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.
1
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.
2
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).
3
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
4
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.”
5
(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.
6
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.
7
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.
8
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
12
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.
9
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
13
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.
10
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 organizationlevel 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 practicaloriented 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
11
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.
14
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.
12
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
13
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.
14
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.
15
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.
16
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).
17
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.
18
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).
19
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
20
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 nonaudit 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…”
21
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
22
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
23
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.
24
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.
25
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.
(Insert Table 2 About Here)
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
26
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
27
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
Audit_Qualityi,t or Logauditfeesi,t or Log(Hours) i,t = α + β1.Number_Words_Perfi,t +
β2.QCIndexi,t + γ.Controlsi,t +Year Fixed Effects+ εi,t,
(3)
and
Audit_Qualityi,t or Logauditfeesi,t or Log(Hours) i,t = α + β1.Number_Words_Perfi,t +
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,
28
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.
29
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 firstyear 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
30
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.
(Insert Table 3 About Here)
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.
31
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%.
(Insert Table 4 About Here)
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
32
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
33
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.
(Insert Table 5 About Here)
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.
(Insert Table 6 About Here)
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
34
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
35
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.
(Insert Table 7 About Here)
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.
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.
34
36
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.
37
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
(Insert Table 8 About Here)
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.
38
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
39
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.
40
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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.
…”
45
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.”
46
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
ScaledAccrualsCFO
An indicator variable equal to one if the ROA (income before extraordinary items
deflated by beginning assets) is positive and less than 3%
An indicator variable equal to one if the inspection of a specific engagement resulted in a
Part I finding
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
PartIFinding
Explanatory Variables:
Number_Words
Number_Words_Perf
Number_Words_Org
QCIndex
Reduced_QC_Index
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
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
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
An index from 0 to 8 built on the PCAOB Part II Findings
Methodology
An index from 0 to 6 built on the PCAOB Part II Findings, excluding findings in audit
methodology and tone at the top
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
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
Proportion_Remediated
47
Variable
Definition
Control Variables:
Client_Importance
FirstYear
Distressed
ForeignPifo
Intangi
City_Leader
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
An indicator variable equal to one for first year audits
An indicator variable equal to one for issuers with negative income before extraordinary
items or negative cash flow from operations
Absolute value of pretax income from foreign operations (PIFO) divided by the absolute
value of pretax income (PI)
One minus gross PP&E divided by assets
Office_Size
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)
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)
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
Length_Relationship
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]
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)
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)
National_Leader
Litigation
48
Table 1: Eight Major organization-level QC Areas Assessed by the PCAOB (Summarized From Public Inspection Reports)
Area
Tone at the top
Description of the area examined
Whether actions and communications by the firm’s
leadership demonstrate a commitment to audit quality
and compliance with applicable regulations and
professional standards
Partner management
Whether partner evaluation, compensation,
promotion, termination and staffing practices
encourage audit quality and technical competence
instead of marketing
Independence policies
Whether the firm complies with independence
requirements, including those related to the provision
of non-audit services
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
Evaluation of the effectiveness of the internal
inspection program to enhance audit quality
Internal inspection
program
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
Policies related to
foreign affiliates
Ensure that the audit work performed by the firm’s
foreign affiliates on U.S. issuers is effective
(Other) practice
monitoring
Assessment of the firm’s response to address QC
deficiencies and other monitoring practices
Examples of procedures conducted
 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
 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
 Review of policies, processes and training programs
 Interviews of personnel
 Sampling of independence consultations
 Review of the firm’s business ventures and arrangements
 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
 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
 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
 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
 Review of the firm’s steps taken to address QC deficiencies
 Re-inspection of engagements that previously identified deficiencies
49
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
Number_Words
80
0.00
Number_Words_Perf
80
0.00
Number_Words_Org
80
0.00
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
Methodology
80
80
3.38
0.73
Tone
80
0.31
QCIndex
80
4.41
Words in Part II Findings Report
Quality Control Index
50
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
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
PartIFinding
51
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:
Number_Words
Distressed
Logat
CFOat
Leverage
BTM
Litigation
Big4
Client_Importance
FirstYear
ForeignPifo
Intangi
City_Leader
National_Leader
Office_Size
CATA
Quick
Geoseg
Busseg
StdSaleGrowth
Restatement
0.208***
[6.188]
0.235***
[3.741]
0.054***
[3.194]
0.200
[1.572]
0.245***
[3.891]
0.174***
[6.051]
0.165**
[2.471]
0.031
[0.310]
Restatement
0.202***
[5.905]
0.211***
[3.376]
0.027
[1.168]
0.000
[0.001]
0.219***
[3.333]
0.163***
[5.696]
0.115
[1.219]
0.071
[0.473]
-15.515
[-1.042]
-0.182*
[-1.677]
0.064
[1.053]
0.209**
[2.284]
-0.072
[-1.171]
0.119*
[1.899]
-0.046*
[-1.687]
-0.460***
[-2.749]
-0.039***
[-2.816]
0.023
[1.511]
0.021
[1.026]
-0.049*
[-1.852]
52
Meet/Beat
0.077***
[2.861]
Meet/Beat
0.077***
[2.784]
0.138***
[8.786]
0.068
[0.847]
0.565***
[9.268]
0.436***
[10.094]
-0.327***
[-5.966]
-0.211***
[-2.769]
0.055***
[2.704]
-0.231***
[-2.828]
0.309***
[4.791]
0.346***
[8.167]
-0.161**
[-2.289]
-0.082
[-0.797]
-5.802
[-0.622]
-0.047
[-0.489]
0.989***
[20.455]
-0.181***
[-2.843]
-0.010
[-0.200]
0.003
[0.046]
-0.027
[-1.225]
-1.456***
[-9.376]
0.011
[0.817]
-0.113***
[-8.451]
0.052***
[3.336]
-0.170***
[-3.120]
Dependent Variables:
DecYE
Restatement
AltmanZ
Length_Relationship
SaleGrowth
Weaknesses
HiTech
Year Fixed Effects
Observations
Pseudo R-squared
Yes
27,837
0.025
Restatement
-0.123*
[-1.810]
0.003
[1.096]
-0.000
[-0.033]
0.025
[0.664]
0.882***
[11.004]
0.116
[1.167]
Yes
27,837
0.038
Meet/Beat
Yes
27,837
0.041
Meet/Beat
-0.013
[-0.231]
-0.004**
[-2.040]
-0.011***
[-2.968]
-0.309***
[-3.922]
0.240***
[2.808]
-0.068
[-0.907]
Yes
27,837
0.087
Panel B: Model (1) with PartIFinding and ScaledAccrualsCFO as measures of audit quality
Dependent Variables:
Number_Words
Distressed
Logat
CFOat
Leverage
BTM
Litigation
Big4
Client_Importance
FirstYear
ForeignPifo
Intangi
City_Leader
National_Leader
Office_Size
CATA
PartIFinding
0.205***
[3.148]
0.010
[0.076]
0.011
[0.288]
-0.566*
[-1.701]
0.203
[1.564]
0.157**
[2.434]
-0.325***
[-2.855]
-0.703***
[-5.080]
PartIFinding
0.220***
[3.288]
0.027
[0.197]
-0.020
[-0.466]
-0.673*
[-1.714]
0.170
[1.213]
0.144**
[2.212]
-0.413***
[-2.856]
-0.521***
[-3.009]
5.030
[0.919]
0.151
[0.859]
0.054
[0.429]
0.349**
[2.349]
-0.126
[-1.117]
0.020
[0.149]
-0.034
[-0.766]
-0.624**
53
ScaledAccrualsCFO
-0.043
[-1.210]
3.424***
[38.021]
-0.001
[-0.080]
2.185***
[16.023]
0.253***
[3.094]
-0.132**
[-2.445]
-0.033
[-0.551]
-0.092
[-0.985]
ScaledAccrualsCFO
-0.032
[-0.901]
3.429***
[38.043]
-0.023
[-1.140]
2.180***
[14.534]
0.176**
[2.126]
-0.126**
[-2.378]
-0.001
[-0.018]
0.098
[0.783]
14.545
[1.149]
0.211
[1.433]
-0.433***
[-7.399]
0.161**
[2.077]
-0.004
[-0.069]
-0.028
[-0.482]
-0.007
[-0.323]
-0.185
Dependent Variables:
PartIFinding
Quick
Geoseg
Busseg
StdSaleGrowth
DecYE
AltmanZ
Length_Relationship
SaleGrowth
Weaknesses
HiTech
Year Fixed Effects
Observations
Pseudo R-squared
Adjusted R-squared
Yes
2,452
0.093
PartIFinding
[-2.169]
-0.076**
[-2.007]
-0.011
[-0.455]
0.035
[1.092]
-0.013
[-0.253]
-0.122
[-1.061]
0.013
[0.957]
-0.003
[-0.383]
0.042
[0.401]
-0.056
[-0.267]
0.141
[0.968]
Yes
2,452
0.101
ScaledAccrualsCFO
Yes
27,837
ScaledAccrualsCFO
[-1.183]
-0.053***
[-3.833]
0.053***
[3.640]
0.007
[0.468]
-0.062***
[-3.619]
-0.028
[-0.498]
-0.009
[-1.512]
-0.006**
[-2.117]
-0.061
[-1.212]
0.455***
[2.901]
0.028
[0.348]
Yes
27,837
0.128
0.134
Panel C: Model (2) and (3) with Restatement and Meet/Beat as measures of audit quality
Dependent Variables:
Number_Words_Perf
Number_Words_Org
Restatement
0.228***
[6.165]
0.006
[0.216]
QCIndex
Full Set of Controls
Year Fixed Effects
Observations
Pseudo R-squared
Yes
Yes
27,837
0.039
54
Restatement
0.227***
[6.304]
0.007
[0.363]
Yes
Yes
27,837
0.039
Meet/Beat
0.008
[0.264]
0.070***
[2.883]
Yes
Yes
27,837
0.087
Meet/Beat
0.019
[0.621]
0.044***
[2.751]
Yes
Yes
27,837
0.087
Panel D: Model (2) and (3) with PartIFinding and ScaledAccrualsCFO as measures of audit quality
Dependent Variables:
Number_Words_Perf
Number_Words_Org
PartIFinding
0.100
[1.415]
0.137**
[2.154]
QCIndex
Full Set of Controls
Year Fixed Effects
Observations
Pseudo R-squared
Adjusted R-squared
Yes
Yes
2,452
0.101
PartIFinding
0.130**
[1.962]
ScaledAccrualsCFO
-0.051
[-1.403]
0.011
[0.393]
0.086**
[2.288]
Yes
Yes
2,452
0.101
ScaledAccrualsCFO
-0.064*
[-1.773]
Yes
Yes
27,837
0.033*
[1.801]
Yes
Yes
27,837
0.134
0.134
Panel E: Results of Model (4)
Dependent Variables:
Number_Words_Perf
Reduced_QC_Index
Restatement
Meet/Beat
PartIFinding
ScaledAccrualsCFO
0.208***
-0.000
0.135*
-0.050
[5.459]
[-0.003]
[1.860]
[-1.333]
-0.003
0.030*
0.095**
0.044**
[-0.134]
[1.824]
[2.260]
[2.128]
0.026
0.098*
-0.014
-0.026
[0.430]
[1.838]
[-0.093]
[-0.408]
0.139**
0.126**
0.233*
-0.009
[2.328]
[2.426]
[1.668]
[-0.137]
Full Set of Controls
Yes
Yes
Yes
Yes
Year Fixed Effects
Yes
Yes
Yes
Yes
Observations
27,837
27,837
2,452
27,837
Pseudo R-squared
0.040
0.087
0.102
Tone
Methodology
Adjusted R-squared
0.134
55
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:
Number_Words
Distressed
Logat
CFOat
Leverage
BTM
Litigation
Big4
Client_Importance
FirstYear
ForeignPifo
Intangi
City_Leader
National_Leader
Office_Size
CATA
Quick
Geoseg
Busseg
StdSaleGrowth
DecYE
Logauditfees
0.021***
[3.330]
0.143***
[10.369]
0.481***
[87.502]
-0.216***
[-8.124]
-0.056***
[-3.249]
-0.044***
[-6.141]
0.045**
[2.500]
0.246***
[10.741]
Logauditfees
0.002
[0.390]
0.093***
[8.501]
0.455***
[78.971]
-0.167***
[-7.338]
0.016
[1.031]
-0.036***
[-6.109]
-0.073***
[-3.857]
0.052*
[1.912]
5.517**
[2.014]
-0.149***
[-8.661]
0.222***
[17.004]
0.199***
[10.722]
0.077***
[6.354]
0.007
[0.499]
0.092***
[16.755]
0.438***
[12.120]
-0.032***
[-13.586]
0.040***
[12.333]
0.027***
[6.270]
-0.008**
[-2.509]
0.069***
Proportion_Nonauditfees
0.018***
[6.401]
-0.017***
[-2.992]
0.012***
[6.060]
0.026**
[2.504]
-0.002
[-0.381]
-0.003
[-1.326]
-0.000
[-0.030]
0.001
[0.073]
56
Proportion_Nonauditfees
0.019***
[6.752]
-0.010*
[-1.761]
0.011***
[5.036]
0.038***
[3.462]
0.003
[0.527]
-0.003
[-1.366]
-0.014
[-1.635]
0.013
[1.151]
0.650
[1.142]
-0.046***
[-4.339]
0.004
[0.692]
0.069***
[8.288]
-0.014**
[-2.282]
0.007
[1.079]
-0.006**
[-2.151]
-0.053***
[-3.062]
0.000
[0.403]
0.001
[0.839]
-0.002
[-1.018]
0.002
[0.730]
-0.037***
Dependent Variables:
Logauditfees
AltmanZ
Length_Relationship
SaleGrowth
Weaknesses
HiTech
Year Fixed Effects
Observations
Adjusted R-squared
Yes
27,837
0.728
Logauditfees
[4.566]
-0.004***
[-4.373]
0.001
[1.198]
-0.015***
[-2.756]
0.489***
[23.754]
0.117***
[5.942]
Yes
27,837
0.806
Proportion_Nonauditfees
Yes
27,837
0.043
Proportion_Nonauditfees
[-5.015]
-0.001***
[-2.784]
0.001
[1.343]
0.003
[0.810]
-0.059***
[-5.295]
0.005
[0.557]
Yes
27,837
0.054
Panel B: Results of Models (2) and (3)
Dependent Variables:
Number_Words_Perf
Number_Words_Org
Logauditfees
0.002
[0.389]
0.000
[0.083]
QCIndex
Full Set of Controls
Year Fixed Effects
Observations
Adjusted R-squared
Yes
Yes
27,837
0.806
Logauditfees
0.004
[0.744]
Proportion_Nonauditfees
0.010***
[3.040]
0.010***
[3.907]
-0.003
[-1.009]
Yes
Yes
27,837
0.806
Proportion_Nonauditfees
0.010***
[2.938]
0.010***
[5.373]
Yes
Yes
27,837
0.055
Yes
Yes
27,837
0.055
Panel C: Results of Model (4)
Dependent Variables:
Number_Words_Perf
Reduced_QC_Index
Tone
Methodology
Logauditfees
Proportion_Nonauditfees
0.003
0.011***
[0.467]
[3.034]
-0.004
0.011***
[-1.354]
[5.802]
-0.006
0.005
[-0.764]
[0.824]
0.014
0.006
[1.407]
[1.249]
Full Set of Controls
Yes
Yes
Year Fixed Effects
Yes
Yes
Observations
27,837
27,837
Adjusted R-squared
0.806
0.055
57
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. Standarderrors 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:
Number_Words_Perf
Number_Words_Org
Logaudithours
-0.053***
[-7.289]
0.040***
[8.021]
QCIndex
Distressed
Logat
CFOat
Leverage
BTM
Litigation
Big4
Client_Importance
FirstYear
ForeignPifo
Intangi
City_Leader
National_Leader
Office_Size
CATA
Quick
Geoseg
0.110***
[7.765]
0.377***
[59.050]
-0.014
[-0.495]
0.032*
[1.700]
-0.016**
[-2.358]
-0.081***
[-3.961]
0.261***
[8.568]
3.458
[1.373]
0.156***
[6.321]
0.186***
[11.210]
0.147***
[7.235]
0.026*
[1.737]
0.009
[0.571]
0.060***
[8.847]
0.417***
[9.778]
-0.036***
[-13.049]
0.035***
[9.911]
Logaudithours
-0.042***
[-6.051]
0.024***
[6.753]
0.111***
[7.816]
0.377***
[59.106]
-0.014
[-0.508]
0.031*
[1.696]
-0.016**
[-2.351]
-0.081***
[-3.973]
0.246***
[8.073]
3.511
[1.395]
0.156***
[6.348]
0.186***
[11.242]
0.149***
[7.299]
0.027*
[1.821]
0.011
[0.658]
0.061***
[8.991]
0.415***
[9.763]
-0.036***
[-13.046]
0.035***
[9.906]
58
Logpartnerhours
-0.111***
[-13.335]
0.037***
[5.605]
0.134***
[8.328]
0.339***
[48.283]
-0.097***
[-2.947]
0.047**
[2.280]
-0.024***
[-3.373]
-0.079***
[-3.484]
0.305***
[8.843]
2.192
[0.913]
0.069**
[2.210]
0.156***
[8.533]
0.154***
[6.609]
0.022
[1.283]
-0.000
[-0.010]
0.042***
[5.605]
0.336***
[6.894]
-0.024***
[-8.690]
0.025***
[6.248]
Logpartnerhours
-0.113***
[-14.740]
0.055***
[12.229]
0.137***
[8.585]
0.338***
[48.704]
-0.093***
[-2.829]
0.048**
[2.332]
-0.024***
[-3.319]
-0.081***
[-3.598]
0.285***
[8.308]
2.299
[0.952]
0.073**
[2.366]
0.155***
[8.588]
0.158***
[6.847]
0.021
[1.262]
-0.000
[-0.009]
0.042***
[5.629]
0.334***
[6.928]
-0.024***
[-8.697]
0.025***
[6.308]
Busseg
StdSaleGrowth
DecYE
AltmanZ
Length_Relationship
SaleGrowth
Weaknesses
HiTech
Year Fixed Effects
Observations
Adjusted R-squared
0.024***
[5.075]
-0.005
[-1.067]
0.018
[1.048]
-0.005***
[-3.335]
-0.001
[-1.038]
-0.030***
[-3.574]
0.368***
[10.929]
0.046**
[2.111]
Yes
13,802
0.733
0.024***
[5.065]
-0.004
[-0.984]
0.018
[1.032]
-0.005***
[-3.309]
-0.001
[-1.023]
-0.030***
[-3.600]
0.366***
[10.856]
0.046**
[2.122]
Yes
13,802
0.733
0.018***
[3.310]
-0.005
[-1.068]
0.020
[1.039]
-0.005***
[-4.678]
-0.002*
[-1.802]
-0.011
[-0.940]
0.470***
[12.371]
0.082***
[3.313]
Yes
13,793
0.571
0.017***
[3.230]
-0.005
[-1.058]
0.021
[1.135]
-0.005***
[-4.563]
-0.002*
[-1.649]
-0.011
[-0.893]
0.465***
[12.205]
0.085***
[3.504]
Yes
13,793
0.577
Panel B: Models (2) and (3) Results With Logeqrhours and Fees_per_hour as Dependent Variables
Dependent Variables:
Number_Words_Perf
Number_Words_Other
Logeqrhours
-0.208***
[-28.591]
0.098***
[18.528]
QCIndex
Distressed
Logat
CFOat
Leverage
BTM
Litigation
Big4
Client_Importance
FirstYear
ForeignPifo
Intangi
0.129***
[8.917]
0.201***
[33.366]
-0.075**
[-2.571]
0.077***
[4.671]
-0.004
[-0.807]
-0.008
[-0.378]
0.089***
[3.033]
1.591
[0.866]
0.113***
[3.722]
0.066***
[4.135]
0.091***
[4.323]
Logeqrhours
-0.186***
[-27.039]
0.075***
[18.943]
0.132***
[9.145]
0.200***
[33.595]
-0.074**
[-2.515]
0.078***
[4.741]
-0.004
[-0.752]
-0.008
[-0.423]
0.045
[1.535]
1.744
[0.950]
0.116***
[3.841]
0.066***
[4.190]
0.095***
[4.552]
59
Fees_per_hour
14.408***
[8.198]
-10.477***
[-8.294]
-1.945
[-0.606]
15.101***
[11.079]
-37.005***
[-5.855]
-3.012
[-0.966]
-4.270***
[-3.590]
-6.404
[-1.427]
-49.408***
[-8.319]
357.977
[1.174]
-62.169***
[-10.273]
13.986***
[3.705]
14.212***
[3.582]
Fees_per_hour
10.884***
[6.954]
-5.020***
[-5.675]
-2.081
[-0.647]
15.108***
[11.073]
-36.733***
[-5.855]
-2.933
[-0.940]
-4.290***
[-3.603]
-6.422
[-1.432]
-45.491***
[-7.639]
346.499
[1.131]
-62.194***
[-10.258]
13.894***
[3.682]
14.042***
[3.537]
City_Leader
National_Leader
Office_Size
CATA
Quick
Geoseg
Busseg
StdSaleGrowth
DecYE
AltmanZ
Length_Relationship
SaleGrowth
Weaknesses
HiTech
Year Fixed Effects
Observations
Adjusted R-squared
-0.015
[-1.003]
-0.029*
[-1.841]
-0.010
[-1.418]
0.202***
[4.756]
-0.015***
[-5.790]
0.014***
[3.806]
0.007
[1.393]
-0.002
[-0.364]
0.057***
[3.387]
-0.004***
[-4.357]
-0.002**
[-2.347]
-0.003
[-0.332]
0.397***
[11.657]
0.078***
[3.526]
Yes
13,698
0.368
-0.013
[-0.869]
-0.027*
[-1.686]
-0.008
[-1.211]
0.198***
[4.717]
-0.015***
[-5.744]
0.014***
[3.792]
0.006
[1.307]
-0.001
[-0.166]
0.057***
[3.420]
-0.004***
[-4.242]
-0.002**
[-2.240]
-0.003
[-0.385]
0.391***
[11.475]
0.081***
[3.656]
Yes
13,698
0.374
7.475**
[2.304]
-4.704
[-1.465]
5.717***
[3.882]
8.369
[0.977]
1.289***
[2.645]
0.828
[1.091]
1.874*
[1.745]
-1.510**
[-2.290]
1.745
[0.498]
-0.314***
[-2.709]
0.518**
[2.324]
3.373
[1.442]
-5.203
[-0.583]
17.517***
[3.780]
Yes
13,802
0.091
7.086**
[2.185]
-5.117
[-1.593]
5.466***
[3.714]
8.629
[1.008]
1.269***
[2.598]
0.849
[1.119]
1.869*
[1.742]
-1.619**
[-2.461]
1.898
[0.543]
-0.316***
[-2.719]
0.521**
[2.341]
3.522
[1.501]
-4.892
[-0.547]
17.606***
[3.801]
Yes
13,802
0.088
Panel C: Model (4) Results
Dependent Variables:
Logaudithours
Logpartnerhours
Logeqrhours
Fees_per_hour
-0.071***
-0.133***
-0.229***
18.522***
[-8.829]
[-14.468]
[-27.969]
[10.979]
-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]
Full Set of Controls
Yes
Yes
Yes
Yes
Year Fixed Effects
Yes
Yes
Yes
Yes
Observations
13,802
13,793
13,698
13,802
Adjusted R-squared
0.735
0.578
0.384
0.095
Number_Words_Perf
Reduced_QC_Index
60
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
0.189***
0.074***
0.157**
-0.032
[5.433]
[2.630]
[2.219]
[-0.853]
-0.401**
0.011
-2.454***
0.135
[-2.284]
[0.072]
[-5.369]
[0.623]
Full Set of Controls
Yes
Yes
Yes
Yes
Year Fixed Effects
Yes
Yes
Yes
Yes
Observations
23,572
23,572
2,089
23,572
Pseudo R-squared
0.032
0.085
0.126
Number_Words
Proportion_Remediated
ScaledAccrualsCFO
Adjusted R-squared
0.136
Panel B: Results With audit fee measures as dependent variables
Dependent Variables:
Number_Words
Proportion_Remediated
Full Set of Controls
Year Fixed Effects
Logauditfees
Proportion_Nonauditfees
0.004
0.013***
[0.856]
[4.716]
-0.046**
0.008
[-2.164]
[0.646]
Yes
Yes
Yes
Yes
Observations
23,572
23,572
Adjusted R-squared
0.807
0.027
Panel C: Results With audit hour measures as dependent variables
Dependent Variables:
Number_Words
Logaudithours
Logpartnerhours
Logeqrhours
Fees_per_hour
-0.003
-0.056***
-0.061***
1.192
[-0.580]
[-7.487]
[-9.684]
[0.934]
-0.075***
-0.135***
0.129***
16.750***
[-3.235]
[-4.555]
[4.237]
[3.399]
Full Set of Controls
Yes
Yes
Yes
Yes
Year Fixed Effects
Yes
Yes
Yes
Yes
Observations
13,802
13,793
13,698
13,802
Adjusted R-squared
0.731
0.565
0.322
0.082
Proportion_Remediated
61
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:
Number_Words
Switch
0.243***
[5.019]
Switch
0.232***
[4.723]
Number_Words_Perf
Number_Words_Org
Distressed
Logat
CFOat
Leverage
BTM
Litigation
Big4
0.497***
[6.880]
-0.387***
[-18.072]
0.134
[1.305]
0.194***
[2.691]
0.172***
[3.459]
-0.194***
[-3.063]
0.165*
[1.870]
0.458***
[6.227]
-0.480***
[-18.581]
0.058
[0.514]
0.154**
[2.097]
0.162***
[3.295]
-0.146*
[-1.735]
0.089
[0.805]
3.165***
[2.907]
-0.927***
[-5.125]
0.131*
[1.778]
0.092
[1.116]
-0.032
[-0.501]
0.152**
[2.094]
0.029
[1.106]
-0.705***
[-4.286]
-0.007
[-0.542]
0.013
[0.844]
0.057***
[2.697]
Client_Importance
FirstYear
ForeignPifo
Intangi
City_Leader
National_Leader
Office_Size
CATA
Quick
Geoseg
Busseg
62
Switch
Switch
0.130***
[2.646]
0.133**
[2.403]
0.497***
[6.878]
-0.387***
[-18.068]
0.134
[1.304]
0.194***
[2.692]
0.172***
[3.458]
-0.194***
[-3.062]
0.167*
[1.889]
0.127***
[2.584]
0.124**
[2.248]
0.458***
[6.224]
-0.480***
[-18.579]
0.058
[0.513]
0.154**
[2.098]
0.162***
[3.294]
-0.146*
[-1.736]
0.091
[0.819]
3.139***
[2.865]
-0.927***
[-5.125]
0.131*
[1.777]
0.092
[1.116]
-0.032
[-0.498]
0.152**
[2.096]
0.029
[1.119]
-0.705***
[-4.284]
-0.007
[-0.543]
0.013
[0.843]
0.057***
[2.695]
Dependent Variables:
StdSaleGrowth
Switch
DecYE
AltmanZ
Length_Relationship
SaleGrowth
Weaknesses
HiTech
Year Fixed Effects
Observations
Pseudo R-squared
Yes
25,675
0.101
Switch
-0.015
[-0.750]
-0.221***
[-3.584]
-0.002
[-1.109]
0.002
[0.420]
-0.039
[-0.807]
1.141***
[12.382]
-0.010
[-0.115]
Yes
25,675
0.123
Switch
Yes
25,675
0.101
Switch
-0.015
[-0.750]
-0.222***
[-3.590]
-0.002
[-1.108]
0.002
[0.421]
-0.039
[-0.805]
1.141***
[12.384]
-0.010
[-0.113]
Yes
25,675
0.123
Switch
0.136***
[2.597]
0.080***
[3.000]
Switch
0.111**
[2.050]
Switch
0.105*
[1.915]
0.071**
[2.503]
0.265***
[3.036]
0.113
[1.450]
No
Yes
Yes
25,675
0.102
0.062**
[2.190]
0.247***
[2.803]
0.111
[1.419]
Yes
No
Yes
25,675
0.123
Panel B: Models (3) and (4) Results
Dependent Variables:
Number_Words_Perf
QCIndex
Switch
0.142***
[2.721]
0.090***
[3.349]
Reduced_QC_Index
Methodology
Tone
Full Set of Controls
Reduced Set of Controls
Year Fixed Effects
Observations
Pseudo R-squared
No
Yes
Yes
25,675
0.101
Yes
No
Yes
25,675
0.123
63
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
Release of
PCAOB Report
corresponding to
Year t inspection
End of Fiscal Year t-1
End of Audit t-1
Year t-1
Year t
Year t+1
Year t PCAOB
Inspection
Fieldwork generally from
March to November
• Year t-1 audits inspected
• QC systems inspection
uses information from
both t-1 and t
One Year Remediation Period
From release date of the
PCAOB Report
64