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Friday 18 November 2011 (HEC - 14:00-16:00 – Room H015)
“Audit and Non-audit Fees in Germany
– Impact of Audit Market Characteristics”
Nicole Ratzinger
University of Ulm
Professor in charge of the seminar: Cédric LESAGE (HEC Paris)
______________________________________________________________________________________________________________________
This document cannot be used without the agreement of the author
Annette G. Köhler and Nicole V. S. Ratzinger-Sakel*
Audit and Non-audit Fees in Germany
– Impact of Audit Market Characteristics**
Abstract
Using a two-stage least squares regression approach, this study investigates the impact
of three German audit market characteristics on audit and non-audit fees for the years
2005 to 2007. We find no relationship between audit and non-audit fees after controlling
for their simultaneous determination, which suggests that audit and non-audit fee determination in Germany does not indicate any particular impairment of auditor independence. Further, we find that fee-cutting for initial audit engagements depends significantly on client size. We find no evidence, however, of network-related fee reporting
bias due to the ambiguous phrasing of German fee disclosure requirements, which implies that German fee data are internationally comparable. The explanatory power of our
model is relatively high with an R2 of 84 %, and most of our results continue to hold
after performing additional statistical and robustness tests, e.g., panel data analysis.
JEL Classification: M42, L11, C30
Keywords: Audit fees; Non-audit fees; Auditor independence; Auditor change; Audit
market segment
* Professor Dr. Annette G. Köhler, Chair of Accounting and Auditing, University of
Duisburg-Essen, Lotharstraße 65, 47057 Duisburg, Germany, Phone: +49 (0203) 3792644, E-Mail: [email protected]
Dr. Nicole V. S. Ratzinger-Sakel, Ulm University, Institute of Accounting and Auditing, Helmholtzstraße 22, 89081 Ulm, Germany, Phone: + 49 (0731) 50-31025,
E-Mail: [email protected]
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Audit and Non-audit Fees in Germany – Impact of Audit Market Characteristics
1
Introduction
While a large body of research examines the determinants and consequences of fees for
audit and non-audit services, to date empirical findings for the German audit market are
limited (Zimmermann (2008); Bigus and Zimmermann (2008); Bigus and Zimermann
(2009); Köhler et al. (2010); Wild (2010)). This is largely due to the fact that, in most
continental European countries, audit and non-audit fee information became publicly
available only recently, following the disclosure obligation in Article 49 of Directive
2006/43/EG. The German legislator implemented this Directive via the Accounting Law
Reform Act (Bilanzrechtsreformgesetz (BilReG)). Effective January 1, 2005, the German Commercial Code (section 314 paragraph 1 no. 9) requires that listed companies
disclose audit and non-audit fees both for the parent company and for subsidiaries
across the following four categories: a) audits, b) other attestation services, c) tax consultancy and d) other services. In contrast, in the U.K. audit fees have been disclosed
since 1967 (Companies Act 1967) and non-audit fees since 1991 (Companies Act
1989).
In this study we exploit the newly available German fee disclosure data to empirically
examine the impact of three audit market characteristics on the fees for audit and nonaudit services in Germany. In particular, using a sample that comprises all German
listed companies subject to fee disclosure requirements for fiscal years 2005 to 2007, we
examine (1) simultaneity effects, (2) audit market segment effects and (3) institutional
setting effects on audit and non-audit fees, where we take fees reported under category
a) above to be audit fees, and fees reported under categories b) through d) to be nonaudit fees. Note that since the wording of the German law lacks precise definition criteria, there is room for interpretation with respect to the categorisation of fees paid to the
auditor (e.g., Zimmermann (2006, 275), Bigus and Zimmermann (2008, 173)).
Prior evidence on the relationship between audit and non-audit fees is mixed. For example, O’Keefe et al. (1994) argue that there should be a negative relationship between
audit and non-audit fees. A negative relationship may call in to question auditor independence, as auditors may use audits to obtain non-audit fees (e.g., Hillison and Kennelley (1988); Hay et al. (2006a)). However, a number of studies find evidence of a posi2
tive relationship (e.g., Davis et al. (2003); Ezzamel et al. (1996); Firth (1997); Bigus
and Zimmermann (2009)), and other studies find no evidence of a relationship between
audit and non-audit fees (e.g., Abdel-Khalik (1990); Barefield et al. (1993); O’Keefe et
al. (1994)). Following Whisenant et al. (2003), Hay et al. (2006a) and Antle et al.
(2006), who suggest that these two types of fees may be endogenously related, we explore whether audit and non-audit fees in Germany are determined simultaneously. We
find that, indeed, client size, client complexity, client risk, audit committee existence
and stock exchange listing simultaneously determine audit and non-audit fees. Accordingly, in our main analysis we control for the joint determination of audit and nonaudit fees using two-stage least squares. We find no evidence of a relationship between
audit and non-audit fees in Germany. Thus, the determination of audit and non-audit
fees in Germany is not consistent with an impairment of auditor independence arising
from potential loss-leadership effects. Additional panel data analysis that controls for
time-invariant individual-specific effects further supports the conclusion that audit fees
are independent of non-audit fees in Germany.
In addition to simultaneity effects, we test for audit market segment effects. Representatives of audit firms state that audit firms’ pricing strategies differ between ‘small’ versus
‘large’ audit client segments, yet there is no empirical evidence as to whether auditors’
fee-cutting in initial audit engagements differs across these segments. To shed light on
this question, we split the sample according to audit firms’ stated client acquisition
strategies while accounting for both market segment differences and Big Four versus
non-Big Four differences. We find that different pricing strategies are applied to ‘small’
versus ‘large’ client segments. In particular, we find that fee-cutting for initial audit
engagements is higher in the ‘large’ client segment than in the ‘small’ client segment,
consistent with cost and reputation effects dominating market structure effects in driving fee-cutting behaviour. However, this result appears to be sensitive to model specification. After controlling for time-invariant individual-specific characteristics, we find
that the less concentrated audit market segment for ‘small’ audit clients shows higher
fee-cutting than the audit market segment for ‘large’ audit clients, consistent with a
market structure explanation for differences in fee-cutting behaviour across segments.
Finally, we also test for effects of the institutional setting on audit and non-audit fees. In
particular, we test for effects stemming from ambiguity in the fee disclosure requirements in Germany. According to the fee disclosure requirements, fees associated with
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audit and non-audit services must be reported. However, the disclosure requirements do
not explicitly specify whether audit and non-audit fees related to services provided by
the statutory auditor’s network members should be reported (Lenz et al. (2006); Köhler
et al. (2010)). As a result, if an audit client has subsidiaries that demand audit or nonaudit services from network members of the statutory auditor, reporting of the related
fees is effectively voluntary. Reported fees of group audits may therefore be systematically biased, which would reduce the international comparability of German audit and
non-audit fee data. To study the impact of the ambiguity of the German fee disclosure
requirements, we include in the model a dummy variable that equals one if the client
firm has foreign subsidiaries, as a bias in reported fees is most likely to be evident in
situations in which the audit client has foreign subsidiaries. We find no evidence supporting network-related systematic fee distortion due to the phrasing of German fee disclosure requirements. Thus, German audit and non-audit fees appear to be internationally comparable. Additional panel data analysis that controls for time-invariant individual-specific effects lends support to the view that German audit and non-audit fees do not
suffer from bias related to ambiguity in the disclosure requirements.
Our paper contributes to the German audit and non-audit fee literature in several ways.
First, we complement Bigus and Zimmermann (2009), who also examine the relationship between audit and non-audit fees in Germany, by taking the relationship between
audit and non-audit fees to be endogenous and using a two-stage least squares approach
to control for their joint determination. Second, we provide novel evidence on differences in auditors’ pricing strategies across ‘small’ versus ‘large’ client segments, as to
the best of our knowledge this paper is the first study of German audit and non-audit
fees to link fee-cutting behaviour to specific client market segments. Third, we build on
prior work highlighting potential fee reporting bias in Germany (e.g., Lenz et al. (2006);
Köhler et al. (2010)) by explicitly testing for whether such a bias exists and impairs the
international comparability of German audit and non-audit fees. Finally, whereas prior
German audit and non-audit fee literature primarily examines a sample period of only
one year, our sample contains company-level observations over a three-year period.
This panel structure of our data allows us to conduct additional analysis in which we
control for individual-specific effects for variables that differ across sample companies
but are constant over time.
4
The paper is organised as follows. Section 2 discusses prior literature and develops our
hypotheses. In Section 3 we describe our data and introduce our model specification.
Section 4 presents our main empirical results. Additional statistical and robustness tests
are reported in Section 5. Finally, Section 6 concludes the paper.
2
2.1
Literature review and hypotheses
The relationship between audit and non-audit fees
The negative impact of non-audit fees on incumbent’s auditor independence has been
discussed in the international literature for quite some time (e.g. Mautz and Sharaf
(1961); Simunic (1984); Barkess and Simnett (1994)). The underlying argument is that
the provision of non-audit services to audit clients may lead to an additional economic
bond between the auditor and the client that is likely to reduce auditor’s independence
(e.g., Mautz and Sharaf (1961); Ezzamel et al. (1996); Firth (2002)).
Prior research delivers mixed insights on the relationship between audit and non-audit
fees. On the one hand, prior work argues that the relationship between audit and nonaudit fees should be negative (O’Keefe et al. (1994)). Two explanations have been proposed for such a relationship. First, Simunic (1984) and Hay et al. (2006a) argue that
knowledge spillovers between the two types of services can lead to cost savings.
Second, Hillision and Kennelly (1988) and Hay et al. (2006a) use the “loss leader argument” whereby auditors reduce audit fees to clients in order to increase sales of nonaudit services (Hay et al. (2006a, 717)). Notice that knowledge spillover effects do not
necessarily suggest an impairment of auditor independence; loss leader effects, however, are likely to be associated with a decrease in auditor independence (Hay et al.
(2006a, 717)).
On the other hand, several prior studies suggest that the relationship between audit and
non-audit fees is positive. For instance, Simunic (1984) shows that both types of services may be driven by client characteristics such as client size or client risk, and Solomon
(1990, 324) suggests that if the market for non-audit services is a monopoly, audit
clients might pay higher audit fees to obtain non-audit services than if only audit services were purchased. Hackenbrack and Knechel (1997, 491) further argue that a positive
relationship may be found to the extent that audit time is used to explain non-audit
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projects to clients. The empirical findings of Davis et al. (1993) and Bell et al. (2001)
for the U.S. audit market, Ezzamel et al. (1996) for the U.K. audit market, Firth (1997)
for the Norwegian audit market and Bigus and Zimmermann (2009) for the German
audit market using a single-equation specification of audit fee models provide support
for a positive relationship between audit and non-audit fees.
Other research finds no evidence of a relationship between audit and non-audit fees.
Abdel-Khalik (1990), Barefield et al. (1993) and O’Keefe et al. (1994), for instance,
each conclude that there is no relationship between audit and non-audit fees. Whisenant
et al. (2003) propose a possible explanation for the finding of no relationship: audit and
non-audit fees are determined simultaneously. Building on this work, Hay et al. (2006a)
show that a positive relationship between audit and non-audit fees obtains when onestage OLS is used, whereas no relationship obtains when a simultaneous specification
based on two-stage least squares is used. However, using both U.K. and U.S. data, Antle
et al. (2006) examine a system of simultaneous equations for audit fees, non-audit fees
and abnormal accruals and find a positive relationship between audit and non-audit fees
in both OLS and jointly determined models. In addition, they find that non-audit services decrease abnormal accruals. They attribute this result to productivity effects of nonaudit services (Antle et al. (2006, 235)). These authors further stress that the assumption
that the provision of non-audit services may impair auditor independence is overly restrictive as auditor independence may be impaired simply due to the payment of extraordinarily large audit fees (Antle et al. (2006, 242)).
The discussion above indicates that the direction of the relationship between audit and
non-audit fees is an open question. However, the one study that focuses on the German
audit market (Bigus and Zimmermann (2009)) found a positive relationship between
audit and non-audit fees. This leads to our first hypothesis:
H1: There is a positive relationship between audit and non-audit fees in the German
audit market.
2.2
Audit market segmentation
Prior research analyses price competition related to auditor changes. Whereas Francis
(1984) and Palmrose (1986) do not find evidence of fee-cutting in initial audit engage6
ments, Simon and Francis (1988) and Ettredge and Greenberg (1990) show that audit
changes are usually associated with a significant decrease in audit fees. The decomposition of the auditor switch variable by Craswell and Francis (1999) delivers additional
insights: fee-cutting is significant only for changes from non-Big X to Big X auditors.
This result might be due to the fact that higher-priced experience goods (such as Big X
audits) allow for deeper discounts as purchase incentives than lower-priced experience
goods. With respect to the German audit market, Bigus and Zimmermann (2009) find
that first-year audit fees do not differ significantly from other audit fees, whereas Köhler
et al. (2010) find a significant decrease in audit fees due to an auditor change. The contradictory results might be due to both the different samples used in these studies as well
as the fact that the findings of Bigus and Zimmermann (2009) are based on one year.
Recent findings for the German audit market (Wild (2010), 523 et seq.) confirm feecutting for initial audit engagements. However, Wild’s results (2010, 523 et seq.) suggest a significant fee discount only for auditor changes to a Big Four auditor. Nonetheless, neither study finds conclusive evidence of lowballing in the German audit market.
To date, however, the above arguments with respect to fee-cutting have not been linked
to specific client groups. This is surprising given that decreases in average costs due to
economies of scale are positively related to client size, which implies that the extent of
fee-cutting for large audit clients should be larger than that for small audit clients, ceteris paribus. Moreover, audit firms are likely to have an incentive to compete for large
audit clients for reputation reasons – for instance, there was fierce competition (including significant fee-cutting) for the audit of Siemens AG after it became clear that the
long-term incumbent auditor was going to be replaced.
Another stream of literature takes client-specific groups into account and argues that the
‘small’ audit client segment is highly competitive because the number of potential suppliers is relatively large compared to the ‘large’ audit client segment, whereas the
‘large’ audit client segment is less competitive because the number of potential suppliers is usually restricted to the Big X, currently the Big Four (e.g., Simunic (1980); Francis (1984); Francis and Stokes (1986); Palmrose (1986); Carson et al. (2004)). In these
studies, Bertrand (1883) competition effects that result in competitive pricing behaviour
– even when there are only two suppliers – are not taken into account; instead, the studies generally focus on the existence and size of fee premiums in the segment in which
competition is taken to be relatively low. The empirical findings are mixed. Simunic
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(1980) does not find evidence of significant fee premiums in the U.S., whereas Francis
(1984) identifies a Big X premium in both the ‘small’ and the ‘large’ client market segments in Australia. Palmrose (1986) and both Francis and Stokes (1986) and Carson et
al. (2004) also find evidence of price premiums among Big X audits (in the U.S. and
Australia, respectively), though only for the ‘small’ audit client segment. The different
results across these studies may be explained in part by the different thresholds used to
distinguish between ‘small’ versus ‘large’ audit market segments: in Simunic (1980,
176) this threshold is total audit client sales of $125 million, while Francis (1984, 145)
uses a median split based on total audit client assets, Francis and Stokes (1986, 386 et
seq.) and Carson et al. (2004, 82) focus on a fixed number of audit clients and Palmrose
(1986, 109) defines the threshold to be total audit client assets of $150 million. Evidence of a Big X premium is also found in Europe either for the Big X group as a whole
(e.g., for the U.K. audit market Chan et al. (1993) and Brinn et al. (1994) or for individual Big X audit firms (e.g., for the Dutch audit market Langendijk (1997), for the Irish
audit market Simon and Taylor (2002) and for the Italian audit market Cameran (2005)).
With respect to the German audit market, both Bigus and Zimmermann (2009) and
Köhler at al. (2010) find that audit fees are positively related with Big Four auditors.
Wild (2010) uses disaggregated Big Four variables and finds that the Big Four premium
is only evident for PwC. However, none of the above studies accounts for audit firms’
different pricing strategies across ‘small’ versus ‘large’ audit client segments.
Taken together, the above discussion shows that prior research suggests that audit firms’
competitive pricing behaviour differs between ‘small’ versus ‘large’ audit client market
segments, but to date there is no empirical evidence linking fee-cutting to specific client
groups. In this study we extend Simunic’s theoretical framework of audit pricing to the
context of fee-cutting behaviour amidst auditor changes. Specifically, we posit that feecutting is greater for initial audit engagements in the ‘small’ client segment than for
those in the ‘large’ client segment, as the ‘small’ client segment has a large number of
suppliers whereas the ‘large’ client segment is highly concentrated and dominated by
Big Four audit firms. This leads to our second hypothesis:
H2: Fee-cutting related to auditor changes in the ’small’ client segment is higher
than in the ‘large’ client segment in the German audit market.
8
To distinguish ‘small’ versus ‘large’ clients, we follow Simunic’s procedure and use as
our cutoff total audit client assets of €500 million. This value, which takes into account
both the market share of Big X audit firms and their growth since the 1980s, has been
confirmed by leading audit firm representatives. For comparison, based on the number
of audit engagements, the market share of the Big Four audit firms is roughly 50 % in
the ‘small’ client market, whereas in the ‘large’ client market it is roughly 85 % (see
Table 2).
2.3
German audit fee reporting bias
The phrasing of the disclosure requirements with respect to audit and non-audit fees in
Germany is ambiguous. The fee disclosure requirements apply to the auditor of the consolidated financial statement, that is, the statutory auditor pursuant to section 318 of the
German Commercial Code, whereas fees for services provided by parties other than the
statutory auditor and its affiliated subsidiaries need not be reported (see IDW RH HFA
1000.6 (2005, ref. no. 6)). However, the disclosure requirements do not explicitly specify whether the fees of international network members have to be disclosed (see IDW
RH HFA 1000.6 (2005, ref. no. 5)). As a consequence, among group audits, while the
disclosure of fees for audit and non-audit services provided by the statutory auditor’s
subsidiaries is actually mandatory, the disclosure of fees for services provided by the
statutory auditor’s network members is voluntary. Reported fees for services provided
by the statutory auditor with network members may therefore be systematically biased
(Lenz et al. (2006); Köhler et al. (2010)), which would reduce the international comparability of German audit and non-audit fee data.
To the extent that network members play an important role mainly in transnational auditor-client relationships, reported fee bias is most likely to be apparent in situations in
which the audit client has foreign subsidiaries: whereas statutory auditors are likely to
inform audit clients with domestic subsidiaries that subsidiary audit and non-audit fees
must be reported, network members auditing foreign subsidiaries are less likely to make
their clients aware of the fact that audit and non-audit fees should be reported by the
parent company. In other words, if there is a reporting bias due to the systematic negligence of network member fees, it should be observable for audits of clients with foreign
subsidiaries. This leads to our third hypothesis:
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H3: There is a negative relationship between the existence of foreign client subsidiaries and audit fees in the German audit market.
3
3.1
Research design
Data
Our sample covers all German listed companies required to disclose audit and non-audit
fee data as a consequence of the legal reporting obligation starting in fiscal year 2005.
This sample comprises listed companies that are registered at a regulated market for
shares or bonds. Recently, the ‘Accounting Law Modernisation Act (Bilanzrechtsmodernisierungsgesetz (BilMoG))’, which became effective on 29 May 2009, extends
the fee disclosure requirement to virtually all German entities subject to statutory audits
(see section 285 sentence 1 no. 17, section 314 paragraph 1 no. 9 and section 288 paragraph 2 sentence 3 of the German Commercial Code). While complete financial data on
fee information are available for these firms for fiscal years 2005 through 2008, we focus our analysis on fiscal years 2005 through 2007 as data are not yet sufficiently available to control for the effect of the 2008 financial and economic crisis.
We collect the data manually from annual/financial reports, corporate governance reports and the websites of the German Stock Exchange as well as the German regional
stock exchanges. Consistent with prior audit fee literature we exclude the financial sector (i.e., banks and insurance companies) because of their specific accounting and corporate governance requirements and their specific balance sheet structures. Furthermore, we exclude insolvent or bankrupt companies, companies with a (one-off) short
fiscal year and companies with missing or imprecise fee data. To facilitate comparability, the analysis is based on IFRS consolidated financial statements. We therefore eliminate US-GAAP consolidated financial statements and single-entity financial statements.
Our final sample consists of 460, 448 and 437 listed firms in the years 2005, 2006 and
2007, respectively.
3.2
Model specification
To examine our hypotheses we begin by estimating the following cross-sectional OLS
regression model, which serves as a reference for further analysis below:
10
LN(AF)=α+β1LN(NAF)+β2Β4toΒ4+β3Β4tonΒ4+β4nΒ4tonΒ4+β5nΒ4toΒ4+β6INT+
β7LN(TA)+β8SQ(ΒUSSEG)+β9RECV+β10EΒIT+β11LEV+β12Deloitte+
β13E&Y+β14KPMG+β15PwC+β16AC+β17LISTED+β18SEC +
[γ0+γ1LN(NAF)+ γ2B4toB4+γ3B4tonB4+ γ4nB4tonB4+γ5nB4toB4+ γ6INT+
γ7LN(TA)+γ8SQ(BUSSEG)+γ9RECV+γ10EBIT+γ11LEV+γ12Deloitte+
γ13E&Y+ γ14KPMG+γ15PwC+γ16AC+ γ17LISTED+ γ18SEC]*SIZE+eAF1 (1)
Audit fees (AF) are regressed on non-audit fees (NAF) to examine the relationship between these variables (hypothesis 1). To test for different pricing strategies across the
‘small’ versus ‘large’ client segments, we look at the fee-cutting behaviour of audit
firms facing initial audit engagements in the two segments (hypothesis 2). Of interest
here is the set of auditor change variables that specify the direction of the auditor
change: Big Four to Big Four (B4toB4), Big Four to non-Big Four (B4tonB4), non-Big
Four to non-Big Four (nB4tonB4) and non-Big Four to Big Four (nB4toB4). To study
the impact of potential reporting bias (hypothesis 3), our variable of interest is INT, a
dummy variable that is coded 1 if the audit client has foreign subsidiaries and 0 otherwise. Interaction variables for the additional effect of market segment affiliation are
included. In particular, β1 to β18 capture the effect of the audit fee determinants for the
‘small ‘client segment whereas γ1 to γ18 capture the additional size effect on audit fees
for the 356 companies with total assets exceeding €500 million in the overall model.
Consistent with prior audit fee literature (see Hay et al. (2006b)), we control for client
size (total assets, TA), client complexity (number of business segments, BUSSEG),
client risk (ratio of accounts receivable to total assets, RECV; ratio of EBIT to total assets, EBIT; ratio of total liabilities to total assets, LEV), Big Four audit engagements,
monitoring by an audit committee (existence of an audit committee, AC) and listing
effects (a listing in one of the leading German indices, LISTING, and a listing in a U.S.
Stock Exchange, SEC).
The model in equation (1) treats non-audit fees as a potential determinant of audit fees
in order to make transparent potential impairment of auditor independence. However,
more recent research (Whisenant et al. (2003); Hay et al. (2006a)) finds that audit and
non-audit fees might be simultaneously determined, in which case the explanatory variable LN(NAF) in equation (1) would be correlated with the error term eAF1, that is, an
OLS assumption would be violated and hence the one-stage OLS approach would yield
inconsistent results. We therefore examine whether LN(NAF) is endogenous in equation
(1). A Durbin-Wu-Hausman (DWH) test produces a robust test statistic (Davidson
11
(2000)). This result suggests that LN(NAF) is indeed endogenously determined
(Sig. 0.024). Specifically, the data suggest that client size, client complexity, client risk,
audit committee existence and stock exchange listing jointly determine audit and nonaudit fees. Thus, following Hay et al. (2006a), we follow a two-stage approach whereby
in a first step we estimate the impact of the explanatory variables on non-audit fees and
in a second step we estimate the influence of the estimated non-audit fees on audit fees.
In particular, we solve the following two-stage least squares regression model to estimate non-audit fees:
LN(NÂF)=α+β1Β4toΒ4+β2Β4tonΒ4+β3nΒ4tonΒ4+β4nΒ4toΒ4+β5INT+β6LN(TA)+
β7SQ(ΒUSSEG)+β8RECV+β9EΒIT+β10LEV+β11Deloitte+β12E&Y+
β13KPMG+β14PWC+β15AC+β16LISTED+β17SEC+
[γ0+γ1B4toB4+γ2B4tonB4+γ3nB4tonB4+γ4nB4toB4+γ5INT+γ6LN(TA)+
γ7SQ(BUSSEG)+γ8RECV+γ9EBIT+γ10LEV+γ11Deloitte+γ12E&Y+
γ13KPMG+γ14PwC+γ15AC+γ16LISTED+γ17SEC]*SIZE+eNÂF
(2)
LN(AF)=α+β1LN(NÂF)+β2Β4toΒ4+β3Β4tonΒ4+β4nB4tonΒ4+β5nΒ4toΒ4+β6INT+
β7LN(TA)+β8SQ(ΒUSSEG)+β9RECV+β10EΒIT+β11LEV+β12AC+
β11LISTED+β14SEC +
[γ+γ1LN(NÂF)+γ2B4toB4+γ3B4tonB4+γ4nB4tonB4+γ5nB4toB4+γ6INT+
γ7LN(TA)+γ8SQ(BUSSEG)+γ9RECV+γ10EBIT+γ11LEV+γ12AC+γ13LISTED+
(3)
γ14SEC+]*SIZE+eAF2
To avoid missing values, we use the measure LN(NAF+1), as this measure allows for
regression results for companies with zero NAF. Our model of non-audit fees (equation
(2)) follows prior literature and controls for client size (Geiger and Rama (2003); Hay et
al. (2006a)), client complexity (Hay et al. (2006a)), client risk (Hay et al. (2006a)), Big
Four audit engagements (Geiger and Rama (2003); Antle et al. (2006); Hay et al.
(2006a)), monitoring by an audit committee (Houghton and Ikin (2001)) and listing effects (Hay et al. (2006a)). As we discuss above, this model delivers estimated values of
non-audit fees, LN(NÂF), which are not a function of audit fees. We next substitute
LN(NÂF) for LN(NAF) in equation (3). Following the argument of Hay et al. (2006a,
720), in equation (3) we omit the variables representing the influence of the Big Four
audit firms (in contrast to the non-Big Four audit firms), as their influence appears to be
more
closely
related
to
non-audit
fees
than
audit
fees.
12
Table 1 summarises the variables used in our multiple regression models.
Table 1
Model Specification and Variable Measurement
Two-stage Least Squares Regression
Variables
Dependent variable
Definition
LN(AF)
Natural logarithm of audit fees
Independent variables
Test variables
LN(NAF)
LN(NÂF)
B4 to B4
B4 to nB4
nB4 to nB4
nB4 to B4
INT
Control variables
LN(TA)
SQ(BUSSEG)
RECV
EBIT
LEV
E&Y*
Deloitte*
KPMG*
PwC*
AC
LISTED
SEC
Interaction variable
SIZE
Predicted
sign
Natural logarithm of non-audit fees
Estimated value of natural logarithm of non-audit fees, not
influenced by audit fees
Dummy variable, coded 1, if there was an intra Big Four
change; otherwise 0
Dummy variable, coded 1, if there was a change from a Big
Four auditor to a non-Big Four auditor; otherwise 0
Dummy variable, coded 1, if there was an intra non-Big Four
change; otherwise 0
Dummy variable, coded 1, if there was a change from a nonBig Four auditor to a Big Four auditor; otherwise 0
Dummy variable, coded 1, if the entity has foreign subsidiaries
+
Natural logarithm of total assets
Square root of number of business segments
Ratio of accounts receivable to total assets
Ratio of earnings before interest and tax to total assets
Leverage; ratio of total liabilities and total assets
Dummy variable, coded 1, if the auditor is E&Y; otherwise 0
Dummy variable, coded 1, if the auditor is Deloitte; otherwise
0
Dummy variable, coded 1, if the auditor is KPMG; otherwise
0
Dummy variable, coded 1, if the auditor is PwC; otherwise 0
Dummy variable coded 1, if an audit committee exists; otherwise 0
Dummy variable, coded 1, if the entity is listed in one of the
four German indices DAX, MDAX, TecDAX or SDAX;
otherwise 0
Dummy variable, coded 1, if firm is SEC listed; otherwise 0
+
+
+
+
+
+
Dummy variable, coded 1, if the firm’s total assets are at least
€500 Mio.
+/-
+
+
+
+
+
+
+
Notes: * Disaggregated Big Four variables used as instrument.
13
4
4.1
Empirical results
Descriptive results
Table 2 presents descriptive statistics and illustrates the difference between the two audit market segments as defined above. The average ratio of non-audit to audit fees is
approximately 0.5 in both the ‘small’ and the ‘large’ segments. However, the mean audit fee for the ‘large’ client market segment is more than 20 times higher than that for
the ‘small’ client market segment, and the mean total assets is 117 times higher for
‘large’ client firms than for ‘small’ client firms. In contrast, client complexity as measured by the number of business segments differs only marginally between the two
groups. The results for the risk variables are mixed: whereas the EBIT ratio for ‘large’
clients is three times higher than that for ‘small’ clients, the leverage of ‘small’ clients is
eight percentage points smaller than the leverage of ‘large’ clients.
Turning to the indicator variables, the results show that 86 % of the ‘large’ clients have
an audit committee, while less than one-third of the ‘small’ clients have an audit committee. In addition, the distribution of INT shows that the vast majority of companies
have foreign subsidiaries, both for the ‘small’ segment and the ‘large’ segment. The
results further indicate that whereas in the ‘small’ segment only 10 % of the companies
are listed in one of the four German indices (DAX, MDAX, TecDAX and SDAX),
roughly 69 % of the ‘large’ clients are listed in one of these indices. Only 2 % of all
sample entities are listed at the SEC, however. With respect to the auditor-specific variables the results show that ‘small’ clients choose Big Four audit firms roughly as often
as non-Big Four audit firms, while in the ‘large’ segment the Big Four audit firms tend
to dominate: in this segment, KPMG has the largest market share, followed by PwC,
E&Y (which dominates the ‘small’ client segment) and Deloitte (which holds position
four in both segments), where ‘market share’ is based on the number of audit assignments. Finally, there are far more auditor switches in the ‘small’ client segment than in
the ‘large’ client segment.
14
Table 2
Descriptive Statistics
Panel Ai: Continuous variables
Variable
Mean
AF (€000)
959.248
NAF (€000)
458.860
TA (€000000)
3873.086
BUSSEG
2.839
RECV
0.179
EBIT
0.042
LEV
0.574
Panel Bi: Indicator variables
Variable
Audit Committee
Foreign subsidiaries
LISTED
SEC listing
SIZE
Big Four audits
Deloitte
E&Y
KPMG
PwC
Auditor Changes
B4 to B4
B4 to nB4
nB4 to nB4
nB4 to B4
No.
621
1150
341
28
356
800
88
270
247
195
132
38
27
32
35
Median
170.000
54.000
136.528
3.000
0.167
0.062
0.595
Std. Dev.
4341.506
2334.837
18563.208
1.428
0.115
0.171
0.235
Minimum
Maximum
10.000 63000.000
0.000 42800.000
1.360 235466.000
1.000
10.000
0.000
0.665
-2.811
0.550
-0.111
2.080
No.
1345
1345
1345
1345
1345
1344
1340
%
46.17
85.50
25.35
2.08
26.47
59.48
6.54
20.07
18.36
14.50
9.81
2.83
2.01
2.38
2.60
15
Table 2 (continued)
Descriptive Statistics by Market Segment (‘small’ versus ‘large’)
Panel Aii: Continuous variables
Variable
Segment
Mean
Median
Std. Dev.
Minimum
Maximum
AF (€000)
small
157.070
122.000
124.356
10.000
1366.000
large
3187.769
774.000
8033.893
30.000 63000.000
NAF (€000)
small
78.918
32.520
133.185
0.000
1217.300
large
1514.375
338.000
4366.921
0.000 42800.000
TA (€000000)
small
122.216
74.492
121.322
1.360
499.890
large 14293.339
1923.211 34006.987
501.535 235466.000
BUSSEG
small
2.582
2.000
1.237
1.000
7.000
large
3.553
3.000
1.663
1.000
10.000
RECV
small
0.186
0.173
0.118
0.000
0.665
large
0.162
0.155
0.103
0.000
0.534
EBIT
small
0.027
0.055
0.194
-2.811
0.550
large
0.083
0.076
0.059
-0.203
0.301
LEV
small
0.551
0.556
0.256
-0.111
2.080
large
0.638
0.639
0.146
0.092
1.000
Panel Bii: Indicator variables
Variable
Segment
Audit Committee
small
large
Foreign subsidiaries
small
large
LISTED
small
large
SEC listing
small
large
Big Four audits
small
large
Deloitte
small
large
E&Y
small
large
KPMG
small
large
PwC
small
large
Auditor Changes
small
large
B4 to B4
small
large
B4 to nB4
small
large
nB4 to nB4
small
large
nB4 to B4
small
large
No.
315
306
815
336
95
246
7
21
501
299
56
32
211
59
134
113
100
95
110
22
26
12
25
2
31
1
28
7
No.
989
356
989
356
989
356
989
356
989
356
988
356
984
356
%
31.85
85.96
82.41
94.38
9.61
69.10
0.71
5.90
50.66
83.99
5.66
8.99
21.33
16.57
13.55
31.74
10.11
26.69
11.12
6.18
2.63
3.37
2.53
0.56
3.13
0.28
2.83
1.97
16
4.2
Multivariate results
The multivariate results are reported in Table 3. Our model has high explanatory power
with an adjusted R2 of 84 %.
Consistent with prior studies that focus on, e.g., Australia, Germany, the U.S. or the
U.K., we find evidence of a strong positive relationship between audit and non-audit
fees when we use a one-stage OLS regression model (see Panel A): LN(NAF) is 0.051
(Sig. 0.000) for the subsample of ‘small’ clients, and 0.086 (Sig. 0.000) for the subsample of ‘large’ clients. However, when we employ the two-stage least squares approach
to control for the joint determination of the two types of fees, we find no relationship
between audit and non-audit fees (see Panel B ii). This result reinforces the view that
audit and non-audit fees are simultaneously determined. More precisely, we find that
variables such as client size, client complexity, client risk, audit committee existence
and stock exchange listing determine both audit fees as well as non-audit fees. This result holds for both the ‘small’ client segment and the ‘large’ client segment, with the
coefficient on LN(NÂF) insignificant for both segments. We therefore find no support
for hypothesis 1. These results are consistent with prior literature on the simultaneous
determination of audit and non-audit fees (e.g., Whisenant et al. (2003); Hay et al.
(2006a)). The results also provide further empirical evidence for the German audit market. In particular, we do not find any indication for audits as loss leaders that might impair auditor independence.
Next, we find no evidence of significant fee-cutting for the ‘small’ client segment but
some evidence of significant fee-cutting for the ‘large’ client segment. The coefficients
on the additional size effect for a non-Big Four to non-Big Four auditor change (-1.054,
Sig. 0.045) as well as for a non-Big Four to Big Four auditor change (-0.301,
Sig. 0.096) are negative and significant. For the ‘large’ client segment the results show
significant fee-cutting except for changes from a Big Four to a non-Big Four audit firm:
B4toB4 (-0.476, Sig. 0.048), nB4tonB4 (-1.314, Sig. 0.046) and nB4toB4 (-0.401,
Sig. 0.060) are significantly negative. Hence, we find no support for hypothesis 2. These
results imply that in Germany, audit market concentration does not explain pricing behaviour around an auditor change. One explanation could rather be that audit firms’ feecutting behaviour may be driven by cost considerations and reputation effects. This
might further be enhanced by higher quasi-rents provided by ‘large’ audit clients subsequent to lowballing. However, none of the German studies (Bigus and Zimmermann
17
(2009); Köhler et al. (2010); Wild (2010)) find conclusive evidence of lowballing in the
German audit market, and thus longer time series are needed to be able to draw further
inferences on lowballing effects.
Turning to our third variable of interest, we find insignificant results on INT for both
segments (‘small’ segment: 0.033, Sig. 0.284; ‘large’ segment: 0.115, Sig. 0.258). We
therefore find no support for hypothesis 3. This result implies that the disclosure of audit and non-audit fees in Germany does not suffer from a bias due to the systematic negligence of fees for services provided by the statutory auditor’s foreign network members, and thus German audit and non-audit fee data should be internationally comparable.
Finally, while all of our control variables have the expected sign and are in line with
prior international (e.g., Simunic (1980); Francis (1984); Palmrose (1986); Cameran
(2005); Simon and Taylor (2002)) and German (Bigus and Zimmermann (2009); Köhler
et al. (2010)) fee literature, not all of our control variables are significant. RECV and
EBIT are not significant for the ‘small’ client segment while EBIT, LEV and AC are not
significant for the ‘large’ client segment. Due to our model specification we can explicitly consider the additional size effect of audit fee determinants in the German audit
market. For the variables LEV, AC and SEC, we observe a mitigating effect on audit
fees: the sign of these coefficients when the additional size effect is included is negative, indicating that the positive relationship between these variables and audit fees is
weaker for clients with total assets of more than €500 million. Furthermore, the results
show a reinforcing effect on audit fees for the variables LN(TA), SQ(BUSSEG), RECV
and LISTED for clients whose total assets exceed €500 million. These variables have
significantly positive coefficients in the market for ‘large’ audit clients. In particular, the
results show that for the LN(TA) variable a one unit increase in client assets in the
‘small’ segment leads to 0.425 higher audit fees and a one unit increase in the ‘large’
segment leads to 0.561 higher audit fees. The fact that the impact of the audit fee determinants is relatively large in the ‘large’ client segment compared to the ‘small’ client
segment also holds for SQ(BUSSEG), RECV and LISTED. We find the highest additional size effect for RECV. While SEC is significant in both segments (Sig. 0.000), the
coefficient on the SEC variable is larger in the ‘small’ audit client segment than in the
‘large’ audit client segment. EBIT is the only variable that is not significant for either
market segment.
18
Table 3
Models of Audit Fees, Non-audit Fees and Control Variables
Panel A: OLS regression
LN(AF)=
α+β1LN(NAF)+β2Β4toΒ4+β3Β4tonΒ4+β4nΒ4tonΒ4+β5nΒ4toΒ4+β6INT+β7LN(TA)+β8SQ(ΒUSSEG)+β9RECV+β10EΒIT+β11LEV+β12Deloitte+
β13E&Y+β14KPMG+β15PwC+β16AC+β17LISTED+β18SEC +
[γ0+γ1LN(NAF)+γ2B4toB4+γ3B4tonB4+γ4nB4tonB4+γ5nB4toB4+γ6INT+γ7LN(TA)+γ6SQ(BUSSEG)+γ9RECV+γ10EBIT+γ11LEV+γ12Deloitte+
γ13E&Y+γ14KPMG+γ15PwC+γ16AC+γ17LISTED+γ18SEC]*SIZE+eAF1
MODEL EFFECT FOR TA ≤ €500 Mio.
Coeff.
t-stat
Sig.
Independent variables
Intercept/SIZE
-0.271
-1.37
0.170 a
LN(NAF)
0.051
5.25
0.000 ***
B4toB4
-0.129
-1.24
0.108
B4tonB4
-0.021
-0.20
0.421
nB4tonB4
-0.089
-0.93
0.177
nB4toB4
-0.186
-1.83
0.034 **
INT
0.025
0.55
0.290
LN(TA)
0.380
20.37
0.000 ***
SQ(BUSSEG)
0.141
3.22
0.001 ***
RECV
0.067
0.45
0.325
EBIT
-0.171
-1.81
0.035 **
LEV
0.498
7.21
0.000 ***
Deloitte
0.255
3.39
0.000 ***
E&Y
0.078
1.75
0.040 **
KPMG
0.244
4.65
0.000 ***
PwC
0.120
2.02
0.022 **
AC
0.147
3.84
0.000 ***
LISTED
0.174
2.82
0.002 ***
SEC
0.949
4.64
0.000 ***
0.855
Adjusted R2
213.710 ***
F-Statistic
ADDITIONAL SIZE EFFECT (γ-Coff.)
Coeff.
t-stat
Sig.
-2.857
0.035
-0.226
-0.482
-0.954
-0.218
0.080
0.138
0.325
1.639
0.131
-0.139
-0.370
-0.135
0.014
-0.036
-0.053
0.054
-0.064
-6.99
1.78
-1.22
-1.24
-1.82
-0.98
0.60
4.29
4.05
5.01
0.24
-0.60
-2.64
-1.21
0.14
-0.33
-0.58
0.59
-0.26
Notes: Significance levels (one-tail tests): * = 0.1, ** = 0.05, *** = 0.01; a = two-tail tests: * = 0.1, ** = 0.05, *** = 0.01.
0.000 a***
0.038 *
0.112
0.108
0.035 **
0.163
0.276
0.000 ***
0.000 ***
0.000 ***
0.404
0.273
0.004 ***
0.113
0.446
0.370
0.282
0.278
0.396
MODEL EFFECT FOR TA > €500 Mio.
Coeff.
t-stat
Sig.
-3.128
0.086
-0.355
-0.503
-1.043
-0.404
0.105
0.518
0.466
1.706
-0.040
0.359
-0.115
-0.057
0.258
0.084
0.094
0.228
0.885
-6.92
3.96
-1.82
-1.06
-1.60
-1.62
0.66
15.64
5.50
4.61
-0.06
1.29
-0.77
-0.44
2.33
0.72
0.90
2.67
5.42
0.000 a***
0.000 ***
0.035 **
0.144
0.055 *
0.053 *
0.255
0.000 ***
0.000 ***
0.000 ***
0.476
0.099 *
0.221
0.330
0.010 ***
0.237
0.185
0.004 ***
0.000 ***
19
Table 3 (continued)
Models of Audit Fees, Non-audit Fees and Control Variables
Panel B i: First-stage of the two-stage least squares regression
LN(NÂF)= α+β1Β4toΒ4+β2Β4tonΒ4+β3nΒ4tonΒ4+β4nΒ4toΒ4+β5INT+β6LN(TA)+β7SQ(ΒUSSEG)+β8RECV+β9EΒIT+β10LEV+β11Deloitte+β12E&Y+β13KPMG+
β14PWC+β15AC+β16LISTED+β17SEC +
[γ0+γ1B4toB4+γ2B4tonB4+γ3nB4tonB4+γ4nB4toB4+γ5INT+γ6LN(TA)+γ7SQ(BUSSEG)+γ8RECV+γ9EBIT+γ10LEV+γ11Deloitte+γ12E&Y+γ13KPMG+
γ14PwC+ γ15AC+ γ16LISTED+γ17SEC]*SIZE+eNÂF
MODEL EFFECT FOR TA ≤ €500 Mio.
Coeff.
t-stat
Sig.
Independent variables
Intercept/SIZE
-2.774
-4.31
0.000 a*
B4toB4
-1.667
-4.92
0.000 ***
B4tonB4
-1.314
-3.81
0.000 ***
nB4tonB4
-0.952
-3.02
0.002 ***
nB4toB4
-0.152
-0.46
0.325
INT
-0.183
1.22
0.112
LN(TA)
0.488
8.24
0.000 ***
SQ(BUSSEG)
0.275
1.91
0.029 **
RECV
0.509
1.05
0.147
EBIT
-0.978
-3.17
0.001 ***
LEV
-0.012
-0.05
0.479
Deloitte
-0.324
-1.31
0.095 *
E&Y
0.053
0.36
0.359
KPMG
0.123
0.71
0.238
PwC
-0.032
-0.16
0.435
AC
0.514
4.11
0.000 ***
LISTED
0.587
2.92
0.002 ***
SEC
0.223
0.33
0.370
0.441
Adjusted R2
31.20 ***
F-Statistic
ADDITIONAL SIZE EFFECT (γ-Coff.)
Coeff.
t-stat
Sig.
-2.474
0.438
-0.734
-0.727
-0.012
0.564
0.131
0.059
-2.992
-1.242
0.519
0.209
0.628
0.090
0.207
-0.434
0.124
1.006
-1.89
0.72
-0.58
-0.42
-0.02
1.28
1.33
0.22
-2.80
-0.71
0.69
0.45
1.72
0.27
0.58
-1.45
0.42
1.20
Notes: Significance levels (one-tail tests): * = 0.1, ** = 0.05, *** = 0.01; a = two-tail tests: * = 0.1, ** = 0.05, *** = 0.01.
0.030 a**
0.235
0.282
0.337
0.163
0.101
0.091 *
0.412
0.003 ***
0.241
0.247
0.325
0.043 **
0.394
0.283
0.074 *
0.338
0.103
MODEL EFFECT FOR TA > €500 Mio.
Coeff.
t-stat
Sig.
-5.268
-1.229
-2.048
-1.679
-0.164
0.381
0.619
0.334
-2.482
-2.220
0.507
-0.115
0.681
0.213
0.175
0.080
0.711
1.229
-4.79
-2.54
-1.74
-1.03
-0.26
0.95
8.17
1.57
-2.70
-1.33
0.37
-0.31
2.12
0.77
0.60
0.30
3.39
3.04
0.000 a***
0.006 ***
0.042 **
0.153
0.397
0.172
0.000 ***
0.058 *
0.004 ***
0.093 *
0.234
0.379
0.018 **
0.222
0.237
0.382
0.000 ***
0.002 ***
20
Table 3 (continued)
Models of Audit Fees, Non-audit Fees and Control Variables
Panel B ii: Two-stage least squares regression
LN(AF)=
α+β1LN(NÂF)+β2Β4toΒ4+β3Β4tonΒ4+β4nB4tonΒ4+β5nΒ4toΒ4+β6INT+β7LN(TA)+β8SQ(ΒUSSEG)+β9RECV+β10EΒIT+β11LEV+β12AC+
β13LISTED+β14SEC+
[γ0+γ1LN(NÂF)+γ2B4toB4+γ3B4tonB4+γ4nB4tonB4+γ5nB4toB4+γ6INT+γ7LN(TA)+γ8SQ(BUSSEG)+γ9RECV+γ10EBIT+γ11LEV+γ12AC+γ13LISTED+
γ14SEC] *SIZE+eAF2
MODEL EFFECT FOR TA ≤ €500 Mio.
Coeff.
t-stat
Sig.
Independent variables
Intercept/SIZE
-0.596
-1.08
0.280 a
LN(NÂF)
-0.010
-0.06
0.478
B4toB4
-0.159
-0.49
0.312
B4tonB4
-0.170
-0.65
0.259
nB4tonB4
-0.260
-1.03
0.152
nB4toB4
-0.100
-0.92
0.180
INT
0.033
0.57
0.284
LN(TA)
0.425
4.65
0.000 ***
SQ(BUSSEG)
0.179
2.56
0.005 ***
RECV
0.125
0.70
0.242
EBIT
-0.247
-1.21
0.112
LEV
0.505
7.06
0.000 ***
AC
0.187
1.84
0.033 **
LISTED
0.191
1.59
0.057 *
SEC
1.034
4.90
0.000 ***
0.844
Adjusted R2
250.913 ***
F-Statistic
ADDITIONAL SIZE EFFECT (γ-Coff.)
Coeff.
t-stat
Sig.
-2.868
0.045
-0.317
-0.504
-1.054
-0.301
0.082
0.136
0.321
1.302
0.292
-0.201
-0.057
0.069
-0.087
-3.06
0.20
-0.80
-0.91
-1.69
-1.31
0.54
1.10
3.01
2.58
0. 49
-0.79
-0.43
0.44
-0.29
Notes: Significance levels (one-tail tests): * = 0.1, ** = 0.05, *** = 0.01; a = two-tail tests: * = 0.1, ** = 0.05, *** = 0.01.
0.002 a***
0.421
0.212
0.181
0.045 **
0.096 *
0.294
0.136
0.001 ***
0.005 ***
0.313
0.215
0.334
0.329
0.385
MODEL EFFECT FOR TA > €500 Mio.
Coeff.
t-stat.
Sig.
-3.464
0.035
-0.476
-0.674
-1.314
-0.401
0.115
0.561
0.500
1.427
0.045
0.304
0.130
0.260
0.947
-3.61
0.21
-1.67
-1.10
-1.69
-1.56
0.65
5.27
4.93
2.39
0.06
0.98
1.20
2.11
3.54
0.000 a***
0.418
0.048 **
0.137
0.046 **
0.060 *
0.258
0.000 ***
0.000 ***
0.009 ***
0.475
0.164
0.115
0.018 **
0.000 ***
21
5
Further statistical tests
5.1
Tests of regression assumptions
With respect to the model described in equation (1), we perform several statistical tests
as follows. First, we perform a Chow test (see Chow (1960)) to determine whether the
model is consistent over time, that is, over the three-year sample period. The calculated
F-ratio is 0.783, which is not significant at α = 0.05, and the critical F-value is 1.301
(see Wooldridge (2009, 449 et seq.)). Thus, the null hypothesis that the slope and intercept are the same over time cannot be rejected, which supports our procedure of pooling
the data over the years 2005 to 2007 in one cross-sectional regression model.
Next, we analyze the residuals of the full sample to test whether further regression assumptions are valid. A Kolmogorov-Smirnov one-sample test of the distribution of the
OLS residuals suggests that the residuals for the full sample do not differ significantly
from the normal distribution, based on a 0.05 significance level. A Goldfeld-Quandt
(1965) test for the full sample further shows that the null hypothesis of homoskedasticity cannot be rejected at α = 0.05: the calculated F-ratio is 0.950 with 651, 651 degrees
of freedom and a critical F-value of 1.138. The regression assumptions of normal distributed residuals and constant variance also hold for both each year considered separately
and the pooled results across the three years. Specifically, the Kolmogorov-Smirnov
one-sample test of the distribution of the OLS residuals suggests that for each year, the
residuals for the full sample do not differ significantly from normal, based on a 0.05
significance level, and the Goldfeld-Quandt test’s calculated F-ratio is 1.080 with 210,
210 degrees of freedom and a critical F-value of 1.256 for 2005, 0.859 with 204, 204
degrees of freedom and a critical F-value of 1.260 for 2006 and 0.959 with 199, 199
degrees of freedom and a critical F-value of 1.263 for 2007, which implies that the null
hypothesis of homoskedasticity cannot be rejected at α = 0.05 in any of the sample
years.
5.2
Robustness test – Market segmentation based on median split
To test the robustness of our results to the choice of ‘large’ versus ‘small’ segment cutoff, we replace the threshold used in our analyses above with a median split based on
clients’ total assets. Our main results are unchanged. More specifically, the positive and
22
significant relationship between audit and non-audit fees continues to hold using a onestage OLS regression approach (see Appendix Table 4, Panel A), whereas the two-stage
least squares regression results continue to find no relationship between audit and nonaudit fees in the German audit market (see Appendix Table 4, Panel B). The coefficients
on the estimated LN(NÂF) variable are not significant for either segment. Moreover, the
estimation results using the median split for market segmentation reinforce the conclusion that both audit and non-audit fees are affected by common determinants such as
client size, client complexity, client risk, audit committee existence and stock exchange
listing. The findings based on a median split also lend support to evidence of no feecutting in the ‘small’ client segment, but of fee-cutting in the ‘large’ client segment (see
Appendix Table 4, Panel B). Finally, estimation of the two-stage least squares regression model based on a median split shows that, consistent with our results above, network-related fee reporting bias due to ambiguous disclosure requirements does not appear to be an issue in Germany – the coefficient on the “distortion” proxy INT is not
significant for either segment. Hence, this sensitivity test lends support to the view that
German audit and non-audit fees are internationally comparable.
5.3
Robustness test – Panel data analysis
We discuss in Section 3.2 that because the OLS assumption E(LN(NAF)|eAF1) = 0 is
violated in equation (1), we use a two-stage least squares approach to obtain consistent
estimates. An alternative way to achieve consistent estimates, however, is to use panel
data analysis (Cameron and Trivedi (2009, 260)). Given the unbalanced panel structure
of our data we use panel data analysis as a robustness test.
Specifically, we consider the following model:
yit = αi+βx’it+εit,
i = 1, 2, …N and t = 1, 2 …T,
(4)
where xi are the regressors, αi are random individual-specific effects, and εit is an idiosyncratic error term (Cameron and Trivedi (2009, 230)).
In contrast to the pooled OLS model, the model in equation (4) allows us to control for
variables that differ across entities but that are constant over time. Two quite different
specifications for αi are the fixed effects (FE) model and the random effects (RE) model. In the FE model, the individual-level effect is allowed to be correlated with the re23
gressors and hence the model permits a limited form of endogeneity, i.e., the error has
two components, a time-invariant component αi that is correlated with the regressors
and can be eliminated through differencing, and a time-varying component that is uncorrelated with the regressors given αi (Cameron and Trivedi (2009, 260)). In the FE
model we assume there is some incidence within the entities that may influence or bias
the explanatory variables and that has to be controlled for. The model removes the effects of these time-invariant characteristics through differencing from regressors so that
we can assess the regressors’ net effects. In the RE model, in contrast, αi is assumed to
be purely random, implying that αi, is uncorrelated with the regressors (Cameron and
Trivedi (2009, 231 et seq.).
The discussion above suggests that the choice between the two models of individualspecific effects depends on the potential correlation of the individual effects with the
regressors. Because we cannot assume that the time-invariant omitted variables are a
priori uncorrelated with the regressors, we conduct a Hausman test. The Hausman test
of fixed versus random effects (see Cameron and Trivedi (2009, 260 et seq.)) strongly
rejects the null hypothesis that RE provides consistent results at the 0.01 level of significance. Thus, assuming that the regressors in our model are correlated with the fixed
effects αi but not with εit we use an FE model to run our regression. Note, however, in an
FE model coefficients on regressors with little within variation or for variables that
change slowly over time will be imprecise (Cameron and Trivedi (2006, 238)).
The FE model results for the ‘small’ client segment and the ‘large’ client segment are
presented in Table 5 (see Appendix). Since the Chow test (Section 5.1) suggests that the
model is consistent over time, time fixed effects are not included. It is notable that the
results of the FE model are not absolutely comparable with respect to the included independent variables with the two-stage least squares approach (see Table 3, Panel B ii),
because the disaggregated Big Four variables are included in the FE model but not in
the two-stage least squares approach. The results confirm our earlier finding of no relationship between audit and non-audit fees, as the coefficient on LN(NAF) is insignificant for both segments. However, the FE model does not support our earlier finding
with respect to fee-cutting in both client segments. Indeed, the FE model suggests that
higher fee-cutting obtains in the ‘small’ client segment than in the ‘large’ client segment. Finally, the estimates of the FE model support our evidence obtained from the
two-stage least squares approach regarding the insignificance of the variable INT. Spe24
cifically, while the result suggests that there is a weak negative correlation between the
existence of foreign subsidiaries and audit fees in the ‘large’ client segment, additional
analysis of the time variation of INT shows that the INT variable is close to timeinvariant. Thus, the weak significance of the variable INT in the ‘large’ client segment
might be due to a bias caused by little variation in INT over time. Turning to the control
variables, while a positive and significant coefficient on SQ(BUSSEG), AC, LISTED
and SEC can be observed in the two-stage least squares approach for the ‘small’ client
segment (see Table 3, Panel B ii) these significances do not hold for the ‘small’ client
segment in the FE-model, suggesting that after controlling for time-invariant individualspecific effects these variables are no more significant determinant for audit fees in the
‘small’ client segment. In terms of the ‘large’ client segment it appears as the positive
and significant coefficients on SQ(BUSSEG), RECV, LISTED, and SEC in the twostage least squares approach also depend on the model specification (see Table 3, Panel
B ii), a control for time-invariant individual-specific effects results in insignificant coefficients regarding these variables (see Appendix, Table 5). Regarding the disaggregated
Big Four variables Deloitte, EY, PwC, and KPMG – only included in the FE-model –
while a positive and significant coefficient can be observed in the ‘small’ client segment
on Deloitte, EY, and KPMG, suggesting a product differentiation that only holds in the
‘small’ client segment but not in the ‘large’ client segment, a negative and significant
coefficient can be observed in the ‘large’ client segment, suggesting that the non-Big
Four audit firms have diseconomies of scale in the ‘large’ client segment (for interpretation of studying audit prices see in detail Simunic (1980, 171); Francis and Stokes
(1986, 384)). In particular, the results show a fee premium in the ‘small’ client segment
for all Big Four audit firms except for PwC, whereas in the ‘large’ client segment a fee
discount can be observed for all Big Four audit firms. We find the highest fee discount
for EY and Deloitte (EY: 64.65 %, calculation: exp(1.040) – 1 = -0.6465; Deloitte:
62.47 %). The results may suggest that EY and Deloitte accept high fee discounts in the
‘large’ client segment in order to increase their market share in this segment. Contrary
to our finding of no fee premium for PwC in the ‘small’ client segment, Wild (2010)
finds a fee premium only for PwC. However, Wild (2010) does not account for different
market segments in his FE analysis. The differing results lend support to the view that
market segmentation is an important determinant of audit fees in the German audit market.
25
In separate tests that are not reported for brevity, we also run the FE model using a balanced panel structure; the results remain essentially unchanged.
6
Summary
In this study we present novel evidence on fees for audit and non-audit services in Germany. Based on the complete set of data available for firms required to disclose audit
and non-audit fees for fiscal years 2005 to 2007 (1,345 observations), we extend prior
German audit market research from both an empirical and a methodological perspective.
We begin by identifying three audit market characteristics that are likely to affect the
fees for audit and non-audit services in Germany: (1) simultaneity effects, (2) audit
market segment effects and (3) institutional setting effects.
With respect to (1), we test for a positive relationship between audit and non-audit fees.
Since we find that non-audit fees are endogenous in our audit pricing model, we use a
two-stage least squares approach that controls for the simultaneous determination of
audit and non-audit fees. In contrast to our hypothesis 1, we find no evidence of a relationship between audit and non-audit fees. In particular, the results do not suggest loss
leaders effects that are likely to be associated with a decrease in auditor independence.
To ensure that the results are not sensitive to model specification, we take advantage of
the panel structure of our data and control for individual-specific effects for variables
that differ across our sample companies but are constant over time. The additional analysis supports the finding of no relationship between audit and non-audit fees. We thus
contribute to the German literature on audit and non-audit fees by providing empirical
findings that show that the relationship between audit and non-audit fees depends to a
large degree on model specification.
With respect to (2), we test for whether the distinction between ‘small’ and ‘large’ audit
clients can help explain the extent of fee-cutting in initial audit engagements. In contrast
to our hypothesis 2, the results of the two-stage least squares approach show that feecutting for ‘large’ audit engagements is higher than for ‘small’ audit engagements.
These findings support the view that, in Germany, cost and reputation effects dominate
market structure effects. However, this result is sensitive to model specification. After
controlling for time-invariant individual-specific effects we find that fee-cutting for
26
‘small’ audit engagements is larger than that for ‘large’ audit engagements. This is in
line with our hypothesis 2 and lends support to the view that market structure is an important determinant of audit pricing. We thus contribute to the literature by providing
evidence of differences in audit firms’ pricing strategies across ‘small’ versus ‘large’
client segments. However, due to the sensitivity of the results to model specification, no
conclusive inference can be drawn as to the nature of these differences.
Finally, with respect to (3), we test whether reported fees for audit and non-audit services are systematically biased, which would raise questions as to the international comparability of German audit and non-audit fees. In contrast to our hypothesis 3, we do not
find evidence of a negative relationship between the existence of foreign subsidiaries
and audit fees in Germany. Thus, the network-related ambiguity currently present in the
fee disclosure requirements in Germany does not appear to impair the comparability of
German audit and non-audit fee data. In additional analysis this result is shown to be
insensitive to model specification. We contribute to the literature by providing novel
evidence on an issue that to date has only been discussed from a theoretical perspective.
The results above are robust to several statistical tests and find support from extremely
high coefficients of determination. Nonetheless, our results are subject to limitations.
First, our sample is limited to listed companies. To generalise the results of this analysis
to non-listed companies, future research will have to rely on the fee information available following from the disclosure requirements of the Accounting Law Modernisation
Act. Second, we examine a time series of three years. To examine whether the results
hold over time, longer time series are needed. Third, given that access to companyspecific information on one-off events such as M&A transactions and restructuring, is
limited, the model explaining non-audit fees suffers from omitted variables. Annual
reports are not of much help in this regard, as they typically do not draw a complete
picture of such events, nor do they provide information about the supplier of related
non-audit services. Fourth, the results with respect to fee-cutting are based on a small
number of cases – typical for the current situation on the German audit market. Fifth, to
capture a potential network-related systematic fee distortion a dichotomous variable is
used. It is notable that the magnitude of a potential bias relates to the number of subsidiaries. However, a specification using the number of foreign subsidiaries to account for a
potential network-related systematic fee distortion would interfere with the use of the
number of foreign subsidiaries as an indicator of audit complexity and audit risk that are
27
assumed to have a positive effect on audit fees. Since it is impossible to disentangle
these countervailing effects, a dichotomous specification is used. Sixth, fee reporting
biases can also result from misclassifications of reported audit fees and non-audit fees.
However, since this problem may emerge in any jurisdiction with fee disclosure requirements, from an international comparability point of view, it should be of minor
importance. Finally, we do not account for the impact of the financial crisis that
emerged in 2008. In our opinion, this crisis considerably affects audit client as well as
auditor risk exposure, which should be reflected in audit fees. Any additional fee decrease currently observed must therefore be analysed in a way that makes more than one
year of the currently available ‘crisis’ fee data necessary. We leave investigation of this
extension to future work.
Appendix
28
Table 4
Median Split Robustness Test
Panel A: OLS regression
LN(AF)=
α+β1LN(NAF)+β2Β4toΒ4+β3Β4tonΒ4+β4nΒ4tonΒ4+β5nΒ4toΒ4+β6INT+β7LN(TA)+
β8SQ(ΒUSSEG)+β9RECV+β10EΒIT+β11LEV+β12Deloitte+β13E&Y+β14KPMG+β15PwC+
β16AC+β17LISTED+β18SEC+eAF3
‘Small’ client segment
Coeff.
t-stat
Independent variables
Intercept
0.025
0.11
LN(NAF)
0.062
5.91
B4toB4
-0.123
-1.01
B4tonB4
0.061
0.61
nB4tonB4
-0.135
-1.50
nB4toB4
-0.208
-1.88
INT
0.031
0.66
LN(TA)
0.347
14.79
SQ(BUSSEG)
0.144
3.13
RECV
0.062
0.40
EBIT
-0.171
-1.97
LEV
0.478
7.18
Deloitte
0.385
4.59
E&Y
0.161
3.44
KPMG
0.314
5.60
PwC
0.134
1.88
AC
0.066
1.54
LISTED
0.013
0.11
SEC
0.728
2.22
0.561b
Adjusted R2
40.522 ***
F-Statistic
Panel B: Two-stage least squares regression
Sig.
0.916 a
0.000 ***
0.157
0.270
0.067 *
0.031 **
0.254
0.000 ***
0.001 ***
0.345
0.025 **
0.000 ***
0.000 ***
0.000 ***
0.000 ***
0.030 **
0.063 *
0.457
0.013 **
‘Large’ client segment
Coeff.
t-stat
Sig.
-1.721
0.058
-0.255
-0.359
0.032
-0.173
0.038
0.462
0.353
0.651
-0.319
0.522
-0.055
-0.113
0.162
0.046
0.144
0.172
1.140
0.791
142.410 ***
-7.35
4.33
-2.03
-1.45
0.12
-1.19
0.46
22.81
6.09
2.97
-1.11
3.58
-0.58
-1.62
2.33
0.64
2.61
3.22
9.02
0.000 a***
0.000 ***
0.021 **
0.073 *
0.453
0.117
0.323
0.000 ***
0.000 ***
0.002 ***
0.134
0.000 ***
0.280
0.053 *
0.010 ***
0.261
0.005 ***
0.001 ***
0.000 ***
LN(NÂF)= α+β1Β4toΒ4+β2Β4tonΒ4+β3nΒ4tonΒ4+β4nΒ4toΒ4+β5INT+β6LN(TA)+β7SQ(ΒUSSEG)+
β8RECV+β9EΒIT+β10LEV+β11Deloitte+β12E&Y+β13KPMG+β14PWC+β15AC+β16LISTED+
β17SEC+eNÂF2
LN(AF)= α+β1LN(NÂF)+β2Β4toΒ4+β3Β4tonΒ4+β4nB4tonΒ4+β5nΒ4toΒ4+β6INT+β7LN(TA)+
β8SQ(ΒUSSEG)+β9RECV+β10EΒIT+β11LEV+β12AC+β13LISTED+β14SEC+eAF4
‘Small’ client segment
Coeff.
t-stat
Sig.
Independent variables
Intercept
LN(NÂF)
B4toB4
B4tonB4
nB4tonB4
nB4toB4
INT
LN(TA)
SQ(BUSSEG)
RECV
EBIT
LEV
AC
LISTED
SEC
0.116
0.131
0.121
0.048
-0.145
-0.057
0.068
0.325
0.142
0.071
-0.130
0.504
0.063
-0.017
0.670
0.16
0.79
0.45
0.21
-0.72
-0.47
1.13
3.09
2.13
0.39
-0.83
7.19
0.69
-0.10
1.72
0.873 a
0.215
0.328
0.416
0.235
0.318
0.259
0.001 ***
0.017 **
0.349
0.203
0.000 ***
0.246
0.462
0.043 **
‘Large’ client segment
Coeff.
t-stat
Sig.
-2.617
-0.120
-0.571
-0.741
0.048
-0.207
0.092
0.577
0.434
0.432
-0.758
0.520
0.196
0.256
1.308
3.88
-0.79
-2.07
-1.80
0.18
-1.38
1.03
6. 50
5.13
1.64
-1.60
3.45
2.56
2.85
6.39
0.000 a***
0.216
0.020 **
0.037 **
0.430
0.084 *
0.152
0.000 ***
0.000 ***
0.050 **
0.055 *
0.000 ***
0.005 ***
0.002 ***
0.000 ***
29
Adjusted R2
F-Statistic
0.459b
41.349 ***
0.782
172.673 ***
Notes: Significance levels (one-tail tests): * = 0.1, ** = 0.05, *** = 0.01; a two-tail tests: * = 0.1, ** = 0.05, *** = 0.01; b a lower
adjusted R2 in the ‘small’ client segment relative to the ‘large’ client segment is in line with comparable international studies (i.e.,
Francis (1984); Carson et al. (2004)).
Table 5
Entity Fixed Effects Model Robustness Test
LN(AF)=
αi+β1LN(NAF)+β2Β4toΒ4+β3Β4tonΒ4+β4nΒ4tonΒ4+β5nΒ4toΒ4+β6INT+β7LN(TA)+
β8SQ(ΒUSSEG)+β9RECV+β10EΒIT+β11LEV+β12Deloitte+β13E&Y+β14KPMG+β15PwC+
β16AC+β17LISTED+β18SEC+eAF5
COMPANIES WITH TA ≤ €500 Mio
COMPANIES WITH TA > €500 Mio
Coeff.
t-stat
Sig.
Coeff.
t-stat
Sig.
Independent variables
0.186
0.38
0.702 a
0.998
0.60 0.549 a
Interceptc
LN(NAF)
0.002
0.03
0.489
0.018
0.93 0.177
B4toB4
-0.085
-1.36
0.087 *
-0.147
-1.16 0.123
B4tonB4
-0.084
-1.26
0.103
-0.134
-0.42 0.338
nB4tonB4
-0.109
-1.91
0.029 **
0.230
0.61 0.270
nB4toB4
-0.222
-3.25
0.001 ***
-0.178
-1.08 0.141
INT
0.012
0.15
0.440
-0.485
-1.50 0.068 *
LN(TA)
0.383
8.61
0.000 ***
0.461
4.14 0.000 ***
SQ(BUSSEG)
0.043
0.78
0.219
-0.043
-0.39 0.350
RECV
0.045
0.25
0.401
0.275
0.42 0.337
EBIT
-0.278
-2.88
0.002 ***
-0.618
-0.92 0.179
LEV
0.273
2.93
0.002 ***
-0.509
-1.51 0.066 *
Deloitte
0.230
2.01
0.022 **
-0.980
-3.34 0.000 ***
E&Y
0.179
2.24
0.013 **
-1.040
-4.42 0.000 ***
KPMG
0.265
2.89
0.002 ***
-0.447
-1.82 0.035 **
PwC
0.097
1.08
0.140
-0.314
-1.34 0.091 *
AC
0.026
0.54
0.293
0.259
1.97 0.025 **
LISTED
-0.013
-0.19
0.426
0.058
0.53 0.299
SEC
0.057
0.17
0.433
0.076
0.40 0.345
0.940d
0.950d
Adjusted R2
7.19 ***
4.26 ***
F-Statistic
Notes: Significance levels (one-tail tests): * = 0.1, ** = 0.05, *** = 0.01; a two-tail tests: * = 0.1, ** = 0.05, *** = 0.0;:c Average
intercept: the average of the individual-specific intercepts; d R2 obtained from least square dummy regression which yield same
results
30
References:
Abdel-Khalik, A. Rashad (1990), The Jointness of Audit Fees and Demand for MAS: A
Self-selection Analysis, Contemporary Accounting Research 6, 295- 322.
Antle, Rick, Elizabeth Gordon, Ganapathi Narayanamoorthy, and Ling Zhou (2006),The
joint determinantion of audit fees, non-audit fees, and abnormal accruals, Review of
Quantitative Finance and Accounting 27, 235-266.
Barefield, Rusell M., Jennifer J. Gaver and Terrence B. O’Keefe (1993), Additional
Evidence on the Economics of Attest: Extending Results from the Audit Market to the
Market Compilations and Reviews, Auditing: A Journal of Practice and Theory 12, 7688.
Barkess, Lynn and Roger Simnett, (1994), The provision of other services by auditors:
Independence and pricing issues, Accounting and Business Research 24, pp. 99-108.
Bell, Timothy B., Wayne R. Landsman and Douglas A. Shackelford (2001), Auditor’s
perceived Business Risk and Audit Fees: Analysis and Evidence, Journal of Accounting
Research 39, 35-43.
Bertrand, Joseph (1883), Théorie Mathématique de la Richesse Sociale, Journal de Savants 67, 499–508
Bigus, Jochen and Ruth-Caroline Zimmermann (2008), Non-audit Fees, Market Leaders
and Concentration in the German Audit Market: A Descriptive Analysis, International
Journal of Auditing 12, 159-179.
Bigus, Jochen, and Ruth-Caroline Zimmermann (2009), Quasirentenmodell und Honorare für Abschlussprüfungen in Deutschland – eine empirische Analyse, Zeitschrift für
Betriebswirtschaft 79, 1283-1308.
BilMoG (2009), Bilanzrechtsmodernisierungsgesetz, Gesetz zur Modernisierung des
Bilanzrechts vom 29. Mai 2009, BGBl. I, 1102.
BilReG (2004), Bilanzrechtsreformgesetz, Gesetz zur Einführung internationaler Rechnungslegungsstandards und zur Sicherung der Qualität der Abschlussprüfung vom
4. Dezember 2004, BGBl. I, 3166.
31
Brinn, Tony, Michael J. Peel and Roydon Roberts (1994), Audit Fee Determinants of
Independent and Subsidiary Unquoted Companies in the UK: An Exploratory Study,
The British Accounting Review 26, 101-121.
Cameran, Mara (2005), Audit Fees and the Large Auditor Premium in the Italian Audit
Market, International Journal of Auditing 9, 129-146.
Cameron, A. Colin and Pravin K. Trivedi (2009), Microeconometrics Using Stata, Stata
Press, Texas.
Carson, Elizabeth, Neil Fargher, Daniel T. Simon and Mark H. Taylor (2004), Audit
Fees and Market Segmentation: Further Evidence on How Client Size Matters within
the Context of Audit Fee Models, International Journal of Auditing 8, 79-91.
Chan, Philip, Mahmoud Ezzamel and David R. Gwilliam (1993), Determinants of Audit
Fees for Quoted UK Companies, Journal of Business Finance & Accounting 20, 765786.
Craswell, Allen T. and Jere R. Francis (1999), Pricing Initial Audit Engagements: A
Test of Competing Theories, The Accounting Review 74, 201-216.
Chow, Gregory C. (1960), Tests of Equality between Sets of Coefficients in two Linear
Regressions, Econometrica 28, 591-605.
Companies Act (1967), Companies Act, Act 25 of 1967, Appointment and Remuneration of Auditors, U.K.
Companies Act (1989), The Companies Act 1989, Disclosure of remuneration for nonaudit work, Regulations 1991, Statutory Instrument, 1991/2128, U.K.
Davidson, James (2000), Econometric Theory, Oxford, Blackwell.
Davis, Larry R., Davis N. Ricchiute and Greg Trompeter (1993), Audit Effort, Audit
Fees, and the Provision of Non-Audit Services to Audit Clients, The Accounting Review
68, 135-150.
Directive 2006/43/EC of the European Parliament and the Council of 17 May 2006 on
statutory audits of annual accounts and consolidated accounts, amending Council Directives 78/660/EEC and 83/349/EEC and repealing Council Directive 84/253/EEC, Official Journal of the European Union L157/87.
32
Ettredge, Michael and Robert Greenberg (1990), Determinants of Fee Cutting on Initial
Audit Engagements, Journal of Accounting Research 28, 198-210.
Ezzamel, Mahmoud, David R. Gwilliam and Kevin M. Holland (1996), Some Empirical
Evidence from Publicly Quoted U.K. companies on the Relationship between the Pricing of Audit and Non-Audit Services, Accounting and Business Research 27, 3-16.
Firth, Michael (1997), The Provision of Non-Audit Services and the Pricing of Audit
Services, Journal of Business Finance & Accounting 24, 511-525.
Francis, Jere R. (1984), The Effect of Audit Firm Size on Audit Prices: A Study of the
Australian Market, Journal of Accounting and Economics 6, 133-151.
Francis, Jere R. and Donald J. Stokes (1986), Audit Prices, Product Differentiation, and
Scale Economies: Further Evidence from the Australian Market, Journal of Accounting
Research 24, 383-393.
Geiger, Marshall A. and Divesh V. Rama (2003), Audit fees, Non-audit fees, and auditor reporting on stressed companies, Auditing: A Journal of Practice and Theory 22, p.
53-69.
Goldfeld, Stephen M. and Richard E. Quandt (1965), Some Tests for Homoscedasticity,
Journal of the American Statistic Association June 1965, 539-547.
Hackenbrack, Karl and W. Robert Knechel (1997), Resource Allocation Decisions in
Audit Engagements, Contemporary Accounting Research 14, 481-499.
Hay, David, W. Robert Knechel and Vivian Li (2006a), Non-audit Services and Auditor
Independence: New Zealand Evidence, Journal of Business Finance & Accounting 33,
715-734.
Hay, David C., W. Robert Knechel and Norman Wong (2006b), Audit Fees: A Metaanalysis of the Effect of Supply and Demand Attributes, Contemporary Accounting Research 23, 141-191.
Hillision, William and Michael Kennelley (1988), The Economics of Nonaudit Services, Accouting Horizonts 2, 32-40.
33
Houghton, Keith A. and Chris C. Ikin (2001), Auditor Porvided Non-Audit Services:
Modelling Fees and Willingness to Buy, Working Paper 2001, University of Melbourne
and University of Tasmania.
IDW RH HFA 1.006 (2005), IDW Rechnungslegungshinweis: Anhangangaben nach
§ 285 Satz 1 Nr. 17 HGB bzw. § 314 Abs. 1 Nr. 9 HGB über das Abschlussprüferhonorar (IDW RH HFA 1.006), Stand: 18. Oktober 2005, Die Wirtschaftsprüfung 58, 12321234.
Köhler, Annette G., Kai-Uwe Marten, Nicole V. S. Ratzinger and Marco Wagner
(2010), Prüfungshonorare in Deutschland – Determinanten und Implikationen, Zeitschrift für Betriebswirtschaft 80, 5-29.
Langendijik, Henk (1997), The Market for Audit Services in the Netherlands, The European Accounting Review 6, 253-264.
Lenz, Hansrudi, Manuela Möller and Balthasar Höhn (2006), Offenlegung der Honorare
für Abschlussprüferleistungen im Geschäftsjahr 2005 bei DAX-Unternehmen, BetriebsBerater 61, 1787-1794.
Mautz, Robert K. and Hussein A. Sharaf (1961), The philosophy of auditing, American
Accounting Association, Florida, 1961.
O’Keefe, Terrence B., Dan A. Simunic and Michael T. Stein (1994), The Production of
Audit Services: Evidence from a Major Accounting Firm, Journal of Accounting Research 32, 241-261.
Palmrose, Zoe-Vonna (1986), Audit Fees and Auditor Size: Further Evidence, Journal
of Accounting Research 24, 97-110.
Simon, Daniel T. and Jere R. Francis (1988), The Effects of Auditor Change on Audit
Fees: Test of Price Cutting and Price Recovery, The Accounting Review 63, 255-269.
Simon, Daniel T. and Mark H. Taylor (2002), A Survey of Audit Pricing in Ireland,
International Journal of Auditing 6, 3-12.
Simunic, Dan A. (1980), The Pricing of Audit Services: Theory and Evidence, Journal
of Accounting Research 18, 161-190.
34
Simunic, Dan A. (1984), Auditing, Consulting, and Auditor Independence, Journal of
Accounting Research 22, 679-702.
Solomon, Ira (1990), Discussion of The Jointness of Audit Fees and the Demand for
MAS: A Self-selection Analysis, Contemporary Accounting Research 6, 323-328.
Whisenant, Scott, Srinivasan Sankaraguruswamy and K. Raghunandan (2003), Evidence on the Joint Determination of Audit and Non-audit Fees, Journal of Accounting
Research 41, 721-742.
Wild, Andreas (2010), Fee Cutting and Fee Premium of German Auditors, Fee Cutting
und Honorarprämien deutscher Abschlussprüfer, Die Betriebswirtschaft 70, 513-527.
Wooldridge, Jeffrey M. (2009), Introductory Econometrics: A Modern Approach, 4th
edition, South-Western Cengage Learning.
Zimmermann, Ruth-Caroline (2006): Gestaltungsspielräume bei Veröffentlichung von
Abschlussprüferhonoraren im Rahmen des BilReG, Zeitschrift für internationale und
kapitalmarktorientierte Rechnungslegung 6, 273-276
Zimmermann, Ruth-Caroline (2008), Abschlussprüfer und Bilanzpolitik der Mandanten
– Eine empirische Analyse des deutschen Prüfungsmarktes, Wiesbaden 2008.
35