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Which Audit Input Matters? An Analysis of the Determinants of Audit
Quality, Profitability, and Audit Fees Using PCAOB Data
Daniel Aobdia
Kellogg School of Management, Northwestern University
Preeti Choudhary
Eller College of Management, University of Arizona
Noah Newberger
Public Company Accounting Oversight Board
This version: September 2018
Daniel Aobdia and Preeti Choudhary were Senior Economic Research Fellows in the Center of Economic Analysis
(now called Office of Economic Research & Analysis – ERA) at the Public Company Accounting Oversight Board
(PCAOB), and conducted this research as part of this affiliation. Noah Newberger is a Senior Research Analyst in
ERA 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 ours and
do not necessarily reflect the views of the Board, individual Board members, or staff of the PCAOB. We are
thankful to PCAOB staff and seminar participants at the PCAOB for helpful comments and suggestions. Daniel
Aobdia gratefully acknowledges generous financial support from the Kellogg School of Management and in
particular the Lawrence Revsine Fellowship.
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Abstract
We exploit proprietary data from the Public Company Accounting Oversight Board (PCAOB) to
analyze how a variety of characteristics regarding the composition of the audit and audit team are
associated with audit quality measured via restatements and regulator-identified audit
deficiencies. We find that more time spent prior to the final phase of the audit is associated with
better audit quality, specifically when more time is spent by the core audit engagement team. We
also decompose the time spent and experience characteristics of the core audit engagement team
into the following roles: lead partner, engagement quality reviewer, and other experienced team
members (comprised of other audit partners, directors, senior managers, and managers). We
generally find that more time spent and more experience of experienced team members other
than the lead partner are associated with better audit quality. Some of the characteristics that
improve audit quality are costly to the client, but not necessarily to the audit firm. Overall, our
analysis highlights several cost-benefit tradeoffs to improving audit quality.
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1. Introduction
In this study, we exploit proprietary data from the Public Company Accounting Oversight
Board (PCAOB) to analyze how a variety of characteristics regarding the composition of the
audit and audit team are associated with audit quality measured via restatements and regulator-
identified (PCAOB) audit deficiencies. Specifically, we focus on the amount of time spent
during different phases of the audit as well as time spent and experience characteristics of the
lead audit engagement team. We partition the lead engagement team into the three following
mutually exclusive roles: lead partner, engagement quality reviewer (EQR), and other
experienced team members, where the latter are comprised of other audit partners, directors,
senior managers, and managers. Lastly, we evaluate whether some of the characteristics
associated with improved audit quality are costly for the audit firm or client, thereby highlighting
cost-quality trade-offs.
This analysis is important and timely given that many audit regulators and firms have
recently introduced audit quality indicator projects to better define and measure audit quality,
and, importantly, understand the determinants of audit quality (e.g., Christensen et al. 2016).1
Despite this increased interest from practitioners about understanding the determinants of audit
quality, archival evidence on the audit process remains scarce, due to lack of comprehensive data
that measures both engagement characteristics and audit quality (DeFond and Zhang 2014). Our
analysis helps open the black box of the role of audit process on audit quality.
1 For example, projects seeking to evaluate audit quality are on the agendas of the U.K. Financial Reporting Council
(FRC 2008), the International Auditing and Assurance Standards Board (IAASB 2013), the Center for Audit Quality
(CAQ 2014), the PCAOB (PCAOB 2015), the Canadian Public Accountability Board (CPAB 2018), Singapore’s
Accounting and Corporate Regulatory Authority (ACRA 2015), Netherlandse Beroepsorganisatie van Accountants
(NBA 2015), Switzerland’s Federal Audit Oversight Authority (FAOA 2015), Chartered Accountants of Australia
and New Zealand (CAANZ, 2015), International Organisation of Securities Commissions (IOSCO 2009), among
others.
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We exploit proprietary data obtained from the PCAOB for the U.S. operations of the
eight largest auditors. One of the roles of the PCAOB’s regulatory activities is to inspect
individual audit engagements of SEC-registered companies to determine whether these audits
were conducted according to relevant audit standards (Section 104 of the Sarbanes Oxley Act of
2002, SOX). If the PCAOB determines that the work performed was not sufficient to support the
audit opinion, the PCAOB issues a Part I Finding (an audit deficiency). As part of the inspection
process, the PCAOB collects specific information about the audit engagement characteristics.
We exploit this information, along with additional PCAOB data. Because our analysis is limited
to inspected engagements that are not randomly chosen, our analysis may not generalize to the
full population. In Section 4.9, we discuss several analyses that mitigate these concerns.
Our dataset contains 2,339 engagements inspected by the PCAOB between 2006 and
2015. It includes a breakdown of audit hours among the different phases of the audit and the
different personnel that form the core audit team, as well as the identity of the senior team
members, from the audit manager level, and their client specific, industry, and overall auditing
experiences. We combine this dataset with Compustat, Audit Analytics, and additional PCAOB
information to build several dependent and control variables. We consider two measures of audit
quality, one based on audit process quality, the other on the client’s financial reporting quality.
Audit process quality is measured by whether the PCAOB identifies any Part I Findings; under
the assumption that inspectors are objective audit experts, such findings yield a measure of
whether the audit was conducted in accordance with PCAOB standards (see Aobdia 2018a for
more details). Financial reporting quality is measured by whether the client restates its fiscal-year
end financial statements, which captures circumstances in which the auditor missed
misstatements at the time it issued the audit opinion. Restatements represent a joint outcome of
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both the client and management (e.g., Aobdia 2018a, Choudhary, Schipper, and Merkley 2018a).
We consider three measures of audit cost, including: audit fees, which capture the cost to the
client, the audit realization rate, which captures the engagement’s profitability, as well as the
total audit hours worked, which capture audit effort.2
Our first set of analyses evaluates the time spent prior to the final phase of the audit,
which typically corresponds to audit work conducted prior to the client’s fiscal year end. Ex ante,
it is unclear whether more time spent during the pre-final phase of the audit positively influences
audit quality. On the one hand, if auditors adjust personnel levels and expertise, as well as the
extent of audit procedures, based on the risk assessment conducted during the initial phases of
the audit (e.g., Johnstone and Bedard 2001) then time spent during the pre-final phase may
matter. Alternatively, if the link between risk assessment and the extent of testing is weak, then
time spent during the pre-final phase may not matter (e.g., Allen et al. 2006).
We find that engagements that spend more time during the pre-final phase exhibit better
audit process and financial reporting quality. These results are mainly driven by the time spent
by the core audit team, not by the information systems auditors, tax auditors, or audit specialists.
Our coefficients imply that an increase of one standard deviation in the proportion of the audit
time spent during the pre-final phase (10%) is associated with reductions in the probabilities of
Part I Findings and restatements of 1.8% and 2.5% points, respectively. The economic effects are
reasonably large given that the average Part I Finding and restatement rates in our sample are
28% and 12%, respectively. However, we find that this improvement in audit quality comes with
2 Realization rate is equal to total audit fees charged divided by the maximum audit fee the auditor would have
charged had all hours been billed at their undiscounted rate, which varies depending on whether the hours are
conducted by partners, managers, seniors or associates. Thus, a 100% realization rate corresponds to an audit where
the auditor works the planned number of hours and bills these hours at their maximum possible rate. The higher the
realization rate, the higher the audit profitability.
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a cost to the client in the form of higher audit fees, but not necessarily to the auditor, as we fail to
find any relation between time during the pre-final phase and audit firm profitability.
In our second set of analyses, we further drill down into the composition of the core audit
engagement team. We evaluate whether the time spent and expertise characteristics of
experienced team members that work under the guidance of the lead partner relate to audit
quality. On the one hand, PCAOB standard AS 1201 clearly states that the lead partner is
ultimately responsible for the engagement and its performance. Consistent with this idea, prior
research outside the U.S. finds significant individual lead partner effects on audit quality (e.g.,
Lennox and Wu 2018). Theoretical economics research about hierarchies also implies a more
important role for the person in charge (e.g., Garicano 2000, Garicano and Hubbard 2007). Yet
prior research within the U.S. surprisingly finds limited evidence that lead partner characteristics
influence audit quality (e.g., Laurion et al. 2016, Aobdia et al. 2018a, Gipper et al. 2018),
perhaps because lead partners delegate their audit quality responsibilities to their engagement
teams. It is also an empirical question whether trade-offs exist between audit quality and audit
cost for the engagement characteristics that positively influence audit quality. Some of these
characteristics might make audits both more efficient and effective; others may be costly to
either the client, the auditor or both.
To decompose the core audit engagement team, we consider the proportion of time spent,
split between lead partner, EQR, and other experienced team members. While we find evidence
consistent with a positive influence of the time spent by the EQR and other experienced team
members on audit quality, we do not find such results when focusing on the lead partner. We
also find that increased quality from other experienced team members’ time spent comes at a
reasonably high cost to the client in the form of higher audit fees. An increase of one standard
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deviation of other experienced team members time (6%) is associated with a 9.4% increase in
audit fees, and a 2.1% reduction in the probability of Part I Finding.
We also assess whether heterogeneity in the expertise of the core audit team experienced
members influences audit quality. Evaluating client-specific expertise, we find that increased
client expertise is associated with better audit quality. These results are driven by the other
experienced team members. A one-standard deviation increase in client specific expertise of
other experienced team members, 1.75 years, is associated with a 2% point decrease in the
probability of restatements. Again, this quality improvement comes with a cost to the client, in
the form of increased audit fees. However, we also find a positive association between client
expertise and audit realization rate, implying no trade-offs between audit profitability and audit
quality from an audit firm’s standpoint. Confirming the results in Gipper et al. (2018), we find no
association (a positive association) between lead partner’s client-specific expertise and audit
quality (audit realization rate).
We also find a positive association between average core audit team members’ industry
specialization and audit quality. The results are driven by the EQR and other experienced team
members. We also find a positive association between EQR overall expertise, proxied by the
EQR total years of audit experience, and audit quality. Consistent with Aobdia et al. (2018a), we
find no association (a positive association) between lead partner industry specialization and audit
quality (audit fees). We fail to find any association between lead partner overall experience and
audit quality. In additional analyses, we fail to find an association between the number of
experienced auditors in the team, or the team gender, and audit quality.
Overall, our results are consistent with several ideas. First, the time spent during the pre-
final phases of the audit positively influences audit quality. Second, other experienced audit team
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members beside the lead partner play an important role in terms of audit quality. Both their time
and client-specific and industry-expertise are positively associated with audit quality. We also
find evidence that the EQR plays an important role in terms of audit quality. In particular, an
EQR’s industry and general expertise positively influence audit quality, whereas client-specific
expertise appears to matter less. Third, while we fail to observe any trade-offs between audit
quality and auditor profitability, we do find that in many instances the audit characteristics that
are associated with better audit quality come with a cost to the client. Thus, depending on a
client’s willingness to pay, trade-offs exist between audit quality and the cost of the audit. We
also find evidence consistent with Laurion et al. (2016), Aobdia et al. (2018a), and Gipper et al.
(2018) that lead partner characteristics are largely unassociated with audit quality in the U.S.
Our analysis helps answer several major gaps in prior audit literature. First, there is an
older line of research on audit production that has access to audit process data but often assumes
that audit quality is constant within the same audit firm (e.g., O’Keefe et al. 1994, Davidson and
Gist 1996, Knechel et al. 2009). However, a different and more recent line of research
documents heterogeneity in audit quality within the same auditor, due to differences in industry
specialization, office characteristics such as size, and lead partner characteristics (e.g., Francis
and Yu 2009, Reichelt and Wang 2010, Gul et al. 2013). The second line of research is generally
constrained by lack of audit process data.3 We expand upon these two lines of research by
evaluating whether some previously unexplored characteristics of the audit team and time spent
are associated with heterogeneous audit quality effects within the same auditor.
3 A recent exception, Cameran et al. (2017), focuses on how audit teams are structured in the Italian setting and find
evidence consistent with audit team diversity affecting audit quality. However, Cameran et al. (2017) does not have
access to a strong measure of audit quality and relies on abnormal working capital accruals instead.
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Second, in exploring heterogeneity within the same audit firm, the audit literature
changed the unit of analysis from audit firms to audit offices, and more recently to engagement
partners (e.g., Lennox and Wu 2018). While some prior research on engagement partners
documents heterogeneities in audit quality due to partner differences (e.g., Gul et al. 2013,
Aobdia et al. 2015), most of the evidence is from outside of the United States. Recent studies
using the U.S. setting document little influence of lead partner characteristics on audit quality
(e.g., Laurion et al. 2016, Aobdia et al. 2018a, Gipper et al. 2018). Our study builds upon these
U.S. based studies to determine what characteristics of an audit engagement ultimately influence
audit quality in the U.S. One possible explanation for the lack of partner influence on audit
quality in the U.S. is that the role of the U.S. lead partner may relate to managing client relations,
whereas other audit team members may focus more on managing the audit. Accordingly, our
analysis of audit team members beyond the lead partner potentially provides additional
explanations for results that question the importance of the lead partner.
Third, prior research studying heterogeneity of audit firms often does not consider the
various trade-offs required to improve audit quality, primarily due to lack of data.4 While several
audit process or engagement team attributes might affect audit quality, these may also come with
a cost, either to the audit firm, or to the client. We exploit proprietary data to evaluate costs
beyond audit fees, including audit hours and engagement realization rates that reflect the
percentage discount in fees received over time spent by a variety of professionals on the audit.
Our analysis is able to pinpoint these trade-offs, thereby highlighting potential conflicts of
interest between auditor profitability or client costs, and audit quality. Generally, our results
4 Two exceptions are Aobdia et al. (2018a) and Gipper et al. (2018).
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regarding the cost/quality tradeoff show that the cost is typically borne by the client, not the
auditor.
Collectively, our results imply that audit practitioners and academics should focus more
on experienced audit team members other than the lead partner to understand their role on audit
quality, and performance in general. While an extensive literature focuses on the role of lead
partners, little is known about the roles below, due to lack of data (Lennox and Wu 2018). Our
results raise the question whether some of the results in prior literature that find that lead partners
influence audit quality or performance might actually be driven by their choices of engagement
team members. Nevertheless, two caveats are in order. First, our analysis is conducted on a
sample where the name of the lead partner is not public. In a regime where the lead partner
names are public, reputation effects might incentivize lead partners to achieve high quality
audits, rather than just striving to reach an acceptable level, and manage the audit process more
proactively. Second, our analyses are based on a sample of engagements inspected by the
PCAOB. Because the PCAOB does not randomly inspect engagements, it is not certain that our
results can generalize to non-inspected engagements.5 Therefore, we encourage academic
researchers to continue exploring this line of research to determine whether our results can
generalize to different samples, regulatory regimes, and countries.
The remainder of this paper is structured as follows. Section 2 includes a review of prior
literature and develops the main hypotheses; Section 3, describes the data and the sample
construction; Section 4, the empirical tests and results. Section 5 concludes.
2. Prior literature and main hypotheses
2.1 Prior literature
5 See the analyses in Section 4.9 that suggest that selection bias concerns are not too severe for our sample.
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At least two streams of literature are relevant to our study. The first is the literature that
studies how the audit is executed (i.e. audit production), which largely holds the output (audit
quality) constant. The second stream of literature on hierarchy and teams is largely theoretical,
and studies how different characteristics of team structure affect the output.
2.1.1 Literature on audit production
An older literature on audit production models an audit as a production function and
focuses on specific units of effort in the audit process necessary to achieve a certain level of
output (e.g., Knechel et al. 2009). In this line of research, the audit output represents the level of
assurance obtained by the audit process, or, in other words, audit quality (O’Keefe et al. 1994).
However, due to the lack of an appropriate measure of assurance obtained, the literature often
assumes that the audit market is competitive, and that audit quality is constant within the same
auditor (e.g., O’Keefe et al. 1994). As a result, prior studies often treat audit production as a cost
minimization problem, holding the output (audit quality) constant. For example, Simunic (1980)
uses audit fees to consider the joint role of audit costs and audit pricing, and models audit fee
determinants based on a variety of client characteristics. Subsequent research exploits direct data
on audit hours and focuses on audit costs only, using aggregated or disaggregated audit hours as
dependent variables, and a variety of client characteristics as explanatory variables (e.g.,
Palmrose 1986, 1989, Davis et al. 1993, O’Keefe et al. 1994, Davidson and Gist 1996,
Hackenbrack and Knechel 1997).
More recent studies, such as Dopuch et al. (2003), and Knechel et al. (2009), introduce
efficiency frontier techniques such as data envelopment analysis to examine relative efficiency in
audit production. In particular, Knechel et al. (2009) extend prior literature by considering audit
effort, i.e. audit hours, as the output, and the cost of staff resources as the input. The underlying
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assumption in Knechel et al. (2009) is that more audit effort in the form of more hours generates
more audit evidence and therefore a higher level of assurance; however, whether this assumption
is valid remains uncertain. In the Greek setting, Caramanis and Lennox (2008) find a positive
association between audit hours and audit quality; however, Aobdia (2018a) does not find any
association between audit hours and PCAOB Part I Findings. Additional studies such as Gong et
al. (2016) and Aobdia (2018b) also find that audit hours do not always move in tandem with
audit quality.
Overall, at least one main concern is that prior studies of audit production typically
assume that audit quality is constant within the same auditor, in part, because they do not have
access to a direct measure of audit quality. However, a more recent line of research documents
significant heterogeneity in audit quality, even within the same auditor. The literature initially
documents variation at the industry or office levels. For example, Balsam et al. (2003) and
Krishnan (2003) find evidence consistent with industry specialist auditors (measured at the
national level) conducting higher quality audits. Francis and Yu (2009) find evidence consistent
with larger audit offices providing higher quality audits, and Reichelt and Wang (2010) find
evidence consistent with industry specialist offices providing higher audit quality when the
auditor is also a national industry specialist. The literature subsequently documents heterogeneity
in audit quality at the partner level outside of the United States. For example, Gul et al. (2013),
Aobdia et al. (2015), Knechel et al. (2015), and Cameran et al. (2018) find evidence that audit
quality depends on the lead partner in China, Taiwan, Sweden, and the U.K., respectively. Chi et
al. (2017) find in Taiwan evidence consistent with lead partner client-specific and industry-
expertise influencing audit quality. Several other studies also show an influence of the lead
partner on audit quality outside of the United States (see Lennox and Wu 2018 for a recent
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review of the literature). Overall, these studies imply that research on the audit process cannot
assume that the output of an audit is constant within the same auditor, and that more precise
measures of the degree of assurance achieved by the audit need to be considered in audit
production models.
2.1.2 Literature on teams and hierarchy
An individual audit engagement requires the use of a team of auditors, with a hierarchy
within the team, to achieve a certain degree of assurance level. The analytical literature in
economics recognizes the importance of team dynamics on output. The literature generally either
focuses on moral hazard issues, or on frictions in the communication of knowledge among team
members. On the one hand, Holmstrom (1982) identifies incentives for team members to shirk,
especially in larger teams when effort is costly, implying that team efficiency and effectiveness
might decrease as the team becomes larger. Georgiadis (2015) studies a dynamic model with a
group of agents that collaborate to complete a project with costly effort. He finds that agents
work harder when the project is closer to completion, implying that team efficiency and
effectiveness might be greater during the final phase of a project. On the other hand, Garicano
(2000) and Garicano and Hubbard (2008) focus on hierarchies in organizations and find that a
hierarchy is a natural way to organize knowledge when matching problems with those who know
how to solve them is costly. Thus, individuals are more likely to influence the overall output of
the organization, as they are closer to the top of the hierarchy.
Despite this extensive theoretical literature on teams and hierarchy, the empirical
literature is more limited, due to lack of data. The results suggest that in our setting the
experienced professionals other than the top (lead) partner influence the output, partially in
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support of these theories. Further, the results suggest that frictions in the transmission of
knowledge are more prevalent in practice relative to moral hazard issues in our setting.
2.2 Hypotheses
2.2.1 Focus on the pre-final phases of the audit
Our first hypothesis evaluates the importance of the pre-final phases of the audit, which
represents audit work done prior to the client’s fiscal year end. The pre-final phase is comprised
of the planning, quarterly review, and interim field phases. During the planning phase of an
audit, PCAOB standards require an auditor to establish the overall audit strategy for the
engagement and to develop an audit plan, which includes planned risk assessment procedures
and planned responses to the risks of material misstatement (AS 2101). In particular, standards
require the engagement team to establish materiality levels and identify and assess the main risks
of material misstatements during the planning phase (AS 2105, AS 2110). Thus, PCAOB
standards grant an important role to planning activities. This is intuitively appealing given that a
proper assessment of risks can allow an auditor to develop an effective audit plan to reduce the
risk of misstatements. Consistent with this idea, O’Keefe et al. (1994) find a positive association
between inherent risk and senior and staff hours used in the audit. Johnstone and Bedard (2001)
and Bedard and Johnstone (2004) further find that audit firms respond to increased fraud and
error risk factors by assigning more high-risk specialist personnel, assigning more industry
expert personnel, and applying more intensive testing and reviews. Some behavioral studies also
find an influence of risk on audit effort decisions (e.g., Libby et al. 1985, Maletta and Kida 1993,
Zimbelman 1997). However, several studies also find that the link between risk assessment and
substantive testing is weaker than one may expect (Allen et al. 2006). For example Fukukawa et
al. (2005), focusing on Japanese audits, find that fraud risk factors have little influence on audit
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planning. Accordingly, it is unclear whether time spent in planning the audit matters for audit
quality.
Other pre-final phases, such as quarterly reviews and interim field, may also have a role
to play in improving audit quality. Quarterly review pertains to audit procedures performed
under AS 4105, Reviews of Interim Financial Statements, such as performing analytical
procedures and making inquiries based upon a review of quarterly financial statements. The
interim phase captures tests of accounting records through inspection, confirmation or
observation, tests of controls, and any substantive tests conducted prior to fiscal year end.
Because audit planning is a continuous and iterative process, quarterly review and interim
procedures may lead to changes in the audit plan (AS 2101). When the audit reaches its final
stage, the audit team may suffer from time compression, which might prevent the team from
properly responding to red flags discovered during the final phase. Consistent with this view,
several studies document a negative influence of time compression on audit quality (McDaniel
1990, Lopez and Peters 2012). Thus, we expect that if auditors properly respond to new evidence
gathered, more time spent prior to the final phase could be associated with better audit quality.
However, prior analytical literature shows that team members work harder as a project nears
completion, possibly implying a more effective output during the final phase of an audit
(Georgiadis 2015).
Eventually, given that prior evidence is mixed on the role of audit planning, quarterly
review, and interim phases on auditor response to increased risk, it remains an empirical question
whether more focus on planning tasks is associated with better audit quality. We state our first
hypothesis in its alternative form:
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H1: More audit effort during the earlier phases of the audit is associated with higher audit
quality.
2.2.2 Importance of other experienced audit team members besides the lead partner
PCAOB standards grant an important role to the lead audit partner, suggesting that his or
her expertise and the time spent on the audit can significantly influence audit quality.
Specifically, AS 1201 states that “[t]he engagement partner is responsible for the engagement
and its performance. Accordingly, the engagement partner is responsible for proper supervision
of the work of engagement team members and for compliance with PCAOB standards, including
standards regarding using the work of specialists, other auditors, internal auditors, and others
who are involved in testing controls.” Consistent with the view that lead partners play an
important role in the audit, prior international (non-U.S.) research finds evidence that lead
partners and their expertise influence audit quality (e.g., Gul et al. 2013, Chi et al. 2017). Further,
Garicano (2000)’s model of hierarchy implies that lead partners knowledge and expertise should
matter more than the knowledge and expertise of audit team members that immediately reports to
him or her. Overall, prior research suggests that lead partners should matter, and relatively more
than other audit team members, as a determinant of audit quality.
However, several forces also imply that the role of a lead audit partner may be more
limited than our priors suggest. First, AS 1201 authorizes lead partners to delegate their duties to
other team members. Second, the time spent by lead partners in U.S. audits is limited (Aobdia
2018b), which perhaps explains why recent studies using the U.S. setting surprisingly find little
evidence that lead partner characteristics influence audit quality (Laurion et al. 2016, Aobdia et
al. 2018a, Gipper et al. 2018). These findings suggest that other experienced team members may
have an important role to play in managing and conducting an audit. For example, AS 1220
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highlights the role of the EQR to evaluate the significant judgments made and conclusions
reached by the engagement team. The standard indicates that the EQR should have competence,
independence, integrity, and objectivity; in other words, the qualifications of an EQR are similar
to that of a lead partner. Audit managers, senior managers, directors and non-lead partners are
also significantly involved in the day-to-day activities of an audit, and depending on the amount
of delegation, in managing the audit as well. Accordingly, the time other experienced personnel
or the EQR spend on these activities, as well as their expertise, could affect audit quality.
Alternatively, characteristics and time spent by different audit team members may not
influence audit quality. Large audit firms in the U.S. have extensive quality control systems to
ensure high quality audits (e.g., Aobdia 2018c), which likely reduces differences in audit quality
across engagements. Recent evidence also suggests that audit firms in the U.S. may strive to
reach the minimum bar imposed by audit standards, and not necessarily go beyond this level
(Donovan et al. 2015, Aobdia 2018b). Either reason would suggest no association between other
experienced team members time or expertise and audit quality, consistent with prior results
found at the lead partner level in the U.S. Eventually, it remains an empirical question whether
the time and expertise of experienced audit team members, excluding the lead partner, influence
audit quality. We state our hypothesis in its alternative form:
H2: Non-lead partner experienced audit-team members’ time, client expertise, and overall
expertise are positively associated with audit quality.
2.2.3 Cost quality trade-offs
The hypotheses above, thus far, focus on the potential benefits of audit team
characteristics on audit quality. However, cost-quality trade-offs likely exist, such that the cost of
higher audit quality may be borne either by the client or the audit firm. For example, more time
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spent during the pre-final phase of the audit could result in the discovery of new risk areas,
requiring additional substantive testing that could be costly to either the audit firm or the client.
The staffing of more experienced team members could lead to higher client costs or lower audit
firm profits due to higher wages paid. Whether it is ultimately the client or the audit firm that
would bear the additional costs depends on clients’ willingness to pay for increased audit quality,
and their bargaining power vis-à-vis the audit firm.
Alternatively, specific engagement characteristics that positively influence audit quality
could also improve audit efficiency, such that we may not observe any trade-offs between audit
quality and the cost of the audit. For example, industry specialist auditors might be able to reduce
audit hours because of learning effects. Consistent with this idea, Bills et al. (2014) find evidence
of cost efficiencies for auditors that specialize in industries with homogenous operations and
complex accounting practices. Gong et al. (2016) find a significant reduction in audit hours
following audit firm mergers in China, without any reduction in audit quality. Eventually, it is an
empirical question whether the engagement characteristics that positively influence audit quality
are associated with increased audit costs, either in the form of higher audit fees or in the form of
reduced audit realization rate. We state our hypothesis in its alternative form.
H3: Some cost-benefit trade-offs may exist in the characteristics associated with better audit
quality.
3. Sample Data Construction and Measures of Engagement Characteristics
3.1 Data construction
Section 104 of SOX instructs the PCAOB to inspect individual engagements of public
accounting firms that audit SEC registered corporations. As part of these inspections, the
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PCAOB collects information from audit firms, which includes a breakdown of the engagement
hours across different audit phases and across different personnel that comprise the audit team.
Specifically, hours are split in a matrix of phases and personnel. The phases include the planning
hours, quarterly review hours, interim field hours, final field hours, as well as hours incurred
after the issuance of the audit report. The personnel generally includes the core audit team, such
as the lead partner, EQR, and other experienced team members, and personnel from other
functions such as IS and tax auditors, and specialists. The PCAOB also collects expertise
information about the senior core audit team members. Specifically, the following information is
available for the lead partner, the EQR, and other experienced members of the core audit team (at
the manager level or above): The number of years the individual has worked with the client, in
the client’s industry (industries are as defined by the audit firm), and in a particular role (such as
senior manager or lead partner).
We obtain engagement characteristics data from the inspection documents that result
from this process. To obtain permission to access these data we submitted a research proposal to
the PCAOB describing the nature of our study, the data necessary to conduct the study, and a
summary of related research and proposed research questions. As a condition of data access, the
PCAOB reviewed our research to approve the release of nonpublic information. After receiving
PCAOB approval, a joint effort was undertaken between PCAOB staff and us to arrange the
collected data into a usable database format for the purpose of several analyses. This process
took a significant amount of time, particularly due to the need to identify the different roles in an
audit team (these roles were not standardized in the original documents). The initial dataset
encompasses 3,051 individual inspections for audit engagements inspected between 2006 and
18
2015 for the eight largest U.S. audit firms (Deloitte & Touche, Ernst & Young, Pricewaterhouse
Coopers, KPMG, BDO, Grant Thornton, Crowe, and McGladrey, renamed RSM in late 2015).
Out of the 3,051 inspections, we eliminate 270 inspections where hours or experience
data are missing. Importantly, we remove all the inspections where hour subtotals are not
reported, because their absence prevents us from checking whether data input or database
compilation mistakes occur. Out of the remaining observations, 175 inspections have errors in
subtotaling the hours across different phases and roles. We eliminate these from the sample. We
also eliminate 28 inspections corresponding to referred work, where the inspected auditor is not
the principal auditor and therefore the core audit team information is missing.
We combine the 2,578 remaining observations that have PCAOB engagement
characteristics with additional PCAOB data about the outcome of the PCAOB inspection (i.e.,
whether a Part I Finding is identified or not), the engagement realization rate (a measure of
profitability of the engagement), as well as Compustat and Audit Analytics data to compute
several dependent and control variables, such as audit fees and whether the fiscal year end
statements are eventually restated. We eliminate 239 observations where additional PCAOB,
Compustat or Audit Analytics data are missing, resulting in a primary sample of 2,339
inspections. Table 1 summarizes the sample selection process.
(Insert Table 1 About Here)
3.2 Sample statistics
Panel A of Table 2 reports the number of observations in the sample, partitioned by
inspection year and whether the auditor is a Big 4 or not. Each of the ten inspection years
19
roughly comprises 10% of the sample, and the Big 4 observations comprise between 69% and
77% of the yearly number of inspections in the sample.
(Insert Table 2 About Here)
Panel B of Table 2 reports the number of observations in the sample split by whether the
inspection results in a Part I Finding, or the fiscal-year end statements are eventually restated.
Out of the 2,339 observations, 652, or 28% receive a Part I Finding, indicating that the PCAOB
believes the audit opinion was not supported according to relevant audit standards. There are 284
engagements, or 12% of the total, that are eventually restated. Consistent with Aobdia (2018a),
the proportion of restated engagements is higher, at 18%, when the PCAOB identifies a Part I
Finding versus 10% when the PCAOB does not identify a Part I Finding.
3.3 Measures of engagement characteristics
We consider 22 measures of engagement characteristics in our empirical analyses,
described below. Table 3, Panel A reports the descriptive statistics for these variables.
(Insert Table 3 About Here)
3.3.1 Pre-final phase
First, we focus on whether more audit effort during the earlier phases of the audit is
associated with higher audit quality (H1). Our variable of interest is %_Pre-final, equal to the
total lead U.S. office hours spent in planning, quarterly review, and interim phases of the audit
divided by the total lead U.S. office hours. Both numerators and denominators include hours for
all auditors in the U.S. lead office, including the core audit team (staff, senior, managers,
directors and partners), the information system (IS) and tax auditors, and the specialists involved
on the audit; they exclude the time spent by other U.S. locations, or the time spent by non-U.S.
20
affiliate audit firms.6 Because our focus is on the quality of the work that leads to the audit
opinion, we also exclude hours incurred after the audit opinion is issued from the denominator.7
The mean %_Pre-final is 56%, and the interquartile ranges from 50-63%, indicating that a
significant portion of the audit is conducted during the pre-final phases. Untabulated analyses
indicate that, on average, quarterly review, planning, and interim field hours represent 18%,
10%, and 28% of the total lead U.S. office hours, respectively.
We also consider whether it is the core audit team that matters, or other team members,
such as the IS and tax auditors that are regularly present to assist with audit work. Accordingly,
we partition %_Pre-final between %_Audit_Pre-final, comprised of the total core audit team
hours during the pre-final phase of the audit divided by the total lead U.S. office hours, and
%_NonAudit_Pre-final, comprised of the total lead U.S. office hours spent by the IS auditors, tax
auditors, and specialists during the pre-final phase of the audit, divided by the total lead U.S.
office hours. The mean %_Pre-final (%_NonAudit_Pre-final) is 46% (10%) and the interquartile
ranges from 41-52% (6-14%), indicating that the bulk of the pre-final work is conducted by the
core audit team.
3.3.2 Time spent at each seniority level for the core audit team
We also evaluate the time spent by the lead audit partner, EQR, and other experienced
core audit team members, to understand their roles on audit quality (H2). %_LeadPartner,
%_EQR, and %OtherExperienced are the proportion of total lead U.S. office hours spent by the
lead partner, the EQR, and other experienced team members, with averages of 5%, 1%, and 18%,
6 Untabulated analyses indicate that these hours are small in comparison to the lead U.S. office hours, perhaps
because they are not well populated in our dataset. On average in our sample, other U.S. office and foreign affiliate
hours only represent 2.5% and 7.8% of total hours, respectively. 7 Hours incurred after the issuance of the audit opinion are generally small in comparison with total lead U.S. office
hours, on average 2.3%. They often relate to audit work-papers archiving tasks and, given that the audit opinion is
already issued at this stage, are unlikely to influence audit quality.
21
respectively. Other experienced team members are comprised of other audit partners (excluding
the lead partner and EQR), directors, senior managers, and managers involved in the core audit
team. Overall, average lead partner and EQR hours represent a small part of the total hours. In
contrast, other experienced team members hours comprise three times the lead partner and EQR
hours combined.
Because we focus on the time spent during the pre-final phases of the audit, we also
consider %_LeadPartner_Pre-final, %_EQR_Pre-final, and %_OtherExperienced_Pre-final,
equal to the time spent by the lead partner, the EQR, and other experienced team members
during the pre-final phases of the audit, divided by the total lead U.S. office hours. The means
are 3%, 1%, and 10%, respectively.
3.3.3 Measures of expertise
We also consider 12 different measures of the core audit team expertise, first based on the
average of the team members, and then partitioned by seniority levels (i.e., lead partner, EQR,
and other experienced) to understand each of their roles on audit quality (H2).
First, we focus on client-specific expertise. Avg_ClientExp is equal to the number of
years of client experience reported by the engagement team, averaged over the lead partner,
EQR, and other experienced core audit team members; the mean is 3.1 years. We subsequently
partition this variable into LeadPartner_ClientExp, EQR_ClientExp, and
OtherExperienced_ClientExp, equal to the number of years of client experience for the lead
partner, the EQR, and the average of the other experienced team members, with reasonably low
means of 3.4, 2.5, and 3.2 years, respectively.8
8 Low client-specific expertise is partly explained by SOX requirements to rotate the lead partner and EQR off a
client every 5 years.
22
Second, we focus on industry-specific expertise. Avg_IndustryExp is equal to the number
of years of industry experience reported by the engagement team, averaged over the lead partner,
EQR, and other experienced team members; the mean is 12.7 years. Similar to Avg_ClientExp,
we partition Avg_IndustryExp into LeadPartner_IndExp, EQR_IndExp, and
OtherExperienced_IndExp to focus on the industry expertise of the lead partner, the EQR, and
the other experienced core audit team members, with reasonably high means of 18.1, 19.0 and
8.0 years, respectively.
Third, we focus on the overall experience of the experienced core audit team, proxied by
the total years they spent in auditing. Avg_Seniority is equal to the number of years of total audit
experience, averaged over the lead partner, EQR, and other experienced team members; the
mean is 17.3 years. Because our dataset only provides the experience at a given seniority level,
we add 12 years of experience to the partner and director levels, 9 years for senior managers, and
6 for managers, consistent with typical promotion paths within audit firms. Similar to the other
expertise variables, we partition Avg_Seniority into LeadPartner_Seniority, EQR_Seniority, and
OtherExperienced_Seniority, to account separately for the overall experience of the lead partner,
the EQR, and the other experienced team members, with high means of 23.8, 25.5, and 11.4
years, respectively.
Finally, we also consider as an explanatory variable Count_OtherExperienced, equal to
the total number of other experienced team members; the mean is 3.1. We include this variable in
all specifications that focus on experience because average experience is mechanically related to
this variable. In other words, more managers or senior managers on an engagement have the
potential to mechanically reduce the average number of years of overall industry or general
expertise. Further, larger teams could suffer from increased free rider effect (e.g., Holmstrom
23
1982), and thus not be as effective as smaller audit teams. We confirm in untabulated analyses
that all our results on audit quality are not sensitive to inclusion of this variable.
4. Empirical Analyses
4.1 Research design
To test our hypotheses, we assess whether a particular engagement characteristic is
associated with audit outcomes using the following regression:
Audit Quality or Audit Effort or Audit Profitability or Audit Feesi,t = α + β1.Engagement
Characteristici,t + γ.Controlsi,t +Auditor Fixed Efforts + Year Fixed Effects + Industry
Fixed Effects+ εi,t,
(1)
where the subscripts i and t correspond to clients and years, respectively.
Model (1) is estimated using ordinary least squares (OLS), but the results are
qualitatively unchanged if we use logistic specifications for binary dependent variables. We use
several dependent variables to assess the relationship between a particular engagement
characteristic and audit quality, effort, profitability and cost. We consider two main measures of
audit quality (for tests of H1 and H2) based on the audit process and the audit outcome.
PartIFinding is an indicator variable equal to one if the PCAOB identifies a Part I Finding on the
engagement. A Part I Finding indicates that the work conducted by the engagement team is not
sufficient to support the audit opinion. Thus, the measure is reflective of audit process issues.
One of its major advantages is that the PCAOB has access to extensive nonpublic information
about the engagement, such as the audit workpapers and direct interactions with the audit team,
to determine whether a Part I Finding exists on the engagement (see Aobdia 2018a for more
details). We also use two financial reporting quality measures based on the propensity for a client
to restate its financial statements. Restatement is an indicator variable equal to one if the fiscal-
year end statements are eventually restated, and Material_Restatement is an indicator variable
24
equal to one when the restatement is considered material and is disclosed in a separate 8-K form
under item 4.02, non-reliance on previously issued financial statements. Material restatements
are more severe than revisions, which are immaterial (e.g. Choudhary, Merkley and Schipper
2017; both are included in the Audit Analytics restatement dataset).
We also consider the potential trade-offs between audit quality and audit effort,
profitability and cost (H3). To measure audit effort, we use Logaudithours, the logarithm of total
hours spent on an engagement (including at the main U.S. office, other U.S. offices, and
international affiliates). To measure audit profitability, we use Realization_Rate, the engagement
realization rate, equal to the total audit fees divided by the hours worked times their
undiscounted billing rates. The higher the realization rate, the higher the profitability of the
engagement. The main advantage of using the audit realization rate is that it represents a direct
measure of auditor profitability. However, the measure comes from a different PCAOB dataset,
is only available for inspections starting in 2009, unavailable for Crowe and RSM audit firms,
and some observations are missing, resulting in significant sample loss.9 To measure the audit
cost to the client, we use Logauditfees, equal to the logarithm of total audit fees charged to the
client from Audit Analytics.
Our main variable of interest, Engagement Characteristic is proxied by the 22
explanatory variables described in section 3.3. Each variable focuses on a different audit
characteristic, such as time spent during the pre-final phases of the audit, or client and industry
knowledge.
9 We consider in untabulated analyses whether this sample restriction could create a power issue for our tests.
Specifically, we restrict the analyses of audit fees and hours to the subsample where realization rate is available. We
generally find that our results on audit fees and hours are similar in the main and restricted samples. However, while
directionally similar, some of the associations with audit hours fall below conventional significance levels in the
restricted sample.
25
Following prior literature (e.g., Francis et al. 2005, Francis and Yu 2009, Reichelt and
Wang 2010, Aobdia 2018a), we include a set of control variables that have been shown to
influence audit quality and audit fees. These control variables are composed of Logat (the natural
logarithm of the client’s total assets, to control for client size), and Leverage (total debt divided
by total debt plus equity, to control for capital structure). To control for client risk, we also
include Loss (an indicator variable equal to one if earnings before extraordinary items is
negative), BTM (the book to market ratio, equal to the client’s book equity divided by fiscal year
end market value), CFOat (cash flows from operations deflated by beginning assets), StdCFOat
(standard deviation of CFOat, measured from year t − 3 to year t), Salegrowth (year-on-year
percentage increase in the client’s revenues), Weakness (indicator variable if material
weaknesses are reported), and Litigation (indicator variable if the client is in a high litigation
industry). To control for client complexity, we also include ForeignPifo (absolute value of pretax
income from foreign operations divided by the absolute value of pretax income), Geoseg
(number of geographic segments, as per Compustat Segments), and Busseg (number of business
segments, as per Compustat Segments). We also control for two specific characteristics of the
audit: DecYE (a dummy that equals one for fiscal year ending in December), to account for
increased time constraints during the busy season, and FirstYear (indicator variable equal to one
for first year audits of new clients), to account for the increased difficulty of first year client
audits (e.g., Bell et al. 2015). Given that the PCAOB is likely to identify additional internal
control over financial reporting (ICFR) audit deficiencies for integrated audits of financial
statements and ICFR, we also include Integrated_Audit (an indicator variable equal to one when
the audit is an integrated audit of financial statements and ICFR). We also incorporate year, audit
firm, and Fama French 12 industry group fixed effects, cluster standard errors at the client level,
26
and winsorize all continuous variables at the 1st and 99th percentiles to limit the influence of
outliers in the specifications. Detailed variable definitions are provided in the appendix.
4.2 Descriptive statistics
We present several descriptive statistics in Panel A of Table 3. In terms of dependent
variables, an average engagement has a 28% likelihood to receive a Part I Finding, a 12% (4%)
likelihood to be restated (have a material restatement), and commands audit fees of $1.5M,
corresponding to 7,300 hours of audit work. The average engagement realization rate is 49%,
indicative that auditors give significant discounts from the official billing rates.
In terms of control variables, our sample contains 77% of integrated audits of financial
statements and ICFR; 73% of the client-years with a December fiscal year end, and 25% that
report losses. Average client assets are $1.6B.
4.3 Sample Partition based on restatements and Part I Findings
Panel B of Table 3 reports the means of the subsamples partitioned on whether the
financial statements are eventually restated as well at t-tests of differences across the two
samples. Several interesting patterns emerge. Engagements of non-restating clients spend more
time during the pre-final phase of the audit as compared with non-restating clients (56.6% versus
53.7%; p<1%). Non-restating engagements also have more time spent by the other experienced
team members relative to restating engagements (18.3% versus 17.6%; p<10%), whereas the
time spent by the lead partner is similar across the two (4.8%; p>10%). The average client
expertise is also higher for non-restating engagements relative to restating ones (3.15 versus 2.84
years; p<1%), partly driven by higher expertise of the other experienced team members (3.3
versus 2.8 years; p<1%). While lead partner client expertise is higher for non-restating versus
27
restating engagements (3.5 versus 3.2 years; p>10%), the difference is not statistically significant
at conventional levels. The overall team’s industry expertise is also higher for non-restating
versus restating engagements (12.8 versus 12.1 years; p<5%); these differences are partly driven
by higher EQR industry experience (19.2 versus 17.9 years; p<5%), and other team members
industry experience (8.0 versus 7.5 years; p<5%). Experienced team members’ seniority, other
dependent variables such as audit fees, hours, and realization rate, and control variables do not
show statistical differences between restating and non-restating groups (p>10%).
Overall, these preliminary univariate results are consistent with several ideas. First, more
time spent during the pre-final phase of the audit is beneficial to audit quality (H1). Second, both
time spent by other experienced team members, and their expertise, appear to be beneficial to
audit quality (H2). Our evidence is also consistent with a positive influence on audit quality of
the other experienced team members’ client-specific and industry-specific expertise. Finally,
consistent with Laurion et al. 2016, Aobdia et al. 2018a, and Gipper et al. 2018, we fail to find
any evidence that specific lead partner characteristics, such as time spent on the audit and
expertise, are associated with audit quality.
Panel C of Table 3 reports an analysis similar to Panel B, where the sample partition is
whether the engagement receives a Part I Finding. Some results in Panel C echo those in Panel
B. For example, engagements without Part I Findings spend more time during the pre-final phase
of the audit than engagements with Part I Findings. However, several results run opposite to
those of Panel B. For example, average industry experience is higher for engagements that
receive Part I Findings. We note that it might be more difficult than in Panel B to interpret these
univariate results without controlling for specific engagement characteristics, because the
engagements that receive Part I Findings appear different from those that do not receive them. In
28
particular, engagements with Part I Findings in our sample are larger, with average assets of
$1.9B, compared with $1.5B for engagements without Part I Findings. Likely reflecting their
larger sizes, they are also comprised of integrated audits to a greater extent (85.9%), as compared
with 73.4% for engagements without Part I Findings. These differences are likely driven by the
PCAOB identifying additional ICFR audit deficiencies for engagements with integrated financial
statements and ICFR audits. Overall, the sample partition in Panel C of Table 3 confirms the
need to control in subsequent multivariate analyses for company-specific characteristics that may
influence both specific engagement characteristics and the probability to receive a Part I Finding.
4.4 Pre-final phase of the audit
Panel A of Table 4 reports multivariate tests of H1, whether more audit effort during the
earlier phases of the audit is associated with better audit quality. Our variable of interest is
%_Pre-final, the proportion of time spent during the pre-final phases of the audit. We estimate
model (1) with %_Pre-final and find negative associations with PartIFinding, Restatement, and
Material_Restatement (p<10%), suggesting that more time spent during the pre-final phases of
the audit positively influences audit quality (H1). A one standard-deviation decline in %_Pre-
final, (10%), is associated with a reduction in the probability of a Part I Finding of 1.8% and a
restatement of 2.5%. These economic effects are reasonably high in comparison with the average
Part I Finding and restatement rates in the sample of 28% and 12%, respectively.
We also find that the increased audit quality comes with a cost to the client, evidenced by
a positive association between %_Pre-final and Logauditfees, driven by a positive association
between %_Pre-final and Logaudithours. In terms of economic magnitude, an increase of one
standard-deviation of %_Pre-final is associated with a non-negligible 6.5% increase in audit
29
fees.10 We do not find an association between %_Pre-final and audit profitability proxied by
Realization_Rate.
(Insert Table 4 About Here)
Next, we separately focus on the pre-final time spent by the core audit team, in
comparison with others involved in the process (IS and tax auditors and specialists). We partition
%_Pre-final into %_Audit_Pre-final and %_NonAudit_Pre-final in Panel B of Table 4. We find
that our initial results in Panel A are generally driven by the time spent by the core audit team in
the pre-final phase, with the exception of material restatements, which are influenced by both the
core audit team and the other auditors involved in the process. We also find positive associations
between both %_Audit_Pre-final and %_NonAudit_Pre-final with Logauditfees and Logaudit
hours, but not realization rate, suggesting the costs borne by the client are driven by both the
audit and non-audit personnel. In subsequent analyses, we focus on the core audit team given
that our data on the non-core audit team are more limited, and the quality effects appear
generally stronger for the core audit team.
In untabulated analyses, we also consider whether any particular pre-final phase of the
audit (quarterly review, planning, and interim field) matters more from an audit and financial
reporting quality standpoint. We partition %_Audit_Pre-final into these three components and
repeat our analyses. We find negative associations between each of the components of pre-final
hours and Restatement and Material_Restatement. However, these associations are insignificant
at conventional levels (p>10%). The main exception is a negative association between the
proportion of planning hours and PartIFinding, which suggests a larger role of the planning
10 This number is computed as e(0.633×0.10)-1.
30
phase compared with the other ones, consistent with some of the arguments in Davidson and Gist
(1996).
Given that we find a positive association between %_Pre-final and Logaudithours in
Table 4, we also consider whether it is the increased audit hours, or the time spent by the auditor
during the pre-final phase that ultimately influence audit quality.11 We replicate our analyses on
audit quality adding audit hours as a control variable and continue to find robust results in the
relationship between %_Pre-final and audit quality (untabulated).
4.5 Time spent by seniority level
We focus in Panel A of Table 5 on the proportion of hours spent by the experienced core
audit team members, including the lead engagement partner (%_LeadPartner), the EQR
(%_EQR), and the other experienced team members (%_OtherExperienced). We find negative
associations between %_EQR and Restatement (p<10%), and between %_OtherExperienced and
PartIFinding (p<5%). The other associations with audit quality are insignificant (p>10%). These
associations provide some evidence that more time spent by more experienced team members
other than the lead partner improve audit quality (H2). However, we do not find any positive
association between the time spent by the lead partner and audit quality; in fact we note a weak
positive association between %_LeadPartner and Material_Restatement, suggesting that too
much time spent by the lead partner on the audit could be detrimental in some instances, perhaps
because it represents an inability to properly delegate to others.
The relationships between time spent by audit team members and cost measures are
mixed. The increased time spent by other experienced team members (%_OtherExperienced )
appears costly to the client with higher audit fees (Logauditfees; p<1%) and more audit hours
11 For example, Caramanis and Lennox (2008) find a positive association between audit hours and audit quality.
31
(Logaudithours; p<1%), and no effect to auditor profitability (Realization Rate; p>10%). An
increase of one standard-deviation of %_OtherExperienced is associated with a reasonably high
9.4% increase in audit fees. In contrast, the additional time spent by the lead partner and EQR
seems to result in more efficient audits or lower audit hours (Logaudithours; p<1%), with
benefits passed to the client in terms of lower fees (Logauditfees; p<1%).
(Insert Table 5 About Here)
Panel B of Table 5 focuses on the time spent by experienced audit team members during
the pre-final phase of the audit. Our results are generally similar to those in Panel A, highlighting
the importance of the pre-final phase of the audit in terms of audit and financial reporting
quality.
4.6 Audit team members’ client expertise
We focus on the role of client-specific expertise in Table 6. Panel A reports the results
with the average client expertise for all experienced team members (Avg_ClientExp). We find
negative associations between Avg_ClientExp and Restatement as well as Material_Restatement
(p<5%). This is consistent with more client-specific expertise positively influencing audit
quality measured as financial reporting quality. A one standard-deviation increase in
Avg_ClientExp (1.33 years) is associated with a 2% reduced probability of restatements. This is
reasonably large given that 12% of the financial statements are eventually restated in our sample.
Focusing on the number of other experienced team members, we do not find any association
between Count_OtherExperienced and audit quality (p>10%); in other words, we fail to find
free-riding issues within larger audit teams.
32
Next, we evaluate whether increased average client expertise comes with trade-offs or
efficiency benefits to the auditor or the client. We find a negative association between
Avg_ClientExp and Logaudithours (p<1%), as well as positive associations with
Realization_Rate and Logauditfees (p<10%). Overall, these results suggest that increased client
expertise has a positive influence on financial reporting quality, but also results in improved
audit efficiency that improves audit profitability. A one standard-deviation increase in
Avg_ClientExp is associated with a 2.73% increase in the audit realization rate. Nevertheless,
increased client expertise comes with a cost to the client in the form of higher audit fees
(p<10%).
(Insert Table 6 About Here)
We focus in Panel B of Table 6 on the separate influence of client expertise for the lead
partner (LeadPartner_ClientExp), the EQR (EQR_ClientExp), and the other experienced team
members (OtherExperienced_ClientExp). Consistent with Gipper et al. (2018), we do not find
any association between LeadPartner_ClientExp and audit quality. We also fail to find any
association between EQR_ClientExp and audit quality (p>10%), but we do find evidence of a
negative association between OtherExperienced_ClientExp and Restatement (p<1%). These
results are consistent with the results of Panel B of Table 5 that suggest that other experienced
team members characteristics are associated with audit quality measured as financial reporting
quality.
In terms of audit profitability, we find positive associations for all the measures of
expertise with Realization_Rate. Extending Gipper et al. (2018) who focus only on lead partner
characteristics, we not only find a negative (positive) association between lead partner client
experience and audit hours (audit profitability), we also find similar directional effects from
33
other experienced team members’ and EQR client experience, suggesting that more client
experience improves audit efficiency and profitability. A one standard-deviation in
OtherExperienced_ClientExp (1.75 years) is associated with a 2.1% decrease in the probability
of restatements, and a 2% increase in realization rate. We also find a positive association with
Logauditfees, suggesting that client-specific experience of other experienced team members
comes with a cost to the client.
We replace the continuous variables of interest of Panel B on client expertise with
indicator variables that equal one when these variables are above their medians in Panel C; the
new variable names have >median appended to reflect the change in definition. We generally
find similar results as in Panel B. However, the association between
OtherExperienced_ClientExp> median and Restatement falls slightly below conventional
significance levels. Instead, we find a negative association between
OtherExperienced_ClientExp>median and PartIFinding. We also find a surprising, but weak,
positive association between LeadPartnerClientExp>median and PartIFinding.
Collectively, our results are consistent with the client specific expertise of other
experienced audit team members playing an important role in explaining financial reporting
quality and audit profitability. Importantly, because these team members are not subject to SOX
mandatory rotation requirements, our results suggest that an audit firm might be able to increase
both audit quality and profitability at the same time, by increasing the level of client expertise
among senior professionals on the audit team.
In additional analyses, we also consider whether non-linearities exist in the relationships
identified above. In particular, it is possible that some of the effects of client specific expertise
taper off as it increases. Following Davidson and Gist (1996), we add in untabulated analyses
34
squared expertise terms in each of the regressions conducted in Table 6. We generally find
negative coefficients on the squared expertise terms in the regression of Realization_Rate,
consistent with the influence of client expertise on audit profitability tapering off as it increases.
We also find negative and positive associations between OtherExperienced_ClientExp and
OtherExperienced_ClientExp2 with PartIFinding, suggesting an initial positive influence of
other experienced team member’s expertise on audit process quality that tapers off. Further, the
association between other experienced team member’s client expertise and audit fees is
moderated when the number of years increase, evidenced by a negative association between
OtherExperienced_ClientExp2 and Logauditfees. Overall, these results are consistent with one
additional year of client specific expertise having a larger influence when client-specific
expertise is lower.
4.7 Audit team members’ industry expertise
In Table 7 we evaluate the role of industry expertise, as defined and reported by the audit
firms. Panel A reports the results when we consider the average industry expertise of the
experienced team members (Avg_IndustryExp). We find a negative association with
Material_Restatement (p<10%), suggesting industry expertise is positively associated with audit
quality. In terms of economic significance, one standard-deviation increase in Avg_IndustryExp,
4.67 years, is associated with a 0.9% decrease in the probability of material restatements, to be
compared with an average probability of 4% in our sample.
(Insert Table 7 About Here)
Panel B decomposes the average industry expertise among the lead partner
(LeadPartner_IndExp), the EQR (EQR_IndExp), and other experienced team members
(OtherExperienced_IndExp). Consistent with Aobdia et al. (2018a), we do not find an
35
association between LeadPartner_IndExp and financial reporting quality, but we do find a
positive association with Logauditfees (p<10%).12 Consistent with our prior results, we find
negative associations between EQR_IndExp, OtherExperienced_IndExp, and
Material_Restatement (p<10%).
We replace the continuous variables of interest in Panel B on industry expertise with
indicator variables in Panel C when these variables are above their respective medians. We
append the names with >median to reflect the change in definition. We generally find similar
results as in Panel B. However, we also find a negative association between
OtherExperienced_IndExp>median and Restatement (p<5%), consistent with a positive effect of
other experienced team members’ industry expertise on audit quality. We also find a surprising
positive relationship between LeadPartnerClientExp>median and PartIFinding, similar to the
one of Panel C of Table 6 (p<1%), perhaps because lead partners become more overconfident or
document their audit procedures less as they gain significant expertise.
Similar to section 4.6, we also consider in untabulated analyses whether non-linearities
exist in the industry expertise relationship with outcome variables, by adding squared industry
expertise terms in the regressions. We find negative associations between
OtherExperienced_IndExp and positive associations between OtherExperienced_IndExp2 with
PartIFinding and Restatement, confirming the importance of the first few years of industry
expertise for other experienced team members.
4.8 Audit team members’ seniority
12 While we do not find any association between LeadPartner_IndExp and Logaudithours in the main sample, we
find a positive association when restricting the sample to where realization rate is available. The coefficient and
statistical significance of the association between LeadPartner_IndExp and Logauditfees also increases when
restricting the sample. Both results are consistent with Aobdia et al. (2018a). We find similar associations with audit
fees and hours and industry experience when focusing on the EQR in the subsample where realization rate is
available.
36
We focus in Table 8 on experienced team member’s overall expertise proxied by the
number of years they worked in auditing. Panel A reports the results for the average years of
seniority in the team (Avg_Seniority). We do not find any association between Avg_Seniority and
audit quality (p>10%).
(Insert Table 8 About Here)
Next, we focus on the seniority of the lead partner (LeadPartner_Seniority), the EQR
(EQR_Seniority), and other experienced team members (OtherExperienced_Seniority) separately
in Panel B of Table 8. We find negative associations between EQR seniority and PartIFinding
(p<10%) as well as Material_Restatements (p<5%). Overall, these results are consistent with the
spirit of PCAOB standard AS 1220 which instructs the EQR to “perform an evaluation of the
significant judgments made by the engagement team and the related conclusions reached in
forming the overall conclusion on the engagement,” implying that significant general expertise is
required for an individual to properly fulfill the EQR role. We replace the continuous variables
of interest of Panel B on general expertise with indicator variables when these variables are
above their medians in Panel C. We append the names of the variables with >median to reflect
the changes in definition. We generally find similar results as in Panel B.
Collectively, our results are consistent with our hypotheses. First, time spent during the
pre-final phase of the audit is positively associated with audit quality (H1). Second, time spent
and expertise of experienced core audit team members other than the lead partner is also
positively associated with audit quality (H2). Third, while some particular engagement
characteristics come with a cost to the client, such as more time spent during the pre-final hours,
there is usually no negative impact on audit profitability proxied by the audit realization rate
(H3). Client-specific experience, on the other hand, tends to improve audit efficiency and audit
37
profitability. We also confirm the results in prior literature that find little influence of specific
lead partner characteristics on audit quality in the U.S. (Laurion et al. 2016, Aobdia et al. 2018a,
Gipper et al. 2018).
4.9 Selection bias concerns
Our sample is based on audits selected for inspection by the PCAOB and, given that the
PCAOB does not randomly select engagements for inspection (Hanson 2012), our results may
not generalize to the full population. Prior research evaluates whether PCAOB-inspected samples
exhibit selection bias and generally finds evidence suggesting that selection bias concerns are not
severe (e.g., Aobdia 2018a, Aobdia et al. 2018b, Choudhary et al. 2018a and b). Further, many
of our explanatory variables of interest during the sample period are not available to the PCAOB
at the time of selection of engagements for inspection. Thus, selection based on these variables is
unlikely. However, the PCAOB has access to lead-partner level information prior to selecting
engagements (e.g., Aobdia et al. 2018a). Therefore, it is important to compare our results of lead
partner effects with those of prior literature, particularly Laurion et al. (2016), Aobdia et al.
(2018a), and Gipper et al. (2018), who analyze much broader samples, not restricted to PCAOB
inspected engagements. The lead partner results are generally consistent with these studies,
suggesting that our results may generalize to the full population.
Given that prior literature typically discusses selection bias issues as an omitted variable
concern (Heckman 1979), we apply a procedure, following Frank (2000) and Larcker and
Rusticus (2010), to estimate the amount of correlation an omitted variable must have with both
independent and dependent variables to overturn our reported results. For each variable of
interest, we compute the impact threshold for a confounding variable (ITCV), which is the
lowest product of the partial correlation between the dependent variable and the omitted variable,
38
and the partial correlation between the explanatory variable of interest and the omitted variable
that makes the coefficient statistically insignificant. Theoretically, a high ITCV indicates that the
OLS results are robust to omitted variable concerns. Following Larcker and Rusticus (2010), we
also compute a benchmark impact for the size of likely correlations involving the hypothetical
(and unobserved) omitted variables, based on the control variables in our sample.
(Insert Table 9 About Here)
Table 9 reports the ITCV for each explanatory variable that has shown statistical
significance in the prior analyses. We also consider the control variables that have higher impact
(in absolute value) than the explanatory variable. In other words, an omitted variable would need
to have an impact comparable to the one of these control variables to overturn our results. For the
audit quality variables, our results indicate that the ITCV of the explanatory variables is high. In
most specifications, none of the control variables, including company size (Logat), has an impact
sufficiently large to overturn the results. For the audit hours and fees variables, only company
size (Logat) generally has a higher impact than our variables. Given that company size predicts a
significant proportion of audit fees and audit hours (over 60%), this is not surprising, and
suggests that the ITCV for the explanatory variables is high. Collectively, out of the 54
coefficients considered, 28 (17), and [9] have no control variable, (Logat only), or [Logat and at
least one other control variable, exactly one for 7 out of 9 cases and more than one for the 2
remaining], respectively, with impact sufficiently large to overturn the results. Overall, these
analyses suggest that our results are reasonably robust to omitted variable concerns.
4.10 Additional analyses
We consider in supplemental analyses the nature of the Part I Finding, in particular
whether the audit deficiency identified by the PCAOB is a financial statements or ICFR
39
deficiency. We also use the count of Part I Findings identified by the PCAOB in each
engagement as an alternative measure of audit process quality. Untabulated analyses indicate that
our results are generally qualitatively unchanged, in comparison with PartIFinding, when we use
these alternative dependent variables. However, the proportion of time spent by other
experienced team members (%_OtherExperienced) is negatively associated with financial
statements Part I Findings and not with ICFR Part I Findings. We also find a negative association
between EQR time (%_EQR) and ICFR Part I Findings, but not with financial statements Part I
Findings. These results are consistent with the ICFR assessment being challenging and requiring
significant expertise (e.g., Hoitash et al. 2008, Bedard and Graham 2011, Kinney et al 2013), and
highlight an important role for the EQR in terms of audit process quality.
We also consider whether specific roles within the other experienced team members
matter more. In untabulated analyses, we decompose the other experienced team members into
two groups: other partners or directors, and senior managers or managers. We generally find that
the effect of client-specific and industry expertise are more pronounced for senior managers or
managers than for other partners or directors. However, we caution about over-interpreting this
evidence for the following reason: Less than half of the engagements in the sample have an audit
director or other audit partner, whereas most engagements have a manager or senior manager.
Thus, the weak results on director could be driven by sample loss (these results are conditional
on having a director or other partner present on the engagement). Consistent with the idea that
other partners and directors matter from an audit quality standpoint, we find in untabulated
analyses that engagements that have a higher proportion of director or other partner time have
better audit quality.
40
We also evaluate the experienced core audit team’s gender. Prior research documents in
experimental and archival settings differences in team performance depending on the team
gender (e.g., Apesteguia et al. 2012, Huang and Kisgen 2013). In untabulated analyses, we count
the number of experienced team members, and consider the gender of each of them, based on
their first name.13 We do not find any association between team gender and any of our measures
of audit and financial reporting quality.
We also consider the introduction of new PCAOB standards. Specifically, we focus on
AS7 (renamed AS 1220 in 2015), which clarified the role of the EQR. We interact EQR related
variables (in Panel B of Tables 5 to 8) with an indicator variable for fiscal years beginning after
12/15/2009, when the standard became effective. In untabulated analyses, we find that several of
our audit quality results on EQR are driven by the post AS 1220-audits, evidenced by significant
F-tests on the sum of the EQR variable and its interaction with post 12/15/2009 fiscal years, but
insignificant coefficients on the main EQR variable, corresponding to pre-AS 1220 audits.
However, the interaction term is generally insignificant. Overall, these results provide weak
evidence that AS 1220 strengthened, or at least did not weaken, the role of the EQR on audit
quality.
We also focus on PCAOB release 2010-004, issued on August 5, 2010, that adopted eight
auditing standards related to the auditor's assessment of and response to risk and superseded six
of the Board's interim auditing standards and related amendments to PCAOB standards. This
standard may have changed the role of pre-final hours in the audit. We interact pre-final hours in
13 We use the database of names from the Social Security Administration of all babies born in the US between 1942-
1990. This database identifies the gender associated with each name for all names used at least 5 times. Because
some names are used for both males and females, we assign a given gender to a particular name if more than 90% of
occurrences of the name are used for a particular gender. For more ambiguous names, we exclude these names from
our analysis.
41
Table 4 with an indicator variable for fiscal years beginning after 12/15/2009, when the standard
became effective. In untabulated analyses, we find a positive (negative) coefficient on the
interaction of this indicator variable and %_Pre-final with Restatement (PartIFinding) as the
dependent variable. This appears to indicate that the changes helped auditors better comply with
audit standards, but financial reporting quality did not eventually benefit.
5. Conclusion
In this study, we evaluate a variety of characteristics regarding the composition of the
audit and audit team to understand whether they influence audit quality. We consider both audit
process quality, measured by regulatory inspection findings, and financial reporting quality,
measured by the propensity of a client to restate the financial statements, as measures of audit
quality. We also explore the trade-offs between increased audit quality and the cost to the client
or the auditor in the form of reduced profitability versus improvements in audit efficiency.
We find that more time spent during the pre-final phase of the audit is associated with
better audit quality. The core audit team drives most of these results. We also find that greater
time spent by other experienced team members is associated with better audit quality. However,
these engagement characteristics come with increased costs to the client, reflecting trade-offs
between audit quality and the cost of the audit to the client. We also find evidence consistent
with EQR and other experienced team members’ expertise, including client specific expertise,
industry expertise, and overall expertise, positively influencing audit quality. Confirming prior
literature on audit partners in the U.S. (Laurion et al. 2016, Aobdia et al. 2018a, Gipper et al.
2018), we fail to find an influence of lead partner time and experience characteristics on audit
quality.
42
Overall, our study is a first attempt at opening the black box of the role of audit processes
and personnel below the lead partner, and responds to recent regulatory initiatives that strive to
better understand the determinants of audit quality. Our results suggest that auditors, audit
regulators, and academics should focus more on non-lead partner level members in audit teams,
to better understand their roles from an audit performance standpoint. While prior audit research
has made significant progress at understanding the role of lead partners on audit performance
(e.g., Lennox and Wu 2018), and finds in several instances that individual partner characteristics
influence audit quality, our study raises the prospect that some of these results might be driven
by the quality of other team members comprising the engagement team, rather than the lead
partners themselves, in case lead partners tend to select specific team members. Future research
might have the opportunity to explore specific engagement characteristics in more details if audit
regulators, such as the PCAOB, or audit firms themselves, directly or through industry
associations, collect more engagement specific data and share them with academic researchers.
43
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47
Appendix: Variable Definitions
Variable Definition
Dependent Variables
PartIFinding An indicator variable equal to one if the inspection of the engagement resulted
in at least one Part I finding, from PCAOB proprietary data.
Restatement An indicator variable equal to one if the fiscal year-end financial statements
are subsequently restated as noted by Audit Analytics.
Material_Restatement An indicator variable equal to one if the fiscal year-end financial statements
are subsequently restated and the disclosure is made in a separate form 8-K
under item 4.02 (non-reliance on previously issued financial statements) as
noted by Audit Analytics.
Logaudithours The logarithm of the total audit hours spent on the engagement (including
other U.S. locations and non-affiliates hours), from PCAOB proprietary data.
Realization_Rate Total audit fees divided by total audit hours times their individual
undiscounted billing rates (which depend on an engagement team member's
seniority). A measure of audit profitability. Obtained from PCAOB proprietary
data but available only for most engagements inspected in 2009 and thereafter.
Logauditfees The logarithm of the engagement audit fees, from Audit Analytics.
Explanatory Variables All explanatory variables are obtained from PCAOB proprietary data.
%_Pre-final Total lead U.S. office hours spent in planning, quarterly review, and interim
phases divided by total lead U.S. office hours (total lead U.S. office hours
exclude hours spent by the non-lead U.S. offices and foreign affiliates). Both
the numerator and denominator include hours spent by IS auditors, tax
auditors, and specialists, but exclude any hours incurred after the audit opinion
is issued.
%_Audit_Pre-final Total lead U.S. office hours spent by the core audit team during the planning,
quarterly review, and interim audit phases divided by total lead U.S. office
hours. The core audit team excludes hours spent by IS auditors, tax auditors,
and specialists (but the denominator includes them). Both the numerator and
denominator exclude any hours incurred after the audit opinion is issued.
%_NonAudit_Pre-final Total lead U.S. office hours spent by the IS auditors, tax auditors, and
specialists in planning, quarterly review, and interim phases divided by total
lead U.S. office hours. The numerator excludes core audit team hours (but the
denominator includes them). Both the numerator and denominator exclude any
hours incurred after the audit opinion is issued.
%_LeadPartner Total lead partner hours divided by total lead U.S. office hours.
%_EQR Total engagement quality reviewer (EQR) hours divided by total lead U.S.
office hours.
%_OtherExperienced Total other experienced team members (other partners excluding the lead
partner and EQR, directors, senior managers and managers) divided by total
lead U.S. office hours
%_LeadPartner_Pre-final Total lead partner hours spent in planning, quarterly review, and interim
phases divided by total lead U.S. office hours.
%_EQR_Pre-final Total EQR hours spent in planning, quarterly review, and interim phases
divided by total lead U.S. office hours.
%_OtherExperienced_Pre-final Total other experienced team members (other partners excluding the lead
partner and EQR, directors, senior managers and managers) spent in planning,
quarterly review, and interim phases divided by total lead U.S. office hours.
Avg_ClientExp Number of years of client experience, averaged over lead partner, EQR, and
other experienced team members
LeadPartner_ClientExp Number of years of client experience for the lead partner.
EQR_ClientExp Number of years of client experience for the EQR.
OtherExperienced_ClientExp Number of years of client experience, averaged over the other experienced
48
Variable Definition
team members.
Avg_IndustryExp Number of years of industry experience, averaged over lead partner, EQR, and
other experienced team members.
LeadPartner_IndExp Number of years of industry experience for the lead partner.
EQR_IndExp Number of years of industry experience for the EQR.
OtherExperienced_IndExp Number of years of industry experience, averaged over other experienced team
members.
Avg_Seniority Number of years of total audit experience, averaged over lead partner, EQR,
and other experienced team members. Because the dataset only provides
experience at a given level, 12 years of experience are added to the partner and
director levels, 9 for senior managers, and 6 for managers.
LeadPartner_Seniority Number of years of total audit experience for the lead partner.
EQR_Seniority Number of years of total audit experience for the EQR.
OtherExperienced_Seniority Number of years of total audit experience, averaged over other experienced
team members. Because the dataset only provides experience at a given role
(i.e. manager, senior manager, etc), 12 years of experience are added to the
partner and director levels, 9 for senior managers, and 6 for managers.
Count_OtherExperienced Total number of other experienced team members on the team.
Control Variables All data from Compustat unless otherwise noted
IntegratedAudit Indicator variable when the audit is an integrated audit of internal controls over
financial reporting and financial statements, from PCAOB proprietary data.
FirstYear Indicator variable when the client is a first-year audit client, from Audit
Analytics.
ForeignPifo Absolute value of pretax income from foreign operations (PIFO) divided by
the absolute value of pretax income (PI).
Logat Logarithm of assets.
Geoseg Number of geographic segments.
Busseg Number of business segments.
DecYE An indicator variable equal to one when the fiscal year ends in December.
StdCFOat Standard deviation of the client's cash flows from operations deflated by
beginning assets, computed over t-3 and t.
CFOat Client's cash flows from operations deflated by beginning assets.
Leverage Total debt divided by debt plus stockholder's equity.
Loss Indicator variable equal to one when income before extraordinary items (IB) is
negative.
BTM Shareholder's equity (book value) deflated by fiscal year end market
capitalization
Litigation Indicator variable if the client is in a high litigation industry (SIC code
between 2833 and 2836, 8731 and 8734, 3570 and 3577, 7370 and 7374, 3600
and 3674, or 5200 and 5961).
SaleGrowth Year-on-year sales growth of the client firm.
Weakness An indicator variable equal to one if the client reports a material weakness.
49
Table 1: Sample Selection Process
This table presents the sample construction from the raw dataset obtained from the PCAOB.
Observations
Engagement Profiles. Inspection Years 2006 - 2015
3,051
Less:
Missing hours data or experience data 270
Errors in sub-totaling hours across phases and roles 175
Inspection of referred work 28
Missing Dependent Variables and Controls (Compustat, Audit Analytics, or PCAOB) 239
Final Sample (client-years)
2,339
50
Table 2: Sample Statistics
This table presents several sample-level statistics. Panel A breaks down the sample by inspection year and auditor
affiliation (Big 4 and non-Big 4), and Panel B breaks down the sample by outcome (restatements and Part I
Findings).
Panel A: Observations by Inspection Year and Big 4
Inspection Year Non Big 4 Big 4 Total
2006 58 169 227
2007 72 184 256
2008 68 151 219
2009 67 196 263
2010 65 178 243
2011 65 178 243
2012 62 165 227
2013 67 182 249
2014 64 186 250
2015 38 124 162
Total 626 1,713 2,339
Panel B: Observations by Outcome (Restatements and Part I Findings)
No Part I Finding Part I Finding Total Part I Finding Rate
No Restatement 1,521 534 2,055 26%
Restatement 166 118 284 42%
Total 1,687 652 2,339 28%
Restatement Rate 10% 18% 12%
51
Table 3: Descriptive Statistics
This table presents overall descriptive statistics in Panel A, and the sample means partitioned between observations
with and without restatements in Panel B, and observations with and without Part I Findings in Panel C. A t-test
assesses the differences in means across the subsamples in Panels B and C. Variable definitions are provided in the
Appendix. Significance levels are * 10%, ** 5% and *** 1%.
Panel A: Descriptive Statistics
Variable Observations Mean StDev Perc25th Median Perc75th
PartIFinding 2,339 0.28 0.45 0.00 0.00 1.00
Restatement 2,339 0.12 0.33 0.00 0.00 0.00
Material_Restatement 2,339 0.04 0.20 0.00 0.00 0.00
Logaudithours 2,339 8.90 0.93 8.25 8.83 9.47
Realization_Rate 1,399 48.52 22.54 30.33 43.00 65.00
Logauditfees 2,339 14.22 1.07 13.47 14.11 14.88
%_Pre-final 2,339 0.56 0.10 0.50 0.57 0.63
%_Audit_Pre-final 2,339 0.46 0.09 0.41 0.46 0.52
%_NonAudit_Pre-final 2,339 0.10 0.06 0.06 0.09 0.14
%_LeadPartner 2,339 0.05 0.02 0.03 0.04 0.06
%_EQR 2,339 0.01 0.01 0.01 0.01 0.01
%_OtherExperienced 2,339 0.18 0.06 0.14 0.17 0.22
%_LeadPartner_Pre-final 2,339 0.03 0.01 0.02 0.02 0.03
%_EQR_Pre-final 2,339 0.01 0.00 0.00 0.00 0.01
%_OtherExperienced_Pre-final 2,339 0.10 0.04 0.07 0.10 0.13
Avg_ClientExp 2,339 3.11 1.33 2.20 3.00 3.83
LeadPartner_ClientExp 2,339 3.43 2.57 2.00 3.00 4.00
EQR_ClientExp 2,339 2.51 1.36 1.00 2.00 4.00
OtherExperienced_ClientExp 2,334 3.21 1.75 2.00 3.00 4.20
Avg_IndustryExp 2,339 12.73 4.67 9.50 13.00 16.00
LeadPartner_IndExp 2,339 18.13 8.84 11.00 19.00 25.00
EQR_IndExp 2,339 19.02 9.22 12.00 20.00 25.00
OtherExperienced_IndExp 2,333 7.98 3.62 5.50 8.00 10.00
Avg_Seniority 2,339 17.31 3.02 15.00 16.83 19.00
LeadPartner_Seniority 2,339 23.82 6.52 19.00 22.75 28.00
EQR_Seniority 2,339 25.46 6.44 21.00 25.00 30.00
OtherExperienced_Seniority 2,333 11.37 2.97 9.50 11.00 13.00
Count_OtherExperienced 2,339 3.09 2.24 2.00 2.00 4.00
IntegratedAudit 2,339 0.77 0.42 1.00 1.00 1.00
FirstYear 2,339 0.08 0.27 0.00 0.00 0.00
ForeignPifo 2,339 0.28 0.60 0.00 0.00 0.35
Logat 2,339 7.40 1.79 6.10 7.30 8.50
Geoseg 2,339 2.45 2.40 1.00 2.00 4.00
Busseg 2,339 2.09 1.73 1.00 1.00 3.00
DecYE 2,339 0.73 0.44 0.00 1.00 1.00
StdCFOat 2,339 0.06 0.07 0.02 0.04 0.07
CFOat 2,339 0.09 0.11 0.02 0.08 0.14
Leverage 2,339 0.37 0.31 0.10 0.34 0.56
Loss 2,339 0.25 0.44 0.00 0.00 1.00
BTM 2,339 0.61 0.63 0.28 0.50 0.82
Litigation 2,339 0.28 0.45 0.00 0.00 1.00
SaleGrowth 2,339 0.15 0.39 -0.03 0.07 0.21
Weakness 2,339 0.05 0.22 0.00 0.00 0.00
52
Panel B: Partition between No Restatements (2,055 observations) and Restatements (284 observations)
Difference in means
(No Restatement - Restatement)
Variable
Mean
No Restatement
Mean
Restatement t-statistic p-value
Logaudithours 8.894 8.926 -0.544 0.586
Realization_Rate 48.322 49.734 -0.808 0.419
Logauditfees 14.218 14.196 0.332 0.740
%_Pre-final 0.566 0.537 4.649*** 0.000
%_Audit_Pre-final 0.464 0.439 4.678*** 0.000
%_NonAudit_Pre-final 0.101 0.097 1.026 0.305
%_LeadPartner 0.048 0.048 -0.054 0.957
%_EQR 0.010 0.010 1.735* 0.083
%_OtherExperienced 0.183 0.176 1.817* 0.069
%_LeadPartner_Pre-final 0.027 0.026 0.980 0.327
%_EQR_Pre-final 0.005 0.005 2.039** 0.042
%_OtherExperienced_Pre-final 0.106 0.098 2.637*** 0.008
Avg_ClientExp 3.149 2.840 3.674*** 0.000
LeadPartner_ClientExp 3.455 3.246 1.280 0.201
EQR_ClientExp 2.501 2.543 -0.494 0.621
OtherExperienced_ClientExp 3.265 2.836 3.883*** 0.000
Avg_IndustryExp 12.811 12.117 2.348** 0.019
LeadPartner_IndExp 18.230 17.401 1.480 0.139
EQR_IndExp 19.171 17.921 2.143** 0.032
OtherExperienced_IndExp 8.046 7.516 2.319** 0.020
Avg_Seniority 17.317 17.271 0.242 0.809
LeadPartner_Seniority 23.868 23.469 0.967 0.334
EQR_Seniority 25.511 25.127 0.943 0.346
OtherExperienced_Seniority 11.375 11.334 0.216 0.829
Count_OtherExperienced 3.098 3.039 0.420 0.675
IntegratedAudit 0.770 0.757 0.497 0.619
FirstYear 0.079 0.077 0.080 0.936
ForeignPifo 0.283 0.288 -0.138 0.890
Logat 7.420 7.245 1.544 0.123
Geoseg 2.451 2.426 0.162 0.872
Busseg 2.089 2.074 0.134 0.894
DecYE 0.740 0.690 1.770* 0.077
StdCFOat 0.058 0.059 -0.170 0.865
CFOat 0.087 0.075 1.716* 0.086
Leverage 0.366 0.413 -2.360** 0.018
Loss 0.250 0.282 -1.145 0.252
BTM 0.610 0.622 -0.320 0.749
Litigation 0.276 0.292 -0.576 0.565
SaleGrowth 0.150 0.152 -0.059 0.953
Weakness 0.043 0.109 -4.790*** 0.000
53
Panel C: Partition between No Part I Finding (1,687 observations) and Part I Finding (652 observations)
Difference in means (No Part I - Part I)
Variable
Mean
No Part I Finding
Mean
Part I Finding t-statistic p-value
Logaudithours 8.908 8.873 0.815 0.415
Realization_Rate 48.477 48.594 -0.091 0.927
Logauditfees 14.242 14.148 1.908* 0.056
%_Pre-final 0.568 0.547 4.542*** 0.000
%_Audit_Pre-final 0.465 0.450 3.743*** 0.000
%_NonAudit_Pre-final 0.102 0.096 2.502** 0.012
%_LeadPartner 0.048 0.048 -0.345 0.730
%_EQR 0.010 0.011 -3.994*** 0.000
%_OtherExperienced 0.183 0.180 0.830 0.407
%_LeadPartner_Pre-final 0.027 0.027 0.448 0.654
%_EQR_Pre-final 0.005 0.006 -3.251*** 0.001
%_OtherExperienced_Pre-
final 0.106 0.102 1.738* 0.082
Avg_ClientExp 3.109 3.119 -0.169 0.866
LeadPartner_ClientExp 3.388 3.536 -1.248 0.212
EQR_ClientExp 2.523 2.461 0.990 0.322
OtherExperienced_ClientExp 3.207 3.229 -0.266 0.791
Avg_IndustryExp 12.569 13.133 -2.624*** 0.009
LeadPartner_IndExp 17.805 18.969 -2.858**** 0.004
EQR_IndExp 18.767 19.670 -2.125** 0.034
OtherExperienced_IndExp 7.892 8.215 -1.932* 0.053
Avg_Seniority 17.300 17.340 -0.285 0.775
LeadPartner_Seniority 23.733 24.043 -1.030 0.303
EQR_Seniority 25.603 25.108 1.667* 0.096
OtherExperienced_Seniority 11.258 11.659 -2.919*** 0.004
Count_OtherExperienced 3.023 3.267 -2.361** 0.018
IntegratedAudit 0.734 0.859 -6.486*** 0.000
FirstYear 0.076 0.084 -0.635 0.525
ForeignPifo 0.278 0.297 -0.676 0.499
Logat 7.337 7.560 -2.702*** 0.007
Geoseg 2.539 2.210 2.982*** 0.003
Busseg 2.076 2.113 -0.465 0.642
DecYE 0.734 0.733 0.035 0.972
StdCFOat 0.062 0.048 4.338*** 0.000
CFOat 0.093 0.066 5.293*** 0.000
Leverage 0.362 0.399 -2.585** 0.010
Loss 0.254 0.253 0.061 0.951
BTM 0.571 0.716 -5.013*** 0.000
Litigation 0.303 0.212 4.463*** 0.000
SaleGrowth 0.158 0.131 1.452 0.147
Weakness 0.049 0.055 -0.593 0.553
54
Table 4: Role of Pre-final Hours
This table presents the results of Model (1) when using %_Pre-final, the proportion of pre-final hours, as the main
explanatory variable of interest in Panel A; and in Panel B the split of this variable between %_Audit_Pre-final and
%_NonAudit_Pre-final, the proportion of pre-final hours for the core audit team and IS, tax and specialist members,
respectively. Variable definitions are provided in the Appendix. The robust standard error, clustered at the client
level (in parenthesis), is below the coefficient. Significance levels are * 10%, ** 5% and *** 1%.
Panel A: Pre-final Hours
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
%_Pre-final -0.182* -0.247*** -0.159*** 0.460*** 5.703 0.633***
(0.0976) (0.0808) (0.0502) (0.115) (4.069) (0.132)
IntegratedAudit 0.0249 -0.0318 0.0121 -0.0366 -1.037 -0.0727*
(0.0281) (0.0247) (0.0157) (0.0331) (1.249) (0.0378)
FirstYear 0.0324 -0.00922 -0.00720 0.222*** -17.28*** -0.114***
(0.0340) (0.0262) (0.0165) (0.0332) (1.458) (0.0378)
ForeignPifo 0.0216 -0.00119 0.00890 0.0848*** -0.297 0.141***
(0.0176) (0.0126) (0.00946) (0.0213) (0.556) (0.0221)
Logat 0.00535 -0.00867 -0.00537* 0.377*** 1.663*** 0.460***
(0.00675) (0.00529) (0.00315) (0.00913) (0.272) (0.0105)
Geoseg -0.0103** -0.000739 -0.00278 0.0387*** -0.310** 0.0402***
(0.00407) (0.00309) (0.00184) (0.00522) (0.142) (0.00536)
Busseg 0.00432 -0.00133 0.00147 0.0603*** 0.213 0.0635***
(0.00531) (0.00422) (0.00239) (0.00666) (0.200) (0.00715)
DecYE -0.0355 -0.0225 -0.0181 -0.00365 -0.343 -0.0121
(0.0223) (0.0183) (0.0121) (0.0253) (0.904) (0.0296)
StdCFOat -0.385*** -0.0459 0.133* 0.410** 1.200 0.651***
(0.133) (0.0997) (0.0774) (0.181) (5.850) (0.217)
CFOat -0.250*** -0.0714 -0.0102 -0.0244 6.394* -0.0287
(0.0874) (0.0688) (0.0497) (0.106) (3.477) (0.124)
Leverage 0.0574* 0.0736*** 0.0462** -0.0164 -1.203 -0.0762*
(0.0334) (0.0274) (0.0201) (0.0384) (1.246) (0.0455)
Loss -0.0422* -0.0149 -0.00530 0.134*** 1.706** 0.152***
(0.0244) (0.0200) (0.0134) (0.0271) (0.861) (0.0306)
BTM 0.0512*** 0.0134 0.00492 -0.0530*** -0.586 -0.0782***
(0.0162) (0.0128) (0.00788) (0.0183) (0.515) (0.0207)
Litigation -0.0140 -0.00189 -0.00468 -0.0798** -0.0798 -0.103***
(0.0293) (0.0238) (0.0152) (0.0365) (1.140) (0.0392)
SaleGrowth 0.00910 0.00503 0.0194 -0.0518* -0.722 -0.0447
(0.0246) (0.0176) (0.0143) (0.0282) (0.867) (0.0296)
Weakness 0.0160 0.133*** 0.0517* 0.393*** 0.599 0.362***
(0.0420) (0.0394) (0.0279) (0.0457) (1.913) (0.0543)
Audit Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,339 2,339 2,339 2,339 1,399 2,339
Adjusted R-squared 0.118 0.035 0.027 0.750 0.738 0.771
55
Panel B: Partition of Pre-final Hours Between Core Audit Team and Other Members
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
%_Audit_Pre-final -0.252** -0.250*** -0.146*** 0.322** 6.045 0.389***
(0.104) (0.0861) (0.0527) (0.127) (4.338) (0.142)
%_NonAudit_Pre-final 0.0232 -0.224 -0.203** 0.840*** 4.339 1.458***
(0.185) (0.142) (0.0902) (0.218) (7.991) (0.257)
IntegratedAudit 0.0258 -0.0317 0.0121 -0.0354 -1.047 -0.0710*
(0.0281) (0.0247) (0.0158) (0.0331) (1.247) (0.0377)
FirstYear 0.0317 -0.00943 -0.00710 0.221*** -17.26*** -0.117***
(0.0340) (0.0262) (0.0165) (0.0332) (1.453) (0.0377)
ForeignPifo 0.0208 -0.00128 0.00902 0.0834*** -0.292 0.139***
(0.0176) (0.0126) (0.00946) (0.0215) (0.557) (0.0216)
Logat 0.00324 -0.00896* -0.00491 0.373*** 1.681*** 0.451***
(0.00685) (0.00540) (0.00323) (0.00927) (0.285) (0.0104)
Geoseg -0.0102** -0.000718 -0.00281 0.0390*** -0.311** 0.0408***
(0.00406) (0.00309) (0.00184) (0.00520) (0.142) (0.00532)
Busseg 0.00416 -0.00138 0.00147 0.0601*** 0.214 0.0631***
(0.00531) (0.00422) (0.00239) (0.00668) (0.199) (0.00716)
DecYE -0.0335 -0.0223 -0.0184 -0.000110 -0.354 -0.00510
(0.0224) (0.0182) (0.0120) (0.0252) (0.905) (0.0293)
StdCFOat -0.377*** -0.0449 0.132* 0.423** 1.081 0.677***
(0.133) (0.0998) (0.0773) (0.179) (5.855) (0.212)
CFOat -0.258*** -0.0725 -0.00880 -0.0380 6.437* -0.0576
(0.0874) (0.0689) (0.0497) (0.106) (3.489) (0.123)
Leverage 0.0617* 0.0745*** 0.0456** -0.00968 -1.248 -0.0612
(0.0336) (0.0276) (0.0204) (0.0385) (1.269) (0.0456)
Loss -0.0425* -0.0149 -0.00522 0.134*** 1.708** 0.151***
(0.0244) (0.0200) (0.0134) (0.0271) (0.862) (0.0306)
BTM 0.0526*** 0.0137 0.00471 -0.0508*** -0.599 -0.0731***
(0.0162) (0.0128) (0.00798) (0.0182) (0.520) (0.0206)
Litigation -0.0123 -0.00203 -0.00519 -0.0757** -0.0906 -0.0959**
(0.0295) (0.0238) (0.0152) (0.0367) (1.142) (0.0392)
SaleGrowth 0.0100 0.00507 0.0191 -0.0499* -0.732 -0.0405
(0.0246) (0.0176) (0.0144) (0.0281) (0.870) (0.0293)
Weakness 0.0140 0.132*** 0.0518* 0.390*** 0.608 0.358***
(0.0419) (0.0394) (0.0278) (0.0455) (1.912) (0.0538)
Audit Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,339 2,339 2,339 2,339 1,399 2,339
Adjusted R-squared 0.119 0.035 0.027 0.750 0.737 0.772
56
Table 5: Role of Experienced Hours
This table presents the results of Model (1) when considering the proportion of hours spent by the lead engagement
partner, the EQR, and the other experienced audit team members, respectively. Panel A focuses on all phases of the
audit, whereas Panel B focuses on the pre-final phase of the audit. Variable definitions are provided in the
Appendix. The robust standard error, clustered at the client level (in parenthesis), is below the coefficient.
Significance levels are * 10%, ** 5% and *** 1%.
Panel A: Proportion of Hours Spent at Different Auditor Levels
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
%_LeadPartner 0.0373 0.490 0.415* -4.614*** 24.51 -3.337***
(0.500) (0.376) (0.238) (0.541) (20.40) (0.628)
%_EQR -0.688 -2.274* -0.927 -15.62*** 1.376 -15.98***
(1.787) (1.289) (0.824) (1.911) (66.48) (2.117)
%_OtherExperienced -0.358** -0.0403 0.0254 1.350*** -9.605 1.491***
(0.147) (0.106) (0.0680) (0.171) (5.988) (0.194)
IntegratedAudit 0.0175 -0.0395 0.00834 -0.0422 -0.989 -0.0716*
(0.0280) (0.0246) (0.0157) (0.0310) (1.239) (0.0367)
FirstYear 0.0334 -0.0118 -0.00794 0.152*** -16.79*** -0.173***
(0.0345) (0.0268) (0.0171) (0.0313) (1.459) (0.0373)
ForeignPifo 0.0244 -0.000678 0.00881 0.0830*** -0.272 0.135***
(0.0176) (0.0128) (0.00947) (0.0218) (0.555) (0.0219)
Logat 0.00343 -0.0151*** -0.00891*** 0.343*** 1.941*** 0.432***
(0.00696) (0.00527) (0.00320) (0.00861) (0.290) (0.0101)
Geoseg -0.00992** -0.000933 -0.00294 0.0357*** -0.306** 0.0369***
(0.00411) (0.00311) (0.00185) (0.00504) (0.143) (0.00518)
Busseg 0.00448 -0.00155 0.00147 0.0526*** 0.236 0.0565***
(0.00531) (0.00424) (0.00239) (0.00640) (0.200) (0.00695)
DecYE -0.0362 -0.0240 -0.0194 0.00743 -0.242 -0.000722
(0.0223) (0.0183) (0.0123) (0.0237) (0.899) (0.0284)
StdCFOat -0.373*** -0.0545 0.124 0.403** 2.044 0.629***
(0.133) (0.101) (0.0780) (0.163) (5.836) (0.212)
CFOat -0.251*** -0.0766 -0.0133 -0.0378 6.387* -0.0402
(0.0878) (0.0697) (0.0505) (0.101) (3.479) (0.121)
Leverage 0.0617* 0.0793*** 0.0495** -0.0211 -1.282 -0.0833*
(0.0335) (0.0271) (0.0199) (0.0367) (1.259) (0.0448)
Loss -0.0397 -0.0138 -0.00490 0.128*** 1.747** 0.144***
(0.0243) (0.0200) (0.0135) (0.0254) (0.859) (0.0295)
BTM 0.0539*** 0.0167 0.00645 -0.0431** -0.641 -0.0712***
(0.0163) (0.0127) (0.00792) (0.0168) (0.512) (0.0196)
Litigation -0.0187 -0.00666 -0.00762 -0.0665* -0.0910 -0.0852**
(0.0292) (0.0236) (0.0152) (0.0347) (1.128) (0.0371)
SaleGrowth 0.0120 0.00653 0.0196 -0.0398 -0.790 -0.0379
(0.0244) (0.0179) (0.0145) (0.0257) (0.874) (0.0280)
Weakness 0.0260 0.141*** 0.0569** 0.350*** 0.500 0.309***
(0.0419) (0.0401) (0.0284) (0.0421) (1.942) (0.0507)
Audit Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,339 2,339 2,339 2,339 1,399 2,339
Adjusted R-squared 0.119 0.032 0.024 0.776 0.738 0.786
57
Panel B: Proportion of Pre-final Hours Spent at Different Auditor Levels
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
%_LeadPartner_Pre-final 0.349 0.391 0.198 -6.253*** 25.43 -4.053***
(0.791) (0.550) (0.358) (0.820) (30.07) (0.979)
%_EQR_Pre-final -0.740 -4.023* -0.941 -27.73*** 165.3 -28.41***
(3.013) (2.260) (1.537) (3.288) (110.8) (3.610)
%_OtherExperienced
_Pre-final
-0.590*** -0.152 -0.0461 2.406*** -8.519 2.743***
(0.218) (0.160) (0.107) (0.266) (8.536) (0.283)
IntegratedAudit 0.0197 -0.0392 0.00812 -0.0350 -0.916 -0.0663*
(0.0280) (0.0246) (0.0158) (0.0305) (1.249) (0.0359)
FirstYear 0.0364 -0.0145 -0.00945 0.143*** -16.84*** -0.185***
(0.0345) (0.0267) (0.0169) (0.0316) (1.454) (0.0376)
ForeignPifo 0.0244 0.000281 0.00978 0.0788*** -0.293 0.131***
(0.0176) (0.0127) (0.00957) (0.0219) (0.548) (0.0213)
Logat 0.00638 -0.0150*** -0.00884*** 0.342*** 2.005*** 0.427***
(0.00703) (0.00540) (0.00332) (0.00882) (0.289) (0.0102)
Geoseg -0.00990** -0.000859 -0.00283 0.0360*** -0.310** 0.0370***
(0.00412) (0.00311) (0.00184) (0.00504) (0.143) (0.00517)
Busseg 0.00463 -0.00162 0.00142 0.0541*** 0.243 0.0577***
(0.00531) (0.00424) (0.00240) (0.00643) (0.199) (0.00693)
DecYE -0.0352 -0.0230 -0.0191 0.00996 -0.278 -0.000628
(0.0224) (0.0184) (0.0124) (0.0238) (0.899) (0.0286)
StdCFOat -0.366*** -0.0437 0.130* 0.416** 1.781 0.626***
(0.133) (0.101) (0.0776) (0.166) (5.794) (0.213)
CFOat -0.252*** -0.0750 -0.0125 -0.0188 6.527* -0.0211
(0.0878) (0.0695) (0.0504) (0.101) (3.451) (0.121)
Leverage 0.0607* 0.0796*** 0.0494** -0.0172 -1.438 -0.0775*
(0.0335) (0.0271) (0.0199) (0.0369) (1.253) (0.0446)
Loss -0.0404* -0.0138 -0.00459 0.126*** 1.734** 0.142***
(0.0243) (0.0200) (0.0134) (0.0254) (0.858) (0.0292)
BTM 0.0523*** 0.0167 0.00652 -0.0420** -0.697 -0.0689***
(0.0163) (0.0128) (0.00799) (0.0170) (0.514) (0.0195)
Litigation -0.0147 -0.00495 -0.00756 -0.0664* -0.118 -0.0894**
(0.0293) (0.0237) (0.0152) (0.0346) (1.132) (0.0372)
SaleGrowth 0.0108 0.00774 0.0206 -0.0408 -0.833 -0.0376
(0.0244) (0.0179) (0.0146) (0.0257) (0.854) (0.0279)
Weakness 0.0208 0.138*** 0.0577** 0.332*** 0.666 0.300***
(0.0421) (0.0401) (0.0285) (0.0424) (1.934) (0.0518)
Audit Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,339 2,339 2,339 2,339 1,399 2,339
Adjusted R-squared 0.119 0.032 0.022 0.775 0.738 0.788
58
Table 6: Role of Client-Specific Expertise
This table presents the results of Model (1) when using client expertise as the main explanatory variable of interest.
Panel A considers average client expertise for the experienced audit team members, and Panel B splits this expertise
between lead partner, EQR, and other experienced team members. Panel C transforms each continuous variable of
interest into an indicator variable for when the variable is above the median of the distribution. Variable definitions
are provided in the Appendix. The robust standard error, clustered at the client level (in parenthesis), is below the
coefficient. Significance levels are * 10%, ** 5% and *** 1%.
Panel A: Average Client Expertise
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
Avg_ClientExp -0.00709 -0.0147** -0.00716** -0.0237*** 2.049*** 0.0180*
(0.00788) (0.00597) (0.00325) (0.00785) (0.264) (0.00936)
Count_OtherExperienced 0.00393 -0.00131 0.000847 0.109*** -0.633*** 0.106***
(0.00507) (0.00398) (0.00266) (0.00653) (0.182) (0.00710)
IntegratedAudit 0.0225 -0.0374 0.00906 0.0148 -0.923 -0.0200
(0.0281) (0.0246) (0.0159) (0.0302) (1.236) (0.0356)
FirstYear 0.0133 -0.0435 -0.0250 0.144*** -12.77*** -0.101**
(0.0378) (0.0292) (0.0185) (0.0359) (1.529) (0.0427)
ForeignPifo 0.0222 -0.000269 0.00951 0.0762*** -0.211 0.134***
(0.0176) (0.0128) (0.00962) (0.0211) (0.557) (0.0218)
Logat 0.0000618 -0.00972 -0.00782** 0.310*** 1.820*** 0.390***
(0.00752) (0.00611) (0.00393) (0.00943) (0.304) (0.0109)
Geoseg -0.0103** -0.000774 -0.00280 0.0395*** -0.318** 0.0410***
(0.00409) (0.00310) (0.00185) (0.00476) (0.139) (0.00505)
Busseg 0.00375 -0.00112 0.00135 0.0449*** 0.325* 0.0484***
(0.00533) (0.00425) (0.00239) (0.00632) (0.195) (0.00675)
DecYE -0.0376* -0.0264 -0.0202* 0.00429 -0.257 0.00224
(0.0223) (0.0183) (0.0123) (0.0233) (0.886) (0.0275)
StdCFOat -0.399*** -0.0548 0.125 0.204 3.344 0.471**
(0.133) (0.100) (0.0779) (0.156) (5.764) (0.199)
CFOat -0.254*** -0.0785 -0.0140 -0.00896 6.232* -0.00158
(0.0874) (0.0693) (0.0503) (0.0957) (3.447) (0.116)
Leverage 0.0589* 0.0738*** 0.0471** -0.0214 -0.618 -0.0735*
(0.0334) (0.0274) (0.0199) (0.0363) (1.228) (0.0432)
Loss -0.0421* -0.0136 -0.00478 0.113*** 1.652** 0.130***
(0.0243) (0.0200) (0.0135) (0.0243) (0.835) (0.0285)
BTM 0.0529*** 0.0146 0.00605 -0.0441** -0.653 -0.0690***
(0.0162) (0.0128) (0.00791) (0.0188) (0.497) (0.0208)
Litigation -0.0178 -0.00709 -0.00801 -0.0688** 0.0480 -0.0887**
(0.0293) (0.0235) (0.0151) (0.0347) (1.109) (0.0374)
SaleGrowth 0.00935 0.00427 0.0193 -0.0538** -0.310 -0.0413
(0.0246) (0.0178) (0.0146) (0.0247) (0.836) (0.0278)
Weakness 0.0176 0.139*** 0.0552** 0.259*** 1.672 0.239***
(0.0417) (0.0400) (0.0280) (0.0385) (1.947) (0.0483)
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,339 2,339 2,339 2,339 1,399 2,339
Adjusted R-squared 0.117 0.033 0.024 0.792 0.750 0.799
59
Panel B: Client Expertise Split Between Lead Partner, EQR, and Other Experienced Team Members
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
LeadPartner_ClientExp 0.00575 -0.000764 -0.00110 -0.00648* 0.457*** -0.00452
(0.00364) (0.00281) (0.00136) (0.00353) (0.109) (0.00396)
EQR_ClientExp -0.00720 0.00550 -0.00237 -0.0167** 0.461** -0.00246
(0.00694) (0.00516) (0.00333) (0.00695) (0.222) (0.00793)
OtherExperienced_
ClientExp
-0.00827 -0.0120*** -0.00280 -0.0102* 1.136*** 0.0182***
(0.00583) (0.00422) (0.00251) (0.00556) (0.201) (0.00653)
Count_Other
Experienced
0.00372 -0.00170 0.000750 0.109*** -0.603*** 0.107***
(0.00508) (0.00401) (0.00268) (0.00649) (0.187) (0.00702)
Control Variables Yes Yes Yes Yes Yes Yes
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,334 2,334 2,334 2,334 1,395 2,334
Adjusted R-squared 0.118 0.034 0.023 0.794 0.750 0.800
Panel C: Client Expertise Split with Indicator Variables for Above Median
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
LeadPartner_ClientExp
>median
0.0325* -0.0152 -0.00332 -0.0333* 2.854*** -0.0105
(0.0190) (0.0144) (0.00896) (0.0187) (0.609) (0.0207)
EQR_ClientExp
>median
-0.0168 0.0123 -0.0131 -0.0518*** 1.958*** -0.00931
(0.0186) (0.0141) (0.00886) (0.0185) (0.613) (0.0214)
OtherExperienced_
ClientExp>median
-0.0520*** -0.0238 0.00499 -0.00133 3.044*** 0.0837***
(0.0193) (0.0156) (0.00972) (0.0194) (0.666) (0.0222)
Count_Other
Experienced
0.00433 -0.00124 0.000823 0.109*** -0.636*** 0.106***
(0.00507) (0.00403) (0.00267) (0.00649) (0.188) (0.00696)
Control Variables Yes Yes Yes Yes Yes Yes
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,334 2,334 2,334 2,334 1,395 2,334
Adjusted R-squared 0.120 0.0319 0.0227 0.793 0.750 0.800
60
Table 7: Role of Industry Expertise
This table presents the results of Model (1) when using industry expertise as the main explanatory variable of
interest. Panel A considers average industry expertise for the experienced audit team members, and Panel B splits
this expertise between lead partner, EQR, and other experienced team members. Panel C transforms each continuous
variable of interest into an indicator variable for when the variable is above the median of the distribution. Variable
definitions are provided in the Appendix. The robust standard error, clustered at the client level (in parenthesis), is
below the coefficient. Significance levels are * 10%, ** 5% and *** 1%.
Panel A: Average Industry Expertise
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
Avg_IndustryExp 0.000479 -0.00223 -0.00195* -0.00277 0.0363 0.00130
(0.00213) (0.00170) (0.00110) (0.00221) (0.0791) (0.00247)
Count_OtherExperienced 0.00269 -0.00189 -0.000126 0.109*** -0.612*** 0.108***
(0.00518) (0.00401) (0.00263) (0.00654) (0.197) (0.00711)
IntegratedAudit 0.0220 -0.0389 0.00778 0.0129 -1.017 -0.0183
(0.0280) (0.0245) (0.0159) (0.0301) (1.235) (0.0356)
FirstYear 0.0289 -0.0136 -0.0105 0.197*** -17.09*** -0.134***
(0.0341) (0.0264) (0.0167) (0.0315) (1.448) (0.0372)
ForeignPifo 0.0225 0.000790 0.0102 0.0772*** -0.341 0.132***
(0.0176) (0.0127) (0.00953) (0.0212) (0.558) (0.0218)
Logat -0.000530 -0.0127** -0.00883** 0.304*** 2.282*** 0.392***
(0.00728) (0.00582) (0.00377) (0.00918) (0.302) (0.0106)
Geoseg -0.0104** -0.000786 -0.00281 0.0395*** -0.315** 0.0410***
(0.00408) (0.00311) (0.00184) (0.00478) (0.140) (0.00506)
Busseg 0.00396 -0.00138 0.00122 0.0445*** 0.306 0.0484***
(0.00533) (0.00426) (0.00240) (0.00635) (0.199) (0.00671)
DecYE -0.0366 -0.0252 -0.0199 0.00692 -0.250 0.000874
(0.0223) (0.0183) (0.0122) (0.0232) (0.898) (0.0275)
StdCFOat -0.395*** -0.0444 0.133* 0.209 2.712 0.450**
(0.133) (0.101) (0.0782) (0.157) (5.850) (0.199)
CFOat -0.252*** -0.0768 -0.0139 -0.0100 6.263* -0.00886
(0.0874) (0.0695) (0.0502) (0.0953) (3.498) (0.116)
Leverage 0.0611* 0.0758*** 0.0473** -0.0158 -1.275 -0.0753*
(0.0337) (0.0272) (0.0201) (0.0362) (1.254) (0.0429)
Loss -0.0420* -0.0135 -0.00460 0.111*** 1.805** 0.128***
(0.0244) (0.0200) (0.0134) (0.0244) (0.853) (0.0284)
BTM 0.0531*** 0.0155 0.00644 -0.0425** -0.724 -0.0696***
(0.0162) (0.0128) (0.00796) (0.0187) (0.510) (0.0208)
Litigation -0.0170 -0.0104 -0.0109 -0.0718** 0.0389 -0.0856**
(0.0295) (0.0239) (0.0152) (0.0348) (1.122) (0.0376)
SaleGrowth 0.0109 0.00434 0.0185 -0.0538** -0.770 -0.0437
(0.0246) (0.0176) (0.0142) (0.0250) (0.849) (0.0280)
Weakness 0.0218 0.144*** 0.0573** 0.266*** 0.972 0.232***
(0.0418) (0.0402) (0.0282) (0.0389) (1.951) (0.0482)
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,339 2,339 2,339 2,339 1,399 2,339
Adjusted R-squared 0.117 0.031 0.024 0.792 0.740 0.800
61
Panel B: Industry Expertise Split Among Lead Partner, EQR, and Other Experienced Team Members
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
LeadPartner_IndExp 0.00175 0.000410 0.000434 0.00146 0.00852 0.00262*
(0.00114) (0.000869) (0.000531) (0.00124) (0.0391) (0.00135)
EQR_IndExp -0.000594 -0.000912 -0.00115** 0.00165 -0.00257 0.00167
(0.00112) (0.000839) (0.000508) (0.00115) (0.0374) (0.00125)
OtherExperienced_
IndExp
-0.00240 -0.00283 -0.00231* -0.00857*** 0.0603 -0.00447
(0.00283) (0.00216) (0.00134) (0.00276) (0.102) (0.00316)
Count_Other
Experienced
0.00275 -0.000726 0.000996 0.112*** -0.630*** 0.108***
(0.00514) (0.00406) (0.00264) (0.00647) (0.195) (0.00700)
Control Variables Yes Yes Yes Yes Yes Yes
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,333 2,333 2,333 2,333 1,395 2,333
Adjusted R-squared 0.117 0.032 0.026 0.794 0.739 0.801
Panel C: Industry Expertise Split with Indicator Variables for Above Median
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
LeadPartner_IndExp
>median
0.0514*** 0.0116 0.00603 0.0199 1.316** 0.0433**
(0.0188) (0.0145) (0.00894) (0.0192) (0.663) (0.0214)
EQR_IndExp
>median
-0.00775 -0.0186 -0.0159* 0.0205 -0.124 0.0341
(0.0195) (0.0146) (0.00842) (0.0198) (0.695) (0.0213)
OtherExperienced
_IndExp>median
-0.0186 -0.0361** -0.0255*** -0.0177 0.0229 0.0120
(0.0187) (0.0145) (0.00852) (0.0198) (0.723) (0.0222)
Count_OtherExperienced 0.00261 -0.0000922 0.00142 0.111*** -0.625*** 0.107***
(0.00515) (0.00408) (0.00264) (0.00646) (0.194) (0.00695)
Control Variables Yes Yes Yes Yes Yes Yes
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,333 2,333 2,333 2,333 1,395 2,333
Adjusted R-squared 0.119 0.0337 0.0270 0.793 0.740 0.801
62
Table 8: Role of Overall Expertise Proxied by Seniority
This table presents the results of Model (1) when using overall expertise proxied by seniority as the main
explanatory variable of interest. Panel A considers average industry expertise for the experienced audit team
members, and Panel B splits this expertise between lead partner, EQR, and other experienced team members. Panel
C transforms each continuous variable of interest into an indicator variable for when the variable is above the
median of the distribution. Variable definitions are provided in the Appendix. The robust standard error, clustered at
the client level (in parenthesis), is below the coefficient. Significance levels are * 10%, ** 5% and *** 1%.
Panel A: Average Overall Expertise
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
Avg_Seniority 0.00126 -0.000454 0.000103 -0.00460 -0.0313 -0.000988
(0.00311) (0.00244) (0.00163) (0.00345) (0.117) (0.00363)
Count_OtherExperienced 0.00315 -0.00166 0.000540 0.106*** -0.626*** 0.104***
(0.00528) (0.00402) (0.00264) (0.00686) (0.200) (0.00727)
IntegratedAudit 0.0219 -0.0380 0.00870 0.0134 -1.051 -0.0196
(0.0281) (0.0246) (0.0159) (0.0301) (1.237) (0.0357)
FirstYear 0.0287 -0.0127 -0.00986 0.198*** -17.09*** -0.134***
(0.0341) (0.0264) (0.0167) (0.0315) (1.446) (0.0373)
ForeignPifo 0.0224 0.000406 0.00981 0.0781*** -0.330 0.133***
(0.0176) (0.0127) (0.00958) (0.0212) (0.555) (0.0218)
Logat -0.000682 -0.0128** -0.00914** 0.307*** 2.285*** 0.395***
(0.00732) (0.00580) (0.00374) (0.00927) (0.300) (0.0107)
Geoseg -0.0104** -0.000790 -0.00281 0.0394*** -0.316** 0.0409***
(0.00408) (0.00311) (0.00185) (0.00478) (0.141) (0.00507)
Busseg 0.00395 -0.00118 0.00137 0.0450*** 0.298 0.0487***
(0.00533) (0.00426) (0.00240) (0.00636) (0.200) (0.00675)
DecYE -0.0368* -0.0245 -0.0193 0.00672 -0.250 -0.000657
(0.0223) (0.0183) (0.0122) (0.0232) (0.897) (0.0275)
StdCFOat -0.395*** -0.0486 0.128 0.209 2.731 0.459**
(0.133) (0.100) (0.0781) (0.158) (5.846) (0.199)
CFOat -0.253*** -0.0749 -0.0125 -0.00499 6.176* -0.00859
(0.0875) (0.0697) (0.0504) (0.0958) (3.490) (0.116)
Leverage 0.0604* 0.0777*** 0.0489** -0.0162 -1.278 -0.0796*
(0.0334) (0.0271) (0.0199) (0.0363) (1.251) (0.0430)
Loss -0.0419* -0.0137 -0.00475 0.112*** 1.800** 0.129***
(0.0244) (0.0200) (0.0134) (0.0245) (0.854) (0.0285)
BTM 0.0532*** 0.0154 0.00639 -0.0447** -0.714 -0.0714***
(0.0162) (0.0128) (0.00794) (0.0188) (0.511) (0.0208)
Litigation -0.0173 -0.00729 -0.00798 -0.0707** 0.00750 -0.0891**
(0.0294) (0.0237) (0.0151) (0.0348) (1.125) (0.0376)
SaleGrowth 0.0110 0.00642 0.0205 -0.0515** -0.841 -0.0442
(0.0245) (0.0178) (0.0145) (0.0251) (0.851) (0.0279)
Weakness 0.0211 0.145*** 0.0582** 0.269*** 0.948 0.232***
(0.0418) (0.0401) (0.0281) (0.0393) (1.946) (0.0484)
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,339 2,339 2,339 2,339 1,399 2,339
Adjusted R-squared 0.117 0.031 0.022 0.791 0.740 0.799
63
Panel B: Overall Expertise Split Between Lead Partner, EQR, and Other Experienced Team Members
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
LeadPartner_Seniority 0.000812 -0.000301 0.000320 0.00332** 0.0448 0.00440***
(0.00142) (0.00118) (0.000735) (0.00145) (0.0533) (0.00163)
EQR_Seniority -0.00246* -0.00115 -0.00147** 0.00293** 0.0433 0.00458***
(0.00142) (0.00101) (0.000627) (0.00148) (0.0515) (0.00158)
OtherExperienced_
Seniority
0.00450 0.0000248 -0.000614 -0.00803** -0.203* -0.00585*
(0.00321) (0.00242) (0.00171) (0.00314) (0.114) (0.00347)
Count_OtherExperienced 0.00155 -0.00147 0.000687 0.109*** -0.573*** 0.105***
(0.00518) (0.00409) (0.00268) (0.00644) (0.193) (0.00705)
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,333 2,333 2,333 2,333 1,395 2,333
Adjusted R-squared 0.118 0.030 0.024 0.793 0.740 0.801
Panel C: Overall Expertise Split with Indicator Variables for Above Median
(1) (2) (3) (4) (5) (6)
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
LeadPartner_Seniority
>median
0.00713 -0.0104 -0.000433 0.0491** 0.363 0.0551***
(0.0182) (0.0147) (0.00904) (0.0191) (0.658) (0.0214)
EQR_Seniority
>median
-0.0348* -0.0178 -0.0136* 0.0242 0.803 0.0441**
(0.0180) (0.0136) (0.00815) (0.0189) (0.643) (0.0210)
OtherExperienced
_Seniority>median
0.00516 -0.000923 -0.00736 -0.0433** -0.470 -0.0328
(0.0182) (0.0146) (0.00914) (0.0188) (0.646) (0.0206)
Count_OtherExperienced 0.00264 -0.00130 0.000927 0.110*** -0.596*** 0.106***
(0.00521) (0.00409) (0.00265) (0.00644) (0.191) (0.00704)
Control Variables Yes Yes Yes Yes Yes Yes
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Observations 2,333 2,333 2,333 2,333 1,395 2,333
Adjusted R-squared 0.118 0.0310 0.0230 0.793 0.740 0.800
64
Table 9: Analysis of the Impact of Potential Selection Bias
This table assesses the impact of potential selection bias following Heckman (1979), who reduces selection bias to
an omitted variable issue, and Frank (2000) and Larcker and Rusticus (2010), who propose an analysis of omitted
variables to understand their minimum impact necessary to overturn results. For each explanatory variable of
interest that loads in prior analyses, we compute the impact threshold for a confounding variable (ITCV), that is the
lowest product of the partial correlation between the dependent variable and the omitted variable, and the partial
correlation between the explanatory variable of interest and the omitted variable that makes the coefficient
statistically insignificant; the square root of the reported values is how much the correlation needs to be with X and
Y to overturn the results. We also evaluate the control variables that have a benchmark impact sufficiently large to
overturn the result. That is, an omitted variable would need to have an impact comparable to these control variables
to overturn the results. ITCV in bold (italics) have no control variable (only Logat) with impact sufficiently large to
overturn the results. Overall, the results are more likely to be robust to selection bias when ITCV is high, and there
are fewer control variables with sufficient impact to overturn the results (that is, the ITCV is either in bold or in
italics). -- denotes coefficients that were not statistically significant at conventional levels in prior analyses and
where an analysis of impact is not conducted.
(1) (2) (3) (4) (5) (6)
Variable
PartI
Finding Restatement
Material_
Restatement
Logaudit
hours
Realization
_Rate
Logaudit
fees
Table 4 - Panel B
%_Audit_Pre-final -0.0101 -0.0205 -0.0176 0.0124 -- 0.0168
%_NonAudit_Pre-final -- -- 0.0245 0.1291 -- 0.1953
Table 5 - Panel A
%_LeadPartner -- -- 0.0660 -0.1402 -- -0.0723
%_EQR -- -0.0109 -- -0.2572 -- -0.2594
%_OtherExperienced 0.0079 -- -- 0.0113 -- 0.0206
Table 5 - Panel B
%_LeadPartner_Pre-final -- -- -- -0.1213 -- -0.0472
%_EQR_Pre-final -- -0.0098 -- -0.2352 -- -0.2293
%_OtherExperienced_Pre-final -0.0075 -- -- 0.0683 -- 0.0890
Table 6 - Panel B LeadPartner_ClientExp -- -- -- 0.0024 0.0634 --
EQR_ClientExp -- -- -- -0.0301 0.0302 --
OtherExperienced_ClientExp -- -0.0334 -- -0.0198 -0.0047 -0.0213
Count_OtherExperienced -- -- -- 0.3832 -0.1109 0.3172
Table 7 - Panel B
LeadPartner_IndExp -- -- -- -- -- -0.0004
EQR_IndExp -- -- -0.0034 -- -- --
OtherExperienced_IndExp -- -- -0.0013 -0.0718 -- --
Count_OtherExperienced -- -- -- 0.3848 -0.1088 0.3219
Table 8 - Panel B
LeadPartner_Seniority -- -- -- 0.0072 -- 0.0163
EQR_Seniority -0.0039 -- -0.0067 0.0304 -- 0.0430
OtherExperienced_Seniority -- -- -- -0.0036 -0.0357 -0.0076
Count_OtherExperienced -- -- -- 0.3755 -0.1108 0.3121