Doctorat HEC. Spécialisation : Information, Comptabilité, Contrôle et Organisation RESEARCH WORKSHOP. Thursday 7 May :00 15:30

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1 Doctorat HEC Spécialisation : Information, Comptabilité, Contrôle et Organisation RESEARCH WORKSHOP Thursday 7 May :00 15:30 Paper: Office Size of Big 4 Auditors and Client Restatements Presenter: Jere R. FRANCIS (University of Missouri-Columbia) Professor in charge of the seminar: Cédric LESAGE This document cannot be used without the agreement of the author

2 Office Size of Big 4 Auditors and Client Restatements* by Jere R. Francis School of Accountancy University of Missouri-Columbia Columbia, MO 65211, USA and Michael D. Yu Department of Accounting Washington State University Pullman, WA 99164, USA Current Draft: April 27, 2009 Version 6.1 *This study is supported by a grant from the PwC INQuires research program of PricewaterhouseCoopers. We appreciate comments on earlier versions of the study presented at the Conference on Corporate Governance and Fraud at George Mason University, the annual meeting of the American Accounting Association, and in workshops at University of Auckland, Bond University, University of Colorado, Indiana University, University of Melbourne, University of Missouri, University of Oregon, Tilburg University, Singapore Management University, and Yale University. We especially thank Paul Brockman, Inder Khurana, Elaine Mauldin, Gary Peters, Raynolde Pereira, Kenny Reynolds, Phil Shane, Mike Stein, Stephen Taylor, and Marlene Willekens for their insightful comments and suggestions.

3 Office Size of Big 4 Auditors and Client Restatements Abstract Big 4 audits of SEC registrants are predicted to be of higher quality when the engagement office administering the audit is of larger size. We argue that larger offices have greater collective inhouse SEC experience and therefore more expertise in conducting such audits. To test this conjecture, we analyze 2,039 restatements by clients of 333 unique Big 4 offices in the United States. A client restatement is suggestive that the auditor did not enforce the correct application of GAAP at the time the original financial statements were issued and therefore that the audit was of low quality. As predicted Big 4 office size is negatively associated with client restatements after controlling for innate client characteristics that may affect restatements (client size, financial performance, industry membership, non-financial measures, off-balance sheet activities, and market-related measures), and a set of controls for other auditor effects such as fee levels and industry expertise. The study has implications for regulators and for accounting firms with respect to audit quality, and raises important questions about the ability of smaller offices to deliver high-quality audits for SEC registrants.

4 Office Size of Big 4 Auditors and Client Restatements 1. Introduction The broad research question posed by this study is whether Big 4 accounting firms can achieve uniform audit quality across their practice offices, particularly for smaller offices that have relatively few SEC registrants as audit clients. Francis and Yu (2009) argue that larger Big 4 offices provide higher quality audits than smaller offices due to greater in-house experience and expertise in the audits of SEC registrants. It turns out there is enormous variation in Big 4 office size based on our sample from Audit Analytics, a commercial data base with comprehensive audit-related data for SEC registrants. 1 The median number of SEC registrants for a Big 4 office in our study is 11 clients (see Table 3). The largest practice office exceeds 300 clients for each Big 4 firm, and the largest individual office is the Chicago office of Deloitte which audited 763 SEC registrants in At the other extreme, 65 individual Big 4 practice offices (20 percent of the sample) have just a single SEC registrant in one or more fiscal years from 2003 through 2006, and 112 offices (34 percent) have three or fewer registrants in one or more years. Intuitively, it is difficult to imagine that small offices would have the same level of expertise as larger offices, even with the technical support and infrastructure that Big 4 firms have to support the office-based delivery of audit services. If auditors in larger Big 4 offices have greater expertise, then we would expect that their clients are more likely to apply GAAP correctly and will have fewer 1 The engagement office of record is determined from the auditor s office address listed on the audit report filed with the SEC and reported in the Audit Analytics data base. Following Reynolds and Francis (2000) an audit is wholly attributed to the engagement office of record. While other offices may participate in an audit engagement, the engagement office of record contracts with the client, has primary responsibility for work done on the audit, and the engagement partner issues the audit report on engagement office letter head. If large offices participate on audit engagements of small offices (and vice-versa) this would only neutralize office size effects and work against the predicted office size/audit quality relation. 1

5 restatements of previously issued financial statements. Our reasoning is that a highquality audit implies enforcement of the correct application of GAAP, so a client restatement is prima facie evidence of low a low-quality audit outcome with respect to the originally-issued financial statements (Panel on Audit Effectiveness, 2000). To test this prediction we examine a sample of 4,302 restated financial statements (2,039 by Big 4 clients) of U.S. based SEC registrants that were originally issued between 2003 and This time period is immediately after the passage of Sarbanes-Oxley in 2002 and the related adoption of new rules regarding internal control systems and implementation of PCAOB inspections of accounting firms. The quality of accounting firm performance became a matter of visibility and public concern following Enron s collapse and the accounting scandal at WorldCom, and what appeared to be Arthur Andersen s culpability in both audits. With SOX and the implementation of independent inspections by the PCAOB, accounting firms now have especially strong incentives to achieve consistent and high-quality audits across all of their practice offices. What do we find? Despite incentives for consistent audit quality, our analysis shows that GAAP application failures (evidenced by client restatements) are systematically different across offices of different size. Big 4 office size is measured by the number of SEC registrants (see footnote 1) and results are robust to alternative office size measures based on client fees. As predicted, clients audited by larger offices have fewer restatements and these results hold for each individual Big 4 accounting firm. These results also hold for 10 of 12 accounting categories of restatements. Restatements 2 Audit Analytics has restatements that were disclosed from calendar year 2000 to the present. Our cutoff date is September 21, The average time lag between the restatement-period-begin date and the disclosure date is 635 days, or approximately two years after the financial statements were originally issued. The most extreme one percent of cases disclose restatements that are over 2,875 days after the restatement-period-begin date. 2

6 due to revenues or to related-party transactions are equally likely across all offices; however, all other types of restatements are more likely to occur for the clients of smaller Big 4 practice offices. Moreover, a year-by-year analysis rules out that the results might simply be a short-term reaction to SOX which was passed in We also examine the four largest non-big 4 firms that have multi-office national practice structures and there is some evidence the office size effect holds for these firms as well. Our study contributes to the understanding of audit practices and audit quality in two related ways. First, it reinforces prior literature which emphasizes the relevance of research that focuses on the engagement office as the unit of analysis in audit research. Second, it provides further evidence linking auditor office size with audit quality. While prior studies such as Francis and Yu (2009) and Choi, Kim, Kim and Zang (2007) document that clients earnings quality is systematically different across auditor offices, it is not clear whether aggressive accounting policies actually cause material financial misstatements and whether GAAP is violated. Hence, this study provides a more convincing measure of audit quality outcomes and the findings have more direct policy implications for both regulators and accounting firms. The remainder of the paper proceeds as follows. The next section discusses related studies, develops the main hypothesis, and addresses model specification and research design issues. Section 3 discusses the sample selection. Empirical tests are reported in section 4 and robustness tests in section 5. Implications of the findings for regulators and accounting firms are discussed in section 6. 3

7 2. Hypothesis Development and Research Design 2.1 Office-Level Analysis Recent studies have argued that empirical audit research should examine individual practice offices of Big 4 accounting firms (Francis, Stokes, and Anderson, 1999; Reynolds and Francis, 2000; Craswell, Stokes, Laughton, 2002). The Big 4 firms have decentralized organizations and operate through a network of local practice offices which have considerable autonomy with respect to contracting with clients and administering audit engagements on the behalf of the firm. In addition, audit reports are issued on the letterhead of the specific Big 4 office administering the audit engagement. Former SEC Commissioner Stephen Wallman (1996) argues that the most critical decisions affecting audit quality are made at the office level and these judgments are affected by the office-level incentives of auditors with respect to their office-specific clienteles. The initial office-level studies examined the influence of client size on auditor independence and constructed office-level client influence metrics to study the influence of large clients on auditor behavior. Craswell, Stokes, and Laughton (2002) find no office-specific effects for a sample of Australian firms, but Reynolds and Francis (2000) find that influential clients in U.S. Big 4 practice offices are actually treated more conservatively by auditors, i.e., more likely to get a going concern report and less likely to have income-increasing abnormal accruals. 3 Consistent with Reynolds and Francis (2000), Li (2009) finds that influential clients in offices are also more likely to receive 3 Client influence is not based on the absolute size of a client when using office-level client influence metrics. Rather, client influence is measured relative to the size of the engagement office, e.g., a client s fee as a percentage of total offices fees. So a client of a given absolute size could be very influential client in a small office, but the same size client may be of little consequence or influence in a large office. 4

8 going-concern opinions in the post-sox period. The primary focus of subsequent officelevel research has been on industry expertise and the question of whether auditor industry expertise is firm-wide or office-specific, and whether Big 4 firms can capture the industry expertise of office-based professionals and distribute that expertise across offices through knowledge sharing practices (e.g., Ferguson, Francis and Stokes, 2003; Francis, Reichelt and Wang, 2005). More recently, Francis and Yu (2009) extend this line of research and examine the absolute size of Big 4 practice offices while controlling for auditor industry expertise. They argue that audit quality with respect to SEC engagements is better in larger offices because such offices have a deeper pool of in-house SEC expertise to draw on in staffing audit engagements. This argument builds on the work of Ferguson et al. (2003) and Francis et al. (2005) and assumes there are practical limits to the ability of Big 4 firms to capture the knowledge/expertise of their office-based professionals and distribute it throughout the organization with knowledge-sharing technologies (see also Vera-Muniz, Ho, and Chow; 2006). In contrast, smaller offices have less collective in-house expertise in the audits of SEC registrants, and may also be stretched in their ability to staff audit engagements with a well-qualified audit team having relevant expertise when staff turnover occurs. In other words, larger offices have more slack in staffing engagements including a deeper reserve of partners to comply with new mandatory rotation rules enacted by SOX. Francis and Yu (2009) examine the association between Big 4 office size and earnings characteristics of clients. They document that clients in smaller offices are more likely to have abnormal accounting accruals that increase reported earnings. Clients in 5

9 smaller offices are also more likely to avoid reporting losses and to avoid earnings declines from the prior year, both of which are consistent with a higher level of unchecked earnings management behavior. Francis and Yu (2009) also document that auditors in smaller offices are less likely to issue going concern reports, ceteris paribus. Choi et al. (2007) also report a negative association between office size and the absolute value of abnormal accruals. Together, these results indicate that audited earnings of clients in smaller offices are more likely to be the product of aggressive accounting policies and earnings management intended to meet management s reporting objectives, which implies that the earnings of these clients are of lower quality (Schipper and Vincent 2003). Thus the evidence in Francis and Yu (2009) and Choi et al. (2007) raises the possibility that audits are not of uniform quality across the spectrum of office size and that smaller offices may have systematically lower audit quality than larger offices in terms of allowing their clients greater discretion to manage reported earnings. A limitation of the earnings management/earnings quality research paradigms is that they only document if reported earnings are out of line relative to statistical norms; that is, such studies cannot determine if aggressive accounting policies are causing material misstatements. Therefore, while the findings in Francis and Yu (2009) and Choi et al. (2007) provide evidence of aggressive accounting policies by clients in smaller offices, it remains unknown if these aggressive policies actually violate GAAP. Thus to make a more explicit connection between an auditor s office size and the quality of audits conducted by individual engagement offices, it is necessary to investigate a measure of poor (low) earnings quality that can be directly mapped to auditor characteristics. As discussed next, client restatement data is used for this purpose in our study. 6

10 2.2 Restatements and Audit Quality Since the passage of Sarbanes-Oxley (SOX) in 2002, Audit Analytics (2007) reports that the number of restatements for all SEC registrants increased monotonically from 563 restatements (514 unique firms) in calendar year 2002, to a peak of 1,876 restatements in 2006 (1,591 unique firms). In 2007, restatements declined to 1,237 for 1,109 unique firms. Some CFOs and auditors have claimed that a large portion of these restatements are defensive disclosures driven mainly by litigation concerns, and that such restatements have little informational value (Johnson, 2007; Nusbaum 2007). This viewpoint gained some support when a recent PCAOB research study found that stock markets responded much less to restatement disclosures after SOX than beforehand. Hranaiova and Byers (2007) analyze restatement disclosures from , and document two-day (0, +1) abnormal returns of percent in the pre-sox period, but only percent in the post-sox period. 4 The results in Hranaiova and Byers (2007) are somewhat at odds with a recent study by Audit Analytics (2008) for a sample of 674 restatements during the 2006 calendar year. Audit Analytics reports a two-day abnormal return of around -1.0 percent, which is similar to that reported in Hranaiova and Byers (2007) for the post-sox period. However, Audit Analytics (2008) also documents that in the 100 trading days surrounding restatement disclosure (50 before and 50 after), the restatement firms lost around 10 percent of their value relative to the market as a whole over the same period. Overall, based on this evidence, we conclude the market continues to view restatements 4 The pre-sox return of percent is consistent with Palmrose, Richardson, and Scholz (2004) who document a negative market reaction of around 9 percent to restatement announcements for a two-day event window. Earlier studies by Dechow, Sloan, and Sweeney (1996) and Anderson and Yohn (2002) also report negative market reactions, and Hribar and Jenkins (2004) estimate that restatement firms experience an increase in the implied cost of equity capital following restatement disclosures. 7

11 negatively and based on this data it is hard to dismiss post-sox restatements as merely defensive disclosures with little information value as is sometimes claimed by some companies and auditors, although Plumlee and Yohn (2008a) report that the materiality threshold for triggering a restatement does seem to have lowered over time. How do restated financial statements provide a measure of audit quality that is relevant to our study? Palmrose and Scholz (2004) argue that restatements provide direct evidence of poor financial reporting quality, and that restatements imply the audits of the originally issued financial statements were also of low quality. That is, a restatement is strongly suggestive of a failure by the auditor to enforce the correct application of GAAP at the time the financial statements were originally issued. Kinney, Palmrose, and Scholz (2004) is the only study we are aware of that examines the effect of auditor characteristics on the likelihood of clients restatements. Specifically they examine the association between restatements and audit fees for 1995 to 2000 as a way of testing if the auditor s fee dependence might compromise audit quality. Other restatement research focuses primarily on the income statement effects of restatements. Palmrose and Scholz (2004) distinguish between core earnings and noncore earnings. The core restatements involve misstatements of revenue; cost of goods sold; selling, general, and administrative costs; and other primary operating expenses. Noncore restatements involve misstatements of a variety of special, one-time transactions and events such as asset impairments, restructurings, mergers and acquisitions, discontinued operations, and extraordinary items. Srinivasan (2005) investigates penalties for outside directors when companies experience accounting restatements, and focuses on the income-decreasing versus income-increasing nature of the restatements, and 8

12 restatements involving technical rather than substantive issues. DeFond and Jiambalvo (1991) also focus on income-increasing restatements. Generally, the above studies find that restatement firms tend to be smaller, less profitable, and slower growing companies with higher debt levels and more serious uncertainties. Earnings overstatements are more likely to occur when firms have diffuse ownership, lower earnings growth, and fewer income-increasing GAAP alternatives (Kinney and McDaniel, 1989; DeFond and Jiambalvo, 1991). More recently Plumlee and Yohn (2008b) and Files, Swanson, and Tse (2009) examine how alternative disclosure strategies affect the market s reaction to restatements. 2.3 Hypothesis and Research Design Based on prior office size research by Francis and Yu (2009) and Choi et al. (2007), we predict that larger offices conduct higher quality audits, and that their clients will have higher quality reported earnings as evidenced by fewer ex post restatements due to GAAP application failures. Therefore, our hypothesis stated in the alternative form is: Hypothesis (H a ): Client restatements are decreasing in Big 4 office size. The null hypothesis is that audit quality is uniform across offices and therefore no differences will exist in client restatement rates across offices of different size. We use a random-effect probit model with standard errors clustered by unique firms to test if the likelihood of restatements due to a GAAP application failure is systematically different across the spectrum of Big 4 office size. With panel data, a random-effect model also provides an additional control for firm-specific omitted variables (Woolridge, 2002). As a robustness test we re-estimate the main results using a standard probit model with Newey-West robust standard errors to correct for 9

13 heteroscedasticity and autocorrelation. To test whether the auditor office size effect changes over the test period, we further estimate these models on a separate year-by-year basis. The general probit model is specified as follows: PROBIT [RESTATEMENT=1] = ƒ (β 0 + β 1 lnoffice + X ' β + ε) (1) where RESTATEMENT is coded 1 if a firm subsequently restates its financial statements due to a GAAP application failure and 0 otherwise. Restatements by U.S. companies of financial statements (which were originally issued in the period ) are identified in the Audit Analytics database as of September 21, The auditor s office size is based on the auditor of record when the restated financial statements were originally issued. The test variable lnoffice is measured as the natural log of the number of U.S. SEC registrants audited by each Big 4 engagement office for a given fiscal year but results are robust to using raw client count data to measure office size. Results are also robust to alternative fee-based measures of office size and are reported in section 4.4. Audit Analytics identifies the engagement office from the Big 4 office letterhead of the audit report filed with the Securities and Exchange Commission. If larger offices provide higher quality audits, then office size should be negatively associated with restatements because of better enforcement of GAAP when the financial statements were originally issued. X is a vector of variables that control for client and auditor characteristics. Our initial tests are based on the Audit Analytics sample. The tradeoff of using Audit Analytics is the benefit of a larger sample than if the Compustat population is used. However, Audit Analytics also has fewer variables so we also test a reduced sample of 10

14 Compustat and CRSP firms with an expanded set of controls. Results are consistent across both samples. The initial client control variables from Audit Analytics are SIZE, LOSS and industry membership (two-digit SIC code). We control for client size with the variable SIZE which is the natural log of a client s total assets. Prior research finds that large clients are more likely to have higher earnings quality, so we expect client size (SIZE) is negatively correlated with GAAP application failures, ceteris paribus. We also predict that firms which report losses have less incentive to manage earnings through GAAP violations, and therefore are less likely to have restatements. LOSS is a dummy variable that takes a value of one if a client s net income is negative, and zero otherwise. If restatements are nonrandom across industries, then industry membership provides an additional control for innate client risk characteristics that potentially give rise to restatements. In addition to client control variables, a set of auditor-related variables are included to control for the possibility that other auditor effects explain client restatements. AUDITOR_CHANGE is a dummy variable that takes the value of 1 if a restatement occurs within the first year of auditor change, 0 otherwise, and controls for the potential effect of a new auditor in bringing about a client restatement. RURAL is a dummy variable that takes the value of 1 if the population of the city in which an auditor is located is 100,000 or less, and 0 otherwise, and controls for the possibility that audit offices located in small cities may have less expertise and therefore a higher likelihood of restatements by their clients. The auditor s industry expertise is captured by the variables NATIONAL_LEADER and CITY_LEADER which control for the auditor s nationallevel and city-specific industry leadership, respectively. Prior studies argue that industry 11

15 expertise results in higher audit quality (Balsam, Krishnan, and Yang, 2003; Krishnan, 2003; Francis et al., 2005). Following these prior studies, we control for the auditor s industry expertise measured at both the national and office-level, and predict that industry leadership should result in fewer client restatements due to GAAP application failures. Specifically, national (office) industry leadership is measured each test year as the specific Big 4 firm (specific Big 4 office in each city) with the largest audit fees from clients in an industry, where industry is defined using 2-digit SIC codes. The remaining auditor variables are proxies for auditor incentives. These variables include the level of client fees from audit and nonaudit services, and are denoted AUDIT and NONAUDIT. High levels of fees have the potential to create an economic bond which gives the client leverage over the auditor and may reduce the auditor s objectivity and professional skepticism (Kinney et al., 2004). If high fees create an economic bond then we would observe a positive association between fees and restatements. Natural logs of nonaudit fees and audit fees are used. As a robustness test we also evaluate abnormal audit fees and nonaudit fees scaled by audit fees. The final auditor variable is INFLUENCE which controls for the size of a client relative to the size of the office administering the audit engagement (Reynolds and Francis, 2000; Li, 2009). INFLUENCE is a client s importance to an office and is measured as the ratio of a specific client s total fees (audit fees plus nonaudit fees) relative to aggregate client fees generated by the office. Note that this is a measure of a client s size relative to the auditor s office size, and is different both conceptually and empirically from absolute office size. Reynolds and Francis (2000) find that client importance is negatively correlated with earnings quality measures, which indicates that auditors are more 12

16 conservative toward larger clients in practice offices because of litigation risk. Based on their study, we predict that INFLUENCE is negatively associated with the likelihood of GAAP application failures. As noted before, the reason we conduct our initial analysis with the Audit Analytics sample is to avoid sample selection bias. A significant number of small firms that file with the SEC are not included in Compustat and CRSP. If we merge Audit Analytics with Compustat and CRSP, the merged sample will be severely biased toward large firms. Given that the primary measure of audit quality is the number of client restatements, we initially use the larger Audit Analytics population to maximize the number of observations with restatements. However, we also analyze a reduced sample of restatements by merging the Audit Analytics sample with Compustat and CRSP, and expanding the set of client control variables. Following prior restatement studies (e.g., Dechow, Sloan, and Sweeney, 1996; Richardson, Sloan, Solimon, and Tuna, 2002; and especially Dechow, Ge, Larson, and Sloan, 2009), we identify the following additional control variables using Compustat and CRSP data. As prior studies show that earnings are generally manipulated through the accruals component, we construct a total accruals measure. High accrual firms are expected to have more restatements. RSST_ACCRUALS is the accruals measure developed by Richardson et al. (2005) which includes working capital accruals and changes in long-term operating assets and longterm operating liabilities, scaled by average total assets for the period. We also include two components of working capital accruals: CHANGE_IN_RECEIVABLES which is the percentage change in accounts receivables, and CHANGE_IN_INVENTORIES which is the percentage change in inventories. We include two additional financial 13

17 performance measures based on the results in Dechow et al., The variable CHANGE_IN_CASH_SALES is the percentage change in cash sales which excludes the accruals component of sales revenue, and is expected to have a positive sign based on Dechow et al. (2009). CHANGE_IN_EARNINGS is the percentage change in earnings and is predicted to have a negative sign based on Dechow et al. (2009). Firms with declining earnings have greater incentives to aggressively manage earnings and misreport. Based on Dechow et al. (2009) we also include a non-financial performance measure and a measure of off-balance sheet activities. CHANGE_IN_EMPLOYEES is the abnormal change in the number of employees and is calculated as the percentage change in the number of employees less the percentage change in total assets. A decline in employees signals declining performance and is expected to be associated with more restatements. OPERATING_LEASES is the percentage change in the present value of future non-cancelable operating lease obligations, and higher values are expected to be associated with more restatements. Finally, we include a set of market-related variables. ACTUAL_ISSUANCE is an indicator variable that takes the value of 1 if a client has issued new debt or equity during the misstatement period and 0 otherwise. Firms issuing securities have greater incentives to misreport. BOOK_TO_MARKET is the ratio of book value of equity to market value of equity and measures a firm s growth opportunities. ABNORMAL_RETURN is the prior 12-month buy-and-hold return adjusted for CRSP value-weighted market returns. Growth firms and firms with high returns have incentives to misreport to avoid having disappointing earnings reports (Skinner and Sloan, 2002). All of the above control variables are year-specific and relative to the time period when the restated financial statements were originally issued. 14

18 To sum up, the research design tests if Big 4 office size is systematically related to restatements of client financial statements. We control for innate client characteristics that may affect the likelihood of restatements such as size, financial performance measures, industry membership, non-financial measures, off-balance sheet activities, and market-related measures, and other auditor effects related to expertise (national and citylevel industry expertise) and fee-related auditor incentives (audit fees, nonaudit fees, client influence). 3. Sample Selection and Results 3.1 Data and Sample Selection The data and initial sample are exclusively from the Audit Analytics data base which has the required restatement data and clientele data for the calculation of Big 4 office size. Audit Analytics has more comprehensive coverage of SEC registrants than Compustat which results in a significantly larger initial sample than the merged sample of Audit Analytics, Compustat and CRSP. This design choice does limit the set of control variables for the multivariate analysis to those in Audit Analytics. For this reason, as noted earlier, we also evaluate auditor office size using the merged Audit Analytics and Compustat/CRSP sample with a broader set of control variables. As of September 21, 2008, there were 9,267 total restatements in the Audit Analytics data base (going back to around the year 2000). Audit Analytics also indicates the year the re-stated financial statements were originally issued and we use this data to construct our sample of restated financials that were originally issued from 2003 through Audit Analytics organizes its restatement data into five broad areas: (1) accounting rule (GAAP) application failures; (2) financial fraud, irregularities, and 15

19 misrepresentations; (3) accounting and clerical application errors; (4) regulatory investigations; and (5) other significant issues. Some restatements are included in two or more these five categories in which case we use the first category listed. It turns out that most of the restatements in Audit Analytics are classified in the first category, accounting rule/gaap application failures, and for this reason the restatement population we use in the study consists of 7,957 total restatements in the Audit Analytics database that are classified as GAAP application failures. As a sensitivity analysis we also examine each the other above-mentioned restatement categories. Since Audit Analytics does not provide quantitative data on restatements, our analysis is limited to identifying those factors that are associated with restatements. However, all restatements are assumed to be material since the reason for a restatement is the correction of what is deemed to be a material deficiency in previously issued financial statements. As indicated earlier, we restrict the sample to Big 4 audited U.S. companies that restated their financial statements which were originally issued from 2003 through 2006, using the Compustat year convention. The auditor identification is based on the auditor of record when the restated financial statements were originally issued. 5 We exclude restatements by foreign filers which gives a final sample of 4,302 restatements of which 2,039 are by U.S. clients of Big 4 auditors and 2,263 are by U.S. clients of non-big 4 auditors. 5 Audit Analytics data is converted to Compustat fiscal year convention, i.e., when a company s fiscal year ends between January 1 and May 31, the previous year is used to denote its fiscal year. For most cases the restatement period end date is used to determine the fiscal year that is being restated. However, if the restatement period end date occurs after the 2006 Compustat fiscal year, the restatement beginning period or begin date is used to determine the fiscal year being restated. The reason for doing this is to maximize the sample size for the test period 2003 through

20 We extract additional data from Audit Analytics to identify the U.S. Big 4 engagement office of record (from the auditor opinion data set), audit fee and nonaudit fee data (from the detailed fee data set), and auditor change information (from the auditor change data set). Clientele data for a Big 4 office is limited to SEC registrants and excludes foreign filers. Audit Analytics also has data on total assets, two-digit SIC industry classification, and net income. Our primary interest is the clients of Big 4 auditors, and the Audit Analytic data base has a total of 35,985 companies audited by Big 4 auditors for the 2003 through 2006 fiscal years (using Compustat notation). As noted before, clients of Big 4 auditors have 2,039 misstatements of the financial statements that were originally issued in fiscal years 2003 through 2006 which gives an overall Big 4 restatement rate of 5.67 percent. Table 1 reports a summary of the 2,039 restatements by Big 4 clients across the four fiscal years. There are 392 observations in the sample with restatements for 2003, 699 for 2004, 559 for 2005, and 389 for The Audit Analytics data base has 10,799 Big 4 clients for 2003, so the 2003 restatement rate is 3.63 percent (392/10,799). There are 8,946 Big 4 clients in Audit Analytics for 2004, so the 2004 restatement rate is 7.81 percent (699/8,946). There are 8,451 Big 4 clients in Audit Analytics for 2005, so the 2005 restatement rate is 6.61 percent (559/8,451). Finally, there are 7,789 Big 4 clients in Audit Analytics for 2006, so the 2006 restatement rate is 4.99 percent (389/7,789). For completeness, we also report details on 2,263 restatements by non-big 4 clients for the same period: 263 for 2003, 549 for 2004, 766 for 2005, and 685 for Given the number of non-big 4 clients per year in the Audit Analytics data base, these correspond to restatement rates of 4.96 percent for 2003 (263/5,300), 9.78 percent for 17

21 2004 (549/5,615), percent for 2005 (766/6,050), and percent (685/6,473) for Two points are worth noting here. First, the overall percentage of restatements is rather large, increasing monotonically from 2003 to 2005 and then decreasing in The second point is that a larger portion of non-big 4 clients have restatements each year relative to Big 4 clients, and the magnitude of the difference has increased over time. By 2006, the non-big 4 rate was over twice the Big 4 rate (10.58 percent vs percent). While not our purpose, these rates are at least suggestive that Big 4 audits are of higher quality since their clients have fewer restatements for GAAP application failures than do the clients of non-big 4 auditors. Table 1 also reports the number of restatements for each of 12 detailed categories with respect to the accounting source of the misstatement: (1) merger and acquisition issues; (2) cash flow statement issues; (3) debt, quasi-debt, warrants, and equity issues; (4) deferred, stock-based compensation issues; (5) tax issues; (6) financial derivatives and hedging issues; (7) inventory, vendor, and cost of sales issues; (8) lease, legal, and FAS 5 contingency and commitment issues; (9) liabilities, payables, reserves, and accrual issues; (10) PPE, intangibles, and other fixed assets issues; (11) revenue recognition issues; and (12) related party issues including consolidation matters. 6 Note that a restatement observation may have multiple accounting areas which are affected by restatements and each one is listed so that the sum of the 12 categories exceeds the number of firms with restatements. Specifically, there are 3,469 accounting areas listed for the 2,039 6 These accounting categories are based on a report by Audit Analytics dated Feb, 2007, titled 2006 Restatements: A six-year Comparison and available on the internet at The report identifies 11 areas of GAAP application failures to which we added one additional category, related party transactions which includes consolidation issues. The 12 categories are defined in more detail in the Appendix. 18

22 restatement firms, or an average of 1.7 accounting areas per observation. The data in Table 1 indicate that restatements of Big 4 clients are fairly evenly distributed across the 12 detailed restatement categories, and that no single category is especially dominant. Restatements range from a low of 136 restatements relating to derivatives, to a high of 399 restatements relating to taxes. For completeness, the 12 categories are also reported in Table 1 for non-big 4 auditors. Table 2 reports the distribution of all 4,302 restatements (Big 4 clients (2,039) and non-big 4 clients (2,263)) across 70 two-digit SIC industry codes, along with the percentage of firms in each industry that had restatements. In absolute numbers, the largest restatements are 609 in SIC 73, Business Services, and 302 in SIC 28, Chemicals and Allied Products. Nine other industries have 100 or more restatements. As a percentage of total industry observations, there are 11 industries which have 15 percent or more observations with restatements: SIC codes 7, 12, 14, 21, 54, 55, 56, 58, 72, 75, and 82. If high-restatement industries are concentrated in smaller offices of Big 4 auditors then it is possible that industry effects rather than auditor effects drive the statistical results on office size, i.e., a classic correlated omitted variable problem. Given the potential non-random distribution of restatements across industries, we control for systematic industry effects on restatements by including a set of industry indicator variables in all of the multivariate models based on two-digit SIC codes. [Insert Table 2 Here] 3.2 Descriptive Statistics and Correlations Table 3 Panel A reports descriptive statistics on the test variable OFFICE. There are 333 unique offices of Big 4 firms and a total of 1,223 offices across the four fiscal 19

23 years (an individual office can appear up to four times). The median office size is 11 SEC clients per office with a range from those offices with a single client to a maximum office size of 763 clients. The first quartile value is four clients per office and the third quartile value is 27 clients, so there is quite a range of Big 4 office size. [Insert Table 3 Here] Panel B of Table 3 reports restatements by quintiles of office size. The 1,223 office-years are classified into quintiles from the smallest to the largest, and Panel C reports restatements for each quintile, in total, and for each of the 12 detailed types of restatements. The Pearson correlation in Panel D shows that overall restatement rates for each of the 12 types of restatements are decreasing in office size (r = -0.10, significant at the.01 level). This can also be seen in Panel B where the total restatement rate rises dramatically (almost doubles) when going from the largest office portfolio (quintile 5) to quintiles 4 and 3, and continues increasing in quintiles 2 and 1. A natural extension of the analysis in Table 3 is to examine whether restatements vary systematically across office size of individual Big 4 auditors. Table 4 Panel A reports that overall restatement rates range from 4.83 percent to 6.41 percent. Ernst & Young has the lowest restatement rate. It audited 9,862 client-year observations, and has 476 client-year restatements during fiscal years , for an overall restatement rate of 4.83 percent. In contrast, Deloitte has the highest restatement rate, auditing 9,501 client-year observations with 609 client-year restatements, resulting in a restatement rate of 6.41 percent. In between these two firms are PricewaterhouseCoopers with a restatement rate of 6.33 percent, and KPMG with a rate of 4.9 percent. [Insert Table 4 Here] 20

24 Table 4 also reports restatement rates across size quintiles of each Big 4 auditor. While the restatement rates are not strictly monotonically decreasing in auditor office size, the Pearson correlations in Panel B indicate a negative and significant correlation at the.01 level between restatements and office size quintiles for each of the individual Big 4 accounting firms. Thus the correlations provide initial evidence to support the predicted negative relation between Big 4 office size and client restatements of previously issued financial statements, and that this relation holds for each of the individual Big 4 accounting firms. Table 5 Panel A reports descriptive statistics for Big 4 clients for both the Audit Analytics sample and the merged Audit Analytics/Compustat/CRSP sample that are used in the multivariate analyses. There are a maximum of 35,985 firm-year observations from the Audit Analytics sample, though some variables have missing values. To preserve sample size, when variables have missing data we assign a value equal to the sample median (of those observations with data) in the multivariate tests. For the reduced sample from the merger of Audit Analytics, Compustat and CRSP, there are 14,848 firm-year observations and we only use those observations with complete (nonmissing) data. [Insert Table 5 Here] Table 5 Panel B reports Pearson correlations among the variables for the multivariate analysis. RESTATEMENT is negatively correlated with OFFICE as predicted and is significant at the.01 level. The correlation between INFLUENCE and OFFICE is and significant at the.01 level, indicating that an individual client is less likely to be important (relative to the office s total portfolio of clients) as the office 21

25 becomes larger. While this is intuitive, it is not tautological. For example, if larger offices have large clients, and smaller offices have small clients, there would not necessarily be a systematic relation between OFFICE and INFLUENCE. Importantly, the potential for collinearity does not appear to be a problem in our main tests because the test variable lnoffice is consistently significant across tests with INLUENCE in the model as a control variable, and INFLUENCE is also significant in most models. Interestingly, client size is negatively correlated with office size, indicating that larger offices tend to have somewhat smaller clients, although the Pearson correlation between SIZE and OFFICE is rather small (-0.02). For the merged Audit Analytics/Compustat/CRSP sample, there are five variables that have negative and significant correlations with the test variable OFFICE around the level: CHANGE_IN_EARNINGS CHANGE_IN_RECEIVABLES, CHANGE_IN_INVENTORIES, CHANGE_IN_CASH_SALES, and CHANGE_IN_EMPLOYEES. However, since lnoffice is consistently significant when these variables are in the model, multicollinearity does not appear to be a problem. 4. Empirical Results 4.1 Multivariate Analysis Table 6 reports the study s results for multivariate model estimation for both the Audit Analytics sample and the reduced Compustat/CRSP sample. Results are based on the random-effect probit model estimation of Equation (1) with standard errors clustered by unique firms. 7 All coefficients are reported as two-tail p-values. The first set of results 7 Alternatively, we follow Hansen (1982) and Newey and West (1987) and estimate a probit model with Newey-West robust standard errors to correct for heteroscedasticity and first-order autocorrelation. There results are consistent. For the first set of results on Table 6, the coefficient of lnoffice is and significant at the.01 level. For the merged Compustat sample without additional control variables, 22

26 is based on the Audit Analytics sample. Missing data are replaced with median values of those observations with data so that the sample size for estimation is 35,985. The coefficient of lnoffice is and significant at the.01 level. SIZE has a coefficient of minus 0.10 and is significant at the.01 level, indicating that larger firms are less likely to have restatements. RURAL is positive and significant at the.05 level, indicating that clients of audit offices located in small cities have more restatements. The variable AUDIT is positive and significant at the.01 level, suggesting that clients with high audit fee levels are more likely to have restatements, ceteris paribus. High fee levels imply economic bonding and potential threats to auditor independence. Consistent with Reynolds and Francis (2000) and Li (2009), the variable INFLUENCE is negative and significant at the.01 level, indicating that larger influential clients in offices are treated more conservatively which results in fewer restatements. NATIONAL_LEADER is positively significant at the.05 level, indicating more restatements when the auditor in an industry expert, as result which is opposite expectations. None of the remaining control variables is significant, i.e., LOSS, AUDITOR_CHANGE, NONAUDIT, and CITY_LEADER. As a robustness test we re-estimate the model for 16,462 observations with non-missing data in the Audit Analytics sample. These untabulated results are comparable to those reported in Table 6: lnoffice is negatively associated with restatements and has a coefficient of which is significant at the.01 level. lnoffice is and significant at the.01 level. For the merged Compustat sample with additional controls, lnoffice is again and significant at the.01 level. The rest control variables are comparable to those reported in Table 6. We also replicate the results in Table 7 with Newey-West robust standard errors. The coefficient of lnoffice for EY, DT, KPMG, and PWC is -0.07, -0.06, -0.11, and -0.16, respectively, and all are significant at the.01 level. Control variables are consistent with those reported in Table 7. 23

27 The second set of results in Table 6 uses the reduced COMPUSTAT/CRSP sample and has 14,848 observations with non-missing data including 1,404 observations with restatements. In order to compare results to the full Audit Analytics sample, we first estimate a model with the same set of control variables. The coefficient of lnoffice is and is significant at the.01 level. Control variables are consistent with the results based on the full Audit Analytics sample, except LOSS which is positive and significant at the.01 level, and NATIONAL_LEADER which becomes insignificant. We conclude from this analysis that the composition of the reduced sample yields results that are consistent with the full Audit Analytics sample and model. The third set of results in Table 6 uses the reduced sample with additional control variables. The coefficient of lnoffice is and significant at the.01 level. The control variables are comparable to those reported earlier. The coefficient of SIZE is and significant at the.01 level indicating that large firms have fewer restatements. LOSS is positive and significant at the.01 level, indicating that loss firms are more likely to have restatements. The coefficient of RURAL is and significant at.10 level, and AUDIT is positively significant at the.01 level. As expected, INFLUENCE has a negative coefficient (-0.55) and is significant at the.01 level. 8 Surprisingly, few of the newly added control variables are significant. The coefficient of RSST_ACCRUALS is and significant at the.05 level; ACTUAL_ISSUANCE is negatively significant at 8 We also interact INFLUENCE with lnoffice as an additional control. For the full Audit Analytics sample, the coefficient on lnoffice is and is significant at.01 level, INFLUENCE is and significant at the.01 level, and the coefficient of the interaction term is and is insignificant. For the Compustat/CRSP sample without additional controls, lnoffice is and significant at the.01 level, INFLUENCE is and significant at.05 level, and the coefficient of the interaction term is and is insignificant. For the Compustat/CRSP sample with additional controls, lnoffice is and significant at the.01 level, INFLUENCE is and significant at the.05 level. The coefficient of the interaction term is and insignificant. 24