big 4 fee premium

Managerial Auditing JournalBig 4 fee premium and audit quality: latest evidence from UK listed companiesDomenico Campa

Article information:To cite this document:Domenico Campa, (2013),"Big 4 fee premium and audit quality: latest evidence from UK listedcompanies", Managerial Auditing Journal, Vol. 28 Iss 8 pp. 680 - 707Permanent link to this document:http://dx.doi.org/10.1108/MAJ-11-2012-0784

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Big 4 fee premium and auditquality: latest evidence from UK

listed companiesDomenico Campa

School of Business, Trinity College Dublin, Dublin, Ireland

Abstract

Purpose Using the most recent observations (2005-2011) from a sample of UK listed companies,This paper aims to investigate whether Big 4 audit firms exhibit a fee premium and, if this is thecase, whether the premium is related to the delivery of a better audit service.

Design/methodology/approach Univariate tests, multivariate regressions and twomethodologies that control for self-selection bias are used to answer the proposed researchquestions. Data are collected from DataStream.

Findings Findings provide consistent evidence about the existence of an audit fee premiumcharged by Big 4 firms while they do not highlight any significant relationship between audit qualityand type of auditor with respect to the audit quality proxies investigated.

Research limitations/implications Evidence from this paper might signal the need forlegislative intervention to improve the competitiveness of the audit market on the basis that itsconcentrated structure is leading to excessive fees for Big 4 clients. Findings might also enhance Big4 client bargaining power. However, as the paper analyses only one country, generalizability of theresults might be a limitation.

Originality/value This study joins two streams of the extant literature that investigate theexistence of a Big 4 audit fee premium and different levels of audit quality among Big 4 and non-Big4 clients. Evidence supports the concerns raised by the UK House of Lords in 2010 that theconcentrated structure of the audit market could be the driver of excessive fees for Big 4 clients as itdoes not find differences in audit quality between Big 4 and non-Big 4 clients.

Keywords Audit fee premium, Big 4, Audit quality, Discretionary accruals, Accounting conservatism,Value relevance, Auditing, Auditors

Paper type Research paper

1. IntroductionSince DeAngelos (1981) research, academics have been interested in investigatingwhether the audit service provided by different audit firms could be, in some way,differentiated. Studies have suggested that larger audit firms provide higher qualityauditing because they have more expertise than smaller competitors as they usuallydeal with larger clients from different industries which enhances the level of auditorsskills (OKeefe and Westort, 1992). Bigger audit firms also have more incentives toprovide better auditing as they have a strong brand reputation to maintain given theirlarge base of clients (Dopuch and Simunic, 1980; Francis and Wilson, 1988).

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0268-6902.htm

The author would like to thank Professor Pearse Colbert and Dr Gerard McHugh for their helpand advices, the editors and the editorial assistant of the journal for their support and, inparticular, two anonymous reviewers for their invaluable help, comments and suggestions thatsignificantly improved this paper. All remaining errors are the authors own.

Managerial Auditing JournalVol. 28 No. 8, 2013pp. 680-707q Emerald Group Publishing Limited0268-6902DOI 10.1108/MAJ-11-2012-0784

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Within this group of bigger audit firms, the Big N have been usually consideredseparately by the extant literature and compared with all the other companies thatprovide the same service. This is due to the fact that the group of the Big N has adominant position in the audit market, especially among listed companies. Despiteexceptions, the majority of studies indicate that the Big N provide better auditing thanother audit firms as their clients exhibit, for example, less earnings management thannon-Big N clients (Becker et al., 1998). This better service comes at a cost to the clients;indeed, it has been shown that the Big N exhibit a fee premium in comparison withtheir smaller competitors (Gonthier-Besacier and Schatt, 2007).

A recent study (Lawrence et al., 2011) has made a significant contribution to thistopic. It suggests that differences in audit quality, measured by proxies for clientsearnings quality, between Big and non-Big audit firms might not depend on the type ofauditor but could be the reflection of their respective clients characteristics. Indeed,Big N audit firms serve bigger companies that are under closer media scrutiny; havemore resources to invest in implementing better accounting systems and strongerinternal controls; could appoint highly qualified directors in their audit committees;and can hire highly skilled professionals in their internal audit departments. Thus, theimprovement in the quality of their earnings might be the consequence of these factorsrather than the conduct of the audit by a Big N firm.

Furthermore, replying to a call for evidence from the Economic Affairs Committeeof the UK House of Lords in 2010, some of the interviewees expressed their belief in theexistence of a Big 4 audit premium that related to the oligopolistic structure of the auditmarket rather than to the delivery of a superior service. The report issued as result of thiscall for evidence indicates that some institutional investors believe the (audit) market(in the UK) is not competitive and that most witnesses believe that the dominance ofthe Big 4 limits competition and choice in the audit market (House of Lords, 2010b).

In addition, the possible lack of a relationship between fee premium and better auditquality might also be due to the fact that, after well-known accounting scandals(e.g. Worldcom, Enron, Parmalat), regulatory reforms have led to an improvement in themonitoring of auditor performance in the conduct of audits. The Sarbanes-Oxley act,national regulations and local corporate governance codes set more rigid requirements foraudit firms such as greater emphasis on independence, firm and partner rotation. Theseinstruments, together with the implementation of updated international standards onauditing, might have made the quality of the audit service more homogeneous across auditfirms.

Using a sample of companies listed on the UK stock market and the most recentdata more specifically, observations from annual reports prepared under IFRS(2005-2011) the aim of this paper is to provide evidence on whether the group of theBig N audit firms (Big 4, in this case) exhibits a fee premium and, if this is the case,whether the premium is related to the delivery of a better audit service. Threedeterminants of clients earnings quality are used to measure audit quality: earningsmanagement, accounting conservatism and the value relevance of earnings.

After using two methodologies that control for self-selection bias the Heckman(1976) procedure and propensity-score matching models and controlling for severalfactors that affect the level of the audit fees, findings indicate that the Big 4 do charge afee premium to their clients. This premium is not followed by an improvement in the

Big 4 feepremium and

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level of service provided as multivariate analyses highlights that audit quality is notbetter among Big 4 clients with respect to the three proxies investigated.

Findings from this study have several implications. They support the concerns of theUK House of Lords that the oligopolistic structure of the audit market results inexcessive fees charged by the audit companies that dominate it. Furthermore, theyindicate that these higher fees are not associated with any improvement in the quality ofthe audit service delivered. This might signal the need for legislative intervention aimedat enhancing the competitiveness of the audit market. Results might also increase Big 4client bargaining power at the moment of contracting audit fees as firms would not bewilling to pay a premium to the Big 4, especially if they were aware that it is not followedby an enhancement of the quality of the audit. This would favour smaller audit firmswhich do not charge any premium and provide the same level of audit quality.

The rest of the paper is organized as follows. Section 2 frames the study into theextant literature and presents the research questions. Section 3 describes themethodology used to gather evidence in order to answer the research questions andthe methods employed to control for self-selection bias. It also details the sampleselection procedure. Section 4 discusses the empirical results and, finally, Section 5concludes this research by highlighting its main implications and limitations.

2. Background and research questionsA consistent body of literature in the past investigated whether all audit firms deliver thesame level of audit service. DeAngelo (1981) was one of the first to provide evidenceagainst the assertion made by regulators and small audit firms that audit firm size doesnot affect audit quality. Indeed, she suggests that bigger audit firms have more tolose by failing to report a discovered breach in clients records. Big audit firms candeliver better service as their employees engage in greater degree of specialization, auditteams backgrounds are more extensive and they offer a higher level of continuingprofessional education (OKeefe and Westort, 1992). They also need to maintain theirestablished brand reputation (Francis and Wilson, 1988) and are able to put morepressure on management, given their larger client portfolios (Lys and Watts, 1994).

Several studies have examined the link between bigger audit firms (usually identifiedby the Big N) and audit quality, using measures of clients earnings quality as proxies foraudit quality. Despite some exceptions ( Jeong and Rho, 2004), most previous studies finda direct association between earnings quality and the conduct of audit by a Big N firm.There is evidence that non-Big 6 clients exhibit discretionary accruals that increaseincome relatively more than Big 6 clients do (Becker et al., 1998). In the same way, theconduct of audit by a Big 5 audit firm is associated with lower discretionary accrualsamong companies going for an initial public offering (Chen et al., 2005), as well ascompanies making seasoned equity offerings (Zhou and Elder, 2008). Francis et al. (1999)provide evidence that the presence of the Big 6 mitigates aggressive reporting as theirclients exhibit higher total accruals but lower discretionary accruals. Krishnan (2003)suggests that there is a greater association between discretionary accruals and futureearnings for Big 6 clients in comparison to those audited by non-Big 6 audit firms. Thehigher audit quality provided by the Big N comes at a price. Indeed, evidence indicatesthat these companies charge an audit fee premium, as found in many countries such asthe UK (Ireland and Lennox, 2002), Australia (Craswell et al., 1995), Hong Kong(DeFond et al., 2000), France (Gonthier-Besacier and Schatt, 2007).

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Recently, there has been a school of thought that linked the perception of a feepremium applied by the Big 4, in the UK, to the concentration of the audit marketrather than to the provision of a better audit service, as highlighted by some of therespondents to a call for evidence from the House of Lords in 2010. More specifically,on 27 July 2010, the Economic Affairs Committee of the UK House of Lords (2010a)issued a call for evidence which states that:

[. . .] audit is dominated globally by the Big Four. The narrow field of choice raises concernsabout competition and the quality of audited accounts, and about possible conflict of interestbetween audit and consulting arms.

The attached questionnaire included the following question (number 2): Does a lack ofcompetition mean clients are charged excessive fees? (House of Lords, 2010a). Theevidence from the answers to this question is not straightforward. While some of therespondents state that there is no evidence of higher audit fees charged by the Big 4(House of Lords, 2010c, pp. 6, 34, 56, 62, 65), others believe, instead, that they do chargea premium (House of Lords, 2010c, pp. 2, 28). However, many of the respondents alsoindicate that there is not enough evidence to permit a precise answer to this question,so a study that investigates this further would appear relevant.

In addition, recent studies have shown that the link between audit quality and thepresence of a Big N audit firm is not as straightforward as it might seem, and that thisrelationship might depend on other factors such as the presence of auditors withindustry-related expertise (Francis, 2004) or on client characteristics (Lawrence et al.,2011). As a matter of fact, the latter study, which uses US companies and three proxiesfor audit quality (discretionary accruals, the ex ante cost-of-equity capital and analystforecast accuracy), provides evidence that there is no difference between the presenceof Big 4 and non-Big 4 audit firms with respect to the three proxies investigated.Lawrence et al. indicate that the differences found between Big 4 and non-Big 4 clientslargely reflect client characteristics and, more specifically, size (more differentiated andbigger among Big 4 audit firms).

The findings of US studies cannot be extended to the UK. Indeed, there are severaldifferences in terms of auditing and financial reporting regulation between these twoinstitutional settings. For example, Li et al. (2009), referring to Huijgen and Lubberink(2005), state that the Security Exchange Commission (SEC) in the US engages inproactive reviewing of registrants accounts to penalise companies for earningsmanipulation. This approach is believed to be more influential than that of theFinancial Reporting Review Panel (FRRP) in the UK, which operates in a responsivemode, doing little or no monitoring of its own and only responding to complaints madeto its. Li et al. (2009) recognise also that there is a different level of litigation forauditors in the USA and the UK. They indicate that, because class actions arepermitted in the USA, there is a huge pool of plaintiffs for US auditors. In addition,Hughes and Snyder (1995) state that, as each party is liable for their own legal fees inthe USA, little disincentive exists for instigating legal action against an audit firm. Bycontrast, in the UK the losing party bears all the fees for taking a matter to court andthis can act as a deterrent (Coffee, 1999). Finally, in accordance with Armour et al.(2002) and Li et al. (2009) point out that in the UK litigation risk and frequency oflawsuits against auditors are much lower than the USA especially because of firmdebt structure. They state that UK firms tend to obtain more private loans from banks


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and have more concentrated debt structures than is typical in the US which has a widepublic debt market.

All these differences affect the perceived audit risk in the two institutional settingsdiscussed which might influence both the level of the audit fees charged to clients andaudit strategies, which in turn has an impact on audit quality.

Bearing all these in mind, the main research questions of this study can be stated asfollows:

RQ1. Do Big 4 audit firms charge an audit fee premium to UK listed companies incomparison with their smaller competitors?

RQ2. Is the presence (absence) of an audit fee premium related to the delivery of higher(comparable) audit quality?

3. Methodology3.1 Investigation of the audit fee premiumThe existence of a fee premium among Big 4 clients is investigated using aregression model which relates the amount of audit fees charged by audit firms to theirclients to several control variables, as explained by the following equation (1):

LNFEEit a b1BIG4it b2MBV it b3LEV it b4LOSSit b5SIZEit b6INVRECit b7ROAit b8QUICKRATIOit 1it 1

where:

LNFEEit natural logarithm of audit fees.BIG4it 1 for Big 4 clients and 0 otherwise.MBVit market to book ratio.LEVit leverage measured as total liabilities over total equity.LOSSit 1 when firm i reports a net loss in year t, and 0 otherwise.SIZEit natural logarithm of total assets.INVRECit sum of inventories and receivables divided by total assets of

firm i in year t.

ROAit return on asset calculated as operating profit over total assets.QUICKRATIOit quick ratio of company i in year t.

The variable BIG4 is included to test the existence of an audit fee premium amongthe group of the Big 4.The existence of a premium would be evidenced by a positiveand significant coefficient b1.

Other variables are included to control for additional factors that may affect theamount of the audit fees. As other studies (Choi and Wong, 2007; Choi et al., 2008, 2010)suggest that SIZE, MBV and INVREC are included to control, respectively, for clientsize, growth and complexity. SIZE affects the number of hours needed to complete theaudit, so a positive coefficient related to this variable is expected. Higher fees are

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expected to be sought from firms with higher levels of growth (Reynolds et al., 2004) soa positive b2 is predicted. A positive relation is also expected between fees and thecomplexity of clients business; for this reason a positive and significant b6 should beobserved. Following other research (Simunic, 1980; Seetharaman et al., 2002; Antle et al.,2006), the model also includes the variables LEV, LOSS and QUICKRATIO to measureclient-related litigation risks to be tolerated by auditors and ROA to control foroperating performance. Simunic and Stein (1996) suggest that auditors charge higherfees to risky clients so a positive (negative) coefficient associated with the variablesLEV, LOSS and QUICKRATIO (ROA) is expected.

3.2 Measures of audit qualityThree determinants of clients earnings quality are used as proxies for audit quality:

(1) earnings management;

(2) accounting conservatism; and

(3) value relevance of earnings.

3.2.1 Earnings management. As far as earnings management is concerned, theanalysis focuses on the most widely used measure of this dimension: discretionaryaccruals.

Discretionary accruals are based on abnormal working capital accruals (AWCA)following DeFond and Park (2001) methodology. Kim et al. (2003), have observed thatalthough previous research has widely used discretionary accruals under Jones (1991)model or its other variants, these models have been criticized as the parameterestimates are biased and measurement errors associated therewith could potentiallyinduce erroneous conclusions about the existence of earnings management (Bernardand Skinner, 1996; Guay et al., 1996; Healy, 1996). The methodology used by DeFondand Park (2001) is independent from potential measurement errors associated withJones (1991) model parameters.

DeFond and Park (2001) estimate abnormal working capital accruals (AWCA) asthe difference between the current years realized working capital accruals and theexpected level of working capital accruals, using the following formula:

AWCAit WCit 2 WCit21=Sit21*Sitwhere:

WCit operating working capital. It is calculated as current assets(DataStream/Worldscope code: WC02201) after subtracting cash andcash equivalent (WC02005), less current liabilities (WC03101) net of thecurrent portion of long term debt (WC18232).

Sit net sales or revenue (WC01001).AWCA captures the deviation of the current years working capital accruals from thenormal level of working capital accruals required to support current sales activitiesand is interpreted as an outcome of opportunistic earnings management (Kim et al.,2003). DeFond and Park also find their measure to be a more powerful test incomparison to a test that uses total accruals. The focus on working capital accruals is


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further supported by other research that suggests that management has the mostdiscretion over such accruals (Becker et al., 1998; Ashbaugh et al., 2003).

AWCAs calculated above are divided by total assets (WC02999) to adjust for firmsize. The absolute value of AWCA is used as the focus of the research on earningsmanagement per se rather than income-increasing or income-decreasing decisions. Toremove potential bias due to the presence of outliers, abnormal accruals are winsorisedusing a top 98 per cent winsorisation: all the data above the 98th percentile are set tothe 98th percentile. No actions are taken at the bottom percentiles because abnormalaccruals are considered in their absolute values; consequently, figures very close tozero cannot be seen as outliers.

AWCAs are finally included in the following regression model (2) to control forother factors that might affect earnings management:

AWCAit a b1BIG4it b2CFOit b3LEV it b4SIZEit b5ROAit b6EISSUEit b7DISSUEit b8MBV it b9GROWTHit 1it 2

where:

AWCAit winsorised absolute value of the abnormal working capital accruals.BIG4it 1 for Big 4 clients and 0 otherwise.CFOit cash flow from operations divided by total assets.LEVit leverage measured as total liabilities over total equity.SIZEit natural logarithm of total assets.ROAit return on asset calculated as operating profit over total assets.EISSUEit annual change in shareholder equity.DISSUEit annual change in liabilities.MBVit market to book ratio.GROWTHit annual change in net sales.

The sign and magnitude of the coefficient b1 associated with BIG4 providesinformation about the relation between earnings management and auditor type. Inparticular, a positive (negative) coefficient means that Big 4 audit firms are associatedwith more (less) earnings management and provides evidence of worse (better) auditquality, as it would indicate, ceteris paribus, higher (lower) discretionary accrualsamong Big 4 clients.

A set of control variables is included in the regression to control for other firm-levelfactors that can influence earnings management. Earlier studies have found thatfinancial leverage (LEV) is positively related to earnings management (Dechow et al.,1995). Cash flow from operations (CFO) and return on assets (ROA) are included in themodel to control for extreme performance, which may affect the level of earningsmanagement (Kothari et al., 2005). Growth of the firm can affect the extent of earningsmanagement (Carey and Simnett, 2006); for this reason the variables GROWTH andMBV are introduced. Shan et al. (2011) show that failure to control for changes infinancing can result in significant earnings management measurement errors and

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erroneous inferences. DISSUE and EISSUE are included in the model to control fordebt and equity issuance, respectively. SIZE, measured as the natural logarithm oftotal assets, is included as earnings management is negatively related to firmdimension, which also captures political costs (Bartov et al., 2000; Park and Shin, 2004).

3.2.2 Accounting conservatism. Beekes et al. (2004) describe accountingconservatism as the asymmetric timeliness in earnings between bad and goodnews. They also indicate that:

[. . .] prior research suggests that managers have significant incentives to disclose bad newson a timely basis, for example to protect themselves against potential legal liability (Skinner,1994; Trueman, 1997). Hence, there is an expectation that bad news will be incorporatedquickly into earnings.

Using a sample of UK companies and analysing the effect of outside directors onaccounting conservatism, Beekes et al. (2004) provide evidence that firms that areactively monitored are expected be more conservative and report bad news on an evenmore timely basis. On the other hand, they also expect that management might haveincentives to accelerate the recognition of good news for opportunistic motivations, suchas an increase in their compensation (Healy, 1985). Effective monitoring should alsoreduce this tendency (Beekes et al., 2004). As Big 4 audit firms could be perceived asstronger monitors, similar expectations to those provided by Beekes et al. (2004) can bepredicted.

Following their methodology, an extension of Basu (1997) model is used toinvestigate accounting conservatism. It is represented by the following equation (3):

Def Eit a b1RETit b2NEGit b3RET*NEGit b4BIG4it b5RET*BIG4it b6NEG*BIG4it b7RET*NEG*BIG4it 1it 3

where:

Def (E)it earnings per share scaled by prior year-end price.RETit 12-month raw returns beginning eight months before the fiscal year-end

and ending four months after the year-end.

NEGit 1 if returns are negative and 0 otherwise.BIG4it 1 for Big 4 clients and 0 otherwise.

In accordance with Beekes et al. (2004), under conservative accounting earnings willhave a higher sensitivity to bad news as compared with good news. However, asexplained above the extent of accounting conservatism in accounting earnings mightdiffer depending on the strength of monitors. If it is believed that the Big 4 better monitorclients activities, the latter are expected to incorporate bad news into earnings on a moretimely basis. In model (3), b7 captures the sensitivity to bad news (exclusive of the effectof good news) for Big 4 clients. For this reason, if they are more conservative thannon-Big 4 clients, the coefficient b7 is expected to be positive and significant.

Regarding good news, Beekes et al. (2004) indicate that there is a tendency formanagers to emphasise their availability for their own bonus and promotionalprospects and that firms under stronger monitoring activity might adopt aconservative approach to recording good news in earnings because of the greater


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constraint placed on managers opportunism. On the hypothesis that a Big 4 firmexercises stronger monitoring activity, a more conservative approach to therecognition of good news in earnings is expected among Big 4 clients. In model (3),the timeliness of good news is captured by the coefficient b1 in relation to non-Big 4clients, while the interaction term b5 captures the marginal timeliness effect for Big 4clients. If the former are aggressive reporters, the coefficient b1 will be significantlylarger than (b1 b5), therefore b5 is expected to be negative and significant.

3.2.3 Value relevance of earnings. The third measure of audit quality takes intoaccount the value relevance of net incomes using the methodology developed byOhlson (1995), which models the value of a firm as a function of its book value andearnings:

Pit a b1BV it b2Eit b3BIG4it b4BV*BIG4it b5E*BIG4it 1it 4where:

Pit price of a share for a firm six months after the fiscal year-end.BVit firm book value per share.Eit firm earnings per share.BIG4it 1 for Big 4 clients and 0 otherwise.

This model highlights how the market perceives the information content of netincomes disclosed by Big 4 clients. If earnings included in their financial statements aremore informative than those reported by non-Big 4 clients, the coefficient b5 should bepositive and significant.

Although the variable of interest is the net income of the company, the model alsoincludes the book value per share as its omission could cause model misspecificationproblems (Collins et al., 1999).

3.3 Control for self-selection biasIn all models reported above, Big 4 and non-Big 4 clients are distinguished by adummy variable, BIG4. The way this variable is included in the models assumes that:

[. . .] auditors are randomly allocated to client firms, which rationalizes the inclusion of BIG4as an exogenous variable in the regression. However, it is widely accepted that clientsself-select their auditor and, from an econometric point of view, self-selection might introducebias when using standard OLS (Chaney et al., 2004).

As in many other studies that investigate audit fees and audit quality (Chaney et al.,2004), I control for self-selection bias. I use two methodologies that address this issue:the two-stage model in accordance with the procedure developed by Heckman (1979)and the propensity-score matching models introduced by Rosenbaum and Rubin(1983).

3.3.1 Heckman (1979) procedure. The first control for self-selection bias is based onthe two-stage model following Heckmans (1979) procedure. I will also deal with themain limitations of this methodology, as highlighted in detail by Lennox et al. (2012),later in Section 3.3.1.1.

In the first stage, the following logistic regression equation (5) is used to estimatethe probability of selecting a Big 4 audit firm. In accordance with Chaney et al. (2004),

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the independent variables are proxies for client size, growth, risk and performance andare the same as those used in model (1):

BIG4it a b1MBV it b2LEV it b3LOSSit b4SIZEit b5INVRECit b6ROAit b7QUICKRATIOit 1it

5

Heckmans (1979) methodology requires that the inverse Mills ratio (INVMILLS) fromequation (5) be included in all regression models because it controls for potentialself-selection bias in the second stage. Accordingly, this ratio will be calculated using Stataand added to all models presented thus far. The significance of the coefficient on INVMILLSindicates the importance of controlling for self-selection bias (Chaney et al., 2004).

3.3.1.1 Dealing with the limitations of the two stage Heckman (1979) procedure.A detailed analysis of the two stage Heckman (1979) procedure is presented byLennox et al. (2012). In their paper, the authors report the main weaknesses of thismethodology and, reviewing 75 articles that employ it, they investigate whether andhow these studies dealt with the limitations highlighted.

Among others, Lennox et al. (2012) identify two main problems. First of all, theyexplain that the sign and the significance of the coefficients of the models in the secondstage might be affected by multicollinearity caused by the introduction of the inverseMills ratio as an additional independent variable. They also point out that thisproblem has been often underestimated by the extant literature, as 72 out of the 75studies reviewed do not include any multicollinearity diagnosis and discussion.Second, they indicate that, in the absence of exclusion restrictions, the results dependentirely on the inverse Mills ratio nonlinearity. Accordingly, in their Section VIsuggestions for better implementation of selection models, Lennox et al. (2012)highlight that one of the best papers that uses selection models is that prepared byFeng et al. (2009), which emphasises the importance of having at least one exclusionrestriction, reports diagnostic tests for multicollinearity and investigates whether thefindings are sensitive to alternative model specifications.

Taking all the above into account, I aim to follow these best practices and I report anddiscuss a diagnostic test for multicollinearity by presenting the variance inflation factor(VIF) coefficients for all regression models. Furthermore, I run several sensitivity checks toinvestigate, in particular, whether results are sensitive to any specific exclusion restrictions.

3.3.2 Propensity-score matching models. The second control for self-selection bias isbased on propensity-score matching models developed by Rosenbaum and Rubin(1983). They highlight that:

[. . .] in non-randomized experiments, direct comparisons between treatment and controlgroups may be misleading because the units exposed to one treatment generally differsystematically from the units exposed to the other treatment. Balancing scores, such aspropensity-scores, can be used to group treated and control units so that directcomparisons are more meaningful (Rosenbaum and Rubin, 1983).

Recently, propensity-score matching models have been employed in studies focused onauditing and, more precisely, on the effect of the presence of Big 4 and non-Big 4 auditfirms on particular variables of interest (Boone et al., 2010; Lawrence et al., 2011).Despite their limitations (Lawrence et al., 2011, p. 262), Lawrence et al. (2011) state thatmatching models are appropriate in such studies as they:


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[. . .] generate samples in which the clients of Big 4 and non-Big 4 auditors are similar,providing a natural framework to parse out the effects of auditor and client characteristics onthe variables of interest, such as audit quality proxies.

Lennox et al. (2012) indicate that these models also have the advantage that they do notneed the inverse Mills ratio variable so that the researcher is not required to find avalid set of control variables as it is needed in the first stage regression of Heckman(1979) procedure.

When employing this methodology, I use the logistic model (5) to estimate theprobability of selecting a Big 4 audit firm, as this is the most prevalent approach toestimating propensity-scores (Lawrence et al., 2011). I then match withoutreplacement a Big 4 client with the non-Big 4 client that has the closest predictedvalue from model (5) following the nearest neighbor matching procedure. I finallyestimate models (1)-(4) based on this propensity-score matched sample.

All models included in the paper are estimated using OLS when the dependentvariable is continuous and a logit regression model when the dependent variable isdichotomous. p-values are calculated using t-statistics from robust standard errorsclustered by firm to correct for serial correlation. Models also include year and industrydummy variables. The latter are based on the Industry Classification Benchmark (ICB),an industry classification taxonomy developed by Dow Jones and FTSE.

3.4 Sample selectionThe most recent data on companies listed on the UK stock market (FTSE All-Share) areused for the analyses. The time period covers 2005-2011, choosing as a starting pointthe first year that International Accounting Standards (IFRS) were made mandatoryfor all companies listed on any European market. Firms operating in the banking andfinancial services industries have been excluded. All data used in the analyses has beengathered from DataStream.

After imposing all the necessary requirements to obtain the annual fees andcalculate earnings management regression variables, I obtained 5,663 firm-yearobservations, in which 3,403 are Big 4 clients and 2,260 are non-Big 4 clients. Afteremploying the propensity-score matching model, I obtained a propensity-scorematched sample of 2,260 Big 4 client and 2,260 non-Big 4 client observations, for a totalof 4,520 firm-year observations. Once I imposed all the necessary requirements for thecalculation of the variables included in the accounting conservatism and valuerelevance models I obtained 3,941 firm-year observations, in which 2,760 are Big 4 and1,181 are non-Big 4 clients, because some of the required data was not available. Afteremploying the propensity-score matching model I obtained a propensity-score matchedsample of 1,181 Big 4 client and 1,181 non-Big 4 client observations, for a total of 2,362firm-year observations.

4. Results and discussion4.1 Descriptive statisticsTable I presents the descriptive statistics for audit fees (LNFEE) as well as for theearnings management measure (AWCA) and for all the other variables used in theregression models. They are presented in aggregate (Panel A) and separated betweenBig 4 and non-Big 4 clients (Panel B). In Panel B, tests of differences in means, mediansand standard deviations between the two groups of companies have been also

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n Mean Median SD n Mean Median SD

Panel A: aggregate descriptive statisticsLNFEE 5,663 5.335 5.056 1.707AWCA 5,663 0.107 0.041 0.256Def(E) 3,941 0.104 0.078 0.156CFO 5,663 20.008 0.064 1.024LEV 5,663 1.394 0.884 3.369LOSS 5,663 0.331 0.000 0.471SIZE 5,663 11.258 11.067 2.472INVREC 5,663 0.281 0.247 0.203ROA 5,663 20.022 0.063 0.642QUICKRATIO 5,663 1.857 0.990 3.835EISSUE 5,663 0.236 0.003 1.121DISSUE 5,663 0.247 0.052 0.808MBV 5,663 2.836 1.662 5.412GROWTH 5,663 0.318 0.089 1.165RET 3,941 0.112 0.037 0.654NEG 3,941 0.464 0.000 0.499BV 3,941 1.542 0.851 2.192E 3,941 23.830 12.560 34.328Panel B: Big 4 vs non-Big 4 clients

Big 4 clients Non-Big 4 clients

LNFEE 3,403 6.155 * * * 6.014 * * * 1.615 * * * 2,260 4.100 4.060 0.911AWCA 3,403 0.072 * * * 0.033 * * * 0.175 * * * 2,260 0.159 0.061 0.336Def(E) 2,760 0.096 * * * 0.076 * * * 0.133 * * * 1,181 0.124 0.084 0.198CFO 3,403 0.062 * * * 0.080 * * * 0.209 * * * 2,260 20.114 0.018 1.595LEV 3,403 1.638 * * * 1.098 * * * 3.510 * * * 2,260 1.026 0.601 3.110LOSS 3,403 0.231 * * * 0.000 * * * 0.421 * * * 2,260 0.481 0.000 0.500SIZE 3,403 12.485 * * * 12.332 * * * 2.183 * * * 2,260 9.410 9.453 1.565INVREC 3,403 0.275 * * * 0.244 * 0.195 * * * 2,260 0.290 0.252 0.214ROA 3,403 0.055 * * * 0.076 * * * 0.219 * * * 2,260 20.138 0.019 0.968QUICKRATIO 3,403 1.661 * * * 0.930 * * * 3.861 2,260 2.150 1.115 3.776EISSUE 3,403 0.199 * * * 0.002 * * * 1.046 * * * 2,260 0.293 0.005 1.222DISSUE 3,403 0.221 * * * 0.051 0.716 * * * 2,260 0.288 0.056 0.928MBV 3,403 3.940 1.940 * * * 62.18 2,260 4.970 1.340 63.24GROWTH 3,403 0.261 * * * 0.082 * 1.008 * * * 2,260 0.404 0.106 1.363RET 2,760 0.108 0.059 * * * 0.516 * * * 1,181 0.122 20.011 0.897NEG 2,760 0.446 * * * 0.000 * * * 0.497 * * * 1,181 0.504 1.000 0.500BV 2,760 1.848 * * * 1.074 * * * 2.394 * * * 1,181 0.825 0.432 1.382E 2,760 29.738 * * * 17.210 * * * 38.170 * * * 1,181 10.023 4.840 15.994

Notes: Significant at: *10, * *5 and * * *1 per cent levels (two-tailed) of differences between means, mediansand standard deviations between Big 4 and non-Big 4 clients

Variables definition:LNFEE is the natural logarithm of audit fees; AWCA is the winsorised absolute value of the abnormal workingcapital accruals; Def(E) is the earnings per share scaled by prior year-end price; CFO is the cash flow fromoperations divided by total assets; LEV is leverage measured as total liabilities over total equity; LOSS is 1 if thefirm reports a net loss and 0 otherwise; SIZE is the natural logarithm of total assets; INVREC is the sum ofinventories and receivables divided by total assets; ROA is the return on asset calculated as operating profit overtotal assets; QUICKRATIO is quick ratio of the company; EISSUE is the annual change in shareholder equity;DISSUE is the annual change in liabilities; MBV is the market to book ratio; GROWTH is the annual change in netsales; RET is 12-month raw returns beginning eight months before the fiscal year-end and ending four monthsafter the year-end; NEG is 1 if returns are negative and 0 otherwise; P is the price of a share for a firm six monthsafter the fiscal year-end; BV is the firms book value per share; E is the firms earnings per share

Table I.Descriptive analysis


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carried out. Differences in means have been calculated using a t-test, differences inmedians have been calculated using the Kruskal-Wallis test and differences in thestandard deviations follow Levenes test.

With respect to the descriptive statistics, it is worth noting that the average LNFEEis 5.335 which corresponds to an average audit fee of 207.4 thousand pounds.Abnormal working capital accruals represent around 10 per cent of firms total assets.Panel B of Table I indicates that the Big 4 charge higher audit fees and that their clientsexhibit lower discretionary accruals. However, in accordance with Lawrence et al.(2011), it also highlights that Big 4 clients are significantly different from those auditedby non-Big 4 firms. Big 4 clients are bigger as suggested by the variable SIZE, moreprofitable (higher ROA and lower frequency of LOSS), exhibit higher cash flow fromoperations and leverage and have lower levels of growth.

As all the features described above might potentially affect the level of fees andabnormal accruals, more reliable conclusions can only be made after a multivariateanalysis.

Table II reports a Pearson correlation matrix.The results mainly support the descriptive statistics. The level of fees (abnormal

accruals) is positively (negatively) related with the variable BIG4. However, aspreviously explained, these variables have significant correlations with other factorsthat will be used as control variables in the regression models; for this reason, only amultivariate analysis can provide statistically reliable evidence about the researchquestions. Indeed, the level of audit fees is also positively related with the cash flow fromoperations, leverage, company size and profitability, while it is negatively related to thelevel of current assets (receivables and inventories), quick ratio and the issuance of newfinancing, both in terms of equity and external debt. The level of abnormal accrualsseems higher among firms that report a loss, have higher levels of current assets and areinvolved in either equity or debt issuance. On the other hand, a negative association isfound with company size, leverage and cash flow, the presence of a Big 4 firm and thereturn on assets.

No comments can be made at this stage in relation to the other dimensions of auditquality investigated accounting conservatism and value relevance of earnings which will be analysed in detail when the multivariate analyses are presented.

4.2 Multivariate analyses4.2.1 Probability of selecting a Big 4 audit firm. Table III reports the estimation of model(5). The reason this model is presented is that, first of all, it provides the basis for thecomputation of the variable INVMILLS and for the implementation of thepropensity-score matching model. Furthermore, Lennox et al. (2012) indicate thatone of the problems with the use of Heckman (1979) procedure from previous studies isthe lack of information about the first stage of this methodology. In the followingTable III, the paper also deals with this observation.

The model is highly statistically significant ( p-value 0.000) and exhibits an R 2 of39.7 per cent. As analysis of the determinants of auditor choice is not the aim of thispaper, the discussion of the results is limited and only points out that the findings aremainly in line with the extant literature where they indicate that the choice of a Big 4 firmis associated with client size, growth and complexity (Simunic and Stein, 1987; Hay andDavis, 2004).

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Notes:

Coe

ffici

ent

isst

atis

tica

lly

sig

nifi

can

tat

:* 1

0,*

* 5,

and

**

* 1p

erce

nt

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els

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ette

r,tw

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iled

Vari

abl

esdefi

nit

ion:

LN

FE

Eis

the

nat

ura

llog

arit

hm

ofau

dit

fees

;AW

CA

isth

ew

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rise

dab

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tev

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eof

the

abn

orm

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ork

ing

cap

ital

accr

ual

s;D

ef(E

)is

the

earn

ing

sp

ersh

are

scal

edb

yp

rior

yea

r-en

dp

rice

;BIG

4is

1fo

rB

ig4

clie

nts

and

0ot

her

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e;C

FO

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eca

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sd

ivid

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tala

sset

s;L

EV

isle

ver

age

mea

sure

das

tota

llia

bil

itie

sov

erto

tale

qu

ity

;LO

SS

is1

ifth

efi

rmre

por

tsa

net

loss

and

0ot

her

wis

e;S

IZE

isth

en

atu

rall

ogar

ith

mof

tota

lass

ets;

INV

RE

Cis

the

sum

ofin

ven

tori

esan

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ceiv

able

sd

ivid

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yto

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sset

s;R

OA

isth

ere

turn

onas

set

calc

ula

ted

asop

erat

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pro

fit

over

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ets;

QU

ICK

RA

TIO

isq

uic

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eco

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any

;EIS

SU

Eis

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ann

ual

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old

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y;D

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UE

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liab

ilit

ies;

MB

Vis

the

mar

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ook

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His

the

ann

ual

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ear-

end

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Table II.Correlation matrix

LN

FE

EA

WC

AD

ef(E

)P

BIG

4C

FO

LE

VLO

SS

SIZ

EIN

VR

EC

RO

A

QU

ICK

RA

TIO

EIS

SU

ED

ISS

UE

MB

VG

RO

WT

HR

ET

NE

GB

VE

LN

FE

EA

WC

A-0.190

***

Def

(E)

-0.108

***

0.13

0***

P0.01

5-0.006

-0.006

BIG

40.59

0***

-0.166

***

-0.083

***

0.04

7***

CF

O0.10

6***

-0.234

***

-0.107

***

-0.005

0.08

4***

LE

V0.14

9***

-0.068

***

-0.008

-0.001

0.08

9***

0.03

3**

LOS

S-0.316

***

0.20

5***

0.08

2***

-0.014

-0.261

***

-0.149

***

-0.054

***

SIZ

E0.89

2***

-0.286

***

-0.128

***

0.01

80.60

9***

0.17

3***

0.14

2***

-0.398

***

INV

RE

C-0.040

***

0.03

7***

0.02

20.00

2-0.038

***

0.00

10.05

0***

-0.144

***

-0.113

***

RO

A

0.18

0***

-0.331

***

-0.110

***

0.00

10.14

7***

0.83

4***

0.04

7***

-0.278

***

0.27

9***

0.03

0**

QU

ICK

RA

TIO

-0.183

***

-0.018

0.04

7***

-0.010

-0.062

***

-0.018

-0.099

***

0.16

8***

-0.158

***

-0.177

***

-0.044

***

EIS

SU

E-0.025

*0.05

3***

-0.003

0.06

4***

-0.041

***

-0.018

-0.003

0.10

1***

-0.066

***

-0.066

***

-0.020

0.07

2***

DIS

SU

E-0.059

***

0.07

1***

-0.058

***

0.05

6***

-0.043

***

0.00

5-0.011

0.05

6***

-0.037

***

-0.099

***

-0.013

0.00

50.10

1***

MB

V0.38

7***

0.04

2***

-0.028

*0.00

10.00

8-0.034

***

0.22

2***

0.01

7-0.062

***

-0.007

-0.004

-0.004

-0.001

-0.000

GR

OW

TH

-0.095

***

0.41

7***

-0.012

0.03

5**

-0.060

***

0.00

2-0.040

***

0.11

7***

-0.088

***

-0.116

***

-0.008

0.07

8***

0.11

0***

0.33

0***

0.00

2R

ET

-0.043

***

0.02

10.13

1***

0.02

0-0.010

0.04

0**

0.00

2-0.060

***

-0.048

***

0.04

9***

-0.019

0.01

00.04

3***

0.04

8***

0.00

90.04

8***

NE

G-0.050

***

0.05

5***

-0.051

***

-0.017

-0.053

***

-0.095

***

-0.016

0.10

9***

-0.060

***

-0.011

-0.054

***

0.00

90.02

40.02

8*-0.018

0.03

2**

-0.592

***

BV

0.38

7***

-0.096

***

-0.072

***

0.27

3***

0.21

4***

-0.007

-0.013

-0.049

***

0.46

1***

-0.065

***

-0.003

0.01

8-0.006

0.00

7-0.018

-0.005

-0.044

***

-0.011

E0.34

6***

-0.045

***

0.10

2***

0.28

6***

0.26

3****

0.06

9***

0.09

5***

-0.075

***

0.39

7***

-0.008

0.10

6***

-0.022

-0.024

-0.015

0.00

2-0.010

-0.052

***

-0.008

0.57

9***


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4.2.2 Big 4 audit firms and audit fee premium. Table IV presents the results of model(1) which investigates the existence of an audit fee premium among Big 4 clients. Thetable includes the estimation of the model using standard OLS before any control forself-selection bias (column A) and after a control for self-selection bias using Heckman(1979) procedure (column B) and the propensity-score matched sample (column C). Thisstructure will also be used consistently for the presentation of the other regressionanalyses.

Model (1) is statistically significant ( p-value 0.000 in all cases) with an R 2 thatgoes from 70.2 (column C) to 82.9 per cent (column B).

Column (A) shows a coefficient b1, associated with the variable BIG4, which ispositive and significant at the 1 per cent level (b 0.216; p-value 0.000). This resultis also supported when a control for self-selection bias is introduced using bothHeckman (1979) procedure (b 0.131; p-value 0.000) and the propensity-score

Dependent variable BIG4

INTERCEPT 26.409 * * *

(224.50)MBV 0.001 * * *

(3.58)LEV 20.002

(20.73)LOSS 0.009

(0.18)SIZE 0.599 * * *

(38.26)INVREC 0.070

(0.58)ROA 20.153 * * *

(24.29)QUICKRATIO 0.020 * * *

(3.66)Observations 5,663R 2 0.397F-stat. 3020.52 * * *

year dummies YesIndustries dummies Yes

Notes: Coefficient is statistically significant at: *10, * *5 and * * *1 per cent levels or better; t-statistics(in parentheses below the coefficients) are calculated using standard errors clustered by firm; forclarity, the year-specific and industry-specific intercepts are omittedRegression models:

BIG4it a b1MBV it b2LEV it b3LOSSit b4SIZEit b5INVRECit b6ROAit b7QUICKRATIOit 1it

Variables definition:BIG4 is 1 for Big 4 clients and 0 otherwise; MBV is the market to book ratio; LEV is leverage measured astotal liabilities over total equity; LOSS is 1 if the firm reports a net loss and 0 otherwise; SIZE is thenatural logarithm of total assets; INVREC is the sum of inventories and receivables divided by totalassets; ROA is the return on asset calculated as operating profit over total assets; QUICKRATIO is quickratio of the company

Table III.Probability of selecting aBig 4 audit firm

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Standard OLS

Control for SSB:Heckman (1979)

procedure

Control for SSB:propensity-scorematched sample

(A) (B) (C)Dependent variable LNFEE LNFEE LNFEE

INTERCEPT 22.699 * * * 25.457 * * * 21.928 * * *

(212.77) (214.32) (28.85)BIG4 0.216 * * * 0.131 * * * 0.300 * * *

(4.65) (7.00) (6.41)MBV 0.000 0.001 * * 0.000

(1.27) (2.28) (0.92)LEV 20.000 20.001 * * * 20.000

(20.84) (24.57) (20.38)LOSS 0.216 * * * 0.177 * * * 0.193 * * *

(6.44) (5.70) (6.43)SIZE 0.633 * * * 0.800 * * * 0.548 * * *

(47.06) (32.28) (35.71)INVREC 0.517 * * * 0.511 * * * 0.522 * * *

(3.67) (3.77) (4.71)ROA 20.188 * * * 20.194 * * * 20.144 * * *

(25.26) (29.27) (25.24)QUICKRATIO 20.015 * * * 20.007 20.013

(23.28) (21.50) (21.32)INVMILLS 0.701 * * *

(0.000)Average VIF of the model 1.90 3.79 4.37VIF BIG4 1.66 1.73 1.47VIF INVMILLS 7.25Observations 5,663 5,663 4,520R 2 0.818 0.829 0.702F-stat. 239.24 * * * 275.58 * * * 133.69 * * *

Year dummies Yes Yes YesIndustries dummies Yes Yes Yes

Notes: Coefficient is statistically significant at: *10, * *5 and * * *1 per cent levels or better; t-statistics (inparentheses below the coefficients) are calculated using standard errors clustered by firm; for clarity, theyear-specific and industry-specific intercepts are omittedRegression models:

Col: A and C : LNFEEit a b1BIG4it b2MBVit b3LEV it b4LOSSit b5SIZEit b6INVRECit b7ROAit b8QUICKRATIOit 1it

Col: B : LNFEEit a b1BIG4it b2MBVit b3LEV it b4LOSSit b5SIZEit b6INVRECit b7ROAit b8QUICKRATIOit b9INVMILLSit 1it

Variables definition:LNFEE is the natural logarithm of audit fees; BIG4 is 1 for Big 4 clients and 0 otherwise; MBV is the marketto book ratio; LEV is leverage measured as total liabilities over total equity; LOSS is 1 if the firm reports a netloss and 0 otherwise; SIZE is the natural logarithm of total assets; INVREC is the sum of inventories andreceivables divided by total assets; ROA is the return on asset calculated as operating profit over total assets;QUICKRATIO is quick ratio of the company; INVMILLS is the inverse Mills ratio from equation (5)

Table IV.The investigation of the

audit fee premium


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matched sample (b 0.300; p-value 0.000). This is evidence that the level of auditfees, after controlling for other factors that affect it, is higher among Big 4 clients.

The other control variables are mainly in line with the extant literature. Simunic andStein (1996) suggest that auditors charge higher fees to risky clients and a positive(negative) coefficient is found here associated with the variable LOSS (ROA) both withand without a control for self-selection bias. In accordance with Simunic (1980) andChoi et al. (2008), a positive coefficient, irrespective of the methodology used, isobserved between the level of fees and the size of the client, as this is a proxy for thenumber of hours needed to carry out the audit, and between fees and the complexity ofthe client, measured here by the variable INVREC. Higher fees are expected to besought from firms with higher levels of growth (Reynolds et al., 2004) and, accordingly,a significant coefficient is found between LNFEE and MBV when a control forself-selection bias using Heckman (1979) procedure is employed. Contrary to theexpectations that fees are higher with respect to the level of debt of the company, anegative coefficient is observed between fees and firm leverage under Heckman (1979)procedure, while the same coefficient is not significant in the absence of a control forself-selection bias and when the propensity-score matched sample is employed. Finally,a non-significant relation is found between fees and the variable QUICKRATIO whena control for self-selection bias is introduced.

The estimation of model (1) presented in column (B) is potentially exposed to boththe main limitations of Heckman (1979) procedure presented in Section 3.3.1.1, namelymulticollinearity and exclusion restriction.

In relation to the multicollinearity issue, the relevant VIF are disclosed in Table IV.The average VIF coefficient of model (1) reported in column (B) is 3.79 while thoseassociated to the main variables of interest, BIG4 and INVMILLS, measure 1.73 and7.25, respectively. On the basis of these coefficients, it can be stated thatmulticollinearity does not represent a concern. Indeed, they are all below the criticalvalue of 10. Values above 10 would indicate that multicollinearity significantly affectsthe stability of the parameter estimates (Dielman, 1991). It is also worth pointing outthat the relevant VIF coefficients are lower than the critical value of 10 in columns (A)and (C) of Table IV as well, which provides assurance that multicollinearity does notaffect the reliability of the analysis of the existence of a fee premium among Big 4clients that has been presented thus far.

Exclusion restriction might be another potential problem when a control forself-selection bias on the basis of Heckman (1979) methodology is used. Indeed, theindependent variables of model (1) are the same as those included in the first stageregression of Heckman (1979) procedure. Feng et al. (2009) stress the importance ofhaving at least one exclusion restriction in the second stage to assess the robustness ofthe results. To investigate the robustness of the coefficient b1 reported in column (B) ofTable IV, I excluded, in turn, each of the independent variables included in model (1).The results (untabled) indicate that none of those variables has a significant effect onthe coefficient associated with BIG4 or on its VIF. Indeed, the former always remainspositive and significant at the 1 per cent level ( p-value 0.000 in all cases); the latterfluctuates between 1.28 and 1.73.

To summarise, evidence reported thus far consistently indicates that, in so far asUK listed company audits are concerned, the Big 4 audit firms charge higher fees totheir clients than their smaller competitors.

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4.2.3 Big 4 audit firms and audit quality. Evidence in Table IV indicates theexistence of a Big 4 audit premium. The rest of the analyses investigate whether thisadditional charge is accompanied by an improvement in audit quality.

Table V reports the evidence from the first audit quality proxy: earningsmanagement, and, more specifically discretionary accruals.

The model is statistically robust ( p-value 0.000 both with and without a controlfor self-selection bias) and the R 2 goes from 21.7(column A) to 28.9 per cent (column C).

The sign and significance of the coefficient associated with the variable BIG4 is notconsistent across the three columns. More precisely, column (A), which includes theestimation of model (2) without controlling for self-selection bias, exhibits anon-significant b1 (b 0.004; p-value 0.689). It suggests that earnings managementthrough the use of discretionary accruals is not less pervasive among Big 4 clients thannon-Big 4 clients.

Column (B) of Table V reports the estimation of model (2) after controlling forself-selection bias using Heckman (1979) procedure. The coefficient b1 becomes,surprisingly, positive and significant at the 5 per cent level (b 0.028;p-value 0.020). This would indicate that the use of abnormal accruals is morepervasive among Big 4 clients. The conduct of an audit by a Big 4 firm would beassociated with higher earnings management and, consequently, lower audit quality.However, it is worth pointing out that this result is not corroborated when thepropensity-score matched sample is used (Table V, column C). In the latter case, thecoefficient, consistent with column (A), is not significant (b 0.017; p-value 0.128).It indicates that the evidence from Heckman (1979) methodology does not hold in asample where client characteristics are balanced across the two auditor groupings.This supports the conclusion that Big 4 clients do not exhibit lower levels ofdiscretionary accruals than non-Big 4 clients and, accordingly, audit quality is nothigher among the former group of companies.

When a control for self-selection bias is introduced, results from Table V alsoindicate that discretionary accruals significantly depend on the size of the company(b 20.017; p-value 0.006, when Heckman (1979) procedure is employed;b 20.036; p-value 0.000, when the propensity-score matched sample is used),as smaller firms are less monitored and exhibit more earnings management(Bartov et al., 2000; Park and Shin, 2004). The use of abnormal accruals is also morepervasive among less profitable companies and higher growth firms (Carey andSimnett, 2006), as indicated by the negative and significant coefficient between AWCAand ROA (b 20.128; p-value 0.000, when Heckman (1979) procedure isintroduced; b 20.127; p-value 0.000, when the propensity-score matchedsample is used), and by the positive and significant coefficient between AWCA andGROWTH (b 0.001; p-value 0.023, under Heckman (1979) procedure; b 0.004;p-value 0.000, when the propensity-score matched sample is employed).

The evidence provided here is not affected by multicollinearity as the relevant VIFcoefficients reported in Table V are all below the critical value of 10. The exclusionrestriction problem, which might arise when Heckman (1979) procedure is employed, isnot a concern in the analysis of audit quality as the independent variables of models(2)-(4) are not exactly the same as those used in the first stage regression of theselection model.


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Standard OLS


procedure


(A) (B) (C)Dependent variable AWCA AWCA AWCA

INTERCEPT 0.420 * * * 20.212 * 0.534 * * *

(13.73) (21.92) (5.62)BIG4 0.004 0.028 * * 0.017

(0.40) (2.32) (1.52)CFO 0.023 * 0.017 0.022

(1.70) (1.34) (1.59)LEV 0.000 * * * 0.000 0.000 * * *

(3.12) (1.22) (4.84)SIZE 20.023 * * * 20.017 * * * 20.036 * * *

(25.92) (22.77) (25.06)ROA 20.137 * * * 20.128 * * * 20.127 * * *

(23.62) (23.94) (23.48)EISSUE 0.000 0.000 20.000

(0.78) (0.70) (20.74)DISSUE 20.000 * * * 20.000 20.000

(22.80) (20.22) (20.30)MBV 0.000 0.000 * * * 0.000

(1.47) (3.36) (1.11)GROWTH 0.001 * * 0.001 * * 0.004 * * *

(2.31) (2.33) (5.69)INVMILLS 0.166 * * *

(4.56)Average VIF of the model 3.45 3.45 4.28VIF BIG4 1.66 1.73 1.47VIF INVMILLS 7.07Observations 5,663 5,663 4,520R 2 0.217 0.244 0.289F-stat. 11.30 * * * 11.64 * * * 9.68 * * *

Year dummies Yes Yes YesIndustry dummies Yes Yes Yes

Notes: Coefficient is statistically significant at: *10, * *5 and * * *1 per cent levels or better; t-statistics (inparentheses below the coefficients) are calculated using standard errors clustered by firm; for clarity, the year-specific and industry-specific intercepts are omittedRegression models:

Col A and C : AWCAit a b1BIG4it b2CFOit b3LEVit b4SIZEit b5ROAit b6EISSUEit b7DISSUEit b8MBVit b9GROWTHit 1it

Col B : AWCAit a b1BIG4it b2CFOit b3LEV it b4SIZEit b5ROAit b6EISSUEit b7DISSUEit b8MBV it b9GROWTHit b10INVMILLSit 1it

Variables definition:AWCA is the winsorised absolute value of the abnormal working capital accruals; BIG4 is 1 for Big 4 clients and0 otherwise; CFO is the cash flow from operations divided by total assets; LEV is leverage measured as totalliabilities over total equity; SIZE is the natural logarithm of total assets; ROA is the return on asset calculatedas operating profit over total assets; EISSUE is the annual change in shareholder equity; DISSUE is the annualchange in liabilities; MBV is the market to book ratio; GROWTH is the annual change in net sales; INVMILLS isthe inverse Mills ratio from equation (5)

Table V.Big 4 audit firms andaudit quality: earningsmanagement

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Overall, the evidence in Table V indicates that the conduct of audit by a Big 4 auditfirm is not associated with better audit quality, as measured by less earningsmanagement. Thus, the significant relation between discretionary accruals and type ofauditor, highlighted in the descriptive analysis and correlation table, is notcorroborated here. The results support those of Lawrence et al. (2011): as earningsmanagement is affected by several firm-level factors, and given that the characteristicsof Big 4 and non-Big 4 clients are significantly different, differences in audit qualityproxies reflect these different features. Indeed, the factors that, on the basis of Table V,are strongly associated with lower abnormal accruals bigger size, higherprofitability, lower levels of growth are exactly some of the main characteristics ofBig 4 clients.

Findings from the accounting conservatism dimension are reported in Table VI.The model is always statistically significant ( p-value 0.000) and the R 2

fluctuates from 4.3 (column C) to 6.9 per cent (column B).Column (A) presents the estimation of model (3) without any control for self-selection

bias. The coefficient b1 is positive and significant at the 1 per cent level (b 0.032;p-value 0.002), indicating that, on average, companies report good news on a timelybasis. The insignificant b3 (b 20.053; p-value 0.177) suggests that firms in thesample do not have a higher propensity to report bad news as compared with good news.The interaction terms RET*BIG4 and RET*NEG*BIG4 are both not significant. Theformer (b 0.010; p-value 0.535) indicates that there is no incremental improvementin the speed of incorporating good news into earnings among Big 4 clients, while thelatter (b 0.017; p-value 0.674) suggests that the conduct of an audit by a Big 4 firmdoes not influence the speed of recognition of bad news either.

All evidence provided thus far holds also when either Heckman (1979) methodologyor the propensity-score matched sample is employed. Indeed, the inferences from thecoefficients presented above remains exactly the same as that reported in the previousparagraph if columns (B) and (C) of Table VI are taken into account.

The results reported in Table VI are not affected by multicollinearity problems sincethe relevant VIF coefficients are, in all cases, lower than the critical value of 10.

Finally, Table VII reports the findings related to the last measure of audit quality:the value relevance of earnings.

The statistical significance of the model is never in question ( p-value 0.000) andits R 2 goes from 50.9 (column C) to 67.5 per cent (column A).

As expected, the coefficients b2 and b3 are, in all columns, positive and significantas market prices are directly sensitive to the earnings and book value of companies.

For the purposes of this paper, the focus in this table is on the coefficient b5, whichsignals whether the market interprets earnings disclosed by Big 4 clients to be moreinformative than those presented by other firms.

In column (A), which reports the estimation of model (4) without any control forself-selection bias,b5 is not significant (b 0.289; p-value 0.726). It provides evidencethat the market does not perceive earnings reported by Big 4 clients to be moreinformative than those presented by non-Big 4 clients. However, this coefficient is notstatistically robust not only because a control for self-selection bias has not been carriedout, but also because its associated VIF (471.26) highlights serious multicollinearityissues.


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StandardOLS

Control for SSB: Heckman (1979)procedure

Control for SSB:propensity-score matched

sample(A) (B) (C)

Dependent variable Def(E) Def(E) Def(E)

INTERCEPT 0.086 * * * 0.050 * * * 0.063 * * *

(6.46) (3.39) (5.21)RET 0.032 * * * 0.028 * * * 0.031 * * *

(3.06) (2.62) (2.90)NEG 20.007 20.011 20.007

(20.45) (20.72) (20.47)RET*NEG 20.053 20.046 20.049

(21.35) (21.19) (21.22)BIG4 20.035 * * * 20.006 20.015

(23.23) (20.47) (21.18)RET*BIG4 0.010 0.010 20.007

(0.62) (0.57) ( 2 0.37)NEG*BIG4 0.023 0.010 0.022

(1.25) (0.57) (0.88)RET*NEG*BIG4 0.017 0.021 0.070

(0.42) (0.51) (1.41)INVMILLS 0.041 * * *

(3.25)Average VIF of themodel

4.80 4.70 5.71

VIF RET*BIG4 3.51 3.51 2.91VIF RET*NEG* BIG4 7.45 7.46 5.62VIF INVMILLS 1.76Observations 3,941 3,941 2,362R 2 0.057 0.069 0.043F-stat. 10.16 * * * 9.93 * * * 6.81 * * *


Notes: Coefficient is statistically significant at: *10, * *5 and * * *1 per cent levels or better; t-statistics (inparentheses below the coefficients) are calculated using standard errors clustered by firm; for clarity, theyear-specific and industry-specific intercepts are omittedRegression models:

Col A and C : Def Eit a b1RETit b2NEGit b3RET*NEGit b4BIG4it b5RET*BIG4it b6NEG*BIG4it b7RET*NEG*BIG4it 1it

Col B : Def Eit a b1RETit b2NEGit b3RET*NEGit b4BIG4it b5RET*BIG4it b6NEG*BIG4it b7RET*NEG*BIG4it b8INVMILLSit 1it

Variables definition:Def(E) is the earnings per share scaled by prior year-end price; RET is 12-month raw returns beginning eightmonths before the fiscal year-end and ending four months after the year-end; NEG is 1 if returns are negativeand 0 otherwise; BIG4 is 1 for Big 4 clients and 0 otherwise; INVMILLS is the inverse Mills ratio fromequation (5)

Table VI.Big 4 audit firms andaudit quality: accountingconservatism

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Column (B) of Table VII includes the estimation of the same model (4) with a control forself-selection bias based on Heckman (1979) methodology. Under this scenario, b5becomes positive and significant (b 6.068; p-value 0.000). This would seem tocontradict the previous finding and would suggest that earnings disclosed by Big 4clients are perceived to be more informative by the market. On the other hand,additional evidence in the same column questions the statistical robustness of thiscoefficient. First of all, its disproportionate VIF (417.35) highlights significantmulticollinearity problems. Furthermore, the coefficient associated to INVMILLS is not

Standard OLS


procedure


(A) (B) (C)Dependent variable P P P

INTERCEPT 811.51 * * * 51.617 281.787 * * *

(2.58) (0.29) (2.60)BV 0.903 * * 108.826 * * 56.889 * * *

(2.01) (2.22) (3.44)E 2.382 * * * 2.043 * * * 2.255 * * *

(11.97) (7.57) (19.89)BIG4 66.824 * * 230.056 23.557

(2.19) (20.16) (1.18)BV*BIG4 93.812 * * * 174.303 28.951

(5.26) (1.25) (1.04)E*BIG4 0.289 6.068 * * * 1.757

(0.35) (13.53) (1.08)INVMILLS 393.182

(1.15)Average VIF of the model 48.72 45.85 5.35VIF E*BIG4 417.26 417.35 1.95VIF INVMILLS 1.88Observations 3,941 3,941 2,362R 2 0.675 0.605 0.509F-stat. 72.65 * * * 123.32 * * * 50.91 * * *


Notes: Coefficient is statistically significant at: *10, * *5 and * * *1 per cent levels or better; t-statistics(in parentheses below the coefficients) are calculated using standard errors clustered by firm; forclarity, the year-specific and industry-specific intercepts are omittedRegression models:

Col A and C : Pit a b1BVit b2Eit b3BIG4it b4BV*BIG4it b5E*BIG4it 1it

Col B : Pit a b1BV it b2Eit b3BIG4it b4BV*BIG4it b 5E*BIG4it b6INVMILLSit 1it

Variables definition:P is the price of a share for a firm six months after the fiscal year-end; BV is the firms book value pershare; E is the firms earnings per share; BIG4 is 1 for Big 4 clients and 0 otherwise; INVMILLS is theinverse Mills ratio from equation (5)

Table VII.Big 4 audit firms and

audit quality: valuerelevance of earnings


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significant (b 393.182; p-value 0.250). This is evidence that a control forself-selection bias, using Heckman (1979) procedure, is not effective in this particularcase and that OLS that ignore self-selection (i.e. OLS that do not include the variableINVMILLS) do not pose a problem with the self-selection bias (Krishnan et al., 2008). Itleads back to the inference made on the basis of column (A) of Table VII which still hasthe multicollinearity problem.

All these issues are eventually overcome in column (C) of Table VII, which reportsthe estimation of model (4) using the propensity-score matched sample. Under thisscenario, b5 is not significant (b 1.757; p-value 0.282), supporting the view thatthe market does not perceive that earnings disclosed by Big 4 clients are moreinformative than those reported by non-Big 4 clients. In addition, the coefficient is nowstatistically robust since it is not affected by multicollinearity problems as itsassociated VIF (1.95) is lower than the critical value of 10.

Finally, in relation to the value relevance of the book value, while its informationcontent seems higher among Big 4 clients without a control for self-selection bias(b 93.812; p-value 0.000), its VIF coefficient (19.70, untabled) does not make itsuitable for a reliable inference. Once the propensity-score matched sample is used, thesame coefficient becomes non-significant (b 28.951; p-value 0.299) andstatistically robust (VIF 1.71, untabled).

To summarize, multivariate analyses support the concerns raised by the UK Houseof Lords, as they indicate that the concentrated structure of the audit market in the UKresults in higher fees for Big 4 clients. In addition, the analyses consistently suggestthat this is happening without any improvement in the level of audit quality providedby the Big 4, at least with respect to the three proxies investigated.

4.3 Robustness testsTo increase the reliability of the findings presented above, some robustness tests(untabled) have been carried out. The relation between type of auditors and auditquality has been repeated using other dependent variables and/or models with andwithout a control for self-selection bias. More precisely, the absolute values ofincome-increasing and income-decreasing abnormal accruals are used separately inmodel (2). Results are consistent with those reported in the main analyses: when I usethe propensity-score matched sample, the relation between the level of abnormalaccruals and the conduct of audits by Big 4 firms is not significant in any circumstance(b 0.015; p-value 0.355, when income-increasing abnormal accruals are used;b 0.019; p-value 0.138, when income-decreasing abnormal accruals are used). Thesame coefficients are also not significant without a control for self-selection bias(b 0.005; p-value 0.588 and b 0.000; p-value 0.983, respectively). Nomulticollinearity problems are observed.

The analysis of audit quality using the propensity-score matching model has beenrepeated by estimating propensity-scores using an extended version of equation (5)where, in accordance with Lawrence et al. (2011), I included additional independentvariables; more precisely, those variables used in the respective audit qualityregressions. Evidence is unchanged.

Finally, another model for the estimation of accounting conservatism, namely theaccruals-cash flow model developed by Ball and Shivakumar (2005), is also used.Evidence consistently shows no differences between Big 4 and non-Big 4 clients as the

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relevant coefficient from that model is not significant either with a control forself-selection bias (b 20.275; p-value 0.912, when Heckman (1979) procedure isemployed; b 0.283; p-value 0.914 when the propensity-score matched sample isused) or without (b 0.241; p-value 0.924). No multicollinearity issues are observedin this case as well.

5. ConclusionsInspired by the concerns raised by the UK House of Lords that the oligopolisticstructure of the UK audit market might lead to excessive fees for Big 4 clients, theaim of this study is the empirical investigation of the existence of a Big 4 audit feepremium and, in that case, whether it is related to the delivery of a better quality audit.

Using the most recent data (2005-2011) from non-financial companies listed in theUK and controlling for self-selection bias, results reveal that Big 4 audit firms docharge their clients an audit fee premium. This premium, however, is not related tothe delivery of a superior audit service as measured using three proxies for clientsearnings quality discretionary accruals, accounting conservatism and valuerelevance of earnings. Indeed, multivariate regressions indicate that Big 4 audit firms,ceteris paribus, do not reduce the use of abnormal accruals and do not improve thespeed of recognition of both good and bad news in earnings. Furthermore, the financialmarket does not perceive earnings disclosed by Big 4 clients to be more informativethan those reported by non-Big 4 clients.

The overall evidence of the paper supports the concerns expressed by the EconomicAffairs Committee of the UK House of Lords and by some respondents to their call forevidence that the concentrated structure of the audit market results in excessiveaudit fees, at least for Big 4 clients, to such an extent that a legislative interventioncould be even required (House of Lords, 2010c). This intervention might also be moreurgent as the findings of this research reveal that these higher audit fees are notaccompanied by an enhancement of the quality of the service provided.

This evidence might also be of interest to Big 4 clients in the event of a renegotiationof the audit fees as it may improve their bargaining power. Big 4 clients would not beready to pay a premium especially if they were aware that it does not result in anyincremental benefit in terms of audit quality. They could be then more willing to hire anon-Big 4 firm, paying lower fees without suffering a decrease in the level of the auditservice received. This situation would consequently decrease the concentration of theaudit market, which is a big concern for many countries, such as the UK, and wouldimprove the overall level of auditing and the price/quality relationship.

This research is not free from limitations. Although the measures employed areamong those commonly used in the literature focused on audit quality, at the sametime, they are not direct measures of this phenomenon as they do not take directly intoaccount factors such as audit hours, audit strategy deficiencies, audit team skills andindependence, etc. For these reasons, the findings reported are not conclusive butrather suggestive in that they carry out comparisons in audit quality among differenttypes of auditors. Second, while the paper explores audit quality using several and themost appropriate dimensions of earnings quality, many other proxies have beendeveloped in the academic literature to analyse the same topic that, if employed, mightchange the evidence here reported. Finally, findings are based on only one country, theUK. This may affect the generalizability of the reported results.


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Future research could extend this analysis to other countries to verify whether Big 4audit firms charge higher fees without providing higher levels of audit quality indifferent institutional settings.

References

Antle, R., Gordon, E.A., Narayanamoorthy, G. and Zhou, L. (2006), The joint determination ofaudit fees, non-audit fees and abnormal accruals, Review of Quantitative Finance andAccounting, Vol. 27 No. 3, pp. 235-266.

Armour, J., Cheffins, B.R. and Skeel, D.A. (2002), Corporate ownership structure and theevolution of bankruptcy law: lessons from the United Kingdom, Vanderbilt Law Review,Vol. 55 No. 6, pp. 1699-1785.

Ashbaugh, H., LaFond, R. and Mayhew, B. (2003), Do non-audit services compromise auditorindependence? Further evidence, The Accounting Review, Vol. 78 No. 3, pp. 611-639.

Ball, R. and Shivakumar, L. (2005), Earnings quality in UK private firms: comparative lossrecognition timeliness, Journal of Accounting and Economics, Vol. 39 No. 1, pp. 83-128.

Bartov, E., Gul, F. and Tsui, J. (2000), Discretionary accruals model and audit qualification,Journal of Accounting and Economics, Vol. 30 No. 3, pp. 421-452.

Basu, S. (1997), The conservatism principle and the asymmetric timeliness of earnings, Journalof Accounting and Economics, Vol. 24 No. 1, pp. 3-37.

Becker, C.L., DeFond, M.L., Jiambalvo, J. and Subramanyam, K.R. (1998), The effect of auditquality on earnings management, Contemporary Accounting Research, Vol. 15 No. 1,pp. 1-24.

Beekes, W., Pope, P. and Young, S. (2004), The link between earnings timeliness, earningsconservatism and board composition: evidence from the UK, Corporate Governance:An International Review, Vol. 12 No. 1, pp. 47-59.

Bernard, V. and Skinner, D.J. (1996), What motivates managers choice of discretionaryaccruals?, Journal of Accounting and Economics, Vol. 22 Nos 1-3, pp. 313-325.

Boone, J.P., Khurana, I.K. and Raman, K.K. (2010), Do the Big 4 and the second-tier firms provideaudits of similar quality?, Journal of Accounting & Public Policy, Vol. 29 No. 4, pp. 330-352.

Carey, P. and Simnett, R. (2006), Audit partner tenure and audit quality, The AccountingReview, Vol. 81 No. 3, pp. 653-676.

Chaney, P.K., Jeter, D.C. and Shivakumar, L. (2004), Self-selection of auditors and au

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