1

OFFICE-LEVEL AUDIT PARTNER ROTATION AND GOING CONCERN

OPINION ISSUANCE

John Goodwin

Associate Professor

School of Accounting and Finance

The Hong Kong Polytechnic University

8/F, Li Ka Shing Tower

Hunghom, Kowloon, HONG KONG

Tel: (852) 2766-7077 Fax: (852) 2330-9845

Email: afgood@inet.polyu.edu.hk

Ferdinand A. Gul

Professor

School of Business

Monash University

Jalan Lagoon Selatan,

Bandar Sunway, 46150,

Selangor Darul Ehsan,

MALAYSIA

Tel: (603) 5514-6000 Fax: (603) 5514-6192

Email: ferdinand.gul@buseco.monash.edu.my

Acknowledgements: The comments of Andy Chui, Robert Halperin, Huang Xu,

Sydney Leung, Huiwen Liu, Chung-ki Min, Bin Srinidhi, Steven Salterio, Srinivasan

Sankaraguruswamy, Nancy Lixin Su, Steve Wei, Franco Wong, Donghui Wu, Wayne

Yu, workshop participants at the City University of Hong Kong, The Hong Kong

Polytechnic University, participants at the 2011 European Accounting Conference in

Rome, the editor, and two anonymous reviewers are much appreciated. We are grate-

ful to The Hong Kong Polytechnic University for financial support (project codes: G-

U514 and G-U774) and the research assistance of Susanna Andreassen and Angel

Sung. We are responsible for any errors.

2

OFFICE-LEVEL AUDIT PARTNER ROTATION AND GOING CONCERN

OPINION ISSUANCE

We find that office-level audit partner rotation is negatively related to going concern

opinion issuance for Australian, financially-distressed clients audited by the Big4

audit firms. When office-level audit partner rotation is higher, issued going concern

opinions are more accurate and the positive relation between high total accruals and

going concern opinion issuance is weaker. But these relations only exist for the years

just after introduction of mandatory partner rotation. We interpret this evidence as

auditors behaving less conservatively with their clients as office level audit partner

rotation increases, and believe this change in auditor behavior is motivated by a desire

to reduce the risk of clients switching. We find no relation between audit partner

rotation at the client level, partner tenure and going concern opinion issuance and we

reconcile our results for partner tenure with prior studies reporting a negative relation.

Keywords: office -level audit partner rotation; audit partner tenure; auditor

conservatism; going concern opinion;

Data availability: data used in this study are available from public sources as

identified in the paper.

3

1. INTRODUCTION

Whether auditor rotation can improve auditor independence has attracted considerable

interest over the last two decades. Early proponents argue that auditor rotation can

prevent client-auditor long term relationships that could impair independence

(DeAngelo 1981; Johnson and Lys 1990; Deis and Giroux 1992; Raghunathan et al.

1994). As a result of these early calls and corporate scandals such as HIH in Australia

and Enron in the U.S., laws were passed requiring audit partners to rotate off clients.

However these rules could have unintended consequences (Nelson 2006). In this

spirit we examine if there is a difference in audit quality between audit offices that

rotate partners relatively more than others.

We use Australian data to examine this question as the names of audit partners are

required to be publicly disclosed on the audit report in that country, and to date

studies have only examined the client-specific link between partner rotation and audit

quality with conflicting results. The evidence in Hamilton et al. (2010), Carey and

Simnett (2006) and Ye et al. (2011) suggests that partner rotation improves audit

quality, Lai and Cheuk (2005) and Chi and Huangs' (2005) evidence suggests partner

rotation has no affect, and evidence in Chen et al. (2008) and Chi et al. (2009)

suggests that partner rotation worsens audit quality.

Auditors do not work in isolation but in teams and across teams, and auditor behavior

is pervasive across an office. For example, major events in the workplace can change

staff behavior, and in their work auditors use decision aids to provide consistency in

their decision making. The introduction of mandatory partner rotation is an event that

can alter auditor behavior because it could have disruptive effects for the office staff

4

or it could change auditor conservatism. If rotation has reverberating effects then this

is an unintended consequence of audit partner rotation.

That changes in worker teams could have disruptive effects is not new and has been

identified in the organizational literature (see for example, Gully et al. 1995; Liang et

al. 1995 and Tesluk and Mathieu 1999). If these types of disruptive effects

reverberate then rotation can have adverse consequences for audit quality across an

audit office. Partners believe that rotation increases the likelihood of client switching

to other audit firms (Daugherty et al. 2009). Faced with impending client loss from

partner rotation, auditors could be less conservative with their clients because auditor

conservatism increases the risk of clients switching (Krishnan 1994). That auditors

have differences in their levels of conservatism and change those levels is also not

new. Cahan and Zhang (2006), Geiger et al. (2006), and Feldman and Read (2010)

provide supporting evidence. We examine which of these two explanations is more

likely in explaining the results.

For each audit office for each fiscal year we measure the office rotation rate in four

different ways which we detail below in Section III, but generally, the rate is the

number of scaled mandatory partner rotations. We then test whether these rates (in

logarithmic form) are associated with the auditor’s propensity to issue a going

concern opinion for a sample of financially-distressed Australian clients. Our results

using 1,619 client years audited by Big 4 firms for 2004 to 2010, show that offices

with a higher rotation rate are associated with a lower propensity to issue going

concern opinions. We find that going concern audit opinions issued in high rotation

rate offices are more accurate than low rotation rate offices, and that high rotation rate

5

offices are less conservative with their clients. We also find that these relations are

not persistent. Collectively, this evidence suggests that high rotation rate offices have

lower audit quality for the average client after the initial shock of mandatory partner

rotation's introduction. We argue that auditors in high rotation rate offices are

motivated to lower conservatism to reduce the risk of clients switching.

This study makes two main contributions. In conducting the first examination of the

office-level effects of partner rotation on audit quality, we provide evidence of an

unintended consequence of partner rotation rules. That being lower auditor

conservatism in the years immediately after introduction of the rotation rule.1 Lower

conservatism is not observed after three years after introduction. Second, we show

that partner tenure is unimportant in explaining going concern opinion issuance,

mainly because tenure is correlated with board size and a proxy for a partner's recent

workload, both of which are correlated with the going concern dependent variable.

We believe that prior studies could have reached erroneous conclusions about partner

tenure and going concern opinion issuance because we find that long-tenured partners

are more accurate with their going concern opinions in the presence of these two

variables.

The next section presents the background of the study and the development of the

hypothesis, while Section III discusses the research methodology. Section IV presents

and discusses the results and Sections V and VI present results and provide discussion

of additional tests. Section VII is a reconciliation with related studies and Section VIII

concludes.

1 This suggests that other audit quality proxies like earnings management could be worse although we

do not examine them for brevity.

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II. BACKGROUND AND HYPOTHESIS

Partner Rotation Requirements

In 2002 the Institute of Chartered Accountants in Australia and CPA Australia issued

Professional Statement F1, requiring partner rotation (hereafter rotation) after seven

consecutive years beginning with assurance reports dated after December 30 2003.

For the vast majority of clients, this rule is equivalent to "the audits of clients with

fiscal years ending on or after 31 December 2003."2 In 2004 rotation was enshrined

in legislation with the passing of the Corporate Law Economic Reform Program 9

(CLERP 9 Commonwealth of Australia 2004), and this law requires rotation after five

consecutive years for fiscal years beginning after June 30 2006. For the vast majority

of clients, this rule is equivalent to "auditing clients with fiscal years ending on or

after 30 June 2007". F1 was subsequently amended to a five-year rotation period to

mirror the law. Similar requirements are included in the Sarbanes-Oxley Act U.S.

House of Representatives 2002) where rotation is required every five years, although

an important difference is that in Australia the partner may begin auditing the client

after two years and the requirement is five years in the U.S. The tenor of the

legislation is that rotation is beneficial for audit quality and that more rotation is

preferred. There are no limits to the number of rotations in any of these

pronouncements and non mandatory rotation continues at only slightly higher rates

after 2003 than before 2003, as Table 2 shows.3

2 There are no listed clients with fiscal year ends of October through November in 2003 and the annual report had

to be lodged within 75 days from fiscal year end in 2003, meaning that most September year end clients would

have lodged their accounts (along with audit reports signed) by 31 December 2003. Perhaps the only exceptions

are clients who have not lodged within the required time and the audit report is signed on or after 31 December

2003. These are probably only suspended clients and these are more common with the non Big4 clients. 3 It is worth noting that voluntary rotation could have occurred before F1's start date as a result of the Australian

Government, in 1996, recommending rotation, and the International Federation of Accountants (IFAC) updated

“Independence Requirements for Assurance Engagements” in November 2001 specifying that lead partners should

rotate after seven consecutive years.

7

Prior Studies

While there are no studies that examine rotation at the office level, we are aware of

seven that examine it at the client level. The most closely related studies to the

present study are Carey and Simnett (2006), who report that partner tenure (hereafter

tenure) more than seven years is associated with a lower likelihood of issuing a going

concern opinion and tenure less than three years is associated with a higher likelihood,

for a sample of distressed Australian clients. They conclude that longer tenure is

associated with lower audit quality.4 And Ye et al. (2001), who report that tenure

(measured continuously) is negatively associated with the likelihood of going concern

opinion issuance, using a sample of distressed Australian clients. As we find no

relation between tenure and going concern opinion issuance, we reconcile these

studies' results with ours in Section VII.

We briefly cover the other five studies using different audit quality proxies to us to

highlight the mixed results among them. Hamilton et al. (2010) find that rotation is

associated with less aggressive accounting for a sample of Australian clients with high

prior-period accruals. They conclude that rotation results in "some improvement in

accounting quality" (Hamilton et al. p 1). Lai and Cheuk (2005) use a sample of

Australian clients to examine the relation between rotation and financial reporting

timeliness, proxied by the audit reporting lag. They find no relation between the two.

Using Taiwanese data, Chi and Huang (2005) initially find that longer tenure is

associated with higher abnormal accruals but that audit firm tenure subsumes much of

tenure's importance when added to their model. These two studies suggest that

rotation has no effect on audit quality. Chen et al. (2008) find that long tenure is

4 Carey and Simnett (2006) also find that long tenure is not associated with discretionary accruals and

some evidence that long tenure is positively associated with just missing and just beating earnings

benchmarks.

8

negatively associated with discretionary accruals after controlling for audit firm

tenure using Taiwanese data, and Chi et al. (2009), find some evidence that rotation is

positively associated with earnings management also using Taiwanese data. These

two studies suggest that rotation is detrimental for audit quality.

What the literature has not considered is whether rotation has any effects for audit

quality across an audit office. A reverberating effect from rotation could manifest

through a psychological effect on staff, including lower audit team cohesiveness. As

noted, the management literature is supportive here. We call this office-level

disruption and expand on it below. Another way is via risk management, including

electronic decision aids and their application of them. Auditors use electronic

decision aids in client continuance decisions because they can provide consistency in

decision making (Bedard et al. 2008 p 200). These aids, their outputs and auditors

application of the outputs, may not be static over time or across offices. The evidence

in Kachelmeier and Messier (1990), Bedard and Graham (2002) and O'Donnell and

Schultz (2005) can support these arguments. We also argue that rotation can effect

auditor conservatism and we expand on this below. Either of the above two

explanations could be observed in regressions because the majority of clients (about

94 percent - see Table 2) are not subject to mandatory rotation in a fiscal year. 5

Audit Quality at the Office Level

Office Level Disruption

Auditors work in teams and can work across teams. We are not aware of any related

research but rotation could change the composition of teams in non rotated clients. A

5 The total of within office and across office mandatory rotations for the 2004 to 2010 years is 361 and

the total client years is 5,789 giving a percentage of about 6 percent (see Table 2).

9

partner may prefer to work with a particular team member, thereby 'forcing' a switch

of a team member, and team members with particular types of experience may move

to another client if the incoming partner is relatively less experienced in the client's

industry or with a client of that size for example. Irrespective of the reason, team

changes can alter the team's psychological traits such as cohesiveness, norms, affect

and cognition, and the extent of within-group agreement; which, in turn can affect the

team's performance, judgments and effectiveness. Turnover in teams can also

adversely impact a team's ability to learn. The management literature supports these

conjectures (see for example, Gully et al. 1995; Jehn 1995; Forgas 1990; Liang et al.

1995; Tesluk and Mathieu 1999 and Carley 1992). On the other hand, job rotation

can be regarded by employees as a career-enhancing experience. In a study of 255

finance employees in a large pharmaceutical company, Campion et al. (1994) found

job rotation to be useful for enhancing employees' business skills, including an

improved knowledge of the organization's functional areas and operations. This

suggests that rotation of other audit team members can have beneficial effects for

audit quality. Another reason is that the introduction of mandatory rotation could

have reduced the incidence of rotation of other audit team members, perhaps because

the audit office believes that team continuity should be preserved as far as possible in

a mandatory rotation regime. Importantly, Campion et al.'s (1994) study also

identified costs of job rotation for non-rotated employees; namely, a decrease in

productivity, job satisfaction and motivation in teams that both gained and lost rotated

employees. Thus, even if the remaining audit team members do not change when a

partner rotates, it does not necessarily follow that there will be no deleterious effects

from rotation. To the extent that these costs of rotation are pervasive then, on balance,

rotation is expected to have adverse consequences for audit quality across the office.

10

While the above arguments could apply at the client level the incentives are stronger

for auditors to behave otherwise at that level. Regulatory and professional body

scrutiny focuses on rotation not on non rotation. For example, the Australian

Securities and Investments Commission (ASIC) conducts periodic reviews of audit

firms as part of its audit inspection program and issues policy statements about

rotation, not specifically about non rotation. This scrutiny could influence partners

with rotated clients, more than partners with non rotated clients to perform within the

spirit of the legislation. Additionally, prior empirical evidence supports a positive

relation between short tenure and going concern opinion issuance (Carey and Simnett

2006 and Ye et al. 2011), although this relation is for the average client and rotated

clients are a small percentage of them.6 With specific regard to disruption, some prior

research is consistent with the link between rotation at the client level and poor

quality audits. For example, using semi-structured interviews with office manager

partners and surveys distributed to U.S. audit partners, Daugherty et al. (2009) find

that rotation may have a negative overall impact on audit quality through its dilution

of client specific and industry expertise. Consequently, frequent rotation increases the

likelihood that partners will specialize in multiple industries at the expense of industry

depth. This suggests that rotation increases the partner work load and the workload of

other members of the audit team during the first few years of the rotation, that could

result in lower audit quality. But partners believe that while client-specific

information is lost at rotation this is not going to affect audit quality (Daugherty et al.

2009).7

6 The total of mandatory and non mandatory rotations for the 2004 to 2010 years is 1,190 and the total

client years is 5,789 giving a percentage of about 21 percent. Under a non mandatory rotation regime

the percentage is smaller (see Table 2). 7 Perhaps partners believe that the knowledge lost can be compensated for by increasing audit effort.

11

Auditor Conservatism

That auditors change their level of conservatism in response to external shocks has

been shown. Relative to non Big4 auditors, Geiger et al. (2006) reports that Big4

auditors were less conservative after introduction of the Private Securities Litigation

Reform Act. Auditors were more conservative after introduction of the Sarbanes

Oxley Act (2002) in the U.S. (Geiger et al. 2005 and Myers et al. 2008) and Cahan

and Zhang (2006) report that ex-Arthur Andersen clients were treated more

conservatively by their new auditors. Auditor conservatism is also not static. While

auditor conservatism increased immediately post SOX, its level returned to pre-Enron

levels in later years (Feldmann and Read 2010).

Auditor conservatism comes with the risk of the client switching to another audit firm

(Krishnan 1994), and auditors may behave less conservatively when faced with risk of

client loss (Vanstraelen 2002; Vanstraelen 2003).8 In the present study we examine

financially-distressed clients as they are more likely to switch (Haskins and Williams,

1990). Daugherty et al. (2009) report that the partners believe that rotation increases

the risk of switching, but reasons are not provided.9

The introduction of mandatory rotation is an external shock to audit firms and offices

that increases the risk of client switching because rotation changes the dynamic

between the audit office and the client. It is a reasonable assumption that offices

would permit their partners who have good client relations to remain with the client

8 For samples of Belgian clients, Vanstraelen (2002) reports a negative relation between going concern

opinion issuance and the proportion of clients lost by an audit firm in the prior fiscal year; and

Vanstraelen (2003) reports that auditors are four times more likely to switch after receiving a going

concern opinion in the last year of a mandated audit firm period, than in other years of that period. 9 The survey also examined the impact of the rotation rules on partners’ quality of life.

12

more often than replace them, meaning that rotation is not likely to improve this

dynamic. We are aware of no empirical studies on rotation and client switching,

perhaps because disclosure of the partner name is not required in most countries, but

results from surveys suggest that a client's relations with its audit team could be

correlated with the rotation event. Addams and Davis (1994, 1996) and Addams et al.

(2002) report that personal relationships between the client and the auditor featured

highly on reasons for audit firm selection, and that four factors all related to the

incumbent auditor's poor service, were most important for switching.10

Supporting

evidence is in Fried and Schiff (1981) and Eichenseher and Shields (1983).

Mandatory rotation introduces uncertainty into the quality of this relation.

A useful strategy for auditors to reduce the risk of client loss is to lower their

conservatism. Lowering conservatism is more likely to succeed than is lowering fees,

even for distressed clients, because a client's bankruptcy risk can increase upon

receiving a going concern opinion for example (George et al. 1996; Pryor and Terza

2002; and Vanstraelen 2003). Thus if auditors behave less conservatively with their

clients, perhaps due to the perceived threat of switching, then the relation between the

office rotation rate and going concern opinion issuance should be negative.

As noted above, regulatory pressures and prior research suggests that the relation

could be different at the client level and these arguments seem to apply equally

whether disruption or lower conservatism is the explanation. With specific regard to

lower auditor conservatism, the threat of client loss is likely higher for non rotated

clients because any 'damage has been done' with them. That is, given that clients

10

The four factors in order of importance are: Not sufficiently proactive, Lack of responsiveness, No

new ideas to help the company and Inadequate understanding of the company (Addams et al. 2002 p

63).

13

place a high value of relations with the auditor, auditors can do less to appease a

rotated client than a non rotated client. As a consequence there is less incentive for

auditors to lower conservatism for rotated clients vis-à-vis non rotated clients.

We test the alternate-form hypothesis (H1) as the predicted relations under either

explanation are the same:

H1: There is a negative relation between the office-level rotation rate and the

issuance of a going concern opinion

III. RESEARCH METHOD

An office-level rotation is coded as unity when a client has a different audit sign-off

partner to that of the prior fiscal year and the audit firm and office which audits the

client are the same for those two fiscal years. We separate mandatory from non-

mandatory rotations and focus on mandatory rotations to provide more meaningful

policy implications from the results.

We proxy for reverberating effects using office rotation rates. We expect them to

capture the collective effects of rotation on an audit office's behavior, similarly to

finance studies using the proportion of busy directors to capture the collective effects

of directors' busyness on a client boards' behavior (see for example, Ferris et al. 2003

and Fich and Shivdasani 2006).

There are no prior empirical studies or theory on office-level partner rotation so we

treat the problem as an empirical exercise and use four different measures.11

Our first

11 We are grateful to the reviewer for identifying different measures of office-level rotation rates.

14

experimental variable is the number of mandatory rotations divided by the number of

clients in the office (MCLI), the second is the number of unique partners involved in

mandatory rotations divided by the number of clients in the office (MUCLI). The

number of mandatory rotations divided by the number of partners in the office is the

third (MPAR), and the number of unique partners involved in mandatory rotations

divided by the number of partners in the office is the fourth (MUPAR). The

corresponding non-mandatory rates are denoted NMCLI, NMUCLI, NMPAR and

NMUPAR respectively. We include these variables in the models because they are

generally correlated with the dependent variable GCREPORT, and they are correlated

with their mandatory counterparts (see Table 4). In multivariate tests we add unity to

these rates prior to taking the logarithm to improve symmetry and to reduce the effect

of outliers.

Although the office rotation rates are strongly positively correlated within each of the

mandatory and non mandatory groups (see Table 4), being the proportion of clients

affected by rotations we favor MCLI and NMCLI as measures of the impact of

rotation on an office. Audit offices generate revenue only from clients, so auditor

behavior is probably more closely related to its client base than with its partner base.

Using number of partners as the base can result in office rotation rates that are

inconsistent with expectations when one is interested in the effect of office rotation on

auditor behavior. Using unique partners in the numerator can also result in

inconsistent numbers because an office that rotates a partner is coded as unity

irrespective of the number of his rotations. For example, it is difficult to argue that

any number of rotations of the same partner has the same effect on auditor behavior in

15

the office ceteris paribus. To illustrate these points we provide two examples for

mandatory rotations from a large and a smaller-sized audit office in Appendix 1.

We examine only the Big4 audit firms as we do not have access to the audit firm's

client lists, meaning that our measures of the office level rotation rate is an estimate of

the 'true' rate. Measurement error in this rate is probably lower using the Big4 as their

client base likely comprises a larger proportion of listed clients. The coefficients for

the office rotation rates are insignificant in all models for the non Big4 sample

(untabulated) which could be caused by higher measurement error in the rates.

Data

The sample of Australian listed clients comprises electronic and hand-collected data

for the years 1995 through 2010, from the Morningstar Datanalysis and Finanalysis

databases. Our empirical tests begin with the first full fiscal year of mandatory

rotation in Australia, namely 2004, as all rotations are subject to endogeneity concerns

in the non-mandatory period and the 2003 mandatory rotations are few (see Table 2).

We exclude banks and insurance companies since the Altman (1968) Z-score control

variable is not appropriate for them. Since we also need usable data for the control

variables, the final sample is 1,619 financially-distressed client years comprising 581

unique clients. Table 1 shows the sample derivation.

Table 1 about here

Descriptive Statistics

Table 2 shows descriptive statistics for 1996 to 2010. The percentages of listed

clients audited by the Big4 has declined from about 65 in 1999 to about 44 in 2010,

consistent with client shedding. The number of audit firms and offices has declined

16

over the period due to the Price Waterhouse and Coopers and Lybrand merger in 1998

and the end of Arthur Andersen in 2002. Tenure has also declined slightly. It is

evident that rotations did occur before 2003 despite there being no requirement to

rotate, and they occurred at about the same rate after 2003. The means of the within-

office non mandatory rates before and after 2003 are about 0.13 for example

(untabulated).12

Our experimental variables use the mandatory within-office rotations

which are shown in the fourth column from the right. There is a 'spike' in mandatory

rotations in the 2007 year, and in Section VII we find that the 2007 year does not only

provide the significance for the experimental variables. As noted, the 2007 year is the

first that the rotation requirement was reduced from seven years to five. Clients who

switched between offices or audit firms and who have new partners as a consequence,

are excluded from the measurement of the experimental variables in order to measure

within-office effects of partner rotation and because clients who switch offices may be

treated differently by the new office. Rotations do occur across offices, but the

second column from the right shows that mandatory rotations of this type are rare,

with only 14 occurring in the sample period.13

We include both of these types of

across office rotations as a control variable denoted as OTHERR.

Table 2 about here

The Model

The dependent variable, GCREPORT, is coded as unity if the client received a going

concern opinion in its audit report for the fiscal year and zero otherwise. As noted,

we have four variables for each type of office-level rotation rate, namely mandatory

12

These raw data come from Table 2. For example, the mean for the 1996 to 2002 years is 681 non

mandatory rotations divided by 5,321 Big4 client years, which is about 13 percent. 13

Rotations can also occur across audit firms when both the client and the partner move to a new audit

firm but this type of rotation is very rare.

17

rotation rates (MCLI, MUCLI, MPAR and MUPAR) and non-mandatory rotation

rates (NMCLI, NMUCLI, NMPAR and NMUPAR). All of these variables are

positively skewed, often have zero values and are usually less than unity. In the

regressions we measure each variable as the natural logarithm of the rate plus unity to

improve their symmetry and to preserve their rankings.

With respect to the controls, we include three client-level rotation variables

corresponding to the data in Table 2. MR is equal to unity if the client had a within-

office mandatory rotation in a fiscal year and zero otherwise. The corresponding non

mandatory rotation is denoted NMR. The data for these two variables are shown in

the third and fourth columns from the right of Table 2. OTHERR is the sum of the

first and second columns from the right of Table 2, and it equals unity of the client

had a rotation that was not within the office in a fiscal year and zero otherwise. As

noted in Section II we have no expectation for these coefficients' signs. TENUREP is

the number of consecutive years that the incumbent audit partner has signed the audit

report for the client and it is capped at 10 years as our dataset begins in 1995. We

expect the TENUREP coefficient to be negative from Carey and Simnett (2006) and

Ye et al. (2011). TENUREF is the number of consecutive years that the incumbent

audit firm has audited the client and it is also capped at 10 years. Prior research is

inconclusive on this relation, (Levinthal and Fichman 1988; Vanstraelen 2000; Geiger

and Raghunandan 2002; Knechel and Vanstraelen 2007), so we have no expectation

for its coefficient. Francis and Krishnan (1999) report that clients with high accruals

are more likely to receive a going concern opinion because auditors act more

conservatively with those clients. We include an indicator equal to unity if the client's

scaled accruals (net income less net operating cashflows divided by lagged total

18

assets), is larger than the sample median and zero otherwise (HIGHTA). Its

coefficient should be positive for consistency with Francis and Krishnan (1999). The

office client-to-partner ratio is positively correlated with the office level mandatory

rotation rates (see Table 4), so we include the natural logarithm of the ratio of clients

to partners as a control for office busyness (CPRATIO). Analogous to studies of

board busyness (Fich and Shivdasani, 2006), we expect its coefficient to be negative.

Kallapur et al. (2010) report that earnings quality is lower when audit market

competition is higher, suggesting that the relation between competition and going

concern opinion issuance could be negative. We include the Herfindahl index

(HERFIND) as a proxy for audit market competition and we expect its coefficient to

be positive for consistency with Kallapur et al. (2010), as higher numbers indicate

lower competition in the present study. The size of the audit office is positively

related to going concern opinion issuance for Big4 firms (Francis and Yu, 2009). So

we include OFFICESIZE, measured as the natural logarithm of the sum of audit fees

paid to the office of the auditor which audits the client by all clients of that office, and

expect the coefficient to be positive. Client influence at the partner level, denoted

INFCLIPAR, is included because Chen et al. (2009) report this variable is important

in explaining audit qualification likelihood in their sample of Chinese listed

companies. It is measured as the ratio of a client's total fees (audit fees plus non audit

fees) relative to aggregate annual fees paid to the practice office for audits signed off

by the partner who audits the client. INCLIOFF is the ratio of a client’s total fees

(audit fees plus non audit fees) relative to aggregate annual fees paid to the practice

office which audits the client. We expect its coefficient to be positive, relying on

similar reasoning from Reynolds and Francis (2000). NATLEADER is an indicator

variable that equals unity if an auditor is the number one auditor in an industry in

19

terms of aggregated audit fees in a specific fiscal year, and zero otherwise, and

CITLEADER is an indicator variable that equals unity if an office is the number one

auditor in terms of aggregated client audit fees in an industry within that fiscal year.

The evidence is mixed on these two variables with Francis and Yu (2009) reporting

no relation and Reichelt and Wang (2009) reporting a negative relation. We have no

expectation for either of these coefficients. The remaining controls are more common

and we do not justify with prior research for brevity. Their expected signs are

consistent with Francis and Yu (2009) and those expectations are shown in Table 5.

SIZE is the natural logarithm of a client’s total assets. CASH is the sum of a client’s

total cash and investments divided by total assets. PRIORGC is an indicator variable

that equals unity if the client received a going concern opinion in its audit report for

the previous fiscal year and zero otherwise. REPORTLAG is the natural logarithm of

the number of days between a client’s fiscal year-end and its earnings announcement

date. DEBT is total liabilities divided by total assets at the end of the fiscal year.

LAGLOSS is an indicator variable that equals unity if earnings before interest, tax

and depreciation is negative in the prior fiscal year and zero otherwise. ALTMAN is

the Altman (1968) Z-score. RETURN is the annual raw stock return measured to the

end of the fiscal year. VOLATILITY is the standard deviation of monthly raw stock

returns measured to the end of the fiscal year. MB is the natural logarithm of the

client’s market value of equity to its book value of equity at fiscal year end. Some of

these variables are winsorized and we provide this detail in Table 3 for brevity. The

HERFIND, OFFSIZE, INFCLIOFF, INFCLIPAR, NLEADER and CLEADER

variables are measured using all listed Australian clients from 2004 through 2010.

We use the following logit regression models for testing hypothesis 1:

20

)1)(///()1( 10 εαα +++== CONTROLSMUCLIMCLIMUPARMPARfGCLogit

Model (1) is estimated using robust standard errors clustered by client and fiscal year

as outlined in Gow et al. (2010), and year and industry fixed effects using GICS

Industry Groups. Results for the industry and year indicator variables are not reported

for brevity.

Table 3 shows the descriptive statistics for the regression sample. It is evident that

even in a mandatory regime, some partners have not complied with the rules as the

maximum tenure is 10 years.

Table 3 about here

Table 4 shows the Pearson and Spearman correlation coefficients between the

regression variables. The experimental variables are generally negatively correlated

with GC_REPORT and the MPAR and MUPAR correlations are stronger. As

expected the correlations between the experimental variables are generally high, but

they range from about 0.55 to about 0.85, for reasons like those mentioned above and

illustrated in Appendix 1.

Table 4 about here

IV. RESULTS AND DISCUSSION

Table 5 shows results from estimating model (1) four times, once for each of the

experimental variables. A financially-distressed client is defined as a client that

reports negative net income and negative net operating cashflows in the same fiscal

year. The experimental variables, shown in the top row of each column in the

21

OFFICE LEVEL VARIABLES section, are all negative and significant at .10 and we

cannot reject Ho1. These results indicate a lower propensity to issue a going concern

opinion in offices with higher rates of mandatory rotation. The significance of these

coefficients declines as one moves from rates that use clients to those that user

partners as the base, probably because using the number of clients as the base better

measures rotations' office-level effects on auditor behavior. Significance also falls as

one moves within the two main rotation types from total rotations to unique partner

rotations (for example from MCLI to MUCLI). This is expected because, as noted

above, unique partner rotation rates are sometimes unrepresentative of office-level

effects.

The client level rotation variables are not significant indicating that the 'fresh eyes'

argument is not supported. One exception is OTHERR in equation (2) but its

importance is subsumed by other variables in the other models. Tenure is not

significant which is inconsistent with Carey and Simnett (2006) and with Ye et al.

(2011). We revisit this relation in section VII as noted. We find no evidence that

audit firm tenure is important consistent with our expectation. HIGHTA is positive

and significant (at .05) consistent with Francis and Krishnan (1999). We use this

variable in further tests below in Section V. Office busyness is not important for

going concern opinion issuance as the results for CPRATIO show. A caveat is that

there are other measures of office busyness (see Fich and Shivdasani, 2006). The

HERFIND coefficient's results indicate that higher audit market competition is

negatively related to going concern opinion issuance. Recall that higher values

indicate weaker market competition here. As noted Kallapur et al. (2010) find the

same directional relation with earnings quality. The OFFICESIZE positive relation is

22

consistent with Francis and Yu (2009). Client influence at the office or partner levels

are not significant. It is important to note here that the result for INFCLIPAR is not

necessarily inconsistent with Chen et al. (2009) because they examine the change in

sign and significance of that coefficient around an event. We find no evidence that

auditor specialization at the City or at the National levels are important, consistent

with Francis and Yu (2009). Of the remaining variables, SIZE, CASH, PRIORGC,

DEBT, BANKRUPTCY, RETURN and to a lesser extent, MB are significant and

consistent with expectations. REPORTLAG, LAGLOSS and VOLATILITY are not

significant at .10 and only LAGLOSS has a sign inconsistent with expectations. The

pseudo R-squared values are about 0.33 for the four models.

Table 4 about here

V. EXPLORING REASONS FOR THE RESULTS

In this section we rule out a competing explanation of knowledge loss and then

attempt to differentiate between the office-level disruption and lower auditor

conservatism explanations outlined above.

It can be argued that more rotations in an office results in more client-specific

knowledge being lost across the office. Thus the observed negative relations reported

in Table 5 could be due to knowledge loss and not due to office disruption or lower

conservatism by audit partners. If knowledge loss is the reason, we should observe a

more significant and negative coefficient for the rotation rates for the sample of

mandatory rotations, compared to the rest of the sample. For brevity, we do not report

results but for the mandatory rotation sample none of the rotation rates' coefficients

are significant and all are negative and significant in the other sample (at .10). This

23

suggests that audit quality could be lower for the non rotated clients. Knowledge loss

does not explain the negative relations and we turn next to our two possible

explanations.

We first examine the accuracy of going concern opinion issuance because the test can

be used as a measure of auditor conservatism (Geiger and Rama 2006) and the

expected relation between office disruption and going concern accuracy is obvious.

Specifically, the rates for Type I errors (going concern reports issued to clients that do

not subsequently become bankrupt) will be higher if office-level disruption is the

explanation for our results but these rates will be lower if auditors behave less

conservatively. This is expected because clients 'at the margin' of subsequent

bankruptcy are less likely to get a going concern report than more financially-stressed

clients if the lower conservatism explanation holds. The rates for Type II errors (not

issuing a going concern report to a client that subsequently becomes bankrupt) should

be higher under either explanation. That is lower Type I coupled with higher Type II

is evidence of lower auditor conservatism (Geiger and Rama 2006).

Univariate and multivariate logistic regression tests are used. Bankrupt clients are

defined as those clients who entered voluntary administration, liquidation or

receivership within twelve months of the audit report date (bankrupt).14

From 2004 to

14

In another Australian study examining bankrupt clients, Jones and Hensher (2004) include clients

that were suspended from quotation on the Australian Stock Exchange because they did not pay their

annual fee, in their bankruptcy definition. We do not include these clients because we believe our

measure more closely approximates that used in U.S. studies. However, there are relatively few

instances of non-fee paying clients that were suspended, and their exclusion did not alter Jones and

Hensher' (2004) inferences (see Jones and Hensher, 2004, footnote 23). As the sample size is so small,

we include 2010 to increase the sample as much as possible, but we only use clients with a fiscal year

end of June or earlier and measure the period to 9 months, because at the time of writing, sufficient

time had not passed to measure other clients reliably. We do not believe this poses any special

problems.

24

2010, there are 452 going concern opinions issued to distressed clients of the Big4

and 30 distressed clients are bankrupt.

We split all mandatory office-level rotation rates at the median for coding. Type I

error results, shown in Table 6, Panel A indicate that for 3 of the 4 tests, offices with

high mandatory rotation rates could be more accurate with their going concern

opinion issuance at the .05 level. For example, for MCLI, 6 (15) of the 224 (228)

clients with going concern opinions in the audit report are subsequently bankrupt in

the low (high) rotation rate offices, and the p-value is 0.049. Unfortunately there are

only 30 distressed, bankrupt clients, precluding a meaningful Chi-square test of Type

II errors so we report Fisher's exact test p-values. The proportions of errors are higher

in high rotation rate offices but none of the differences are significant at .10.

We can test Type I errors using logistic regression but not Type II errors due to the

small sample size. We estimate a model where the dependent variable is equal to

unity if the client is subsequently bankrupt after receiving a going concern opinion

and include all controls from model (1). Initial estimation proved unsuccessful due to

quasi complete separation, which is unsurprising given that we have only 18 bankrupt

clients in this sample. The three year indicators causing the problem were removed as

we are not aware of another solution. The results should be interpreted with some

caution. Panel B of Table 6 shows the summary results.

These results also indicate that high rotation rate offices are more accurate than low

rotation rate offices for MCLI and to a lesser extent MPAR but MUCLI and MUPAR

are highly insignificant. This suggests that MCLI has more accurate opinions at very

25

high levels of that rotation rate than do the other rates. To reconcile with the chi-

square tests we split each rate at its sample median and the results shown in Panel D

show that all are significant, consistent with the accurate prediction observations for

MUCLI and MUPAR being more bunched around the median than the other two

rates, despite all rates having more accurate observations above than below the

median. These results are suggestive of lower auditor conservatism, so we use

another test which does not have the concerns of small sample size.

Our second test relies on the arguments in Francis and Krishan (1999). If auditors

behave less conservatively in high rotation rate offices, their threshold for issuing

going concern opinions should be higher, that is, these auditors should be willing to

accept more inherent risk (Francis and Krishan 1999). If high office rotation causes

disruption the auditor’s threshold for issue going concern opinions should not be

higher. We include the joint effect of the accruals indicator variable (HIGHTA) and

the office rotation rates. A negative coefficient for the joint effect is expected if

conservatism is the explanation but we do not expect a negative coefficient if

disruption is the explanation.

Panel A of Table 7 shows summary results for the coefficients. Except for MULCI,

the coefficients for the joint effect are negative and significant at .10, suggesting that

the relation between going concern issuance and high accruals could be weaker in

high rotation rate offices. To be more confident, we estimate the predicted

probabilities of going concern opinion issuance at quartiles of office rotation rates, for

the significant joint effect coefficients. All other explanatory variables are set to their

26

medians. The industry and year indicator variables, MR, NMR, OTHERR,

NLEADER and CLEADER and PRIORGC all have zero medians .

Results in Panel B show that probabilities are higher for high accrual clients (except at

the maximum values), but the difference between the probabilities declines as the

rotation rate increases. For example at the 25th percentile of MCLI the difference is

about 10 percentage points and it is about 4 percentage points at the 75th percentile.

The other rotation rates show similar patterns. This means that the likelihood of

receiving a going concern opinion for clients with low versus high accruals becomes

closer as the rotation rate increases. Alternatively, the change in the probability as

one moves to higher percentiles is much larger (about three times as large from the

25th to the 75th for MCLI), for high rotation rate offices. This evidence is consistent

with lower auditor conservatism in higher rotation rate offices and supports the

accuracy results above. We conclude that the lower likelihood of going concern

issuance is caused by lower auditor conservatism and not by office-level disruption.

VI. IS THE LOWER CONSERVATISM AN EQUILIBRIUM CONDITION?15

The year 2007 is an outlier with 127 mandatory rotations (see Table 2). To examine

the influence of that year on the results and the persistence of lower conservatism, we

estimate model (1) and model (1) including the accruals joint effect with rotation rates

for the years 2004 to 2006, 2007 and 2008 to 2010. The results for the rotation rates

from model 1, shown on the top row of each Panel in Table 8, reveal that office

rotation rates are all negative and significant at (.05) for the pre-2007 years sample,

insignificant for the 2007 year and for the 2008 to 2010 years sample. Thus the 2007

15 We are grateful to the reviewer for pointing this out.

27

year is not causing the main results in Table 5 and the negative relation does not

persist after about 3 years after the introduction of mandatory rotation. In the bottom

row of each panel the interaction coefficients show that high rotation rate offices are

less likely to issue a going concern opinion for the 2004 to 2006 years sample, and the

2007 and the 2008 to 2010 years samples, all the coefficients for the joint effects are

insignificant, indicating that the lower conservatism in high rotation rate offices does

not persist either. This is consistent with an initial shock of mandatory rotation

temporarily reducing auditor independence. Small samples rule out meaningful going

concern opinion accuracy tests.

It is important to note that other measures of audit quality may not give the same

results for later years. Auditors in high rotation rate offices could be more willing to

tolerate higher accruals management for example in later years after rotation because

accruals management is within GAAP and a less risky choice for auditors than is

lowering their independence. As DeFond (2010) suggests, triangulation of audit

quality measures need not always be expected.

VII. RECONCILIATION WITH OTHER STUDIES

In this section we attempt to reconcile our findings for partner tenure with findings

from two studies reporting results that conflict with ours. In reporting a negative

relation between going concern issuance and tenure, Carey and Simnett (2006) and

Ye et al.s' (2011) results could support a rotation policy due to its client-specific

effect. Our results do not support a rotation policy due to its client-specific effect.16

16

Our results do not support a partner rotation policy in the initial years after introduction of the

rotation rules. Whether the reported lower conservatism behavior persists is another matter. Auditors

in high rotation rate offices could become more conservative if they perceive that their clients are less

likely to switch to other audit firms for example.

28

Carey and Simnett (2006) estimate a cross sectional logit regression using data from

1995, and Ye et al. (2011) estimate the same type of model using data from 2002.

Their estimations do not control for correlation in the regression residuals across

clients. Standard errors will be inefficient in the presence of across client residual

correlation (Petersen 2009 and Gow et al. 2010). Nor do their models include some

important explanatory variables so their estimates could be inefficient and biased.

Lastly, our results in Table 6, Panel C, indicating that long tenured partners are more

accurate is not consistent with lower audit quality as partner tenure increases.

We can only measure tenure from 1995, so we use the 2002 year, recode the tenure

variable to cap it at eight years, and estimate Ye et al.'s (2011) model as closely as

possible. We use the Ye et al. (2011) model since they also use a continuous measure

for tenure (their two models are quite similar).17

One caveat is we do not have access

to their ALUMNI variable and we do not expect its omission to pose any special

problems. Results of the replication, shown in the second column from the left in

Table 9, reveal that tenure is negative and significant only at .09 and Ye et al. (2011)

report a p-value of about .05 (far left column). Our sample size is 15 fewer (611

versus 626), possibly because we also need legitimate values for other controls for our

tests here, so this could explain this difference. Nevertheless, it is the change in

tenure's significance that matters in the present analysis. Estimating the model with

robust standard errors clustered by client and including all significant omitted

variables results in a more significant and negative coefficient, similar to Ye et al.

(2011) as the middle column shows. To arrive at this model specification we initially

17

Carey and Simnett (2006) use short and long tenured indicator variables (less than 3 and longer than

7 years respectively) and when we use their model in the subsequent analysis in the present section the

results are qualitatively the same.

29

add all our extra variables (except the duplicated variables) and then progressively

delete the most insignificant variable after each estimation, unless the variable is well

supported by theory or prior research (SIZE remains despite its insignificance for

example). After improving the model specification these results could still be used to

support rotation due to a client specific effect, so we conduct further tests.

We observe that it is the 56 clients with tenure longer than seven years that result in

the tenure coefficient's importance, because when we remove those clients its

coefficient is highly insignificant (p-values > 0.50), whether we use Ye et al.'s (2011)

model or our extended version (untabulated). And we observe client and partner

specific differences between these 56 clients and the rest of the sample. These 56

clients are smaller and have lower market-to-book ratios suggesting that their

governance mechanisms could be weaker. These partners have done more audits in

recent times, suggesting that they could have more expertise and experience, that they

could be overworked or that they could be a dominant partner in the office for

example.18

We add a board size variable, because larger boards can be associated

with better monitoring (Klein, 2002). We only have data to 2007 for board size. The

experimental variable (BOARDSIZE) is the natural logarithm of the number of

directors on the board. A variable proxying for the abovementioned partner attributes

has not been used before, so for each partner for each fiscal year, we measure the total

of the number of audits he did in the five fiscal years prior to the current year using a

rolling window, from 2002 to 2007. Five years is chosen only because it equals the

maximum number of consecutive years for a partner to audit a client under current

18 For the short and long (more than 7 years) tenured samples respectively, the means of some variables

are, Number of directors on the board: 4.34 and 3.98, Market to book ratio: 3.22 and 2.60, natural

logarithm of client size: 16.19 and 15.78 and the total of the number of audits signed off on by the

partner in the five fiscal years before the current year: 29.66 and 39.71. All these differences are

significant at .05.

30

rules. The experimental variable (PWLOAD) is the natural logarithm of the

aforementioned total plus unity. Consistent with our expectations, BOARDSIZE is

negatively correlated with tenure (Pearson = -0.05) and PWLOAD is positively

correlated with tenure (Pearson = 0.33), and they are both negatively correlated with

the going concern opinion issuance dependent variable (Pearsons = -0.12 and -0.08

respectively).19

When we add BOARDSIZE and PWLOAD to the model a different picture emerges.

These extra two variables' coefficients are negative and significant (at .05) as the

second column from the right shows, and tenure becomes significant only at .24. We

repeated the analysis using the pooled sample from 2002 to 2007 with more dramatic

effect, and these results are shown in the far right column of Table 9. Further,

untabulated results showed a negative, significant (at .10) tenure coefficient using Ye

et al.'s (2011) model and using the model without the extra two variables for this

pooled sample, and when we add these variables to the model predicting Type I errors

(see Panel C, Table 6), tenure remains significant at the .05 level. Board size and the

total of a partner's recent number of audits subsumes much of the information in

partner tenure when explaining going concern opinion issuance. Examining the

reasons for the negative relations for BSIZE and PWLOAD is beyond the scope of the

present paper.

Why do our results for tenure differ without the BOARDSIZE and PWLOAD

variables? The answer lies in other omitted variables and the Big4 sample that we use.

19 These reported Pearson correlation coefficients are for a pooled sample from 2002 to 2007.

Spearman coefficients are similar. All coefficients are significant at .01 except that the Spearman

coefficient between BOARDSIZE and TENUREP is significant at .10 in the pooled sample. The

coefficients for 2002 are almost the same except for the Pearson and Spearman correlation coefficients

between PWLOAD and GC_OPINION, which are negative and insignificant.

31

If we estimate Ye et al.'s (2011) model on the Big4 sample from 2002 to 2010, the

coefficient is -0.06 (p = .04), adding the significant omitted variables (except for

BOARDSIZE and PWLOAD), industry indicators and using two-dimension

clustering by client and year gives a negative coefficient with a p-value of about 0.16

(untabulated).

We do not use the same samples as these two studies, and we exclude ALUMNI from

our model due to data limitations, but the analysis in this section suggests that model

misspecification in those studies could be the reason for the differences in results. A

negative relation for tenure after controlling for board size and partner history may be

found, but long tenured partners are more accurate with their opinions as the results in

Table 7 show. A negative coefficient in explaining going concern issuance does not,

of itself, indicate lower audit quality. It could be that fewer, less-accurate opinions

are issued.

VIII. CONCLUSION

This study examines for the first time, the association between mandatory rotation at

the office level and going concern opinion issuance. Using a sample of financially-

distressed Australian clients from 2004 through 2010, audited by the Big4 audit firms,

we make several contributions to the auditing literature.

We find that audit offices with high rotation rates are less likely to issue a going

concern opinion. We find that the going concern audit opinions are generally more

accurate in high rotation rate offices. Tests using total accruals provide evidence

consistent with lower auditor conservatism in high rotation rate offices. We also find

32

that these relations are only observed in the years immediately after the introduction

of mandatory rotation. We argue that relatively high office rotation creates an

incentive for auditors to behave less conservatively to reduce the risk that clients will

switch to other audit firms. It seems that mandatory rotation's introduction

temporarily exacerbated the risk of client loss and the consequential change in auditor

conservatism is observable. We cannot test the switching explanation due to paper

constraints but this is an interesting area for future research. We find that the

propensity to issue a going concern opinion is not associated with rotations at the

client level, nor is it associated with tenure. A reconciliation with prior studies shows

that board size and a proxy for the size of the partner's recent workload render the

tenure variable unimportant in explaining going concern opinion issuance.

Reinvestigation of prior studies might be warranted. Further exploration of these two

extra variables' relations, including the underlying reasons for them, is left to future

research. We suggest monitoring as a reason for the former and partner expertise,

partner dominance in the office and overwork as reasons for the latter. Although not

our focus, we provide some insight into audit market competition and going concern

opinion issuance. As noted, Kallapur et al. (2010) report that higher audit market

competition is associated with higher earnings management, and we report that higher

audit market competition is associated with a lower propensity to issue a going

concern opinion. These results appear consistent but we did not examine the accuracy

of those opinions for paper scope reasons. We leave this to future research.

There are limitations to note. Although endogeneity concerns are probably weak for

mandatory rotations we cannot be as confident about non-mandatory rotations. If

endogeneity is present then inferences could change. Results for the four office

33

rotation rate variables are not always consistent. Nevertheless, we do not observe any

case where they predict opposite relations. We believe that MCLI is the best of the

four to proxy for the office-level effects of rotation.

34

TABLE 1

DERIVATION OF THE SAMPLE - 2004 - 2010

Client

years

Unique

clients

ASX-listed clients audited by Australian auditors 12,157 2,294

Sample after excluding non Big4 clients 5,789 1,297

Sample after excluding non-distressed clients 2,028 706

Sample after excluding clients in banks and insurance industries 2,018 701

Final sample after excluding clients with unusable control variables 1,619 581

A client with negative net income and negative net operating cash flow in the same fiscal year is defined as a distressed

client.

35

TABLE 2 - DESCRIPTIVE STATISTICS FOR ALL BIG4 CLIENTS 1996 - 2010

Year Clients Audit

Firms

Audit

Offices Partner Tenure

Within Office

Rotations

Within Audit Firm,

Across Office Rotations

N % of

total Mean Median

Mandatory

Non-

mandatory

Mandatory

Non-

mandatory

1996 687 65 6 42 - - 0 92 0 19

1997 701 65 6 39 - - 0 76 0 12

1998 734 65 5 33 - - 0 61 0 9

1999 758 65 5 33 - - 0 95 0 15

2000 800 63 5 35 - - 0 96 0 18

2001 828 63 5 35 - - 0 143 0 13

2002 816 62 4 30 - - 0 118 0 15

2003 796 59 4 30 - - 5 156 0 14

2004 808 55 4 32 2.81 2.00 29 85 3 16

2005 821 52 4 31 2.89 2.00 16 98 1 14

2006 863 51 4 30 2.77 2.00 30 114 2 9

2007 860 47 4 30 2.15 2.00 127 111 3 11

2008 836 45 4 29 2.26 2.00 54 114 1 15

2009 788 43 4 30 2.29 2.00 52 147 1 12

2010 813 44 4 30 2.58 2.00 39 78 3 5

Totals

2004-2010 5,789 48 - - 2.54 2.00 347 747 14 82

All 11,909 55 - - 2.54 2.00 352 1,584 14 197 Partner tenure is probably biased below the 'true' partner tenure more in earlier years, as we begin tenure's measurement in 1995 and it is capped at 10 years. We

say probably because we use only clients that are listed, as do all other studies that we have read, meaning the 'true' partner tenure is unknown when only listed

client data are used. A within-office rotation is recorded when the audit firm and audit office which audits the client are the same and the audit partner who audits

the client is different over two consecutive fiscal years. A within audit firm, across office rotation is recorded when the audit firm which audits the client is the

same and the audit office which audits the client and the audit partner who audits the client are different over those two consecutive fiscal years. The year 1995 is

'lost' because we need to measure partner changes for rotations. With regard to the Price Waterhouse and Coopers and Lybrand merger in 1998 and the Arthur

Andersen and Ernst and Young merger in 2002, the new merged audit firms are treated as different audit firms to the pre-merged firms.

36

TABLE 3 - DESCRIPTIVE STATISTICS FOR THE SAMPLE 2004 - 2010

Variables N Min Median Mean Max StdDev

GCREPORT 1,619 0 0 0.248 1.000 0.043

MCLI 1,619 0 0.047 0.057 0.470 0.060

NMCLI 1,619 0 0.104 0.119 0.442 0.082

MUCLI 1,619 0 0.037 0.044 0.470 0.041

NMUCLI 1,619 0 0.076 0.078 0.405 0.046

MPAR 1,619 0 0.134 0.214 1.124 0.234

NMPAR 1,619 0 0.318 0.413 1.386 0.316

MUPAR 1,619 0 0.115 0.165 0.693 0.139

NMUPAR 1,619 0 0.248 0.276 0.693 0.158

MR 1,619 0 0 0.051 1.000 0.219

NMR 1,619 0 0 0.150 1.000 0.357

OTHERR 1,619 0 0 0.024 1.000 0.153

TENUREP 1,619 1.000 2.000 2.566 10.000 1.545

TENUREF 1,619 1.000 5.000 5.503 10.000 3.073

HIGHTA 1,619 0 0 0.500 1.000 0.500

CPRATIO 1,619 0 1.153 1.390 2.386 0.592

HERFIND 1,619 0.159 0.233 0.251 1.000 0.104

OFFSIZE 1,619 9.259 16.564 16.470 18.659 1.267

INFCLIOFF 1,619 0.000 0.004 0.018 1.000 0.084

INFCLIPAR 1,619 0.001 0.088 0.207 1.000 0.280

NLEADER 1,619 0 0 0.276 1.000 0.447

CLEADER 1,619 0 0 0.366 1.000 0.482

SIZE 1,619 9.105 16.589 16.674 23.952 1.631

CASH 1,619 -0.002 0.248 0.346 3.977 0.310

PRIORGC 1,619 0 0 0.192 1.000 0.394

REPORTLAG 1,619 2.773 4.317 4.303 6.482 0.268

DEBT 1,619 0.001 0.137 0.305 2.908 0.432

LAGLOSS 1,619 0 1.000 0.899 1.000 0.301

ALTMAN 1,619 -67.602 0.879 1.976 82.606 15.302

RETURN 1,619 -0.899 -0.172 0.120 4.500 1.006

VOLATILITY 1,619 0.029 0.199 0.224 0.801 0.124

MB 1,619 -6.700 2.010 3.297 23.920 4.672

Variable Definitions:

Dependent Variable:

GCREPORT = indicator variable that equals unity if a client receives a going-concern opinion

in its audit report in a fiscal year, and zero otherwise;

Experimental Variables:

MCLI = natural logarithm of the number of mandatory partner rotations that occurred in an

office in a fiscal year divided by the number of clients in the office plus 1;

MUCLI = natural logarithm of the number of unique partners involved in mandatory rotations

that occurred in an office in a fiscal year divided by the number of clients in the office plus 1;

MPAR = natural logarithm of the number of mandatory partner rotations that occurred in an

office in a fiscal year divided by the number of partners in the office plus 1;

37

MUPAR = natural logarithm of the number of unique partners involved in mandatory

rotations that occurred in an office in a fiscal year divided by the number of partners in the

office plus 1;

Control Variables (Office-level Partner Rotation Variables):

NMPAR = natural logarithm of the number of non mandatory partner rotations that occurred

in an office in a fiscal year divided by the number of partners in the office plus 1;

NMUPAR = natural logarithm of the number of unique partners involved in non mandatory

rotations that occurred in an office in a fiscal year divided by the number of partners in the

office plus 1;

NMCLI = natural logarithm of the number of non mandatory partner rotations that occurred in

an office in a fiscal year divided by the number of clients in the office plus 1;

NMUCLI = natural logarithm of the number of unique partners involved in non mandatory

rotations that occurred in an office in a fiscal year divided by the number of clients in the

office plus 1;

Control Variables (Client-level Partner Rotation Variables):

MR = indicator variable that equals unity if the client has a within-office mandatory rotation

of an audit partner and zero otherwise;

NMR = indicator variable that equals unity if the client has a within-office non mandatory

rotation of an audit partner and zero otherwise;

OTHERR = indicator variable that equals unity if the client has an across-office or an across-

audit firm partner rotation and zero otherwise;

Other Control Variables:

TENUREP = number of consecutive years that the partner's name appears at the bottom of

the audit report for the client capped at 10 years;

TENURF = number of consecutive years that the audit firm audits the client capped at 10

years;

HIGHTA = indicator equal to unity if net income less net operating cashflows divided by

lagged total assets is greater than the sample median and zero otherwise;

CPRATIO = natural logarithm of the ratio of the number of clients in the office to the

number of partners in the office;

HERFINDAHL = sum of the squared market shares (where market share is measured using

audit fees paid to the incumbent audit office) of all audit firms in the city divided by the

number of audit firms in that city;

OFFSIZE = natural logarithm of the sum of audit fees paid to the office of the auditor which

audits the client, by all clients of that office;

INFCLIOFF = ratio of a specific client’s total fees (audit fees plus non audit fees) relative to

aggregate annual fees generated by the practice office which audits the client;

INFCLIPAR = ratio of a specific client’s total fees (audit fees plus non audit fees) relative to

aggregate annual fees generated by the partner in that office;

NLEADER = indicator variable that equals unity if an auditor is the number one auditor in an

industry in terms of aggregated audit fees in a fiscal year, and zero otherwise;

CLEADER = indicator variable that equals unity if an office is the number one auditor in

terms of aggregated client audit fees in an industry within that city in a fiscal year, and zero

otherwise;

SIZE = natural logarithm of a client’s total assets;

CASH = sum of a client’s total cash and investments divided by total assets;

PRIORGC = indicator variable that equals unity if a client received a going-concern opinion

in its prior fiscal year, and zero otherwise;

REPORTLAG = natural logarithm of the number of days between a client’s fiscal year-end

and its earnings announcement date;

DEBT = client’s total liabilities deflated by total assets, winsorized at the 1st and 99

th

percentiles;

LAGLOSS = indicator variable that equals unity if operating income after depreciation in

previous fiscal year is negative, and zero otherwise;

ALTMAN = Altman (1968) Z-score winsorized at the 1st and 99

th percentiles;

38

RETURN = client's 12-month raw stock return for the fiscal year, winsorized at the 1st and

99th percentiles;

VOLATILITY = standard deviation of the client's 12 monthly stock returns for the fiscal year,

winsorized at the 1st and 99th percentiles;

MB = client’s market value of equity to its book value of equity, winsorized at the 1st and

99th percentiles;

39

TABLE 4 -

PEARSON AND SPEARMAN CORRELATION COEFFICIENTS, (PEARSON ABOVE THE DIAGONAL)

VARIABLES

GC

RE

PO

RT

MC

LI

NM

CL

I

MU

CL

I

NM

UC

LI

MP

AR

NM

PA

R

MU

PA

R

NM

UP

AR

MR

NM

R

OT

HE

RR

GCREPORT

-0.040* 0.025 -0.004 0.056* -0.078* -0.049* -0.062* -0.063* -0.029 0.010 0.040*

MCLI -0.001

-0.085* 0.862* -0.001 0.830* -0.137* 0.771* -0.091* 0.243* -0.019 -0.005

NMCLI 0.035 0.045*

0.019* 0.734* -0.113* 0.778* -0.022* 0.653* -0.010 0.358* 0.003

MUCLI 0.009 0.965* 0.122*

0.118* 0.548* -0.106* 0.711* -0.074* 0.196* 0.008 -0.008

NMUCLI 0.054* 0.135* 0.816* 0.199*

-0.197* 0.298* -0.146* 0.510* 0.011 0.234* 0.036

MPAR -0.048* 0.890* -0.055* 0.831* -0.092*

0.072* 0.884* 0.083* 0.202* -0.010 -0.010

NMPAR -0.058* -0.093* 0.739* -0.066* 0.415* 0.095*

0.186* 0.835* -0.013 0.294* -0.027

MUPAR -0.050* 0.853* -0.033 0.828* -0.102* 0.984* 0.137*

0.174* 0.184* 0.025 -0.017

NMUPAR -0.054* 0.015 0.704* 0.028 0.582* 0.155* 0.902* 0.180*

0.001 0.230* -0.015

MR -0.029 0.221* -0.003 0.195* 0.020 0.195* -0.033 0.178* 0.001

-0.097* -0.036

NMR 0.010 0.012 0.315* 0.028 0.213* 0.007* 0.255* 0.013 0.235* -0.097*

-0.066*

OTHERR 0.040 -0.012 0.001 -0.005 0.017 -0.021 -0.034 -0.020 -0.021 -0.036 -0.066*

TENUREP -0.059* -0.114* -0.216* -0.125* -0.190* -0.087* -0.134* -0.088* -0.159* -0.285* -0.518* -0.194*

TENUREF -0.047* 0.026 0.023 0.013 0.011 0.010 -0.008 -0.004 -0.028 0.161* 0.071 0.039

HIGHACC 0.179* -0.001 -0.032 -0.008 -0.041* 0.027 0.015 0.032 0.007 -0.045* -0.019 0.085*

CPRATIO -0.138* -0.072* -0.182* -0.150* -0.471* 0.322* 0.462* 0.350* 0.355* -0.018 0.001 -0.031

40

TABLE 4 - CONTINUED

SPEARMAN CORRELATION COEFFICIENTS

VARIABLES

GC

RE

PO

RT

MC

LI

NM

CL

I

MU

CL

I

NM

UC

LI

MP

AR

NM

PA

R

MU

PA

R

NM

UP

AR

MR

NM

R

OT

HE

RR

HERFIND 0.074* -0.176* -0.017 -0.098* 0.197* -0.376* -0.330* -0.370* -0.289* -0.028* -0.073* 0.053*

OFFSIZE 0.123* 0.284* 0.087* 0.306* 0.257* 0.070* -0.234* 0.037 -0.182* 0.047* -0.037 0.007

INFCLIOFF -0.008 -0.144* 0.015 -0.154* -0.067* -0.065* 0.124* -0.053* 0.102* -0.006 0.055* -0.008

INFCLIPAR 0.137* 0.063* 0.118* 0.108* 0.253* -0.122* -0.196* -0.131* -0.136* 0.077* 0.122* 0.033

NLEADER -0.014 0.030 -0.078* 0.044* -0.056* 0.031 -0.075* 0.025 -0.093* 0.002 -0.020 -0.034

CLEADER 0.010 0.107* -0.073* 0.098* -0.103* 0.152* -0.037 0.145* -0.022 0.006 0.011 0.014

SIZE -0.165* 0.067* 0.097* 0.076* 0.133* 0.001 -0.016 -0.009 0.001 0.039 -0.018 -0.043*

CASH -0.245* 0.029 -0.083* 0.016 -0.083* 0.063* -0.026 0.059* -0.017 0.015 0.016 -0.006

PRIORGC 0.488* -0.009 -0.025 -0.007 -0.004 -0.032 -0.081* -0.032 -0.068* 0.002 -0.029 0.077*

REPORTLAG 0.014 -0.049* -0.072* -0.072* -0.192* 0.115* 0.171* 0.130* 0.139* 0.008 0.012 0.038

DEBT 0.331* -0.001 0.050* 0.023 0.098* -0.080* -0.074* -0.078* -0.077* 0.007 -0.015 0.014

LAGLOSS 0.003 -0.033 -0.025 -0.046* -0.070* 0.012 0.046* 0.014 0.028 -0.026 0.003 0.026

ALTMAN -0.386* -0.005 0.014 -0.022 -0.012 0.035 0.080* 0.030 0.086* -0.018 0.007 -0.032

RETURN -0.252* -0.043* -0.118* -0.060* -0.106* 0.013 -0.015 0.014 -0.017 0.038 -0.084* -0.038

VOLATILITY 0.093* 0.084* 0.035 0.082* -0.026 0.141* 0.092* 0.149* 0.075* -0.003 0.004 0.003

MB -0.065* 0.016 -0.088* 0.001 -0.106* 0.060* -0.005 0.062* -0.021 0.024 -0.003 0.027

41

TABLE 4 - CONTINUED

PEARSON AND SPEARMAN CORRELATION COEFFICIENTS, (PEARSON ABOVE THE DIAGONAL)

VARIABLES T

EN

UR

EP

TE

NU

RE

F

HIG

HT

A

CP

RA

TIO

HE

RF

IND

OF

FS

IZE

INF

CL

IOF

F

INF

CL

IPA

R

NL

EA

DE

R

CL

EA

DE

R

SIZ

E

CA

SH

GCREPORT -0.076* -0.046* 0.179* -0.129* 0.039 0.110* -0.009 0.085* -0.014 0.010 -0.156* -0.177*

MCLI -0.096* 0.032 0.001 -0.042* -0.047* 0.141* -0.056* 0.030 0.012 0.122* 0.054* 0.031

NMCLI -0.202* 0.029 -0.037 -0.059* -0.125* -0.001 -0.100* 0.044* -0.101* -0.050* 0.063* -0.058*

MUCLI -0.108* 0.022 -0.022 -0.205* 0.049* 0.188* -0.053* 0.091* 0.049* 0.076* 0.069* 0.021

NMUCLI -0.177* 0.008 -0.044* -0.417* 0.025 0.163* -0.114* 0.211* -0.061* -0.088* 0.128* -0.047*

MPAR -0.067* 0.014 0.037* 0.379* -0.246* 0.015* -0.082* -0.135* -0.042* 0.181* -0.010 0.048*

NMPAR -0.116* 0.016 0.009* 0.516* -0.262* -0.190* -0.112* -0.193* -0.106* -0.024 -0.050* -0.036

MUPAR -0.088* 0.004 0.025* 0.384* -0.240* 0.039 -0.102* -0.137* 0.002 0.172* -0.014 0.044*

NMUPAR -0.097* 0.000 0.011 0.464* -0.231* -0.148* -0.149* -0.158* -0.126* -0.050* -0.030 -0.027

MR -0.234* 0.161* -0.045* -0.014 0.006 0.041* -0.004 0.031 0.002 0.006 0.036 0.006

NMR -0.426* 0.066* -0.019 0.008 -0.069* -0.025 -0.034 0.102* -0.020 0.011 -0.009 0.020

OTHERR -0.159* 0.037 0.085* -0.047* 0.035 -0.010 0.033 0.066* -0.034 0.014 -0.051* -0.015

TENUREP

0.227* -0.021 0.059* 0.036 0.001 0.020 -0.107* 0.046* -0.006 0.000 -0.011

TENUREF 0.227*

-0.041* -0.020 0.003 0.001 0.026 0.034 0.062* -0.037 -0.009 -0.021

HIGHACC -0.027 -0.045*

0.074* -0.055* 0.025 -0.028 0.003 -0.040 0.002 -0.158* 0.014

CPRATIO 0.059* -0.013 0.066*

-0.313* -0.293* -0.190* -0.430* -0.036 0.068* -0.161* 0.024

42

TABLE 4 - CONTINUED

PEARSON AND SPEARMAN CORRELATION COEFFICIENTS, (PEARSON ABOVE THE DIAGONAL)

VARIABLES

TE

NU

RE

P

TE

NU

RE

F

HIG

HT

A

CP

RA

TIO

HE

RF

IND

OF

FS

IZE

INF

CL

IOF

F

INF

CL

IPA

R

NL

EA

DE

R

CL

EA

DE

R

SIZ

E

CA

SH

HERFIND 0.065 0.017 -0.074* -0.478* -0.282* 0.324* 0.148* 0.010 0.133* -0.011 -0.003

OFFSIZE -0.008 0.003 0.001 -0.458* 0.133* -0.465* 0.070* 0.083* 0.069* 0.143* 0.018

INFCLIOFF -0.028 0.008 0.001 0.156* -0.024 -0.684* 0.289* -0.019 0.126* 0.071* -0.028

INFCLIPAR -0.145* 0.020 -0.027 -0.477* 0.250* 0.148* 0.289* -0.024 0.051* 0.250* -0.087*

NLEADER 0.042* 0.065* -0.040 -0.011 0.044* 0.081* -0.035 -0.005 0.216* 0.002 0.049*

CLEADER -0.005 -0.032 0.002 0.087* -0.021 0.110* -0.014 0.018 0.216* 0.055* 0.040*

SIZE -0.002 -0.009 -0.151* -0.161* 0.031 0.122* 0.321* 0.280* -0.007 0.047* -0.327*

CASH -0.020 -0.013 0.024 0.079* -0.008 -0.007 -0.190* -0.117* 0.027 0.035 -0.331*

PRIORGC -0.044* 0.017 0.159* -0.087* 0.026 0.062* -0.021 0.064* -0.003 0.004 -0.214* -0.136*

REPORTLAG -0.077* -0.072* 0.058* 0.343* -0.280* -0.242* 0.019 -0.269* -0.023 -0.048* -0.124* -0.071*

DEBT -0.004 0.022 0.160* -0.180* 0.147* 0.116* 0.229* 0.276* 0.001 0.076* 0.127* -0.308*

LAGLOSS -0.018 -0.048* 0.031 0.115* -0.055* -0.066* -0.112* -0.139* 0.018 -0.023 -0.210* 0.139*

ALTMAN 0.015 -0.067* -0.245* 0.090* -0.080* -0.076* -0.073* -0.148* 0.001 -0.054* 0.247* 0.026

RETURN 0.058* 0.024 -0.089* 0.123* 0.001 -0.081* -0.063* -0.135* 0.023 -0.001 0.041* 0.136*

VOLATILITY -0.094* -0.104* 0.152* 0.087* -0.125* -0.060* -0.065* -0.090* 0.006 -0.038 -0.230* 0.082*

MB -0.011 -0.004 0.096* 0.130* -0.047* -0.065* -0.055* -0.109* -0.025 -0.012 -0.189* 0.323*

43

TABLE 4 - CONTINUED PEARSON CORRELATION COEFFICIENTS

PR

IOR

GC

RE

PO

RT

LA

G

DE

BT

LA

GL

OS

S

AL

TM

AN

RE

TU

RN

VO

LA

TIL

ITY

GCREPORT 0.488* 0.032 0.338* 0.003 -0.284* -0.194* 0.080*

MCLI -0.016 -0.031 -0.005 -0.016 -0.010 0.089* 0.047*

NMCLI -0.032 -0.014 0.023 0.004 0.028 -0.100* 0.074*

MUCLI -0.011 -0.052* 0.007 -0.035 -0.009 0.035 0.064*

NMUCLI -0.010 -0.126* 0.073* -0.051* -0.018 -0.065* -0.005

MPAR -0.030 0.094* -0.070* 0.029 0.034 0.110* 0.091*

NMPAR -0.062* 0.156* -0.058* 0.063* 0.078* -0.028 0.117*

MUPAR -0.030* 0.121* -0.081* 0.028 0.042* 0.088* 0.131*

NMUPAR -0.064* 0.132* -0.052* 0.043* 0.073* 0.023 0.072*

MR 0.002 0.012 -0.010 -0.026 -0.012 0.021 0.005

NMR -0.029 0.016 -0.029 0.003 0.037 -0.061* 0.007

OTHERR 0.077* 0.050* 0.010 0.026 -0.041* -0.031 -0.015

TENUREP -0.061* -0.062* -0.016 -0.012 -0.004 0.034 -0.110*

TENUREF 0.018 -0.082* 0.001 -0.050* -0.072* -0.001 -0.086*

HIGHTA 0.159* 0.059* 0.167* 0.031 -0.195* -0.018* 0.149*

CPRATIO -0.076* 0.293* -0.132* 0.115* 0.090* 0.106* 0.082*

44

TABLE 4 - CONTINUED

PEARSON AND SPEARMAN CORRELATION COEFFICIENTS,

(PEARSON ABOVE THE DIAGONAL)

PR

IOR

GC

RE

PO

RT

LA

G

DE

BT

LA

GL

OS

S

AL

TM

AN

RE

TU

RN

VO

LA

TIL

ITY

HERFIND 0.008 -0.085* 0.101* -0.060* -0.064* -0.031* -0.087*

OFFSIZE 0.050* -0.198* 0.048* -0.036 -0.023 -0.073* -0.033

INFCLIOFF -0.004 -0.023 0.063* -0.129* -0.048* -0.041* -0.048*

INFCLIPAR 0.036 -0.179* 0.191* -0.146* -0.073* -0.086* -0.079*

NLEADER -0.003 -0.015 0.005 0.018 0.018 0.011 0.007

CLEADER 0.004 -0.018 0.075* -0.023 -0.031 -0.012 -0.009

SIZE -0.217 -0.157* -0.012 -0.240* 0.307* 0.017 -0.202*

CASH -0.077* -0.100* -0.185* 0.121* -0.082* 0.131* 0.056*

PRIORGC

0.059* 0.292* 0.095* -0.277* -0.063* 0.104*

REPORTLAG 0.027

0.032 0.107* 0.002 0.082* 0.133*

DEBT 0.240* -0.183*

-0.078* -0.506* -0.162* -0.037

LAGLOSS 0.095* 0.144* -0.121

-0.037 0.088* 0.058*

ALTMAN -0.318* 0.142* -0.788* -0.047*

0.122* 0.018

RETURN -0.106* 0.091* -0.173* 0.093* 0.172*

0.277*

VOLATILITY 0.102 0.204* -0.122* 0.064* 0.008 0.106*

MB -0.018 0.034 0.035 0.150* -0.202* 0.427* 0.065*

* = significant at .10

45

TABLE 5 - GOING CONCERN AUDIT OPINION TESTS

FOR DISTRESSED CLIENTS, 2004 - 2010 Variable (1) (2) (3) (4)

INTERCEPT

-5.501 -5.455 -5.437 -5.655

(0.100) (0.106) (0.104) (0.108)

OFFICE-LEVEL ROTATION VARIABLES

MCLI

-

-5.577

(0.040)

NMCLI

? 0.161

(0.858)

MUCLI

-

-6.547

(0.056)

NMUCLI

? -0.084

(0.965)

MPAR

-

-1.293

(0.066)

NMPAR

?

0.062

(0.751)

MUPAR

-

-2.006

(0.099)

NMUPAR

? -0.193

(0.775)

PARTNER-LEVEL ROTATI