Download - Audit Firm Appointments, Audit Firm Alumni, and Audit Committee Independence

Audit Firm Appointments, Audit Firm Alumni, and Audit Committee Independence*

CLIVE S. LENNOX, Hong Kong University of Science and Technology

CHUL W. PARK, Sungkyunkwan University

1. Introduction

Audit firms have large numbers of alumni (that is, former employees) and theyinvest significant resources in generating and maintaining good relations with theiralumni (Istwan and Wollman 1976). In a survey study, Iyer, Bamber, and Barefield(1997) find that alumni are inclined to provide economic benefits to their formeraudit firms. One way that an alumnus can benefit his former firm is to influence theappointment of that firm as the company’s auditor. The alumnus likely has influ-ence over the choice of audit firm if he is a senior officer within the company (forexample, chief executive officer [CEO] or chief financial officer [CFO]). Priorstudies find that audit quality can be impaired if the company is audited by a firmthat formerly employed one of the company’s senior officers (Menon and Williams2004; Lennox 2005). Independent audit committees may perceive that officer–auditor affiliations represent a potential threat to audit quality and thus they maynot sanction the formation of affiliations. In this paper, we investigate (a) whetheralumni influence audit firm appointment decisions, and (b) whether independentaudit committees mitigate the tendency of companies to select officers’ formerfirms.

To test (a), we use a sample of 1,198 companies that change auditors andappoint Big 5 firms in the period 1995 to 2000. We model the company’s decisionto appoint one Big 5 firm instead of another, conditional upon the occurrence of anauditor change and the company’s decision to appoint a Big 5 firm. Of the 1,198auditor change companies, 189 have a chief executive officer or an accounting /finance officer who is an alumnus of a Big 5 firm. The accounting and finance posi-tions include chief financial officer, vice-president of finance, chief accountingofficer, controller, and treasurer. We find that the presence of an alumnus has amajor influence on the audit firm appointment decision. In particular, an audit firmis more likely to be appointed if the company has an officer who is an alumnus ofthat firm. For example, we estimate that Deloitte & Touche is appointed with a

Contemporary Accounting Research Vol. 24 No. 1 (Spring 2007) pp. 235–58 © CAAA

* Accepted by Michael Willenborg. We appreciate helpful comments from Mike Willenborg (asso-ciate editor) and two anonymous referees. We also thank Gary Biddle, Jim Frederickson, MarshallGeiger, Gilles Hilary, Suil Pae, and seminar participants at the International Symposium of AuditResearch (Florida, 2004), the European Accounting Association (Prague, 2004) and the AmericanAccounting Association (Florida, 2004). Lennox thanks Hong Kong’s Research Grants Council(HKUST 6204/04H) for its financial support. Park appreciates support from the Korean ResearchFoundation Grant (KRF-2003-041-600301).

236 Contemporary Accounting Research

mean probability of 19.5 percent if the company does not have an officer who pre-viously worked for Deloitte & Touche; this probability increases to 43.1 percent ifthe company has an officer who previously worked for Deloitte & Touche. Simi-larly, we find that audit firm appointment probabilities are significantly higher forthe other Big 5 firms if officers formerly worked for those firms.

To test (b), we focus on the 189 companies whose officers are alumni. Thecompanies without alumni are dropped from the sample because these companieshave no choice but to forgo affiliations. Of the 189 companies with alumni, 105companies form affiliations by appointing their officers’ former firms while theremaining 84 companies choose not to form affiliations. Following prior research,we classify audit committee members as independent if they are neither employeesof nor affiliated with the company (e.g., Klein 2002a; Abbott and Parker 2000).We find that companies are less likely to appoint officers’ former firms if auditcommittees are more independent. Therefore, we conclude that independentaudit committees reduce the incidence of officer–auditor affiliations. This findingis consistent with audit committees perceiving affiliations to be a potential threat toaudit quality. In addition, we find that companies are more likely to appoint officers’former audit firms if officers left those firms more recently. This indicates that thealumni relation is stronger if the officer’s affiliation with the former audit firm ismore recent.

Our study makes two contributions to the auditing literature. First, we provideevidence on how alumni provide economic benefits to their former firms. Iyer et al.(1997) find that alumni are inclined to send business to their former audit firms, butthere is no archival evidence that alumni provide such business by influencingaudit firm appointments. We document that an audit firm is more likely to beappointed if the company’s officer is an alumnus of that firm. We also documentthat a company is more likely to appoint the alumnus’s former audit firm if thealumnus left that firm more recently.

Second, our study contributes to an emerging literature on the ties betweenofficers and their companies’ audit firms. Menon and Williams (2004) find thatofficer–auditor ties are associated with greater earnings management, and Lennox(2005) finds that such ties are negatively associated with the issuance of going-concern opinions. While these papers suggest that officer–auditor affiliations canimpair audit quality, this study examines the formation of such affiliations. Specif-ically, we find that companies form affiliations by appointing officers’ former auditfirms, but companies are less likely to form affiliations if audit committees aremore independent. Thus, our study responds to a call by DeFond and Francis 2005for further evidence on officer–auditor affiliations.

The results are also relevant to recent changes in the regulation of audit firmsand audit committees. First, the Sarbanes-Oxley Act of 2002 requires audit firms tobe appointed directly by audit committees rather than by company officers. Priorto the Act, audit committees were expected, but not required, to take responsibilityfor appointing audit firms. Our results indicate that both alumni and audit commit-tees influenced audit firm appointments prior to the Act. This suggests that alumnihad close ties with their former audit firms and these ties influenced the company’s

CAR Vol. 24 No. 1 (Spring 2007)

Audit Firm Appointments, Alumni, and Audit Committee Independence 237

choice of audit firm. Our results support regulators’ concerns about officer–auditorties and the need for audit firms to be appointed by independent audit committees.

Second, section 301 of the Act requires audit committee members to be inde-pendent of the company’s officers, although the Securities and Exchange Commission(SEC) may make exemptions on a case-by-case basis. We find that companies areless likely to appoint officers’ former firms if audit committees are more independ-ent. Therefore, our study provides further evidence that independent audit committeesstrengthen audit quality (Abbott and Parker 2000; Klein 2002a). In addition, ourstudy supports regulators’ attempts to improve audit committee independence.

The remainder of this paper is organized as follows. Section 2 develops thehypothesis and describes the research design. Section 3 provides descriptive statis-tics and documents that companies tend to appoint their officers’ former auditfirms. Section 4 tests whether independent audit committees mitigate the tendencyof companies to appoint officers’ former firms. Section 5 concludes with the mainfindings and limitations of the study.

2. Hypothesis development and research design

Officer–auditor affiliations and audit committees

Recent studies indicate a threat to audit quality if officers previously worked fortheir companies’ audit firms. Menon and Williams (2004) find that earnings man-agement is more prevalent when officers are former partners of their companies’audit firms. Their results suggest that affiliated audit firms are less likely to detectearnings management, or affiliated auditors are more likely to waive companies’earnings management attempts. Lennox (2005) finds a lower frequency of going-concern audit opinions when officers are affiliated with their companies’ auditfirms. Therefore, affiliated auditors are less likely than unaffiliated auditors toreport going-concern problems. Since affiliations may threaten audit quality, someaudit firms may choose not to bid for engagements that would result in affiliations.Although we cannot observe this, we do observe that some audit firms acceptengagements that result in affiliations. Presumably, firms accept such engagementsbecause they do not perceive a threat to audit quality or because audit fees are suf-ficient to compensate them for the perceived risk.

We expect audit committees to be sensitive to the possibility that affiliationsthreaten audit quality. Even if audit committee members truly believe that there isno imminent threat, they may still refuse to sanction the appointment of theofficer’s former firm. This is because the audit committee may have to explain whyit permitted an affiliation if there is a subsequent audit failure. Audit committeeshave significant influence over audit firm appointments because regulators andaudit committee guidelines recommend that audit firms should be appointed by auditcommittees (e.g., Braiotta, Hickok, and Biegler 1999; National Office of Audit andAccounting 2001). Moreover, audit committees function more effectively if com-mittee members are independent of the company’s officers. Therefore, we expectindependent audit committees to be less likely to sanction the appointment ofofficers’ former audit firms.



HYPOTHESIS. There is a negative association between the formation ofaffiliations and audit committee independence.

Audit firm appointments and audit firm alumni

Before we test whether independent audit committees mitigate the tendency forcompanies to appoint their officers’ former audit firms, it is necessary to documentthat companies have such a tendency. Therefore, we first test whether an audit firmis more likely to win the engagement of a company whose officer(s) previouslyworked for that firm. Conditional upon finding a positive alumni effect, we subse-quently examine whether independent audit committees mitigate the strength ofthis effect.

Iyer et al. (1997) examine the attitudes of alumni toward their former auditfirms. They find that the characteristics of audit firms and their alumni affect thedegree to which alumni have attachments with their former firms. An alumnus ismore likely to feel attached if the former firm is more prestigious (p-value � 0.001),there is a closer fit between the values of the alumnus and the former firm (p-value� 0.001), and there is a closer relationship between the alumnus and his mentor(p-value � 0.020). In turn, an attachment increases the inclination of the alumnusto provide economic benefits to his former firm (p-value � 0.001). Iyer et al. alsofind that alumni are more inclined to provide economic benefits if the former firmmaintains strong alumni relations (p-value � 0.060). Audit firms maintain relationswith alumni by distributing directories and newsletters, organizing social events, andmaintaining personal communications with former colleagues. Survey studies alsoindicate that the company’s working relationship with its auditors is one of the mostimportant factors driving the choice of audit firm (Eichenseher and Shields 1983;Beattie and Fearnley 1995). Given the importance of a smooth working relation-ship, an alumnus may prefer to appoint his former audit firm.

To test whether companies tend to appoint their officers’ former audit firms,we estimate the following equations:

AA � �0 � �1ALUMNUS_AA � CONTROLS � u1 (1a)

DT � �0 � �1ALUMNUS_DT � CONTROLS � u2 (1b)

EY � �0 � �1ALUMNUS_EY � CONTROLS � u3 (1c)

KPMG � �0 � �1ALUMNUS_KPMG � CONTROLS � u4 (1d)

PWC � �0 � �1ALUMNUS_PWC � CONTROLS � u5 (1e).

The dependent variables indicate the identities of the incoming audit firms(Arthur Andersen, Deloitte & Touche, Ernst & Young, KPMG, and Pricewater-houseCoopers, respectively). For example, AA equals 1 if the company appointsArthur Andersen and 0 otherwise. Each alumnus variable indicates whether the



company has a CEO or an accounting/finance officer who previously worked forthe audit firm. For example, ALUMNUS_AA equals 1 if the company has such anofficer who previously worked for Arthur Andersen and 0 otherwise. The alumnivariables for the other Big 5 firms are defined similarly (ALUMNUS_DT,ALUMNUS_EY, ALUMNUS_KPMG, and ALUMNUS_PWC). The model for Price-waterhouseCoopers is complicated by the 1998 merger between Price Waterhouseand Coopers & Lybrand. ALUMNUS_PWC equals 1 if the officer previouslyworked for PricewaterhouseCoopers, Price Waterhouse, or Coopers & Lybrandand 0 otherwise. Similarly, PWC equals 1 if the incoming audit firm is Pricewater-houseCoopers (1998–2000), Price Waterhouse (1995–98), or Coopers & Lybrand(1995–98) and 0 otherwise.

We expect that an audit firm is more likely to be appointed if the company hasan officer who previously worked for that audit firm. Therefore, we predict that�1 0, �1 0, �1 0, �1 0, and �1 0 in (1a) to (1e). We hand-collect thealumni data from 10-K filings and proxy statements, and it would be prohibitivelycostly to collect such data for Big 5 clients that do not change auditors. Thus, weestimate (1a) to (1e) for a sample of 1,198 companies that change auditors and thatappoint Big 5 firms.

We now discuss the control variables in (1a) to (1e). There is evidence thatindustry-specialist audit firms supply higher-quality services than do nonspecialists(e.g., Balsam, Krishnan, and Yang 2003). Thus, a company may prefer to appointan audit firm that specializes in the company’s industry. On the other hand, industry-specialist auditors charge higher fees (Ferguson, Francis, and Stokes 2003; Francis,Reichelt, and Wang 2005), thereby reducing the likelihood that an industry specialistis appointed as the company’s new auditor.1 We include specialization variables in(1a) to (1e) to control for the potential association between industry specializationand the company’s choice of auditor. SPECIALIST_AA equals 1 if Arthur Andersenis a specialist in the company’s industry and 0 otherwise. Similarly, we includeindustry specialization variables for the other Big 5 firms (SPECIALIST_DT,SPECIALIST_EY, SPECIALIST_KPMG, and SPECIALIST_PWC). The specializa-tion variables indicate whether the audit firm has the largest market share of salesin the company’s two-digit Standard Industrial Classification (SIC) industry code.

Shu (2000) provides evidence that auditor–client mismatch drives realign-ments between auditors and their clients. Therefore, we control for how closely thecharacteristics of the auditor-change company match those of other clients withinthe audit firm’s portfolio. To construct our matching variables, we first use COM-PUSTAT to identify Big 5 clients in the period 1995 to 2000 (37,078 observations).We drop companies with missing auditor fields in COMPUSTAT. Next, we esti-mate five models in which the dependent variables indicate the identities of theBig 5 firms. The independent variables capture (a) client size (log of assets),(b) financial health (Zmijewski 1984 bankruptcy score), and (c) the company’stwo-digit SIC industry code (we include each industry sector that has at least 100observations). Untabulated results reveal that KPMG’s and Arthur Andersen’s cli-ents are relatively small, PWC’s clients are relatively large, Ernst & Young’s clientsare relatively healthy, and Deloitte & Touche’s clients are less healthy compared



with other Big 5 firms.2 Using the models’ coefficients, we predict the Big 5 prob-abilities for each company in the auditor change sample. Our objective is to deter-mine how closely the auditor-change company matches other companies in theaudit firm’s portfolio. For example, an auditor-change company is more closelymatched with Arthur Andersen than with alternative Big 5 firms if it has the fol-lowing predicted probabilities: 27 percent for Arthur Andersen, 23 percent forDeloitte & Touche, 20 percent for Ernst & Young, 17 percent for KPMG, and 13percent for PricewaterhouseCoopers. MATCH_AA equals 1 if the company is moreclosely matched with the clientele of Arthur Andersen than with the clientele of theother Big 5 firms and 0 otherwise. Similarly, we construct dummy variables to cap-ture how closely the company matches the other Big 5 firms (MATCH_DT,MATCH_EY, MATCH_KPMG, and MATCH_PWC ). The matching variables indi-cate whether the characteristics of audit firms’ portfolios influence the company’schoice of auditor.

Finally, we control for company size using the log of assets (Ln(ASSETS)) andwe control for the size of the outgoing audit firm using a dummy variable(FORMER_BIG5), which equals 1 if the outgoing firm is one of the Big 5 and 0otherwise.

The formation of affiliations and audit committee independence

If (1a) to (1e) indicate that companies tend to appoint their officers’ former firms,the next step is to test whether independent audit committees mitigate this ten-dency. We test the impact of audit committee independence on the formation ofaffiliations by estimating the following equation:

AFF � 0 � 1%IND_AUDCOM � OTHER_VARIABLES � v (2).

The dependent variable (AFF) equals 1 if the company forms an affiliation (that is,the company appoints the officer’s former audit firm) and 0 otherwise. We classifyaudit committee members as independent if they are neither employees nor “gray”directors (Weisbach 1988; Klein 2002a).3 Our audit committee independence vari-able is the percentage of independent members (%IND_AUDCOM). If independ-ent audit committees deter the formation of affiliations, we expect 1 � 0.

We now discuss the other explanatory variables in (2). Extant research usesmeeting frequency as a proxy for the diligence with which audit committees exe-cute their duties. Beasley, Carcello, Hermanson, and Lapides (2000) find that auditcommittees of fraud companies meet less often than do audit committees of com-parable companies without reported fraud. Abbott, Parker, and Peters (2004) showthat the frequency of accounting restatements is negatively associated with thenumber of meetings. Anderson, Mansi, and Reeb (2004) find that audit committeemeeting frequency is associated with a lower cost of debt financing and lower yieldspreads. Audit committee meeting frequency is also related to the type of auditorchange made by the company. Abbott and Parker (2000) find that companies aremore likely to appoint industry-specialist auditors if independent audit committeesmeet at least twice a year. This suggests that active audit committees are more



closely involved in selecting audit firms. We therefore investigate the associationbetween the number of audit committee meetings (#MEET_AUDCOM) and thelikelihood that the company appoints its officer’s former audit firm. We expectcompanies to be less likely to appoint officers’ former firms if audit committeesmeet more frequently.

According to regulators, the audit committee should assume responsibility forhiring the audit firm and for overseeing audit quality. Therefore, we expect that theaudit committee is the most important governance mechanism affecting audit firmappointments. Nevertheless, audit committee characteristics are embedded withina larger corporate governance process and audit committee variables are positivelycorrelated with board variables (Klein 2002b). Thus, we control for board independ-ence (%IND_BOARD) and the number of board meetings (#MEET_BOARD).Unlike the audit committee variables, we do not form a prediction for the associa-tion between affiliations and board characteristics.

We expect alumni to have stronger ties with their former audit firms if they leftthose firms recently. Therefore, we predict that a company is more likely to forman affiliation if the officer left the audit firm recently. Although corporate filingsdisclose when the officer joined the company, they often fail to disclose when theofficer left the audit firm. For example, suppose a company discloses that an officerjoined the company in 1990 and, prior to 1990, the officer worked for a differentcompany and an audit firm. In this case, we do not know the precise year that theofficer left the audit firm but we do know that the officer left in 1990 or before. Ifthe auditor change occurs in 1998, we know that the officer left the audit firm atleast eight years prior to the auditor change. Thus, we can calculate a lower boundfor the number of years between the officer leaving the audit firm and the auditorchange. Since we only observe a lower bound, we use a dummy variable to capturethe length of time since the officer left. RECENT equals 1 if the officer left theaudit firm no more than two years prior to the auditor change and 0 otherwise. Insensitivity tests, we investigate whether our results are sensitive to choosing twoyears as the cutoff.

Alumni may have stronger ties with their former certified public accountants(CPA) firms if they held more senior positions (for example, partner). Unfortu-nately, the positions formerly held at audit firms are disclosed for less than half ofthe alumni, which is a limitation of our test for this effect. Nevertheless, in a sup-plementary test, we report that a company is more likely to form an affiliation ifthe alumnus was previously a partner at the audit firm.

A company may change audit firm because the alumnus lobbies for the incum-bent audit firm to be replaced by his former firm. In this case, the alumnus influencesthe dismissal decision as well as the choice of incoming firm. Although we cannotobserve such lobbying directly, 8-K filings disclose whether audit firm changes areinitiated by companies (“dismissals”) or by outgoing audit firms (“resignations”).We test whether the frequency of affiliations is significantly different between dis-missals and resignations. The rationale for this test is that the alumnus may influencethe company’s decision to dismiss the incumbent firm but the alumnus does notinfluence the outgoing audit firm’s decision to resign. Thus, the resignation sample



provides a benchmark to evaluate how frequently companies would appoint officers’former firms if alumni do not lobby for incumbent firms to be dismissed. If alumnido not lobby for dismissals, we would expect no significant difference in thefrequency of affiliations between dismissals and resignations. If alumni do lobbyfor incumbent firms to be dismissed, we would expect more frequent affiliations fordismissals than for resignations. We include a dummy variable (DISMISSAL),which equals 1 if the company dismisses the outgoing audit firm and 0 if the out-going firm resigns. If alumni lobby for incumbent firms to be dismissed, we expecta positive association between affiliations and DISMISSAL.

Companies may form affiliations if officers’ former firms are experts in thecompany’s industry, so we control for an industry specialization effect. Our spe-cialization variable (SPECIALIST) equals 1 if the incoming audit firm has the larg-est market share of the company’s industry and 0 otherwise. Market share is theproportion of industry sales, where the industry is measured using the two-digitSIC code. We expect a positive association between affiliations and SPECIALIST.

Audit firms have incentives to invest in alumni relations in order to markettheir services and earn more revenue (Iyer et al. 1997). The expected return frominvesting in alumni relations is greater if the alumnus manages a larger companybecause the alumnus may recommend his former firm to more influential businesscontacts. In addition, the audit firm gains more revenue if it is appointed by a largercompany. Therefore, audit firms have stronger incentives to maintain close rela-tions with alumni who are officers in larger companies. If alumni in larger companieshave stronger ties with their former audit firms, we expect a positive associationbetween affiliations and company size. We control for company size using the logof assets (Ln(ASSETS)). We use the log of assets rather than market capitalizationbecause market values are unavailable for 19 companies.

Prior research indicates that financial distress and the demand for insuranceaffect the decision to change auditor and the choice of incoming auditor (Willen-borg 1999). We control for financial distress using the Zmijewski bankruptcy score(ZMIJ), where higher values indicate a higher probability of bankruptcy (Zmijew-ski 1984). We control for the insurance motive by including a dummy variable(LITIG), which equals 1 if the company is in an industry that has high litigationrisk and 0 otherwise. Following Ali and Kallapur 2001 and Matsumoto 2002, weassume that industries are high risk if they have the following SIC codes: 2833–2836, 3570–3577, 3600–3674, 5200–5961, or 7370–7374. We do not form a pre-diction about the association between affiliations and these variables.

3. Audit firm appointments and audit firm alumni

The sample

We begin our sample collection using the Auditor-Trak data base, which provides acomprehensive list of public companies that change audit firms. Our sample beginsat January 1, 1995 and ends at December 31, 2000 (we do not want our data to becontaminated by auditor changes in 2001–2 that are caused by Arthur Andersen’sdemise). We impose four restrictions on the sample. First, we require that 10-K



filings and proxy statements are available during the auditor-change yearbecause we use these to identify officers who previously worked for audit firms.Second, we require that data are available for total assets, industry sector, theZmijewski bankruptcy score, the type of auditor change (dismissal or resignation),and the identity of the outgoing audit firm. Third, we drop non–Big 5 appoint-ments because prior research indicates that they are qualitatively different fromBig 5 appointments (e.g., DeFond 1992). Fourth, we drop the company if thealumnus is not a CEO or an accounting /finance officer. We focus on CEOs andaccounting / finance officers because they have more control over audit firmappointments than do officers in other positions. As discussed later, our resultscontinue to hold when the sample includes companies that appoint non–Big 5firms and when it includes alumni who are not CEOs or accounting / financeofficers.

To identify officers who previously worked for audit firms, we use the careerbiographies in 10-K filings, proxy statements, and the Dun & Bradstreet ReferenceBooks of Corporate Management. Career biographies are available for senior offic-ers (for example, chief financial officers) but not for junior managers. Some 10-Kfilings and proxy statements contain only recent biographical information (typi-cally five years), so we may not identify alumni who joined companies more thanfive years prior to the auditor change. Dun & Bradstreet’s Reference Books ofCorporate Management contain more complete biographical information butthey do not include all companies in the sample (they cover the largest 12,000 U.S.companies). Since our data sources do not provide complete career biographies,some companies may be incorrectly coded as not having alumni. This would likelyact against finding the predicted results.

Our final sample consists of 1,198 companies that change audit firms and thatappoint Big 5 firms as incoming auditors. Table 1, panel A shows that 189 of thesecompanies have at least one CEO or accounting/finance officer who is an alumnusof a Big 5 firm (ALUMNUS � 1). Within this subsample of 189 alumni companies,105 companies form affiliations by hiring their officer’s former audit firm (AFF � 1).4

It should be noted that companies cannot form affiliations if they do not havealumni (ALUMNUS � 0). This is shown in panel A of Table 1 where there are zeroobservations for the outcome ALUMNUS � 0, AFF � 1. Since the companieswithout alumni have no choice but to forgo affiliations, there is no point in usingthese observations to estimate the model of affiliation formation. Thus, the estima-tion sample for (2) does not include companies that have no alumni. As a result,while we estimate (1a) to (1e) using the full sample (n � 1,198), we estimate (2)using the alumni sample alone (n � 189).

Table 1, panel B shows that there are 214 officers who formerly worked forBig 5 firms (166 companies have one alumnus, 21 have two alumni, and 2 havethree alumni). Of the 214 alumni, 16 are CEOs and 198 are accounting /financeofficers (panel C). Table 2 reports the breakdown of incoming and predecessoraudit firms (panel A) and the former firms of alumni (panel B). The number of newappointments ranges from 217 for KPMG to 275 for Ernst & Young. The numberof companies that have alumni ranges from 31 for KPMG to 46 for Arthur Andersen,



implying that there are sufficient alumni per Big 5 firm to reliably estimate thealumni coefficients in (1a) to (1e).

The results

Table 3 reports the results for (1a) to (1e). As expected, we find positive and highlysignificant associations between the alumni variables and the choice of incomingaudit firm. For example, the ALUMNUS_AA coefficient in (1a) is positive and signif-icant at better than the 1 percent level (z-statistic � 5.27). Therefore, a company is


TABLE 1Composition of the sample of auditor changes

Panel A: Alumni and the formation of affiliations

ALUMNUS � 0 1,009 0 1,009ALUMNUS � 1 84 105 189Totals 1,093 105 1,198

Panel B: Officers who are alumni of audit firms

1 166 1662 21 423 2 6Totals 189 214

Panel C: Corporate positions of alumni

Accounting or finance 198CEO 16Total 214

Notes:

ALUMNUS � 1 if the company has at least one officer who formerly worked for a Big 5 audit firm and 0 otherwise. AFF � 1 if the company appoints one of its officer’s former audit firms and 0 otherwise.

To be in the alumni sample, a company has to satisfy the following data requirements: (a) the company newly appoints a Big 5 audit firm; (b) the company has a CEO or accounting/finance officer who previously worked for a Big 5 firm; and (c) data are available for all variables. To be in the no-alumni sample, a company has to satisfy the following data requirements: (a) the company newly appoints a Big 5 audit firm; (b) the company does not have an officer who previously worked for an audit firm; and (c) data are available for the company’s size, industry, and Zmijewski 1984 bankruptcy score.

AFF � 0 AFF � 1 Totals

Number of officers per company who are alumniNumber of companies

Number of officers

Number of officers



TABLE 2Incoming/outgoing audit firms and former audit firms of alumni

Panel A: Incoming/outgoing audit firms

Arthur Andersen 268 22.4 130 10.9Deloitte & Touche 218 18.2 121 10.1Ernst & Young 275 23.0 150 12.5KPMG 217 18.1 171 14.3Coopers & Lybrand, Price Waterhouse,

PricewaterhouseCoopers 220 18.4 296 24.7Non–Big 5 firm — 330 27.5Total 1,198 1,198

Panel B: Former audit firms of alumni

Arthur Andersen (ALUMNUS_AA � 1) 46Deloitte & Touche (ALUMNUS_DT � 1) 32Ernst & Young (ALUMNUS_EY � 1) 44KPMG (ALUMNUS_KPMG � 1) 31Coopers & Lybrand, Price Waterhouse, PricewaterhouseCoopers

(ALUMNUS_PWC � 1) 42Total* 195

Notes:

ALUMNUS_AA � 1 if the company has an officer who previously worked for Arthur Andersen and 0 otherwise. ALUMNUS_DT � 1 if the company has an officer who previously worked for Deloitte & Touche and 0 otherwise. ALUMNUS_EY � 1 if the company has an officer who previously worked for Ernst & Young and 0 otherwise. ALUMNUS_KPMG � 1 if the company has an officer who previously worked for KPMG and 0 otherwise. ALUMNUS_PWC � 1 if the company has an officer who previously worked for PricewaterhouseCoopers, Price Waterhouse, or Coopers & Lybrand and 0 otherwise.

* There are 189 companies with at least one officer who formerly worked for a Big 5 firm (see Table 1). However, the total is 195 in Table 2 because a company can have more than one alumnus and the alumni may come from different firms (for example, the company’s CEO may be an alumnus of KPMG and the CFO may be an alumnus of Arthur Andersen). In some cases, the company has more than one alumnus but the alumni come from the same firm (for example, the CEO and CFO may both be alumni of KPMG). Thus, the total of 195 is less than the number of Big 5 alumni (214).

Audit firm

Incoming auditor

Predecessor auditor

n % n %

Audit firmNumber of companies


more likely to appoint Arthur Andersen if the company has an officer who previ-ously worked for Arthur Andersen. Similar results are reported in (1b) to (1e) for theother Big 5 audit firms. The ALUMNUS_DT, ALUMNUS_EY, ALUMNUS_KPMG,and ALUMNUS_PWC coefficients are all positive and highly significant (the z-statistics range from 3.12 to 4.63). Thus, companies tend to appoint officers’former audit firms rather than unaffiliated audit firms.

To gauge the strength of the alumni effect, we predict audit firm appoint-ment probabilities when the alumni variables are switched “on” (for example,ALUMNUS_AA � 1) and “off” (for example, ALUMNUS_AA � 0). If the com-pany does not have an officer who formerly worked for Arthur Andersen (AA), weestimate that AA would be appointed with a mean probability of 23.2 percent.5 If

TABLE 3Audit firm appointments and the alumni of audit firms (z-statistics are reported in parentheses)

AA � �0 � �1ALUMNUS_AA � CONTROLS � u1 (1a)

DT � �0 � �1ALUMNUS_DT � CONTROLS � u2 (1b)

EY � �0 � �1ALUMNUS_EY � CONTROLS � u3 (1c)

KPMG � �0 � �1ALUMNUS_KPMG � CONTROLS � u4 (1d)

PWC � �0 � �1ALUMNUS_PWC � CONTROLS � u5 (1e)

ALUMNUS_xx � 1.64 1.15 1.32 1.30 1.59

(5.27)‡ (3.12)‡ (4.11)‡ (3.54)‡ (4.63)‡

SPECIALIST_xx � 0.33 0.35 �0.01 0.13 0.18

(1.60) (1.43) (�0.09) (0.71) (1.02)

MATCH_xx � 0.26 �0.12 0.58 0.11 0.08

(1.49) (�0.47) (2.78)‡ (0.25) (0.46)

Ln(ASSETS) ? 0.01 �0.07 0.04 0.02 0.00

(0.35) (�1.73)* (1.16) (0.47) (0.08)

FORMER_BIG5 ? 0.02 0.32 0.21 0.13 0.63

(0.14) (1.77)* (1.30) (0.71) (3.25)‡

Intercept �1.36 �1.35 �1.53 �1.55 �1.90

(�6.90)‡ (�6.49)‡ (�7.57)‡ (�8.24)‡ (�7.31)‡

Pseudo R2 2.8% 1.5% 2.5% 1.2% 4.0%Number of companies 1,068 1,077 1,048 1,027 902

(The table is continued on the next page.)

Predicted sign (1a) (1b) (1c) (1d) (1e)



TABLE 3 (Continued)

Notes:* Significant at the 10 percent level (two-tailed).

† Significant at the 5 percent level (two-tailed).

‡ Significant at the 1 percent level (two-tailed).

The sample sizes are different because we estimate the models after dropping companies whose outgoing audit firm corresponds to the dependent variable’s incoming audit firm. For example, in (1a), we drop 130 companies whose outgoing firm is Arthur Andersen because it is impossible for these companies to appoint Arthur Andersen as the new audit firm. Therefore, the sample size is 1,068 (1,198 � 130) in (1a). We apply similar sample selection criteria in (1b) to (1e). The models are estimated using logistic regression, with standard errors that are robust to heteroscedasticity.

AA � 1 if the company hires Arthur Andersen as the incoming audit firm and zero otherwise. DT � 1 if the company hires Deloitte & Touche as the incoming audit firm and 0 otherwise. EY � 1 if the company hires Ernst & Young as the incoming audit firm and 0 otherwise. KPMG � 1 if the company hires KPMG as the incoming audit firm (0 otherwise). PWC � 1 if the company hires PricewaterhouseCoopers (1998–2000), Price Waterhouse (1995–98), or Coopers & Lybrand (1995–98) as the incoming audit firm and 0 otherwise. ALUMNUS_AA, ALUMNUS_DT, ALUMNUS_EY, ALUMNUS_KPMG, and ALUMNUS_PWC are as defined in Table 2. SPECIALIST_xx � 1 if the audit firm (AA, DT, EY, KPMG, or PWC) has the largest market share in the company’s industry and 0 otherwise. Market share is the proportion of industry sales, where industry is measured using the two-digit SIC code. MATCH_xx � 1 if the company is more closely matched with the audit firm (AA, DT, EY, KPMG, or PWC) than with any of the other Big 5 firms and 0 otherwise. To determine how closely the company is matched with each of the Big 5 firms, we estimate auditor choice models for the COMPUSTAT population of Big 5 clients (1995–2000). The dependent variables in the five auditor choice models indicate the identities of the Big 5 firms. The independent variables are the company’s size, financial health, and two-digit SIC industry code. We use the model coefficients and the independent variables to predict the Big 5 probabilities for each of the 1,198 companies in the auditor-change sample. For example, an auditor-change company is more closely matched with Arthur Andersen than with any of the other Big 5 firms if it has the following predicted probabilities: 27 percent for Andersen, 23 percent for Deloitte & Touche, 20 percent for Ernst & Young, 17 percent for KPMG, and 13 percent for PricewaterhouseCoopers. Ln(ASSETS) � log of total assets. FORMER_BIG5 � 1 if the company was audited by a Big 5 firm prior to the change in auditor and 0 otherwise.



the company does have an officer who formerly worked for AA, we estimate thatAA would be appointed with a mean probability of 60.6 percent.6 Therefore, AA ismore than twice as likely to be appointed if the company has an officer who for-merly worked for AA (60.6 percent 23.2 percent). Similarly, the appointmentprobabilities for the other Big 5 firms are significantly higher if officers are alumniof those firms. If the company does not have an officer who worked for Deloitte &Touche (D&T), D&T would be appointed with a mean probability of 19.5 percent.If the company does have an officer who worked for D&T, D&T would beappointed with a probability 43.1 percent. For Ernst & Young, an alumnusincreases the appointment probability from 24.5 percent to 54.5 percent; forKPMG, the probability increases from 20.6 percent to 48.7 percent; for Price-waterhouseCoopers, the probability increases from 22.7 percent to 57.4 percent.Therefore, our results consistently demonstrate that alumni significantly influenceaudit firm appointments.

The coefficients on the control variables are generally insignificant in the fivelogit models. These results are not directly comparable to extant research becausewe model a company’s choice of auditor among the Big 5 firms (for example, (1a)models the company’s decision to appoint Arthur Andersen rather than an alterna-tive Big 5 firm). In contrast, prior studies examine a company’s decision to changeauditor and the choice between hiring a Big 5 firm or a non – Big 5 firm (e.g.,Francis and Wilson 1988). Our five models have low explanatory power (thepseudo R 2 range from 1.2 percent to 4.0 percent), indicating that it is difficult toexplain why a company hires one Big 5 firm instead of another. Nevertheless, ourresults suggest that the alumnus connection is the most important factor affecting acompany’s choice between alternative Big 5 firms.

The coefficients on the specialization variables are insignificant, indicatingthat companies are not more likely to appoint industry specialists. One reason maybe that industry specialists charge higher audit fees (Ferguson et al. 2003; Franciset al. 2005). The coefficients on the matching variables are also insignificant(except for Ernst & Young). Therefore, the characteristics of audit firms’ client port-folios do not generally influence the company’s choice between alternative Big 5firms. The FORMER_BIG5 coefficient is significantly positive for Pricewater-houseCoopers (PWC), indicating that companies are more likely to appoint PWCif the outgoing auditor is a Big 5 firm. This makes sense because PWC is the largestBig 5 firm and so the new clients of PWC are more likely to come from Big 5 firmsthan are the new clients of other Big 5 firms.7

Robustness tests

We perform three robustness tests on the models in Table 3. First, we use thealumni sample (n � 189) as an alternative to using the full sample (n � 1,198).This is a useful test because, as noted above, (2) should be estimated using thealumni sample alone. Despite the smaller sample size, the results are very similarto those in Table 3. In particular, the alumni coefficients range from 1.66 to 2.33and the corresponding z-statistics range from 3.64 to 5.63. Therefore, our findings arerobust to using either the alumni sample or the full sample to estimate (1a) to (1e).



Second, we include control variables that have been used in prior research onauditor changes and the hiring of Big 5 or non–Big 5 audit firms (Francis andWilson 1988; Johnson and Lys 1990; Shu 2000; Lennox 2000). In these studies,new acquisitions, litigation risk, financial health, new financing, and the prior issu-ance of modified opinions are significant independent variables. In contrast tothese studies, we model a company’s choice among the Big 5 audit firms, condi-tional upon the company’s changing to a Big 5 firm. Untabulated results reveal thatnew acquisitions, litigation risk, financial health, new financing, and the prior issu-ance of modified opinions do not explain the choice between alternative Big 5firms, whereas the alumni coefficients remain highly significant.

Finally, we estimate the models using multinomial logit rather than binarylogit to account for the fact that companies are choosing between five alternativeaudit firms. We find that the alumni coefficients are positive and statistically signifi-cant at the 1 percent level (the z-statistics range from 3.71 to 5.64), so the multinomiallogit results are similar to those tabulated.

4. The formation of affiliations and audit committees

As documented in section 3, companies are apt to appoint their officers’ formeraudit firms. In this section, we investigate whether independent audit committeesreduce the likelihood that companies appoint officers’ former firms. A companycannot form an affiliation if the company does not have an officer who is an alumnus.Therefore, this section focuses on the companies that have alumni (ALUMNUS � 1,n � 189).

Table 4 compares the 105 companies that form affiliations (AFF � 1) with the84 companies that do not (AFF � 0). The mean percentage of independent auditcommittee members (%IND_AUDCOM ) is 69.0 percent in the affiliation samplecompared with 78.8 percent in the no-affiliation sample. Similarly, the medianaudit committee independence is 66.7 percent in the affiliation sample comparedwith 81.7 percent in the no-affiliation sample. These differences in means andmedians are statistically significant, and they are consistent with our predictionthat independent audit committees deter the formation of affiliations. The othercorporate governance variables are statistically insignificant.

Table 4 reveals significant differences between the affiliation and no-affiliationsamples for three independent variables. First, a company is more likely to form anaffiliation if the alumnus left the audit firm recently (RECENT). This is consistentwith alumni having stronger ties with their former firms if the alumni left thosefirms recently. Second, a company is more likely to form an affiliation if theauditor change is initiated by the client (DISMISSAL) rather than by the outgoingaudit firm. This is consistent with alumni lobbying for their former audit firms toreplace the incumbent firms. Third, company size (Ln(ASSETS)) is positively asso-ciated with the formation of affiliations. Affiliations are not significantly associatedwith the size of the outgoing audit firm (FORMER_BIG5), the appointment of anindustry specialist (SPECIALIST ), financial health (ZMIJ ), or litigation risk(LITIG).


TA
BL

E 4

Uni

vari

ate

resu

lts f

or th

e al

umni

sam

ple

(AL

UM

NU

S �

1, n

� 1

89)

%IN

D_A

UD

CO

M0.

690

0.66

70.

290

0.78

80.

817

0.23

9�

0.09

8†�

0.15

0†

#ME

ET

_AU

DC

OM

2.20

02.

000

1.55

32.

524

2.00

02.

241

�0.

324

0.00

0%

IND

_BO

AR

D0.

499

0.50

00.

176

0.51

50.

500

0.18

8�

0.01

60.

000

#ME

ET

_BO

AR

D7.

248

6.00

06.

374

7.00

06.

000

4.51

20.

248

0.00

0R

EC

EN

T0.

190

0.00

00.

395

0.08

30.

000

0.27

80.

107†

0.00

0†

DIS

MIS

SAL

0.93

31.

000

0.25

10.

833

1.00

00.

375

0.10

0†0.

000†

FO

RM

ER

_BIG

50.

724

1.00

00.

449

0.71

41.

000

0.45

40.

010

0.00

0SP

EC

IAL

IST

0.19

00.

000

0.39

50.

202

0.00

00.

404

�0.

012

0.00

0L

n(A

SSE

TS

)4.

577

4.74

71.

749

4.12

34.

198

1.83

20.

454*

0.54

9*

ZM

IJ�

1.38

1�

1.71

53.

334

�1.

349

�2.

017

4.53

5�

0.03

2�

0.30

2L

ITIG

0.29

50.

000

0.45

80.

310

0.00

00.

465

�0.

015

0.00

0

(The

tabl

e is

con

tinue

d on

the

next

pag

e.)

The

com

pany

app

oint

s its

offi

cer’

sfo

rmer

aud

it fir

m(A

FF

� 1

, n �

105

)

The

com

pany

doe

s no

t app

oint

its

offic

er’s

for

mer

aud

it fir

m(A

FF

� 0

, n �

84)

Dif

fere

nces

in

mea

nsD

iffe

renc

es

in m

edia

nsM

ean

Med

ian

s.d.

Mea

nM

edia

ns.

d.



Table 5 reports a correlation matrix. The correlation between audit committeeindependence (%IND_AUDCOM ) and board independence (%IND_BOARD) ispositive and significant. Similarly, there is a significant positive correlationbetween audit committee meetings (#MEET_AUDCOM ) and board meetings(#MEET_BOARD). The positive relation between audit committee characteristicsand board characteristics is consistent with prior research (Klein 2002b; Kara-manou and Vafeas 2005).

Multivariate results

Table 6 reports multivariate results for (2). The audit committee independence variable(%IND_AUDCOM) has negative and significant coefficients (z-statistics � �2.83,�2.77). Therefore, companies are less likely to appoint officers’ former firms ifaudit committees are more independent. The coefficients on audit committee meet-ing frequency (#MEET_AUDCOM ) are also negative and significant. Therefore,companies are less likely to appoint officers’ former firms if audit committees meetmore often. The multivariate regressions reveal a significant negative association

TABLE 4 (Continued)



Tests for differences in means are based on t-statistics for continuous variables and z-statistics for proportions. Nonparametric tests for median differences are based on the Wilcoxon rank sum test.

ALUMNUS � 1 if the company has at least one officer who formerly worked for a Big 5 audit firm and 0 otherwise. AFF � 1 if the company appoints one of its officer’s former audit firms and 0 otherwise. %IND_AUDCOM � the percentage of independent audit committee members. #MEET_AUDCOM � the number of meetings held by the company’s audit committee. %IND_BOARD � the percentage of independent board members. #MEET_BOARD � the number of meetings held by the company’s board of directors. RECENT � 1 if the alumnus left the audit firm no more than two years prior to the auditor change and 0 otherwise. DISMISSAL � 1 if the company dismisses the outgoing audit firm and 0 if the outgoing audit firm resigns. FORMER_BIG5 � 1 if the company was audited by a Big 5 firm prior to the change in auditor and 0 otherwise. SPECIALIST � 1 if the incoming audit firm has the largest market share in the company’s industry and 0 otherwise. Market share is the proportion of industry sales, where industry is measured using the two-digit SIC code. Ln(ASSETS) � log of total assets. ZMIJ � Zmijewksi 1984 bankruptcy score (�4.803 � 3.599*(Net income/Total assets) � 5.406*(Total liabilities/Total assets) � 0.10*(Current assets/Current liabilities)). LITIG � 1 if the company belongs to a high-risk industry (SIC codes 2833–2836, 3570–3577, 3600–3674, 5200–5961, and 7370–7374) and 0 otherwise.




TAB

LE

5C

orre

latio

n m

atri

x fo

r th

e al

umni

sam

ple

(n �

189

)

1.A

FF

1.00

2.%

IND

_AU

DC

OM

�0.

18†

1.00

3.#M

EE

T_A

UD

CO

M�

0.09

0.07

1.00

4.%

IND

_BO

AR

D�

0.04

0.57

‡0.

061.

005.

#ME

ET

_BO

AR

D0.

02�

0.08

0.15

†0.

071.

006.

RE

CE

NT

0.15

†0.

020.

18†

0.06

�0.

021.

007.

DIS

MIS

SAL

0.16

†0.

010.

110.

030.

02�

0.00

1.00

8.F

OR

ME

R_B

IG5

0.01

0.03

0.20

‡0.

090.

03�

0.01

�0.

031.

009.

SPE

CIA

LIS

T�

0.02

0.00

�0.

010.

010.

050.

030.

010.

011.

0010

.L

n(A

SSE

TS

)0.

13*

0.12

0.46

‡0.

100.

16†

0.10

0.

140.

36‡

�0.

081.

0011

.Z

MIJ

�0.

000.

05�

0.05

0.21

‡0.

12*

0.05

�0.

11�

0.01

�

0.05

�0.

22‡

1.00

12.

LIT

IG�

0.02

0.04

0.01

0.11

0.09

�0.

100.

05�

0.03

0.02

�0.

02

�0.

04

Not

es:

*Si

gnifi

cant

at t

he 1

0 pe

rcen

t lev

el (

two-

taile

d).

†Si

gnifi

cant

at t

he 5

per

cent

leve

l (tw

o-ta

iled)

.

‡Si

gnifi

cant

at t

he 1

per

cent

leve

l (tw

o-ta

iled)

.

All

vari

able

s ar

e as

defi

ned

in T

able

4.

1.2.

3.4.

5.6.

7.8.

9.10

.11

.


between affiliations and meeting frequency, whereas the univariate association isinsignificant (see Table 4). We find the univariate test is insignificant because of thelack of control for company size. Specifically, audit committees meet more often inlarger companies, and larger companies are more likely to form affiliations. Failingto control for company size therefore creates a spurious positive relation betweenaffiliations and audit committee meetings. This spurious positive relation, whenuncontrolled, offsets the significant negative relation found in the multivariateregressions and results in an insignificant relation in the univariate test.

In contrast to the audit committee variables, we find insignificant coefficientsfor board independence (%IND_BOARD) and board meetings (#MEET_BOARD).Of the remaining explanatory variables, three are statistically significant. First,a company is more likely to form an affiliation if the alumnus left the formeraudit firm recently (RECENT). This indicates that ties between alumni and theirformer firms are greater if alumni left recently. Second, client-initiated dismissals(DISMISSAL) are more likely to result in affiliations than are auditor-initiated res-ignations. This suggests that alumni influence the dismissals of incumbent firms aswell as the appointments of incoming firms. Finally, affiliations are positivelyassociated with company size (Ln(ASSETS)). This is consistent with audit firmsmaintaining stronger ties with alumni who work in larger companies.

Further analysis and sensitivity tests

This section describes the untabulated results from six additional tests. First, weargue that an independent audit committee may not sanction the appointment ofthe officer’s former firm because the committee may be concerned that officer–auditor affiliations threaten audit quality. To test whether affiliations actuallyimpair audit quality, we collect press releases disclosing news of audit qualityimpairment in the three-year period following the audit firm’s appointment. Wesearch LexisNexis for the following news: (a) the company is investigated by theSEC because of accounting irregularities, (b) the company restates its financialstatements because of accounting irregularities, or (c) the company is sued byinvestors because of accounting irregularities. We restrict our sample to includeirregularities that occur after the audit firm’s appointment. Of the 84 companiesthat do not form affiliations, only 5 (6.0 percent) are subsequently involved inaccounting irregularities. Of the 105 companies that form affiliations, 12 (11.4 per-cent) are subsequently involved in accounting irregularities. Although there is apositive association between affiliations and audit quality impairment, the associa-tion is not very significant (the one-tailed p-value is 0.10).

Second, we investigate whether the alumnus has more influence over theaudit firm’s appointment if the alumnus sits on the audit committee or the boardof directors. We include dummy variables indicating whether the alumnus is anaudit committee member (ALUMNUS_AUDCOM) or a board member (ALUMNUS_BOARD). We find that 10 companies have an alumnus on the audit committee and60 companies have an alumnus on the board. The ALUMNUS_AUDCOM coefficientis positive and insignificant (z-statistic � 1.29), although the lack of significancecould be attributable to low power. The ALUMNUS_BOARD coefficient is negative




TABLE 6The formation of affiliations in the alumni sample (n � 189) (z-statistics are reported in parentheses)

AFF � 0 � 1%IND_AUDCOM � OTHER_VARIABLES � v (2)

%IND_AUDCOM � �1.76 �2.03(�2.83)‡ (�2.77)‡

#MEET_AUDCOM � �0.28 �0.28(�2.96)‡ (�2.89)‡

%IND_BOARD ? �0.92 0.70(�1.05) (0.65)

#MEET_BOARD ? �0.00 �0.01(�0.02) (�0.18)

RECENT � 1.25 0.96 1.23(2.42)† (1.94)* (2.39)†

DISMISSAL � 1.20 1.02 1.19(2.35)† (1.97)† (2.34)†

SPECIALIST � 0.03 �0.04 0.02(0.07) (�0.09) (0.06)

Ln(ASSETS) � 0.31 0.13 0.30(2.80)‡ (1.45) (2.82)‡

ZMIJ ? 0.03 0.02 0.03(0.71) (0.54) (0.59)

LITIG ? 0.06 0.02 0.04(0.18) (0.06) (0.12)

Intercept ? �0.37 �0.86 �0.46(�0.52) (�1.26) (�0.62)

Pseudo R2 10.7% 4.7% 10.8%



‡ Significant at the 1 percent level (two-tailed).

The models are estimated using logistic regression, with standard errors that are robust to heteroscedasticity.

All variables are as defined in Table 4.

Predicted sign Model 1 Model 2 Model 3


but insignificant (z-statistic � �0.03). The coefficients on audit committee inde-pendence and audit committee meetings remain negative and significant at lessthan the 1 percent level.

Third, we investigate the alumnus’s former position at the audit firm. An alum-nus who held a senior position (for example, partner) may have stronger ties to hisformer firm compared with an alumnus who held a more junior position. There-fore, a company may be more likely to form an affiliation if the alumnus held amore senior position. Alumni positions at audit firms are disclosed by only 89companies, so there are too many missing observations to draw firm conclusionsabout the effect of the alumnus’s former position. Nevertheless, the small sampleindicates that the alumnus’s position may be a factor. We find that 31 companieshave an officer who was a partner or senior partner at the audit firm and, of these,22 companies (71.0 percent) form affiliations. We find that 58 companies haveofficers who were below the partner level and, of these, only 33 companies (56.9percent) appoint the officer’s former firm. The difference between these two fre-quencies (71.0 percent versus 56.9 percent) is quite large, but it is not statisticallysignificant because the sample is small (z-statistic � 1.30).

Fourth, we further investigate the finding that a company is more likely toform an affiliation if the officer left the audit firm recently. In Table 6, the RECENTvariable equals 1 if the officer left the audit firm no more than two years prior tothe auditor change and 0 otherwise. In additional tests, we redefine the RECENTvariable to capture horizons longer than two years. The RECENT coefficientsremain positive and significant at the 10 percent level for horizons of up to sixyears, but they become insignificant for longer horizons. The audit committee vari-ables remain significant at less than the 1 percent level in each regression.

Fifth, our sample excludes companies that hire non – Big 5 firms and itexcludes alumni who are not CEOs or accounting/finance officers. We investigatewhether the results still hold when these observations are added to the sample. Theresulting alumni sample increases from 189 to 267 companies. In untabulatedresults, affiliations remain negatively associated with audit committee independ-ence and meeting activity (significant at less than the 1 percent level). We alsoinvestigate whether the results remain when the 53 non–Big 5 predecessor auditfirms are dropped from the sample (n � 136). In untabulated results, the auditcommittee independent and meeting coefficients remain significant at less than the5 percent level.

Finally, we include four additional corporate governance variables: (a) a dummyvariable indicating whether the CEO is chairman of the board, (b) the percentagestock ownership of audit committee members, (c) the ownership of directors andofficers as a group, and (d) the CEO’s ownership. These four variables are insig-nificant in explaining the formation of affiliations, whereas the audit committeeindependence and meeting coefficients remain significant at less than the 1 percentlevel.



5. Conclusions

This study investigates how audit committees and the alumni of audit firms influ-ence the company’s choice of incoming audit firm. There are two main findings.First, companies tend to appoint their officers’ former audit firms rather than alter-native firms. Therefore, alumni confer significant economic benefits on theirformer firms. Interestingly, the tendency to form affiliations is significantly greaterif alumni left their former audit firms more recently. Second, we find that compan-ies are less likely to appoint officers’ former firms if audit committees are moreindependent. Therefore, independent audit committees reduce the influence ofalumni over audit firm appointments.

A limitation of our study is that we are unable to discriminate between alterna-tive explanations for why alumni prefer to appoint their former firms. One reasonmay be that alumni have attachments toward their former firms (Iyer et al. 1997).Alternatively, officers may want smooth working relations with their auditors(Beattie and Fearnley 1995). Finally, officers may value the influence that theyexert over affiliated audit firms (Menon and Williams 2004; Lennox 2005). A secondlimitation of the study is that companies are not required to disclose more than fiveyears’ biographical information for their officers. Therefore, our alumni samplemay not include all officers who previously worked for audit firms.

Endnotes1. Prior studies document mixed evidence on the effect of industry specialization on

auditor changes. Williams (1988) finds that an auditor change is more likely if the incumbent firm is not a specialist in the company’s industry. On the other hand, Eichenseher and Shields (1983) find no association between auditor changes and industry specialization.

2. The pseudo R2 in the five models are 2.3 percent for AA, 2.0 percent for DT, 1.9 percent for EY, 1.5 percent for KPMG, and 1.7 percent for PWC. The low explanatory power indicates that it is difficult to explain why a company hires one Big 5 firm instead of another.

3. “Gray” directors include former employees of the company or a related entity, relatives of management, professional advisers to the company (for example, legal counsel), interlocking directors, and directors who have significant transactions or business relationships with the company (as defined by items 404(a) and (b) of Regulation S-X).

4. There are 92 companies in which the outgoing audit firm’s departure results in the breakup of an existing affiliation. In general, an affiliation can be broken or created when there is either a change of auditor or a change of executive (see Lennox 2005). In this study, we examine auditor changes but not executive changes. Therefore, the number of newly created affiliations (105) is not directly comparable to the number of broken affiliations (92).

5. If the company does not have an AA alumnus, the company hires AA with a predicted probability equal to exp(0.33*SPECIALIST_AA � 0.26*MATCH_AA � 0.01*Ln(ASSETS) � 0.02*FORMER_BIG5 � 1.36) (1 � exp(0.33*SPECIALIST_AA � 0.26*MATCH_AA � 0.01*Ln(ASSETS) � 0.02*FORMER_BIG5 � 1.36).



6. If the company has an AA alumnus, the company hires AA with a predicted probability equal to exp(1.64 � 0.33*SPECIALIST_AA � 0.26*MATCH_AA � 0.01*Ln(ASSETS) � 0.02*FORMER_BIG5 � 1.36) [1 � exp(1.64 � 0.33*SPECIALIST_AA � 0.26*MATCH_AA � 0.01*Ln(ASSETS) � 0.02*FORMER_BIG5 � 1.36)].

7. According to the Audit Analytics data base, the Big 5 firms earned the following audit fees (total fees) in 2001: Arthur Andersen $465 million ($1,439 million); Deloitte & Touche, $537 million ($1,664 million); Ernst & Young, $581 million ($1,671 million); KPMG, $492 million ($1,245 million); and PricewaterhouseCoopers, $978 million ($3,548 million). According to annual reports filed with the SEC Practice Section, the Big 5 firms had the following numbers of personnel (CPAs) in 2001: Arthur Andersen, 27,867 (4,441); Deloitte & Touche, 22,300 (6,288); Ernst & Young, 22,256 (7,218); KPMG, 18,176 (5,761); and PricewaterhouseCoopers, 43,134 (8,391). Also, the annual reports disclose that the Big 5 firms had the following numbers of SEC clients in 2001: Arthur Andersen, 2,311; Deloitte & Touche, 2,877; Ernst & Young, 2,923; KPMG, 1,808; and PricewaterhouseCoopers, 3,025. Thus, by all these criteria, PricewaterhouseCoopers is the largest of the Big 5 audit firms.

ReferencesAbbott, L. J., and S. Parker. 2000. Auditor selection and audit committee characteristics.

Auditing: A Journal of Practice & Theory 19 (2): 47–66.Abbott, L. J., S. Parker, and G. F. Peters. 2004. Audit committee characteristics and

restatements. Auditing: A Journal of Practice & Theory 23 (1): 69–87.Ali, A., and S. Kallapur. 2001. Securities price consequences of the Private Securities

Litigation Reform Act of 1995 and related events. The Accounting Review 76 (3): 431–61.

Anderson, R. C., S. A. Mansi, and D. M. Reeb. 2004. Board characteristics, accounting reporting integrity, and the cost of debt. Journal of Accounting and Economics 37 (3): 315–42.

Balsam, S., J. Krishnan, and J. Yang. 2003. Auditor industry specialization and earnings quality. Auditing: A Journal of Practice & Theory 22 (2): 71–97.

Beasley, M., J. V. Carcello, D. R. Hermanson, and P. D. Lapides. 2000. Fraudulent financial reporting: Consideration of industry traits and corporate governance mechanisms. Accounting Horizons 14 (4): 441–54.

Beattie, V., and S. Fearnley. 1995. The importance of audit firm characteristics and the drivers of auditor change in UK listed companies. Accounting and Business Research 25 (100): 227–39.

Braiotta, L., Jr., R. S. Hickok, and J. C. Biegler. 1999. The audit committee handbook, 3rd ed. New York: Wiley Europe.

DeFond, M. 1992. The association between changes in client firm agency costs and auditor switching. Auditing: A Journal of Practice & Theory 11 (1): 16–31.

DeFond, M., and J. R. Francis. 2005. Does auditing matter? Auditing: A Journal of Practice & Theory 24 (Supplement): 5–30.

Dun & Bradstreet, Inc. D&B reference book of corporate management: America’s corporate leaders. Short Hills, NJ: Dun & Bradstreet.



Eichenseher, J. W., and D. Shields. 1983. The correlates of CPA-firm change for publicly-held companies. Auditing: A Journal of Practice & Theory 2 (2): 23–37.

Ferguson, A., J. R. Francis, and D. Stokes. 2003. The effects of firm-wide and office-level industry expertise on audit pricing. The Accounting Review 78 (2): 429–48.

Francis, J. R., and E. R. Wilson. 1988. Auditor changes: A joint test of theories relating to agency costs and auditor differentiation. The Accounting Review 63 (4): 663–82.

Francis, J. R., K. Reichelt, and D. Wang. 2005. The pricing of national and city-specific reputations for industry expertise in the U.S. audit market. The Accounting Review 80 (1): 113–36.

Istwan, D. F., and J. B. Wollman. 1976. Turnover in CPA firms. CPA Journal 46 (7): 21–5.Iyer, V. M., E. M. Bamber, and R. M. Barefield. 1997. Identification of accounting firm

alumni with their former firm: Antecedents and outcomes. Accounting, Organizations and Society 22 (3–4): 315–36.

Johnson, W. B., and T. Lys. 1990. The market for audit services: Evidence from voluntary auditor changes. Journal of Accounting and Economics 12 (1–3): 281–308.

Karamanou, I., and N. Vafeas. 2005. The association between corporate boards, audit committees, and management earnings forecasts: An empirical analysis. Journal of Accounting Research 43 (3): 453–86.

Klein, A. 2002a. Audit committee, board of director characteristics, and earnings management. Journal of Accounting and Economics 33 (3): 375–400.

Klein, A. 2002b. Economic determinants of audit committee independence. The Accounting Review 77 (2): 435–52.

Lennox, C. 2000. Do companies successfully engage in opinion-shopping? Evidence from the UK. Journal of Accounting and Economics 29 (3): 321–37.

Lennox, C. 2005. Audit quality and executive officers’ affiliations with CPA firms. Journal of Accounting and Economics 39 (2): 201–31.

Matsumoto, D. A. 2002. Management’s incentives to avoid negative earnings surprises. The Accounting Review 77 (3): 483–514.

Menon, K., and D. Williams. 2004. Former audit partners and abnormal accruals. The Accounting Review 79 (4): 1095–118.

National Office of Audit and Accounting. 2001. Audit committee guide for public companies. Bloomington, MN: McGladrey & Pullen, LLP.

Shu, S. 2000. Auditor resignations: Clientele effects and legal liability. Journal of Accounting and Economics 29 (2): 173–205.

Weisbach, M. 1988. Outside directors and CEO turnover. Journal of Financial Economics 20 (1): 431–60.

Willenborg, M. 1999. Empirical analysis of the economic demand for auditing in the IPOs market. Journal of Accounting Research 37 (1): 225–38.

Williams, D. D. 1988. The potential determinants of auditor change. Journal of Business, Finance and Accounting 15 (2): 243–61.

Zmijewski, M. E. 1984. Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research 22 (Supplement): 59–82.
