1

AUDIT FIRM TENURE, NON-AUDIT SERVICES AND INTERNAL ASSESSMENTS

OF AUDIT QUALITY

ABSTRACT

We use data from internal assessments of audit quality in a Big 4 firm to investigate the impact

of audit firm tenure and non-audit services (NAS) on audit quality. We find that first-year audits

receive lower assessments of audit quality, but quality improves significantly after the first year

and is sustained over very long tenure for SEC registrants. However, long tenure is associated

with lower audit quality for the sub-sample of privately-held firms. We also find that audit fees

are discounted for first-year audits but auditor effort is higher than in subsequent years,

suggesting a steep learning curve for new clients. We find no association between total non-

audit service fees and audit quality in the full sample but observe that total NAS fees are

positively associated with quality for SEC registrants and negatively associated with quality for

privately-held clients. We do not find an association between audit and NAS fees but auditor

effort can be higher when NAS are present. These results suggest that NAS does not undermine

audit quality in SEC registrants, nor do they necessarily create knowledge spillovers.

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AUDIT FIRM TENURE, NON-AUDIT SERVICES AND INTERNAL ASSESSMENTS

OF AUDIT QUALITY

1. Introduction

After decades of debate and research, the auditing profession, regulators, and researchers

continue to wrestle with two longstanding concerns about threats to auditor independence and

audit quality: (1) social bonding—becoming personally friendly with, or increasingly trusting of,

client management, and (2) economic bonding—becoming financially dependent on multi-period

fees from audits and non-audit services (NAS) provided to the client. Regulators have argued

that social bonding can erode professional skepticism and lead to auditor complacency, while

economic bonding may prompt auditors’ concessions or shirking in response to managements’

financial reporting demands.1

The financial crisis of 2008 reignited the debate on these issues and

both the PCAOB [2011] and the European Commission [2010; 2011] have suggested that

mandatory audit firm rotation can mitigate the loss of auditor objectivity. The European

Commission [2011] has also proposed new restrictions on NAS provided by auditors of public-

interest entities. On the other hand, the auditing profession has argued that learning benefits and

knowledge spillovers arise from extended tenure and NAS. This study examines the impact of

audit firm tenure and non-audit services (NAS) on audit quality using a unique proxy for quality,

namely, assessments of audit process attributes made by internal quality review teams at a large

international audit firm.

Extensive prior research has examined the effects of auditor tenure and NAS on audit

quality with mixed results. Most of the research on the impact of tenure finds evidence of a

1 Regulators have also argued that auditor provision of certain types of NAS, e.g., services involving bookkeeping,

financial information systems design and implementation, appraisals and valuations, and internal audit outsourcing

may compromise independence. In 2000, the SEC adopted a ban on auditor provision of these NAS services (SEC

[2000]), later extended by the Sarbanes-Oxley Act of 2002.

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positive association between tenure and audit quality (Myers, Myers, and Omer [2003]),

although there is also some evidence of a negative (Cahan and Zhang [2006]) or a non-linear

(Brooks, Cheng, and Reichelt [2012]) association. Research findings on the association between

NAS and audit quality are even more diverse with evidence suggesting a negative association

(Frankel, Johnson, and Nelson [2002]), a positive association (Davis, Soo, and Trompeter

[2009]) or no association (Ashbaugh, Lafond, and Mayhew [2003]). A possible reason for the

inconsistent findings is that researchers have had to cope with an important limitation, namely,

the need to use observable proxies for audit quality such as discretionary (or abnormal) accruals

or accounting restatements. These indirect measures may not accurately reflect the quality of

audits, at least as perceived by practitioners and regulators.

Inferences made by researchers who use financial reporting outcomes to proxy for audit

quality overlook the fact that every audit has non-zero residual risk as reflected in the audit risk

model, i.e., abnormal accruals (regardless of how they are measured) and post-audit restatements

are treated as prima facie evidence of low audit quality. In contrast, the professional view of

audit quality reflects the risk that some financial reporting problems may remain undetected and

it is the correspondence between the quality of the audit process and auditing standards and firm

policies that determine the quality of an audit. A researcher who uses abnormal accruals to proxy

for audit quality presupposes that quality decreases with increases in “abnormal accruals.”

However, the profession—through internal and external inspection processes—may judge the

audits of clients with significant abnormal accruals to be acceptable because the audits are

executed in accordance with standards and policies, and the audit opinions are considered to be

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justified by the evidence obtained during the course of the audit.2 Similarly, there may be

instances where proxies signal high audit quality (i.e., low accruals, no restatements), but the

audit process is judged to be substandard by inspectors.3 The PCAOB has noted that they “have

found no direct statistical relationship between the size of an abnormal accrual and the

probability that inspections staff would detect an audit failure” (PCAOB [2011], 37).

In this study, we develop audit quality measures using direct assessments of individual

audit process activities and the overall quality of the audit process made by internal reviewers at

a large international audit firm for a sample of 265 U.S. audit engagements. 4

More specifically,

our data include internal reviewer assessments of 55 separate audit process activities plus overall

conclusions regarding the appropriateness of the client’s accounting principles and disclosures,

the appropriateness of the auditor’s report, the sufficiency of audit evidence, and the overall

quality of the audit. We believe that a major contribution of this paper is the use of audit quality

measures developed from direct assessments of the audit process to examine the effects of

auditor tenure and non-audit services on overall audit quality for both SEC registrants and

privately held clients.5 The findings presented in the paper should help reconcile prior mixed

evidence on these issues.

Our results indicate that first-year audits are more likely to receive lower assessments of

audit quality, while higher levels of audit quality are attained in the years immediately thereafter.

2 Consistent with this view, Schelleman and Knechel [2010] provide some evidence suggesting that the quantity and

quality of audit evidence obtained by auditors increase with the magnitude of accruals. 3 This also follows from the relatively few restatements that have been triggered by the Part 1 findings of PCAOB

inspections. 4 Internal quality reviewers include some of the most experienced, best and brightest practicing auditors within a

firm. Incentives, separate lines of authority, and reviewer training help ensure reviewers are competent and

independent. Engagement deficiencies can impact training agendas, performance evaluations, and auditor

compensation. These factors give credence to the reliability of our audit quality metric. 5 Examination of audit quality differences between SEC registrants and privately held clients allows us to compare

our results to prior research which overwhelmingly utilizes publicly traded companies. In addition, our results

concerning the SEC registrants should be of greater importance to regulators.

5

Specifically, the probability that a first-year engagement receives a high quality rating is 15

percentage points lower than continuing engagements. Additional analyses show that the lower

audit quality observed in first-year clients is not caused by lower audit effort in spite of

discounted fees, i.e., audit effort is higher in the first year than subsequent years. For our sub-

sample of SEC registrants, higher levels of audit quality are also sustained with increasing

tenure, even for very long-tenure engagements, whereas we observe a small decline in audit

quality for the sub-sample of private clients when tenure is very long. Finally, using a new

measure of partner industry specialization which aligns the lead engagement partner’s area of

specialization with their clients’ industry, our results indicate that partner specialization can

mitigate the negative effects of short tenure in complex industries. Overall, these findings are

consistent with the notion that obtaining client-specific knowledge sufficient to produce a high

quality audit is difficult and costly on a first-year engagement, and that the development of

client-specific knowledge occurs over repeat engagements.

We also examine the association between NAS and audit quality using measures of both

the combined fees from all NAS provided to the client and separate fees for individual types of

NAS (tax, management advisory, securities offerings, and other services). Our results indicate

that neither the presence nor the magnitude of total NAS fees is associated with audit quality, a

result consistent with many prior studies. However, we find evidence of a positive association

between total NAS fees and quality of audits of SEC registrants and a negative association

between total NAS and audit quality for private clients. The likelihood of a high quality rating

for SEC registrants with auditor-provided NAS is 22 percentage points higher, on average, than

for a private client with NAS. This result may be due to knowledge spillovers, or could suggest

that provision of NAS for public clients increases auditor business risk and auditors respond to

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this risk by performing higher quality audits. The observation that audits where NAS is provided

often have higher effort levels supports the latter explanation. Analyses for individual types of

NAS show that fees obtained from services related to management advisory services for SEC

registrants are positively associated with audit quality, while we find a marginally negative

association between tax fees, management advisory fees and audit process quality among private

clients. In additional analyses, we find that higher audit quality is associated with larger

discretionary accruals, thus discretionary accruals appear to be ineffective proxies for the

underlying audit quality.

Our results are subject to several caveats because the sample comes from a single firm,

may lack power, and over-weights risky engagements. Further, the audit quality metrics are

based on judgments made by individual reviewers. Nevertheless, our findings have implications

for recent policy initiatives. Our findings suggest mandatory rotation as proposed by the

PCAOB in 2011 would impose significant start-up costs on newly-appointed auditors who face a

steep learning curve when obtaining client-specific knowledge in a time-constrained

environment. Downward price pressure in the competitive market for audits and the short time

frame during which the first-year audit must be completed exacerbate the challenge of a first

time audit of large, complex business organizations. Our results also contribute to the research

stream examining auditor tenure because they suggest that the continuous measure of tenure may

not accurately reflect knowledge acquisition over time and that controlling for first year effects

may be more appropriate. Relatedly, future research can supplement our findings by also

examining the variation of audit fees over time. The study’s results also should be informative to

regulators as they consider whether to impose additional restrictions on NAS. In particular, our

results suggest that the presence of SEC monitoring and enforcement powers likely increase an

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auditor’s perceived business risk when providing NAS, and that auditors respond to these risks

by increasing effort and quality. Finally, future research can benefit from using partner-client

industry misalignment as an alternative measure of partner knowledge and specialization.6

The remainder of the paper is organized as follows. Section 2 provides a review of

relevant prior research findings and development of hypotheses. Section 3 explains the data and

research design. Section 4 presents the results for our primary hypotheses tests and supplemental

analyses, and Section 5 discusses the results and their limitations and concludes the paper.

2. Background and Development of Hypotheses

2.1 BACKGROUND

Two opposing arguments are central to the ongoing debate about the impact of auditor

tenure and NAS on audit quality. On one hand, some argue that regulatory restrictions on audit

firm tenure and NAS improve audit effectiveness by dampening the effects of social and

economic bonding. Others argue that such restrictions decrease audit effectiveness and efficiency

by causing a loss of client-specific knowledge accumulated over successive audits or from

knowledge spillovers arising from auditor-provided NAS. Interest in these issues generally

intensifies following economic downturns when business performance declines and business

improprieties are revealed. Such was the case following the slide of the NASDAQ index in 2000,

the implosion of Enron and Worldcom in 2001, and the financial crisis of 2008.

Audit quality is traditionally defined in the academic literature as the joint probability of

discovering and reporting a material misstatement (DeAngelo [1981]). The ability to discover a

material error, when one exists, depends on the auditor’s technical competence and effort level,

whereas the willingness to report a discovered error relates to the auditor’s independence.

6 This observation is potentially relevant to the ongoing debate as to whether the identity of the audit partner should

be disclosed in the US, (Knechel, Vanstraelen and Zerni [2014]).

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Competence and independence are often treated as being independent but competence depends

on getting to know the client, which may lead to social bonding and impaired independence.

Because the actual outcome of the audit is generally unobservable, (Francis [2004], Barton

[2005]), a great deal of prior research has used financial reporting proxies such as accruals or

restatements to measure audit quality. The profession tends to look at audit quality from the

perspective of whether the audit process is executed in accordance with auditing standards and

whether the auditors’ conclusions are supported by sufficient, competent evidence. The process

perspective is also consistent with external peer review (Castarella, Jensen, and Knechel [2009]),

the PCAOB inspection process,7 and legal criteria used to adjudicate alleged audit failures

(Missal [2008]).8 At this point, we know very little about how proxies for outcome-based

(financial reporting) quality relate to assessments of audit process quality, and whether process

quality is affected by auditor tenure and NAS.

2.2 AUDITOR TENURE AND AUDIT QUALITY

The longstanding policy debate regarding the effect of auditor tenure on audit quality has

centered on the issues of economic and social bonding arising from continuing auditor

association with a client and its management. Extensive academic research examining the

relationship between audit firm tenure and various external proxies of audit quality generally

finds that quality is positively associated with auditor tenure, consistent with knowledge

accumulation by an auditor (e.g., Palmrose [1987; 1991], Johnson, Khurana, and Reynolds

[2002], Myers, Myers, and Omer [2003], Chung and Kallapur [2003], Chen, Lin, and Lin

[2008], Srinidhi, Leung, and Gul [2010], Gul, Fung, and Jaggi [2009]).

7 See, e.g., Rule 4000 in the PCAOB Bylaws and Rules and Firm Inspection Reports at

http://pcaobus.org/inspections/reports/pages/default.aspx. 8 See Missal [2008] for an example of the criteria applied by a bankruptcy examiner to determine whether there are

potential causes of action against an accounting firm.

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Restricting tenure by mandating a change in auditors can be costly and may adversely

affect audit quality in the short run.9 Significant incremental costs include those related to the

tendering process, client and auditor transition activities, and the steep learning curve facing the

auditor (Causholli [2013]). The logistics of an auditor change may be quite complex and time

constrained. Much depends on when a client puts the audit out for tender, which may not be

until after the most recent audit is completed. The tender process itself will take considerable

time and effort (Fiolleau, Hoang, Jamal, and Sunder [2013]). In the end, the successor auditor

may not be able to start the engagement until late in the fiscal year, providing little time to plan

the audit or conduct interim and control testing.10

Quarterly reporting may further confound the

problem of transitioning to a new auditor. In short, an auditor of a newly-acquired client faces

significant knowledge acquisition challenges in a time-constrained setting.

While post-year-end audit effort can partially compensate for such constraints, it takes a

significant time investment to fully develop client-specific knowledge. Successive engagements

help an auditor to build the knowledge base needed to assess a client’s systems, transactions,

risks and controls, and the accumulation of knowledge should improve the quality of audits over

time (PCAOB AS #5, ¶9 [2007], PricewaterhouseCoopers [2012]). The question then arises:

How long does it take for the benefits from client-specific knowledge to accrue to an auditor? In

the second year of an engagement, the auditor will be less constrained in conducting interim and

control testing, attending client meetings, or addressing significant and unusual transactions.

9 There is little evidence on the actual startup costs for a new engagement but the GAO [2003] reported that

incremental costs in the initial year of an audit can be as high as 20 percent of the overall audit cost. 10

Other undesirable effects arising from audit firm rotation may include (1) less competition among audit firms, (2)

higher internal costs for clients, (3) capacity constraints and scheduling problems, (4) shifting focus and effort

towards preparation of proposals for new engagements, (5) lower incentives to invest in specialization, and (6)

higher audit fees (GAO [2003], PCAOB [2011], Ernst and Young [2012]).

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However, the rate at which an audit team’s knowledge develops may depend on the size and

complexity of the client so improved learning and quality might continue over a few years.

As tenure gets longer, knowledge acquisition may slow down significantly while other

circumstances could cause quality to deteriorate. For example, client-specific knowledge can

depreciate as team members rotate off an engagement; the auditor might place too much reliance

on findings from prior audits, especially if no significant financial reporting problems were

detected; or combined fee increases over time for both the audit and NAS might strengthen the

auditor’s economic bond to the client (PCAOB [2011]). Some recent studies have found that

audit quality increases with tenure up to a certain point, but may decline when tenure becomes

very long (e.g., Davis, Soo, and Trompeter [2009] Chu, Church, and Zhang [2012], and Brooks,

Cheng, and Reichelt [2012]).11

Collectively, these observations about auditor tenure lead to our

first three hypotheses:

H1a: Audit quality will be lower for first year engagements when compared to subsequent

engagements.12

H1b: Audit quality will improve during the first few years following a first year

engagement.

H1c: Audit quality will begin to decline as audit firm tenure becomes very long.

Prior evidence suggests that auditors respond to perceived auditor business risk (i.e.,

litigation, regulatory sanctions, impaired reputation) by increasing audit effort (Bell, Landsman,

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The link between auditor tenure and audit quality is further complicated if mandatory audit firm rotation is

considered. The PCAOB has argued that mandatory rotation creates an incentive for the outgoing auditor to perform

high quality audits knowing that a new auditor is going to scrutinize that work (PCAOB [2011]). In contrast, Elizur

and Falk [1996] show that when an auditor knows the end term of the audit engagement, the quality of planning

declines. Evidence in countries where mandatory rotation has been tried suggest that audit quality deteriorates when

clients are forced to change auditors (Cameran, Prencipe, and Trombetta [2009], Ruiz-Barbadillo, Gomez-Aguilar,

and Carrera [2009] and Kwon, Lim, and Simnett [2010]). 12

An additional argument for H1a is that economic bonding may actually be more severe in the early years of tenure,

especially if an auditor needs to recover costs associated with a lowball fee (Dye [1991]).

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and Shackelford [2001], O’Keefe, Simunic, and Stein [1994], Bell, Doogar, and Solomon

[2008], and Schelleman and Knechel [2010]). Auditors can reduce their business risk by

avoiding high-risk clients, transferring risk through liability insurance, or managing residual risk

through a rigorous audit process. Audits of SEC registrants are generally considered to be higher

risk than audits of private companies. Long tenured engagements among SEC registrants may

present even higher risk to an auditor due to regulatory scrutiny, broad stakeholder focus, and the

magnitude of potential legal exposure. We expect that auditors will react to increased risk so any

negative effects from short tenure (such as lack of client specific knowledge) and long tenure

(such as impaired independence) will be moderated among SEC registrants and strongest for

private clients. This perspective leads to our fourth hypothesis:13

H1d: Relative to audits of privately-held clients, audit quality will decline less for audits

of SEC registrants as audit firm tenure becomes very short or very long.

2.3 NON-AUDIT SERVICES AND AUDIT QUALITY

Another issue of concern to regulators and investors is the potential effect on audit

quality of auditor-provided NAS. Again, there are two opposing arguments: One argument is that

increasing fees from NAS intensifies the economic bond between the auditor and client, reducing

auditor independence.14

The contrary argument suggests that client-specific knowledge obtained

from NAS informs the audit process and improves audit quality (Simunic [1984], Beck, Frecka,

and Solomon [1988], Antle and Demski [1991], and Wu [2006]). To date, empirical research

has generally been inconclusive regarding an association between NAS and audit quality, with

13

Our sample data include 152 SEC registrants and 113 private clients. Of the SEC clients, 87 (77 percent) have

elevated auditor business risk as assessed by the audit team. There is a significant positive correlation between SEC

status and perceived auditor business risk (p=0.00). 14

This argument does not reflect market and regulatory mechanisms which serve to discipline an auditor and limit

the effects of economic bonding, e.g., litigation and reputation risk (DeAngelo [1981], Palmrose [1988], and Weber,

Willenborg, and Zhang [2008]) and regulator monitoring and enforcement actions.

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little evidence of a decline in audit quality.15

On the other hand, there is some evidence

supporting the knowledge spillover argument (Antle, Gordon, Narayanamoorthy, and Zhou

[2006], Koh, Rajgopal, and Srinivasan [2013], and Knechel and Sharma [2012]). Because

findings in prior research are mixed, and because the primary concern of regulators and users of

financial statements is that NAS reduces audit quality due to economic bonding, we formulate

our next hypothesis to test for a negative association between NAS and audit quality:16

H2a: Audit quality will decrease as combined fees from auditor-provided NAS

increase.

As noted in the discussion of auditor tenure, there is extensive evidence that auditors

adapt their engagements in response to perceived auditor business risk. Significant NAS fees

from SEC registrants can exacerbate an auditor’s business risk by increasing regulators’ scrutiny

of registrants’ financial statements and audits (e.g., using NAS as a risk factor when selecting

risk-based samples for regulatory monitoring). This could lead to an increase in the likelihood of

SEC lawsuits, fines, and sanctions (Schmidt [2012]); stimulate policy changes imposing new

restrictions on NAS; and imply a plausible motive for compromised independence that can be

used by plaintiffs in civil litigation against the auditor. Concerns such as these increase auditor

incentives to attain a high level of audit quality on audits of SEC clients as NAS increases. Our

next hypothesis states that any negative association between NAS fees and audit quality is

mitigated for audits of SEC clients:

H2b: Relative to audits of privately-held clients, audit quality will decline less for clients

having publicly-listed securities as combined NAS fees increase.

15

Exceptions include Frankel, Johnson, and Nelson [2002], Srinidhi and Gul [2007], Geiger and Blay [2011] and

Causholli, Chambers and Payne [2013]. 16

While it is possible that NAS may improve audit quality, the debate about NAS for regulatory purposes is whether

NAS harms audit quality. In a test for evidence of harm, it is not necessary to show that quality improves with NAS,

only that quality does or does not decline (Knechel and Sharma [2012]). Thus, we formulate this hypothesis to test

for a negative association between NAS and audit quality.

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While auditor provision of any particular type of NAS may increase the economic bond

between auditor and client, some types of NAS may have different implications for

independence and knowledge spillovers that could affect audit quality. For example, Kinney,

Palmrose, and Scholz [2004] find a negative association between fees received for tax services

and the likelihood of a restatement. Fees from tax services are associated with a higher

incidence of going concern opinions (Robinson [2008]), better estimates of tax reserves (Gleason

and Mills [2011]), and reduced earnings management (Krishnan and Visvanathan [2011]).17

However, results are generally less clear for other types of NAS. Kinney, Palmrose, and Scholz

[2004] do not find an association between either information technology or internal audit

services and audit quality. Koh, Rajgopal, and Srinivasan [2013] find that information system

services are positively associated with measures of financial reporting quality. Prawitt, Sharp,

and Wood [2012] find that auditor-provided internal audit services result in lower accounting

risk relative to internal audit or outsourcing to a professional service firm other than the external

auditor. Paterson and Valencia [2011] find that audit-related NAS are negatively related to the

likelihood of restatement. In light of prior mixed results, our final hypothesis is non-directional:

H2c: Different types of NAS will have differential effects on audit quality.

3. Data and Research Design

3.1 DATA

The data used in this study consist of audit quality assessments, audit firm tenure, audit

and NAS fees, total and staff-level audit labor hours, and other key client and engagement

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Cook and Omer [2013] do not find an association between tax services and reporting quality. Lim and Tan [2008]

find that the effect of NAS on audit quality is conditional on auditor specialization and that auditor specialists

provide higher quality audits and are also more likely to benefit from knowledge spillovers. A similar finding is

reported in Donohoe and Knechel [2013].

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characteristics for 265 U.S. audits conducted by a Big 4 firm. Audit firm personnel collected the

data as part of the annual internal quality reviews performed during late spring and early fall of

2003. The data pertain to the most recent annual audits at that time and the audits and internal

reviews were completed before the effective date of SOX.

The firm initially selected 307 audit engagements for internal quality review using a

stratified sampling approach that oversampled engagements with higher auditor business risk and

audit risk (the selection criteria are proprietary).18

Approximately one-third of the original

sample consists of first-year audits where Arthur Andersen had been the predecessor auditor.19

We drop 19 engagements because of missing data, 20 engagements involving government and

non-profit clients, and 3 engagements which were interim reviews. Our analysis is based on the

265 remaining audit engagements. The clients are from four industry sectors: consumer and

industrial products (113), financial services (68), information, communication and entertainment

services (57), and health care (27). Of the final sample, 120 are first-year engagements (96, or

80%, of which are former AA clients) and 145 are continuing engagements.

3.2 RESEARCH DESIGN

3.2.1 Audit Quality Measures. For each audit, a review team under the supervision of the

firm’s risk management department assessed the contents of the audit work papers, interviewed

audit team personnel, and assessed the accuracy of a variety of data provided by the audit team

(including audit labor hours, fees, and other client and engagement characteristics). On average,

the fieldwork portion of a single review was 52 hours, with the longest inspection consuming

18

Firm personnel indicate that about 50% of engagements were selected randomly. Oversampling based on risk

precludes meaningful extrapolation of our results to the firm’s full client portfolio. On the other hand, oversampling

of high risk audits might bias our results towards finding a negative effect on audit quality. Nevertheless, the

sampling selection is consistent with the PCAOB’s risk-based approach to selecting engagements for inspection

(CAQ [2012], PCAOB [2012]). 19

We report separate tests regarding the ex-AA clients in the robustness section of the paper.

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854 hours. Members of the review teams had not previously worked on the audits that they

reviewed.

Initially, the reviewers evaluated the adequacy of 55 individual audit activities of the

audit process. If any of the 55 activities were deemed to be inadequate a “deficiency” was noted.

(see Appendix A for the list of the 55 activities and the frequency of deficiencies for each

activity and by audit phase). The total number of deficient activities, TOTDEFIC, ranges from

zero to 12 per audit. At the conclusion of the review, the team made composite assessments of

four attributes of the audit process: (a) sufficiency of evidence obtained to support the audit

opinion, (b) appropriateness of the accounting principles used, (c) appropriateness and

completeness of presentations and disclosures in the financial statements, and (d) appropriateness

of the audit report. The review team also made a composite assessment of the overall audit

quality for the engagement. A 4-point scale was used for the five composite assessments: (1)

unqualified satisfactory, (2) satisfactory with comments, (3) needs improvement and (4)

unsatisfactory. Audit firm quality control policies mandate “a negative response form should be

completed and discussed with the reviewing partner in charge” for all engagements receiving

assessments other than “unqualified satisfactory” on any of the five composite assessments. For

our analysis, we create a dummy variable AQ equal to 1 for audit engagements rated “unqualified

satisfactory”, and equal to 0 otherwise (i.e., the other three quality categories).

Because not all deficiencies will indicate a serious problem (Peecher and Solomon

[2014], Hansen [2014]), we believe that the aggregate quality assessment (AQ) made by an

examiner reflects more than a simple summation of the number of deficiencies because those

16

judgments reflect the relative importance or weighting of the 55 process attributes. 20

Nevertheless, to provide a broad view of audit quality, we also use TOTDEFIC as an alternative

measure of audit quality while acknowledging that this analysis implicitly assumes that all

deficiencies are of equal importance.21

Table 1, Panel A, presents the frequency distribution of overall audit quality ratings: 112

(42 percent) audit engagements are assessed “unqualified satisfactory,” 133 (50 percent) are

assessed “satisfactory with comments”, and 18 (7 percent) and 2 (1 percent) are assessed “needs

improvement” and “unsatisfactory,” respectively. Audits of clients operating in the financial

services sector received the highest proportion of “unqualified satisfactory” assessments (57

percent), followed by non-financial/non-health care sectors (38 percent) and the health care

sector (30 percent).22

<<<<< Insert Table 1 about here >>>>>

Table 1, Panel B, presents the frequency distribution of TOTDEFIC: 17 percent of the

engagements have no deficiencies, 28 percent have one, 33 percent have two or three, and 22

percent have four or more deficiencies. Table 1, Panel C, breaks down the distribution of

TOTDEFIC into more detail by separating engagements rated as “unqualified satisfactory”

(SAT), “satisfactory with comments” (SWC) and “needs improvement” or “unsatisfactory”

(UNSAT). Notably, 79 percent of engagements in the SAT group have no or one deficiency,

compared to only 23 percent and 0 percent in the SWC and UNSAT groups. Panel C also

20

We regressed the number of deficiencies related to (1) planning, (2) internal control evaluation, (3) substantive

testing and (4) wrap-up work on AQ with the natural log of total assets as a control variable. All four categories of

deficiencies are significant and negative, suggesting that problems with any of the four phases of the audit may

influence a reviewer’s overall audit quality rating. 21

In a recent speech, PCAOB board member Jay Hansen stated that currently the inspection reports “…provide no

information about which of the deficiencies are relatively minor… and which indicate a much more significant

problem with the audit.” 22

The χ2 test on the 2x3 table (not tabulated) indicates observed proportions differ significantly from expected

proportions (p <0 .01).

17

presents descriptive statistics for the association between TOTDEFIC and overall quality ratings.

Tukey tests for differences between TOTDEFIC for all pairs of rating groups (i.e., SWC vs. SAT,

UNSAT vs. SAT and UNSAT vs. SWC) are all significant at the 0.01 level. A t-test for the

difference between TOTDEFIC conditional on AQ indicates TOTDEFIC is significantly lower

(p<0.01) for engagements where AQ=1.23

We also ran a logit regression model using AQ as a dependent variable and independent

variables that included client size and 21 of the 55 engagement attributes.24

The results (not

tabulated) indicate that deficiencies in three of the attributes were perfectly correlated with low

AQ (i.e., AQ=0): (1) inappropriate reliance on external experts employed by the client, (2)

problems with the preparation of consolidated financial statements, and (3) weaknesses in

working paper documentation. Other deficiencies that were systematically associated with low

AQ (p<0.05) indicate problems with the precision of analytical procedures, evaluation of the

client’s tax accounting, valuations involving asset impairments, valuations involving other

significant estimates, legal letters obtained from an attorney, deficiencies in the management

representation letter, evaluation of subsequent events, conclusions regarding passed audit

differences, performance of final analytical procedures, and final preparation of financial

statement disclosures. Many of these areas are considered to be the most difficult and

challenging areas in an audit so the results are not surprising.

3.2.2 Tests of Audit Firm Tenure. We use the following general form of equation (1) to

test our hypotheses related to audit firm tenure and audit quality:

23

We also tested the association of the 4 intermediate assessments and the overall audit quality measure using an

ordered logit regression. The results indicate that sufficiency of evidence (p<0.01) and appropriateness of

accounting principles (p<0.06) are significantly associated with overall audit quality. 24

Many of the 55 attributes did not occur frequently enough in the sample to be meaningfully included in this

analysis. These are omitted to preserve degrees of freedom.

18

AUDIT QUALITY= a0 + a1(TENURE) + a2(LAST) +a3(PUB) + a4(ROMM)

+ a5(LEV) + a6(ROA) + a7(COMPLEX) + a8(FS)

+ a9(HC) + a10(ICE) + u (1)

where AUDIT QUALITY is either AQ or TOTDEFIC. TENURE represents our various test

variables. For H1a we replace TENURE with FIRST, which is equal to 1 for first-year audit

engagements and 0 for continuing engagements. For H1b, we replace TENURE with TEN2-4,

which equals 1 for engagements with tenures in the 2 to 4 years range and measures the audit

quality for the TEN2-4 group relative to the first-year group of engagements. We also include

TEN5-N to control for quality differences in engagements with tenure of five years or longer. We

expect the coefficient for TEN5-N to be positive but not necessarily different from the coefficient

for TEN2-4. To test H1c, we replace TENURE with LONG and again include FIRST, where

LONG is defined using two thresholds suggested by regulators: 11+ years (based on the 10 years

suggested by the PCAOB) and 9+ years (based on the 8 years suggested by the European

Commission).25

For H1d, in addition to FIRST and LONG we add the interactions PUB*FIRST

and PUB*LONG to measure the incremental effect of long tenure on audit quality for SEC

registrants relative to private clients.

<<<<< Insert Table 2 about here >>>>>

Panel A of Table 2 reports that the mean (median) level of audit firm tenure for the full

sample is 6 (2) years and tenure at the 90th

percentile is 16 years. New audits (FIRST) constitute

45 percent of the sample while audits in years two through four (TEN2-4) are 20 percent of the

sample. Engagements with tenures longer than 10 years comprise 20 percent of the sample, and

tenure exceeds 20 years for 16 engagements (not tabulated).

25

Prior research has used different cut-offs for long tenure but there is little theoretical justification for assuming

quality starts to decline at a specific point in time. In the most recent provisional agreement the European

Commission is also considering rotation of auditors after a period of 10 years (EC [2013]).

19

Equation (1) also includes a number of control variables which reflect conditions where a

review team might be more sensitive to deficiencies or more conservative in their quality

assessments:26

LAST = natural log of total assets.

PUB = 1 if the client is an SEC registrant.

LEV = total liabilities divided by total assets.

ROMM = 1 if the auditor-assessed risk of material misstatement at the overall financial

statement level is moderate or high and equals 0 if the assessed risk of material

misstatement is low.

ROA = ratio of income (loss) from continuing operations to total assets.

COMPLEX = client’s operational complexity ranging from 1 (very simple) to 5 (very

complex).

FS = 1 for clients in the financial services sector; 0 otherwise.

HC = 1 for clients in the health care industry, 0 otherwise.

ICE = 1 for clients in the information, communications and entertainment industries; 0

otherwise.

The mean (median) value for total assets is $2.9 billion ($261 million). We control for

differences in engagement risk using PUB, ROMM, and LEV. Fifty-seven percent of our sample

clients have publicly-listed securities (PUB) and 52 percent have a high or moderate risk of

material misstatement (ROMM). Mean (median) LEV is 64 percent (60 percent) and mean

(median) ROA is -0.05 (0.016). Finally, we control for industry differences with dummy

variables for financial services (FS), health care (HC), and information, communications and

entertainment (ICE).

26

Numerous studies use similar control variables in tests of audit quality (Pratt and Stice [1994], Frankel, Johnson,

and Nelson [2002], DeFond, Raghunandan, and Subramanyam [2002], Ashbaugh, Lafond, and Mayhew [2003],

Carcello and Nagy [2004], Nagy [2005], Gul, Fung, and Jaggi [2009], Carey and Simnett [2006], Lim and Tan

[2008], and Knechel, Rouse, and Schelleman [2009]).

20

3.2.3. Tests of Non-audit Services. We use the following general form of equation (2) to

test our hypotheses related to auditor-provided non-audit services and audit quality:

AUDIT QUALITY= b0 + b1(NAS) + b2(LAST) +b3(PUB) + b4(ROMM)

+ b5(LEV) + b6(ROA) + b7(COMPLEX) + b8(FS)

+ b9(HC) + b10(ICE) + u (2)

where NAS represents various test variables. In our dataset, NAS fees are separated into five

categories: tax (TAX), management advisory services (MAS), services pertaining to public

securities offerings (OFFER), services for merger or acquisition activities (MA), and other

unspecified NAS (UNSP). Total NAS fees are the sum of fees for the five categories.27

The

control variables are the same as those in equation (1).

For H2a, we replace NAS with PNNAS_AUD, which is equal to the ratio of total non-audit

service fees to audit fees. A negative coefficient on PNNAS_AUD indicates lower audit quality

associated with NAS fees. To test H2b, we add the interaction term PUB* PNNAS_AUD. We

expect the coefficient for the interaction term to be positive. Finally, to test H2c, we replace NAS

with separate measures for the following four categories of NAS:

PNTAX_AUD = ratio of tax fees to audit fees.

PNMAS_AUD = ratio of management advisory fees to audit fees.

PNOFFR_AUD = ratio of fees billed for services involving client public securities

offerings to audit fees.

PNOTH_AUD = ratio of fees billed for services involving client merger & acquisition

activities and other unspecified NAS to audit fees.

27

Note that NAS fees reported in our data base do not match those reported in Audit Analytics for public clients in

about 60% of our public company sample. This does not necessarily suggest coding errors because there are a

number of possible reasons for such discrepancies: (1) Audit Analytics reports the numbers provided by the client in

their public disclosures, while we report the numbers according to the firm’s internal bookkeeping, and the client

numbers are not subject to audit. (2) There may be timing differences as to when the client and firm record NAS

fees. (3) Fees for public reporting may be subject to estimation or calculated on either a cash flow or accrual basis,

especially for audits involving foreign operations. If we replace the total NAS fees in our database with the fees per

Audit Analytics (when they differ) our results are qualitatively the same as reported in our primary analysis.

21

Not all clients purchase NAS, so we also include NAS_Dummy, which is equal to 1 for clients

that purchase any non-audit services, and 0 otherwise.

Descriptive statistics for combined NAS fees and NAS fees by type of service are shown

in Panel B of Table 2: 77 percent of clients purchase NAS and the proportion of clients

purchasing TAX, MAS, OFFER, UNSP and MA are 67, 17, 20, 28 and 7 percent, respectively.

Mean fees for combined NAS are $184,351, of which TAX fees are the largest ($102,618),

followed by UNSP ($27,162), OFFER ($24,778), MAS ($21,952) and MA fees ($7,842).28

The

average proportion of combined NAS fees to audit fees (PNNAS_AUD) is 63 percent, whereas

for TAX, MAS, OFFER and OTHER these proportions are 35, 9, 11 and 9 percent, respectively.

4. Results

4.1 TENURE AND AUDIT QUALITY

4.1.1. Tests of Hypotheses. We first consider the hypotheses related to auditor tenure.

Table 3 presents the correlation matrix of variables in equations (1) and (2). AQ is inversely and

significantly correlated with TOTDEFIC indicating some degree of commonality between the

two measures of audit quality. FIRST and AQ (TOTDEFIC) are negatively (positively) correlated

supporting H1a. The correlation between TEN2-4 and AQ is positive and marginally significant,

suggesting immediate improvement in audit quality after the first year, supporting H1b. LONG9+

is negatively correlated with TOTDEFIC indicating fewer deficiencies are found in long tenured

engagements. Correlations among the independent variables indicate no significant problems

with multicollinearity. 29

28

Mean fees (n) for only those clients purchasing the indicated service type are: Tax=$152,773 (178),

MAS=$132,212 (44), OFFER=$126,270 (52), MA=$115,447 (18) and UNSP=$97,270 (74). 29

All correlations between AQ and total deficiencies within each of the four audit phases—audit planning, internal

control evaluation and control risk assessment, substantive testing and audit wrap-up activities—are negative and

significant at the p=0.001 level.

22

<<<<< Insert Table 3 about here >>>>>

Results from the tests of our tenure hypotheses are shown in Table 4. We use logit

regressions for AQ and Poisson regressions for TOTDEFIC. All models show reasonably good

fit, as evidenced by the model p-values and Nagelkerke’s/adjusted r-squares. The association

between LAST and AQ (TOTDEFIC) is negative (positive) and significant, suggesting that audits

of large clients are more likely to receive lower quality ratings (more deficiencies). Also, AQ is

often negatively associated with leverage (LEV) but is not systematically influenced by any of

the other control variables. Finally, audits of clients in the financial services industry generally

receive higher quality ratings and fewer deficiencies.

<<<<< Insert Table 4 about here >>>>>

Table 4, Panel A presents results for hypotheses H1a (TENURE=FIRST) in columns (1)

and (2) and H1b (TENURE=TEN2-4) in columns (3) and (4). For H1a, the coefficient on FIRST is

negative and significant for AQ, and positive and significant for TOTDEFIC, i.e., first year audit

engagements are more likely to receive low quality ratings and have more deficiencies.30

The

marginal effect of FIRST on AQ, holding all other independent variables constant at their means,

indicates that the probability that a first-year engagement receives a high quality rating is 15

percentage points lower than that of continuing engagements. The incidence rate ratio for the

coefficient on FIRST and TOTDEFIC indicates that deficiency counts increase by 26 percent for

first year clients relative to all other clients.

For H1b, the coefficient for TEN2-4 is positive for AQ, indicating that audit quality for the

TEN2-4 group is higher, on average, than quality for first-year engagements. The marginal effect

indicates that the probability that TEN2-4 engagements receive a high quality rating is 21

30

All our tests are two-tailed.

23

percentage points higher than that of a first-year engagement while holding all the other variables

constant at their means. We include TEN5-N in our tests of H1b to control for quality differences

in engagements with tenure longer than four years. The coefficient on TEN5-N is positive and

significant for AQ, indicating that audit quality for the TEN5-N group of engagements remains

higher, on average, than that of first year engagements. Our results for TOTDEFIC indicate that

the number of deficiencies decreases for both TEN2-4 and TEN5-N but only the coefficient on

TEN5-N is significant. Since not all deficiencies are of equal importance in regard to overall

quality, this pattern of results suggests that auditors deal with the issues that are most likely to

influence overall quality immediately after the first year while resolving less important sources

of deficiencies in subsequent audits.31

Panel B of Table 4 presents the results for H1c, the association between long tenure

(LONG9+, LONG11+) and audit quality. The coefficients for long tenure measure audit quality

differences relative to medium tenure engagements (captured in the intercept). For AQ, although

the coefficients on the LONG variables are not significant, they are consistently negative. The

marginal effect for LONG11+ in the AQ model indicates that the probability that a long tenure

engagement receives a high quality rating is 9.47 percentage points lower than that of medium

tenure engagements, holding all other independent variables constant at their means. The results

for TOTDEFIC suggest that the number of deficiencies decreases when tenure is long, although

only significantly so for LONG9+. The incidence rate ratio indicates that the number of

31

When we compare the coefficients for TEN2-4 and TEN5-N the difference is not significant for both AQ and

TOTDEFIC. We also ran the following models: (1) include FIRST and a continuous measure for tenure and (2)

dropping first year clients while testing for the continuous measure of tenure. From (1), the coefficient on FIRST is

negative and significant for AQ and positive but insignificant for TOTDEFIC, while the continuous tenure measure

is insignificant in both models (but approaching significance for TOTDEFIC (p=0.11). For (2) the continuous tenure

measure is not significant for AQ, and negative and close to significance for TOTDEFIC (p=0.11).

24

deficiencies decreases by about 16 percent for long tenured engagements relative to those with

medium tenure.32

Panel C of Table 4 presents the results for H1d, which tests whether audit quality is higher

for audits of SEC registrants, compared to audits of privately held clients, when tenure is very

short or very long. TENURE = FIRST, LONG, PUB*FIRST and PUB*LONG in all models.

Results for LONG 9+ years are shown in columns (1) and (2) and 11+ years in columns (3) and

(4). Coefficients for these four test variables measure audit quality differences relative to

medium tenure engagements for privately-held clients (captured in the model intercepts). The

PUB variable controls for quality differences for audits of medium tenure publicly-listed clients,

compared to audits of medium tenure private clients Considering short tenure first, the

coefficients for PUB*FIRST are positive for AQ but not significantly different from zero,

whereas the coefficients on FIRST are negative and significant, in both AQ models. For

TOTDEFIC, the coefficients on PUB*FIRST are negative but not significant, while FIRST is

positive but only significant for the LONG11+ model. In untabulated tests, we re-run the

analysis of FIRST separately for SEC registrants and private clients. The coefficients in the AQ

model for FIRST are -0.6679, p = 0.07 (SEC) and -0.7196, p = 0.12 (private).33

Overall these

results suggest that the negative effect of FIRST on audit quality is found for both public and

32

In additional analyses, we test alternative cut-offs for long tenure ranging from 12 to 20 years. The results

(untabulated) continue to show no statistically significant quality differences between long and medium range

tenures, although the coefficients in the AQ model tend to be negative. We also test several alternative models to

examine tenure effects by including the following terms: (a) TEN2 (tenure=2 years) and TEN3-N (tenure>2 years);

(b) TEN2-3 (tenure=2 or 3 years) and TEN4-N (tenure>3 years); and (c) the natural log of tenure. In tests of (a) and

(b) we delete FIRST. For (a), we find that TEN3-N is positive (negative) and significant (p<0.05) in AQ

(TOTDEFIC) models and TEN2 is positive and significant in the AQ model (p<0.10) but insignificant in TOTDEFIC

model. For (b), we find that both TEN2-3 and TEN4-N are positive and significant for AQ, but only TEN4_N is

negative and significant for TOTDEFIC (p values range from 0.02 to 0.03).These findings are consistent with our

general conclusions that overall audit quality rebounds shortly after the first year. For (c), the natural log of tenure is

insignificant in the AQ model, but negative and significant at 1 percent in the TOTDEFIC model. 33

We re-perform the analysis for private clients by dropping the insignificant control variables (ROMM, LEV, ROA,

COMPLEX) in order to preserve degrees of freedom and obtain a significantly negative coefficient on FIRST (-

0.7888, p =0.06) for the sub-sample of private clients.

25

private clients although the magnitude is larger for private clients. This is not consistent with our

hypothesis but is consistent with the argument that auditors find it very challenging to overcome

the lack of client-specific knowledge on a first-year audit, generally resulting in lower audit

quality.

For long tenure, in the AQ models, the coefficients for the interaction terms PUB*LONG

are positive but insignificant. LONG11+ is negative and marginally significant, and LONG9+ is

negative but not significant suggesting that lower audit quality from long tenure is present only

in the private client subsample. Neither measure of long tenure, nor its interaction with PUB, is

significant in the model for TOTDEFIC although the coefficients are negative (fewer

deficiencies).

In one set of untabulated tests we re-perform the analyses related to H1d separately for the

SEC and private samples. For the private sample we obtain negative and marginally significant

coefficients for LONG11+ (AQ, -1.0060, p = 0.09) and negative but insignificant for LONG9+

(AQ, -0.8709, p = 0.13). The marginal effect on LONG11+ indicates that the probability that a

first-year audit of a private client receives a high quality rating is about 25 percentage points

lower than that of medium term engagements.34

For the SEC subsample, LONG11+ and

LONG9+ are positive but not significant. For TOTDEFIC, the coefficients for LONG9+/11+ are

not significant for the private sample, but LONG9+ is negative and significant for the SEC

registrants suggesting that for public companies there is some evidence that long tenure is

associated with fewer deficiencies and higher audit quality.

In other untabulated tests, we replace the tenure dummy variables with a continuous

measure of tenure (i.e., the number of years the audit firm has audited the client). The continuous

34

Interpreting differences in the SEC and private company subsamples must be done with care because observed

differences can be due to multiple factors such as differences in risk, personnel, and complexity.

26

variable is negative and significant for TOTDEFIC but insignificant for AQ, suggesting some

evidence than an increase in tenure is associated with fewer deficiencies. We also add the square

of tenure to measure non-linearity in tenure effects. In this case, neither the continuous measure

for tenure nor tenure-squared is significant for AQ, but continuous tenure remains negative for

TOTDEFIC while its square is not significant.

Finally, we break continuous tenure into five discrete segments: TEN2-4, TEN5-7, TEN8-

10, TEN11-13, and TEN14-N. The results show a positive and significant coefficient for TEN2-4

for AQ and a negative and significant coefficient for TEN14-N for TOTDEFIC. We also observe

that the five coefficients in the AQ model decline as tenure increases suggesting that the

improvements in audit quality relative to the first year decrease with tenure.35

None of the

coefficients are significantly different from each other. However, the coefficient on TEN2-4 is

statistically different from TEN14-N (AQ, 1.3366 vs. 0.0433, p=0.06) for the private subsample,

again suggesting that the decline in quality over time is confined to private firms.

Overall, our results indicate that both SEC and private clients experience relatively lower

overall audit quality in the first year of an engagement. In long tenure, audit quality may

deteriorate for private clients but audits of SEC registrants maintain a relatively high level of

overall audit quality while also experiencing fewer audit deficiencies.

4.1.2. Tenure, Audit Effort and Audit Fees. A follow up question that arises is what

causes quality to be lower in first-year audits. Regulators are particularly concerned with this

question because an auditor that obtains a client via a discounted (i.e., “lowball”) fee may have

incentives to reduce the effort level for such audits. To explore this issue in more detail, we

examine the associations between FIRST and audit fees and audit effort. We use fee and hours

35

The coefficients for TOTDEFIC also decline, becoming more negative (i.e. fewer deficiencies as tenure

increases).

27

models similar to Bell, Doogar, and Solomon [2008]36

which include the usual controls for client

and engagement specific variables (Hay, Knechel, and Wong [2006]) plus our various test

variables. The dependent variables in our two fee equations are LNFEE (natural log of total audit

fees billed) and FEEPERHR (total audit fees billed divided by total audit hours, i.e., average

price per unit of audit labor), and the dependent variable in our audit effort model is LNHRS

(natural log of total audit hours).

<<<<< Insert Table 5 about here >>>>>

Results for our test variables are summarized in Table 5. In Panel A, we show the mean

values of several audit production variables and how these differ between first-year audits and

continuing engagements. The mean audit fee per hour is significantly lower in a first-year

engagement ($147) relative to a continuing engagement ($167), consistent with lowballing. Also

partners and managers expend relatively more time on a first-year engagement. Panel C shows

the results of the regression of audit fees, fee per hour, and total hours on various engagement

characteristics. Consistent with findings in many prior studies, Table 5 indicates the presence of

lowballing because the coefficients on FIRST are negative and significant for LNFEE and

FEEPERHR. Table 5 also shows that LNHRS is significantly higher for first-year audits.

We also disaggregate total hours into hours by specific labor ranks and test for

associations between FIRST and effort by partners, managers, in-charge, other staff, and

specialists (e.g., tax and technology). The results (untabulated) suggest a strong positive

association (p<0.05) between FIRST and all of the measures of audit effort with the exception of

technology specialists. When considered in combination with the fee results, we observe that

auditors devote more effort to first-year engagements but at a reduced average rate per hour.

36

The sample used in Bell, Doogar, and Solomon [2008] did not include financial services and health care clients.

28

This is not the result that would obtain if auditors were intentionally cutting corners on new

audits and is also consistent with the presence of a significant learning curve (Causholli [2013]).

4.2 NON-AUDIT SERVICES AND AUDIT QUALITY

4.2.1. Tests of Hypotheses. We now turn to our tests of the NAS hypotheses. Pairwise

correlations between measures of NAS and audit quality (Table 3) show that tax and other

services are positively correlated with TOTDEFIC, while offer services are positively associated

with AQ thus indicating that different NAS types might have a different implication for audit

quality. Results from the tests of our three NAS hypotheses using equation (2) are shown in

Table 6.

<<<<< Insert Table 6 about here >>>>>

Panel A in Table 6 presents the results for H2a (NAS_Dummy, PNNAS_AUD) and H2b

(PUB*PNNAS_AUD). The H2a tests are shown in columns (1) and (2) for AQ and TOTDEFIC,

respectively, and the H2b tests appear in columns (3) and (4). For H2a, the coefficients for

NAS_Dummy and PNNAS_AUD are not significant in either model, suggesting that neither the

presence nor magnitude of total NAS is associated with audit quality or deficiencies. In tests of

H2b, the coefficients on the interaction variable PUB*PNNAS_AUD are positive (negative) and

significant for AQ (TOTDEFIC), indicating that the probability that an audit of a publicly-listed

client receives a high quality rating increases as total NAS increases. We use the Norton, Wang,

and Ai [2004] procedure37

to compute interaction effects for each of our 265 sample observations

and find that, relative to privately-held clients, the mean probability of a high quality audit for

public clients that purchase auditor-provided NAS is 22 percentage points higher. This result is

37

Ai and Norton [2003] note that in non-linear models such as logit, the magnitude of an interaction does not equal

the marginal effect of the interaction term and the statistical significance of the interaction should not be tested using

a simple Z test. Following their recommended procedure, we compute the interaction effect for each of our 265

sample observations and find it is positive and statistically significant for 95% of the sample observations .

29

consistent with the notion that provision of NAS to a publicly-listed audit client produces

knowledge spillovers.

Alternatively, it could indicate an increase in perceived auditor business risk causing

auditors to respond by conducting higher quality audits. However, we also note that the

coefficient on PNNAS_AUD is negative (positive) and significant for AQ (TOTDEFIC)

suggesting that audit quality declines with increases in NAS for private clients (generating the no

net effect result in the test of H2a). The marginal effect of PNNAS_AUD on AQ, holding all the

other independent variables constant at their means, indicates that as the magnitude of

PNNAS_AUD increases by one percentage point, the probability of receiving a high quality

rating is 0.17 percentage points lower among private clients. In the TOTDEFIC regression,

incidence rate ratios suggest that the number of deficiencies in private clients increases by 29

percent as non-audit services increase.38

In untabulated tests, we re-run the analysis separately

for SEC registrants and private clients: the coefficients in the AQ model for PNNAS_AUD are

0.3623, p = 0.09 (SEC) and -0.6254, p = 0.06 (private) and for TOTDEFIC the coefficients are

0.0143, p = 0.81 (SEC), and 0.2304, p=0.00.39

Panel B of Table 6 reports results for H2c, where NAS fees are broken into four

components: PNTAX_AUD, PNMAS_AUD, PNOFFR_AUD, and PNOTH_AUD. Two of the

coefficients for AQ (TOTDEFIC) are negative (positive) while two are positive (negative),

suggesting mixed impacts on audit quality. However, only two coefficients are significant: (1)

PNOFFR_AUD is positive for AQ and (2) PNTAX_AUD is positive for TOTDEFIC. The former

38

We also run models that include (1) only NAS_Dummy, (2) only TAX_Dummy, MAS_Dummy, OFFR_Dummy,

and OTH_Dummy, (3) only PNNAS_AUD, and (4) only PNTAX_AUD, PNMAS_AUD, PNOFFR_AUD,

PNOTH_AUD. The results we obtain are consistent with our primary results. 39

We re-perform the analysis for private clients by dropping the insignificant control variables (ROMM, LEV, ROA,

COMPLEX) in order to preserve degrees of freedom and obtain a significantly negative coefficient on FIRST (-

0.7888, p =0.06) for the sub-sample of private clients.

30

result suggests that provision of services involving new securities offerings may have a positive

effect on the quality of the audit due to knowledge spillovers between the audit and the closely

related work necessary to prepare a public offering. The latter result suggests that provision of

tax services may be associated with lower audit quality and is inconsistent with prior research

(Kinney, Palmrose, and Scholz [2004]). In additional (untabulated) analysis, we find that the

negative association between tax fees and audit quality is present only in the private client

subsample and is significant for both AQ (-0.7072, p=0.10) and TOTDEFIC (0.2760, p=0.06).40

We extend our analysis of SEC registrants by replacing PNNAS_AUD with the four NAS

components and PUB*PNNAS_AUD with the comparable component interactions. The results

are reported in Panel C of Table 6. We observe a positive (negative) and significant interaction

for MAS and AQ (TOTDEFIC). Taken together, these results suggest that for SEC registrants

MAS is associated with higher audit quality. The results for TOTDEFIC suggest that the

previously observed increase in TOTDEFIC associated with tax services is confined to the

sample of private clients, while we also observe an increase in deficiencies for private companies

purchasing MAS services.41

4.2.2. Non-Audit Services, Audit Effort and Audit Fees. As observed above, if NAS has

an association with audit quality, it is mostly positive with the possible exception of tax services

40

The data collection instrument had two questions that are tax-related: (1) Do the work papers include

documentation of the client’s tax calculations with appropriate evidence of procedures performed and conclusions

reached?, and (2) Was an appropriate Tax Review performed and documented? Our sample includes 72 tax-related

deficiencies, of which 46 (64%) and 11 (15%) occur on engagements receiving quality ratings of “satisfactory with

comments” and “needs improvement or unsatisfactory,” respectively. 41

We obtain somewhat unusual results for offering services—a large decrease in deficiencies for private companies

but a large increase for public companies—but these results should be interpreted with care. Not all companies in the

sample issuing new securities are classified as public entities. Some government-sponsored and other not-for-profit

hospitals are able to issue public debt without meeting all of the requirements of an SEC registrant (Horner and

Makens [1997]). In our sample there are five such entities that engaged the auditor for non-audit services related to

such an offering. When the analysis is run for only the public companies, we find that the association of

TOTDEFIC and PNOFFR_AUD is not significant (p=.760, untabulated).

31

provided to private clients. However, the prior analysis does not indicate if a positive association

is due to knowledge spillovers or an auditor addressing increased auditor business risk. Panel B

of Table 5 reports the mean values of several audit production variables and how these differ

between engagements with NAS =1 vs. NAS=0. This table shows that the mean audit fee is

significantly higher for NAS purchasers, but none of the other production characteristics differ

across the two groups. In a multivariate analysis, Table 5, Panel C indicates a negative

association between FEEPERHR and both PNOFFR_AUD and PNOTH_AUD, as well as a

marginally negative association between LNFEE and PNOTH_AUD. For LNHRS, the coefficient

for NAS_Dummy is positive and marginally significant, indicating that audit hours increase when

auditor-provided NAS are present and that the auditor may be compensating for increased

auditor business risk rather than benefiting from knowledge spillovers, i.e., we do not find

evidence suggesting that audit effort declines in the presence of auditor-provided NAS.

In contrast to the overall effect, the PNTAX_AUD is negatively associated with both total

audit effort and audit fees. This suggests a potential for knowledge spillovers consistent with

Donohoe and Knechel (2013). In untabulated analyses, PNTAX_AUD is negatively associated

with hours expended by partners (-0.1739, p<0.05), managers (-0.1787, p<0.05) and technology

specialists (-0.3347, p<0.10). When considered together with our finding that TOTDEFIC is

positively associated with PNTAX_AUD (at least for private clients) this pattern of results is

consistent with the notion that the auditor considers and relies on tax specialists’ non-audit work

as part of their body of evidence, making the audit process potentially more efficient.42

However,

the link between the audit and tax services may not be well documented thus resulting in

42

The negative association between PNTAX_AUD and audit hours is highly sensitive to the inclusion of the

NAS_Dummy. When NAS_Dummy is omitted, the association is insignificant. Further, including a variable

TAX_Dummy in the LNHRS model is also insignificant. The additional analyses suggest that any link between tax

services and auditor effort is quite weak.

32

deficiencies. Overall, the results we obtain on TAX services could be consistent with either the

presence of knowledge spillovers or a general decrease in the level of skepticism that auditors

exhibit when relying on evidence gathered by the consulting team (Joe and Vandervelde [2007]).

4.3 ADDITIONAL ANALYSES

4.3.1. Alternative measures of audit quality. We conducted a number of tests using

alternative measures of audit quality. First, we consider the possibility that some of the

engagements included in “satisfactory with comments” may have actually been of high quality

but with minor deficiencies. Table 1, Panel C, shows that no engagement in the UNSAT group

has fewer than two deficiencies. Consequently, we re-classify 31 engagements as high quality

that received “satisfactory with comments” ratings and had TOTDEFIC<2 to create AQREV,

resulting in 143 (54 percent) and 122 (46 percent) observations where AQREV=1 and 0,

respectively. AQ and AQREV have a positive correlation of 0.79 (p<0.01). Our results using

AQREV are similar to those using AQ except for H2c where we obtain a negative coefficient for

PNTAX_AUD (-0.4730, p=.08).

We also perform ordered logit regressions by replacing AQ in models (1) and (2) with the

4-point scale overall audit quality variable tabulated in Panel A of Table 1. Overall audit quality

ranks the quality of audit engagements in decreasing order, i.e., a rank of 1 is “unqualified

satisfactory” and a rank of 4 is “unsatisfactory”.43

The results (untabulated) are qualitatively

similar to our primary results. Specifically, for our four tenure hypotheses we find that FIRST is

positive (p<0.05), TEN2-4 is negative (p<0.05), LONG9+/LONG11+ are insignificant,

PUB*FIRST and PUB*LONG9+/LONG11+ are insignificant although for H1d, the coefficient on

LONG11+ is positive and marginally significant (p<0.10). For our NAS hypotheses, we find

43

Because of the manner in which we define AQ, we expect the coefficients on the variables of interest to be of the

opposite sign relative to those reported in Tables 4 and 6.

33

that PNNAS_AUD is insignificant, PUB*PNNAS_AUD is negative (p<0.05) and PNOFFR_AUD

is negative (p<0.10).

Finally, we replace AQ with one of the components of audit quality, the sufficiency of

audit evidence (EVID) and set EVID equal to one if the sufficiency of audit evidence is rated

“unqualified satisfactory” (n=190, 72%), zero otherwise.44

We present the results for EVID in

Table 7. In Panel A, we find that EVID is lower in first year engagements (H1a), and increases for

engagements with tenures between 2 and 4 years (H1b). Similar to our AQ results, in Panel B,

PUB*LONG9+/LONG11+ are not significant, but when restricted to private clients

LONG9+/LONG11+ are significantly negative, consistent with H1d. Panel C shows the results

of the association between NAS variables and EVID. None of the NAS variables are significantly

associated with EVID.

<<<<< Insert Table 7 about here >>>>>

4.3.2. Industry Specialization. Prior research suggests that industry specialization can

improve audit quality (Solomon, Shields, & Whittington, [1999], Owhoso, Messier, & Lynch,

[2002], Lim and Tan [2008]). Our database includes an indication of the lead partner’s

designated industry specialization. As a result, unlike prior studies that use market shares to

measure specialization,45

we use actual partner industry knowledge to proxy for industry

specialization. In 233 engagements (87.9%), the partner’s specialization matches the client’s line

of business. To test if an industry mismatch at the partner level negatively affects audit quality,

we estimate a model that includes FIRST, NAS_Dummy, PNNAS_AUD, control variables and

four dummy variables to indicate that the audit partner is not a specialist in the industry of the

44

The other three components of audit quality lack sufficient variation to be analyzed. 45

The audit firm’s measure of client industry differs from the 2-digit SIC codes employed in extant research.

Minutti-Meza (2013) concludes that industry specialization proxies based on industry market share of the audit firm

are not reliable measures for auditor knowledge and specialization.

34

client: NOT-FS-SP (n=7), NOT-HC-SP (4), NOT-ICE-SP (8) and NOT-CIP-SP (13).46

Our

findings indicate that only the coefficient on NOT-FS-SP is negative and significant for AQ (-

2.4242, p<0.05) and positive and significant for TOTDEFIC (0.7323, p<0.02). This result

suggests that knowledge from industry specialization benefits audits in the financial services

sector the most. This is consistent with the notion that knowledge from industry specialization

can have the greatest benefits in industries with complex transactions and regulation (Owhoso,

Messier, and Lynch [2002], Moroney [2007]).

We also examine whether industry specialization can mitigate the negative effect of first-

year audit engagements. Gul, Fung, and Jaggi [2009] find that auditors with industry experience

can perform better on new engagements, suggesting that industry knowledge partially offsets a

lack of client-specific knowledge. To test the effect of misaligned industry specialization on first-

year audits, we define NOT-SPEC to be equal to one if the lead audit partner on an engagement

is not a specialist in the client’s industry. We then estimate the model that includes NOT-SPEC

and the interaction term FIRST*NOT-SPEC. The coefficient on FIRST* NOT-SPEC is negative

but not significant for AQ and is positive and marginally significant for TOTDEFIC (p<0.10).

When the analysis is run on the SEC/private subsamples, the negative results of partner

misalignment are only observed in the private client sample. 47

4.3.3. Audit Process Quality and Accruals. Much prior research uses estimates of

discretionary accruals (DA) as proxies for audit quality in a variety of contexts. The underlying

46

The four industries are financial services (FS), health care (HC), information, communication and entertainment

(ICE), and consumer and industrial products (CIP). 47

Prior research emphasizes that in order to tease out the effects of specialization, misalignment between the area of

partner specialization and client industry should be relatively large (Solomon, Shields, and Whittington [1999]). It

is possible that specialized knowledge in the ICE sector is aligned sufficiently for audits of clients in the CIP sectors

and vice versa. Combining ICE and CIP into a single industry class, the coefficient on FIRST* NOT-SPEC is

negative and significant in the AQ model (p<0.05) and positive and significant in the TOTDEFIC model (p<0.01).

35

rationale is that the joint probability of earnings management and low auditor quality increases as

DA increase. For a variety of reasons, DA estimates are limited as a measure of audit quality,

i.e., GAAP allows alternative accounting treatments and discretion when estimating the possible

range of values for an accounting estimate (Knechel, Krishnan, Pevzner, Shefchik and Velury

[2013]). Further, many of the DA models fail to capture the economic shocks that give rise to

accruals and this leads to cases of high abnormal accruals being erroneously classified as

earnings management. For these reasons, some of the variability in a single-period cross section

of DA estimates may be attributable to the inherent imprecision of GAAP rather than low audit

quality. Internal or external reviewers examine evidence about the audit process and auditor

judgments that goes beyond publicly observable accrual measures, and can evaluate this

evidence to assess compliance with auditing standards, the validity of auditor judgments, and the

quality of audit documentation. Therefore, evidence of an association between DA estimates and

audit process quality would support the validity of using DA estimates as proxies for audit

quality.

Our sample includes 102 audit engagements for which we have sufficient financial

statement information to develop DA metrics using the following model (Ashbaugh et al. [2003],

Kothari et al. [2005]):

(3)

where CA is current accruals and equals income before extraordinary items plus depreciation

(COMPUSTAT variables IBC +DPC), minus operating cash flows (OANCF). ΔSALE is the

change in sales equal to SALEt – SALEt-1. IB is income before extraordinary items and AT equals

total assets. We estimate equation (3) within 2-digit SIC codes using all Compustat firms in the

1

1

3

1

2

1

1

1

1

t

t

t

t

tt

t

AT

IB

AT

SALE

ATAT

CA

36

industry with available data in fiscal year 2002. We use four versions of discretionary accruals:

DA (firm-specific residual), ABSDA (absolute value of DA), POSDA (negative DA values set

equal to zero) and NEGDA (positive DA values set equal to zero).

Table 8, Panel A shows that none of the four DA estimates are negatively correlated with

AQ. On the contrary, we find a positive correlation between AQ and DA (p<0.10), ABSDA

(p<0.01) and POSDA (p<0.01). TOTDEFIC is not significantly correlated with any of the DA

measures. We find no significant correlations between the DA measures and our primary

measures of tenure (FIRST, TEN2-4 and TEN5-N) and NAS (PNNAS_AUD, PNTAX_AUD,

PNMAS_AUD, PNOFFR_AUD and PNOTH_AUD). Table 8 also shows that in many cases, the

magnitude of DA is positively associated with audit effort. These results are consistent with the

notion that auditors conduct higher quality audits for clients having large discretionary accruals.

We also conduct a multivariate analysis of accruals. Based on prior research (e.g. Lim

and Tan 2010) we control for (1) the percent change in sales (GROWTH), (2) the ratio of

operating cash flow to total assets (CFO), (3) the ratio of total liabilities to total assets (LEV), (4)

whether the company is from a high litigation industry (LITIG) defined as industries with SIC

codes equal to 2833-2836, 3570-3577, 3600-3674, 522-5961, 7370-7474, (5) market to book

ratio (MB), (6) the natural log of the market value of equity (MVE), (7) whether a company has

negative net income (LOSS), (8) whether the company experienced a percentage change in long-

term debt greater or equal to 20 percent, or the percentage change in common shares outstanding,

adjusted for stock splits, greater or equal to 10 percent (FIN), and (9) the absolute value of

lagged current accruals (LCA). Because of the additional required variables our sample is

reduced to 97 observations. Table 8, Panel B presents the results of this regression. Similar to

our univariate results, there is a positive and significant association between AQ and the

37

magnitude of discretionary accruals (ABSDA and POSDA). There is also a marginally significant

positive association between TOTDEFIC and NEGDA. Overall, these results suggest that audit

quality measures that focus on process quality do not map well into financial reporting quality

measures such as discretionary accruals.

<<<<< Insert Table 8 about here >>>>>

4.4 ROBUSTNESS TESTS

4.4.1. Arthur Andersen Clients. Eighty percent of the first-year audits were previously

audited by Arthur Andersen (AA). The unusual circumstances surrounding the audits of ex-AA

clients may have influenced the review of these engagements. In some cases, members of the

AA audit teams may have followed their clients to the new audit firm. However, it was the

policy of the audit firm to replace the audit partner, manager and concurring review partner of an

ex-AA client with their own personnel. Also, although some staff personnel might continue on

the audit of an ex-AA client, all critical audit decisions made during the audit were the

responsibility of successor firm personnel. Finally, it is also possible that a greater proportion of

lower-quality assessments were made for this subset of audits because the ex-AA auditors were

inadequately trained on the new firm’s audit methodology. We re-run equation (1) and include

both FIRST and a dichotomous variable indicating first-year clients that are not former AA

clients. A positive coefficient on the interaction would indicate that first time audits of non-AA

clients were of higher quality than ex-AA clients. We find that the interaction term is positive

and significant using AQ (p<0.05) but is insignificant for TOTDEFIC. So, while there is some

evidence that the results for FIRST are influenced by ex-AA clients, not all of our results are

driven by former Andersen clients.

38

4.4.2. Combined Effects of Tenure and NAS. In our primary tests of the hypotheses, we

did not include the variables for tenure and NAS in the same model. We test the robustness of

our primary results by including tenure and NAS variables in the same models. In each of the

models presented in Table 4, we add NAS_Dummy and PNNAS_AUD, and in each of the models

shown in Table 6 we add FIRST. The results are qualitatively similar to our primary results with

the exception that PUB*PNNAS_AUD is not significant for TOTDEFIC (p=0.16) and

PNOFFR_AUD is not significant for AQ (p=0.13).48

4.4.3. Bootstrapping. We re-estimate our regressions by using the bootstrap method

(Brownstone and Valletta [2001]). This method runs each regression multiple times and uses the

variability of the model coefficients to estimate the standard errors. We perform the bootstrap

technique on all of the models shown in Tables 4 and 6 using 1000 repetitions. Our results

(untabulated) are similar to our primary results except PUB*PNNAS_AUD is not significant for

TOTDEFIC (p=0.12), and PNOFFR_AUD is not significant for AQ.

4.4.4 Office Size and Audit Quality. We use two methods to examine the effect of office

size on audit quality. First, based on the location of the office available in our dataset, we

classified each engagement as being performed by a large or small office where large offices are

those that serve clients in densely populated metropolitan areas with large levels of business

activity. The analyses (untabulated) show that office size is not significantly associated with AQ,

but is negatively associated with TOTDEFIC (-0.2022, p<0.05). Second, we matched our sub-

sample of SEC registrants with Audit Analytics to obtain information on the size of each office

that audits the SEC registrant. The matching resulted in a final sample of 111 observations.

48

We also estimate all the models in Table 6 after replacing PNNAS_AUD with the natural log of total NAS fees for

tests of H2a and H2b and the natural log of the fees from each NAS type in tests of H2c. The results (untabulated) are

qualitatively the same as our primary results. MAS services are not provided to any public companies where the

engagement received either a “needs improvement” or “unsatisfactory” overall audit quality rating.

39

Following Francis and Yu (2009) we measure office size as the natural log of audit fees at the

office level and include this variable in all of our main analyses. Using this approach, office size

is not significantly related to audit quality.

5. Summary and Concluding Remarks

We use data from internal assessments of audit quality in a Big 4 accounting firm to

investigate the impact of audit firm tenure and auditor-provided NAS on audit quality. We find

that first-year audits are more likely to receive a lower assessment of audit quality, but audit

quality improves significantly in the years following the first year, suggesting that lower audit

quality is limited to the first year. Also, we find that audit quality is sustained in very long tenure

engagements for SEC registrants, but long tenure is negatively associated with audit quality for

privately-held clients. Further, we observe that lowballing occurs in new engagements but

auditors exert greater effort for first-year audits. Higher auditor effort indicates that auditors

spend considerable time acquiring client-specific knowledge in a first-year engagement.

Altogether, our findings reveal a significant learning cost in the first year of an audit which may

contraindicate a policy of mandatory rotation.

On the issue of NAS, we find no evidence that NAS leads to a loss of audit quality for

SEC registrants. On the contrary, we observe that total NAS and management advisory services

are associated with higher audit quality for SEC registrants. We do observe some loss of audit

quality for privately-held clients as fees from NAS increase, especially those that acquire

management advisory and tax services. Audit effort, either in total or when classified by category

of labor, is often higher in engagements that are linked to non-audit services even though audit

40

fees are not higher.49

Taken together, these results suggest that the improvement in audit quality

is less likely to be due to knowledge spillovers and more likely to be due to auditors providing

higher quality audits in response to an increase in perceived auditor business risk from NAS.

Our study has limitations. First, the data come from only one firm and the sample size is

limited. To the extent the processes are unique to this firm, or the tests lack power, the results

may not generalize to other settings. Second, the data are from a time period characterized by an

increased awareness and scrutiny of the audit process. This may affect the rigor of internal audit

quality assessments and produce evaluations that are more stringent than those produced during

normal periods. However, the setting we use closely resembles the current regulatory

environment in which audit firms are evaluated by the PCAOB inspectors. Third, our measures

of audit quality are based on judgments of individuals and subject to the biases and limitations of

those individuals. Finally, even though the engagements selected for review cover a wide range

of offices and types of engagements, there appears to be an oversampling of risky engagements.

Although this could bias the results and affect our ability to generalize our conclusions, it is

nevertheless similar to the PCAOB’s selection of high risk engagements for inspection.

Our study extends current research and provides some unique evidence to consider when

addressing regulatory policies for the audit profession. Because our measures of audit quality

focus strictly on the quality of the audit process as a separate activity within the financial

reporting process, we are able to circumvent the usual problems associated with prior research,

i.e., endogenous auditor tenure and management effects on financial reporting quality. In

addition, our study provides a more in-depth perspective since we can address not only the nature

49

This pattern of results may suggest a synergy of services rather than a cross-subsidization and is consistent with

the economic benefits that can be obtained from bundling professional services (Hitt, Bierman, Shimizu, and

Kochhar [2001]).

41

of the association between audit firm tenure and NAS and audit quality, but also the fee and audit

production effects associated with these important issues. Interestingly, the loss of independence

due to economic or social bonding that is the concern of regulators is only observed for

privately-held clients and does not occur for companies under the jurisdiction of the SEC.

42

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49

TABLE 1

AUDIT QUALITY MEASURES

Panel A: Composite review-team assessments for overall audit quality

Assessment N Percent

Unqualified Satisfactory 112 42%

Satisfactory with Comments 133 50%

Needs Improvement 18 7%

Unsatisfactory 2 1%

265

AQ = Definitions of AQ = 1 and 0

1 Engagements receiving “unqualified

satisfactory” assessments 112 42%

0 Engagements receiving other than

“unqualified satisfactory” assessments 153 58%

Industry Sectors AQ = 1 AQ = 0 Total

Financial Services 39 (57%) 29 (43%) 68

Information,

Communication &

Entertainment

22 (39%) 35 (61%) 57

Consumer &

Industrial Products 43 (38%) 70 (62%) 113

Health Care 8 (30%) 19 (70%) 27

Total 112 153 265

50

TABLE 1 continued

Panel B: Frequency distribution of total deficiencies (TOTDEFIC) per engagement

TOTDEFIC

# of

Engagements Percent

0 44 16.6%

1 75 28.3%

2 53 20.0%

3 34 12.8%

4 22 8.3%

5 18 6.8%

6 8 3.0%

7 2 0.8%

8 4 1.5%

9 3 1.1%

11 1 0.4%

12 1 0.4%

51

TABLE 1 continued

Panel C: Descriptive statistics for individual assessed deficiencies(1)

by RATING

RATING

SAT

Unqualified

Satisfactory

SWC

Satisfactory with

Comments

UNSAT

Needs

Improvement &

Unsatisfactory

N 112 133 20

TOTDEFIC

Cumulative Distribution of Engagements

Total

Sample

0 42

(37.5%)

2

(1.5%)

0

(0.0%)

44

(16.6%) Tukey Tests for

Differences in Means

1 88

(78.6 %)

31

(23.3%)

0

(0.0%)

119

(44.9%)

Δ TOTDEFIC

Means for:

Signed

p-value

2 105

(93.8%)

63

(47.4%)

4

(20.0%)

172

(64.9%) SWC - SAT

+.0000

***

3 109

(97.3%)

91

(68.4%)

6

(30.0%)

206

(77.7%) UNSAT - SAT +.0000

***

4 111

(99.1%)

107

(80.5%)

10

(50.0%)

228

(86.0%)

UNSAT -

SWC +.0000

***

5 112

(100%)

122

(91.7%)

12

(60.0%)

246

(92.8%)

ANOVA Model

F-Statistic = 81.5, 2 df

p-value = .0000***

6 112

(100%)

128

(96.2%)

14

(70.0%)

254

(95.8%)

7 112

(100%)

128

(96.2%)

16

(80.0%)

256

(96.6%) t-test for Difference in

Mean TOTDEFIC By AQ

8 112

(100%)

131

(98.3%)

17

(85.0%)

260

(98.1%) AQ=1 AQ=0

9 112

(100%)

133

(100%)

18

(90.0%)

263

(99.2%) .94 3.3

11 112

(100%)

133

(100%)

19

(95.0%)

264

(99.6%)

t-Statistic = 11.8

p-value = .0000***

12 112

(100%)

133

(100%)

20

(100.0%)

265

(100.0%)

TOTDEFIC

Means

(Standard

Errors)

.94

(.1564)

2.96

(.1435)

5.30

(.3701)

2.10

(.1290)

Notes: (1)See APPENDIX A for abbreviated definitions of individual audit activities and frequencies of engagements with assessed

deficiencies by audit phase.

SAT refers to overall audit quality of “unqualified satisfactory,” SWC refers to “satisfactory with comments” and UNSAT

refers to “needs improvement” or “unsatisfactory;” AQ = 1 for engagements receiving an “unqualified satisfactory”

assessment for overall audit quality, 0 otherwise; TOTDEFIC = total number of assessed deficiencies across all 55

individual audit activities; *** indicates significance at 1 percent.

52

TABLE 2

DESCRIPTIVE STATISTICS FOR THE SAMPLE

Panel A: Client and engagement measures

Variables Mean Median Std Dev P10 P90

ASSETS($000) 2,880,402 261,038 16,600,000 37,809 3,267,000

PUB 0.57 1 0.50 0 1

ROMM 0.52 1 0.50 0 1

LEV 0.64 0.60 0.503 0.16 0.96

ROA -0.048 0.016 0.676 -0.20 0.117

COMPLEX 2.61 3 0.87 2 4

TENURE 5.89 2 8.743 1 16

FIRST 0.45 0 0.50 0 1

TEN2-4 0.20 0 0.40 0 1

LONG, 11+ years 0.20 0 0.40 0 1

Panel B: Audit and non-audit fee measures

Variables Mean Median Std Dev P10 P90

AUDITFEES($) 361,862 210,000 563,021 69,000 647,000

NASFEES($) 184,351 83,000 287,754 0 480,000

TAXFEES($) 102,618 25,000 198,527 0 293,000

MASFEES($) 21,952 0 74,129 0 83,000

OFFERFEES($) 24,778 0 74,348 0 80,000

MAFEES($) 7,842 0 42,816 0 0

UNSPFEES($) 27,162 0 79,517 0 84,000

NAS_Dummy 0.77 1 0.42 0 1

TAX 0.67 1 0.47 0 1

MAS 0.17 0 0.37 0 1

OFFER 0.20 0 0.40 0 1

MA 0.07 0 0.25 0 0

UNSP 0.28 0 0.45 0 1

PNNAS_AUD 0.63 0.36 0.83 0 1.71

PNTAX_AUD 0.35 0.13 0.54 0 0.95

PNMAS_AUD 0.09 0 0.34 0 0.18

PNOFFR_AUD 0.11 0 0.46 0 0.20

PNOTH_AUD 0.09 0 0.22 0 0.30 Notes: ASSETS($000) = client total assets in thousands of dollars; PUB = 1 if the client has publicly-listed securities (equity,

debt, or both), 0 otherwise; ROMM = 1 if the auditor-assessed risk of material misstatement at the overall financial statement

level is moderate or high and equals 0 if the assessed risk of material misstatement is low; LEV = total liabilities divided by total

assets; ROA = ratio of income (loss) from continuing operations to total assets; COMPLEX = client’s operational complexity

ranging from 1 (very simple) to 5 (very complex); TENURE = number of years the auditee has been a client of the audit firm;

FIRST = 1 for first-year audit engagements, 0 otherwise; TEN2-4 = 1 for engagements with tenures in the 2 to 4 years range, 0

otherwise; LONG, 11+ years = 1 for engagements with tenures longer than 10 years, 0 otherwise; NASFEES($) = dollar amount

of total fees billed for non-audit services; AUDITFEES($) = dollar amount of audit fees billed; TAXFEES($) = dollar amount of

53

fees billed for tax services; MASFEES($) = dollar amount of fees billed for management advisory services; OFFERFEES($) =

dollar amount of fees billed for services involving client public securities offerings; MAFEES($) = dollar amount of fees billed

for services involving client mergers and acquisitions; UNSPFEES($) = dollar amount of fees billed for other unspecified NAS;

NAS_Dummy = 1 for engagements where the sum of non-audit service fees is greater than zero, 0 otherwise; TAX = 1 if fees

billed for tax services are greater then zero, 0 otherwise; MAS = 1 if fees billed for management advisory services are greater

than zero, 0 otherwise; OFFER = 1 if fees billed for services involving client public securities offerings are greater than zero, 0

otherwise; MA = 1 if fees billed for services involving client mergers and acquisitions are greater than zero, 0 otherwise; UNSP

= 1 if fees billed for other unspecified NAS are greater than zero, 0 otherwise; PNNAS_AUD = ratio of total non-audit service

fees to audit fees; PNTAX_AUD = ratio of tax fees to audit fees; PNMAS_AUD = ratio of management advisory fees to audit

fees; PNOFFR_AUD = ratio of fees billed for services involving client public securities offerings to audit fees; PNOTH_AUD =

ratio of fees billed for services involving client merger & acquisition activities and other unspecified NAS to audit fees.

54

TABLE 3

CORRELATION MATRIX

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 AQ 1

2 TOTDEFIC -0. 55***

1

3 FIRST -0.15**

0.14**

1

4 TEN2-4 0.11* -0.03 -0.45

*** 1

5 LONG9+ 0.03 -0.15** -0.50

*** -0.27

*** 1

6 NAS Dummy -0.06 0.07 -0.01 -0.02 0.05 1

7 PNNAS_AUD 0.03 0.07 -0.17

*** 0.15

** 0.02 0.42

*** 1

8 PNTAX_AUD -0.03 0.10

* -0.07 0.02 0.03 0.35

*** 0.66

*** 1

9 PNMAS_AUD 0.02 -0.04 -0.07 0.08 0.02 0.14

** 0.39

*** 0.02 1

10 PNOFFR_AUD 0.12

* -0.03 -0.03 0.14

** 0.01 0.13

** 0.56

*** -0.02 -0.03 1

11 PNOTH_AUD -0.07 0.15

** -0.04 0.10 -0.05 0.22

*** 0.34

*** 0.04 -0.06 0.12

* 1

12 LAST -0.12

* 0.07 -0.09 0.01 0.10 0.18

*** 0.01 0.02 -0.00 -0.05 0.09 1

13 PUB -0.07 0.11

* 0.06 -0.10

* -0.03 0.33

*** 0.14

** 0.06 -0.08 0.18

*** 0.13

** 0.17

*** 1

14 ROMM -0.06 -0.04 -0.02 0.07 -0.05 -0.12

* -0.11

* -0.09 -0.04 -0.06 0.00 -0.07 -0.01 1

15 LEV -0.16

** 0.02 0.03 0.08 -0.08 0.03 -0.02 -0.04 0.13

** -0.07 -0.02 -0.02 -0.10 0.10 1

16 ROA 0.04 0.02 -0.05 0.01 0.04 0.08 0.04 0.05 0.02 0.00 -0.01 0.02 -0.01 -0.00 -0.01 1

17 COMPLEX -0.08 0.00 -0.04 0.03 0.03 0.14**

-0.05 -0.02 0.02 -0.09 0.03 0.46***

0.13**

0.02 0.03 -0.13**

1

Notes: AQ= 1 for engagements receiving an “unqualified satisfactory” assessment for overall audit quality, 0 otherwise; TOTDEFIC = total number of assessed audit

deficiencies; FIRST = 1 for first-year audit engagements, 0 otherwise; TEN2-4 = 1 for engagements with tenures in the 2 to 4 years range, 0 otherwise; LONG9+ = 1 for

engagements with tenures longer than 8 years, 0 otherwise; NAS_Dummy = 1 for clients that purchase any auditor-provided non-audit services, 0 otherwise; PNNAS_AUD =

ratio of non-audit service fees to audit fees; PNTAX_AUD = ratio of tax fees to audit fees; PNMAS_AUD = ratio of management advisory fees to audit fees; PNOFFR_AUD

= ratio of fees billed for services involving client public securities offerings to audit fees; PNOTH_AUD = ratio of fees billed for services involving client merger &

acquisition activities and other unspecified NAS to audit fees; LAST = natural log of total assets; PUB = 1 if the client has publicly-listed securities (equity, debt, or both); 0

otherwise; ROMM = 1 if the auditor-assessed risk of material misstatement at the overall financial statement level is moderate or high and equals 0 if the assessed risk of

material misstatement is low; LEV = total liabilities divided by total assets; ROA = ratio of income (loss) from continuing operations to total assets; COMPLEX = client’s

operational complexity ranging from 1 (very simple) to 5 (very complex); ***, **, * indicate significance at 1, 5 and 10 percent, respectively.

55

TABLE 4

THE ASSOCIATION BETWEEN AUDIT FIRM TENURE AND AUDIT QUALITY

AUDIT QUALITY= a0 + a1(TENURE) + a2(LAST) +a3(PUB) + a4(ROMM) + a5(LEV) + a6(ROA) + a7(COMPLEX) + a8(FS) + a9(HC) +

a10(ICE) + u

Panel A: Tests of tenure hypotheses H1a and H1b Tests of H1a : TENURE = FIRST Tests of H1b : TENURE = TEN2-4, TEN5-N

(1) (2) (3) (4)

AQ TOTDEFIC AQ TOTDEFIC

TENURE Variables Coef. p-value Coef. p-value Coef. p-value Coef. p-value

FIRST (H1a) -0.6230 0.02** 0.2318 0.04**

TEN2-4 (H1b) 0.8709 0.02** -0.1370 0.44

TEN5-N 0.4900 0.10* -0.2860 0.02**

Control Variables

LAST -0.2345 0.01*** 0.0710 0.02** -0.2330 0.01*** 0.0724 0.02**

PUB -0.2123 0.48 0.1537 0.23 -0.1929 0.53 0.1597 0.22

ROMM -0.2336 0.38 -0.0514 0.64 -0.2447 0.36 -0.0550 0.62

LEV -0.8783 0.02** 0.0683 0.32 -0.8899 0.02** 0.0604 0.38

ROA 0.0744 0.72 0.0367 0.46 0.0767 0.71 0.0369 0.44

COMPLEX 0.0599 0.74 -0.0784 0.28 0.0566 0.75 -0.0812 0.27

FS 0.9497 0.01*** -0.3864 0.02** 0.9303 0.01*** -0.3921 0.02**

HC -0.4246 0.39 -0.0782 0.69 -0.4986 0.32 -0.1023 0.61

ICE 0.0004 1.00 -0.0598 0.66 -0.0407 0.91 -0.0705 0.62

Constant 4.9800 0.00*** -0.4800 0.39 4.3564 0.01*** -0.2586 0.64

Model Fit

Model χ2 31.70 20.33 32.75 22.07

p-value 0.0004 0.0262 0.0006 0.0238

Nagelkerke’s R2 0.1516 0.1563

Area under ROC curve 0.7000 0.7046

N 265 265 265 265

56

TABLE 4 continued

Panel B: Tests of H1c: TENURE = FIRST, LONG

(1) (2) (3) (4)

AQ TOTDEFIC AQ TOTDEFIC

TENURE Variables Coef. p-value Coef. p-value Coef. p-value Coef. p-value

FIRST -0.7893 0.01*** 0.1123 0.41 -0.7599 0.01*** 0.1716 0.20

LONG (H1c):

LONG, 9+ years -0.3858 0.29 -0.2998 0.07*

LONG, 11+ years -0.3908 0.30 -0.1768 0.30

Control Variables

LAST -0.2322 0.01*** 0.0756 0.02** -0.2284 0.01*** 0.0750 0.02**

PUB -0.2112 0.49 0.1530 0.23 -0.2225 0.46 0.1502 0.24

ROMM -0.2474 0.36 -0.0570 0.60 -0.2538 0.35 -0.0562 0.61

LEV -0.8955 0.02** 0.0543 0.43 -0.8997 0.02** 0.0602 0.39

ROA 0.0775 0.71 0.0377 0.42 0.0791 0.70 0.0381 0.43

COMPLEX 0.0634 0.72 -0.0831 0.25 0.0624 0.73 -0.0809 0.26

FS 0.9376 0.01*** -0.3969 0.02** 0.9364 0.01** -0.3921 0.02**

HC -0.4763 0.34 -0.1149 0.57 -0.4569 0.36 -0.0920 0.64

ICE -0.0483 0.90 -0.0869 0.54 -0.0392 0.92 -0.0721 0.61

Constant 5.1276 0.00*** -0.4151 0.46 5.0365 0.00*** -0.4750 0.40

Model Fit

Model χ2 32.85 24.49 32.81 21.99

p-value 0.0006 0.0108 0.0006 0.0245

Nagelkerke's R2 0.1567 0.1565

Area under ROC curve 0.7037 0.7027

N 265 265 265 265

57

TABLE 4 continued

Panel C: Tests of H1d: TENURE = FIRST, LONG, PUB*FIRST, PUB*LONG

(1) (2) (3) (4)

AQ TOTDEFIC AQ TOTDEFIC

TENURE Variables Coef. p-value Coef. p-value Coef. p-value Coef. p-value

FIRST -0.9421 0.05** 0.3059 0.14 -0.9718 0.04** 0.3787 0.07*

PUB*FIRST 0.2652 0.68 -0.3134 0.23 0.3606 0.56 -0.3319 0.20

LONG, 9+ years -0.8113 0.14 -0.1894 0.44

PUB*LONG9+ 0.7675 0.29 -0.1859 0.58

LONG, 11+ years -1.0051 0.07* -0.0101 0.97

PUB*LONG11+ 1.1464 0.13 -0.2820 0.40

Control Variables

LAST -0.2450 0.01*** 0.0780 0.02** -0.2546 0.01*** 0.0799 0.02**

PUB -0.5145 0.30 0.3435 0.13 -0.6107 0.20 0.3621 0.11

ROMM -0.2373 0.38 -0.0592 0.59 -0.2413 0.37 -0.0573 0.61

LEV -0.8990 0.02** 0.0518 0.50 -0.9113 0.02** 0.0605 0.43

ROA 0.0796 0.71 0.0478 0.30 0.0790 0.71 0.0486 0.31

COMPLEX 0.0768 0.67 -0.0787 0.27 0.0974 0.59 -0.0814 0.27

FS 0.9080 0.01*** -0.4124 0.01*** 0.9320 0.01** -0.4115 0.01***

HC -0.5025 0.32 -0.1124 0.57 -0.4669 0.36 -0.0943 0.63

ICE -0.0535 0.89 -0.0934 0.51 -0.0373 0.92 -0.0785 0.58

Constant 5.5239 0.00*** -0.5807 0.34 5.6854 0.00*** -0.6944 0.26

Model Fit

Model χ2 33.98 26.03 35.11 23.54

p-value 0.0012 0.0169 0.0008 0.0356

Nagelkerke's R2 0.1618 0.1668

Area under ROC curve 0.7018 0.7040

N 265 265 265 265

Notes: AQ= 1 for engagements receiving an “unqualified satisfactory” assessment for overall audit quality, 0 otherwise; TOTDEFIC = total

number of assessed audit deficiencies; FIRST = 1 for first-year audit engagements, 0 otherwise; TEN2-4 = 1 for engagements with tenures in the

2 to 4 years range, 0 otherwise; TEN5-N = 1 for engagements with tenures longer than 4 years, 0 otherwise; LONG, 9+ years = 1 for

engagements with tenures longer than 8 years, 0 otherwise; LONG, 11+ years = 1 for engagements with tenures longer than 10 years, 0

otherwise; LAST = natural log of total assets; PUB = 1 if the client has publicly-listed securities (equity, debt, or both); 0 otherwise; ROMM = 1

58

if the auditor-assessed risk of material misstatement at the overall financial statement level is moderate or high and equals 0 if the assessed risk

of material misstatement is low; LEV = total liabilities divided by total assets; ROA = ratio of income (loss) from continuing operations to total

assets; COMPLEX = client’s operational complexity ranging from 1 (very simple) to 5 (very complex); FS = 1 for clients in the financial

services sector, 0 otherwise; HC = 1 for clients in the health care sector, 0 otherwise; ICE = 1 for clients in the information, communications and

entertainment sectors, 0 otherwise; ***, **, * indicate significance at 1, 5 and 10 percent respectively

59

TABLE 5

ANALYSES OF AUDIT FEE AND PRODUCTION ATTRIBUTES AS FUNCTIONS OF

TENURE AND NAS

Panel A: Mean values of audit production measures for first-year vs. continuing engagements

Variables

First Year

Engagements

Continuing

Engagements

t-test

p-value

AUDITFEES($) $410,666 $321,472 0.199

FEEPERHR $146.53 $166.67 0.003***

HOURS/ASSETS 0.0099 0.0078 0.092*

AUDITFEES($)/ASSETS 0.00134 0.00122 0.512

PTR_MGR-HRS/ASSETS 0.0027 0.0018 0.044**

OTH_HRS /ASSETS 0.0072 0.0059 0.156

PTR_MGR-HRS /HOURS 0.25 0.23 0.035**

Panel B: Mean values of audit production variables by NAS

Variables NAS = 1 NAS=0

t-test

p-value

AUDITFEES($) $407,815 $208,180 0.015**

FEEPERHR $154.74 $166.96 0.135

HOURS/ASSETS 0.0084 0.0097 0.402

AUDITFEES($)/ASSETS 0.0012 0.0015 0.116

PTR_MGR-HRS/ASSETS 0.0022 0.0022 0.913

OTH_HRS /ASSETS 0.0062 0.0075 0.223

PTR_MGR-HRS /HOURS 0.24 0.22 0.111

60

TABLE 5 continued

Panel C: OLS Regression of Audit Hours, Fee per hour, and Audit Fees on engagement characteristics

LNHRS= c0 + c1(FIRST) + c2(NAS_Dummy) +c3(PNTAX_AUD) + c4(PNMAS_AUD) + c5(PNOFFR_AUD) + c6(PNOTH_AUD) + c7(LAST) + c8(FRG) +

c9(COMPLEX) + c10(NREP) + c11(LEV) +c12(PUB) + c13(ABR) + c14(ROMM) + c15(MRELY) + c16(HRELY) + c17(DEBTCOV) + u

FEEPERHR= d0 + d1(FIRST) + d2(NAS_Dummy) +d3(PNTAX_AUD) + d4(PNMAS_AUD) + d5(PNOFFR_AUD) + d6(PNOTH_AUD) + d7(LAST) +

d8(FRG) + d9(COMPLEX) + d10(NREP) + d11(LEV) +d12(PUB) + d13(ABR) + d14(ROMM) + d15(MRELY) + d16(HRELY) + d17(DEBTCOV) +

d18(PPNPTRHRS) + d19(PPNMGRHRS) + d20(PPNINHRS) + u

LNFEE= f0 + f1(FIRST) + f2(NAS_Dummy) +f3(PNTAX_AUD) + f4(PNMAS_AUD) + f5(PNOFFR_AUD) + f6(PNOTH_AUD) + f7(LAST) +

f8(FRG) + f9(COMPLEX) + f10(NREP) + f11(LEV) +f12(PUB) + f13(ABR) + f14(ROMM) + f15(MRELY) + f16(HRELY) + f17(DEBTCOV) +

f18(LNPTRHRS) + f19(LNMGRHRS) + f20(LNINHRS) + f21(LNOTHHRS) + u

(1)

(2)

(3)

LNHRS FEEPERHR LNFEE

Variables Coef. p-value Coef. p-value Coef. p-value

FIRST 0.3075 0.00*** -29.3529 0.00*** -0.1126 0.01***

NAS_Dummy 0.1621 0.10* -4.9116 0.58 -0.0124 0.82

PNTAX_AUD -0.1454 0.03** 5.3606 0.38 -0.0685 0.08*

PNMAS_AUD -0.0248 0.81 -4.0067 0.67 -0.0212 0.72

PNOFFR_AUD 0.1201 0.13 -20.8130 0.00*** -0.0600 0.18

PNOTH_AUD 0.1327 0.40 -30.9678 0.03** -0.1526 0.09*

LAST 0.2469 0.00*** 5.9617 0.00*** 0.0891 0.00***

FRG -0.0024 0.34 0.5753 0.01** 0.0027 0.05*

COMPLEX 0.2906 0.00*** -7.7910 0.07* 0.0107 0.70

NREP 0.0114 0.00*** -0.0179 0.96 0.0021 0.37

LEV 0.1228 0.09* 0.6387 0.92 0.0033 0.94

PUB 0.3154 0.00*** 5.0487 0.50 0.0621 0.22

ABR 0.0698 0.44 18.3740 0.02** 0.1271 0.01**

ROMM 0.1480 0.05* 8.3634 0.22 0.0857 0.05**

MRELY 0.2866 0.00*** -18.5655 0.04** -0.0610 0.29

HRELY 0.1446 0.28 -15.7713 0.19 -0.0424 0.58

DEBTCOV 0.1741 0.02** -18.3593 0.01*** -0.1009 0.02**

LNPTRHRS 0.1442 0.00***

LNMGRHRS 0.2829 0.00***

LNINHRS 0.2846 0.00***

61

LNOTHHRS 0.1132 0.00***

PPNPTRHRS 206.8684 0.04**

PPNMGRHRS 157.2083 0.01***

PPNINHRS 71.7326 0.01***

Constant 0.7288 0.08* 30.6798 0.47 5.9701 0.00***

Adjusted R square 0.64 0.24 0.89

N 265 265 265

Notes: AUDITFEES($) = dollar amount of audit fees billed; FEEPERHR = total audit fees billed divided by total audit hours; HOURS/ASSETS = ratio of total

audit hours to total assets($000); AUDITFEES($)/ASSETS = ratio of audit fees billed to total assets($000); PTR_MGR-HRS/ASSETS = ratio of the sum of partner

and manager audit hours to total assets($000); OTH_HRS/ASSETS = ratio of the sum of audit hours worked by all audit personnel other than partners and

managers to total assets($000); PTR_MGR-HRS/HOURS = ratio of the sum of partner and manager audit hours to total audit hours; LNHRS = natural log of total

audit hours; LNFEE = natural log of total audit fees billed; FIRST = 1 for first-year audit engagements, 0 otherwise; NAS_Dummy = 1 for clients that purchase

any auditor-provided non-audit services, 0 otherwise; PNTAX_AUD = ratio of tax fees to audit fees; PNMAS_AUD = ratio of management advisory fees to audit

fees; PNOFFR_AUD = ratio of fees billed for services involving client public securities offerings to audit fees; PNOTH_AUD = ratio of fees billed for services

involving client merger & acquisition activities and other unspecified NAS to audit fees; LAST = natural log of total assets; FRG = proportion of client total

assets located outside the United States; COMPLEX = client’s operational complexity ranging from 1 (very simple) to 5 (very complex); NREP = total number

of audit reports rendered for the engagement; LEV = total liabilities divided by total assets; PUB = 1 if the client has publicly-listed securities (equity, debt, or

both); 0 otherwise; ABR = 1 if assessed auditor business risk is moderate or high, 0 if low; ROMM = 1 if the auditor-assessed risk of material misstatement at

the overall financial statement level is moderate or high and equals 0 if the assessed risk of material misstatement is low; MRELY = 1 if the auditor placed

moderate reliance on the client’s internal control system, 0 otherwise; HRELY = 1 if the auditor placed high reliance on the client’s internal control system, 0

otherwise; DEBTCOV = 1 if the client is bound by significantly restrictive debt covenants, 0 otherwise; LNPTRHRS, LNMGRHRS, LNINHRS and LNOTHHRS

are natural logs of audit labor hours at the partner, manager, in-charge and other (predominantly staff) ranks, respectively; PPNPTRHRS, PPNMGRHRS, and

PPNINHRS = ratios of partner, manager, and in-charge hours to total audit hours, respectively; ***, **, * indicate significance at 1, 5 and 10 percent

respectively.

62

TABLE 6

THE ASSOCIATION BETWEEN NON-AUDIT SERVICES AND AUDIT QUALITY

AUDIT QUALITY= b0 + b1(NAS) + b2(LAST) +b3(PUB) + b4(ROMM) + b5(LEV) + b6(ROA) + b7(COMPLEX) + b8(FS) + b9(HC) +

b10(ICE) + u

Panel A: Tests of NAS Hypotheses H2a and H2b

Tests of H2a:

NAS = NAS_Dummy, PNNAS_AUD

Tests of H2b:

NAS = NAS_Dummy, PNNAS_AUD,

PUB*PNNAS_AUD (1) (2) (3) (4)

AQ TOTDEFIC AQ TOTDEFIC

NAS Variables Coef. p-value. Coef. p-value Coef. p-value Coef. p-value

NAS_Dummy -0.0535 0.89 -0.0247 0.89 0.2683 0.51 -0.0923 0.58

PNNAS_AUD (H2a) 0.0732 0.68 0.0766 0.34 -0.6868 0.06* 0.2563 0.01***

PUB*PNNAS_AUD (H2b) 1.0224 0.01*** -0.2415 0.09*

Control Variables

LAST -0.2161 0.02** 0.0671 0.04** -0.2325 0.01*** 0.0701 0.03**

PUB -0.2499 0.44 0.1545 0.26 -0.8842 0.03** 0.3102 0.04**

ROMM -0.2012 0.45 -0.0447 0.69 -0.1451 0.60 -0.0570 0.61

LEV -0.9463 0.02** 0.0753 0.22 -1.0471 0.01*** 0.0745 0.22

ROA 0.0985 0.65 0.0332 0.51 0.1102 0.63 0.0306 0.54

COMPLEX 0.0822 0.64 -0.0700 0.33 0.0898 0.62 -0.0701 0.33

FS 0.9771 0.01*** -0.4322 0.01*** 1.0578 0.00*** -0.4505 0.01***

HC -0.3578 0.47 -0.1388 0.48 -0.3166 0.53 -0.1678 0.41

ICE -0.0123 0.97 -0.0833 0.58 -0.0247 0.95 -0.0788 0.60

Constant 4.3192 0.01*** -0.3292 0.56 4.7891 0.00*** -0.4292 0.45

Model Fit

Model χ2 26.55 16.25 33.88 20.04

p-value 0.0054 0.1322 0.0007 0.0663

Nagelkerke's R2 0.1282 0.1613

Area under ROC curve 0.6851 0.7016

N 265 265 265 265

63

TABLE 6 continued

Panel B: Test of H2c – NAS = PNTAX_AUD, PNMAS_AUD, PNOFFR_AUD,

PNOTH_AUD, NAS_Dummy (1) (2)

AQ TOTDEFIC

NAS Variables Coef. p-value Coef. p-value

PNTAX_AUD (H2c) -0.2769 0.32 0.1672 0.07*

PNMAS_AUD (H2c) 0.1915 0.65 -0.0707 0.63

PNOFFR_AUD (H2c) 0.9813 0.07* -0.1286 0.41

PNOTH_AUD (H2c) -0.8659 0.20 0.4938 0.15

NAS_Dummy 0.1473 0.71 -0.0905 0.60

Control Variables

LAST -0.2127 0.02** 0.0600 0.08*

PUB -0.3995 0.23 0.1783 0.19

ROMM -0.2066 0.45 -0.0595 0.59

LEV -0.9783 0.01*** 0.0808 0.21

ROA 0.0881 0.69 0.0397 0.46

COMPLEX 0.1039 0.56 -0.0727 0.30

FS 1.0315 0.00*** -0.4473 0.00***

HC -0.5734 0.28 -0.0666 0.74

ICE -0.0030 0.99 -0.0939 0.53

Constant 4.2601 0.01*** -0.1861 0.75

Model Fit

Model χ2 34.21 23.44

p-value 0.0019 0.0535

Nagelkerke's R2 0.1628

Area under ROC curve 0.7110

N 265 265

64

TABLE 6 continued

Panel C: Test of H2b – NAS = PUB*NAS type

(1) (2)

AQ TOTDEFIC

NAS Variables Coef. p-value Coef. p-value

PNTAX_AUD -0.5251 0.16 0.2423 0.06*

PNMAS_AUD -1.3646 0.16 0.2363 0.08*

PNOFFR_AUD 1.6443 0.62 -2.7689 0.09*

PNOTH_AUD -1.6682 0.19 0.7485 0.16

PUB*PNTAX_AUD (H2b) 0.2677 0.62 -0.0959 0.56

PUB*PNMAS_AUD (H2b) 5.3960 0.02** -1.1314 0.03**

PUB*PNOFFR_AUD (H2b) -0.9004 0.79 2.6811 0.10*

PUB*PNOTH_AUD (H2b) 1.2165 0.42 -0.3258 0.63

NAS_Dummy 0.3722 0.38 -0.1355 0.43

Control Variables

LAST -0.2213 0.02** 0.0620 0.06*

PUB -0.8738 0.04** 0.2968 0.05**

ROMM -0.1034 0.72 -0.0760 0.48

LEV -1.0482 0.01*** 0.0617 0.33

ROA 0.0907 0.69 0.0408 0.44

COMPLEX 0.0894 0.63 -0.0629 0.39

FS 1.0852 0.00*** -0.4532 0.00***

HC -0.2631 0.65 -0.1079 0.63

ICE 0.1436 0.72 -0.1221 0.41

Constant 4.4939 0.00*** -0.2549 0.66

Model Fit

Model χ2 46.55 38.16

p-value 0.0002 0.0037

Nagelkerke's R2 0.2166

Area under ROC curve 0.7275

N 265 265 Notes: AQ= 1 for engagements receiving an “unqualified satisfactory” assessment for overall audit quality, 0

otherwise; TOTDEFIC = total number of assessed audit deficiencies; PNNAS_AUD = ratio of total non-audit

service fees to audit fees; PNTAX_AUD = ratio of tax fees to audit fees; PNMAS_AUD = ratio of management

advisory fees to audit fees; PNOFFR_AUD = ratio of fees billed for services involving client public securities

offerings to audit fees; PNOTH_AUD = ratio of fees billed for services involving client merger & acquisition

activities and other unspecified NAS to audit fees; NAS_Dummy = 1 for clients that purchase any auditor-

provided non-audit services, 0 otherwise; LAST = natural log of total assets; PUB = 1 if the client has publicly-

listed securities (equity, debt, or both); 0 otherwise; ROMM = 1 if the auditor-assessed risk of material

misstatement at the overall financial statement level is moderate or high and equals 0 if the assessed risk of

material misstatement is low; LEV = total liabilities divided by total assets; ROA = ratio of income (loss) from

continuing operations to total assets; COMPLEX = client’s operational complexity ranging from 1 (very

simple) to 5 (very complex); FS = 1 for clients in the financial services sector, 0 otherwise; HC = 1 for clients

in the health care sector, 0 otherwise; ICE = 1 for clients in the information communications and entertainment

sectors, 0 otherwise; ***, **, * indicate significance at 1, 5 and 10 percent respectively.

65

TABLE 7 AUDIT FIRM TENURE, NON-AUDIT SERVICES, AND SUFFICIENCY OF EVIDENCE

Panel A: Tenure and Sufficiency of Audit Evidence (EVID)

EVID= g0 + g1(TENURE) + g2(LAST) +g3(PUB) + g4(ROMM) + g5(LEV) + g6(ROA) + g7(COMPLEX) + g8(FS) + g9(HC) +

g10(ICE) + u

(1) (2) (3) (4)

Tests of H1a –

TENURE = FIRST

Test of H1b –

TENURE = TEN2-4,

TEN5-N

Tests of H1c –

TENURE = FIRST,

LONG ( 9+ years)

Tests of H1c –

TENURE = FIRST,

LONG ( 11+years)

TENURE Variables Coef. p-value Coef. p-value Coef. p-value Coef. p-value

FIRST (H1a) -0.5234 0.07* -0.7532 0.03** -0.7334 0.03**

TEN2-4 (H1b) 1.2080 0.01***

TEN5-N 0.2358 0.46

LONG, 9+ years (H1c) -0.4968 0.22

LONG, 11+ years (H1c) -0.5390 0.19

Control Variables

LAST -0.2573 0.01*** -0.2574 0.01*** -0.2542 0.01*** -0.2495 0.01***

PUB 0.5163 0.12 0.5674 0.09* 0.5276 0.12 0.5134 0.13

ROMM -0.3045 0.30 -0.3147 0.28 -0.3136 0.28 -0.3206 0.27

LEV 0.5259 0.22 0.5220 0.23 0.5116 0.24 0.5014 0.24

ROA 0.3580 0.16 0.4050 0.10 0.3717 0.14 0.3719 0.14

COMPLEX -0.1039 0.58 -0.1187 0.53 -0.1089 0.57 -0.1074 0.57

FS 0.6065 0.12 0.5647 0.15 0.5881 0.13 0.5862 0.13

HC 0.4811 0.37 0.3600 0.52 0.4443 0.42 0.4625 0.40

ICE -0.4018 0.30 -0.4751 0.23 -0.4525 0.25 -0.4441 0.26

Constant 5.9983 0.00*** 5.5313 0.00*** 6.2084 0.00*** 6.1072 0.00***

Model Fit

Model χ2 21.78 26.34 23.26 23.47

p-value 0.0163 0.0058 0.0162 0.0152

Nagelkerke’s R2 0.1133 0.1359 0.1207 0.1217

Area under ROC curve 0.6694 0.6927 0.6812 0.6797

N 265 265 265 265

66

TABLE 7 continued

Panel B: Tenure, Public Clients, and Sufficiency of Audit Evidence (EVID)

EVID= g0 + g1(TENURE) + g2(LAST) +g3(PUB) + g4(ROMM) + g5(LEV) + g6(ROA) +

g7(COMPLEX) + g8(FS) + g9(HC) + g10(ICE) + u

(1) (2)

Tests of H1d –

TENURE = FIRST,

LONG9+

PUB*FIRST

PUB*LONG9+

Test of H1d –

TENURE = FIRST,

LONG11+

PUB*FIRST

PUB*LONG11+

TENURE Variables Coef. p-value Coef. p-value

FIRST -0.4904 0.37 -0.5948 0.28

PUB*FIRST -0.4209 0.56 -0.2031 0.77

LONG, 9+ years (H1c) -0.9935 0.09*

PUB*LONG9+ 0.9957 0.24

LONG, 11+ years (H1c) -1.3196 0.03**

PUB*LONG11+ 1.6691 0.06*

Control Variables

LAST -0.2793 0.01*** -0.2998 0.00***

PUB 0.4785 0.42 0.2670 0.64

ROMM -0.3109 0.29 -0.3278 0.27

LEV 0.4435 0.31 0.4362 0.32

ROA 0.4047 0.11 0.3970 0.12

COMPLEX -0.0473 0.81 -0.0105 0.96

FS 0.4810 0.22 0.5131 0.20

HC 0.4302 0.44 0.4915 0.38

ICE -0.4894 0.22 -0.4803 0.22

Constant 6.6467 0.00*** 7.0557 0.00***

Model Fit

Model χ2 26.89 29.29

p-value 0.0129 0.0060

Nagelkerke’s R2 0.1386 0.1503

Area under ROC curve 0.6918 0.7020

N 265 265

67

TABLE 7 continued

Notes: EVID = 1 for engagements receiving an “unqualified satisfactory” assessment for the sufficiency of evidence obtained to support the audit opinion, 0

otherwise; FIRST = 1 for first-year audit engagements, 0 otherwise; TEN2-4 = 1 for engagements with tenures in the 2 to 4 years range, 0 otherwise; TEN5-N = 1

Panel C: Non-audit services and Sufficiency of Audit Evidence (EVID) EVID= g0 + g1(NAS) + g2(LAST) +g3(PUB) + g4(ROMM) + g5(LEV) + g6(ROA) + g7(COMPLEX) + g8(FS) + g9(HC) + g10(ICE) + u

(1) (2) (3)

Tests of H2a –

NAS = NAS Dummy,

PNNAS_AUD

Test of H2b –

NAS = NAS Dummy,

PNNAS_AUD,

PUB*PNNAS_AUD

Tests of H2c –

NAS = PNTAX_AUD, PNMAS_AUD, PNOFFR_AUD,

PNOTH_AUD, NAS Dummy

NAS Variables Coef. p-value Coef. p-value Coef. p-value

NAS_Dummy -0.3879 0.36 -0.3201 0.46 -0.2623 0.54

PNNAS_AUD (H2a) 0.0051 0.98 -0.1615 0.62

PUB*PNNAS_AUD (H2b) 0.2322 0.54

PNTAX_AUD (H2c) -0.3979 0.14

PNMAS_AUD (H2c) 0.1245 0.80

PNOFFR_AUD (H2c) 1.5446 0.14

PNOTH_AUD (H2c) 0.0855 0.90

Control Variables

LAST -0.2404 0.01** -0.2436 0.01*** -0.2423 0.01***

PUB 0.6001 0.09* 0.4549 0.29 0.4077 0.26

ROMM -0.3190 0.28 -0.3075 0.30 -0.3597 0.23

LEV 0.4583 0.27 0.4453 0.28 0.4412 0.29

ROA 0.3841 0.15 0.3896 0.15 0.4077 0.16

COMPLEX -0.0740 0.69 -0.0729 0.70 -0.0634 0.74

FS 0.6247 0.11 0.6442 0.11 0.6419 0.11

HC 0.6161 0.26 0.6327 0.25 0.3531 0.54

ICE -0.4740 0.23 -0.4798 0.22 -0.4628 0.25

Constant 5.6385 0.00*** 5.7350 0.00*** 5.7507 0.00***

Model Fit

Model χ2 19.54 19.92 26.64

p-value 0.0520 0.0687 0.0214

Nagelkerke’s R2 0.1021 0.1040 0.1374

Area under ROC curve 0.6604 0.6650 0.6903

N 265 265 265

68

for engagements with tenures longer than 4 years, 0 otherwise; LONG, 9+ years = 1 for engagements with tenures longer than 8 years, 0 otherwise; LONG, 11+

years = 1 for engagements with tenures longer than 10 years, 0 otherwise; LAST = natural log of total assets; PUB = 1 if the client has publicly-listed securities

(equity, debt, or both); 0 otherwise; ROMM = 1 if the auditor-assessed risk of material misstatement at the overall financial statement level is moderate or high and

equals 0 if the assessed risk of material misstatement is low; LEV = total liabilities divided by total assets; ROA = ratio of income (loss) from continuing

operations to total assets; COMPLEX = client’s operational complexity ranging from 1 (very simple) to 5 (very complex); FS = 1 for clients in the financial

services sector, 0 otherwise; HC = 1 for clients in the health care sector, 0 otherwise; ICE = 1 for clients in the information, communications and entertainment

sectors, 0 otherwise; ***, **, * indicate significance at 1, 5 and 10 percent respectively

69

TABLE 8

ACCRUALS AND AUDIT PROCESS QUALITY

Panel A: Correlations between discretionary accruals and audit quality, effort,

audit firm tenure and non-audit services

Discretionary Accruals Measures

DA ABSDA POSDA NEGDA

Audit Process Quality Variables

AQ 0.19* 0.30*** 0.28*** -0.04

TOTDEFIC 0.04 -0.11 -0.03 0.13

Audit Effort Variables

LHRPRAST 0.07 0.22** 0.15 -0.10

PPNPTRHRS 0.23** 0.14 0.22** 0.13

PPNMGRHRS 0.19* 0.10 0.17* 0.12

Tenure Variables

FIRST 0.07 -0.01 0.05 0.08

TEN2-4 0.00 -0.07 -0.04 0.06

TEN5-N -0.08 0.07 -0.02 -0.14

NAS Variables

PNNAS_AUD 0.02 -0.03 0.00 0.05

PNTAX_AUD -0.13 -0.09 -0.13 -0.06

PNMAS_AUD -0.07 -0.10 -0.10 0.01

PNOFFR_AUD 0.15 0.08 0.14 0.09

PNOTH_AUD -0.00 -0.06 -0.03 0.04

70

TABLE 8 continued

Panel B: OLS Regression of Discretionary Accruals on Measures of Audit Quality

DISCRETIONARY ACCRUALS= h0 + h1(AUDIT QUALITY) + h2(GROWTH) + h3(CFO) + h4(LITIG) + h5(LOSS) + h6(LEV + h7(MB) +

h8(MVE) + h9(FIN) + h10(LCA) +h11(HC) + h12(ICE) + u

(1) (2) (3) (4) (5) (6)

DISCRETIONARY ACCRUALS

ABSDA ABSDA POSDA POSDA NEGDA NEGDA

Variables Coef. p-value Coef. p-value Coef. p-value Coef. p-value Coef. p-value Coef. p-value

AQ 0.0745 0.01*** 0.0546 0.05** -0.0199 0.38

TOTDEFIC -0.0013 0.80 0.0044 0.32 0.0057 0.07*

GROWTH 0.0897 0.00*** 0.0994 0.01*** 0.1034 0.01*** 0.1098 0.01*** 0.0137 0.57 0.0105 0.64

CFO -0.1875 0.02** -0.2184 0.01*** -0.3091 0.00*** -0.3357 0.00*** -0.1216 0.02** -0.1173 0.02**

LITIG 0.0218 0.41 0.0061 0.81 0.0044 0.85 -0.0071 0.75 -0.0174 0.43 -0.0132 0.50

LOSS -0.0129 0.67 -0.0087 0.78 -0.0413 0.12 -0.0367 0.16 -0.0284 0.17 -0.0280 0.19

LEV 0.1119 0.01*** 0.0915 0.04** 0.1226 0.01*** 0.1029 0.03** 0.0106 0.72 0.0114 0.71

MB 0.0013 0.25 0.0009 0.48 0.0018 0.18 0.0014 0.34 0.0005 0.06* 0.0005 0.05**

MVE -0.0039 0.53 -0.0056 0.38 -0.0021 0.76 -0.0033 0.64 0.0018 0.70 0.0023 0.59

FIN -0.0124 0.64 -0.0137 0.62 -0.0311 0.17 -0.0331 0.15 -0.0187 0.49 -0.0194 0.47

LCA 0.1699 0.13 0.2041 0.10* 0.1728 0.07* 0.2231 0.04** 0.0029 0.98 0.0190 0.84

HC 0.0884 0.43 0.1261 0.37 0.1132 0.42 0.1536 0.35 0.0248 0.56 0.0275 0.49

ICE 0.0597 0.01*** 0.0639 0.01*** 0.0569 0.01*** 0.0621 0.01*** -0.0028 0.89 -0.0018 0.93

Constant 0.0104 0.83 0.0614 0.18 -0.0139 0.77 0.0072 0.87 -0.0243 0.50 -0.0542 0.07*

Adjusted R square 0.4552 0.4002 0.5421 0.5167 0.1190 0.1291

N 97 97 97 97 97 97

Notes: Panel A shows the pairwise correlation coefficients between discretionary accruals, and measures of audit quality, audit effort, audit firm tenure, and non-audit

services. Panel B shows the results of the OLS regression testing for an association between discretionary accruals and audit quality. AUDIT QUALITY = AQ OR

TOTDEFIC; DA = discretionary accruals measured as the residual of model (3); ABSDA = absolute value of discretionary accruals; POSDA = DA if DA > 0, 0

otherwise; NEGDA = DA if DA < 0, 0 otherwise; AQ = 1 for engagements receiving an “unqualified satisfactory” assessment for overall audit quality, 0 otherwise;

TOTDEFIC = total number of assessed deficiencies across all 55 individual audit activities; LHRPRAST = natural log of total audit hours divided by natural log of

total assets; PPNPTRHRS = ratio of partner hours to total audit hours; PPNMGRHRS = ratio of manager hours to total audit hours; FIRST = 1 for first-year audit

engagements, 0 otherwise; TEN2-4 = 1 for engagements with tenures in the 2 to 4 years range, 0 otherwise; TEN5-N = 1 for engagements with tenures longer than 4

years, 0 otherwise; PNNAS_AUD = ratio of total non-audit service fees to audit fees; PNTAX_AUD = ratio of tax fees to audit fees; PNMAS_AUD = ratio of

71

management advisory fees to audit fees; PNOFFR_AUD = ratio of fees billed for services involving client public securities offerings to audit fees; PNOTH_AUD =

ratio of fees billed for services involving client merger & acquisition activities and other unspecified NAS to audit fees; GROWTH is equal to the percent change in

sales; CFO equals operating cash flows scaled by total assets; LITIG is a dummy variable equal to one if the company is in a high litigation SIC code: 2833-2836,

3570-3577, 3600-3674, 5200-5961, 7370-7374, zero otherwise; LEV equals total liabilities scaled by total assets; MB equals the market-to-book ratio; MVE equals the

natural log of market value of equity; LOSS is a dummy variable equal to one if net income is less than zero, zero otherwise; FIN is a dummy variable indicating new

financing when the percentage change in long-term debt is greater or equal to 20 percent, or the percentage change in common shares outstanding (adjusted for stock

splits, etc.) is greater or equal to 10 percent, zero otherwise; LCA is the absolute value of lagged total accruals, scaled by total assets; ***, **, * indicate significance

at 1, 5 and 10 percent respectively

72

APPENDIX A

DEFINITIONS OF ASSESSED AUDIT ACTIVITIES AND FREQUENCIES OF

ENGAGEMENTS WITH ASSESSED DEFICIENCIES BY COMPOSITE RATINGS AND

AUDIT PHASE

Appendix A presents abbreviated definitions for the 55 individual audit activities

assessed by the firm’s internal quality control review teams, grouped within four audit phases:

audit planning activities (Panel A), internal control evaluation and control risk assessment

activities (Panel B), substantive testing activities (Panel C), and audit wrap-up activities (Panel

D). For each individual audit activity the review team assessed whether audit team performance

was deficient or not (i.e., a dichotomous rating). For example, for the “Analytical Procedures-

Precision” activity within the substantive testing phase, review teams assessed the following

activity:

Did the engagement team perform planned analytical procedures at a

sufficient level of precision, and investigate and obtain explanations

and corroborative evidence for any variances from expectations

outside an acceptable difference? Yes or No?

Each panel presents frequencies of deficiencies found in the total sample of 265 engagements for

each within-phase audit activity and for engagements receiving the following three overall audit

quality ratings: “unqualified satisfactory (RATING =1),” “satisfactory with comments

(RATING=2)” and “needs improvement or unsatisfactory (RATING=3).” Frequencies of total

deficiencies across all within-phase audit activities are presented at the bottom of each panel.

73

TABLE A

Frequency Count of Deficiencies Classified by Stage of the Audit and Overall Audit Quality50

50

Each panel of this table provides the frequency count for each type of deficiency in the four separate phases of the

audit, i.e., Panel A addresses 14 possible deficiencies related to Audit Planning. These deficiencies are then

categorized by the overall quality rating of the engagement (which is based on all deficiencies). Zero deficiencies in

a phase of the audit is not the same as zero deficiencies in total, i.e., TOTDEFIC is the sum of the deficiencies across

the four phases of the audit. Table 1, Panel C reveals that there are no engagements with zero total deficiencies that

are rated as “unsatisfactory”. Two engagements are rated as “satisfactory with comments” even though they have

no deficiencies. Removing those two observations from the sample does not change our results.

Panel A: Audit Planning Activities

Overall Audit Quality RATING

Abbreviated Definitions of

Individual Audit Activities

Total

Sample

1

Unqualified

Satisfactory

2

Satisfactory

with

Comments

3

Needs

Improvement

or

Unsatisfactory

N 265 112 133 20

Business Understanding 2 2

Analytical Procedures 12 4 7 1

Fraud Risk Assessment 4 1 3

Business Risks & Processes 5 1 1 3

Client Information Technology 11 3 7 1

Integration-Tech. Specialists 23 6 12 5

Other Locations-Coordination 2 2

Oth. Locations-Documentation 4 2 2

Proper Audit Plan 5 2 3

Audit Plan Consistency w Risk 2 2

Evaluation of Internal Audit 3 2 1

Work of Another Auditor 2 2

Client’s Service Organizations 9 1 6 2

Client’s External Experts 18 14 4

Total 102 20 63 19

# Assessed Deficiencies

Per Engagement

# of Engagements

(Percent)

0 181

(68%)

92

(82%)

83

(62%)

6

(30%)

1 68

(26%)

20

(18%)

38

(29%)

10

(50%)

2 14

(5%)

0

(0%)

11

(8%)

3

(15%)

3 2

(1%)

0

(0%)

1

(1%)

1

(5%)

74

Panel B: Internal Control Evaluation and Control Risk Assessment Activities

Overall Audit Quality RATING

Abbreviated Definitions of

Individual Audit Activities

Total

Sample

1

Unqualified

Satisfactory

2

Satisfactory

with

Comments

3

Needs

Improvement

or

Unsatisfactory

N 265 112 133 20

Cycle Approach – Proper 6 5 1

Understanding of Processes 5 1 3 1

Risk Assmt. & Control Eval. 5 2 3

Audit Programs 22 3 15 4

Controls Testing 21 5 15 1

Total 59 11 41 7

# Assessed Deficiencies

Per Engagement

# of Engagements

( Percent)

0 215

(81%)

161

(85%)

45

(75%)

9

(60%)

1 42

(16%)

26

(14%)

13

(22%)

3

(20%)

2 7

(3%)

2

(1%)

2

(3%)

3

(20%)

3 1

(0%)

1

(0%)

0

(0%)

0

(0%)

75

Panel C: Substantive Testing Activities Overall Audit Quality RATING

Abbreviated Definitions of

Individual Audit Activities

Total

Sample

1

Unqualified

Satisfactory

2

Satisfactory

with

Comments

3

Needs

Improvement

or

Unsatisfactory

N 265 112 133 20

Analytical Procedures-Precision 32 4 22 6

Tests of Details 23 8 11 4

Inventory Observation 7 6 1

Receivables Confirmations 15 3 11 1

Head-Office Consultations 7 2 4 1

Revenue Recognition 14 3 8 3

Tax-Related Audit Procedures 62 13 39 10

Tax Review 10 2 7 1

Consolidations/ Equity Method 12 10 2

Business Combinations 9 1 4 4

Stock Options/Warrants/Rights 10 1 8 1

Equity Transactions 1 1

New Accounting Standards 6 1 4 1

Valuation & Asset Impairment 32 4 24 4

Derivative Instruments 6 1 4 1

Other Significant Estimates 22 3 13 6

Total 268 46 176 46

# Assessed Deficiencies

Per Engagement

# of Engagements

(Percent)

0 113

(43%)

73

(65%)

35

(26%)

5

(25%)

1 85

(32%)

34

(30%)

47

(35%)

4

(20%)

2 36

(14%)

4

(4%)

30

(23%)

2

(10%)

3 19

(7%)

0

(0%)

17

(13%)

2

(10%)

4 7

(3%)

1

(1%)

2

(2%)

4

(20%)

5 4

(2%)

0

(0%)

2

(2%)

2

(10%)

6 1

(0%)

0

(0%)

0

(0%)

1

(5%)

76

Panel D: Audit Wrap-Up Activities

Overall Audit Quality RATING

Abbreviated Definitions of

Individual Audit Activities

Total

Sample

1

Unqualified

Satisfactory

2

Satisfactory

with

Comments

3

Needs

Improvement

or

Unsatisfactory

N 265 112 133 20

Laws & Regulations 1 1

Sworn Statements by Officers 1 1

Management Certifications 13 6 6 1

Letter of Audit Inquiry 14 1 12 1

Mgmt. Representation Letter 12 1 10 1

Related Party Transactions 7 1 3 3

Subsequent Events 16 2 9 5

Debt Covenant Violations 6 1 2 3

Going Concern Evaluation 9 2 5 2

Final Analytical Procedures 24 4 14 6

F/S Consistency w Other Info 3 1 1 1

Presentation & Disclosure 17 3 13 1

Passed Audit Differences 15 2 13

Work Paper Documentation 14 6 8

Audit Checklists 2 2

Other Required Workpapers 5 1 4

Partner & Manager Reviews 5 1 3 1

Required In-Depth Reviews 3 1 1 1

Other Review Policies 1 1

Auditor’s Report 8 8

Total 176 28 114 34

# Assessed Deficiencies

Per Engagement

# of Engagements

(Percent)

0 149

(56%)

89

(79%)

56

(42%)

4

(20%)

1 79

(30%)

19

(17%)

52

(39%)

8

(40%)

2 24

(9%)

3

(3%)

17

(13%)

4

(20%)

3 6

(2%)

1

(1%)

4

(3%)

1

(5%)

4 5

(2%)

0

(0%)

4

(3%)

1

(5%)

5 1

(1%)

0

(0%)

0

(0%)

1

(5%)

6 1

(0%)

0

(0%)

0

(0%)

1

(5%)

77

APPENDIX B

PCAOB INSPECTION PROCESS AND AUDIT FIRM INTERNAL REVIEWS

The internal quality review process used by the firm is undertaken to gauge the

performance of individual auditors. The results of the review are used to adjust training,

improve the firm’s audit process and methodology, and assist in the evaluation of personnel.

Poor performance can result in recommendations for better or different training, and may affect

an individual’s performance evaluation, promotion, compensation and, potentially, their

termination from the firm. This type of review predates PCAOB inspections. Because of the

concern for the quality of specific types of engagements, the firm uses a stratified approach to

select engagements based on perceived risk and coverage of all partners. For example, while an

individual partner may not be reviewed every year, at least one engagement of each partner will

be selected for review on a regular basis. New engagements are an obvious high risk area that is

oversampled. Further, based on anecdotal information, the risk-based approach used by the firm

is similar to what is understood about the PCAOB’s current risk-based approach for selecting

engagements for inspection. Although, the PCAOB did not exist at the time when the reviews

were completed, the firm was preparing for such inspections. However, the firm had no way to

know the format of those inspections. We cannot directly address how current reviews within

audit firms are affected by the PCAOB’s actions.

Relative to the PCAOB’s inspection process we note some known differences and

similarities. First, in terms if similarities both internal reviews and PCAOB follow a risk-based

approach to selecting engagements for review. For example, the PCAOB has stated that “audit

work is selected for inspection largely on the basis of an analysis of factors that, in the PCAOB

inspection staff's view, heighten the possibility that auditing deficiencies are present, rather than

through a process intended to identify a representative sample of the audit firm's work (PCAOB

78

[2012, 3]). Second, while the PCAOB provides information only on the simple counts of

deficiencies they detect (similar to TOTDEFIC), they do not yet provide information as to the

severity of these deficiencies. This may change in the future as recently noted by PCAOB board

member Jay Hansen: “…inspection reports would be more meaningful if we provided more

context about the severity of each finding. Currently, each inspection finding in the public

portion of a PCAOB inspection report must meet the threshold I just described — that the audit

firm did not gather sufficient evidence to render an opinion — but the reports provide no

information about which of the deficiencies are relatively minor or involved only a portion of a

disclosure, and which indicate a much more significant problem with the audit. I would like to

see the Board consider whether it would be possible to classify inspection findings by severity

level, in order to provide that additional context.” Third, the PCAOB inspections have a different

focus each year. According to the PCAOB, “individual audits and areas of inspections are most

often selected on a risk-weighted basis and not randomly” (PCAOB [2012, i]. For example, in a

recent inspection cycle they focused on internal controls, whereas the internal reviews are more

general assessing compliance with auditing standards. It is probable that the PCAOB focus in a

given cycle affects the focus on internal reviews also. We cannot say if the emphasis in the

firm’s inspections changed over time but clearly the introduction of a large number of ex-AA

clients was an issue the firm considered. Fourth, Part I of the PCAOB’s inspections states

whether the inspection staff found that the auditor failed to gather sufficient audit evidence.

Similar to the focus of the PCAOB, sufficiency of evidence emerges as the primary driver of

audit quality in our analysis (see EVID, Table 7).