Audit Quality and Office Size: Investigation of Large Grant Thornton Audit Offices in Chicago and New York

Transcription

1 Audit Quality and Office Size: Investigation of Large Grant Thornton Audit Offices in Chicago and New York Dick van Rosmalen

2 Audit Quality and Office Size: Investigation of Large Grant Thornton Audit Offices in Chicago and New York Master Thesis Department Accountancy, Faculty of Economics and Business Studies, Tilburg University Name: Dick van Rosmalen Student number: Supervisor: Dr. S. Hollander RA Date:

3 Preface Audit quality and office size: Investigation of large Grant Thornton audit offices in Chicago and New York is a thesis written as the completion of my Master Accounting at Tilburg University. Especially, I would like to thank my supervisor Stephan Hollander for his support during the process of writing this thesis. With his enthusiasm, useful comments and advice he guided me through the process. I also want to thank my family and friends for their trust and support during the last six months. September 25, 2012 Dick van Rosmalen 3

4 Abstract Prior literature indicates that audit quality differences between Big 4 auditors and especially Second-tier auditors are diminishing. Also, the audit office is regarded as a more appropriate unit of analysis than the whole audit firm. In this study audit quality differences between audit offices of Grant Thornton, Big 4, BDO Seidman, and Third-tier audit offices are examined within two cities, Chicago and New York. In these cities, Grant Thornton has audit offices that are similar or even larger in size than Big 4 audit offices. Office size is measured by the number of SEC registrant clients per office. A large number of these clients do not specify audit fees. Most studies exclude these observations when measuring the number of clients, but since a separate audit opinion is given, and a separate audit report is written, it is hard to argue that the clients that do not specify audit fees are not actual clients of the audit office. Audit quality is proxied by means of the first principal component, extracted from three different measures of discretionary accruals. No significant coefficients are found in the regressions for Chicago and New York. This is probably due to the small sample sizes. It is therefore hard to say anything about audit quality differences between the Big 4, Grant Thornton, BDO Seidman, and Third-tier audit offices. Further research on this topic, with other data or variables will be necessary. 4

5 Table of Contents Preface... 3 Abstract Introduction Theory & hypotheses Audit quality Reputation Local audit office Audit quality of Grant Thornton in Chicago and New York Hypotheses Research method Audit office size Audit quality Different measures of audit quality Model descriptions Regression models Samples and descriptive statistics Audit office size sample Samples Descriptive statistics Audit quality samples Samples Principle component analysis Descriptive statistics Results Results of univariate tests Results of multivariate tests Sensitivity tests Conclusions Conclusions Caveats Suggestions for further research References Appendix

6 1. Introduction Motivated by the emergence of large international audit firms besides the established Big 4 audit firms 1, the so-called Second-tier audit firms, Boone, Khurana, & Raman (2010) compare the audit quality of Big 4 and these Second-tier audit firms, Grant Thornton and BDO Seidman, in the US. These are the fifth and sixth largest audit firms in the world, respectively. Although previous literature recognizes that Big 4 audit firms provide audits of higher quality, there are several reasons why the audit quality of these two Second-tier audit firms is similar to that of Big 4 audit firms nowadays. The next chapter will elaborate on these reasons. Most important is that because of the growth of Second-tier firms and regulatory changes, audit quality differences between Big 4 and Second-tier firms are diminishing. This notion is supported by a number of empirical studies (Whisenant, 2006; Boone et al., 2010; Chang, Cheng & Reichelt, 2010). Also quite recently, another stream of accounting literature argues that audit quality should be assessed at the office/city level instead of assessing it at the firm/national level. The most important argument here is that local offices of Big 4 audit firms are semi-autonomous and most of the important decisions on the specific audit are made by the local office (Narayanan, 1995; Wallman, 1996; Francis, 2004; Francis, Reichelt, & Wang, 2005). Empirical studies that evaluate audit quality at the office level find significant differences between local offices of audit firms and stress the importance of the office/city level analysis (Basioudis & Francis, 2007; Gaver & Paterson, 2007; Francis & Yu, 2009; Choi, Kim, Kim, & Zang, 2010). This study combines the two streams of literature that state that audit quality differences are diminishing and that analysis at the office level is probably more accurate. When measured by the number of SEC registrant clients, the audit offices of Grant Thornton in the two cities Chicago and New York are similar or even larger in size when compared to Big 4 audit offices in the same cities. As a consequence, this provides an even more convincing setting in which audit quality of Big 4 audit firms and a Second-tier audit firm will be similar. The research question addressed in this study is whether audit quality differences still exist at the office level in Chicago and New York, where Grant Thornton offices are similar or even larger in size than Big 4 audit offices. The audit quality of these two Grant Thornton offices is also compared to that of the other Second-tier audit office, BDO, and the Third-tier audit offices 2 in the same cities to determine the position of Grant Thornton in the local audit markets in Chicago and New York. Audit office size is measured by the number of SEC registrant clients per audit office. Unlike previous studies, observations without a reported audit fee in Audit Analytics are included. Based on 1 In this paper, Big 4 also refers to previously Big 5, 6, or 8, depending on the year. The current Big 4 audit firms are Deloitte & Touche, Ernst & Young, KPMG, and PwC. 2 All audit offices other than the Big 4 and Second-tier audit offices. 6

7 this measure, the Grant Thornton offices in Chicago and New York are the second and third largest audit offices in the US for the period Audit quality is measured by discretionary accruals. Since there are different metrics for estimating discretionary accruals, that all have measurement error, this study combines three different metrics. Using principal component analysis, the performance-adjusted modified Jones model (Reichelt & Wang, 2010), the McNichols model (2002), and the DeFond & Park model (2001) are reduced to one parsimonious measure. This reduces the measurement error associated with estimating discretionary accruals. First, a regression is estimated with all available data in Audit Analytics and Compustat for the US. So, all local audit offices in the US with one or more SEC registrant clients are taken. This is done to determine whether there still exist audit quality differences between the Big 4 audit offices, Second-tier audit offices, and Third-tier audit offices as a group. Then, separate regressions are estimated for Chicago and New York to answer the research question and to determine the position of Grant Thornton in the local audit markets in Chicago and New York. No consistent results are obtained from these regressions. The variables of interest do not show significant coefficients. For the city level analysis, this is probably due to the small sample sizes. The remainder of the paper is structured as follows. The next chapter discusses the previous literature on this topic and states the hypotheses. The third section contains the research method. Chapter four provides an overview of the sample selection and shows descriptive statistics of the sample. Chapter five presents the results. In the last part of the paper conclusions are drawn, limitations are discussed and some suggestions for further research are mentioned. 7

8 2. Theory & hypotheses This chapter elaborates on the development of the research question and hypotheses. The first paragraph discusses the literature on audit quality in relation to the audit firm. The second paragraph considers the importance of reputation. The third paragraph discusses the literature on the local audit office. The fourth paragraph discusses the reasons why audit quality of Big 4 audit offices and Grant Thornton offices in the cities Chicago and New York will be similar. The last paragraph contains the hypotheses. 2.1 Audit quality Francis (2011) argues that audit quality is a concept that is complex and cannot be defined in one simple way. Audit quality is affected by many factors at many different levels. He recognizes that the audit firm is an important unit of analysis. Audit quality is mostly determined by the resources of the audit firm and the incentives the audit firm has to perform audits of high quality. The two most suggested incentives for the audit firm to perform high-quality audits are the potential reputation loss audit firms might suffer and potential litigation costs. A large body of literature focuses on the difference in audit quality between Big 4 audit firms and non-big 4 audit firms. Francis (2004) provides an overview of the many studies on this issue. These indicate that Big 4 audit firms provide audits of higher quality than non-big 4 audit firms. Compared to non-big 4 audit firms, Big 4 audit firms earn a fee premium (Moizer, 1997; Hay, Knechel, & Wong, 2006; Choi, Kim, Liu, & Simunic, 2008), have lower litigation activity (Palmrose, 1988; Feroz, Park, & Pastena, 1991), have more disagreements with clients about accounting practices (McConnell, 1984; DeFond & Jiambalvo, 1993), set lower planning materiality (Blokdijk, Drieenhuizen, Simunic, & Stein, 2003), exert more audit effort (Caramanis & Lennox, 2008), issue more accurate audit reports (Lennox, 1999a), are more conservative (Francis & Krishnan, 1999), and are better able to constrain discretionary accruals (Becker, DeFond, Jiambalvo, & Subramanyam, 1998; Francis, Maydew, & Sparks, 1999). This is often called the Big N/non-Big N dichotomy. Whether the higher audit quality of Big 4 audit firms is attributable to the reputation loss hypothesis or to the litigation avoidance hypothesis is not entirely clear. Big 4 audit firms both have the strongest reputation and the largest litigation costs, so both theories predict that larger audit firms will deliver audits of higher quality. In addition, litigation is likely to damage the reputation of the auditor and, the other way around, events that damage the reputation of the auditor can lead to litigation costs. Research that tries to separate these two effects find mixed results. Lennox (1999b) and Khurana & Raman (2004) suggest that audit quality is driven by potential litigation costs rather than by reputation loss concerns, while Datar & Alles (1999) show that when litigation concerns decrease, reputation still provides sufficient incentive to maintain audit quality. Other studies 8

9 conclude that it is hard to separate these two effects (Baber, Kumar, & Verghese, 1995; Franz, Crawford, & Johnson, 1998; Weber, Willenborg, & Zhang, 2008; Gao, Jamal, Liu, & Luo, 2011; Murase, Numata, & Takeda, 2011; Skinner & Srinivasan, 2012). 2.2 Reputation One of the most used definitions of audit quality is developed by DeAngelo (1981): audit quality is the joint probability that an auditor both (1) detects a breach in the accounting system of the client, and (2) reports this breach. The probability that an auditor detects a breach is dependent on the knowledge and competence of the auditor, the procedures that have to be followed, the technological capabilities of the auditor etcetera. The probability that the auditor also reports a detected breach is conditional upon the independence of the auditor with regard to the client. Independence is thus of particular importance to the audit firm. The auditor needs to be independent to express a reliable opinion about the fairness and reliability of the financial statements that are issued by the client, because otherwise the opinion of the auditor would be of no value to investors and other stakeholders. Based on the definition developed by DeAngelo (1981), the two drivers of reputation are perceptions of competence and independence. Francis (1984) states that audits are, in effect, a market for reputations (p. 134). Watts & Zimmerman (1981) discuss the evolvement of the audit profession and suggest that large audit firms were able to establish their own brand name and that this serves as a signal of competence and independence. They conclude that this brand name (i.e. reputation) introduces an incentive for audit firms to be independent. Also in later studies, loss of reputation is observed as an important mechanism that supplies auditors with an incentive to maintain independence (Watts & Zimmerman, 1983; Antle, 1984). Because loss of reputation can lead to economic losses, which in the case of audit firm are a loss of clients and litigation costs, firms protect their reputation by maintaining product and service quality (Klein & Leffler, 1981; Shapiro, 1983). Reputation is thus important for retaining current clients. In addition, it is an appropriate mechanism for attracting new clients, and recruiting and retaining good employees (Khurana & Raman, 2004). Dopuch & Simunic (1982) and Moizer (1997) argue that, since the details of audits are not observable, audit quality must be associated with indirect measures that can be observed. They propose that the name of the audit firm then is an important item. There is a lot of empirical evidence consistent with the notion that reputation is an important driver of audit quality. This will be discussed below. Firth (1990) and Franz et al. (1998) test the impact of information damaging the auditor s reputation and find that the stock price of clients of criticized auditors is negatively affected. Investors seem to be concerned about the credibility of the financial statements of the auditor s 9

10 clients. Firth (1990) also shows that criticized auditors lose clients and market share. Initial public offerings are often carried out by a Big 4 audit firm because the reputation of the auditor increases the value (Titman & Trueman, 1986; Beatty, 1989; Datar, Feltham, & Hughes, 1991). Teoh & Wong (1993) and Francis & Ke (2006) show that the earnings response coefficient is greater for companies audited by Big 4 firms. However, it declines when information damaging the audit firm s reputation becomes public (Moreland, 1995). The reputation of the audit firm is thus important and introduces incentives to maintain their service quality. Datar & Alles (1999) take a different perspective and investigate the auditor s reputation with the client s manager. They argue that the inherent risk of the audit 3 is reduced by reputation for deterring misreporting. This allows the auditor to reduce substantive testing, without increasing the probability of biased reports. They show that the auditor has a strong incentive to maintain his reputation for high audit quality, even if the auditor has less to fear from litigation. Thus, Big 4 audit firms have stronger incentives to provide audits of high quality. A study in New-Zealand provides evidence that even just the change of name of an audit firm to that of a Big 4 auditor generated a fee premium (Firth, 1993). According to Mayhew (2000), reputation serves as an endogenous mechanism that generates high audit effort, which in turn leads to high audit quality. He shows in an experimental market that auditors that have a reputation for highquality audits continue to supply high audit effort in order to maintain their reputation. However, a follow-up experiment of Mayhew, Schatzberg, & Sevcik (2001) concludes that when there is uncertainty about the appropriate accounting treatment for a transaction, reputation alone is not sufficient to mitigate impairment of objectivity by auditors. Several studies which investigate the stock market reactions of firms that switch auditors, suggest that these switches are generally viewed as unbeneficial (i.e. reactions are negative). This is probably caused by significant start-up costs for a new auditor. However, these studies also suggest that a switch from a non-big 4 to a Big 4 audit firm is viewed more favorably than a switch from Big 4 to non-big 4 or a switch within the Big 4 (Moizer, 1997). Krishnan (1994) defines firms that switch from non-big 4 to Big 4 firms as movers in search of credibility (p. 211), implying that Big 4 firms provide more credibility. There are some studies that look at auditor changes around accounting scandals to investigate the importance of reputation to an audit firm. Chaney & Philipich (2002) show that clients of Arthur Andersen experienced significant negative market reactions after the Enron fraud came out. They attribute this to the damaged reputation of Arthur Andersen. Nelson, Price, & Rountree (2008) assess this study and document significant confounding effects. They conclude that there is no evidence of a reputation effect. However, several other studies investigating this event provide evidence that there is a significant effect of auditor reputation and independence on 3 The inherent risk of an audit is the risk that the financial statements contain material misstatements due to error or fraud, before considering the effectiveness of internal controls (Arens, Elder, & Beasley, 2012). 10

11 perceived audit quality (Barton, 2005; Krishnamurthy, Zhou, & Zhou, 2006). Studies of accounting scandals and related auditor changes in different countries also provide evidence consistent with the view that auditor reputation is valued by the market (Weber et al. 2008; Gao et al., 2011; Murase et al., 2011; Skinner & Srinivasan, 2012). These latter studies are all conducted in relative low litigation countries, because in the US it is hard to separate litigation and reputation effects. As discussed earlier, in the US the largest audit firms both have the strongest reputations and the largest litigation incentives. Although these latter results might not be generalizable to the US, it appears that auditor reputation does matter. The next paragraph discusses the importance of the local audit office as a unit of analysis. The last paragraph provides different arguments why audit quality of Big 4 and Second-tier offices similar in size, in the same city, will be the same. Although not expected, if results show that the Big 4 audit offices in the same city still provide audits of higher quality, this can probably be attributed to reputation (i.e. brand name). 2.3 Local audit office More recent research focuses on the local audit office instead of the whole audit firm as a unit of analysis for several reasons. First, Big 4 audit firms are decentralized organizations and local offices are semi-autonomous in their decisions about the audit of a specific client (Narayanan, 1995; Wallman, 1996; Francis, 2004; Francis et al., 2005). The local office contracts the client, executes and monitors the audit and issues the audit report with the name of the local office on the letterhead (Reynolds & Francis, 2001; Ferguson, Francis, & Stokes, 2003). Second, large offices have more experience, more and more competent colleagues and advisors, and a better business network than small offices. These are conditions that enhance audit quality (Francis & Yu, 2009). Third, expertise is only partially transferrable between different offices of the same firm, and auditor expertise and client-specific knowledge is tied to the individual professional. This knowledge and expertise is hard to capture and not easily distributed among other offices (Vera-Muñoz, Ho, & Chow, 2006; Reichelt & Wang, 2010). Fourth, to perform the audit, auditors are required to go to the client and to investigate documents and interview personnel at the client s location. Therefore, audit services are less transportable than goods or services in other industries, such as manufacturing. This implies that local markets are a more appropriate unit of analysis for the accounting industry (Penno & Walther, 1996). Empirical evidence is generally consistent with this arguing. Francis, Stokes, & Anderson (1999) show that, although Big 4 audit firms are market leader nationally, Big 4 audit firms in 70% of the cases are not market leader at the city level. As market leadership enhances the visibility and reputation of a firm, it will influence audit quality. 11

12 Reynolds & Francis (2001) are the first to investigate audit quality at the office level and find that larger clients have lower discretionary accruals. Audit offices treat relative larger clients more conservative. On the contrary, Chung & Kallapur (2003) show that the size of the client does not influence the magnitude of discretionary accruals. Gaver & Paterson (2007) investigate the accuracy and bias in accounting estimates of losses on outstanding claims of insurance companies at the office level and show that there are no significant differences between Big 4 and non-big 4 auditors. They highlight the importance of the office-level analysis. These findings cast doubt on the view that larger auditors perform audits of higher quality. However, an important limitation of the last study is that it only focuses on insurance companies and thus lacks generalizability. Most other studies at the office level document a significant effect of office size. Basioudis & Francis (2007) observe a strong effect of office level reputation on audit fees in the UK. The effect on audit fees of office level reputation is even larger than the effect of firm level reputation. Ferguson et al. (2003) and Francis et al. (2005) investigate the effect of auditor industry expertise on audit fees at the national and at the local level. Both studies find that there is only an effect on audit fees when the audit firm is industry expert at both the national and the local level. There is also evidence that within the Big 4, audit quality differs dependent on the size of the audit office. Clients of larger offices report less discretionary accruals and larger offices are more likely to issue a going-concern (Francis & Yu, 2009). Larger offices thus deliver audits of higher quality. Reichelt & Wang (2010) show that clients of Big 4 audit offices report less discretionary accruals than clients of non-big 4 audit offices. Choi et al. (2010) investigate non- Big 4 offices as well as Big 4 offices. They show in line with Reichelt & Wang (2010) that audit quality, measured by the magnitude of discretionary accruals, is higher for larger offices. In addition, they show that audit fees are higher for larger offices, which also implies a higher audit quality for larger offices. In their regression analysis of discretionary accruals, the dummy variable for Big 4 audit firms is significantly negative. This is evidence supporting the view that Big 4 audit offices, as a group, provide audits of higher quality than non-big 4 audit offices. 2.4 Audit quality of Grant Thornton in Chicago and New York Regulators and interest groups promoted the two Second-tier audit firms, Grant Thornton and BDO Seidman, as a viable alternative to the Big 4 audit firms, arguing that the audit quality of these firms is very similar to that of Big 4 audit firms (American Assembly, 2005; SEC, 2005; PCAOB, 2006; US Chamber of Commerce, 2006). There are several reasons why the audit quality of Big 4 and Second-tier audit firms will not differ nowadays. First, the Second-tier firm that is examined in this study, Grant Thornton, is the fifth largest audit firm internationally and in the US. In size, it is comparable to the Big 8 audit firms studied in the 1980s (Boone et al., 2010). Second, it has also developed an international and national 12

13 reputation for high-quality audits. The incentives to maintain independence and audit quality, reputation loss and litigation costs, thus might be similar to that of Big 4 audit firms (Boone et al., 2010). Third, the International Federation of Accountants (IFAC) is primarily founded by the Big 4 audit firms and two Second-tier audit firms (Carson, 2009). Together, these six firms also recommended changes in auditing and financial reporting standards (DiPiazza, Rake, McDonnell, Samyn, Parrett, & Turley, 2006). Apparently, the Big 4 audit firms considered these two Second-tier firms as valuable partners and they present themselves as one group. This could indicate that the differences between the Big 4 and Second-tier audit firms are limited. Fourth, investors view a smaller difference in audit quality between Big 4 and non-big 4 audit firms because of the involvement of Big 4 audit firms in the accounting scandals of the early 2000s (Chang et al., 2010). Fifth, recent regulatory changes, as the Sarbanes Oxley Act (SOX) of 2002, have increased the incentives for both the client and the auditor to provide financial reports of high quality (Nelson, 2006; Chang et al., 2010). This can lead to a more uniform audit quality in general. Opposed to these arguments, Grant Thornton, the largest Second-tier audit firm, is still significantly smaller than the smallest Big 4 audit firm in terms of revenues, number of partners, employees, and public company clients (GAO, 2008; US Department of the Treasury, 2008). As a consequence, Grant Thornton has less invested capital and smaller quasi-rents than Big 4 audit firms. Big 4 audit firms thus still have potentially larger litigation costs because of their superior financial resources and are assumed to be more independent because of its larger quasi-rents. Boone et al. (2010) examine data from and find that there is very little difference in actual audit quality, but that the perceived audit quality is higher for Big 4 audit firms. Chang, et al. (2010) investigate auditor switches before and after the implementation of SOX. They document relatively less negative market reactions to switches from a Big 4 audit firm to a non-big 4 audit firm after the implementation of SOX. Also quite recently, Whisenant (2006) reports that the stock market response is non-negative when firms switch from a Big 4 audit firm to Grant Thornton. A shortcoming of these studies is that they ignore the previously discussed stream of literature that suggests that analysis at the audit office level is probably more accurate than an analysis at the audit firm level (Francis, 2004). In Chicago and New York, a cleaner test on reputation can be done by comparing the largest offices of Second-tier audit firms to offices of Big 4 audit firms. At the audit office level, Big 4 and Second-tier audit firms are obviously more similar in size than at the firm level. Therefore, it can be argued that the reputation loss and litigation cost incentives are also very similar for these offices. So, ceteris paribus, the only difference here is the brand name of the audit office. When measured by number of SEC registrant clients, the Grant Thornton Chicago office is suitable for the years and the Grant Thornton New York office is suitable for the years Therefore, this 13

14 study will examine the audit quality of the two audit offices of Grant Thornton, and the audit quality of Big 4 audit offices in Chicago and New York. To determine the position of Grant Thornton in these two local markets, the audit quality of the two Grant Thornton offices is also compared to that of the other Second-tier office, BDO Seidman, and to that of Third-tier audit offices. 2.5 Hypotheses As discussed above, a large body of evidence indicates that the Big 4 audit firms, as a group, deliver audits of higher quality than non-big 4 audit firms. However, this study argues that more recently the brand name effect of Big 4 audit firms is diminishing, in particular when compared to a Second-tier audit firm at the city level in Chicago and New York, with offices similar in size. Therefore, hypothesis H 1 is as follows: H 1 : Audit quality of the Grant Thornton office in Chicago and New York is not different from the audit quality delivered by a Big 4 audit office in the same city. When comparing the audit quality of Grant Thornton and the other Second-tier firm, BDO, in the same city, the argument of similar office size is not applicable anymore. The Grant Thornton offices in Chicago and New York have many more SEC registrant clients than BDO offices in the same cities. Although in general the two Second-tier firms are expected to deliver similar audit quality, for Chicago and New York it is expected to differ because of the larger office size of the Grant Thornton offices. Consistent with the previous literature discussed in paragraph 2.3, larger offices are expected to deliver audits of higher quality. Therefore, hypothesis H 2 is as follows: H 2 : Audit quality of the Grant Thornton office in Chicago and New York is higher than the audit quality delivered by a BDO Seidman office in the same city. Previous research indicates that because of reputation loss and litigation cost incentives the audit quality of Big 4 and Second-tier firms is higher than that of Third-tier firms. In addition, the two Grant Thornton offices in Chicago and New York are much larger than the Third-tier offices. Therefore, hypothesis H 3 is as follows: H 3 : Audit quality of the Grant Thornton office in Chicago and New York is higher than the audit quality delivered by a Third-tier audit office in the same city. 14

15 3. Research method To test the hypotheses, different kinds of information are needed. All audit offices in the US have to be identified. Then, the largest audit offices of Grant Thornton have to be determined and the Big 4 audit offices and offices of other audit firms in the same cities have to be identified. In addition, the delivered audit quality by that specific offices must be determined. The size of an office and the quality of the audits provided by the different audit offices can be measured in many different ways. Some methods that could be used for this study are discussed in this chapter. The methods that are chosen for this study will be based on this discussion and will be explained in detail. 3.1 Audit office size First, the different local audit offices need to be identified and the size of these offices has to be determined. There are several ways to measure the size of an audit office. It can be measured by number of clients (Francis & Yu, 2009; Choi et al., 2010) or total revenues (Craswell, Stokes, & Laughton, 2002; Francis & Yu, 2009; Choi et al., 2010). The total revenues for an audit office can also be measured in two ways. Besides the audit, audit offices may provide additional services to clients. Examples of these services are tax advice, comfort letters, or statutory audits. An audit office thus has two revenue streams, audit fees and non-audit fees. Office size therefore can be measured by determining only the audit fees or by determining the total fees, audit fees plus non-audit fees. Total assets are also an accepted way to measure size in general. These are easy to determine. However, in a service industry as the audit industry, total assets do not fully capture the size of an office. In addition, total assets can be determined in different ways by different offices and measurement of intangible assets is hard. This would introduce bias. Choi et al. (2010) estimate the size of a local audit office by using the sum of audit fees a local office earned from SEC registrant clients as well as the number of SEC registrant clients. Their results are similar for both measures. Francis & Yu (2009) also determine office size by aggregating audit fees received from SEC registrant clients. In additional analyses they use total fees and number of SEC registrant clients as alternative measures. Their results are robust to the use of the first, but not to the use of the latter. This is however due to the large number of ties in their data. That will not be a problem in this research, since only a limited number of offices will be studied that almost all have a different number of clients. In addition, audit fees are affected by client characteristics as size, complexity, and risk and these are related to audit quality. Although a model can control for these factors, there could be a problem with omitted correlated variables (Francis & Yu, 2009). Further, obtaining audit fee data requires the use of another section of Audit Analytics. The database is split in different parts and the audit fee cannot be drawn from the same part of the database as the opinion of the local audit office. So to get the audit fees of a local office two datasets 15

16 have to be merged 4. Because of the absence of fee data for a large number of audit opinions, many observations must be eliminated. For example, the addition of fee data reduces the number of observations for Grant Thornton New York in the year 2010 from 1059 to 23 and for Deloitte & Touche Chicago from 982 to 101. Many studies exclude observations without fee data, but it is hard to argue that the, respectively, remaining 1039 and 881 are not clients of the audit offices of Grant Thornton New York and Deloitte & Touche Chicago. Most of these clients are trust funds or securities portfolios. For example, a client of Grant Thornton New York is Van Kampen Unit Trusts, Municipal Series According to the audit report, this unit trust is comprised of two income trusts. These unit trust series continue to the number 700, and there are other series of these unit trusts, so it is obvious that this concerns a lot of clients. The reports of these clients that are filed with the SEC specify several fees, but do not specify the audit fee. Auditing expenses are captured under the sum of operating expenses of the trust 6. So when audit fee data is extracted from Audit Analytics, these clients are nowhere to be found. There is also no aggregate audit fee available for these clients. However, a separate audit opinion is given and a separate audit report is written for these clients, so auditors worked on this and the client also must have paid for this. In case the audit office fails to detect an error or fraud and issues a wrong audit report, the audit office risks losing all of these clients. Excluding all of these observations leads to a completely different picture of audit office size. Therefore, this study uses the number of SEC registrant clients audited by a local office as a proxy for office size, irrespective of the availability of audit fees. The database Audit Analytics is used to identify the local audit offices. This study assumes that in each city there is only one office of an audit firm, because Audit Analytics does not allow to identify different offices of the same audit firm in the same city. To determine the size of the office, the total number of SEC registrant clients are counted for each office. In the remainder of the paper, number of clients means number of SEC registrant clients. 3.2 Audit quality As evident from the literature discussed in the previous chapter, there is no one method to measure audit quality. Therefore, different measures of audit quality are discussed first and the 4 After the process of data collection, the Audit Analytics database (accessed by using the Wharton Research Data Services) was updated. It is now possible to match the audit opinion given by the local firm to the SEC registrant client with the total fees and audit fees in the fiscal year of interest. 5 Unit trusts are collective investment schemes. A large number of investors pool their money and a fund manager invests this in different types of stocks and securities. All unit trusts have an own investment objective and strategy. Profits are proportionally divided among the investors. More information about these trusts can be found at and in the book of Sin (1998). 6 See 16

17 chosen method is motivated. Then, the different models that will be used in this research are discussed one by one Different measures of audit quality A first method used to measure audit quality is the number of restatements a firm has to make in the issued financial statements. A major drawback of this method is that there are only a limited total number of restatements each year, so this data is quantitatively limited. In addition, databases that contain this information often contain restatements that are immaterial and are economically insignificant (Dechow, Ge, & Schrand, 2010). A measure that captures audit quality for sure is the number of fraud cases. However, this number is even more limited than the number of restatements. Another measure closely related to this is how often the firm is sued by various stakeholders. Lawsuits are most of the time caused by a (significant) restatement that the client has to make, or are caused by fraud. DeFond & Jiambalvo (1993) take a different point of view and look at the number of disagreements between the auditor and the client. When firms switch auditors, the SEC requires to disclose disagreements. More disagreements would indicate a more independent auditor and thus a higher audit quality. For a period of four years, they are able to analyze only 40 firms. Because this research looks at just a few audit offices, data about these four measures will be too limited. Some studies use the issue of a modified opinion as a proxy for audit quality. However, because modified audit opinions are only given in relative extreme situations, they do not differentiate audit quality for a broad cross-section of firms (Myers, Myers, & Omer, 2003). In addition, because of the low variability in audit opinions, statistical power will be reduced (Choi et al., 2010). Audit quality can also be measured by the degree of conservatism. Under the conservatism principle, bad news (e.g. losses) has to be reported earlier or faster than good news. More conservative financial reporting implies higher audit quality (Dechow, 1994; Basu, 1997). Ball & Shivakumar (2006) integrate the conservatism principle in an accruals model, which is also used by Choi et al. (2010). This model is complex and results of Choi et al. (2010) show that this metric does not lead to different results than somewhat easier models, like the Jones model (1991). An indirect measure of audit quality is audit fees. Higher fees imply higher quality audits. However, evidence on this area is not inconclusive and fees are dependent on many different factors (Dechow et al., 2010). Also, as indicated in the previous paragraph, audit fee data are not available for a lot of clients. Choi et al. (2010) use both the magnitude of discretionary accruals and audit fees. The results obtained for both metrics are similar. This research will focus on audit quality and not on (effects on) audit fees. The discretionary accruals are that part of accruals that are assumed to be managed by managers. To determine earnings quality or earnings management, many studies measure the 17

18 magnitude of discretionary accruals. Two recent studies document a direct link between audit quality and earnings quality measures (Francis, 2011). Earnings quality and earnings management are closely related and can both be measured by means of the magnitude of discretionary accruals (Dechow & Dichev, 2002). When a firm has high discretionary accruals, this means that management has a lot of discretion in determining the earnings number and so can manage or manipulate this number. Financial statements of firms that manage earnings (i.e. have high discretionary accruals), have a lower quality (Frankel, Johnson, & Nelson, 2002). Managers have to estimate future results, like provisions for non-paying debtors and depreciation, to determine the current earnings number. These estimations are subjective by nature and can only partly be verified by auditors before they occur. Therefore, auditors try to limit the use of discretionary accruals as much as possible to enhance earnings quality (Francis & Krishnan, 1999). Consequently, auditors of firms with low discretionary accruals deliver higher quality audits than auditors of firms with high discretionary accruals Model descriptions The most established and used methods of estimating discretionary accruals are the Jones model (1991), modified Jones model (Dechow, Sloan, & Sweeney, 1995), the performance-matched Jones model (Kothari, Leone, & Wasley, 2005) and a model developed by Dechow & Dichev (2002). Dechow et al. (1995) evaluate different models to estimate discretionary accruals and conclude that their modified version of the Jones model (1991) is the most accurate one. Later, Kothari et al. (2005) improved this model by adding a performance measure to the regression. Two more recent studies by Jones, Krishnan, & Melendrez (2008) and Galle (2009) evaluate the models that were proposed after Dechow et al. (1995). They both conclude that the Dechow & Dichev model (2002) and the McNichols model (2002) yield the best results when measuring earnings management. The McNichols model (2002) is a modification of the Dechow & Dichev model (2002). The McNichols model (2002) will be discussed below. However, all of these methods require a lot of data. For each two-digit SIC industry, there is a minimum of ten observations. Because in this research only a few audit offices are studied, this requirement cannot be met. Therefore, the regressions are estimated using all observations. This introduces some measurement error. To alleviate the measurement error concern, three metrics are computed and the first principal component is used as the dependent variable. The three metrics are the performance-adjusted modified Jones model (Reichelt & Wang, 2010), the McNichols model (2002), and a simpler model developed by DeFond & Park (2001). This last model does not have the requirement of a minimum of ten observations per industry. Of all metrics, the absolute value is taken because managers have different incentives in different circumstances. Earnings are managed 18

19 upwards or downwards, depending on the particular situation the manager is in (Healy, 1985; Warfield, Wild, & Wild, 1995; Levitt, 1998; Kasznik, 1999). In additional analysis signed accruals are also examined. The first metric to estimate the discretionary accruals is the performance-adjusted modified Jones model (Reichelt & Wang, 2010). A regression is estimated and the coefficients of the regression are used to calculate the expected accruals. The discretionary accruals are the difference between the total accruals and the expected accruals. As explained earlier, the discretionary component is the part of accruals that is assumed to be managed. The larger the discretionary accruals, the lower the audit quality. The model looks as follows: where: TA = β, + β REV + β PPE + β ROA, + ε (1) TA = total accruals for company i in year t divided by total assets (AT) 7 at the end of year t-1. Total accruals are calculated as earnings before extraordinary items and discontinued operations (IB) minus operating cash flows from continuing operations (CFO). A, = total assets for company i at the end of year t-1. REV = change in revenue (SALE) from prior year, for company i in year t divided by total assets at the end of year t-1. PPE = gross value of property, plant, and equipment (PPEGT) for company i in year t divided by total assets at the end of year t-1. ROA, = return on assets for company i in year t-1,, calculated as net income (NI) divided by average total assets. ε = error term. The coefficient estimates of equation (1) are used to estimate the expected total accruals, with an adjustment for the change in accounts receivable: where: ETA = β, + β ( REV REC ) + β PPE + β ROA, (2) ETA = expected total accruals for company i in year t. β to β = estimated coefficients from equation (1). REC = change in accounts receivable (RECT) from prior year, for company i in year t. 7 The Compustat variable name is in parentheses. 19

20 Discretionary accruals is the difference between total accruals and expected total accruals: DACC = TA ETA (3) The second method relates the total accruals to cash flows from the previous year, the current year, and the next year. They measure to what extent the realized cash flows match the total accruals. Accruals are used to shift or adjust the recognition of revenues and costs from one year to another. This is done to better reflect the performance of the firm (Dechow, 1994). The realized cash flows that offset these accruals are assumed to happen within a certain period. Therefore, the poorer the match between the realized cash flows and the total accruals, the lower the accrual quality and, consequently, the audit quality. The second metric is the absolute value of the residual of the McNichols model (2002). Following Jones et al. (2008), all variables are scaled by total assets at the beginning of the year. The model looks as follows: WC = b + b CFO, + b CFO + b CFO, + b REV + b PPE + ε (5) where: WC = change in working capital from prior year, for company i in year t. Calculated as the change in accounts receivable (RECCH), plus the change in inventory (INVCH), plus the change in accounts payable (APALCH), plus the change in taxes payable (TXACH), plus the change in other current assets (AOLOCH), multiplied by 1. CFO = cash flow from continuing operations for company i in year t, calculated as the net operating cash flow (OANCF) minus the cash flow from extraordinary items and discontinued operations (XIDOC). REV = change in revenue (SALE) from prior year, for company i in year t divided by total assets at the end of year t-1. PPE = gross value of property, plant, and equipment (PPEGT) for company i in year t divided by total assets at the end of year t-1. ε = error term. The third and last method used to determine the magnitude of the discretionary accruals is the model developed by DeFond & Park (2001). This model is simpler because it does not require a regression. The discretionary accruals are determined through working capital. The difference between realized working capital and expected working capital measures the magnitude of the discretionary accruals. The original model uses quarterly data and signed discretionary accruals. However, following other studies, this research will use yearly data and unsigned (i.e. absolute) discretionary accruals (Kim et al., 2003; Carey & Simnett, 2006). This is done because discretionary 20

21 accruals can be positive as well as negative. The closer to zero, the higher the audit quality. The model looks as follows: Where: DACC = WC WC, S S, DACC = discretionary accruals for company i in year t. WC = working capital (excluding cash) for company i in year t, calculated as (current assets cash and short-term investments) (current liabilities short-term debt). 8 S = sales for company i in year t. Respectively, the three measures are referred to as DACC MJ, DACC MN, en DACC DP. Then, following Bharath, Sunder, & Sunder (2008) these three metrics are reduced to one parsimonious measure using Principal Component Analysis (PCA). The first principal component from this analysis is named audit quality (AQ). Originally, I tried to include the Dechow & Dichev model (2002) instead of the DeFond & Park model (2001). However, after necessary truncation, the Dechow & Dichev model (2002) experienced a significant drop in correlation with the other two metrics. Therefore, it was not possible to retain this model in the principal component analysis. Because the other two models did not show that much sensitivity to truncation, I decided to include the DeFond & Park model (2001). This model is also insensitive to truncation. In additional analysis, another method to estimate discretionary accruals is examined and a model with going concern reports as the dependent variable is estimated. These will be discussed later on. 3.3 Regression models To come to a proper conclusion about the effect of brand name on audit quality, regression models are developed and different regressions are estimated. First, a regression is estimated with all available data in Audit Analytics and Compustat for the US to determine whether there still exist audit quality differences between the Big 4 audit offices, Second-tier audit offices, and Third-tier audit offices as a group. So, all local audit offices in the US with one or more SEC registrant client are taken in this regression. Then, for Chicago and New York separate regressions are estimated. The dependent variable is the first principal component retrieved from the PCA analysis. Several control variables need to be added that also influence the magnitude of the discretionary accruals. The regression models are shown per hypothesis. The regression model with all available data in Audit Analytics and Compustat is shown in the next chapter. 8 Respectively, the Compustat variable names are ACT, CHE, LCT, and DLC. 21

22 The first hypothesis is concerned with the audit quality of the Big 4 offices and the Grant Thornton offices in Chicago and New York. The variable of interest is thus GT. This is a dummy variable that equals 1 for an audit office of Grant Thornton, and 0 for a Big 4 audit office. The full regression model including the control variables looks as follows: AQ = α + α GT +α INDSPEC + α LNTA + α CHGSALE + α MB + α LOSS + α BANKRUPTCY + α DEBT + α ISSUE + α CFO + α TENURE + α VOLATILITY + α LAGGACC + ε All variables are as defined in table 3.1, for company i in year t. The second hypothesis compares the audit quality of the other Second-tier audit offices, BDO, and the Grant Thornton offices in Chicago and New York. The variable of interest is again GT. It is a dummy variable that equals 1 for an audit office of Grant Thornton, and 0 for a BDO Seidman audit office. The control variables are taken together for brevity. This regression model looks as follows: AQ = β + β GT + Controlvariables + ε The third and last hypothesis examines the audit quality of Third-tier audit offices and the Grant Thornton offices in Chicago and New York. The variable of interest is GT. Again, this is a dummy variable that equals 1 for an audit office of Grant Thornton, and 0 for a Third-tier audit office. Also here, the control variables are taken together for brevity. This regression model looks as follows: AQ = γ + γ GT + Controlvariables + ε In all models, AQ is the audit quality measure. Consistent with hypothesis H 1, the coefficient on GT in the first regression is expected to be zero. Consistent with hypotheses H 2 and H 3, negative signs are expected for GT in the second and third regression. Several control variables are added that were found to be significant in other studies. Industry specialism of the audit office enhances audit quality because the audit office has more knowledge of the business of the industry. Therefore, the auditor will be better able to judge the financial statements of the client (Balsam, Krishnan, & Yang, 2003). Reichelt & Wang (2010) show that firms with a city-specific industry specialist auditor have lower abnormal accruals. INDSPEC is a dummy variable that, equals 1 for city-specific industry leaders, 0 otherwise. A negative coefficient is expected for this variable. LNTA is the log of total assets and controls for the size of the client. Larger clients more often have higher earnings quality (Becker et al., 1998), and larger clients are treated more conservatively by auditors (Reynolds & Francis, 2001; Francis & Yu, 2009). Therefore, a negative coefficient is expected. Following Francis & Yu (2009) and Choi et al. (2010), the change in sales (CHGSALE) and the market-to-book ratio (MB) are included to control for firm growth. Consistent 22