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Apprendre à oser ® 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] 1 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 3 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 5 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 7 (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: 9 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. 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