The Pricing of Industry Specialization
by Auditors in New Zealand
David Hay, University of Auckland and
Debra Jeter, Vanderbilt University
February 2008
Abstract
A number of recent research papers examine the impact of industry
specialization by auditors on audit fees in a variety of contexts. Most propose that
auditors who specialize in an industry are able to perform higher quality audits, which
are recognized and valued by financial statement users and thus by managers to such
an extent that higher audit fees are warranted and paid to such specialists. Evidence
consistent with this proposition has been presented in some studies, while others have
generated conflicting findings. To the extent that specialist premiums are supported,
this result is a satisfying one for auditors, as it suggests that managers value high
quality audits and view audit services as something other than a necessary evil. In
this study, we partition New Zealand companies into various market segments to
consider the possible benefits of hiring a specialist auditor, and whether (and when)
such benefits justify higher audit fees.
We find that there are fee premiums for auditor specialization defined at the
city level but not at the national level. The issue of self-selection, whereby clients
choose auditors based on the same variables that determine audit fees, does not appear
to arise with respect to the choice of a specialist auditor in our setting. Somewhat
surprisingly, we find that audit premiums for specialization apply most consistently to
larger client firms, and to unlisted companies and low-risk companies. One
explanation for this finding is that auditors without specialist expertise in an industry
have to offer discounts to attract the most desirable clients, i.e. those that are large,
those that have are associated with less danger of liability, and those that exhibit low
risk; while clients are not able to negotiate fees as successfully with auditors who
have differentiated themselves via industry specialization. The value of this study is
that it extends the previous research to a new setting, and obtains similar results; it
examines the issue of whether specialist premiums are simply due to auditors and
clients self-selecting each other; and it examines subsets of the market to test
alternative explanations for specialisation premiums.
DRAFT – PLEASE DO NOT COPY OR QUOTE WITHOUT PERMISSION FROM
THE AUTHORS
Acknowledgments:
We appreciate helpful comments by Jeff Casterella, Paul Chaney, David Emanuel,
Jere Francis, Carlos Jimenez, Gary McGill, Kevin McMeeking and participants at the
University of Florida International Conference on Assurance and Corporate
Governance; the Australian National Centre for Auditing and Assurance Research
Conference at Australian National University and a workshop at the University of
Exeter. We also gratefully acknowledge the contribution made in preliminary work by
Jasmine Kwong.
The Pricing of Industry Specialization
by Auditors in New Zealand
1. Introduction
A large body of evidence (see Craswell, Francis & Taylor 1995, Hogan and
Jeter 1999, and Knechel, Naiker and Pacheco 2007, for example) suggests that audit
firms are desirous of acquiring specialization skills and/or being known as industry
specialists. Further, in the U.S., the Government Accounting Office (GAO, 2003)
pinpointed “the desire to increase industry expertise” as one of the reasons for the
mergers of the Big CPA firms into the current four firms. Possible benefits, from the
auditors’ perspective, include the ability to earn higher profits through either (or both)
higher fees or lower production costs, reduced risk of audit failures through enhanced
understanding of the industries represented, and enhanced growth opportunities.
A number of studies (Francis et al. 2005; Ferguson et al. 2003; DeFond et al.
2000) have presented evidence that industry specialist auditors earn a fee premium,
using data from settings as diverse as the U.S., Australia, and Hong Kong. The body
of evidence on fee premiums, however, is inconclusive as a number of other studies
found little or no evidence of a fee premium (Palmrose, 1986; Ferguson and Stokes
2002), or evidence of such a premium only for certain subsets of firms (Craswell, et
al. 1995, only for large clients). The usual assumption in studies finding evidence of
a specialist fee premium is that the premium relates to the reputation of the auditor.
For example, Craswell, et al. (1995) state: “Industry specialization will increase a Big
8 auditor’s reputation (within that industry) but will require development of expertise .
. . additional investment in expertise will require a positive return resulting in an audit
fee premium.”
A question that has arisen in some of the more recent studies with respect to
industry specialization by auditors is whether city-specific leadership alone is a
sufficient condition to warrant a fee premium, or whether both national and city
leadership are needed. Ferguson et al. (2003) find a fee premium only for firms that
are joint national and city-level leaders in their industry (in Australia). Francis, et al.
(2005) find that national leaders do not earn a premium, but that joint national and
1
city leaders do, as well as city-specific leaders, and concluded that “auditor reputation
for industry expertise is neither strictly local nor strictly national in character” (based
on U.S. evidence). Basioudis and Francis (2007) found that there was a premium for
city level specialists but not for national leadership and that joint national-city
leadership adds nothing beyond the premium for city leadership (in the UK).
We use a dataset of New Zealand firms to explore industry specialization by
auditors. Our data provide a unique opportunity to examine the presence of fee
premiums for specialist auditors in a smaller country where there is less opportunity to
build up extensive experience with a narrowly-defined industry sector, and
transferability of office-specific expertise is relatively easy to achieve. Further, many
previous studies have not been able to include a complete measure of each audit
firm’s industry share, as data for the subsidiaries of overseas companies (such as the
international motor companies or oil companies) are not available and thus not
included. In New Zealand these companies must disclose their audited financial
statements, and we are able to include unlisted as well as listed firms to obtain a more
accurate measure of industry market share. We also examine the effects of
specialization within a variety of segments of the market for audit services. We
provide insight into the following questions related to industry specialization by
auditors:
(1) Do any firms pay a fee premium for national and/or city-specific specialist
auditors in our setting?
(2) Do all firms with specialist auditors pay a fee premium, or only certain subsets
of firms?
(3) If the premiums affect only certain subsets of firms, what does this tell us
about the benefits of specialization and the pricing of specialization expertise
by auditors?
(4) Why do client firms often prefer specialist auditors, and is this choice
influenced by client size, risk and complexity?
We present evidence that firms in our sample of listed and unlisted New
Zealand clients do pay fee premiums to specialist auditors, and that such premiums
are observed almost exclusively at the city rather than national level. We find that the
fee premiums apply consistently, regardless of our specification, to larger client firms,
2
to unlisted firms, and to low-risk clients. Because we measure specialist premiums
relative to non-specialist fees, as is common in the literature, one interpretation of this
finding is that non-specialists must offer fee discounts to attract this desirable
clientele. We conclude that, even in this relatively simple setting, audit fee
determination reflects a complex interplay of demand and supply factors. We find no
evidence that selection of a specialist auditor is associated with the variables in the
audit fee model, thus lessening the concern that self-selection issues are affecting our
interpretation.
The rest of this paper is organized as follows. Section 2 provides an overview
of the prior research. Section 3 discusses our motivation and research design. Section
4 describes the sample and method and results, and section 5 summarizes and
concludes.
2. Prior Research
A series of papers by Danos and Eichenseher (1981, 1982, 1986) suggests that
auditor concentration within industries is related to scale and cost economies,
extending even earlier work on auditor concentration by Zeff and Fossum (1967),
Rhode et al. (1974), and Schiff and Fried (1976). Eichenseher and Danos (1981)
argue that auditor concentration should be higher in industries where the auditor can
use scale economies to reduce average production costs, and they expect that scale
economies will dominate in industries where the auditor has to make large
investments in industry-specific knowledge or expertise. Danos and Eichenseher
(1982) argue that cost effectiveness is related to the audit firm's overall market share,
the auditor's client- industry market share, and regulation in the client industry. They
speculate that in regulated industries, large audit firms can increase market share
because of scale-related cost advantages (e.g., related to specialized knowledge),
while in non-regulated industries, large audit firms lose market share because of a
lack of scale-related cost advantages. Danos and Eichenseher (1986) find that changes
in Big 8 market shares are positively related to regulation and negatively related to
growth.
3
More recent evidence on auditor concentration (Hogan and Jeter 1999) reveals
that auditor concentration has increased in non-regulated industries over time, while
concentration in regulated industries has remained unchanged (though still higher than
in non-regulated industries). These findings suggest that industry-specific knowledge
requirements extend to non-regulated industries, and that these knowledge
requirements increased during Hogan and Jeter’s sample period, 1976-1993. Kwon
(1996) takes a slightly different perspective and focuses on auditor dominance in an
industry rather than levels of auditor concentration. Kwon contends that firms in
concentrated industries will be reluctant to engage the same auditor because of
concerns that proprietary information may be inadvertently passed to their
competitors. He finds a negative relationship between his measures of auditor
dominance and the four-firm concentration ratio in the industry, consistent with an
argument that proprietary concerns are important.
Prior studies presenting evidence that specialist auditors provide higher quality
auditors include Solomon et al. (1999), who suggest that industry-specialist auditors
have greater knowledge within their specialist industry; Carcello and Nagy (2004),
who find a negative association between auditor industry specialization and client
financial fraud; and Balsam, Krishnan and Yang (2003), who document higher
earnings response coefficients for clients of specialist auditors. Gramling and Stone
(2001) provide an extensive review of the literature in this area, remarking that the
evidence is quite mixed, and that “many critical issues related to audit firm industry
expertise remain under- or un-investigated.”
O’Keefe et al. (1994) suggest that the provision of audit services to a client
requires investments in general knowledge, industry-specific knowledge, and clientspecific knowledge. We posit in this paper that one of the benefits to the client of an
auditor with superior levels of industry-specific knowledge is that such knowledge
facilitates the efficiency of the audit process. In other words, the client is not called
upon to explain to the auditor the nature of aspects unique to the client’s industry as
the industry specialist auditor is already well-versed in those aspects.
4
Demand side: Do client firms pay more for an audit by a specialist; and if so, why?
Recent papers examining audit fees find some evidence of a premium for
auditor industry specialization (at either the city or national level, or in some cases
both), generally assumed to indicate that clients value an auditor with greater
expertise in their industry enough to pay more for such services. Craswell et al.
(1995) state: “Some accounting firms voluntarily invest in expertise beyond the
minimum required by professional standards and therefore have incentives to
maintain their reputations by producing higher-quality audits.” Craswell et al. (1995)
present evidence of specialist premiums measured at the national level in Australia in
the 1980s. However, their results are quite sensitive to the percentage market share
used for the cut-off in their definition of specialization, with statistical significance
levels varying widely.
Cullinan (1998) proposes a framework in which the use of cost-based versus
market-based pricing for audit services determines whether premiums or discounts are
associated with greater perceived industry expertise. DeFond et al. (2000) finds that
Big 5 industry specialists in Hong Kong earn a fee premium, while specialist non-Big
5 firms charge discounted fees to clients in their specialist industries.
Although most of the earlier specialization studies identified specialists based
on national market share, more recent studies have turned to local or city leadership
measures. Ferguson and Stokes (2002) find no evidence of specialist premiums
measured at the national level in Australia in the 1990s. In contrast, Ferguson et al.
(2003), also using Australian data, find that specialist premiums are earned by
auditors who were identify as specialists at both the city and national levels. Francis,
Reichelt and Wang (2005) also find evidence of a specialist premium where the
auditor is a specialist at both the national and city level in the U. S. Francis, et al.
(2005) further suggest that city-specific leadership alone is likely to be a sufficient
condition, whereas Ferguson et al. (2003) argue that leadership at both city and
national levels is needed to warrant a fee premium.
However, the city-alone premium documented in Francis, et al. (2005) is not
supported in two of their robustness tests, and the authors conclude that their results
are mixed and inconclusive on this point. They find no evidence of a fee premium for
firms that were leaders at the national level only, and they conclude that there is “at
5
least weak evidence of transferability of expertise and firm-wide reputation benefits,”
but not strong transferability effects. Using UK data, Basioudis and Francis (2007)
find specialization premiums at the city level but not in national market share
measures, suggesting that industry specialization benefits are more likely to occur in
narrower market segments than in national market share measures.
Mayhew and Wilkins (2003) draw on Porter (1985) to suggest that auditor
specialization is a form of differentiation, leading not only to greater efficiency and
lower costs, but also (potentially) to greater value to the client. Where audit firms are
successful in differentiating themselves from their competitors, then specialization
will lead to fee premiums. When they are unsuccessful in achieving differentiation,
they must offer a discount to attract clients, where “achieving differentiation” means
obtaining a significantly greater market share than one’s competitors (at least 10%
more).
Casterella et al. (2004) also apply Porter’s (1985) competitive strategy
framework, arguing that specialist auditors adopt a differentiation strategy which
allows them to charge higher fees. They find that relatively smaller clients are charged
a premium, while larger clients are not, and they attribute this difference to the greater
bargaining power of larger companies. In contrast, Craswell et al. (1995) find a
significant specialist premium only for large companies, and not for small companies.
They attribute this to the larger companies having greater agency costs and hence
more need for a specialist auditor. Both studies rely on median size to distinguish
between large and small clients. Large companies in Casterella et al. (2004) are U.S.
companies with assets in excess of U.S.$123 million in 1993; while in Craswell et al.
(1995), “large” companies consist of Australian firms with more than A$18.2 million
in assets in 1987.
In summary, the results are mixed with respect to a prediction based on agency
theory that specialist auditors should earn a fee premium. According to a metaanalysis study by Hay, Knechel and Wong (2006), auditor specialization is significant
in three studies out of nine and the combined result of the meta-analysis is significant.
Furthermore, the evidence to date is also mixed with respect to whether specialist
premiums, if they exist, accrue only to specialists exhibiting both national and city
6
leadership in their industry, or whether one designation is sufficient; and whether
large or small client firms are most likely to pay such a fee premium.
Supply side: Why do audit firms invest in developing specialization expertise?
Prior studies (Casterella et al., 2004; Mayhew and Wilkins, 2003; Porter,
1985) suggest that auditors have incentives to specialize in order to offer a
differentiated product or service and to establish themselves as high-quality providers.
They then strive to create a competitive advantage in the market for audit services
over non-specialist auditors through one of several channels: recognition of their
superior reputation, cost reductions, value provided in the quality of advisory services
and assurance or monitoring; or some combination of these. Mayhew and Wilkins
(2003) present arguments that these investments are most beneficial in offering
services to a fairly homogeneous group of clients such as same-industry firms.
Turning to the auditing experimental literature, we find evidence that specialist
auditors benefit from lower cost, more efficient audits. Low (2004), for example,
finds that specialist auditors are more flexible and quicker to adjust to needed changes
in audit programs inherited from predecessor auditors. Owhoso et al. (2002) presents
evidence that members of specialist teams are more effective and work in a more
complimentary fashion to detect errors than non-specialist teams.
From the perspective of the auditor, we posit that the specialist auditor has the
opportunity to pass along, or alternatively to withhold, economies or cost savings, if
they exist. Further we suggest that specialist auditors will make these decisions with
the goal of maximizing not only current, but also future revenues and profits. Factors
likely to enter the pricing decision strategy include the following:
3.

The client’s earnings history;

The client’s potential for future profitability;

The perceived risk of litigation;

The client’s current liquidity.
Motivation and Research Design
Why may client firms prefer specialist auditors?
7
We suggest four non-mutually exclusive reasons why clients may prefer to
hire industry specialists as auditors, and we attempt to distinguish among those
reasons. First, consistent with the arguments presented by Danos and Eichenseher
(1982) and by Eichenseher and Danos (1981), specialist auditors may experience
economies of scale in auditing clients in the industries of expertise (e.g. regulated
industries). Further, they may pass a portion of these economies on to their client
firms. In this case, clients might actually benefit by hiring a specialist auditor without
paying a fee premium and, in fact, could enjoy a fee discount. If we present evidence
of a fee discount to specialist auditors in our setting, we would conclude that our
results are consistent with the economies of scale argument.
The argument most commonly presented in prior research is that clients hire
specialist auditors because of their superior reputation. Clients provide a signal of the
quality of their reporting by their choice of a specialist auditor under this theory, which
may lead to other benefits such as lowered cost of capital. Various researchers (e.g., Watts
and Zimmerman, 1986) argue that the demand for auditing services arises from conflicts
of interest among managers, creditors and outside shareholders. Given the existence of
such agency issues, managers and entrepreneurs have incentives to reduce agency costs
through high quality monitoring. Titman and Trueman (1986) present a model wherein
firms with favorable information signal their quality by selecting auditors that are costly,
but that provide relatively more precise information about the firms’ final cash flows. In
the context of these theories, a specialist auditor fee premium could be interpreted as the
price paid, by clients signaling their quality, for an auditor of superior reputation. If the
reputation theory is the dominant reason for the choice of industry specialists, we would
suggest that fee premiums would be more likely if the client firm is concerned about
agency issues, is in the public eye, or is planning to enter the public debt or equity markets
in the near future.
An assumption inherent in the auditor reputation view of fee premiums is that the
individuals or groups for whom the signal is relevant are able to distinguish between
higher and lower quality auditors. To the extent that brand name (Big 4 versus non-Big 4,
for example) captures audit quality, it is easy to distinguish between auditor types.
However, when we extend this theory to the case of auditor specialization, it becomes
much more ambiguous. Not even researchers can agree upon the best definition of an
8
industry specialist, or how to measure such a designation. Hogan and Jeter (1999) present
evidence that the industries listed by auditors on their websites as their areas of
specialization were not highly correlated with their market share in such industries,
implying that Web listings may constitute a sort of wish list for the future. This raises a
question as to whether firms would be willing to pay a fee premium for the “reputation”
associated with a designation that is largely uncertain (even to those most interested in
defining and measuring the construct) unless they believe that the reputation conveys
additional benefits.
A third possibility is that clients believe specialist auditors to provide superior
services in other dimensions such as advice offered during the course of the audit. In
their survey of the 500 fastest-growing private companies in America, Addams and
Davis (1994) found the following to be the highest and second highest ranked factors
in auditor choice: (1) personal relationships, including the relationship between the
CPA firm’s key people and the company’s key decision-makers, and confidence in the
audit firm; and (2) technical expertise, made up of the quality of the technical
expertise of the assigned team and technical expertise in accounting as well as
industry specific experience. If these aspects play an important role in the hiring of
specialist auditors, we would expect that smaller clients would be more likely to
benefit, and hence to pay fee premiums, than larger clients with their own staffs of
experts. Some prior studies (Addams et al. 1994, for one) have suggested that client
firms were more motivated in choosing or switching auditors by their perception of
the auditors’ helpfulness in addressing company concerns (helping the company grow,
foreseeing problems, understanding the client’s business, e.g.) than by audit fees or by
their perception of the auditors’ reputation or professionalism. Beattie & Fearnley
(1995) also found that the “quality of working relationship with partner” ranked third
highest in importance of factors that clients consider in choosing auditors, and that
“guidance on accounting” and “advice to management” also ranked highly.
Fourth, we suggest that an audit is an arduous undertaking for client firms, and
that the audit proceeds more efficiently when the auditor is a specialist. Here we
recognize that client firms do not like to waste their time educating their auditors
about their business, and that specialist auditors do not require as much time from the
client’s management and clerical staff in figuring out transactions or in understanding
9
the business environment of the client. Thus, the cost of the total audit (including not
only the audit fee but also administrative and office time absorbed, and other related
expenses) is likely to be lower than the alternative total cost of hiring an auditor less
versed in the client’s industry. This would be true even if the specialist auditor
charges a fee premium so long as the fee premium does not exceed the cost savings
from other related expenses. In distinguishing between the last two explanations for
fee premiums, we suggest that the advisory facet is more likely to be relevant among
relatively small client firms, while the convenience factor would apply to clients of all
sizes.
Among the cost considerations (both direct and indirect) related to the audit of
a firm’s financial statements are the following:

The audit fee

The cost of secretarial and administrative time, supplies, etc. in assisting the
auditors

The cost of capital, either equity or debt, that may be lowered as a result of a
signal of an auditor of superior quality or reputation

The benefit (cost reduction or revenue potential) from valuable advice from
the auditor.
The last two considerations have the potential to lower the total cost of the
audit. We contend that the client wishing to maximize profits will choose to minimize
the total cost of the audit, or the net effect of the factors above, rather than to
minimize the fee only.
New Zealand setting
The audit market in New Zealand has been examined in previous studies (e.g.,
Johnson et al. 1995; Baskerville and Hay 2006) and is similar in many respects to
markets in the United States and other western countries. The Big 4 firms dominate
the market for audit services. Audit fees are a function of client size, risk, and
complexity, similar to pricing in other western countries. The market for audit
services in New Zealand is also similar in terms of legal requirements and auditing
standards to that in other countries examined in previous auditor specialization
research. The country is relatively small, and the economy is dominated by the largest
10
city (Auckland) and the capital (Wellington). In this regard, New Zealand is similar to
Australia, where 84% of listed companies have their headquarters in three cities
(Francis et al. 2005). Because the country is relatively small, New Zealand provides a
unique environment for auditor specialist research with the potential for a clear-cut
test of the explanations discussed. As in other countries examined in prior studies,
auditors are concerned about liability issues and the effect of recent scandals and
reforms, though perhaps to a lesser extent. This study also has the advantage of data
for both listed and unlisted companies, which enables us to examine differences in
specialization choices and fees between those groups.
Our setting of New Zealand firms appears to be comparable to that of
Ferguson et al. (2003), in that it is even more centralized and less geographically
dispersed than the Australian audit market. Hence, the transferability of expertise
across offices would appear to be relatively easy, suggesting the possibility that
national leadership alone might be sufficient in our setting to justify a specialist
premium.
Model Specification
Most studies examining the existence (or non-existence) of a specialist fee
premium estimate some variation of the following OLS regression (see, for example,
Ferguson, et al. (2003).
Fi = / Xi +  Speci + i,
(1)
where Fi is the audit fee; Speci is an indicator variable that takes the value 1 if the actual
auditor is deemed a specialist auditor, and 0 otherwise. The remaining explanatory
variables (Xi) capture other client and auditor characteristics that affect fees. A significant
positive coefficient for  in the above model is generally interpreted as evidence consistent
with the existence of a specialist fee premium. We perform the following OLS regression
to insure comparability with the results of prior studies on auditor specialization:
LAF = b0 + b1 LTA + b2 SSUB +b3 CATA +b4 QUICK + b5 DEBTTA + b6 ROA +
b7 Foreign + b8 Loss + b9 BIG 4 + b10 Specialization + e
(1)
where:
LAF
= Log of audit fees
11
LTA
SSUB
CATA
QUICK
DEBTTA
ROA
Foreign
Loss
BIG 4
Specialization
= Log of total assets
= Square root of the number of subsidiaries
= Current assets to total assets
= Quick ratio
= Debt to total assets
= Return on assets computed as earnings before interest & taxes
divided by total assets
= Percentage of foreign assets
= Indicator variable for presence of a current-year loss
= Indicator variable for a Big 4 auditor
= Indicator variable(s) for measures of specialization.
Alternative models are estimated using national-level specialist measures, citylevel specialist measures, and both. Consistent with Francis, et al. (2005), a specialist
auditor is identified as the audit firm with the largest market share in each industry.
Alternative criteria are applied (such as designating both the first and second-ranked
market leaders as specialists), with similar results. Client firms are classified using the
New Zealand Stock Exchange industry classification (NZX).1 In addition, we estimate the
models for several subsets of our sample firms in order to distinguish among the various
explanations laid out in Section 3 of this paper. These include: the entire sample (NZ Top
200 companies in 2003 plus listed firms outside the Top 200); relatively large firms (in the
top half of the Top 200 list); relatively small firms (outside the top half of the Top 200
list); listed companies; unlisted companies; growth firms (those that have improved their
ranking in the Top 200 list by ten places or more); non-growth firms; firms that are
subsidiaries of overseas companies; firms that are not subsidiaries of overseas companies;
firms in relatively high-risk industries (proxied by industry volatility); and firms in lowerrisk industries. Note that our measure of growth reflects past, rather than future growth, as
evidenced by documented changes during the past year.
Self-selection issues
The above OLS regression should allow for valid inferences if self-selection issues
are not prevalent. However, as pointed out in Chaney, et al. (2004), companies are not
randomly assigned to audit firms, but select their auditors. The client and audit firm
1
As a sensitivity test we used a statistical classification, the Australian and New Zealand
Standard Industry Classification (ANZSIC).
12
characteristics that affect auditor choice may be similar to the variables in our fee models.
Some analytical studies, including Titman and Trueman (1986), Datar et al. (1991), and
Hogan (1997), develop theoretical models to explore the self-selection of auditors by
clients. In these models, clients with favorable private information choose higher quality
auditors despite higher costs. From an econometric perspective, self-selection introduces a
bias in the standard OLS regressions. The self-selection problem arises because fees are
observable only after a firm has chosen its auditor, while the fees under an alternative
auditor choice remain unobserved. This causes the expected error in the standard OLS
specification of audit fees to be non-zero and the auditor choice (specialization) variable to
be endogenous.
As a first step in dealing with this issue, we estimate separate models for clients of
specialist and non-specialist auditors. We do this for the entire sample, and for the
subgroups of interest. We then apply the coefficients of each model to the group of clients
making the alternate choice; in other words, the coefficients from the model for clients of
specialist auditors are applied to clients of non-specialists, and vice versa within each
group (large clients, small clients, listed companies, unlisted companies etc).2 Thus, we
allow slope coefficients to differ across auditor groups. This enables us to consider one of
the arguments advanced by Chaney, et al. (2004)—that slope coefficients on complexity,
size, and risk might be smaller for higher quality auditors, while the intercept should
reflect higher fixed costs to recoup the greater investment in specialization training.
We next test whether self-selection is an issue by estimating a model of auditor
specialist choice using the variables in our audit fee model as explanatory variables, and
consider whether they are significantly related to the choice of a specialist auditor. If so, a
two-stage model may be appropriate.
4.
Results
Sample description
Our sample consists of New Zealand companies in 2003. The list of the Top
200 companies published in Management magazine was used, supplemented by data
This approach is sometimes referred to as the ‘two-part OLS model’ (Leung and Yu, 1996)
and in some circumstances this method has been argued to be more robust than the Heckman
2
13
from the remaining listed companies not in the Top 200. After excluding companies
in the finance industry and observations with incomplete data, we are left with 218
observations.
Descriptive statistics
Table 1 summarizes industry specialists at the national level and shows the
second, third and fourth-ranked auditors based on audit fees. The overall market
leader is PwC with 34% of total audit fees, followed by KPMG with 28%. City
specialists exist in Auckland and Wellington, but not Christchurch, Dunedin or any
other city. Auckland has a specialist in each of the NZX industry codes, while
Wellington has a specialist in 13 of 16 codes. The national specialist is the same firm
as the city specialist in 19 cases (out of 32 possible cases). There are small numbers of
companies in some industries, and we conduct sensitivity tests to consider whether
their inclusion affects our results.
INSERT TABLE 1 HERE
Descriptive statistics for our continuous variables and frequencies for our
indicator variables and subset components are reported in Table 2. Big 4 audit firms
are chosen by 88% of companies, while 35% are audited by the national market leader
for that industry, and 32% by the city leader. The ranges and distributions of the
values of the variables are approximately as expected, based on those reported in prior
studies. The companies in our study are smaller than those in the U.S. study by
Francis et al. (2005), with mean assets of $485 million compared to $1,912 million,
but no more so than might be expected. The other independent variables are
comparable to Ferguson et al. (2003) and Francis et al. (2005).
INSERT TABLE 2 HERE
OLS Regression Results
The audit fee model discussed previously was estimated first for our entire
sample, and then for various subsets of the data. Results for the full sample are
presented in table 3, while the subset results are shown in table 4. In both tables,
alternative models were run using national-level specialist measures, city-level
method employed to control for self-selection in some other audit fee studies.
14
specialist measures, and both. As previously mentioned, we also used two alternative
industry coding schemes: the New Zealand Stock Exchange industry classification
(NZX), and the Australian and New Zealand Standard Industry Classification
(ANZSIC).
Overall, we find that: (1) there are no national-level premiums; (2) there are
statistically significant city-level premiums; (3) when both city and national specialist
designations are included, there is no greater premium than that earned for the city
designation alone. We also estimate our models designating both the first and secondranked market leaders as specialists. The second ranked firms did not earn significant
fee premiums in any case. The models were also computed excluding each Big 4 firm,
one at a time, to ensure that the results are not driven by any one firm. The
significance levels remained the same in each instance. The results are robust across
the two alternative industry classification schemes, and when industries with few
observations are omitted. Further sensitivity tests included omitting those industries
where the market leader is only narrowly ahead (15% or less) of the next ranked firm;
and adding a dummy variable to control for higher costs in Auckland, which has
higher salary costs than other cities. In both cases, the coefficients and significance
levels of the various specialization variables were approximately the same.
INSERT TABLE 3 HERE
Table 4 summarizes the results of our estimation of all models for the following
ten sub-sets of data (as well as for the entire sample):
-
Companies in the top 100 of the Top 200 list3
Companies outside the top 100 of the Top 200 list
Listed companies
Unlisted companies
Growth companies (those that improved their ranking in the Top 200 list by 10
places or more in the past year)
Non-growth companies
Higher risk companies (in relatively volatile industries based on the variability
of reported revenue)4
3
These companies had total assets of about $102 million or more.
As an alternative means of differentiating between high and low-risk firms, we used
measures based on losses, debt and liquidity; results were consistent with those presented.
4
15
-
Lower risk companies
Subsidiaries of overseas companies
Not subsidiaries of overseas companies.
Panel A of Table 4, which summarizes our results designating specialists only at
the national level, reveals no significant coefficients on the specialist variable. Panel
B, in direct contrast, reveals consistent evidence of audit fee premiums for city-level
specialists for the full sample and for all subsets except for small clients, high-risk
clients and listed companies. Nonetheless, the implications are clear. In our setting,
city-level designation as a specialist merits a fee premium far more often than
national, and is nearly always sufficient for a premium to apply.
To analyze the implications of the subset analysis further, let us focus on the
subsets with significant premiums in Panel B. We find consistent evidence of
specialist premiums at the city level for large clients, for unlisted companies, for lowrisk firms, both for companies that are subsidiaries of overseas companies and
companies that are not, and both for clients with demonstrated growth over the past
year and clients which have not. We find no evidence of premiums to specialist
auditors for high-risk firms or listed companies (after controlling for risk, complexity,
etc. in the pricing model). We find some (marginally not significant) evidence of
specialist premiums for smaller clients.
One plausible interpretation of our result is that non-specialist auditors are forced
to offer a fee discount to attract those clients that are most desirable because they are
large, are unlisted and therefore attract less risk of lawsuits, or are in a low-risk
industry. Under this explanation, what we are observing might be viewed as a
discount to non-specialists (since a specialist fee premium is defined as an increment
paid to specialists above the fee paid to non-specialists after controlling for risk, size,
and complexity). In contrast, even the most desirable clients are not able to negotiate
fees as successfully with auditors who have differentiated themselves via industry
specialization.
INSERT TABLE 4 HERE
16
Potential self-selection and other robustness issues
As Chaney et al. (2004) show with regard to Big 4 premiums, there are
limitations in using the standard OLS estimation in a setting of audit fees and auditor
choice. It does not take into account potential self-selection, and measures only the
intercept effects, not the slope effects. We next examine counterfactuals, or treatment
effects. We use a two-step approach to testing for differences in slope coefficients.
We estimate a model of fees for clients with specialist auditors; we then apply the
coefficients from this model to the clients with non-specialist auditors; and we test
whether these counterfactuals are significantly different from zero. We next repeat
this test in the opposite direction (model for clients with non-specialists applied to
clients with specialists). Table 5 presents audit fee models for the full sample, and
separate audit fee models for the clients of specialists and non-specialists (measured at
the city level).
INSERT TABLE 5 HERE
Chaney et al (2004) suggest that Big 4 firms have higher fixed costs due to
more extensive staff training, investments in technology, etc., but lower variable costs
relative to non-Big 4 auditors. Thus, it seems reasonable to conjecture that a similar
issue might apply to specialists compared to non-specialist auditors. There are some
differences between the models. For example, the coefficient on CATA (current assets
as a proportion of total assets) is slightly higher for clients of specialist auditors
(1.277) than clients of non-specialists (1.010), while the coefficient on foreign assets
is much lower for clients of specialist auditors (.399) than non-specialists (1.739).
These differences suggest that clients with relatively low levels of current assets and
substantial foreign operations, for example, should find a specialist auditor to be an
economically efficient choice. However, as Panel B of Table 5 shows, these
differences in the slope coefficients are not significantly different from zero (although
the difference in the intercept is).
We next estimate counterfactual audit fees to examine whether fees are
different for the data broken down by category: large clients with specialists, large
clients with non-specialists, then small clients with specialists, small clients with non-
17
specialists, listed companies and unlisted companies and so on (details of these
models are not reported). These results again show that specialists charge significantly
more (or non-specialists significantly less), regardless of self-selection effects, for the
sample overall; for large clients; for unlisted companies; and for lower-risk
companies. These results are generally consistent with the models reported earlier in
the paper.
In Table 6, we present a logit model of auditor specialist selection (using the
city-level specialist designation). Arguments presented earlier in the paper suggest
that choosing a specialist auditor might also be affected by the variables in the audit
fee models. Table 6 shows that these variables are not significantly associated with
the choice of a specialist auditor. We find no evidence of self-selection bias in our
estimation, and we conclude that a two-stage model is not needed in our setting. We
also consider whether choice of a specialist auditor is affected by a company’s
overseas subsidiary status or its status with respect to issuing securities, demonstrated
growth, or industry risk. These tests (not reported) models reveal some evidence of a
significant relation between specialist choice and size (significant at p=0.000 when
these other dummy variables are included), suggesting that larger firms are more
likely to choose specialist auditors. There is no significant relation between issuing
status, growth or risk and the choice of a specialist auditor.
INSERT TABLE 6 HERE
Our results with respect to the overall level of fee premiums paid at national
and city-level designations to specialist auditors are next compared to those in some
previous studies, summarized in Table 7. Our results are similar to those of Francis et
al. (2005) and Basioudis & Francis (2007) in our finding that premiums exist at the
city level, whether or not the firm is a specialist at the national level, and unlike those
of Ferguson et al. (2003), who found that both national and city designations were
necessary for a premium to be earned. Further, we find premiums only for the topranked firm, whereas Ferguson et al. (2003) found evidence of premiums for the top
two firms.
18
5. Summary and Conclusion
In summary, we present strong evidence of fee premiums to city-level
specialists in New Zealand and no evidence of premiums to national specialists. With
respect to client size, prior research has produced mixed results, with Casterella et al.
(2004) finding that small clients are most likely to pay a specialist premium and
Craswell et al. (1995) presenting evidence of a specialization premium only for
relatively large clients. Our results are generally consistent with Craswell et al.
(1995) and inconsistent with Casterella et al. (2004). We find much stronger evidence
of specialist fee premiums paid by large clients and weaker, less consistent evidence
of premiums paid by small clients. In sensitivity testing we use a variety of cut-offs,
as well as a continuous size variable, and our results are robust for large clients. Thus,
in our setting, we do not find evidence that large client firms use their bargaining
power to negotiate lower fees, or to negotiate away specialist premiums. The evidence
suggests instead that large clients are able to negotiate lower fees when their auditor is
not a specialist.
We next turn to our various subsets of firms in an attempt to shed insight into
the source or nature of specialist fee premiums. In our initial research design, we
posited the following as a means of distinguishing among alternative explanations:
1. If no fee premiums are paid to specialist auditors (or if discounts are
documented), our findings are most consistent with economies of scale.
2. If fee premiums are paid only by the largest clients in our sample, or only by
those active in the public equity or debt markets, our findings are most consistent
with the reputation theory.
3. If fee premiums are paid only by the smaller clients in our sample, or to those
that are unlisted but growing rapidly, our findings are most consistent with the
advisory theory.
4. If fee premiums are paid by all the clients in our sample, our findings are most
consistent with the convenience or efficiency factor theory.
Taken together, our models show that there are strong and consistent city-level
premiums for the whole sample, for larger companies, for unlisted companies, for
companies with or without demonstrated growth, for low-risk companies and for both
subsidiaries of overseas companies and non-subsidiaries. There is no evidence of a
19
fee premium paid by listed companies or high-risk companies, and less consistent
evidence of specialist premiums paid by smaller companies. The absence of a
premium at the national level is generally consistent with previous studies (see Table
7 for more details).
These results are quite clear in showing that explanations number 1, 3 and 4
are not supported (these results are even more convincing in that they are generally
not only inconsistent with the predictions, but frequently opposite them in sign).
There is some support for the reputation effect explanation (number 2) to the extent
that the larger clients in our sample are most likely to pay fee premiums. However,
listed companies are not.
Since our results are not entirely consistent with any of the scenarios proposed
based strictly on the client’s need or demand for a specialist auditor, we turn to
supply-side considerations in interpreting our findings. As mentioned earlier,
auditors’ pricing decisions may reflect a combination of the following: the client’s
earnings history; the client’s potential for future profitability; the perceived risk of
litigation; and the client’s ability to pay. Our findings are consistent, at least in part,
with an argument that auditors in New Zealand take into account the ability of some
clients to afford a fee premium.
Thus, we conclude that, even in this relatively simple setting, audit fee
determination reflects a complex interplay of demand and supply factors. Further, our
findings suggest that specialist auditors in New Zealand are not likely to succumb to
pressures to lower prices for large clients with greater bargaining power. Instead, nonspecialists may be forced to offer lower fees to the most desirable clients, such as
larger clients, clients with demonstrated recent growth, and low-risk clients. Thus, the
bargaining power may be most likely to shift to the client in the case of desirable
clients negotiating with non-specialist auditors.
Conclusion
A number of recent research papers examine the impact of industry
specialization by auditors on audit fees in a variety of contexts. Some propose that
auditors who specialize in an industry (or industries) are able to perform higher
quality audits, which are recognized and valued by financial statement users and/or by
managers to the extent that higher audit fees are warranted and paid to such
20
specialists. The evidence to date is, however, mixed with respect to: (a) the existence
of a specialist premium; (b) whether such a premium accrues only to national and
city-specific specialists, or whether one designation is sufficient; and (c) whether
large or small client firms are most likely to pay such a fee premium. Our results
strongly support the proposition that specialist premiums exist, in the sense that
specialist auditors are able to charge higher fees than their non-specialist counterparts,
and that such premiums are best captured at the city level.
This finding is particularly interesting in light of previous research in the U. S.
and Australian settings. Francis et al. (2005) suggested that “since the Big 5
accounting firms have many more offices in the U. S. compared to just a handful in
Australia, it may be more difficult for Big 5 firms in the U. S. to achieve the firmwide transferability of office specific industry expertise and, more generally, to
achieve a uniform firm-wide reputation across a divergent range of offices.” Thus, we
might have expected to find that transferability (and thus the role of national rather
than local expertise) would play a more important role in New Zealand, which is
heavily concentrated relative to the other settings and in which only two cities have
auditor industry specialists. To the contrary, we find strong and consistent evidence
that, in our setting, city leadership is sufficient to justify fee premiums. This finding
is consistent with Francis, et al.’s (2005) U. S. study and Basioudis & Francis’s
(2007) U.K. study, and inconsistent with Ferguson, et al.’s (2003) Australia-based
study where both city and national levels of leadership were needed. However,
Francis, et al. acknowledged that their results for city-specific leadership (alone) were
mixed and inconclusive. Thus, our findings add strength to the argument that cityspecific industry leadership alone is sufficient to merit a fee premium.
We also examine the issue of self-selection. While it seems reasonable to
argue that auditor industry specialists are selected by clients for reasons of economic
efficiency, and that the same variables which determine audit fees might also
influence the choice of a specialist or non-specialist, the evidence shows that selfselection does not appear to be a significant issue in our setting.
By focusing on various subsets of firms, we are able to provide some insight
into why firms are willing to pay a fee premium for specialist auditors, and to
eliminate some seemingly feasible explanations. We find consistent evidence of city-
21
level premiums for the whole sample, for larger companies, for unlisted companies
and for companies with in industries that are less risky. There is no evidence of a fee
premium paid by listed companies, and mixed evidence of premiums paid by smaller
companies. These results are rather surprising, and we interpret them as evidence that
companies which are most desirable as audit clients (large firms, non-issuers, and
low-risk clients) are able to negotiate a reduced fee with non-specialist auditors in our
setting, though not with specialists who have achieved a stronger bargaining position
as a result of product differentiation. The likelihood that litigation and market
concerns are less extreme in New Zealand than in the U. S. raises a question as to
whether these same results would generalize to other settings, and poses an interesting
question for future research.
22
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26
Table 1: Top-ranking industry specialists at the national level in New Zealand
NZX Sectors
n
A01 Agriculture & Fishing
A02 Mining
A03 Forestry
A04 Building
A05 Energy
A06 Food
A07 Textiles & Apparel
A08 Intermediate & Durables
A09 Property
A10 Transport
A11 Ports
A12 Leisure & Tourism
A13 Consumer
A14 Media & Communications
A15 Finance & Other Services
A16 Investment
18
4
10
7
21
20
4
27
8
5
7
6
40
15
13
17
TOTAL
Total
audit
fees
5,221
300
3,865
2,540
4,823
2,447
611
2,715
492
1,750
426
889
4,199
3,932
4,467
910
222
39,587
Topranked
firm
%
KPMG
PwC
PwC
KPMG
PwC
PwC
EY
PwC
PwC
DTT
DTT
PwC
PwC
KPMG
PwC
EY
74%
67%
38%
63%
37%
43%
76%
44%
46%
52%
48%
52%
40%
63%
72%
40%
PwC
34%
Secondranked
PwC
KPMG
DTT
PwC
DTT
EY
KPMG
KPMG
SCW
EY
PwC
KPMG
EY
PwC
EY
DTT
KPMG
%
Thirdranked
11%
26%
30%
24%
33%
24%
12%
24%
21%
22%
32%
39%
25%
19%
10%
27%
DTT
GT
EY
DTT
EY
DTT
BDO
DTT
KPMG
KPMG
ANZ
EY
DTT
EY
SR
KPMG
7%
4%
27%
13%
17%
17%
12%
14%
14%
17%
8%
9%
14%
10%
7%
10%
EY
CDFK
KPMG
--KPMG
KPMG
--EY
DTT
PwC
DTT
--KPMG
DTT
KPMG
PwC
7%
3%
5%
DTT
17%
EY
16%
27%
%
Fourthranked
Names of firms
DTT – Deloitte Touche Tohmatsu; EY – Ernst & Young; KPMG – KPMG; Pwc – PricewaterhouseCoopers; SCW – Sherwin Chan & Walsh;
BDO – BDO Spicers; ANZ – Audit New Zealand; CDFK – Carlton DFK; GT – Grant Thornton; SR – Staples Rodway
27
%
10%
16%
9%
10%
8%
7%
12%
8%
6%
10%
Table 2: Descriptive statistics
Mean
AF
LAF
TA
LTA
SUB
SSUB
CATA
QUICK
DebttoTAs
ROA
FOREIGN
Frequencies
Loss
BIG 4
Growth
Listed
High-risk
Sub of overseas
Specialization
180.130
4.428
485,016
11.607
5.720
1.926
0.447
2.784
0.196
-0.192
0.040
Median
Std.
Deviation
76.000
4.331
110,885
11.616
3.000
1.832
0.458
1.077
0.111
0.086
0.000
365.319
1.174
1,171,855
1.896
7.079
1.436
0.293
8.759
0.235
3.874
0.118
Number
63
200
43
116
57
70
Number
Clients of national specialists (market
leader)
Clients of city specialists (market leader)
Where:
AF
LAF
TA
LTA
SUB
SSUB
CATA
QUICK
DebttoTAs
ROA
FOREIGN
Loss
BIG 4
Growth
Listed
High-risk
Sub of overseas
Minimum
Maximum
3000.000
8.006
10,746,000
16.190
40.000
6.325
1.000
85.947
1.486
1.421
1.000
77
4.000
1.386
66
6.519
0.000
0.000
0.004
0.037
0.000
-57.621
0.000
Percent
27.9%
87.8%
19.7%
52.8%
25.6%
30.8%
Percent of
audits
35%
70
32%
52%
Percent of
fees
53%
= Audit fees in thousands
= Log of audit fees (in thousands)
= Total assets in thousands
= Log of total assets (in thousands)
= Number of subsidiaries
= Square root of the number of subsidiaries
= Current assets to total assets
= Quick ratio
= Debt to total assets
= Return on assets computed as EBIT to total assets
= Percentage of foreign assets
= 1 if firm incurred a current year loss; 0 otherwise.
= 1 if Big 4 auditor; 0 otherwise.
= 1 if company improved by at least 10 places in the Top200 list; 0 otherwise.
= 1 if firm had equity or debt securities listed on the NZ Stock Exchange; 0
otherwise.
= 1 if company was in the following industry categories: Agriculture & Fishing;
Mining; Forestry; Building; Leisure & Tourism; Other Services; Investment; 0
otherwise.
= 1 if company was a subsidiary of an overseas company; 0 otherwise.
28
Table 3 Audit fee models for national leader, city leader and combinations
Model 1
Estimate
Constant
LTA
SSUB
CATA
QUICK
DebttoTAs
ROA
FOREIGN
LOSS
BIG4
National leader
City leader
National and city
leader
National but not
city leader
City but not
national leader
F-statistic
Adjusted R2
Sample size
where:
Dependent
variable
LTA
SSUB
CATA
QUICK
DebttoTAs
ROA
FOREIGN
LOSS
BIG 4
-2.229
.478
.186
1.018
-.015
.168
-.027
1.167
.277
.196
.019
tstatistic
Prob.
-6.035
15.310
5.363
6.123
-2.967
.858
-2.316
3.074
2.648
1.333
.202
.000
.000
.000
.000
.003
.392
.022
.002
.009
.184
.840
56
.716
218
Model 2
Estimate
tstatistic
Prob.
Model 3
Estimate
-2.116
.466
.190
1.028
-.014
.159
-.027
1.017
.262
.108
-5.903
15.336
5.652
6.383
-2.772
.835
-2.393
2.747
2.581
.763
.000
.000
.000
.000
.006
.405
.018
.007
.011
.446
.334
3.544
.000
.000
60
.732
218
.000
= Log of audit fees (in thousands)
= Log of total assets (in thousands)
= Square root of the number of subsidiaries
= Current assets to total assets
= Quick ratio
= Debt to total assets
= Return on assets computed as EBIT to total assets
= Percentage of foreign assets
= 1 if current-year loss occurred; 0 otherwise.
= 1 if Big 4 auditor; 0 otherwise.
29
Prob.
-2.165
.469
.197
1.048
-.014
.155
-.026
1.023
.269
.132
tstatistic
-6.035
15.439
5.801
6.491
-2.830
.813
-2.349
2.770
2.649
.918
.234
2.118
.035
-.110
-.831
.407
.492
3.180
.002
51
.734
218
.000
.000
.000
.000
.000
.005
.417
.020
.006
.009
.360
Table 4: Coefficient and significance levels of specialization variables in models for
subsets of companies according to size, listing status, growth, risk and whether a
subsidiary.
Panel A: Models examining National specialist
National specialist
Coefficient
Significance
(Model 1)
All companies
.019
.840
Large
.007
.963
Small
.027
.831
Listed
.050
.683
Unlisted
-.050
.735
Growth
-.268
.244
Non-growth
.069
.529
High-risk
.020
.927
Low-risk
.000
.999
Sub of overseas
.042
.816
Non-sub of overseas
.070
.527
Panel B: Models examining City specialist
NZX City (Model 2)
Coefficient
Significance
All companies
.334
.000
Large
.442
.007
Small
.262
.051
Listed
.223
.113
Unlisted
.437
.030
Growth
.470
.047
Non-growth
.308
.006
High-risk
.306
.156
Low-risk
.366
.000
Sub of overseas
.476
.007
Non-sub of overseas
.271
.018
***
***
**
**
***
***
***
**
*** Significant at p less than .01
** Significant at p less than .05
Tables report coefficient and significance levels of coefficient b10 on auditor specialization in this model:
LAF = b0 + b1 LTA + b2 SSUB +b3 CATA +b4 QUICK + b5 DEBTTA + b6 ROA + b7
Foreign + b8 Loss + b9 BIG 4 + b10 Specialization + b11Industry + e
‘National’ indicates specialist defined as leading firm at national level; ‘City’ at the city level.
Companies in finance industry categories excluded.
Large
Small
Listed
Unlisted
Growth
Non-growth
High-risk
Companies in Top 100 of the Top 200 list
Companies outside the Top 100 of the Top 200 list
Listed companies and debt issuers
Others
Companies that had improved their ranking in the Top 200 list by ten places or more
Others
Companies in the following industry categories: Mining; Forestry; Building; Leisure
& Tourism; Other Services; Investment.
30
Low-risk
Sub of overseas
Non-sub of overseas
Others
Companies that were subsidiaries of overseas companies
Others
31
Table 5: Models estimated for clients of specialists and non-specialists
Panel A: regression models of audit fees for clients of specialist and non-specialist
auditors
Constant
LTA
SSUB
CATA
QUICK
DebttoTAs
ROA
FOREIGN
LOSS
Big4
F-statistic
Adjusted R2
Sample size
Specialists
Estimate t-statistic
-1.621
-2.431
.465
8.559
.210
3.561
1.277
3.957
-.083
-2.047
.101
.276
-.013
-.027
.399
.768
.184
.825
25
.743
67
Prob.
.018
.000
.001
.000
.045
.783
.979
.446
.413
Non-specialists
Estimate t-statistic
-2.232
-4.889
.475
12.300
.180
4.333
1.010
5.209
-.012
-2.419
.185
.816
-.028
-2.386
1.739
3.219
.357
2.779
.094
.634
.000
37
.685
151
Prob.
.000
.000
.000
.000
.017
.416
.018
.002
.006
.527
.000
Panel B: F-tests for difference intercept and slope coefficients between clients of
specialist and non-specialist auditors
Test
Joint F-test that all slope coefficients and the intercept are the
same across clients of specialists and non-specialists
Joint F-test that all slope coefficients excluding the intercept are
the same across clients of specialists and non-specialists
F-test that the intercept is the same across clients of specialists
and non-specialists
Notes:
Specialists – auditor is city-level industry market leader
Non-specialists – other
Where:
LAF
LTA
SSUB
CATA
QUICK
DebttoTAs
ROA
FOREIGN
LOSS
Big4
= Log of audit fees (in thousands) (dependent variable)
= Log of total assets (in thousands)
= Square root of the number of subsidiaries
= Current assets to total assets
= quick ratio
= Debt to total assets
= Return on assets computed as EBIT to total assets
= Percentage of foreign assets
= 1 if current-year loss; 0 otherwise.
= 1 if Big 4 auditor; 0 otherwise.
32
F-statistic
2.352
sig.
0.015
1.071
0.386
12.569
0.000
Table 6: Logistic regressions of auditor specialist choice.
[Dependent variable = City specialist (top-ranked auditor in industry for given city)]
LTA
SSUB
CATA
QUICK
DebttoTAs
ROA
FOREIGN
LOSS
Big4
Constant
Chi squared
Cox & Snell
R Square
Nagelkerke
R Square
where:
Dependent
variable
LTA
SSUB
CATA
QUICK
DebttoTAs
ROA
FOREIGN
LOSS
BIG 4
Coefficient
.189
-.073
-.074
-.048
.137
.017
1.988
.168
20.245
-23.004
Prob.
.105
.571
.909
.298
.845
.839
.129
.656
.998
.998
31.508
.135
.000
.190
= City specialist (top-ranked auditor in industry for given city)
= Log of total assets (in thousands)
= Square root of the number of subsidiaries
= Current assets to total assets
= Quick ratio
= Debt to total assets
= Return on assets computed as EBIT to total assets
= Percentage of foreign assets
= 1 if current-year loss occurred; 0 otherwise.
= 1 if Big 4 auditor; 0 otherwise.
33
Table 7 – Comparison with previous studies
Specialists at national-level, city-level and both
Country
Period
No of observations
FFS
Australia
1998
1,046
FRW
U.S.
2000-2001
3,994
B&F
UK
2002-2003
506
This study
NZ
2003
218
Model 1: National
leader coefficient
(significance)
.094
(.069)
.075
(<.001)
.026
(.63)
.019
(.840)
Model 2:
City leader
coefficient
(significance)
.143
(.010)
.121
(<.001)
.141
(.001)
.334
(.000)
Model 3:
Both national and
city coefficient
(significance)
.213
(.001)
.170
(<.001)
.125
(.05)
.234
(.035)
National but not
city coefficient
(significance)
-.018
(.392)
-.032
(.318)
.002
(.98)
-.110
(.407)
City but not
national
coefficient
(significance)
.081
(.169)
.074
(.002)
.160
(.02)
.492
(.002)
FFS – Ferguson, Francis and Stokes (2003)
FRW – Francis, Reichelt and Wang (2005)
B & F – Basioudis & Francis (2007)
34