Does the Organization and Culture of the Largest Audit ... · 1 Does the Organization and Culture of the Largest Audit Firms Influence their Audit Quality and Efficiency? Daniel Aobdia

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Does the Organization and Culture of the Largest Audit Firms

Influence their Audit Quality and Efficiency?

Daniel Aobdia

Kellogg School of Management, Northwestern University

[email protected]

This version: December 2016

This research paper was prepared while the author was a Senior Economic Research Fellow at the PCAOB. The

PCAOB, as a matter of policy disclaims responsibility for any private publication or statement by any of its

Economic Research Fellows and employees. The views expressed in this paper are the views of the author and do

not necessarily reflect the views of the Board, individual Board members, or staff of the PCAOB. I would like to

thank David Aboody, Preeti Choudhary, Michael Gurbutt, Robert Knechel (discussant), Patricia Ledesma, Robert

Magee, Robert Pawlewicz (discussant), Luigi Zingales, PCAOB staff and seminar participants at the 2016

Duke/UNC Fall Camp, the 2016 George Mason Conference on Investor Protection, Corporate Governance, and

Fraud Prevention, National Taiwan University, UCLA, the 22nd

University of Illinois Symposium on Auditing

Research, and the PCAOB for helpful discussions on earlier versions of this work. I acknowledge generous financial

support from the Kellogg School of Management and in particular the Lawrence Revsine Fellowship.

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Does the Organization and Culture of the Largest Audit Firms

Influence their Audit Quality and Efficiency?

Abstract

This study investigates the association between the largest audit firms’ internal organization and

audit quality and efficiency. Using a unique dataset of firm-wide deficiencies in the quality

control (QC) systems identified by the PCAOB during its inspections of audit firms, I find a

negative association between firm-level QC deficiencies and audit quality. Furthermore, audits

conducted by audit firms with more organization-level deficiencies appear less efficient, as

evidenced by more hours worked on the engagements, despite audit fees remaining unchanged

and audit quality being worse. These results appear to be partly driven by deficiencies in the tone

at the top (a proxy for culture) and the audit methodology. In contrast, audit firms with more

deficiencies of a practical nature (audit performance issues) do not appear to spend enough effort

on their audit, consistent with a “shirking” hypothesis. Overall, these results suggest that while

both practical-level and organization-level deficiencies negatively influence audit quality, their

root causes are different.

Keywords: Audit Quality, Audit Efficiency, Quality Control Systems, PCAOB Inspections,

Culture, Audit Methodology, Impact of Regulation.

JEL Classification: M42, M48, M14, L51.

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1. Introduction

The purpose of this study is to determine empirically the role of an audit firm’s organization

and culture for its audit quality and efficiency, by specifically focusing on a firm’s quality

control (QC) systems. Even though this question is of interest to academics and regulators, little

is empirically known about the influence of these systems, mainly due to the unavailability of

public data.1 For example, Francis (2011, p138), indicates that “research on the relation between

accounting firms and audit quality is severely limited by the availability of data on

characteristics of accounting firms. To date, research on this topic has relied on variables that

can be constructed from public disclosures such as client-based measures of industry expertise

or office size. However, these measures do not go inside the “black-box” of the accounting

firm’s organizational structure and operations.” DeFond and Zhang (2014, p304) further

confirm that “we currently know little about basic characteristics of audit firms such as their

choice of ownership structure, governance systems, audit quality control systems, compensation

schemes, or audit technology.” This study uses a unique dataset of QC deficiencies identified by

the Public Company Accounting Oversight Board (PCAOB) in its annual inspections of the eight

largest audit firms to answer this question.

The analytical accounting literature highlights the importance of an audit firm’s QC systems

as a mechanism designed to align individual incentives of the partners with the overall incentives

of the firm. While the auditing literature often treats an audit firm as a single person “auditor”,

and relies on the notion of reputation and litigation to incentivize firms to conduct high quality

audits (e.g., DeAngelo 1981, Dye 1993), in practice, partners make decentralized decisions in

performing various tasks (e.g., Liu and Simunic 2005). Every partner is simultaneously an owner

1 Academic researchers have instead used experimental and survey settings to try to answer the question (e.g.,

Bedard et al. 2008, Jenkins et al. 2008).

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and an agent of the partnership (e.g., Huddart and Liang 2003), and has incentives to shirk when

effort is unobservable (e.g., Holmstrom 1982). While the literature shows that mutual monitoring

and peer pressure can reduce this moral hazard (e.g., Balachandran and Ramakrishnan 1987,

Kandel and Lazear 1992), in practice these are likely to be effective only when the size of the

partnership is small (e.g., Huddart and Liang 2003). Under these conditions, Huddart and Liang

(2005) show that in larger partnerships the specialization of a small number of partners in

monitoring and supervision tasks, while the other ones produce the output, lessens shirking in

both monitoring and production tasks. Thus, the implementation of this specialized monitoring

and supervision in the form of a firm’s QC system is essential to increase audit effort and quality.

The economics literature also proposes a similar role for organizational culture: In the absence of

proper ex-ante incentives to regulate individual employee behavior, culture is valuable as a

safeguard to prevent employees from making decisions with a short-term benefit but that are

very detrimental in the long-run in light of the overall incentives of the firm (O’Reilly 1989,

Kreps 1990, Guiso et al. 2015b). These theories are supported by anecdotal evidence at Arthur

Andersen, whereby a culture shift in the organization combined with poor QC systems

contributed to several failed audits, including Boston Chicken, Waste Management, and Enron

(Brown and Dugan 2002, Eichenwald and Norris 2002, Richard and Thurm 2002, Toffler and

Reingold 2003, Wyatt 2004, Gendron and Spira 2009). Furthermore, survey evidence suggests a

link between culture or QC systems, and auditors engaging into low quality behavior (Otley and

Pierce 1996, Malone and Roberts 1996).

Based on this theoretical background, I ask the following questions to assess the relevance of

the audit firms’ QC systems: Are deficiencies in the audit firms’ QC systems associated with

lower audit quality? If so, how does this impact audit efficiency and profitability? Are

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deficiencies of a more practical nature (audit performance) different from more fundamental,

organization-level deficiencies? Are clients more likely to switch auditors when an audit firm has

more deficiencies in its QC systems? And what is the specific role of an audit firm’s culture and

audit methodology vis-à-vis other types of QC deficiencies?2 The answers to these questions

provide new insights into the role of QC systems, culture and audit methodology for audit quality

and efficiency. Further, they highlight the regulatory role of the PCAOB in detecting these QC

deficiencies, which are not initially publicly reported and will remain nonpublic only if the audit

firm addresses the criticism to the Board's satisfaction no later than 12 months from the date of

the audit firm’s inspection report.

I use a unique dataset of QC deficiencies, built from the nonpublic versions of the PCAOB

inspection reports to answer these questions. The PCAOB is a non-profit organization

established by the Sarbanes-Oxley Act of 2002 (SOX) to oversee the audits of public companies

(referred to as issuers or client issuers in the remainder of this paper) and improve audit quality.

In particular, the PCAOB conducts inspections of public accounting firms that audit issuers.

These inspections are annual for firms that regularly provide audit reports for more than 100

issuers, and at least triennial otherwise (Section 104 of SOX). For annually inspected firms, the

PCAOB inspections mainly focus on two elements: A review of individual audit engagements,

and a review of the firm’s QC systems (e.g., PCAOB 2015).3 The review of the firm’s QC

systems is itself composed of two parts, which are detailed in Figure 1, and are closely aligned

with the corresponding auditing standard QC 20.

2 Audit methodology issues that I consider for this particular analysis are composed of design issues in the audit

methodology or training programs, and do not include issues with the application of the audit methodology, which

are part of the more practical-level types of deficiencies (audit performance deficiencies). 3 The review of the audit firms’ QC systems is a SOX requirement (section 104) that instructs the PCAOB to

“evaluate the sufficiency of the quality control system of the firm, and the manner of the documentation and

communication of that system by the firm.”

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(Insert Figure 1 About Here)

The first part of the QC review corresponds to a bottom-up generalization of deficiencies

identified in individual audit engagements. QC deficiencies in this area are called audit

performance deficiencies and correspond to similar issues identified in several individual

engagements (see Appendix A for an example). The second part corresponds to top-down

analyses complemented by additional insights derived from the inspection of specific individual

audit engagements and interviews in individual audit offices. Overall, this top-down review

focuses on organization-level issues of an audit firm (see Appendix B for an example), and

focuses, among others, on tone at the top, a proxy for culture in the audit firm (e.g., POB 2000,

Jenkins et al. 2008, Berson et al. 2008, Guiso et al. 2015b), and on the design of the audit

methodology. The PCAOB discusses QC system deficiencies under Part II of the inspection

reports (hereafter I also refer to a QC system deficiency as a Part II Finding). These Part II

Findings are not released to the public if addressed satisfactorily within one year of the issuance

of an inspection report (Section 104(g)(2) of SOX, Gradison and Boster 2010). 4

I obtain the nonpublic versions of the PCAOB inspection reports for the U.S. operations of

the Big 4 and other annually inspected “second-tier” auditors, and build several measures based

4 Note that the statutorily prescribed criterion for whether a quality control criticism is made public is whether the

criticism has been “addressed by the firm, to the satisfaction of the Board” within 12 months after the Board issues

the inspection report [SOX 104(g)(2)]. The Board has explained that, in evaluating whether it is satisfied with a

firm’s remediation action in the 12 months, the Board recognizes that “with respect to some types of quality control

criticisms, a firm may not, realistically, be able to implement practices and procedures that completely achieve the

desired objectives in a 12-month period,” and that, accordingly, a favorable Board determination “does not

necessarily mean that the firm completely and permanently cured any particular quality control defect.” (PCAOB

2006a, pp6 and 7). Consequently, a QC deficiency may not be completely remediated one year following the

issuance of an inspection report, even if the PCAOB does not make public the Part II Finding. This continuous

nature of the remediation process prevents me from using it in the main tests as a quasi-exogenous shock to the

firms’ QC systems. I still use the remediation process in supplemental tests to better understand its role.

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on these reports.5,6

Because at present no other researcher has access to this highly confidential

and proprietary dataset, I aim to remove any element of subjectivity in the construction of the

measures. First, I count the number of words in each Part II Finding report. Because Part II

sections of a PCAOB report are clearly separated between audit performance deficiencies and

top-down organization-level deficiencies, I also split this word count between these two types of

deficiencies. I further delve into the organization-level QC deficiencies, which the PCAOB has

analyzed and reported quite consistently over time, and build a firm-wide QC index of such

deficiencies. The QC index is built from eight categories, which include, among others,

deficiencies in tone at the top, audit methodology, partner management, independence policies

and management of foreign affiliates, and is higher when deficiencies are identified in more

categories.7 In additional tests, I also separate from this index deficiencies identified in the tone

at the top and audit methodology to focus on the influence of culture and tools in an organization

on its efficiency and performance in general.

In general, the descriptive statistics indicate that QC deficiencies are frequently identified by

the PCAOB. This contrasts with limited instances of their public release, indicating that most

deficiencies are addressed by the audit firms to the satisfaction of the Board within the one-year

period following the release of the inspection report.8 I also confirm, in untabulated analyses,

5 The “second tier” auditors are defined as in Hogan and Martin (2009) and include Grant Thornton, BDO, Crowe

Horwath and McGladrey. 6 According to the PCAOB (2006a, p9 and 10), “The quality control procedures at larger firms are typically far

more complex, extensive, and formal than those at smaller firms. Board inspection procedures are correspondingly

more extensive, and inspection report discussions of those quality control systems are usually set out in extensive

and specific terms… Board inspection critiques of those systems are correspondingly more detailed.” Consequently,

I only focus on larger firms in the analyses in order to derive meaningful comparisons across firms. 7 I am unable to include in the index “Audit Performance” deficiencies because these correspond to different themes

(e.g., Evans et al. 2011) that are more transient and idiosyncratic. 8 Note that, consistent with footnote 4, this does not indicate that the deficiency is completely remediated within one

year. Consistent with remediation being a continuous process, I find reasonably high autocorrelations in the index of

QC deficiencies, suggesting that it can take in practice several years to remediate a particular type of deficiency.

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that there is sufficient variation in the QC deficiencies dataset to conduct meaningful empirical

analyses.9

In the first set of analyses, I find a negative association between an audit firm’s QC

deficiencies and audit quality, measured using the propensity to restate financial statements, the

propensity to meet/beat the zero earnings threshold, and deficiencies identified by the PCAOB in

the inspection of individual engagements (Part I Findings).10,11

I find some evidence that both

audit performance and organization-level deficiencies are negatively associated with audit

quality. Because audit performance deficiencies are generally derived from the inspections of

individual engagements, and the selection of these engagements is risk-based (e.g., Hanson

2012), the result on audit performance deficiencies suggests that to a certain extent individual

engagement-level deficiencies identified by the PCAOB can be generalized to other

engagements of the firm. Within organization-level deficiencies, deficiencies in the audit

methodology also matters in terms of audit quality, highlighting the importance of this area. This

study is the first to empirically document results consistent with the theories mentioned above.

Furthermore, these results suggest that the PCAOB identifies relevant QC deficiencies that

ultimately influence audit quality.

Consistent with audit fees being determined by a competitive process in the audit market and

not by internal differences in the organization of the audit firms (Doogar and Easley 1998,

9 I am unable to provide detailed descriptive statistics on the QC deficiencies because of the very clear SOX

requirements that preclude these deficiencies from being publicly disclosed if remediated within one year of

issuance of the PCAOB inspection report. 10

Aobdia (2015a) finds that these measures are appropriate to measure audit quality. I also use a measure of accruals

based on Leuz et al. (2003) that represents a valid proxy for audit quality (Aobdia 2015a). However, the results are

weaker using this measure, perhaps because this measure is a relatively weak predictor of audit quality (Aobdia

2015a). 11

I caveat against putting too much weight in the Part I Findings as a measure of audit quality for this particular

study, because both Part I Findings and Part II Findings are determined by the PCAOB inspectors. Thus, a

mechanical relation could exist between these two types of deficiencies, even though I choose a different timing of

measurement to reduce this concern.

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Donovan et al. 2014), I find no evidence that QC deficiencies are associated with audit fees.

However, I find that firms with more QC deficiencies tend to provide more non-audit services to

their audit clients, even though the economic effect is reasonably limited.12

The relation between QC deficiencies and engagement hours is unclear and is likely to

depend on the type of QC deficiencies identified. On the one hand an audit firm with more

deficiencies could “shirk” and not spend enough effort on its audits in general. On the other

hand, such a firm may 1) improperly scope and price its audits at the time of client acceptance or

continuation by selecting the “wrong clients”, or 2) conduct inefficient audits because of lack of

expertise, training, or inefficient methodologies (both possibilities indicate inefficiencies in the

way an audit firm conducts its day to day operations). Consistent with the first “shirking”

alternative, I find negative associations between audit performance deficiencies with both audit

hours (including total partner hours and engagement quality review - EQR - partner hours), and

audit quality. Collectively, these results suggest that the auditor did not spend enough time on the

engagements to bring audit quality to a satisfactory level. Given that audit fees are not associated

with audit performance deficiencies, these inferences also result in a positive association

between such deficiencies and average hourly fees. In contrast with the “shirking” results, I find

1) a positive association between organization-level deficiencies and audit hours (this association

is mainly driven by tone at the top and methodology issues), 2) a negative association with audit

quality, and 3) no association with audit fees (and thus a negative association with audit fees per

hour). Collectively, these results are consistent with inefficiencies existing at audit firms with

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The limited economic significance of these results is consistent with SOX Section 201 that prohibits audit firms

from providing many types of non-audit services, and subjects any non-audit service to pre-approval by the issuer’s

audit committee.

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poor organization-level QC systems because of audit firms selecting the “wrong clients” or being

inefficient in their day to day operations, as mentioned above.

I also test whether remediation of these QC deficiencies has any influence on audit quality

and efficiency. I find some evidence of a positive association between the proportions of

deficiencies remediated and audit quality, and a negative association with audit hours and a

positive association with fees per hour. These results suggest that both audit quality and

efficiency improve following remediation of the Part II Findings, consistent with QC issues

negatively influencing audit quality and efficiency and their remediation improving it.

In a final set of tests, I assess whether clients are more likely to switch auditors that have

more QC deficiencies. On the one hand, audit firms are very unlikely to communicate the

nonpublic part of the inspections to their clients (e.g., PCAOB 2012). Anecdotal evidence

suggests that audit firms routinely refuse to share them with audit clients and that they even

restrict their distribution internally (e.g., Evans et al. 2011).13

On the other hand, deficiencies in

the firms’ QC systems could have some consequences visible by the client, such as more difficult

interactions with the audit team during the engagement in case hours are wasted and some of

them involve client time. Consistent with the second alternative, I find that clients of audit firms

with more QC deficiencies are more likely to switch auditors the following year. This result

suggests more client dissatisfaction when the auditor has more QC deficiencies, even if the

deficiencies are not directly communicated to the issuer.

This study makes several contributions. First, this study contributes to the audit literature and

responds to the observation by Francis (2011) and DeFond and Zhang (2014) that more research

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One potential reason audit firms may do so is because clients do not have access to the other audit firms’

proprietary parts of the reports, and therefore no benchmark is available to determine whether a report indicates

good, bad or average performance for a specific audit firm.

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is needed to explore the role of audit firms’ characteristics for audit quality. I find, in support of

prior theoretical literature, evidence that the audit firms’ QC systems matter for audit quality. My

results also highlight the importance of the roles of a firm’s culture and audit methodology for

audit quality. They contribute to an emerging literature in economics and finance that focuses on

culture, responding to the call by Guiso et al. (2015a) for more research on the role of culture in

corporations. Notably, I find a positive association between firm’s culture and performance,

consistent with Guiso et al. (2015b) who find such results for publicly traded corporations.

My study also contributes to the literature on PCAOB inspections. Several recent studies find

that PCAOB inspections improve both perceived and actual audit quality (e.g., DeFond and

Lennox 2015, Fung et al. 2015, Gipper et al. 2016, Shroff 2015). However, the mechanism by

which they do so is not understood very well. Aobdia (2015b) suggests that the mere possibility

of inspection of an individual engagement provide a deterrence effect. This study provides

evidence that QC deficiencies negatively influence firm-wide audit quality and their remediation

has the potential to improve it. My study also suggests that audit firms with worse organization-

level QC systems are less efficient for similarly priced audits, or, in other words, less profitable.

This result is more surprising than the negative association identified between audit performance

deficiencies and audit hours, which is more suggestive of audit firms with more practical-

oriented deficiencies “shirking” on their engagements. Because the PCAOB is focused on

inspecting the firms’ QC systems and making sure that the firms remediate the deficiencies

identified during the inspection process, these results suggest that, excluding audit-performance

deficiencies, firms that remediate organization-level types of deficiencies have the potential to

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become more profitable over the long run.14,15

This highlights an unusual impact of regulation in

the form of potential efficiency improvements leading to higher profitability for the regulated

entities. In this particular context, this goes against the conventional wisdom that regulation is

costly for the regulated entities (e.g, Franks et al. 1998, Iliev 2010, Anderson and Sallee, 2011).16

The remainder of this paper is structured as follows. Section 2 provides some background on

the relevant elements of a QC system, the PCAOB inspections and a review of the prior

literature; Section 3, the hypothesis development; Section 4, the data and sample construction;

and Section 5, the main empirical tests. Section 6 provides additional empirical analyses and

Section 7 concludes.

2. Background on the relevant elements of a QC system, PCAOB inspections and prior

literature

Audit practitioners and regulators have emphasized over the years the need for an audit firm

to have a proper QC system. For example, the PCAOB adopted in 2003 as an interim standard

the AICPA standard QC 20, which indicates that “a CPA firm shall have a system of quality

control of its accounting and auditing practice.” This standard indicates that an audit firm QC

system needs to encompass the five following elements: Independence, integrity and objectivity;

Personnel management; Acceptance and continuance of clients and engagements; Engagement

performance; and Monitoring.

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The initial remediation is likely to be costly, and consequently, it is unclear whether audit firms that successfully

remediate the QC deficiencies identified by the PCAOB can become more profitable over the short-run.

Furthermore, remediating QC deficiencies is likely to be a necessary, but not sufficient condition for a firm to

become more profitable. Last, I am unable to include for this analysis the costs of the PCAOB inspections for the

audit firms, which could be quite substantial. 15

An increase in profits in the form of a reduction of hours also assumes that the mix of hours between the different

types of personnel composing the audit team does not change too much. This assumption is reasonable given that the

analysis shows that most types of hours are higher when more QC deficiencies are identified at the auditor. 16

This result applies to the specific context of auditing, a highly concentrated oligopoly (e.g., Aobdia et al. 2015),

where the initial incentives to improve efficiency may not be as high as in other industries.

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Similarly, the AICPA indicates in its standard QC 10 that “The firm must establish and

maintain a system of quality control.” Furthermore, this standard indicates that a firm’s QC

system needs to address each of the following elements: Leadership responsibilities for quality

within the firm (the tone at the top); Relevant ethical requirements; Acceptance and continuance

of client relationships and specific engagements; Human resources; Engagement performance;

and Monitoring. Overall, these requirements indicate that an audit firm must be organized such

that they are addressed. PCAOB inspections aim to ensure that an audit firm does so.

2.1 PCAOB inspections

Prior to SOX, audit firms were self-regulated through, among other things, the AICPA’s peer

review program, started in the 1970s (e.g., Hermanson, Houston and Rice, 2007; Lennox and

Pittman, 2010). This changed following several well-known accounting scandals at Enron,

WorldCom and elsewhere (e.g., Hanson, 2012). As part of SOX, Congress established

independent oversight of the accounting profession by the PCAOB for audits of issuers. Since its

creation, the PCAOB has conducted hundreds of inspections of registered public accounting

firms that audit issuers. These inspections are annual for firms that regularly provide audit

reports for more than 100 issuers, and at least triennial otherwise (Section 104 of SOX). The

PCAOB began its inspection program with limited inspections of the Big 4 in 2003 (e.g.,

PCAOB 2004), and conducted its first full inspections in 2004.

PCAOB inspections of annually inspected firms mainly focus on two elements: A review of

individual audit engagements, and a review of the firm’s QC systems (e.g., PCAOB 2012, Center

for Audit Quality 2012, PCAOB 2015).17

For the latter review, the assessment is based on

17

A limitation of the PCAOB inspections of individual audits is that the inspectors do not have access to the client,

but can only focus on the audit work papers and interviews of the audit firm. Thus, PCAOB inspectors may not have

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specific analyses that focus on the firms’ QC policies and procedures, and from inferences

derived from the review of individual engagements (e.g., PCAOB 2015). Overall, the nature of

the inspection of a firm’s QC systems is consistent with the requirements in PCAOB standard

QC 20 and the AICPA standard QC 10. The review of the firm’s QC systems eventually yields

two types of deficiencies. The first type, audit performance deficiencies, is directly derived from

deficiencies identified in the inspections of individual audit engagements and corresponds to the

different themes identified in these individual inspections. While the identification of individual

audit deficiencies through the PCAOB inspections of individual engagements does not always

indicate that there are QC level deficiencies, a repeated instance of a similar type of audit issues

is likely to be considered a QC deficiency and included in the audit performance part of the Part

II section of the report (e.g., Evans et al. 2011, also see Appendix A for an example). Because

the nature of the deficiencies identified in individual engagements has changed over time, these

deficiencies also tend to be reasonably more transient in nature.

The second type of deficiencies is based on an overall assessment of the organization and

corresponds to organization-level issues. Accordingly, the assessment is conducted at the firms’

National Offices but also involves specific procedures based on individual engagements or

interviews at specific offices (e.g., PCAOB 2005). The publicly disclosed portions of the

PCAOB inspection reports discuss the different QC areas reviewed and provide a description of

the types of procedures performed. These organization-level areas have remained stable over

time. For example, for the limited inspections conducted in 2003, they included a review of

seven functional areas, including the tone at the top, partner management, independence policies,

client acceptance and retention policies, the internal inspection program, the audit policies,

access to some information available to the auditors at time of the engagement if this information was not

documented in the audit work papers.

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procedures and methodologies, including training, and the policies related to foreign affiliates

(e.g., PCAOB 2004). Even though the description slightly changed over time, the most recent

inspection reports indicate that these areas are still part of the review of the firms’ QC systems.

The major innovation over time was the introduction of additional procedures related to practice

monitoring beyond the firms’ internal inspection programs.

(Insert Table 1 About Here)

Table 1 provides additional details and examples (all based on the public portions of the

inspection reports) of procedures that are conducted by the PCAOB inspectors for the part of the

QC inspections that focus on the audit organization (also see Appendix B for an example of

publicly released audit organization QC deficiency).18

In particular, the analysis of the tone at the

top is very detailed. PCAOB inspectors not only assess what the tone at the top stated by the

leadership is, but also how it is perceived and implemented in the different practice offices by

junior staff. Appendix C provides an excerpt about the procedures conducted by the PCAOB

with regard to tone at the top, as described in the 2004 inspection report of Deloitte (PCAOB,

2005). Prior research suggests that tone at the top represents an overall assessment of the culture

of the audit firm (e.g., Guiso et al. 2015b), which is related to the tone originating from its

leaders (e.g., POB 2000, Jenkins et al. 2008, Berson et al. 2008). Based on Table 1, “audit

policies, procedures and methodologies” represent an assessment of the tools available to the

engagement teams to conduct their day-to-day audit activities. The other six areas intuitively

relate to audit quality but some of them might be more limited in nature. For example, the

18

For example, the 2003 inspection reports are very detailed in terms of procedures performed by the inspection

team. More recent reports are less detailed but still provide a description of the analyses conducted.

16

policies related to the foreign affiliates are relevant only to issuers with international operations

(e.g., PCAOB 2011, pC-3).

The PCAOB issues a Part I Finding when it identifies deficiencies in individual audit

engagements. These Part I Findings are disclosed to the public, with the name of the issuer

masked. The situation is different when the PCAOB identifies QC systems deficiencies and

issues a Part II Finding. Part II Findings are not disclosed to the public if remediated within one

year of the issuance of an inspection report (Section 104(g)(2) of SOX, Gradison and Boster

2010).19

Over time, several large audit firms had some (but not necessarily all) Part II Findings

publicly disclosed. However, because the Part II Findings remediated within a year are not

publicly disclosed, academic researchers and the public in general have struggled to understand

the exact nature of a Part II Finding. In order to provide more transparency, the PCAOB has

indicated several times that Part II Findings for large firms are not unusual (e.g., PCAOB 2012),

and even provided an illustrative example of a nonpublic portion of a large-firm inspection report

(Evans et al. 2011). This illustrative example includes, besides audit performance deficiencies,

three main categories of organization-level deficiencies: Partner management, audit

methodology, and tone at the top, and suggests that these categories are not rare.

(Insert Figure 2 About Here)

The typical timeline of an inspection is provided in Figure 2. Inspection fieldwork for the

individual audit engagements with issuer fiscal years ending between April 1st of year t-1 and

March 31st of year t is typically conducted between March and November of year t, after these

engagements are completed (e.g., Aobdia 2015b). The PCAOB also analyzes the firm’s QC

19

See footnote 4 for more information about the remediation process, which is reasonably continuous in nature.

17

policies and procedures at the same time. However, these analyses may not always pertain to the

prior year’s policies and also assess “real time” concerns.20

Due to this potential for “real time”

assessment, in the remainder of the analyses, when using dependent variables measured for year t

audits, I also measure Part II Findings based on the year t inspection cycle.21

2.2 Prior empirical literature

Because of the lack of availability of data, limited empirical research exists on the relation

between audit firms’ organizational structure and audit quality and efficiency (e.g., Francis 2011,

DeFond and Zhang 2014). Limited empirical studies in the area include several in the 1980s that

explore the relation between structured and unstructured audit technology and several outcome

variables, including preferences for auditing standards and audit report timeliness (Cushing and

Loebbecke 1986, Kinney 1986, Williams and Dirsmith 1988, Morris and Nichols 1988).

However, these studies are dated and predate the PCAOB regime.22

In a similar vein, because the PCAOB is precluded by SOX (Section 104(g)(2)) from

disclosing QC system deficiencies when these are addressed by the firms within 12 months after

the issuance of the inspection report (e.g., PCAOB, 2012), very limited research exists on the

role of the PCAOB inspections of the firms’ QC systems. Most current research focuses, due to

lack of identification otherwise, on the market share impact of the PCAOB inspections when QC

criticisms are eventually made public due to lack of remediation by the audit firms (e.g., Nagy

20

For example, recent inspection reports suggest that the PCAOB reviews current documents in its review of the

tone at the top (e.g., PCAOB, 2011, pC-2). 21

I use the Part I Findings identified in Year t+1, corresponding to Year t audits, when using Part I Finding as a

measure of audit quality. 22

Given that the audit office is disclosed in the auditor report, several recent papers also use office-level

characteristics to test for audit quality and fees (e.g., Francis et al. 2005, Francis and Yu 2009, Reichelt and Wang

2010).

18

2014, and Boone, Khurana and Raman 2015).23

Aobdia (2015b) is the first to use the PCAOB

proprietary inspection data to further understand the impact of the PCAOB inspection process.

However the focus in Aobdia (2015b) is on the PCAOB’s inspections of individual engagements,

and not on the inspections of the firms’ QC systems.

3. Hypothesis development

3.1. Audit firm QC deficiencies and audit quality

While the PCAOB and the AICPA have emphasized over the years the need for an audit firm

to have proper QC systems, this need is also supported by the analytical literature. In particular,

QC systems are designed to align the individual incentives of the partners with the overall

incentives of the firm to conduct high quality audits (the incentives of the firm are driven by

reputation and litigation, as shown in prior literature). For example, in Huddart and Liang

(2005)’s analytical model, partners have incentives to shirk production tasks because effort is

unobservable. Huddart and Liang (2005) further show that symmetric mutual monitoring is

insufficient to reduce the issue, because partners also have incentives to shirk the monitoring

task. Instead, they identify that task specialization, in which some partners mainly monitor while

other ones are engaged in production, is beneficial for the organization. Thus, the

implementation of this specialization in the form of a firm’s hierarchy, tone and the top and QC

systems in general has the potential to increase both audit effort and quality.

Anecdotally, QC systems appear to matter. For example, Arthur Andersen auditors were able

to overrule the authority of their national specialists on the Enron engagement (e.g., Eichenwald

and Norris 2002), thereby leading to the failed audit of Enron, and eventually the demise of the

entire firm. Survey evidence also suggests that QC systems perceived as strong are associated

23

An exception is Drake et al. (2015) who focus on income tax accounts following the publicly disclosed QC

deficiencies of Deloitte for the 2007 inspection.

19

with a lower propensity of auditors to engage in low quality behaviors (e.g., Otley and Pierce

1996, Malone and Roberts 1996).24

Consequently, I test the following hypothesis:

H1a: An audit firm’s QC deficiencies are negatively associated with audit quality

The management literature defines culture as “a set of norms and values that are widely

shared and strongly held throughout the organization” (O’Reilly and Chatman 1996, Guiso et

al. 2015b), a definition applicable to public accounting firms. Anecdotal evidence, especially

based on the demise of Arthur Andersen, suggests that culture in an audit firm has an impact on

audit quality. For example, Wyatt (2004) suggests that the rise of consulting in public accounting

firms led to a culture change that focused on revenue to the detriment of audit quality, an

observation confirmed by former partners of Arthur Andersen (e.g., Gendron and Spira 2009,

Toffler and Reingold 2003).25

This anecdotal evidence is consistent with theories that consider

company culture to be relevant because employees will face choices that cannot be properly

regulated ex-ante (O’Reilly 1989, Kreps 1989), leading culture to act as a safeguard for

employees from making short-term based decisions that may have detrimental consequences on

the long-run to the entire organization (Guiso et al. 2015b). I test the following hypothesis using

tone at the top deficiencies to operationalize for culture (see Subsection 2.1):

H1b: Culture is an important factor of the association between QC issues and audit quality

24

In particular, according to Bedard et al. (2008, p188-189), “Extant research examines the frequency of such QTB

[quality threatening behavior] as collection of insufficient audit evidence, inadequate workpaper (i.e., audit

documentation) review, other violations of generally accepted auditing standards (GAAS), violations of generally

accepted accounting principles (GAAP), failure to book material adjustments, truncating sample sizes, accepting

doubtful evidence, relying on internal audit work of questionable quality, insufficient risk adjustment in audit

procedure planning, false or premature sign-off, failure to do thorough research, and under-reporting of time.

Across these studies, a surprisingly large proportion of auditors admit to engaging in QTB.” 25

This focus on revenue was new for Arthur Andersen, a company whose founder, Arthur Andersen, put reputation

over profit and refused to approve questionable transactions even if this meant the loss of the business (e.g., Brown

and Dugan 2002).

20

An audit methodology represents the backbone of the day-to-day execution of an audit.

Anecdotal evidence suggests that, at least in the past, large accounting firms’ auditors were likely

to be more knowledgeable about their audit firm methodology than about current auditing

standards. This highlights the potential reliance of engagement team personnel on the firm’s

methodology. Prior research also suggests that an audit methodology has an important impact on

auditor actions (see the discussion in Subsection 2.2). Audit firms also recognize the importance

of their audit methodologies on audit quality.26

For example, in Grant Thornton’s public

response to its 2004 PCAOB inspection (PCAOB 2006b), the letter indicates that “As the result

of the inspection of the eight largest accounting firms, the PCAOB inspection staff is in a unique

position. These inspections give them an unprecedented, in-depth understanding of each of the

firms’ audit methodologies, policies and procedures. In 2002 […] we recommended that the

AICPA coordinate a review of the major accounting firm’s audit methodologies so that best

practices could be determined and shared.” Consequently, I test the following hypothesis:

H1c: Audit methodology issues are an important factor of the association between QC

deficiencies and audit quality

3.2. Audit firm QC deficiencies, audit pricing, and the provision of non-audit services

On the one hand, several studies suggest that the audit market is characterized by price

competition (e.g., Johnson and Lys 1990, Pearson and Trompeter 1994), and some even suggest

that audit differentiation does not matter (Doogar and Easley 1998, Donovan et al. 2014). This

suggests that audit fees, determined by a competitive market pricing, are not associated with QC

deficiencies internal to individual audit firms. On the other hand, if auditors clients care about

the quality of the audit services they receive, and auditors are able to credibly convey their

26

Audit firms often advertise their methodologies on their websites. See for example

http://www.pwc.com/us/en/audit-assurance-services/publications/pwc-audit-transformation-white-book.html

21

quality, then it is possible that firms with fewer QC deficiencies are able to price a premium to

their clients for this quality.27

For example, prior research documents that differentiation affects

audit fees (e.g., Francis et al. 2005, Numan and Willekens 2012), and some studies suggest that

higher audit fees represent a signal of higher audit quality (e.g., Ball et al. 2012). Consequently, I

test the following hypothesis:

H2a: There is no association between audit firms’ QC deficiencies and audit pricing

The relation between QC deficiencies and the provision of non-audit services is unclear. On

the one hand, deficiencies in the QC system could lead audit firms to advertently or inadvertently

sell more non-audit services. On the other hand, Section 201(g) of SOX severely restricts the

scope of non-audit services, and Section 201(h) conditions any non-audit service to the approval

of the audit committee. Consequently I test the following hypothesis:

H2b: There is no association between audit firms’ QC deficiencies and the provision of non-

audit services

3.3. Audit firm QC deficiencies and audit efficiency

The relationship between QC deficiencies and audit efficiency is unclear. On the one hand,

QC deficiencies could lead to engagement teams not working enough on their audits (the

“shirking” alternative). For example, deficiencies in internal inspections, other types of

monitoring, or in the audit methodology could lead engagement teams to not conduct enough

work on specific parts on the audit, consistent with lower audit quality and hours spent on each

engagement for firms with more QC deficiencies. The detection of audit performance QC

27

Note that this would at minimum require audit firms to share the confidential portion of their PCAOB inspection

reports with their clients (it is still unclear whether clients would be able to act on the information if the audit firms

do not disclose this information to the capital markets). However, according to the PCAOB (2012), “some firms …

have nevertheless expressed reluctance to disclose to audit committees nonpublic portions of reports and other

nonpublic inspection information…”

22

deficiencies by the PCAOB could also be suggestive of engagement teams “shirking” on their

audits, if these deficiencies obtained from risk-based inspections of individual engagements are

representative of the remainder of the firm’s engagements. On the other hand, issues in the tools

available to the engagement teams, their expertise, or in the way partners are staffed and lead

their audits may drive the engagement team to spend unnecessary time on irrelevant parts of the

audit, or too much time in general because of lack of expertise and training, despite low audit

quality overall. Furthermore, an audit firm may select the “wrong clients”, those that require

higher audit effort despite similar audit fees. Consequently, I test the following hypothesis, for

both audit performance and organization level QC deficiencies:

H3a: There is no association between audit firms’ QC deficiencies and audit efficiency

Prior research suggests that, for publicly traded corporations, a culture of integrity is

associated with higher operational performance (Guiso et al. 2015b). In the accounting context,

Jenkins et al. (2008), based on Ponemon and Gabhart (1993), mention that “the behavioral

research suggests that employees who fit well with a firm’s culture remain with the firm longer

and contribute more to the financial success of the firm.” This discussion suggests that a proper

tone at the top could be associated with better audit efficiency. I test the following hypothesis:

H3b: There is no association between tone at the top deficiencies and audit efficiency

Similar to the discussion of H3a, on the one hand, deficiencies in the audit methodology

could lead auditors to spend unnecessary time on several irrelevant areas of the audit. Poorly

trained auditors may also have to spend more time on specific tasks. Thus, deficiencies in the

audit methodology may be associated with poor audit efficiency. On the other hand, deficiencies

in the audit methodology could lead auditors to not spend enough time on important areas of the

23

audit. Therefore, it is unclear whether deficiencies in the audit methodology have a positive or

negative impact on audit efficiency. I test the following hypothesis:

H3c: There is no association between audit methodology issues and audit efficiency

In subsequent empirical tests, inferences on audit efficiency must be made by collectively

comparing the evidence on audit quality, audit fees and audit hours. For example, if audit quality

and engagement hours are both low, then the evidence would suggest a “shirking” issue. If audit

quality is low while audit hours are high and fees unchanged, this would suggest that either the

auditor extra effort does not bear fruit, or the client was much harder to audit to begin with,

despite the engagement being priced at the same fee. Thus the evidence would be suggestive of

efficiency issues in the way an engagement is conducted or in the choice of audit clients.

3.4 Potential measurement issues

The empirical tests of the hypotheses above represent joint tests of whether QC system

deficiencies matter, and importantly, whether the assessment of such deficiencies by the PCAOB

is accurate. Such tests are important given the widespread skepticism regarding the value of the

PCAOB inspections. In particular, the test of H1 becomes highly relevant to validate whether the

PCAOB assessment is accurate, especially in the case of audit performance deficiencies, which

are derived from the inspection of individual audit engagements. Because the inspection of

individual audit engagements is risk-based (e.g., Hanson 2012), it remains unclear whether the

results of individual inspections, even when observed in several instances, can generalize to the

remainder of the firm. For example, Deloitte mentions in its initially non-public response to the

Part II section for its 2007 inspection (subsequently publicly released in 2011) that: “In most

sections of Part II of the Draft Report, the Board expresses "...cause for concern about the

24

effectiveness of the Firm's quality controls...". However, in some cases the concerns noted by the

Board are based only on limited instances observed by the inspection team, yet the implication is

that such concerns indicate a broad issue in our audit practice as a whole. We believe that

observations should not be included in Part II when there are only a limited number of instances

on which they are based, certain of which instances we disagreed with in our written responses

to the inspection team's comments.”

4. Data and sample construction

4.1 QC deficiencies

I obtain QC deficiency information at the audit firm level from the PCAOB for the

inspections conducted between 2004 and 2013. Because the PCAOB inspections generally apply

a comparable framework across firms that are annually inspected (e.g., PCAOB 2006a, PCAOB

2015), I restrict the sample to the U.S. operations of the largest eight auditors, including the Big

4 firms and the four “second tier” firms, as defined in the General Accountability Office reports

(GAO 2003 and GAO 2006) and in Hogan et al. (2009).28

I obtain the full, nonpublic versions of

the PCAOB reports for these firms and these years. This leaves a sample of 80 firm-year

observations with the appropriate measures of QC deficiencies.

First, I count the number of words in each Part II section of the inspection report that

discusses QC issues and create a variable called Number_Words.29

This number represents an

aggregate measure of the issues reported in the Part II section of the report. Because the Part II

section includes two different types of deficiencies, audit performance deficiencies and

28

The results are qualitatively unchanged if I restrict the sample to the six “global firms”, which include the Big 4,

Grant Thornton and BDO. 29

Some inspection reports incorporate the response of the firm to the draft inspection report in the Part II section

(see for example the report of the 2012 inspection of ABBM Group released on September 27 2012). Also, as

discussed in PCAOB (2006a) footnote 17, some reports may include nonpublic portions even though they do not

include quality control criticisms. I make sure to restrict the word count to sections related to QC issues.

25

organization-level deficiencies I also split this word count between these two types of

deficiencies and create Number_Words_Perf, the number of words corresponding to audit

performance deficiencies, and Number_Words_Org, equal to Number_Words minus

Number_Words_Perf. For ease of interpretation and comparison of the coefficients on these

three variables in subsequent regressions, I normalize their distributions to a mean of zero and a

standard deviation of one. This transformation ensures that the coefficients on these variables can

directly be compared in the tables and that the economic significance of these coefficients

corresponds to a change of one standard deviation of the variable.

I further refine the analysis on organization-level QC deficiencies, group these deficiencies

into the eight different categories described in Table 1, and build an index of deficiencies,

QCIndex: I add one point to the index if deficiencies are identified within a given category. For

example, if a tone at the top issue is identified, then I add one point to the index. Thus, the index

goes from zero (no deficiency identified in any of the categories) to eight (deficiencies identified

in each category). The main advantage of an index resides in its simplicity, an attribute that has

led several well-known studies to adopt them in the past (e.g., La Porta et al. 1998, Gompers et

al. 2003, Bebchuk et al 2009, Garmaise 2011). I later separate specific categories for tone at the

top (Tone) and audit methodology (Methodology), and build a sub-index based on the six

remaining categories (Reduced_QC_Index).30

Note that audit performance Part II Findings are

excluded in the construction of QCIndex. Given that the PCAOB inspections are risk-based (e.g.,

Hanson, 2012), these deficiencies are more idiosyncratic and transient in nature, and even though

generalizable to a subset of engagements of the firm, may not be applicable to all engagements. I

30

This research design is preferable to including indicator variables for each of the eight categories to avoid

multicollinearity concerns, especially on the analyses of audit hours where the sample is further restricted to the

years between 2008 and 2013.

26

still control for these in all relevant analyses by including Number_Words_Perf as an

explanatory variable, but am unable to build a similar index of audit performance deficiencies

due to their more transient nature.


Table 2, Panel A presents descriptive statistics for Number_Words, QCIndex and their

components. Because I compute these measures for eight firms over a period of ten years, there

are 80 firm-year observations. The mean value of 4.41 for QCIndex suggests that, despite limited

public disclosures, QC deficiencies are frequently identified by the PCAOB. This is consistent

with Evans et al. (2011) and PCAOB disclosures (PCAOB 2012) that indicate that it is not

unusual for an inspection report of one of the large, annually inspected firm to include criticisms

of several aspects of the firm’s system of quality control. Untabulated analyses indicate that the

standard deviations of these measures are reasonably large, indicating that there is sufficient

variation in the dataset to conduct meaningful analyses, and that the measures are quite persistent

over time, with, for example, an average autocorrelation above 0.5 for QCIndex. This result is

consistent with the idea expressed in footnote 4 that it takes time in practice for firms to fully

remediate the audit deficiencies identified by the PCAOB, even though the deficiencies have

been deemed by the Board to be satisfactorily addressed within the 12 months period following

the release of the inspection report. Untabulated analyses also indicate that the correlations

between QCIndex and its components are mostly above 0.5 and significant at 5% or better,

suggesting that the use of an index is appropriate in this context as it captures a great portion of

the variation among all individual constituents. Correlations are lower between QCIndex and

Methodology or Tone, (and even lower when comparing these two variables with

Reduced_QC_Index), confirming that these categories may represent a separate assessment of a

27

firm’s QC systems. The correlation between QCIndex and Number_Words_Org is also high,

above 0.5, consistent with longer organization-level sections of the Part II report incorporating

more organization-level deficiencies, while, by construction, the correlation between QCIndex

and Number_Words_Perf is lower.

4.2 Data and sample construction

I merge the QC deficiencies dataset, available for the U.S. operations of large audit firms,

with Compustat and Audit Analytics to obtain publicly available information on auditors,

restatements, audit fees, audit offices, and other audit quality measures and control variables.

After imposing the restriction to have all audit quality variables, audit fees, and control variables

available, this leaves a primary sample of 27,837 issuer-year observations. I also merge these

data with audit hours obtained from the PCAOB. These data cover the fiscal years 2008 to 2013,

are comprehensive for the U.S. engagements of the Big 4 and more sparsely populated for the

other large auditors, and include the total hours spent on the engagement, total partner hours and

hours spent by the quality review partner. Using this data greatly reduces the sample size for the

analyses focusing on audit hours. While total audit hours are generally well populated, the other

types of hours are not always available for every year and this further reduces the sample size for

more detailed analyses focusing on the partner and engagement review partner hours. The

sample size eventually varies with the analysis conducted and data availability for the particular

dependent variable considered. I do not restrict the sample to the intersection of all data

available, because doing so would considerably reduce the sample size for most analyses.

I also obtain individual PCAOB inspections and Part I Findings data, as a measure of audit

quality, from the PCAOB. These data cover the inspections corresponding to financial statements

with fiscal years between 2003 and 2013, and include the name of the issuer inspected, its

28

Central Index Key (CIK), its auditor, the year of the inspection, and whether a Part I Finding is

issued or not. The final sample for this analysis, restricted to engagements that were inspected by

the PCAOB, is composed of 2,452 issuer-years.

5. Main empirical tests

5.1 Research design

I test whether QC index deficiencies are associated with audit quality, audit fees and audit

hours using the following regressions:

Audit_Qualityi,t or Logauditfeesi,t or Log(Hours) i,t = α + β1.Number_Wordsi,t +

γ.Controlsi,t +Year Fixed Effects+ εi,t, (1)

and

Audit_Qualityi,t or Logauditfeesi,t or Log(Hours) i,t = α + β1.Number_Words_Perfi,t +

β2.Number_Words_Orgi,t + γ.Controlsi,t +Year Fixed Effects+ εi,t, (2)

and


β2.QCIndexi,t + γ.Controlsi,t +Year Fixed Effects+ εi,t, (3)

and


β2.Methodologyi,t + β3.Tonei,t + β4.Reduced_QC_Indexi,t + γ.Controlsi,t +Year Fixed Effects

+ εi,t, (4)

where the subscripts i and t correspond to issuers and years, respectively.

Models (1) to (4) are estimated using a logistic regression when the dependent variables are

binary and OLS otherwise. The dependent variables are composed of Audit_Quality (to test H1),

itself composed of the four following proxies: Restatement, Meet/Beat and PartIFinding,

29

indicator variables equal to one when the company restates its financial statements, has a return

on assets between 0% and 3%, or its audit engagement receives a Part I Finding, respectively;

and ScaledAccrualsCFO, the absolute value of accruals deflated by cash flows from operations.

All these variables are computed in a similar fashion as in Aobdia (2015a), who finds that

Restatement, Meet/Beat, and ScaledAccrualsCFO represent adequate measures of audit quality

when compared with the PCAOB Part I Findings. Because Aobdia (2015a) finds a larger

economic importance of Restatement and Meet/Beat in predicting a Part I Finding, I expect the

regressions using these measures to be more powerful than the ones using accruals. I also use the

identification of a Part I Finding by the PCAOB as a measure of audit quality. To prevent the

issue of simultaneity of measurement of the Part I Findings and Part II Findings, I measure the

Part I Findings for the next year’s inspection cycle compared to the measurement of Part II

Findings.31

This measurement timing is appropriate given the timeline presented in Figure 2. I

still caveat, for this particular study, that the identification of a Part I Finding is unlikely to be the

strongest measure of audit quality available, because both Part I and Part II Findings are

determined by the same PCAOB inspection teams.

I also use the following dependent variables (to test H2): Logauditfees is equal to the

logarithm of audit fees, and Proportion_Nonauditfees is equal to the non-audit fees divided by

the audit fees for a given issuer. I also use Log(Hours), composed of three different proxies, (to

test H3): Logaudithours, equal to the logarithm of the total engagement hours, Logpartnerhours,

equal to the logarithm of total partner hours, and Logeqrhours, equal to the logarithm of the

quality review partner hours. Detailed variable definitions are provided in Appendix D.

31

Notably, because Part II Findings are determined in part from the inspection of selected engagements, a

simultaneous measurement of Part I and Part II Findings could introduce a mechanical relationship between these

two variables. This concern is partially alleviated when measuring the Part I Findings one year later.

30

The explanatory variable of interest in model (1) is Number_Words, equal to the number of

words related with QC criticisms in the Part II section of the report. For model (2), it is

Number_Words_Perf and Number_Words_Org, equal to the number of words for audit

performance and organization-level deficiencies, respectively. For model (3), besides

Number_Words_Perf, the explanatory variable of interest is QCIndex, equal to the index built in

Section 4.1. I do not include Number_Words_Org in this specification because, by construction,

QCIndex is a substitute for this variable. For model (4), the explanatory variables include Tone,

an indicator variable when a tone at the top Part II Finding is identified by the PCAOB,

Methodology, an indicator variable when Part II Findings in audit methodology are identified,

and Reduced_QC_Index, a reduced index, from zero to six, based on the remaining categories of

deficiencies presented in Table 1.

I control for variables that are likely to affect audit fees and quality and are commonly used

in prior audit literature (e.g., Francis et al. 2005; Francis and Yu 2009; Reichelt and Wang 2010,

Aobdia et al. 2015). In particular, I include the following set of control variables:

Client_Importance (total client fees charged by the auditor, audit and non-audit fees, divided by

the total fees charged by the auditor during the year), FirstYear (a dummy equal to one for first-

year audits), Distressed (a dummy equal to one if either the firm’s income before extraordinary

items or cash flows from operations is negative), ForeignPifo (absolute value of pretax income

from foreign operations divided by the absolute value of pretax income), Intangi (one minus

gross PP&E divided by assets), City_Leader (a dummy equal to one when the total office audit

fees for a two-digit SIC industry are the largest in the core business statistical area –CBSA– in

that year), NationalLeader (a dummy equal to one if the total fees for that auditor in a two-digit

SIC industry are the largest), Office_Size (logarithm of audit fees for the office year), Logat (the

31

natural logarithm of the firm’s total assets), CATA (current assets divided by total assets), Quick

(current assets less inventory divided by current liabilities), Geoseg (number of geographic

segments), Busseg (number of business segments), Stdsalegrowth (the standard deviation of the

firm’s sales growth, computed from year t − 3 to year t), DecYE (a dummy that equals one for

fiscal year ending in December), CFOat (cash flows from operations deflated by beginning

assets), Leverage (total debt divided by total debt plus equity), Salegrowth (percentage increase

in the firm’s revenues), BTM (the book to market ratio, equal to the issuer’s book equity divided

by fiscal year end market value), Altman (the Altman Z-score), Litigation (a dummy equal to one

for high-litigation industries and zero otherwise), Length_Relationship (number of years the

audit firm has continuously audited the client firm, obtained from Compustat), Big4, an indicator

variable when the auditor is a Big 4 firm, Weaknesses (indicator variable when internal control

weaknesses are reported) and HiTech (indicator variable for high-tech industries).32

Notably,

City_Leader, National_Leader and Office_Size control for the role of industry specialization and

audit offices, identified to have an influence on audit quality and fees in prior studies (e.g.,

Francis et al. 2005; Francis and Yu 2009; Reichelt and Wang 2010). I also include year fixed

effects, cluster standard errors at the issuer level, and winsorize all continuous variables at the 1st

and 99th

percentiles to limit the influence of outliers. I also estimate parsimonious regressions

with a set of basic controls, including Distressed, Logat, CFOat, Leverage, BTM, Litigation and

Big4, to ensure that the results are not driven by the inclusion of specific control variables.


32

I exclude Distressed from the regression when using Meet/Beat as a dependent variable to avoid a mechanical

relationship between both in this particular specification.

32

Table 3 shows the descriptive statistics. 11% of the issuers’ financial statements are

eventually restated, while 14% of them meet/beat the zero earnings threshold. 23% of PCAOB

inspected engagements in the sample receive Part I Findings. An average audit commands total

fees of approximately $1.1M, representing 5,998 hours of work, including 352 partner hours.

The average audit fees per hour are approximately $235, with a standard deviation of $134.

5.2 Audit quality results

The results of model (1), with audit quality measures, are shown Panels A and B of Table 4.

Panel A shows that the more words in the Part II section of the PCAOB report, the higher the

probability of restatements and of meet/beat. The results do not appear to be very sensitive to the

number of control variables, as evidenced by reasonably similar coefficients when comparing the

columns with a limited set of controls with the ones with a full set. Untabulated analyses suggest

that an increase of Number_Words by one standard deviation approximately increases the

probability of restatement and meet/beat by 1.7% and 0.8%, respectively, which are reasonably

large increases in comparison with the mean probabilities of 11% and 14%.


Panel B confirms the results when using PartIFinding as a measure of audit quality.

Untabulated analyses suggest that one standard deviation increase of Number_Words

approximately increases the probability of a Part I Finding by 3.6%, to be compared with an

average of 23% in the sample. Number_Words loads insignificantly in the regressions with

ScaledAccrualsCFO as the dependent variable. This result is not completely surprising given that

the dependent variable is a noisy proxy for audit quality (Aobdia 2015a). Overall, the results in

Panels A and B suggest that firm-wide QC deficiencies have a negative influence on audit

quality. This result is consistent with the intent of SOX (section 104) that establishes the PCAOB

33

and instructs the Board to “evaluate the sufficiency of the quality control system of the firm, and

the manner of the documentation and communication of that system by the firm.”

Panels C and D present the results using models (2) and (3). Overall, I find some evidence

that QC deficiencies in both audit performance and organization-level areas are associated with

poor audit quality, as evidenced by positive coefficients on Number_Words_Perf,

Number_Words_Org, and QCIndex with the different measures of audit quality. However, each

individual result is weaker than in Panels A and B. For example, there is no association between

Number_Words_Org and Restatement, while the variable is positively associated with Meet/Beat

and PartIFinding. Collectively, these results still suggest that both types of QC deficiencies

matter in terms of audit quality.

Panel E presents the results using model (4). The results highlight the importance of

deficiencies in the audit methodology on audit quality, with positive associations identified

between Methodology and Restatement, Meet/Beat, and PartIFinding. They highlight the

“conventional wisdom” expressed by Grant Thornton in its public response to its 2004 PCAOB

inspection of the importance of a proper audit methodology (see section 3.1).

5.3 Audit fee results and results on provision of non-audit services

Panel A of Table 5 presents the results of model (1) using audit fees as the dependent

variable. I do not find any association between Number_Words and Logauditfees in the second

column (the association is positive in the first, but likely results from the lack of specific control

variables). This result is consistent with the internal organization of the audit firm having little

influence on the pricing of audit services, consistent with Doogar and Easley (1998). However, I

find a positive association between Number_Words and the proportion of non-audit fees. This

result is consistent with audit firms with quality control issues being more reliant on non-audit

34

services. However, the result is not very large economically. An increase of one standard

deviation of Number_Words is associated with an increase in the proportion of non-audit fees of

1.9%, to be compared with a sample average of 23%. This is consistent with SOX Section 2.1

that limits the possibilities for audit firms to cross-sell non audit services to their clients.


Panels B and C presents similar analyses when using models (2) to (4). The results are

similar to Panel A and do not show any association between QC deficiencies and Logauditfees,

regardless of the nature of the deficiencies. They also show a positive association between both

audit performance and organization level deficiencies and Proportion_Nonauditfees.

5.4 Audit hour results

Table 6 presents the results of models (2) to (4) using audit hours as the dependent variables.

I do not tabulate the results of model (1), because, as shown in Panels A and B when using the

results of models (2) and (3), the results go in opposite directions depending on the nature of the

QC deficiency. Thus, model (1) only shows a non-informative average of these opposite results.


Panels A and B show that, on the one hand, Number_Words_Perf is negatively associated

with all forms of audit hours and positively associated with Fees_per_hours. These results

suggest that the negative associations identified between audit performance deficiencies and

audit quality in Table 4 are driven by not enough effort spent on each engagement by the

auditors (the “shirking” alternative). On the other hand, I find positive associations between

Number_Words_Org and QCIndex with all forms of audit hours, and negative associations with

Fees_per_Hour. These results, combined with those of a negative association with audit quality

35

in Table 4, and no association with audit fees in Table 5, are consistent with inefficiencies being

present at auditors that have more organization-level deficiencies identified by the PCAOB.

Inefficiencies can arise from two possibilities: 1) audits are poorly conducted, with the

engagement team spending too much time on unnecessary parts of the audit (and not enough on

its important parts), or 2) the auditor selects the “wrong clients”, those that are difficult to audit

and do not pay extra fees to compensate for the increased difficulty of the audit.

Panel C confirms this result when using Model (4). I find positive associations between Tone,

Methodology, and all forms of hours. The results on Reduced_QC_Index are not as strong and

are even insignificant when using logaudithours and Fees_per_hour as dependent variables. The

positive coefficient on Methodology is consistent with the first inefficiency explanation, whereby

engagement teams of auditors with deficiencies in their methodologies do not have the proper

tools to conduct an audit properly. The coefficient on Tone is also large, perhaps because tone at

the top issues may lead auditors to poorly choose their clientele (this would be consistent with

the second inefficiency explanation). Overall, these results are suggestive of organization-level

QC deficiencies leading to inefficiencies in the way audits are being conducted.

6. Additional empirical tests

6.1 QC deficiencies remediation

In this section I test whether remediation of QC deficiencies influences audit quality. As

previously discussed, it is difficult to quantify and measure the influence of the remediation

process because it is continuous in nature and may take more than 12 months to materialize.

However, for this Section, I consider the QC deficiencies that are not remediated and thus are

subsequently made public by the PCAOB as these are instances where remediation was possibly

insufficient. I add a new variable to model (1), Proportion_Remediated, equal to the proportion

36

of the number of words in the Part II section of the report relating to QC issues that are not

publicly released. This proportion is equal to one if no QC criticism is subsequently released by

the PCAOB, and to zero if all of them are.33

Because, based on the timeline in Figure 2,

remediation is likely to influence audit quality following the issuance of the PCAOB report, I

consider, for a given Proportion_Remediated, the issuer fiscal year end immediately following

the release of the corresponding PCAOB inspection report, and before the next PCAOB report is

released. Because Proportion_Remediated is available only for issuer-year observations that

occur subsequently to the release of the first PCAOB report for their audit firm, the sample size

is reduced accordingly.


The results are presented in Table 7. I find in Panel A a negative association between

Proportion_Remediated and both Restatement and PartIFinding, suggesting a positive influence

of QC deficiency remediation on audit quality. Interestingly, I also find a negative association

between Proportion_Remediated and logaudithours, and a positive association with

Fees_per_hour, consistent with the assertion that remediation of the QC deficiencies identified

by the PCAOB may allow audit firms to become more efficient and profitable on the long run.34

Collectively, these results provide some evidence that remediation of QC deficiencies has a

positive influence on both audit quality and audit efficiency.35

6.2 Robustness tests

33

I set the variable to one if there is no word in the Part II section of the report that relates to QC issues. 34

I also find a puzzling positive association between Proportion_Remediated and Logauditfees, suggesting that

audit firms pass on to their clients a portion of the cost savings derived from remediation. 35

Because the number of Part II Findings publicly released, especially the non-audit performance ones, is limited, it

is not possible to empirically separate Proportion_Remediated between audit performance and other types of QC

deficiencies. This limitation of the data may explain why Proportion_Remediated loads positively when using

Logeqrhours as the dependent variable.

37

I conduct several robustness tests to confirm the validity of the results in Section 5. First,

because the PCAOB Part II Findings are in part determined from the inspection of individual

engagements, there could be a concern that the results are partly driven by the individually

inspected engagements. This issue is mostly solved by the timing of measurement in the paper.

Notably, I look at audit quality and efficiency the year following the measurement of quality of

individual engagements. Nevertheless, to provide additional confidence, I remove from the

sample all individually inspected engagements (that contributed to the identification of Part II

Findings) and find qualitatively unchanged results.

Second, given the potential timing uncertainties to which engagements a QC deficiency

relates to (see subsection 2.1), I rerun the analyses lagging the QCIndex variable, and its

components Tone, Methodology and Reduced_QC_Index, by one year, and find generally

unchanged results for most specifications. However, Number_Words loads negatively in the

specification with Logaudithours as the dependent variable. I also find a negative association

between QCIndex and Logauditfees, but no association between Number_Words_Org and

Logauditfees in these specifications.

Third, I conduct additional tests on the analysis that uses PartIFinding as the dependent

variable. Notably, because the engagements selected by the PCAOB for inspection are not

randomly chosen, but risk-based (e.g., Olson, 2008; Hanson, 2012; Church and Shefchik, 2012),

it is possible that, only for this particular specification, a selection bias may result in coefficient

bias (Lennox et al. 2012). Consequently, I conduct a robustness test using a bivariate probit

model with selection (Van de Ven and Van Pragg, 1981).36

In the first stage, I model the

probability of selection for inspection of a particular engagement. I then control for this selection

36

This model is similar to the Heckman (1979) model, but with binary dependent variables in the second stage.

38

in the second stage model. An important aspect of using such a model is to identify exclusion

restrictions in the first stage that can convincingly be excluded from the second stage regression

(Little, 1985; Lennox et al., 2012). I identify two exclusion restrictions, based on internal

discussions at the PCAOB.37

Two categories of issuers were less likely to be selected for

inspection for reasons unrelated to risk assessment. These exclusion restrictions are similar to the

ones used in Aobdia (2015a), and load similarly in the first stage regressions. In unreported

results, I still find that all results hold when attempting to control for selection bias.

6.3 Additional analysis of client switches

The results above suggest that auditors with more QC deficiencies have lower quality but still

charge their clients the same amount of fees. An unanswered question remains whether clients

could be more dissatisfied by their audit services. This dissatisfaction could result from a

negative audit committee assessment of the quality provided by the auditor, or from unnecessary

interactions of company management with the engagement team. I test for this idea using an

auditor switching model. I use models (1) to (4) and replace the dependent variable with Switch,

an indicator variable equal to one if the client switches auditors the next year.38


The results of this analysis are presented in Table 8. Panel A presents the results of models

(1) and (2), and Panel B of models (3) and (4). I find positive associations between QC

deficiencies and auditor switch, suggesting that clients using auditors with more QC deficiencies

are more dissatisfied with their work and switch away from their auditor more often. Untabulated

37

I am unable to describe in further details these variables, to preserve the confidentiality of the PCAOB inspection

selection process. 38

The results in this section are robust if the dependent variable is replaced by an indicator variable equal to one if

the client switches auditors the next year and the auditor did not resign from the engagement, as determined by

Audit Analytics.

39

analyses, based on the second column of Panel A, indicate that the probability of switching

increases by approximately 0.7% for one standard deviation increase of Number_Words,

compared with an average probability of switching of 5.0%. The coefficients on

Number_Words_Perf and Number_Words_Org are quite similar from each other, based on the

last two columns of Panel A. Splitting QCIndex between its components in the last two columns

of Panel B, I find that Methodology loads positively, suggesting that deficiencies in the audit

methodology have an influence on the clients’ switching activity.

7. Conclusion

This study examines the association between large audit firm’s QC systems, in particular

organization-level systems, culture and audit methodology, with audit quality and efficiency.

Using a unique dataset obtained from the PCAOB, I find negative associations between QC

deficiencies and audit quality, between audit performance QC deficiencies and audit effort, and

between organization-level QC deficiencies and audit efficiency. Furthermore, I find that

deficiencies in culture and audit methodology represent a large component of this latter

association. Additional evidence suggests that QC deficiency remediation has a positive

influence on audit quality. Further, client issuers are more likely to switch auditors when their

auditor’s QC systems have deficiencies, suggesting more client dissatisfaction with their auditor,

even if their auditors are highly unlikely to communicate to them the nonpublic parts of the

PCAOB reports.

These results contribute to the auditing literature, by documenting a previously suspected, but

never empirically detected, relation between an audit firm’s QC systems and audit quality. They

also contribute to the literature on the economics of culture, suggesting that a company with

deficiencies in its culture suffers from poor performance. Finally, they contribute to the literature

40

on the economics of regulation, and suggest a novel impact of the role of regulation in the form

of improved audit efficiency and audit firm profitability once the firms remediate the

organization-level deficiencies identified by the PCAOB.

41

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Appendix A: Example of Publicly Released Audit Performance Part II Finding from the

2007 Inspection of Deloitte (see pp11 and 12 of PCAOB release No. 104-2008-070A)

“A. Audit Performance

A firm's system of quality control should provide reasonable assurance that the firm's audit work

will meet professional standards and regulatory requirements. Not every deficiency in an audit

indicates that a firm's quality control system is insufficient to provide that assurance, and this

report does not discuss every auditing deficiency observed by the inspection team. On the other

hand, some deficiencies, or repeated instances of a similar deficiency, may indicate a potentially

significant defect in a firm's quality control system even if the deficiency has not resulted in an

insufficiently supported audit opinion. As described below, some deficiencies reported by the

inspection team do suggest that the Firm's system of quality control may in some respects fail to

provide sufficient assurance that the Firm's audit work will meet applicable standards and

requirements.

…

b. Management Estimates

The engagement reviews provide cause for concern that the Firm's system of quality control may

not do enough to assure that the Firm performs appropriate procedures to audit significant

estimates, including evaluating management's assumptions and testing the data supporting the

estimates. In addition to seven engagements […] described in Part I.A, the inspection team

identified eight engagements […] (two of which are also discussed in Part I.A with respect to

different estimates) with deficiencies in the Firm's testing of management estimates. …”

46

Appendix B: Example of Publicly Released Organization-level Part II Finding from the

2012 Inspection of BDO USA (see pp20 and 21 of PCAOB release No. 104-2013-265A)

“Deficiencies in Quality Controls Related to the Firm's Internal Inspection

Program

The PCAOB inspection results continue to indicate that the Firm's internal inspection program

should be improved. * * * *.

…

In 2012, the PCAOB inspection team inspected three audits […] that the internal inspection team

had reviewed, including two audits […] that the internal inspection team had rated as

"unsatisfactory." In each of these three audits, the PCAOB inspection team identified one or

more deficiencies that were not identified by the internal inspectors but are of such significance

that they are included in Part I.A of this report. The inspection team identified a total of six such

deficiencies in these audits that were not identified by the internal inspectors, even though they

had reviewed the relevant area. …”

Appendix C: Example of Publicly Released PCAOB Description of its Procedures to

Analyze the Tone at the Top, from the 2004 Inspection Report of Deloitte (see pB-6 of

PCAOB release No. 104-2005-089)

“The primary objective of the review of Deloitte's "tone at the top" was to assess whether actions

and communications by Deloitte's leadership demonstrate a commitment to audit quality and

compliance with the Act, the rules of the Board, the rules of the SEC and PCAOB standards in

connection with Deloitte's performance of audits, issuance of audit reports, and related matters

involving issuers. Toward that end, the inspection team reviewed and analyzed information at

Deloitte's National Office. Such information included Deloitte's code of conduct; documents

relating to measuring and monitoring audit quality; descriptions of the duties of, and

relationships between and among, Deloitte staff and leadership; results of surveys of staff and

clients; public company audit proposals; internal and external communications from

management; descriptions of Deloitte's financial structure and business plan; and agendas and

minutes of Deloitte's board of directors. In addition, the inspection team interviewed 19 members

of Deloitte's leadership team.

The inspection team conducted interviews at 24 of Deloitte's practice offices to obtain

perspectives on communications from Deloitte's leadership relating to audit quality and tone at

the top. The inspection team interviewed members of the leadership at each of these offices, as

well as certain audit partners and senior managers assigned to engagements that were reviewed.

In addition, the inspection team conducted two focus group meetings in 12 of the practice offices

to assess the participants' understanding of, among other things, the messages conveyed by the

National Office, practice office leadership and their supervisors, and how such messages might

affect their actions on audits, as well as to hear their perspectives on the tone at the top. One

of these focus groups meetings consisted of audit senior managers and audit managers, and the

other was composed of audit senior accountants and audit staff.”

47

Appendix D: Variables Definitions

Variable Definition

Dependent Variables:

Restatement An indicator variable equal to one if financial statements for the year are restated

Meet/Beat An indicator variable equal to one if the ROA (income before extraordinary items

deflated by beginning assets) is positive and less than 3%

PartIFinding An indicator variable equal to one if the inspection of a specific engagement resulted in a

Part I finding

ScaledAccrualsCFO Absolute value of accruals deflated by cash flow from operations

Logauditfees The logarithm of the engagement audit fees, from Audit Analytics

Logaudithours The logarithm of the audit hours spent on the engagement

Logpartnerhours The logarithm of total partner hours spend on the engagement

Logeqrhours The logarithm of the hours spent by the engagement quality review partner

Fees_per_hour Total audit fees divided by total engagement hours

Proportion_Nonauditfees Non-audit fees divided by total audit fees, both variables taken from Audit Analytics

Switch An indicator variable when the client switches auditor between year t and t+1

Explanatory Variables:

Number_Words The number of words in the PCAOB Part II Section of the report that describe QC

issues. For ease of comparison of the coefficients, the distribution is normalized with a

mean of zero and a standard deviation of one

Number_Words_Perf The number of audit-performance related Part II Findings deficiencies. For ease of

comparison of the coefficients, the distribution is normalized with a mean of zero and a

standard deviation of one

Number_Words_Org The total number of words in the PCAOB Part II Section of the report that describe QC

issues less the number of audit-performance related Part II Findings deficiencies. For

ease of comparison of the coefficients, the distribution is normalized with a mean of zero

and a standard deviation of one

QCIndex An index from 0 to 8 built on the PCAOB Part II Findings

Reduced_QC_Index An index from 0 to 6 built on the PCAOB Part II Findings, excluding findings in audit

methodology and tone at the top

Methodology An indicator variable equal to one if Part II Findings in audit methodology are identified

Tone An indicator variable equal to one if tone at the top Part II Findings are identified

Foreign An indicator variable equal to one if foreign affiliates Part II Findings are identified

Partner An indicator variable equal to one if partner management Part II Findings are identified

Internal_Inspection An indicator variable equal to one if internal inspection Part II Findings are identified

Independence An indicator variable equal to one if independence Part II Findings are identified

Client_Acceptance An indicator variable equal to one if client acceptance Part II Findings are identified

Other_Monitoring An indicator variable equal to one if monitoring (excluding internal inspections) Part II

Findings are identified

Proportion_Remediated The proportion of words in the Part II Findings that are not subsequently publicly

released. The variable is set to one if there are no word in the Part II Section that relate to

QC issues

48

Variable Definition

Control Variables:

Client_Importance Total client fees charged by the auditor (audit and non-audit fees) divided by total fees

charged by auditor for all clients during the year

FirstYear An indicator variable equal to one for first year audits

Distressed An indicator variable equal to one for issuers with negative income before extraordinary

items or negative cash flow from operations

ForeignPifo Absolute value of pretax income from foreign operations (PIFO) divided by the absolute

value of pretax income (PI)

Intangi One minus gross PP&E divided by assets

City_Leader An indicator variable equal to one if the auditor office is the largest in the core business

statistical area (CBSA) in terms of fees for the issuers's industry (defined at the two-digit

SIC code)

National_Leader An indicator variable equal to one if the auditor is the largest in terms of fees for the

issuer's industry (defined at the two-digit SIC code)

Office_Size Logarithm of the total office fees charged to clients during the year

Logat Logarithm of assets

CATA Current assets divided by total assets

Quick Current assets less inventories divided by current liabilities

Geoseg Number of geographic segments

Busseg Number of business segments

StdSaleGrowth Standard deviation of the issuer's sales growth, computed over t-3 and t

DecYE An indicator variable equal to one when the fiscal year ends in December

CFOat Issuer's cash flows from operations deflated by beginning assets

Leverage Total debt divided by debt plus stockholder's equity

BTM Book shareholder's equity deflated by fiscal year end market capitalization

AltmanZ Altman Z-score. Defined as [1.2 × (Working Capital/ Assets)] + [1.4 × (Retained

earnings / Assets)] + [3.3 × (Earnings Before Interest and Taxes / Assets)] + [0.6 ×

(Market value of equity / Book value of liabilities) + Sale/Assets]

Litigation An indicator variable equal to one if the firm is in a litigation industry (SIC code

between 2833 and 2836, 8731 and 8734, 3570 and 3577, 7370 and 7374, 3600 and 3674,

or 5200 and 5961)

Length_Relationship Number of years the auditor has continuously audited a given client (from Compustat)

Big4 An indicator variable equal to one if the issuer is audited by a Big 4 auditor

SaleGrowth Year-on-year sales growth of the client firm

Weaknesses An indicator variable equal to one if the issuer reports a material weakness

HiTech An indicator variable equal to one when the firm is in a hi-tech industry (three-digit SIC

code equal to 272, 283, 355, 357, 360, 361, 362, 363, 364, 365, 366, 367, 369, 381, 382,

386, 481, 484, 489, 573, 596, 621, 679, 733, 737, 738, or 873)

49

Table 1: Eight Major organization-level QC Areas Assessed by the PCAOB (Summarized From Public Inspection Reports)

Area Description of the area examined Examples of procedures conducted

Tone at the top Whether actions and communications by the firm’s

leadership demonstrate a commitment to audit quality

and compliance with applicable regulations and

professional standards

Interviews of the firm’s leadership

Review of significant management reports and documents, including the

code of conduct and results of surveys of staff and clients

Interviews at the firm’s practice offices of local leadership and

engagement team members to obtain perspectives on the tone at the top

Focus groups, in practice offices, of lower-level team members

Partner management Whether partner evaluation, compensation,

promotion, termination and staffing practices

encourage audit quality and technical competence

instead of marketing

Assessment of documents related with partner evaluation, compensation,

nomination and termination

Assessment of partner staffing

Partner and firm leadership interviews

Review of some partners’ personnel files

Independence policies Whether the firm complies with independence

requirements, including those related to the provision

of non-audit services

Review of policies, processes and training programs

Interviews of personnel

Sampling of independence consultations

Review of the firm’s business ventures and arrangements

Client acceptance and

retention policies

Whether the firm does not associate with clients

which management lacks integrity, and whether it

undertakes only engagements within its professional

competence

Review of policies and procedures for client acceptance and retention

Sampling of client acceptance and continuance packages

Assessment of whether audit procedures are responsive to risks identified

during the process

Internal inspection

program

Evaluation of the effectiveness of the internal

inspection program to enhance audit quality Analysis of the current year’s inspection program, its findings and the

firm’s evaluation of the results

Review of the qualification and experience of the firm’s internal inspectors

PCAOB Inspection of some internally inspected engagements

Audit policies

procedures and

methodologies, including

training

Whether the design of the firm’s audit methodology,

and the design of training programs of personnel are

adequate to promote audit quality

Review of methods for developing policies and procedures

Review of internal guidance and training materials distributed to audit

personnel

Assessment of whether the firm is responsive to external changes that

could have an impact on its audit methodology

Policies related to

foreign affiliates

Ensure that the audit work performed by the firm’s

foreign affiliates on U.S. issuers is effective Assessment of policies and procedures related to the supervision of foreign

affiliates on U.S. audit clients

Assessment of the audit guidance related to multinational engagements

Assessment of whether employees in foreign affiliates have sufficient

understanding of U.S. GAAP and PCAOB standards

(Other) practice

monitoring

Assessment of the firm’s response to address QC

deficiencies and other monitoring practices Review of the firm’s steps taken to address QC deficiencies

Re-inspection of engagements that previously identified deficiencies

50

Table 2: Descriptive Statistics for Different Measures of QC Deficiencies

This table presents descriptive statistics for Number_Words, its split between Number_Words_Perf and

Number_Words_Org, and for QCIndex, its eight components, and the reduced index, Reduced_QC_Index. All

variables are measured at the audit firm-year level. There are 80 firm-year observations in the dataset, corresponding

to 8 audit firms over a period of 10 years. For ease of comparison of the coefficients on Number_Words,

Number_Words_Perf and Number_Words_Org in subsequent regressions, their distributions are normalized with a

mean of zero and a standard deviation of one.

Variable Observations Mean

Words in Part II Findings Report

Number_Words 80 0.00

Number_Words_Perf 80 0.00

Number_Words_Org 80 0.00

Quality Control Index

Foreign 80 0.41

Partner 80 0.66

Internal_Inspection 80 0.70

Independence 80 0.63

Client_Acceptance 80 0.40

Other_Monitoring 80 0.58

Reduced_QC_Index 80 3.38

Methodology 80 0.73

Tone 80 0.31

QCIndex 80 4.41

51

Table 3: Descriptive Statistics

This table presents the descriptive statistics for the analyses of audit quality, fees and hours used in models (1) to

(4). All variables are defined in Appendix D.

Variable Observations Mean Std 25th

Perc. 50th

Perc. 75th

Perc.

Restatement 27,837 0.11 0.31 0.00 0.00 0.00

Meet/Beat 27,837 0.14 0.34 0.00 0.00 0.00

PartIFinding 2,452 0.23 0.42 0.00 0.00 0.00

ScaledAccrualsCFO 27,837 1.61 4.21 0.32 0.58 1.09

Logauditfees 27,837 13.90 1.12 13.16 13.88 14.61

Logaudithours 13,802 8.70 0.97 8.09 8.69 9.31

Logpartnerhours 13,793 5.86 0.94 5.24 5.78 6.40

Logeqrhours 13,698 3.88 0.70 3.42 3.87 4.33

Fees_per_hour 13,802 235.40 133.96 170.09 210.94 261.72

Proportion_Nonauditfees 27,837 0.23 0.32 0.04 0.13 0.30

Client_Importance 27,837 0.00 0.01 0.00 0.00 0.00

FirstYear 27,837 0.04 0.20 0.00 0.00 0.00

Distressed 27,837 0.35 0.48 0.00 0.00 1.00

ForeignPifo 27,837 0.24 0.44 0.00 0.00 0.31

Intangi 27,837 -0.50 0.41 -0.74 -0.38 -0.18

City_Leader 27,837 0.53 0.50 0.00 1.00 1.00

National_Leader 27,837 0.27 0.44 0.00 0.00 1.00

Office_Size 27,837 17.21 1.44 16.23 17.44 18.27

Logat 27,837 6.37 1.99 5.01 6.32 7.70

CATA 27,837 0.496 0.247 0.301 0.492 0.689

Quick 27,837 2.33 2.82 0.98 1.50 2.58

Geoseg 27,837 2.42 2.21 1.00 2.00 4.00

Busseg 27,837 2.02 1.62 1.00 1.00 3.00

StdSaleGrowth 27,837 0.42 1.49 0.07 0.14 0.28

DecYE 27,837 0.70 0.46 0.00 1.00 1.00

CFOat 27,837 0.06 0.25 0.03 0.09 0.15

Leverage 27,837 0.32 0.45 0.01 0.24 0.48

BTM 27,837 0.43 1.19 0.24 0.43 0.70

AltmanZ 27,837 3.63 10.97 1.53 3.10 5.30

Litigation 27,837 0.37 0.48 0.00 0.00 1.00

Length_Relationship 27,837 9.14 7.85 3.00 7.00 13.00

Big4 27,837 0.84 0.36 1.00 1.00 1.00

SaleGrowth 27,837 0.16 0.65 -0.02 0.08 0.20

Weaknesses 27,837 0.05 0.21 0.00 0.00 0.00

HiTech 27,837 0.40 0.49 0.00 0.00 1.00

Switch 25,675 0.05 0.22 0.00 0.00 0.00

52

Table 4: Audit Quality Results

This table presents the results of models (1) to (4) when using audit quality measures as the dependent variables.

Panels A and B present the results of Model (1) using Restatement and Meet/Beat, and PartIFinding and

ScaledAccrualsCFO as dependent variables, respectively. Panels C and D present the results of Models (2) and (3),

and Panel E the results of Model (4). Variable definitions are provided in Appendix D. The z- or t-statistic (in

parenthesis) is below the coefficient. Standard-errors are clustered at the issuer-level. Significance levels are * 10%,

** 5% and *** 1%.

Panel A: Model (1) with Restatement and Meet/Beat as measures of audit quality

Dependent Variables: Restatement Restatement Meet/Beat Meet/Beat

Number_Words 0.208*** 0.202*** 0.077*** 0.077***

[6.188] [5.905] [2.861] [2.784]

Distressed 0.235*** 0.211***

[3.741] [3.376]

Logat 0.054*** 0.027 0.138*** 0.055***

[3.194] [1.168] [8.786] [2.704]

CFOat 0.200 0.000 0.068 -0.231***

[1.572] [0.001] [0.847] [-2.828]

Leverage 0.245*** 0.219*** 0.565*** 0.309***

[3.891] [3.333] [9.268] [4.791]

BTM 0.174*** 0.163*** 0.436*** 0.346***

[6.051] [5.696] [10.094] [8.167]

Litigation 0.165** 0.115 -0.327*** -0.161**

[2.471] [1.219] [-5.966] [-2.289]

Big4 0.031 0.071 -0.211*** -0.082

[0.310] [0.473] [-2.769] [-0.797]

Client_Importance

-15.515

-5.802

[-1.042]

[-0.622]

FirstYear

-0.182*

-0.047

[-1.677]

[-0.489]

ForeignPifo

0.064

0.989***

[1.053]

[20.455]

Intangi

0.209**

-0.181***

[2.284]

[-2.843]

City_Leader

-0.072

-0.010

[-1.171]

[-0.200]

National_Leader

0.119*

0.003

[1.899]

[0.046]

Office_Size

-0.046*

-0.027

[-1.687]

[-1.225]

CATA

-0.460***

-1.456***

[-2.749]

[-9.376]

Quick

-0.039***

0.011

[-2.816]

[0.817]

Geoseg

0.023

-0.113***

[1.511]

[-8.451]

Busseg

0.021

0.052***

[1.026]

[3.336]

StdSaleGrowth

-0.049*

-0.170***

[-1.852]

[-3.120]

53


DecYE

-0.123*

-0.013

[-1.810]

[-0.231]

AltmanZ

0.003

-0.004**

[1.096]

[-2.040]

Length_Relationship

-0.000

-0.011***

[-0.033]

[-2.968]

SaleGrowth

0.025

-0.309***

[0.664]

[-3.922]

Weaknesses

0.882***

0.240***

[11.004]

[2.808]

HiTech

0.116

-0.068

[1.167]

[-0.907]

Year Fixed Effects Yes Yes Yes Yes

Observations 27,837 27,837 27,837 27,837

Pseudo R-squared 0.025 0.038 0.041 0.087

Panel B: Model (1) with PartIFinding and ScaledAccrualsCFO as measures of audit quality

Dependent Variables: PartIFinding PartIFinding ScaledAccrualsCFO ScaledAccrualsCFO

Number_Words 0.205*** 0.220*** -0.043 -0.032

[3.148] [3.288] [-1.210] [-0.901]

Distressed 0.010 0.027 3.424*** 3.429***

[0.076] [0.197] [38.021] [38.043]

Logat 0.011 -0.020 -0.001 -0.023

[0.288] [-0.466] [-0.080] [-1.140]

CFOat -0.566* -0.673* 2.185*** 2.180***

[-1.701] [-1.714] [16.023] [14.534]

Leverage 0.203 0.170 0.253*** 0.176**

[1.564] [1.213] [3.094] [2.126]

BTM 0.157** 0.144** -0.132** -0.126**

[2.434] [2.212] [-2.445] [-2.378]

Litigation -0.325*** -0.413*** -0.033 -0.001

[-2.855] [-2.856] [-0.551] [-0.018]

Big4 -0.703*** -0.521*** -0.092 0.098

[-5.080] [-3.009] [-0.985] [0.783]

Client_Importance

5.030

14.545

[0.919]

[1.149]

FirstYear

0.151

0.211

[0.859]

[1.433]

ForeignPifo

0.054

-0.433***

[0.429]

[-7.399]

Intangi

0.349**

0.161**

[2.349]

[2.077]

City_Leader

-0.126

-0.004

[-1.117]

[-0.069]

National_Leader

0.020

-0.028

[0.149]

[-0.482]

Office_Size

-0.034

-0.007

[-0.766]

[-0.323]

CATA

-0.624**

-0.185

54


[-2.169]

[-1.183]

Quick

-0.076**

-0.053***

[-2.007]

[-3.833]

Geoseg

-0.011

0.053***

[-0.455]

[3.640]

Busseg

0.035

0.007

[1.092]

[0.468]

StdSaleGrowth

-0.013

-0.062***

[-0.253]

[-3.619]

DecYE

-0.122

-0.028

[-1.061]

[-0.498]

AltmanZ

0.013

-0.009

[0.957]

[-1.512]

Length_Relationship

-0.003

-0.006**

[-0.383]

[-2.117]

SaleGrowth

0.042

-0.061

[0.401]

[-1.212]

Weaknesses

-0.056

0.455***

[-0.267]

[2.901]

HiTech

0.141

0.028

[0.968]

[0.348]


Observations 2,452 2,452 27,837 27,837

Pseudo R-squared 0.093 0.101

Adjusted R-squared 0.128 0.134

Panel C: Model (2) and (3) with Restatement and Meet/Beat as measures of audit quality


Number_Words_Perf 0.228*** 0.227*** 0.008 0.019

[6.165] [6.304] [0.264] [0.621]

Number_Words_Org 0.006

0.070***

[0.216]

[2.883]

QCIndex

0.007

0.044***

[0.363]

[2.751]

Full Set of Controls Yes Yes Yes Yes


Observations 27,837 27,837 27,837 27,837

Pseudo R-squared 0.039 0.039 0.087 0.087

55

Panel D: Model (2) and (3) with PartIFinding and ScaledAccrualsCFO as measures of audit quality


Number_Words_Perf 0.100 0.130** -0.051 -0.064*

[1.415] [1.962] [-1.403] [-1.773]

Number_Words_Org 0.137**

0.011

[2.154]

[0.393]

QCIndex

0.086**

0.033*

[2.288]

[1.801]



Observations 2,452 2,452 27,837 27,837

Pseudo R-squared 0.101 0.101

Adjusted R-squared

0.134 0.134

Panel E: Results of Model (4)

Dependent Variables: Restatement Meet/Beat PartIFinding ScaledAccrualsCFO

Number_Words_Perf 0.208*** -0.000 0.135* -0.050

[5.459] [-0.003] [1.860] [-1.333]

Reduced_QC_Index -0.003 0.030* 0.095** 0.044**

[-0.134] [1.824] [2.260] [2.128]

Tone 0.026 0.098* -0.014 -0.026

[0.430] [1.838] [-0.093] [-0.408]

Methodology 0.139** 0.126** 0.233* -0.009

[2.328] [2.426] [1.668] [-0.137]



Observations 27,837 27,837 2,452 27,837

Pseudo R-squared 0.040 0.087 0.102

Adjusted R-squared

0.134

56

Table 5: Audit Fee Results

This table presents the results of models (1) to (4) when using audit fee measures as the dependent variables. The

specifications use Logauditfees and Proportion_Nonauditfees as dependent variables. Panel A shows the results of

Model (1), Panel B the results of Models (2) and (3), and Panel C the results of Model (4). Variable definitions are

provided in Appendix D. The t-statistic (in parenthesis) is below the coefficient. Standard-errors are clustered at the

issuer-level. Significance levels are * 10%, ** 5% and *** 1%.

Panel A: Results of Model (1)

Dependent Variables: Logauditfees Logauditfees Proportion_Nonauditfees Proportion_Nonauditfees

Number_Words 0.021*** 0.002 0.018*** 0.019***

[3.330] [0.390] [6.401] [6.752]

Distressed 0.143*** 0.093*** -0.017*** -0.010*

[10.369] [8.501] [-2.992] [-1.761]

Logat 0.481*** 0.455*** 0.012*** 0.011***

[87.502] [78.971] [6.060] [5.036]

CFOat -0.216*** -0.167*** 0.026** 0.038***

[-8.124] [-7.338] [2.504] [3.462]

Leverage -0.056*** 0.016 -0.002 0.003

[-3.249] [1.031] [-0.381] [0.527]

BTM -0.044*** -0.036*** -0.003 -0.003

[-6.141] [-6.109] [-1.326] [-1.366]

Litigation 0.045** -0.073*** -0.000 -0.014

[2.500] [-3.857] [-0.030] [-1.635]

Big4 0.246*** 0.052* 0.001 0.013

[10.741] [1.912] [0.073] [1.151]

Client_Importance

5.517**

0.650

[2.014]

[1.142]

FirstYear

-0.149***

-0.046***

[-8.661]

[-4.339]

ForeignPifo

0.222***

0.004

[17.004]

[0.692]

Intangi

0.199***

0.069***

[10.722]

[8.288]

City_Leader

0.077***

-0.014**

[6.354]

[-2.282]

National_Leader

0.007

0.007

[0.499]

[1.079]

Office_Size

0.092***

-0.006**

[16.755]

[-2.151]

CATA

0.438***

-0.053***

[12.120]

[-3.062]

Quick

-0.032***

0.000

[-13.586]

[0.403]

Geoseg

0.040***

0.001

[12.333]

[0.839]

Busseg

0.027***

-0.002

[6.270]

[-1.018]

StdSaleGrowth

-0.008**

0.002

[-2.509]

[0.730]

DecYE

0.069***

-0.037***

57


[4.566]

[-5.015]

AltmanZ

-0.004***

-0.001***

[-4.373]

[-2.784]

Length_Relationship

0.001

0.001

[1.198]

[1.343]

SaleGrowth

-0.015***

0.003

[-2.756]

[0.810]

Weaknesses

0.489***

-0.059***

[23.754]

[-5.295]

HiTech

0.117***

0.005

[5.942]

[0.557]


Observations 27,837 27,837 27,837 27,837

Adjusted R-squared 0.728 0.806 0.043 0.054

Panel B: Results of Models (2) and (3)


Number_Words_Perf 0.002 0.004 0.010*** 0.010***

[0.389] [0.744] [3.040] [2.938]

Number_Words_Org 0.000

0.010***

[0.083]

[3.907]

QCIndex

-0.003

0.010***

[-1.009]

[5.373]



Observations 27,837 27,837 27,837 27,837


Panel C: Results of Model (4)

Dependent Variables: Logauditfees Proportion_Nonauditfees

Number_Words_Perf 0.003 0.011***

[0.467] [3.034]

Reduced_QC_Index -0.004 0.011***

[-1.354] [5.802]

Tone -0.006 0.005

[-0.764] [0.824]

Methodology 0.014 0.006

[1.407] [1.249]

Full Set of Controls Yes Yes

Year Fixed Effects Yes Yes

Observations 27,837 27,837


58

Table 6: Audit Hours Results

This table presents the results of models (2) to (4) when using audit hour measures as the dependent variables.

Model (1) is not tabulated because the results are inconclusive and only reflect different forces as shown in the other

models. Panels A and B present the results of Models (2) and (3) using Logaudithours and Logpartnerhours, and

Logeqrhours and Fees_per_hour as dependent variables, respectively. Panel C presents the results of Model (4).

Variable definitions are provided in Appendix D. The t-statistic (in parenthesis) is below the coefficient. Standard-

errors are clustered at the issuer-level. Significance levels are * 10%, ** 5% and *** 1%.

Panel A: Models (2) and (3) Results With Logaudithours and Logpartnerhours as dependent variables

Dependent Variables: Logaudithours Logaudithours Logpartnerhours Logpartnerhours

Number_Words_Perf -0.053*** -0.042*** -0.111*** -0.113***

[-7.289] [-6.051] [-13.335] [-14.740]

Number_Words_Org 0.040*** 0.037***

[8.021] [5.605]

QCIndex 0.024*** 0.055***

[6.753] [12.229]

Distressed 0.110*** 0.111*** 0.134*** 0.137***

[7.765] [7.816] [8.328] [8.585]

Logat 0.377*** 0.377*** 0.339*** 0.338***

[59.050] [59.106] [48.283] [48.704]

CFOat -0.014 -0.014 -0.097*** -0.093***

[-0.495] [-0.508] [-2.947] [-2.829]

Leverage 0.032* 0.031* 0.047** 0.048**

[1.700] [1.696] [2.280] [2.332]

BTM -0.016** -0.016** -0.024*** -0.024***

[-2.358] [-2.351] [-3.373] [-3.319]

Litigation -0.081*** -0.081*** -0.079*** -0.081***

[-3.961] [-3.973] [-3.484] [-3.598]

Big4 0.261*** 0.246*** 0.305*** 0.285***

[8.568] [8.073] [8.843] [8.308]

Client_Importance 3.458 3.511 2.192 2.299

[1.373] [1.395] [0.913] [0.952]

FirstYear 0.156*** 0.156*** 0.069** 0.073**

[6.321] [6.348] [2.210] [2.366]

ForeignPifo 0.186*** 0.186*** 0.156*** 0.155***

[11.210] [11.242] [8.533] [8.588]

Intangi 0.147*** 0.149*** 0.154*** 0.158***

[7.235] [7.299] [6.609] [6.847]

City_Leader 0.026* 0.027* 0.022 0.021

[1.737] [1.821] [1.283] [1.262]

National_Leader 0.009 0.011 -0.000 -0.000

[0.571] [0.658] [-0.010] [-0.009]

Office_Size 0.060*** 0.061*** 0.042*** 0.042***

[8.847] [8.991] [5.605] [5.629]

CATA 0.417*** 0.415*** 0.336*** 0.334***

[9.778] [9.763] [6.894] [6.928]

Quick -0.036*** -0.036*** -0.024*** -0.024***

[-13.049] [-13.046] [-8.690] [-8.697]

Geoseg 0.035*** 0.035*** 0.025*** 0.025***

[9.911] [9.906] [6.248] [6.308]

59

Busseg 0.024*** 0.024*** 0.018*** 0.017***

[5.075] [5.065] [3.310] [3.230]

StdSaleGrowth -0.005 -0.004 -0.005 -0.005

[-1.067] [-0.984] [-1.068] [-1.058]

DecYE 0.018 0.018 0.020 0.021

[1.048] [1.032] [1.039] [1.135]

AltmanZ -0.005*** -0.005*** -0.005*** -0.005***

[-3.335] [-3.309] [-4.678] [-4.563]

Length_Relationship -0.001 -0.001 -0.002* -0.002*

[-1.038] [-1.023] [-1.802] [-1.649]

SaleGrowth -0.030*** -0.030*** -0.011 -0.011

[-3.574] [-3.600] [-0.940] [-0.893]

Weaknesses 0.368*** 0.366*** 0.470*** 0.465***

[10.929] [10.856] [12.371] [12.205]

HiTech 0.046** 0.046** 0.082*** 0.085***

[2.111] [2.122] [3.313] [3.504]


Observations 13,802 13,802 13,793 13,793


Panel B: Models (2) and (3) Results With Logeqrhours and Fees_per_hour as Dependent Variables

Dependent Variables: Logeqrhours Logeqrhours Fees_per_hour Fees_per_hour

Number_Words_Perf -0.208*** -0.186*** 14.408*** 10.884***

[-28.591] [-27.039] [8.198] [6.954]

Number_Words_Other 0.098*** -10.477***

[18.528] [-8.294]

QCIndex 0.075*** -5.020***

[18.943] [-5.675]

Distressed 0.129*** 0.132*** -1.945 -2.081

[8.917] [9.145] [-0.606] [-0.647]

Logat 0.201*** 0.200*** 15.101*** 15.108***

[33.366] [33.595] [11.079] [11.073]

CFOat -0.075** -0.074** -37.005*** -36.733***

[-2.571] [-2.515] [-5.855] [-5.855]

Leverage 0.077*** 0.078*** -3.012 -2.933

[4.671] [4.741] [-0.966] [-0.940]

BTM -0.004 -0.004 -4.270*** -4.290***

[-0.807] [-0.752] [-3.590] [-3.603]

Litigation -0.008 -0.008 -6.404 -6.422

[-0.378] [-0.423] [-1.427] [-1.432]

Big4 0.089*** 0.045 -49.408*** -45.491***

[3.033] [1.535] [-8.319] [-7.639]

Client_Importance 1.591 1.744 357.977 346.499

[0.866] [0.950] [1.174] [1.131]

FirstYear 0.113*** 0.116*** -62.169*** -62.194***

[3.722] [3.841] [-10.273] [-10.258]

ForeignPifo 0.066*** 0.066*** 13.986*** 13.894***

[4.135] [4.190] [3.705] [3.682]

Intangi 0.091*** 0.095*** 14.212*** 14.042***

[4.323] [4.552] [3.582] [3.537]

60

City_Leader -0.015 -0.013 7.475** 7.086**

[-1.003] [-0.869] [2.304] [2.185]

National_Leader -0.029* -0.027* -4.704 -5.117

[-1.841] [-1.686] [-1.465] [-1.593]

Office_Size -0.010 -0.008 5.717*** 5.466***

[-1.418] [-1.211] [3.882] [3.714]

CATA 0.202*** 0.198*** 8.369 8.629

[4.756] [4.717] [0.977] [1.008]

Quick -0.015*** -0.015*** 1.289*** 1.269***

[-5.790] [-5.744] [2.645] [2.598]

Geoseg 0.014*** 0.014*** 0.828 0.849

[3.806] [3.792] [1.091] [1.119]

Busseg 0.007 0.006 1.874* 1.869*

[1.393] [1.307] [1.745] [1.742]

StdSaleGrowth -0.002 -0.001 -1.510** -1.619**

[-0.364] [-0.166] [-2.290] [-2.461]

DecYE 0.057*** 0.057*** 1.745 1.898

[3.387] [3.420] [0.498] [0.543]

AltmanZ -0.004*** -0.004*** -0.314*** -0.316***

[-4.357] [-4.242] [-2.709] [-2.719]

Length_Relationship -0.002** -0.002** 0.518** 0.521**

[-2.347] [-2.240] [2.324] [2.341]

SaleGrowth -0.003 -0.003 3.373 3.522

[-0.332] [-0.385] [1.442] [1.501]

Weaknesses 0.397*** 0.391*** -5.203 -4.892

[11.657] [11.475] [-0.583] [-0.547]

HiTech 0.078*** 0.081*** 17.517*** 17.606***

[3.526] [3.656] [3.780] [3.801]


Observations 13,698 13,698 13,802 13,802


Panel C: Model (4) Results

Dependent Variables: Logaudithours Logpartnerhours Logeqrhours Fees_per_hour

Number_Words_Perf -0.071*** -0.133*** -0.229*** 18.522***

[-8.829] [-14.468] [-27.969] [10.979]

Reduced_QC_Index -0.000 0.038*** 0.039*** 1.386

[-0.085] [7.232] [8.899] [1.134]

Tone 0.125*** 0.136*** 0.279*** -33.041***

[10.402] [8.003] [19.761] [-10.841]

Methodology 0.092*** 0.075*** 0.048*** -18.401***

[6.553] [4.928] [3.458] [-5.574]



Observations 13,802 13,793 13,698 13,802


61

Table 7: Remediation, Audit Quality and Audit Hours

This table presents the results of model (1) augmented with an additional explanatory variable,

Proportion_Remediated, equal to the proportion of words in the Part II Findings not publicly released, and measured

for the corresponding issuer fiscal year end after the corresponding report is released and before the next PCAOB

report is released. Panel A reports the results with measures of audit quality as the dependent variables, Panel B with

audit fee variables, and Panel C with audit hour variables. The coefficients on the control variables are not reported

for brevity. Variable definitions are provided in Appendix D. The t- or z- statistic (in parenthesis) is below the

coefficient. Standard-errors are clustered at the issuer-level. Significance levels are * 10%, ** 5% and *** 1%.

Panel A: Results With audit quality measures as dependent variables

Dependent Variables: Restatement Meet/Beat PartIFinding ScaledAccrualsCFO

Number_Words 0.189*** 0.074*** 0.157** -0.032

[5.433] [2.630] [2.219] [-0.853]

Proportion_Remediated -0.401** 0.011 -2.454*** 0.135

[-2.284] [0.072] [-5.369] [0.623]



Observations 23,572 23,572 2,089 23,572

Pseudo R-squared 0.032 0.085 0.126

Adjusted R-squared

0.136

Panel B: Results With audit fee measures as dependent variables

Dependent Variables: Logauditfees Proportion_Nonauditfees

Number_Words 0.004 0.013***

[0.856] [4.716]

Proportion_Remediated -0.046** 0.008

[-2.164] [0.646]

Full Set of Controls Yes Yes

Year Fixed Effects Yes Yes

Observations 23,572 23,572


Panel C: Results With audit hour measures as dependent variables

Dependent Variables: Logaudithours Logpartnerhours Logeqrhours Fees_per_hour

Number_Words -0.003 -0.056*** -0.061*** 1.192

[-0.580] [-7.487] [-9.684] [0.934]

Proportion_Remediated -0.075*** -0.135*** 0.129*** 16.750***

[-3.235] [-4.555] [4.237] [3.399]



Observations 13,802 13,793 13,698 13,802


62

Table 8: Client Switches

This table presents the results of models (1) to (4) with client switching (Switch) as the dependent variable. Panel A

presents the results of models (1) and (2) and Panel B the results of models (3) and (4). Variable definitions are

provided in Appendix D. The z-statistic (in parenthesis) is below the coefficient. Standard-errors are clustered at the

issuer-level. Significance levels are * 10%, ** 5% and *** 1%.

Panel A: Models (1) and (2) Results

Dependent Variables: Switch Switch Switch Switch

Number_Words 0.243*** 0.232***

[5.019] [4.723]

Number_Words_Perf

0.130*** 0.127***

[2.646] [2.584]

Number_Words_Org

0.133** 0.124**

[2.403] [2.248]

Distressed 0.497*** 0.458*** 0.497*** 0.458***

[6.880] [6.227] [6.878] [6.224]

Logat -0.387*** -0.480*** -0.387*** -0.480***

[-18.072] [-18.581] [-18.068] [-18.579]

CFOat 0.134 0.058 0.134 0.058

[1.305] [0.514] [1.304] [0.513]

Leverage 0.194*** 0.154** 0.194*** 0.154**

[2.691] [2.097] [2.692] [2.098]

BTM 0.172*** 0.162*** 0.172*** 0.162***

[3.459] [3.295] [3.458] [3.294]

Litigation -0.194*** -0.146* -0.194*** -0.146*

[-3.063] [-1.735] [-3.062] [-1.736]

Big4 0.165* 0.089 0.167* 0.091

[1.870] [0.805] [1.889] [0.819]

Client_Importance

3.165***

3.139***

[2.907]

[2.865]

FirstYear

-0.927***

-0.927***

[-5.125]

[-5.125]

ForeignPifo

0.131*

0.131*

[1.778]

[1.777]

Intangi

0.092

0.092

[1.116]

[1.116]

City_Leader

-0.032

-0.032

[-0.501]

[-0.498]

National_Leader

0.152**

0.152**

[2.094]

[2.096]

Office_Size

0.029

0.029

[1.106]

[1.119]

CATA

-0.705***

-0.705***

[-4.286]

[-4.284]

Quick

-0.007

-0.007

[-0.542]

[-0.543]

Geoseg

0.013

0.013

[0.844]

[0.843]

Busseg

0.057***

0.057***

[2.697]

[2.695]

63


StdSaleGrowth

-0.015

-0.015

[-0.750]

[-0.750]

DecYE

-0.221***

-0.222***

[-3.584]

[-3.590]

AltmanZ

-0.002

-0.002

[-1.109]

[-1.108]

Length_Relationship

0.002

0.002

[0.420]

[0.421]

SaleGrowth

-0.039

-0.039

[-0.807]

[-0.805]

Weaknesses

1.141***

1.141***

[12.382]

[12.384]

HiTech

-0.010

-0.010

[-0.115]

[-0.113]


Observations 25,675 25,675 25,675 25,675

Pseudo R-squared 0.101 0.123 0.101 0.123

Panel B: Models (3) and (4) Results


Number_Words_Perf 0.142*** 0.136*** 0.111** 0.105*

[2.721] [2.597] [2.050] [1.915]

QCIndex 0.090*** 0.080***

[3.349] [3.000]

Reduced_QC_Index

0.071** 0.062**

[2.503] [2.190]

Methodology

0.265*** 0.247***

[3.036] [2.803]

Tone

0.113 0.111

[1.450] [1.419]

Full Set of Controls No Yes No Yes

Reduced Set of Controls Yes No Yes No


Observations 25,675 25,675 25,675 25,675

Pseudo R-squared 0.101 0.123 0.102 0.123

64

Figure 1: Description of a PCAOB Inspection of a Firm’s QC Systems

This figure describes a typical inspection of an audit firm’s QC systems for a large audit firm. Eventually two types

of QC deficiencies are identified. Top-down, organization-level deficiencies corresponding from analyses conducted

at the National Offices in conjunction with inferences from the review of specific audit engagements and interviews

at practice offices. And bottom-up, audit performance deficiencies corresponding to similar deficiencies identified in

the inspections of individual engagements.

Figure 2: Typical Inspection Process Timeline

Year t-1 Year t Year t+1

End of Fiscal Year t-1

End of Audit t-1

Year t PCAOBInspection Fieldwork generally fromMarch to November• Year t-1 audits inspected• QC systems inspection

uses information from both t-1 and t

Release of PCAOB Reportcorresponding toYear t inspection

One Year Remediation PeriodFrom release date of the PCAOB Report

Documents

Does the Organization and Culture of the Largest Audit ... · 1 Does the Organization and Culture of the Largest Audit Firms Influence their Audit Quality and Efficiency? Daniel Aobdia