restricted to survey data rather than reported data. Finally, the types of NAS studied do not explore the extent to which they are audit related and therefore where there are opportunities for knowledge spillovers. Economies of scope for the audit firm from sharing know-how input (“knowledge spillovers”) across the services are generated if the cost of joint provision of audit and NAS is less than the costs of providing each service separately by different auditing firms in the market. From a supply side perspective, industry specialist auditors are in a strong position to persuade the client about the benefits of purchasing NAS from them. The client knows that the specialist has deep knowledge of the business of the company and its industry and this will contribute to obtaining a higher quality NAS outcome as well as -5-

a more competent audit. Moreover, client experience with the specialist through the audit makes it easier for the incumbent specialist to disclose value to existing audit clients in a credible way about their capacity to supply more audit-related NAS. Consequently, if the specialist auditor is able to detect opportunities to provide more audit related NAS to an incumbent audit client, transaction costs are reduced and the cost of supplying the new service will be lower. From a demand side perspective, since non-audit services are not a mandatory requirement, the client retains discretion on purchase and choice of supplier, and therefore an incumbent industry specialist auditor cannot assume that they can recoup a normal rate of return on their investment in auditor industry specialization through the supply of NAS. Accordingly returns to being an industry specialist auditor would need to be priced in the audit and prior evidence suggests that on average this is reflected in audit fee premiums (see e.g., Ferguson and Stokes 2002).4 Moreover to the extent there is client discretion in purchasing NAS and in choosing a (multiple) supplier(s) beyond the incumbent auditor, competition for the supply of NAS will put pressure on price for these audit-related NAS. The industry specialist incumbent can supply the audit at marginal cost. Moreover, from the client’s perspective, search costs for identifying a credible supplier are reduced and they know the supplier’s costs are also reduced because it has already invested in acquiring deep client knowledge as well as industry knowledge for the audit. The capacity for industry specialist auditors to detect opportunities to provide NAS and convince the client of value in purchasing these NAS will be greater the more audit-related the NAS because these opportunities leverage the specialist’s expertise. The incumbent industry specialist auditor is positioned to be more competitive in supplying these audit-related NAS compared to an alternative supplier and compared to nonindustry specialists because there are the transaction cost advantages outlined above in organizing supply that leverages their capacity as auditor specialists. This suggests two hypotheses about the range of more audit-related NAS supplied and the fees paid for such services by industry specialist incumbent auditors:

H1: Relative to non-industry specialist auditors, industry specialist

4

In un-tabulated results, we find industry specialist auditors in our sample earn audit fee premiums. This supports the prior research findings and the arguments here.

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auditors are more likely to supply their audit clients a greater range of NAS that are more audit-related.

H2: Relative to non-industry specialist auditors, industry specialist auditors are more likely to charge lower fees to their audit clients for NAS that are more audit-related.

3.

Research Design The hypotheses are tested using two models; a cross-sectional range of NAS

model, and a NAS fee model. Both models are estimated on Australian listed company data for the period 2002-2004. This period is chosen because in this period CLERP legislation was introduced requiring companies to make detailed disclosure of types of NAS and their fees. In order to classify the NAS according to their relation to audit services, a list of all the different types of NAS disclosed by Australian listed companies in this period were rated on a 5-point Likert scale:

1 - Highly likely audit-related 2 - Moderately likely audit-related 3 - Neutral on whether it is audit-related 4 - Moderately unlikely audit-related 5 - Highly unlikely audit-related

Each of the authors and one other audit academic at UTS independently rated the services. None of the raters could condition the results because they were only provided a list of all NAS listed by the companies in the sample and did not know which auditors were associated with what services. A “very strong consensus” rating of a service type being audit related was assigned if all of the raters assigned a score of 1 or 2 to the service. A “strong consensus” rating of a service being audit related was assigned if at least 3 of the 4 assigned scores of 1 or 2 to the service. Tests are run using these two groups of audit related services. Table 1 Panel A shows the NAS with a very strong consensus rating and the additional NAS included under a strong consensus rating on being audit related are added in Panel B.

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INSERT TABLE 1 HERE

Notably some of the NAS services identified in Panel B of Table 1, e.g., nontaxation related NAS, were ruled out by legislation as non-allowable NAS to be supplied by the incumbent auditor.5 In that sense tests on the two separate panel groups in Table 1 allows us to consider whether the theory’s application is sensitive to the type of service considered by regulators to be in conflict with the role of auditor. The dependent variable for Hypothesis 1 is an indicator variable labeled ARRGNAS (for a greater range of more audit-related NAS) which takes the value of 1 when the incumbent auditor provides more types of more audit-related NAS than the median of these services provided and disclosed in the client industry and 0 otherwise. ARRGNAS1 is used to designate this measure based on the more audit-related services included in Table 1 Panel A (under the very strong consensus rating) and AR-RGNAS2 is used for the services in Panel A along with the services in Panel B (under the strong consensus rating). The dependent variable for Hypothesis 2 is AR-NAF, being the fees paid for the more audited related identified NAS. AR-NAF1 represents the fees for the more audit-related services included in Table 1 Panel A (very strong consensus) and ARNAF2 is the sum of the fees for the services in Panel A and the fees for the services in Panel B (strong consensus). The experimental variable of interest is whether an auditor is an industry specialist auditor or not. The extant literature identifies industry specialist auditors using various measures; for example, market share based on client sales [Palmrose (1986), Abbott and Parker (2000), Carcello and Nagy (2004)], market share based on client assets [Hogan and Jeter (1999), Abbott and Parker (2000), Carcello and Nagy (2004)], market share based on audit fees [Ferguson and Stokes (2002), Carson et al. (2002)] and market share based on number of clients [Craswell et al. (1991), Craswell et al. (1995), Abbott and Parker (2000), Chen and Elder (2001), Ferguson and Stokes (2002)]. This study uses three alternative approaches to classify an auditor as an industry specialist: a) A fee-based approach under which an auditor is an industry specialist if they earn more than 20% of the audit fees of clients within the industry;

5

For example, after 2002, the Sarbanes-Oxley Act of 2002 and the SEC Release No. 33-8183 (refer to http://www.sec.gov/rules/final/33-8183) limited the supply of NAS.

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b) An assets-based approach under which the auditor is the industry specialist if they audit more than 20% of the assets of clients within the industry. c) A client-based approach under which the auditor is an industry specialist if they audit more than 20% of the clients within the industry. While a 20% cut-off is arbitrary, it is widely used across the literature. An industry is also required to have more than 15 companies in order to be eligible for classifying specialists existing in the industry. The predicted sign of the association between industry specialist auditor and AR-RGNAS is positive under Hypothesis 1 and negative with AR-NAF under Hypothesis 2. The models estimated to test the hypotheses with these dependent and experimental variables are summarised in Table 2 below.

INSERT TABLE 2 HERE A number of control variables suggested by the prior literature affecting client demand for NAS and the fees clients are prepared to pay for the NAS are included in the models. Firth (1997) argues that companies that face potentially higher agency costs are concerned that if they are supplied high levels of NAS by their auditor, the higher economic bond between the client and the auditor will lower auditor independence and the quality of the audit perceived by their claimholders. As such, they would demand fewer NAS of any kind and pay lower NAS fees to their auditors. Firth argues that agency costs are lower in companies that have low debt-equity ratios (DE), so the lower the DE ratio the greater the demand for NAS and the higher NAS fees paid to their auditors. Larger clients (proxied by total assets – TA) and those that have complex operations (proxied by the number of subsidiaries – Subs - and the number of foreign subsidiaries – Forsubs) are likely to demand more audit related NAS because their audits are likely to be very complex creating opportunities to deliver more NAS. Clients with higher levels of inherent and control risks (proxied by a smaller quick ratio – Quick; reported losses in the last three years – Loss; higher levels of inventories – INV - and receipt of a qualified audit opinion – Opinion = 1) are likely to demand more NAS (Antle et al. 2006), especially those that are audit related because these risk sources are relevant in determining the extent of testing in the audit. For example, larger investments in inventories carry greater risk of misappropriation of assets, and more complex valuation and obsolescence issues. All of these are factors that could lead clients to demand audit related valuation and asset protection NAS services from their auditors. Companies with

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a greater proportion of their total assets invested in intangibles (Intang) are likely to have a greater demand for audit-related NAS impacting valuations of the assets for asset value carrying determinations in the audit. Finally, in the NAS fee model an indicator variable for June 30 fiscal year-end variable (YE) is included to control for peak load effects on the supply price of NAS. Consistent with prior related studies on NAS, logarithm transformations6 are made to meet statistical assumptions applicable to model estimations (Subs, Foreign, Inv, Intang, TA, AR-NAF).

3.1 Data sources and sample selection The sample consists of Australian Stock Exchange (ASX) listed firms from 2002 through 2004. Data are made available from the Capital Markets CRC - UTS database on Australian listed company audits collected from public sources. Table 3 Panel A provides the distribution of sample firms by year. From an available population of 3,693 firm years over the period, firm years were excluded where companies failed to provide a breakdown of non-audit fees. The final sample was reduced to 1,631 firm years which includes: a) companies that have purchased NAS from their auditors and have disclosed the breakdown of these services (n=805) and b) companies that indicate they have not purchased NAS from their auditors (n=826).

The sample by year consists of 577

companies for 2002, 659 companies for 2003 and 395 companies for 2004. The firms are not clustered in any particular year. Approximately 50% of the available population of firms makes up the sample in 2002 and 2003. The lower 33% for 2004 reflects data were only available for the Top 200 ASX companies at the time of analysis.7

INSERT TABLE 3 HERE

Table 3 Panel B reports descriptive comparisons for companies included in the sample and the remaining companies from the available population. The companies included in the sample are more profitable and have larger inventories, more intangibles, a larger number of subsidiaries, higher audit and non-audit fees than other companies from the available population. They are otherwise similar to the rest of the population on size and foreign subsidiaries. 6

The cases of zero values on any variable are given a value 1 before taking the natural log. The sensitivity of the results to this sample selection constraint is addressed (see 4.1) using sub-samples partitioned as follows: a) Total sample, b) Only Top 200 ASX companies from 2002 through 2004, c) All ASX companies from 2002 through 2003. 7

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In Australia, companies have been required to disclose in their annual reports the level of audit and non-audit fees paid to the incumbent auditor since the 1960s. Details of individual NAS supplied to companies began to emerge in the 2002 accounts of Australian companies which facilitates the identification of more audit related NAS.

3.2 Audit and NAS market shares More detailed market share descriptive data on the sample for the audit and NAS services from 2002 to 2004 are presented in Table 4. The table shows that average audit fees per company have increased during the period covered by the sample while average non-audit fees paid to the incumbent auditor per company have decreased. In untabulated descriptives, the mean AR-NAF1 is $55,900 and the mean AR-NAF2 is $193,479 over the period. In 2002 and 2003 the market share of industry specialists, on average, is 50% according to the three measures of industry specialization used (assets, audit fees, clients) while in 2004 the market share is on average 25% but 2004 is restricted to the ASX Top 200 companies.

INSERT TABLE 4 HERE In un-tabulated descriptives, all the GICS8 industry sectors are represented in the total sample and in each individual year and the distribution of sample companies among the industry sectors is very similar to the distribution of companies among the industry sectors in the total population. In 6 industry sectors fewer than 20% of the companies purchase NAS and provide the breakdown of the types of services purchased: Household and

Personal

Products

(0%),

Consumer

Durables

and

Apparel

(9.52%),

Telecommunication Services (13.48%), Materials (14.21%), Energy (14.44%) and Capital Goods (15.38%). There is a group of 5 industry sectors in which more than 45% of the companies in each sector purchase NAS and provide the breakdown of the types of services purchased: Utilities (45.65%), Transportation (51.85%), Food and Drug Retailing (52.17%), Insurance (61.11%) and Banks (63.89%). These percentages are similar throughout the years included in the sample. In 2002, 50% of the Top 200 ASX

8

GICS (Global Industry Classification Standard) is a joint Standard and Poor’s/Morgan Stanley Capital International product aimed at standardising industry definitions. To bring Australia in line with the rest of the world Standard and Poor’s have reclassified all ASX listed entities according to GICS. From 1 July 2002 the ASX industry classification became redundant. Source: ASX website.

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listed companies purchased NAS and disclosed the breakdown of the services, in 2003 the percentage climbed to 61% and in 2004 the percentage was 58%. The data reveal that companies in certain industries, namely Insurance, Media, Food & Beverage, Banks, Real Estate and Transportation, tend to demand higher levels of more audit-related services than the others. Drilling into the more audit-related NAS purchased across industry sectors reveals that some services are heavily concentrated in certain industries. Thirty-four percent of the Regulatory services are purchased by companies in the Pharmaceutical, Banks, Insurance and Utilities and 45% of the Accounting Services are concentrated in the Transport, Insurance, Real Estate, Technology & Hardware and Utilities industries. Forty percent of the Acquisition Services are concentrated in the Transport, Food and Beverage, Real Estate and Technology & Hardware and Utilities, while 44% of the Compliance services are purchased by companies in the Transportation and Real Estate. Out of the 23 GICS industries, 9 had fewer than 15 companies each year (Transportation, Automobiles and Components, Consumer durables and apparel, Food and

Drug

retailing,

Household

and

personal

products,

Banks,

Insurance,

Telecommunication and Utilities) and were classified as not having industry specialists while 14 industries met the minimum of 15 companies each year to classify their auditors as industry specialists or not: Energy, Materials, Capital goods, Commercial services and supplies, Hotels-Restaurants and Leisure, Media, Retailing, Food-Beverage and Tobacco, Health care equipment and services, Pharmaceuticals and biotechnology, Diversified financials, Real Estate, Software and Services and Technology hardware and equipment. EY and KPMG show a strong specialization in industries such as Energy, Materials and Capital Goods, sharing honors according to the three different measures throughout the sample period (2002-2004). A similar situation appears with three of the Big 4 (EY, KPMG and PWC) which are all specialists in Food-Beverage and Tobacco and Real Estate industries. DTT is a specialist in only one industry, Commercial Services and Supplies but shares the specialist label with PWC from 2002 until 2004. The same happens in the Software industry where EY and PWC are classified as specialists. There are two industries where only one auditor is specialist according to the three measures in the three years: KPMG in Health Care and Equipment and PWC in Diversified Financials.

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4. Results The AR-RGNAS probit model estimation results using the three specialization measures to test H1 are presented in Table 5. Specifications issues with the models are discussed further below.

INSERT TABLE 5 HERE

Hypothesis 1 is tested using all sample firm-year observations (1631 firms). The results using the most restrictive measure for the range of more-audit related NAS (ARRGNAS1) are reported in Panel A. The regression model has a pseudo R square of 0.3410 with a significant Chi-square. The positive and significant coefficient of the experimental variable is consistent with our prediction and gives strong support to H1, suggesting that industry specialist auditors supply a greater range of more audit-related NAS to their audit clients after controlling for other factors affecting the demand for NAS. The results are not sensitive to the different industry specialization measures used. Panel B reports the results of the model using the less restrictive measure of the range of more audit-audit related services (AR-RGNAS2). The coefficient of the INDUSTRY SPECIALIST AUDITORS variable is positive and significant for the three industry specialization measures, suggesting in conjunction with the Panel A results, that the support for hypothesis 1 is robust to alternative classifications of industry specialist and audit-related NAS. The coefficient of the industry specialist variable ranges from 0.472 to 0.925 across the models suggesting that, on average, industry specialist auditors supply between 60% and 152% more audit-related NAS to their clients than non-industry specialist auditors.9 The AR-NAF model estimation results using the three specialization measures to test hypothesis 2 are presented in Table 6. The model is estimated on a sub-sample of firm-year observations where audit-related NAS (AR-NAF1>0, NAS with a very strong consensus rating of being more audit related) are purchased from incumbent auditors (349 firm-year observations). The test is also run for firm year observations where ARNAF2>0, that is, firm-year observations where NAS with a strong consensus rating of

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The procedure described in Simon and Francis (1988, p.263, footnote 7) and Craswell et al. (1995, p.307) is used to calculate the magnitude of the percentage shift in the RGNAS regression model to infer the magnitude of changes in the level of more audit-related NAS attributable to industry specialization. The formula used to calculate the percentage is the following: exp(coef) –1].

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being more audit related are purchased. The results are not sensitive to this change and are therefore not reported.

INSERT TABLE 6 HERE

The adjusted R squares of the models are around 42% with a significant F test result (p .05. In year by year tests of H2, the coefficients on the three industry specialist auditor variables used each year remain negative in nearly all models, but significance levels rise above the .05 level. 4.2.2 Sample bias and time period effects Tests were also conducted to address potential sample bias issues with restriction in 2004 to the Top 200 companies. The AR-RGNAS1 and AR-NAF1 models were estimated on (a) the 2002 and 2003 sample of all ASX listed companies that disclosed a breakdown of NAS in these two years; and (b) Top 200 companies for the period 20022004. The results, not tabulated, are consistent with those reported in Tables 5 and 6 and support both hypotheses. We also estimated the AR-RGNAS1 and AR-NAF1 models using three year dummies for each year 2002, 2003 and 2004. The objective of including the dummies is to establish whether the range of audit-related NAS and fees charged are time period dependent. With increased regulatory scrutiny of NAS supply by auditors approaching 2004, we would expect the range of audit-related NAS and fees derived by auditors for those services to decline. The un-tabulated results indicate that the range of NAS is negatively associated with each year but that the effect is more pronounced in 2004, consistent with expectations. After controlling for these effects, the industry specialization variables continue to be positively associated with AR-RGNAS1 thus supporting hypothesis H1. In the AR-NAF1 model estimations, the un-tabulated results indicate AR-NAF1 fees increase in 2002 relative to other years but the 2003 and 2004 dummies are not significant. After controlling for these effects, the coefficients on the three industry

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Values of variance inflation factors (VIF) exceeding 10 are often regarded as indicating multicollinearity, but in weaker models (such as logistic regressions), values above 2.5 may be a cause of concern, [Lardaro (1993)].

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specialist auditor variables used each year remain negative in all models and significant at the p < .05 level in the case of the client based measure of industry specialization.

5.

Conclusions This study investigates the capacity of industry specialist auditors to provide a

greater range of more audit-related NAS and whether the joint provision of audit and these types of NAS generate economies of scope that produce knowledge spillovers to clients taking these two services. Under a theory that industry-specific information held by the specialist auditor and gathered during the audit process will serve as a common input when providing audit services and more audit-related NAS to the client, industry specialist auditors have a competitive advantage to provide more of these audit-related NAS and generate cost savings that manifest in lower fees for these services. The results, based on disclosures available for a sample of Australian companies for the period 2002-2004, show that on average, industry specialist auditors supply between 60% and 152% more of the “more audit-related NAS” to their clients than nonindustry specialist auditors. The results also show that industry specialist auditors, relative to non-industry specialist auditors, charge between 8.15%-15.72% lower fees for more audit-related NAS to their audit clients. The results are robust to a number of model specification and sensitivity tests and to different classifications of services judged to be more audit-related. Notably, the results show the spillover benefits are largely confined to just the audit related non-audit services with the lower fees not materialising when industry specialist auditors supply non-audit related NAS. Importantly too, the results do not suggest that spillover effects are sensitive to the type of services supplied by incumbent auditors judged by regulators to be in conflict with the independence of the auditor. The results are subject to certain limitations, notably that reliance is placed on three of the available industry specialist measures in the literature and the findings weaken in relation to fee reduction effects in model estimations made on individual years in the three year window of investigation. We do not control for the possibility of industry specialization on NAS spilling over to audit. To do so would require data on the market for NAS beyond that populated by auditors so that specialists can be identified. In Australia, clients taking NAS from suppliers other than their auditors are not required to

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disclose the services or the fees charged which would bias any attempt to rate an auditor as a specialist in the NAS market. With these caveats, we believe our findings contribute to the literature and regulatory debate on the existence of knowledge spillovers by arguing that the propensity to generate knowledge spillover economies depends upon the supplier of the NAS (whether they are an industry specialist auditor or not) and the types of NAS (whether the NAS are more audit related or not). The results also contribute to enhancing an understanding of the underlying demand and supply for NAS by demonstrating that there are benefits to companies taking more audit related NAS from their industry specialist auditors. This raises questions over regulatory solutions aimed at restricting joint supply of NAS. Our evidence suggests the existence of economies of scope benefits in the supply of audit-related NAS in particular need to be balanced against regulatory claims of a lack of auditor independence with NAS. Our findings further suggest that in studies of accounting quality outcomes focussed on whether auditors supply more NAS, consideration of the nature of the audit supplier and type of NAS could have a bearing on the accounting outcomes. The latter are issues for further research.

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Table 1 Classification of More Audit-Related NAS using consensus analysis with survey's responses Panel A - Services with a Very Strong Consensus on them being audit related Assurance services Attestation Audit related work Compliance Internal Audit Related parties transactions review Review Half year Rep Panel B – Additional services with a Strong Consensus on them being audit related Accounting Services Acquisition Audit of Issuer sponsored share register Corporate Governance Due diligence Financial Reporting Independent Accountant's Report International Accounting Standards advice Other Statutory Services R&D Services / Rebate Regulatory Services Taxation Services Transaction Services Workers Compensation Audit

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Table 2 Range of audit related NAS (H1) and fee (H2) models Dependent variable Hypothesis

AR-RGNAS

AR-NAF

H1

H2

+

-

Experimental variable Audited by industry specialist (ISA) Control variables Debt-equity ratio (DE)

-

-

Size (TA)

+

+

Number of subsidiaries (Subs)

+

+

Foreign subsidiaries (Foreign)

+

+

Quick ratio (Quick)

-

-

Loss in the last three years (Loss)

+

+

Intangibles (Intang)

+

+

Inventories (Inv)

+

+

Qualified opinion (Opinion)

+

+ +

30/6 year-end (YE) Where: AR-RGNAS = dummy variable with an assigned value of 1 when the

DE= debt divided by equity

incumbent auditor provides and discloses more types of more audit-related

TA= total assets

NAS than the median of the industry, 0 otherwise.

Subs = total number of client’s subsidiaries

AR-NAF = non-audit fees paid to the incumbent auditor for the more

Foreign = proportion of total subsidiaries that are foreign

audit-related NAS

Quick = quick ratio

ISA = dummy variable assigned a value of 1 if the auditor is the industry

Loss = dummy variable set equal to 1 if reported earnings are negative in

specialist according to one of the following measures: they audit more

the last 3 years, 0 otherwise

than 20% of the assets of clients within the industry / they earn more than

Intang = capitalized R&D

20% of the audit fees of clients within the industry / they audit more than

Inv = total inventories

20% of the clients within the industry , 0 otherwise

Opinion = 1 if the client received a qualified opinion, 0 otherwise. YE = 1 if the client has a June 30th year-end, 0 otherwise.

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Table 3 Sample Selection PANEL A - Distribution by year (2002-2004) of the available population of ASX listed companies and of companies included in the sample. Available Population Final Sample % within Year Frequency Percent Frequency Percent Year 2002 1238 33.52% 577 35.38% 46.61% 2003 1256 34.01% 659 40.40% 52.47% 2004 1199 32.47% 395 24.22% 32.94% Total 3693 100.00% 1631 100.00% 44.16% PANEL B - Descriptive Statistics for companies included and not included in the sample (Period 2002-2004)

Total Assets Total Inventory Total Intangibles EBIT Subsidiaries Foreign Subs Audit Fees Non-Audit Fees

Mean Std. Mean Std. Mean Std. Mean Std. Mean Std. Mean Std. Mean Std. Mean Std.

Companies included in Sample 101,884,586 545,544,776 25,951,689 154,565,511 51,523,148 261,972,958 29,505,982 203,604,935 16.46 43.17 4.55 18.31 293,802 986,001 314,998 1,512,036

Companies not included in Sample 83,836,309 350,188,494 16,809,663 112,188,071 17,784,841 143,295,040 13,951,349 101,992,350 11.79 52.66 3.59 36.67 125,052 582,743 107,898 655,553

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t-statistic (Difference) -1.209

2-tailed p-value 0.227

-2.067

0.039

-4.942

0.000

-2.998

0.003

-2.878

0.004

-0.960

0.337

-6.423

0.000

-5.542

0.000

Table 4 Market Share Descriptive Statistics 2002 - 2004

Number of Companies Total Assets - Average per company Audit Fees - Average per company Non-Audit Fees - Average per company INDUSTRY SPECIALIST AUDITORS a Market Share (total assets) INDUSTRY SPECIALIST AUDITORS Market Share (audit fees) INDUSTRY SPECIALIST AUDITORS Market Share (number of companies) Number of Audit Firms Number of Companies per Audit Firm BINDER DIJKER OTTE &CO (BDO) DELOITTE TOUCHE TOHMATSU (DTT) ERNST & YOUNG (EY) GRANT THORNTON HALL CHADWICK HLB MANN JUDD HORWARTH KPMG PANNELL KERR FOSTER (PKF) PRICE WATERHOUSE COOPERS (PWC) STANTON PARTNERS OTHERS TOTAL a

2002 Sample 577 $ 3,036 million $279,059 $449,169

2003 Sample 659 $ 2,434 million $284,989 $265,244

2004 Sample 395 $ 4,421 million $390,890 $208,155

37.61%

42.79%

23.80%

48.35%

48.41%

25.06%

51.13% 49

60.55% 59

26.08% 55

21 40 103 15 4 7 14 116 0 161 9 87 577

23 37 147 24 8 12 14 131 27 143 12 81 659

11 43 71 15 10 12 4 57 26 50 13 83 395

Based on holding 20% of the total assets under audit (audit fees/number of companies) of clients within the industry.

While a 20% cut-off is arbitrary, it is widely used across the literature. An industry is also required to have more than 15 companies in order to be eligible for classifying specialists existing in the industry.

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Table 5 Test of H1: Probit model for AR-RGNAS PANEL A: Dependent variable AR-RGNAS1 Period: 2002 – 2004 Sample: Full Sample INDUSTRY INDUSTRY INDUSTRY SPECIALIST SPECIALIST SPECIALIST AUDITORS=20% AUDITORS=20% AUDITORS=20% AFEES ASSETS CLIENTS Coefficient Sig Coefficient Sig Coefficient Sig INDUSTRY SPECIALIST AUDITORS 0.672 0.000 0.472 0.001 0.683 0.000 LnSUB -0.039 0.586 -0.053 0.458 -0.020 0.780 QUICK -0.021 0.015 -0.020 0.017 -0.019 0.022 DE -0.003 0.928 -0.004 0.931 -0.004 0.930 LnFOREIGN -0.038 0.689 0.005 0.960 -0.018 0.849 LnINV -0.011 0.405 -0.005 0.717 -0.009 0.466 LnINTANG 0.063 0.000 0.059 0.000 0.061 0.000 LnTA 0.857 0.000 0.843 0.000 0.843 0.000 OPINION -0.358 0.176 -0.363 0.169 -0.330 0.213 LOSS 0.137 0.412 0.133 0.426 0.116 0.485 Constant -8.454 0.000 -8.197 0.000 -8.413 0.000 Chi-square 420.330 0.000 408.615 0.000 420.242 0.000 R2 34.10% 33.30% 34.10% No. Companies 1631 1631 1631 Note: p-values are two-tailed PANEL B: Dependent variable AR-RGNAS2 Period: 2002 – 2004 Sample: Full Sample INDUSTRY INDUSTRY INDUSTRY SPECIALIST SPECIALIST SPECIALIST AUDITORS=20% AUDITORS=20% AUDITORS=20% AFEES ASSETS CLIENTS Coefficient Sig Coefficient Sig Coefficient Sig INDUSTRY SPECIALIST AUDITORS 0.925 0.000 0.655 0.000 0.873 0.000 LnSUB -0.182 0.009 -0.198 0.004 -0.161 0.022 QUICK -0.012 0.014 -0.012 0.015 -0.011 0.020 DE -0.006 0.930 -0.007 0.943 -0.008 0.944 FOREIGN 0.064 0.490 0.118 0.198 0.091 0.323 LnINV -0.032 0.011 -0.023 0.062 -0.029 0.017 LnINTANG 0.038 0.001 0.033 0.004 0.035 0.002 LnTA 0.978 0.000 0.954 0.000 0.954 0.000 OPINION -0.334 0.155 -0.341 0.145 -0.307 0.191 LOSS -0.147 0.356 -0.136 0.388 -0.180 0.255 Constant -8.491 0.000 -8.110 0.000 -8.335 0.000 Chi-square 511.711 0.000 507.700 0.000 515.512 0.000 R2 37.90% 36.50% 37.10% No. Companies 1631 1631 1631

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Note: p-values are two-tailed Where: AR-RGNAS1 = dummy variable set to 1 when the incumbent

LnSubs = natural log of the total number of client’s subsidiaries

auditor provides and discloses more types of more audit-

QUICK= quick ratio

related NAS (Includes NAS in Very Strong Consensus list,


Table 1 Panel A) than the median of the industry, 0 otherwise.

LnForeign = natural log of the proportion of client’s

AR-RGNAS2 = dummy variable set to 1 when the incumbent

subsidiaries that are foreign

auditor provides and discloses more types of more audit-

LnInv = natural log of total inventories

related NAS (Includes NAS in Very Strong Consensus and

LnIntang = natural log of capitalized R&D

Strong Consensus lists, Table 1 Panel A and B) than the

LnTA= natural log of total assets

median of the industry, 0 otherwise.

OPINION = 1 if the client received a qualified opinion,

INDUSTRY SPECIALIST AUDITORS = dummy variable set

0 otherwise.

equal to 1 if the auditor is the industry specialist according to

LOSS = dummy variable set equal to 1 if reported earnings are

one of the following measures: they audit more than 20% of

negative in the last 3 years, 0 otherwise

the assets of clients within the industry / they earn more than 20% of the audit fees of clients within the industry /

they

audit more than 20% of the clients within the industry , 0 otherwise

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Table 6 Test of H2: OLS Model for AR-NAF Dependent variable: LnAR-NAF1 Period: 2002 – 2004 Sub-sample: AR-NAF1 > 0 INDUSTRY SPECIALIST AUDITORS = 20% FEES Coefficient Sig INDUSTRY SPECIALIST AUDITORS -0.085 LnSUB 0.019 QUICK 0.001 DE -0.022 YE 0.036 LnFOREIGN 0.076 LnINV -0.008 LnINTANG 0.010 LnTA 0.370 OPINION 0.003 LOSS 0.119 Constant 1.360 F 24.028 No. Companies 349 R2 42.10% Note: p-values are two-tailed

0.198 0.540 0.919 0.887 0.651 0.055 0.206 0.086 0.000 0.985 0.096 0.000 0.000

INDUSTRY SPECIALIST AUDITORS = 20% ASSETS Coefficient Sig

-0.129 0.017 0.001 -0.020 0.019 0.067 -0.008 0.010 0.382 -0.008 0.115 1.283 24.382 349 42.40%

0.050 0.583 0.886 0.900 0.817 0.087 0.202 0.063 0.000 0.956 0.106 0.000 0.000

INDUSTRY SPECIALIST AUDITORS = 20% CLIENTS Coefficient Sig

-0.171 0.010 0.001 0.008 0.028 0.075 -0.007 0.009 0.372 -0.017 0.120 1.415 24.831 349 42.90%

0.010 0.754 0.952 0.961 0.724 0.055 0.259 0.116 0.000 0.900 0.089 0.000 0.000

Where: LnAR-NAF = natural log of non-audit fees paid to the


incumbent auditor for more audit-related NAS

YE = 1 if the client has a June 30th year-end, 0 otherwise.

INDUSTRY SPECIALIST AUDITORS = dummy variable


set equal to 1 if


the auditor is the industry specialist

according to one of the following measures: they audit


more than 20% of the assets of clients within the industry /


they earn more than 20% of the audit fees of clients within


the industry /

OPINION = 1 if the client received a qualified opinion,

they audit more than 20% of the clients

within the industry , 0 otherwise

0 otherwise.

LnSubs = natural log of the total number of client’s

LOSS = dummy variable set equal to 1 if reported earnings are

subsidiaries

negative in the last 3 years, 0 otherwise

QUICK= quick ratio

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Table 7 INDUSTRY SPECIALIST AUDITORS choice model and control for self-selection bias Panel A - Probit model for INDUSTRY SPECIALIST AUDITORS Period: 2002 – 2004 Sample: Full Sample INDUSTRY INDUSTRY INDUSTRY SPECIALIST SPECIALIST SPECIALIST AUDITORS=20% AUDITORS=20% AUDITORS=20% Dependent variable -> ASSETS CLIENTS AFEES Coefficient Sig Coefficient Sig Coefficient Sig LnSUB -0.201 0.001 -0.152 0.014 -0.295 0.000 DE 0.009 0.478 0.029 0.777 0.010 0.582 LnFOREIGN 0.061 0.466 0.267 0.001 0.153 0.069 LnINV 0.054 0.000 0.029 0.007 0.055 0.000 LnINTANG -0.006 0.562 -0.033 0.002 -0.023 0.027 LnTA 0.125 0.077 0.331 0.000 0.173 0.016 OPINION -0.327 0.059 -0.409 0.035 -0.366 0.030 LOSS -0.175 0.213 -0.082 0.567 -0.370 0.008 Constant -1.521 0.007 -3.232 0.000 -1.772 0.002 Chi-square 258.690 0.000 261.450 0.000 301.980 0.000 R2 11.62% 12.23% 13.36% Note: p-values are two-tailed Panel B – Choice model residual correlation with residuals of NAS models Model -> Pearson Correlation Rho(ρ) Sig. AR-RGNAS1 0.00031 0.990 AR-NAF1 -0.00053 0.992 Where: INDUSTRY SPECIALIST AUDITORS = dummy variable set


equal to 1 if the auditor is the industry specialist according to


one of the following measures: they audit more than 20% of the


assets of clients within the industry / they earn more than 20%


of the audit fees of clients within the industry / they audit more


than 20% of the clients within the industry , 0 otherwise

OPINION = 1 if the client received a qualified opinion, 0

LnSubs = natural log of the total number of client’s subsidiaries

otherwise.


LOSS = dummy variable set equal to 1 if reported earnings are negative in the last 3 years, 0 otherwise

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