The Validity of Publicly Available Measures of Audit Quality.
Evidence from the PCAOB Inspection Data

Abstract
This study investigates the ability of several commonly used measures of audit quality derived
from publicly available data to predict an accurate measure of audit process quality derived fromaudit deficiencies of individual engagements (Part I Findings) identified during the PCAOB
inspections process. Using a unique dataset of inspected engagements, I find that several
measures of audit quality used in prior literature are predictive of audit deficiencies, consistent
with these measures conveying audit quality. However, I also find that going concern opinions
positively predict Part I Findings, consistent with a disclaimer effect and not with an argument ofhigher audit quality used in prior literature. I also find that the collective predictive power ofpublicly available measures of audit quality on Part I Findings is low, perhaps because mostmeasures are a joint function of financial reporting and audit process quality. Overall, theseresults provide some guidance to researchers about which measures of audit quality to use andsuggest that some results in prior literature may need to be interpreted with caution.

Keywords: Audit Process Quality, PCAOB Inspections, Measures of Audit Quality.
JEL Classification: M42, C80.

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1. Introduction
This study provides an empirical assessment of the validity of widely used measures of audit
quality based on publicly available data. Multiple proxies have been used in prior audit literature
as output measures of audit quality, with little consensus on which measures are best (e.g.,
DeFond and Zhang, 2014). In a review paper, DeFond and Zhang (2014) qualitatively argue thatmany of these measures represent valid measurements of audit quality.1 However, DeFond andZhang also caution that each measure has some potential weaknesses. This study provides an
empirical assessment of each measure in light of a unique and accurate measure of audit process
quality, the Public Company Accounting Oversight Board (PCAOB) inspections data.2
The PCAOB is a nonprofit corporation established by the Sarbanes-Oxley Act of 2002 (SOX)
to oversee the audit of public companies (referred to as issuers in the remainder of this paper)and improve financial reporting. In particular, the PCAOB conducts inspections of public
accounting firms that audit issuers. These inspections are annual for firms that regularly provide
audit reports for more than 100 issuers, and at least triennial otherwise (see Section 104 of
SOX).3 As part of an inspection, the PCAOB selects specific completed audit engagements forreview, and usually sends a team of inspectors, all experienced former auditors, to review theaudit work performed.4 The inspectors spend, on average, approximately one week assessing thework of the engagement team, including by analyzing the audit work papers and frequently
interacting with the engagement team (e.g., Fischer, 2006; Riley et al., 2008; Center for Audit

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The literature has debated about how to exactly define audit quality (e.g., DeFond and Zhang, 2014). In this paper,
I take a process-based view of audit quality, and define a good audit as one that performed sufficient work to support
the audit opinion, as defined by relevant audit standards.
2
Even though the PCAOB, in its publicly released inspection reports, mentions the Part I Findings, the identity of
the issuers is masked. Consequently, the dataset of inspected engagements and Part I Findings is not publicly
available.
3
Also see http://pcaobus.org/Inspections/Pages/InspectedFirms.aspx
4
See Appendix B for the job requirements of an inspector.
2

Quality, 2012).5 In the event that the inspection team determines that the work conducted by theaudit engagement team was not sufficient to support the audit opinion, the PCAOB issues a PartI Finding. Because the PCAOB inspections are designed to make sure that the engagement teamfulfilled its role as an auditor and the PCAOB inspectors are all experienced former auditors who
devote a significant amount of effort to the inspection process, a Part I Finding is a strong andprecise signal of poor audit process quality for a specific engagement.6 Perhaps as importantly,
because the PCAOB focuses only on whether the engagement team fulfilled its role thedetermination of a Part I Finding, as a measure of audit process quality, is not confounded by the
initial financial reporting quality of the issuer. Consequently, a Part I Finding represents an
accurate measurement of poor audit process quality that does not suffer from joint measurements
issues of financial reporting and audit quality, an issue commonly encountered when using
output measures of audit quality (e.g., DeFond and Zhang, 2014).7
To assess the validity of publicly available output measures of audit quality, I use a uniquedataset obtained from the PCAOB, that indicates, for each year, which specific engagements
were inspected and whether each engagement received a Part I Finding or not. The dataset spans
the years 2003 to 2012. I merge this dataset with Compustat and Audit Analytics data to compute
publicly available output measures of audit quality and some other variables. I rely on priorliterature (e.g., Lim and Tan, 2008; Francis and Yu, 2009; DeFond and Zhang, 2014) to compute
several commonly used measures of audit quality, including discretionary accruals, Dechow andDichev (2002) residuals, the propensity to meet/beat specific earnings benchmarks, the issuance

5Additional work reviewing the audit work papers is also often performed at the PCAOB offices (Riley et al., 2008).
Audit firms are also given many opportunities during the inspection process to provide any additional informationor perspective about the audit (e.g., PCAOB, 2012; Center for audit quality, 2012), thereby increasing the accuracyof Part I Findings as a signal of poor audit quality.
7
For example, the PCAOB can still issue a Part I Finding if the inspectors deem that the engagement team did poor
work, even if the financial statements of the issuer are clean.
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of a going concern opinion, and whether restatements occur or not. I also study less commonlyused measures of audit quality based on total accruals and accruals deflated by cash flows fromoperations (CFO), as in Leuz, Nanda and Wysocki (2003). Because the PCAOB generally
inspects only a subset of individual audit engagements conducted by an audit firm for each year
in which the firm is inspected, my dataset is reduced to 3,997 issuer-year observations. I thenassess whether each output measure of audit quality is predictive of Part I Findings, controllingfor a wide range of other factors.
In the first set of tests, I find that many of the output measures of audit quality used in priorliterature are predictive of Part I Findings. Specifically, unsigned discretionary accruals, thepropensity to meet/beat the zero earnings threshold, and restatements positively predict theissuance of a Part I Finding. In particular, a restatement increases the probability of a Part I
Finding by 11%, the propensity to meet/beat the zero earnings threshold by 7%, and an increaseof one standard deviation in discretionary accruals by 2%. These numbers are reasonably large,
especially for restatements and the propensity to meet/beat the zero earnings thresholds, in lightof the sample average probability of a Part I Finding, equal to 27%. I also find that unsigned total
accruals or unsigned total accruals deflated by CFO, measures that are not commonly used inprior literature, are also predictive of Part I Findings. I do not find any association between
signed accruals, Dechow and Dichev residuals, or the propensity to meet/beat last year’s
earnings with the issuance of a Part I Finding. In contrast to several claims in prior literature thatargue that issuance of a going concern opinion is a measure of good audit quality, I actually finda positive association between the issuance of a going concern opinion and audit deficiencies asmeasured by inspection Part I Findings. This result is consistent with a disclaimer role of thegoing concern opinion, suggesting that the engagement team reduces the amount of work4

performed during the audit, because of the perceived or actual lessened litigation risk afforded by
the going concern opinion (Mutchler, 1984; Carcello and Palmrose, 1994; Kaplan and Williams,
2013).8 This result is also consistent with recent findings in an experimental setting byKachelmeier, Schmidt and Valentine (2014), and in an empirical setting by Czerney, Schmidt
and Thompson (2014), and Czerney (2015), that suggest that the inclusion of non-standard
wording in the audit opinion acts as a disclaimer role of the audit.9
When including all the measures of audit quality that predicted the issuance of a Part I
Finding in “kitchen sink” regressions, I still find an association with Part I Findings for each ofthem. This result suggests that each of these measures represents an independent assessment ofaudit quality, controlling for the other ones, and is consistent with the arguments presented in
DeFond and Zhang (2014). However, I find that the explanatory power of publicly available
output measures of audit quality on Part I Finding is relatively limited.10 Collectively, thesemeasures explain only between 6% and 15% of the likelihood of issuance of a Part I Finding,perhaps because they are a joint function of financial reporting and audit quality.11
In further tests, I find that both positive and negative discretionary accruals are predictive ofPart I Findings, consistent with results in prior literature that indicates that auditors constrain
both positive and negative accruals (Myers, Myers and Omer, 2003; Francis and Yu, 2009).
Focusing on signed discretionary accruals, I find that they are predictive of Part I Findings when

8

Such an idea is consistent with the analytical model of Dye (1993), who shows that the audit effort is dependent on
the risk of litigation in case of audit failure.
9This finding is also consistent with distressed issuers having very poor accounting or internal control systems,
which could affect the engagement team’s ability to conduct the audit.
10Note that because inspections focus on specific areas of selected engagements, this could result in a lower
predictive power of overall measures of audit quality. Consequently, it could still be the case that measures of audit
quality based on publicly available variables are reasonably explanatory of the true underlying audit quality, but thatthe power of my tests is reduced because PCAOB inspections focus on specific areas of the audit.
11
The 6% is computed from a model including several control variables. When control variables are excluded, thepredictive power of the publicly available measures of audit quality is, collectively, as high as 15%.
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restricting the sample to engagements that meet or slightly beat the zero earnings threshold,consistent with the use of signed accruals being appropriate when an appropriate incentive tomanipulate earnings upwards can be found (e.g., Dechow et al., 2010). Collectively, this set ofresults suggest that, in the absence of restrictions on the sample based on management incentives,
unsigned accruals is a more appropriate measure of audit quality than signed accruals, consistent
with the arguments advanced in Reynolds and Francis (2000) and Hribar and Nichols (2007).
However, signed discretionary accruals are still useful when the researcher can identify theproper management incentives to increase earnings.
Because the engagements selected by the PCAOB for inspections are not randomly chosen,
but risk-based (e.g., Olson, 2008; Hanson, 2012; Church and Shefchik, 2012), I conduct arobustness test using a bivariate probit model with selection (Van de Ven and Van Pragg,
1981).12 I identify two exclusion restrictions, based on internal discussions at the PCAOB, andstill find that the results hold when attempting to control for selection bias.
Overall, this study contributes to the literature by providing some guidance to researchers
about which output measures of audit quality, for U.S. listed issuers, are predictive of PCAOBPart I Findings. In particular, this study extends DeFond and Zhang (2014) who provide a
qualitative assessment about these measures. I find that several output measures of audit quality
are actually predictive of Part I Findings, while the issuance of a going concern opinion goes in
the opposite direction. Consequently, this study provides some validity to prior auditing literaturethat relies on these measures. Based on the economic significance, and excluding going concern
opinions (which direction is problematic), the most promising measures appear to be the issuanceof a restatement and the propensity to meet/beat the zero earnings threshold. A note of caution is
12This model is similar to the Heckman (1979) model, but with binary dependent variables in the second stage.
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that the predictive power of publicly available measures of audit quality on Part I Findings
appears low. This suggests that many unobservable or nonpublic factors influence audit quality,
and calls for additional research on finding stronger measures of audit quality. Taking a different
perspective, my results also suggest that a poorly conducted audit based on relevant standards,
and identified as deficient by the PCAOB, is associated with worse reporting outcomes,
including higher accruals and a higher probability of restatements. Thus, to a certain extent, theseresults also speak of the value, from a financial reporting standpoint, of a properly conducted
audit.
The remainder of this paper is structured as follows. Section 2 provides some background on
the PCAOB inspections and commonly used measures of audit quality; Section 3, the data;
Section 4, the main empirical tests; and Section 5, several additional tests. Section 6 concludes.

2. Background on PCAOB inspections and measures of audit quality
2.1 PCAOB Inspections
2.1.1 Background
Prior to SOX, audit firms were self-regulated through, among other things, the AICPA’s peer
review program, started in the 1970s (e.g., Hermanson, Houston and Rice, 2007; Lennox and
Pittman, 2010). This changed following several well-known accounting scandals at Enron,WorldCom and elsewhere (e.g., Hanson, 2012). As part of SOX, Congress establishedindependent oversight of the accounting profession by the PCAOB for audits of issuers. Since its
creation, the PCAOB has, each year, conducted hundreds of inspections of registered public
accounting firms that audit issuers. These inspections are annual for firms that regularly provide
audit reports for more than 100 issuers, and at least triennial otherwise (Section 104 of SOX).

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One element of the inspections program involves the selection of specific engagements forreview. Given its limited resources, the PCAOB cannot inspect all the engagements of aparticular audit firm every year and uses a risk-based approach to select the engagements thatwill be inspected. The PCAOB then notifies the audit firm, and, after an initial data request (e.g.,
Eskow, 2004; Fischer, 2006; Center for Audit Quality, 2012), sends a team of inspectors, allexperienced former auditors (see Appendix B for the current job requirements of an entry-level
inspector), who conduct fieldwork at the audit firm’s office involved with the engagement.13
This fieldwork lasts approximately one week, although inspections can finish in four businessdays or take as long as two weeks (e.g., Riley et al., 2008; Johnson, Keune and Winchel, 2014).14
During the fieldwork, the PCAOB inspectors dissect the audit work papers, interact frequently
with the engagement team to strengthen their understanding of the work completed during theaudit, and determine whether the work performed by the engagement team is sufficient tosupport the audit opinion. In the event that it is not, the PCAOB issues a Part I Finding for thatspecific engagement. Part I Findings are made public in the inspection reports of individual audit
firms, disclosed by the PCAOB. However, the name of the issuer is masked in the public reports.
Further, the specific engagements selected for inspection are not publicly disclosed.
Consequently, an important part of the inspection process is not disclosed to the public and only
aggregate inference can be made with publicly available data.
2.1.2 Advantages and potential issues of using PCAOB inspection data as a measure of audit
process quality
There are several advantages of using PCAOB inspection data as a measure of audit process
quality. First, because a PCAOB inspection focuses on whether regulatory auditing standards are13

For smaller audit firms all the inspection fieldwork may take place in one of the PCAOB offices (PCAOB Annual
Report, 2005).
14
In many instances, additional work is also conducted within the PCAOB offices outside of the one-weekfieldwork window (e.g., Riley et al., 2008).
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being met, the inspection results are based on a clearly established, standards-based definition, ofaudit quality. Specifically, a Part I Finding indicates that the engagement team did not perform
sufficient work to support its audit opinion, as defined by current auditing standards, and is aclear signal of poor audit process quality in that engagement.
Second, the PCAOB spends a considerable amount of effort conducting inspections,suggesting that the Part I Findings are a precise signal of poor audit process quality. In particular,
the PCAOB inspection teams are all composed of experienced and knowledgeable former
auditors (PCAOB, 2011; Carcello et al., 2011). For example, Lennox and Pittman (2012) reportthat inspectors average 12 years of public practice experience, an observation corroborated by
the current job requirements for inspectors, detailed in Appendix B.15 Many of the PCAOB
inspectors previously worked for the Big 4 firms at the manager level or above, audited publicly
traded companies, and have extensive knowledge of auditing practices. Further, they are
independent from the public accounting profession (Lennox and Pittman, 2010; PCAOB, 2011;
Carcello et al., 2011). The inspectors spend a significant amount of time conducting fieldwork,
analyzing the work papers of the inspected engagement, and interacting with the audit
engagement team to refine their understanding of the particular engagement, the mindset, and theprocedures conducted by the engagement team. Notably, audit firms are given many
opportunities during the inspection process to provide any additional information or perspective
about the audit (e.g., PCAOB, 2012; Center for Audit Quality, 2012), thereby increasing theaccuracy of Part I Findings. Survey evidence also suggests that PCAOB inspectors have theproper qualifications to conduct their role and that their fieldwork is appropriate. For example,

15

Also see the PCAOB Annual Report for 2006 that mentions that the inspection team leaders for large inspections
have an average of 25 years of relevant work experience, and all other inspection team members average 14 years of
relevant work experience.
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Houston and Stefaniak (2013), in a survey of 107 partners of large auditing firms, find thatpartners are in agreement with the following statements: that the PCAOB inspections are very
detailed within the areas inspected, that the inspectors concentrate on assessing sufficiency ofevidence gathered, concentrate on assessing judgments made during substantive testing,concentrate on assessing audit documentation, and concentrate on assessing internal control
evaluations. Further survey evidence from triennially inspected audit firms also shows that thesefirms agree that the inspectors possess adequate technical knowledge, exercise appropriate
professional conduct, and that their focus on audit work paper documentation and substantive
audit procedures is appropriate (Daugherty and Tervo, 2010).16
Third, survey data finds that PCAOB inspections are unpredictable (Houston and Stefaniak,
2013). Specifically, the partners of large audit firms indicate that they cannot predict in advancewhich of their audits and which year they will be chosen for a PCAOB inspection. This suggeststhat the engagement team is unable to strategically improve quality on their audit in anticipationof an inspection. Such strategic actions may have the potential to distort the relationship between
publicly available measures of audit quality and the inspection results.
Last but not least, the PCAOB inspections primary focus is on the quality of the audit, not on
the quality of the client’s financial reporting.17 Consequently, a Part I Finding can be issued in

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An argument has been made in the literature (e.g., Palmrose, 2006; Glover et al., 2009; Lennox and Pitman, 2010)that there is a trade-off for the PCAOB inspectors between expertise and independence. In particular, because the
PCAOB inspectors are not currently conducting audits, they may be unable to keep up with the latest developmentsin the profession. However, this claim goes contrary to survey evidence that indicates that the PCAOB inspectors are
qualified (Daugherty and Tervo, 2010). Further, each PCAOB inspector is involved with the review of many
engagements every year, in contrast to auditors, who only focus on a handful of clients. Thus, one argument could
be made that the PCAOB inspectors, by encountering many different types of audits each year and specializing in
the inspection process, actually build experience faster than practicing auditors (e.g., Carcello et al., 2011).
17
The PCAOB inspectors may observe the quality of the client’s financial reporting and internal controls in
conducting the inspection. For example, when the inspectors find issues with the financial statement of a specificissuer, the usual practice is for them to report that information to the SEC (see for example the inspection report of10

case the engagement team did a poor audit job, even if the financial statements of the issuer are
clean. This is a major improvement over most publicly available measures of audit quality, thatcan only measure a joint event of poor financial statement quality combined with poor audit
quality (DeFond and Zhang, 2014).
There are also some potential drawbacks in using PCAOB inspections data. The mostobvious one is sample loss resulting from the limited number of inspections the PCAOB is able
to conduct every year. Further, because the number of engagements selected for inspection islimited, the probability that a given engagement is selected several times over the sample period
is low, and this results in the lack of availability of time-series data. Consequently, all the resultsof this paper are relevant mostly for studies that focus on cross-sectional analyses.18 An
additional issue comes from the fact that a Part I Finding is a zero-one outcome. Even though aPart I Finding is a powerful signal of poor audit process quality, no information is available on
the audits that passed the inspection, especially how close they were to the failure threshold. Last
but not least, the PCAOB inspections focus on limited areas of the engagement, often those thatappear to the inspectors to be the most critical for the audit (Hanson, 2012; PCAOB, 2012;
Center for Audit Quality, 2012). Even though, interestingly, surveyed audit partners of large
firms neither agree nor disagree with the statement that a PCAOB inspection touches on all audit
areas in an engagement (Houston and Stefaniak, 2013), suggesting that in practice the inspectors
do focus on the most critical areas of the audit, the PCAOB focus on sub-areas of the audit
indicates that, for a given engagement, not all audit deficiencies may be identified by the
PCAOB. This might introduce noise in the measure that could lower the power of the tests
Deloitte for 2013, p20, footnote 3). However, the inspectors’ primary focus is on the actions taken by theengagement team in light of the client’s financial reporting and existing internal controls.
18
Many papers in auditing use cross-sectional identification designs, and consequently the results of this study areapplicable to them. However, a handful of papers, including Gul et al. (2012) and Aobdia et al. (2015) use timeseries identification.
11

conducted in the empirical analyses, and could at least partially explain the low explanatory
power of the regressions used in this paper.
2.2 Measures of audit quality
Most publicly available measures of audit quality, except for the going concern opinion,
are jointly determined by the issuer’s initial financial reporting quality and the work of theauditor performed. Specifically, most of the measures will not be able to differentiate a bad audit
from a good audit when initial financial reporting quality of the issuer is good. The following
measures are widely used in auditing literature as measures of audit quality.
2.2.1 Going Concern opinions
An auditor issues a going concern opinion when there is substantial doubt about theissuer’s ability to continue as a going concern for a reasonable period of time, not to exceed one
year beyond the date of the financial statements being audited (PCAOB, AU Section 341).19
Prior literature has argued that the issuance of a going concern opinion is a measure of good
audit quality, from an auditor independence standpoint, because issuers have incentives topressure auditors to issue clean opinions (DeFond and Zhang, 2014). Consistent with this idea,
the issuance of a going concern opinion leads to an increased probability of auditor switching(Chow and Rice, 1982; Krishnan, 1994). However, prior literature also finds evidence that theissuance of a going concern opinion reduces both the perceived and actual risk of auditor
litigation (e.g., Mutchler, 1984; Carcello and Palmrose, 1994), and the settlement amount in casethe auditor is still named in a lawsuit (Kaplan and Williams, 2013). Similarly, recent evidence inboth experimental (Kachelmeier et al., 2014) and empirical settings (Czerney et al., 2014;
Czerney, 2015) also suggest that the inclusion of non-standard wording in the audit opinion acts

19

See http://pcaobus.org/Standards/Auditing/Pages/AU341.aspx
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as a “disclaimer” of the audit. This may leave the door open to auditors to conduct poor quality
audits and issue going concern opinions as disclaimers, in an effort to shield themselves fromliability in case something goes wrong. This argument is consistent with the analytical model of
Dye (1993), who, using a moral hazard setting, shows that the effort conducted by the auditor isdependent on the liability faced in case of audit failure.
2.2.2 Accrual based measures of audit quality
Prior auditing literature widely uses discretionary accruals as a measure of audit quality(e.g. Reynolds and Francis, 2000; Balsam, Krishnan and Yan, 2003; Krishnan, 2003; Myers,
Myers and Omer, 2003; Lim and Tan, 2008; Francis and Yu, 2009; Carcello and Li, 2013). The
basic idea is that high quality audits constrain opportunistic earnings management, and therefore
should have a negative impact on discretionary accruals (DeFond and Zhang, 2014). A majoradvantage of accruals based measures of audit quality lie in their continuous nature, and the factthat they are also expected to detect within GAAP earnings manipulation. However, little
consensus for measurement exists for accruals. Further, the measures can be noisy and evenbiased in some instances (Dechow et al., 2010).
There are several methods to measure accruals. The first one, based on the Jones (1991)
model, is to model a level of normal accruals, based on sales growth and PP&E. The difference
between total accruals and normal accruals, the residual of the regression, are the discretionary
accruals. As noted in Dechow et al. (2010), the explanatory power of the Jones model is low.
Further, the residuals are highly positively correlated with total accruals (Dechow et al., 2003),
and discretionary accruals are usually less powerful than total accruals at detecting earnings
management in SEC enforcement releases (Dechow et al., 2011), thereby suggesting that total

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accruals themselves could be used as a measure of audit quality. For example, Leuz et al. (2003),
use two measures of earnings quality based on total accruals.
Both signed and unsigned discretionary accruals are used in the literature. Unsigned accruals
are often used in absence of a particular directional prediction (e.g., Hribar and Nichols, 2007),and signed accruals models when an incentive to manage earnings upwards exists (e.g., Dechow,
Hutton, Kim and Sloan, 2012).
Accruals can also be measured using the Dechow and Dichev (2002) model, which focuseson the matching of accruals to cash flows and considers the residual of a regression of short-termworking capital accruals on current, prior and future cash flows, as a measure of earnings quality.
The model is unsigned and only focuses on short-term working capital accruals (Dechow et al.,2010). While the Dechow and Dichev model was originally built as a measure of earnings
quality and is extensively used as such for good reasons, it is difficult to assess whether themodel can directly be extended as a measure of audit quality. Specifically, the role of the auditor
extends beyond short-term working capital accruals. Further, auditors also assess the mapping of
accruals to cash flows itself (for example, by auditing the rate of recognition of deferred
revenues), which, by construction, is excluded from the measure.
2.2.3 Other measures of audit quality
Restatements are also commonly used as a measure of audit quality because they indicatethat the auditor issued an unqualified opinion on financial statements that were misstated.
Consequently, a restatement is a strong indication of poor audit quality (DeFond and Zhang,2014). However, restatements occur reasonably rarely, are subject to whether detection occurs or
not, and show poor audit quality only conditional on poor initial financial reporting quality.

14

Prior literature also uses the propensity to meet/beat specific earnings thresholds as ameasure of audit quality (e.g., Francis and Yu, 2009). Survey and empirical evidence confirm
that executives care about earnings thresholds and are willing to take actions to meet/beat them(e.g., Burgstahler and Dichev, 1997; Matsumoto, 2002; Graham et al., 2005). Specifically,
Graham et al. (2005) survey 401 Chief Financial Officers who indicate, in their vast majority,that meeting or beating earnings benchmarks are important to them. When asked what theywould do to meet the desired earnings target in case of a shortfall, 40% indicate that they wouldbook revenues now rather than next quarter (if justified in either quarter), 28% that they would
draw down on reserves previously set aside, 21% that they would delay taking an accounting
charge, and 8% that they would alter accounting assumptions (such as allowances, pensions, etc.).
These areas fall under the purview of the auditor. Consequently, issuers slightly meeting or
beating these expectations are likely to be suspect (Roychowdhury, 2006).

3. Data construction
3.1 Sample Construction
I obtain individual PCAOB inspections and Part I Findings data from the PCAOB. These
data cover the fiscal years 2003 to 2012 and include the name of the issuer inspected, its Central
Index Key (CIK), its auditor and fiscal year inspected, and whether a Part I Finding is issued ornot. The data is comprehensive for the firms identified as Global Network Firms by the PCAOB
and covers the fiscal years 2007 to 2012 for the other ones.20 There are 6,640 unique inspection
engagement reviews in the dataset. I merge this dataset with Compustat and Audit Analytics toobtain appropriate measures of audit quality and control variables. This reduces the sample size

20

Current audit firms identified Global Network Firms include the Big 4 audit firms, Grant Thornton and BDO. See
http://pcaobus.org/Registration/Firms/Pages/GlobalNetworkFirms.aspx.
15

to 5,354 observations. I only keep the inspected engagements in the dataset for most analyses,
except for the bivariate probit with selection analysis, presented in subsection 5.3. I also restrict
the sample to two-digit SIC industries with more than ten observations each year to allow for thecomputation of the accruals based measures. After inclusion of all control variables, the final
sample is restricted to 3,997 inspected engagements.21 I also use, in the bivariate probit withselection analysis, a larger sample, corresponding to all engagements that took place between
2003 and 2012, derived from the intersection of Audit Analytics and Compustat and restricted toissuer-years that have control variables available. This sample is composed of 55,736 issuer-year
observations.
Table 1 presents descriptive statistics on the PCAOB inspections in the sample. Panel A
shows the number of inspections by year in the sample. There are approximately 400 inspectedengagements each year, with some variation depending on the year inspected. The number ofinspections in the dataset increases in 2007 because of increased data coverage after this date.
Panel B shows the inspection split by Big 4 / non-Big 4 auditors. Some inspections are available
for non-Big 4 auditors prior to 2007, because several of the non-Big 4 firms are considered to bepart of the Global Network Firm inspection program. The number of non-Big 4 inspections morethan doubles after 2007, consistent with an increase in the data coverage and not necessarily withincreased inspections for non-Big 4 auditors starting in 2007.
(Insert Table 1 About Here)
3.2 Measures of Audit Quality
3.2.1 Accruals and Discretionary Accruals
21

Some studies exclude from the analyses financial sector (SIC codes 60-69) and regulated industries (SIC Codes
44-49). The results are qualitatively unchanged when excluding these industries. Given this result, I keep theseindustries to increase the sample size.
16

Following prior literature (e.g., Lim and Tan, 2008; Francis and Yu, 2009; Reichelt andWang, 2010), I estimate discretionary accruals using the cross-sectional modified Jones model(Dechow, Sloan, and Sweeney, 1995; Kothari, Leone, and Wasley, 2005):
TAt/ASSETt-1 = β1(1/ASSETt-1) + β2(ΔSALESt)/ASSETt-1 + β3PPEt/ASSETt-1 + β4ROAt-1 + εt,(1)where TA is total accruals measured as earnings before extraordinary items (IB) minus net
cash flow from operations excluding extraordinary items and discontinued operations (OANCFXIDOC), ΔSALES is change in net sales, PPE is gross property, plant, and equipment, and ROA
is the rate of return on assets. Following Kothari et al. (2005), I control for ROA to increase thepower of the model. I deflate both the dependent and independent variables by lagged total assets
and estimate equation (1) by industry (two-digit SIC code) and year. Discretionary accrual (DA)is the residual from the model, εt. I use both DA and its absolute value, AbsDA, as potential
measures of audit quality.
Following Leuz et al. (2003), I also consider the two following accrual-based measures ofaudit quality. Accruals equals TAt/ASSETt-1, and AccrualsCFO equals total accruals (TA) deflatedby the absolute value of the issuer’s cash flows from operations (OANCF-XIDOC). I take theirabsolute values, denominated AbsAccruals and AbsAccrualsCFO in the following analyses.
3.2.2 Dechow and Dichev residuals
I also estimate the Dechow and Dichev (2002) residuals. Although the most frequently
used measure of financial reporting quality in auditing is based on the Jones (1991) discretionary
accruals model, some prior studies in auditing have measured audit quality using accrual quality
measured by the Dechow and Dichev (2002) model (DeFond and Zhang, 2014). I augment the
Dechow and Dichev model following McNichols (2002) and Francis et al. (2005).

17

TCAt = β0+β1CFOt-1 + β2CFOt + β3CFOt+1+ β4 ΔSALESt+ β5PPEt+ εt,(2)where TCA is total current accruals (= Δ Inventories + Δ Account Receivable - Δ AccountPayable - Δ Income Tax Payable + Net change in other assets and liabilities, all taken from the
cash flow statement), and CFO is cash flow from operations (OANCF-XIDOC). All variables
are scaled by the average assets during the year. I estimate equation (2) by industry (two-digit
SIC code) and year. The absolute value of the residual from equation (2), AbsDD, is a potential
measure of audit quality.22
3.2.3 Other measures of audit quality
Restatements are commonly used as a measure of audit quality. I define Restatement as anindicator variable equal to one if the financial statements for the year are restated due toaccounting or fraud related reasons. The data are from Audit Analytics.
Going concern opinions are also commonly used as a measure of audit quality. I define GC
as an indicator variable equal to one if the auditor issued a going concern opinion for the year.
The data are taken from Audit Analytics. Because most issuers with going concern opinions do
not go bankrupt, I also define GCNoBankruptcy as an indicator variable equal to one if theauditor issues a going concern opinion and the issuer does not declare bankruptcy the following
year. I use both UCLA Lopucki and SDC databases to determine whether an issuer declares
bankruptcy or not.
Several studies have used the propensity to meet/beat earnings thresholds as measures ofaudit quality. Following prior studies (e.g., Francis and Yu, 2009), I define SmallProfit as an
22

Both Dechow et al. (2010) and Dechow and Dichev (2002) suggest that the absolute value of the residual from themodel is an appropriate measure of accrual quality. They also propose using the standard deviation of the residuals.
While this measure is fine from an accruals quality standpoint, the main disadvantage, from an audit quality
standpoint, is that it is not directly linked with a particular audit anymore, but with a series of audits over theestimation period. Untabulated analyses indicate that the results are unchanged if I take the standard deviation of theresiduals over a period of five years, instead of the absolute value of the residuals.
18

indicator variable equal to one if the ROA (income before extraordinary items deflated by
beginning assets) is more than 0 and less than 3%.23 I also define SmallBeat as an indicatorvariable equal to one if the year-on-year change in ROA is less than 1%.
4. Main Empirical Tests
4.1 Research Design
I estimate whether a given measure of audit quality predicts Part I Findings using thefollowing regression:
Part I Findingt = β0+β1 Audit Quality Measuret + β Controlst + εt,(3)Part I Finding is an indicator variable equal to one when the PCAOB releases a Part I
Finding for a given engagement. Because of the binary nature of the dependent variable, theregression is estimated using a logistic specification. Audit Quality Measure is a measure of audit
quality, previously defined in Subsection 3.2. Controls is a vector of controls variables that priorresearch has identified as potentially impacting audit fees and quality (e.g., Francis, Reichelt andWang, 2005; Francis and Yu, 2009; and Reichelt and Wang, 2010). This vector also potentially
impacts the issuance of a Part I Finding, and is composed of Logat, the natural logarithm of theissuer’s assets, to control for issuer size, ForeignPifo, the absolute value of pretax income fromforeign operations divided by the absolute value of pretax income, Geoseg, the number ofgeographic segments of the issuers, and Busseg, the number of business segments of the issuer tocontrol for issuer’s complexity. Decye is an indicator variable equal to one if the issuer’s fiscal
year ends in December, to control for the busy season of the auditor. I also include the issuer’s
leverage ratio, Leverage, defined as total debt divided by total debt plus book equity, its book-tomarket ratio, BTM, CFOat, the issuer’s cash flows from operations deflated by beginning assets,

23

Results are qualitatively unchanged when using other numbers, such as 2%, 4% and 5%.
19

StdCFOat, the standard deviation of CFOat computed from years t-3 to year t, and Salegrowth,
the year-on-year sales growth of the issuer, to control for other business factors that could impact
the issuance of a Part I Finding. I measure CFOat for the prior year, in order to not create
potential issues in the regressions where the issuers’ accruals are used as an explanatory
variable.24 Big 4 is an indicator variable equal to one when the audit firm is one of the Big 4.
Finally, I include Weaknesses, the number of internal control weaknesses in a fiscal year, HiTech,an indicator variable equal to one when the issuer is in a hi-tech industry, and Litigation, an
indicator variable equal to one when the issuer is in a high-litigation industry. Additional details
about the explanatory and control variables are provided in Appendix A. All continuous
variables are winsorized at the 1st and 99th percentiles to reduce the impact of outliers in thespecifications.
4.2 Descriptive Statistics
Descriptive statistics for the inspected engagements are presented in Table 2. Overall, 27%of the inspections result in a Part I Finding. 6% of the inspected engagements received a going
concern opinion, while 11% of the issuers’ financial statements were eventually restated. 64% of
the inspections are for Big 4 audit firms, while 70% of the issuers have December year ends andthus correspond to busy-season audits.
(Insert Table 2 About Here)
Table 3, Panel A, partitions the descriptive statistics between inspections that received no
Part I Findings and those that received one. Inspections that received a Part I Finding are much
more likely to receive a going concern opinion, at 9% compared with 5%, and the issuer’s

24

Specifically, because earnings equal accruals plus cash flows from operations, this could create mechanicalrelationships in the regressions. Results are qualitatively unchanged if I measure CFOat for the same year.
20

financial statements are more likely to be restated, at 15% compared with 10%. Further, they are
more likely to report small profits, at 17% compared with 13%, and their absolute discretionary
accruals, absolute accruals and absolute accruals deflated by cash flows from operations arehigher. All of these results are statistically significant at 1% or better using a t-test of difference
in means, and at 5% or better when using a Wilcoxon test of differences in distribution. Overall,these initial results suggest that the measures mentioned above are potentially good candidates asmeasures of audit quality. They also suggest that poor audit process quality, as defined by a PartI Finding, is associated with worse financial reporting outcomes, including higher accruals, anincreased probability of restatements, and a higher propensity to meet/beat the zero earnings
thresholds, potentially highlighting the role of a high-quality audit as defined by applicablestandards. However, the direction of the going concern opinion goes opposite to prior claims in
the literature. This result is consistent with a disclaimer effect of the going concern opinion on
the audit. Further, there is no significant difference between groups for the signed accruals, theDechow and Dichev accruals, and the propensity to beat last year’s earnings. In terms of control
variables, engagements receiving Part I Findings are for smaller issuers, as evidenced by asmaller asset size, have smaller cash flows from operations and higher book to market, and areless likely to belong to a high litigation industry or to be audited by a Big 4 firm. This suggests
the need to control for these variables in multivariate specifications, which is the focus of thenext subsection.
(Insert Table 3 About Here)
Panel B of Table 3 shows the correlations among the main variables of interest. Overall, theresults corroborate the ones shown in Panel A. In particular, the correlations between Part I
Finding and GC, GCNoBankruptcy, Restatement, SmallProfit, AbsDA, AbsAccruals and21

AbsAccrualsCFO load positively at 5% or better for both Spearman and Pearson correlations.
However, the correlations are relatively low, between 3% and 10%, depending on the variable
used.
4.3 Empirical Results
Table 4 presents the results of the main analyses. Panel A presents the results for accrualsbased measures. Unsigned Discretionary Accruals (AbsDA), Unsigned Scaled Accruals(AbsAccruals), and Accruals deflated by CFO (AbsAccrualsCFO) load positively in thespecifications. On the other hand, signed discretionary accruals (DA) and Dechow and Dichev
residuals (AbsDD) do not predict Part I Findings, as evidenced by an insignificant coefficient.
Interestingly, Big 4 loads negatively in all specifications, consistent with Big 4 auditors receiving
fewer Part I Findings than non-Big 4 auditors during the sample period. This result is consistent
with the argument made in prior literature of larger auditors being of higher quality (e.g,
DeAngelo, 1981; Palmrose, 1988).
(Insert Table 4 About Here)
Table 4 Panel B presents the results for the other variables. Consistent with priorexpectations, I find a positive association between Part I Finding and both SmallProfit andRestatement. I do not find any association between Part I Finding and SmallBeat. I find apositive association between GC and Part I Finding, indicating that audits where a going
concern opinion is issued are more likely to receive a Part I Finding.25 This result becomes25

The results on the going concern opinion are robust to inclusion of control variables for financial distress, such as
the Altman Z-score and whether the issuer reports losses during the year. The results are also robust to restricting thesample to issuers that report negative earnings or negative cash flows from operations, as commonly done in theaudit literature focusing on going concern opinions (e.g., Reynolds and Francis, 2000; DeFond et al., 2002).
22

stronger when using GCNoBankruptcy as the explanatory variable.26 Overall, this result suggeststhat auditors may use the issuance of a going concern opinion as a disclaimer, consistent with theresults in Czerney et al. (2014) who look at other atypical language in the audit reports.
(Insert Table 5 About Here)I conduct “kitchen sink” regressions, using all the explanatory variables that worked in theanalysis shown in Table 4, to determine whether the measures of audit quality that are predictiveof Part I Findings have independent components from each other. Results are presented in Table
5. Overall, I find that each measure of audit quality still loads significantly when controlling forthe other ones.27 Overall, these results suggest that each measure explains audit quality
independently from the other, and are consistent with the arguments presented in DeFond andZhang (2014). In terms of economic significance, based on the results of Column (2), I find, in
untabulated analyses, that one standard deviation increase in AbsDA raises the probability of PartI Finding by 1.9%, while a restatement, small profit, or going concern not followed bybankruptcy increases this probability by 11.0%, 6.6%, and 16.3%, respectively. Based on the
results of Column (3), I find that one standard-deviation increase in AbsAccrualsCFO increases
this probability by 1.4%. These numbers need to be compared with the average probability of aPart I Finding, equal to 27% according to Table 2. Overall, they suggest that large differences
can be found in terms of these audit quality variables when an inspection results in a Part I
Finding, especially for engagements with restatements, small profits, or going concern opinions.

26

Untabulated analyses indicate that only GCNoBankruptcy loads when including both GCNoBankrutpcy and GC as
explanatory variables. Consequently, I use this variable in the regressions onwards.
27
I do not run the regressions including both unsigned discretionary accruals and unsigned accruals, because these
two measures are substitutes for each other by definition.
23

I use the Receiver Operating Characteristic (ROC) curves to estimate the predictive power ofthe regressions. The ROC curve is a parametric plot of the probability of detection versus thefalse positive rate (e.g., Schmidt, 2012). Specifically, I compute the area under the curve (AUC),
a measure of fit of the model, and present this statistic under the Pseudo R-square of each
regression. A value of 0.5 of this statistic corresponds to a random model, while a value of 1.0
means perfect predictive power of the explanatory variables. Column (1)’s AUC, without
including the explanatory variables, equals 0.586, indicating that the initial model is not good at
predicting Part I Findings. Inclusion of all the measures of audit quality that work only increase
the model AUC to 0.613, or an increase of 0.03. This increase is significant at 1% in an
untabulated chi-square test. This result suggests that, collectively, all measures of audit quality
included together have an incremental explanatory power on the probability of a Part I Finding.
However, they only explain approximately 6% of the probability of a Part I Finding, arguably a
low number.28 Consequently, this suggests that publicly available measures of audit quality lack
power as proxies of audit quality. One caveat, though, is that individual PCAOB inspections
delve into specific areas. Consequently, the lack of power may also be driven by noise
introduced in the inspection process itself and the Part I Finding measure of audit quality.
5. Additional tests
5.1 Positive and negative discretionary accruals
Even though DA, the signed discretionary accruals, does not load significantly in Table 4, it
is still possible that only large positive discretionary accruals could be correlated with Part I
Findings. Consequently, following prior studies (e.g., Myers, Myers and Omer, 2003; Francis

28

This number is computed as (0.613-0.586)/(1-0.5). If I exclude all control variables from the specification but thefive measures of audit quality, the ROC of this specification equals 0.576. Consequently, without inclusion of thecontrol variables an argument can be made that, collectively, publicly available measures of audit quality explain up
to 15% [computed as (0.576-0.5/(1-0.5)]of the Part I Findings.
24and Yu, 2009), I rerun the specifications partitioning the sample into a positive discretionary
accruals subsample and a negative discretionary accruals subsample. I keep AbsDA as the
explanatory variable for both subsamples. Results are presented in Table 6, Columns (1) and (2).
I find that both large positive and large negative accruals positively predict Part I Findings,
consistent with the role of the auditor not being limited to large income increasing accruals.
(Insert Table 6 About Here)
5.2 Additional tests of signed accruals
While the use of unsigned accruals is suitable in absence of a particular directional prediction(e.g., Hribar and Nichols, 2007), signed accruals models are usually more effective when an
incentive to manage earnings upwards exists (e.g., Dechow, Sloan and Sweeney, 1995; Dechow,
Hutton, Kim and Sloan, 2012). My initial tests of whether accruals are predictive of Part I
Findings do not consider any specific incentive, and this could explain the difference in results
for signed and unsigned discretionary accruals found in Table 4. Because executives have
incentives to manipulate earnings upwards to meet/beat earnings thresholds (Graham et al., 2005;
Dechow, Ge and Schrand, 2010), I conduct similar analyses to the ones shown in Table 4, but by
restricting the sample to issuers that just met or beat the zero earnings threshold or last years’
earnings (SmallProfit and SmallBeat equal to one, respectively). Results are presented inColumns (3) and (4) of Table 6. I find a positive association between signed accruals and theprobability of a Part I Finding for issuers that just met or beat the zero earnings threshold, but noassociation for those that met or beat last year’s earnings. These results suggest that the use ofsigned accruals is suitable when an incentive variable can be found. They are also consistent withthe results presented in Table 4 on SmallProfit predicting Part I Findings, but not SmallBeat.
5.3 Selection Bias Concerns
25One concern of the analyses shown in Table 4 is related with the risk-based selection processof the PCAOB. Because the observations I use are non-randomly chosen, this could result in thepotential for coefficient bias in the analyses (Lennox, Francis and Wang, 2012). Consequently, I
attempt to control for selection bias using a bivariate probit model with selection. This model issimilar to the Heckman (1979) model, but with binary dependent variables in the second stage(Van de Ven and Van Pragg, 1981). The idea of selection models is to use a two-stage regression.
In the first stage, I model the probability of selection for inspection of a particular engagement. I
then control for this selection in the second stage model. An important aspect of using such amodel is to identify exclusion restrictions in the first stage that can convincingly be excluded
from the second stage regression (Little, 1985; Lennox et al., 2012).
The first stage model uses as dependent variable Inspection, an indicator variable equal to
one when an engagement is selected for inspection. I include, as explanatory variables, all thecontrol variables identified in Model (3), as well as all the measures of audit quality that I use in
the second stage regression. I also identify two exclusion restrictions. Two categories of issuers
were less likely to be selected for inspection for reasons unrelated to risk assessment. I use
indicator variables in the first stage regression for when issuers belong to these categories. These
exclusion thresholds are likely to have an impact on which engagement is selected for inspection.
However, they are unlikely to be related to whether one particular engagement will receive a PartI Finding, conditional on this specific engagement being selected for inspection.29 Due to the29

As often with exclusion variables, it is impossible to fully argue that the exclusion variables will always beunrelated to the dependent variable in the second stage. For example, even in one of the best known choice of an
exclusion restriction, Angrist (1990), some concerns can be found about the complete independence of the exclusion
variable. In the case of this particular study, a potential concern is related to Audit Firms potentially reverseengineering the inspection selection process of the PCAOB, and thereby putting less effort in audits of issuers thatare inspected less often. In additional untabulated specifications, I also use another restriction variable, based on the
proportion of issuers inspected each year by the PCAOB for a given firm. This proportion can be argued to be, atleast, partially exogenous, given that it is dependent on whether an audit firm is inspected triennially or annually and26need to preserve the confidentiality of the selection process of which engagements are subject toinspection, I am unable to provide more details on these two exclusion restrictions or to tabulate
the first stage regressions. However, the two identified exclusion restrictions load negatively at 1%
in the first stage regressions, with t-statistics of approximately -4 and -6, respectively. In terms of
economic significance, the categories corresponding to the exclusion restrictions have an average
probability to be chosen for inspection that is 1.6% and 2.2% lower, respectively. Given that the
average probability of being chosen for inspection is 7.2%, this decrease is important. The
overall explanatory power of the first stage model is modest, evidenced by an AUC of 0.63,
because the PCAOB uses a wide range of public and non-public information to determine theengagements that will be subject to inspection.
(Insert Table 7 About Here)
Results of the second stage regression, controlling for selection bias, are shown in Table 7. I
find that the results shown in Table 5 are robust to controlling for selection, as evidenced bysimilar directions and statistically significant coefficients on all output measures of audit quality.
6. Conclusion
This paper assesses the validity of publicly available measures of audit quality in light of an
accurate measure of audit process quality based on the PCAOB inspections process. I find thatmany of the measures widely used in prior literature are positively correlated with audit process
quality, especially restatements and whether the issuer meets/beats the zero earnings threshold.
However, the validity of a going concern opinion as a measure of audit quality is to beinterpreted with caution. I find results consistent with a disclaimer effect hypothesis, linked withon the PCAOB’s resources available for a given year. Results also hold using this exclusion variable. However,
because inspections are risk based, it is likely that the PCAOB will choose more risky engagements, on average,
when the proportion of audit inspected is lower for a particular firm.
27

a reduction of liability when the auditor issues such an opinion. This study also provides some
guidance to which measure of accruals researchers should be looking at when conducting
analyses of audit quality.
I also find that, collectively, measures of audit quality based on publicly available data are
only able to explain between 6% and 15% of the Part I Findings. This result is somewhat
concerning as it implies that some of the results in prior literature based on one measure of audit
quality, on small samples, or that find null results, may be reasonably weak. Overall, this study
suggests the need to create a more powerful measure of audit quality that could be made publicly
available.

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32

Appendix A: Variables Definitions

Variable

Definition

Dependent Variable:
Part I Finding

An indicator variable equal to one if the inspection resulted in a Part I finding.

Test Variables:DA

The residual of a regression of accruals (deflated by beginning assets) on gross property,
plant and equipment (PP&E, deflated by beginning assets), the year-on-year change inrevenues (deflated by beginning assets), one over beginning assets, and prior year returnon asset (ROA, defined as income before extraordinary items deflated by averageassets). Accruals are defined as income before extraordinary items (Compustat IB), lesscash flow from operations (Compustat OANCF) excluding extraordinary items anddiscontinued operations (Compustat XIDOC).

AbsDA

The absolute value of DA.

AbsDD

The absolute value of the residual of a regression of total current accruals deflated byaverage assets on cash flow from operations deflated by average assets for the current
year, the following year, and the prior year, gross PP&E deflated by average assets, andchange in revenues deflated by average assets. Total current accruals are defined aschange in inventories plus change in account receivable minus change in accountpayables minus change in income taxes payable plus net change in other assets andliabilities. All variables appearing in the computation of total current accruals are fromthe cash flow statement.

AbsAccruals

Absolute value of accruals deflated by beginning assets.

AbsAccrualsCFO

Absolute value of accruals deflated by cash flow from operations.

Restatement

An indicator variable equal to one if the financial statements for the year are restated due
to accounting or fraud related reasons.

SmallProfit

An indicator variable equal to one if the ROA (income before extraordinary items
deflated by beginning assets) is less than 3%.

SmallBeat

An indicator variable equal to one if the year-on-year change in ROA (income before
extraordinary items deflated by beginning assets) is less than 1%.

GC

An indicator variable equal to one if the auditor issued a going concern opinion.

GCNoBankruptcy

An indicator variable equal to one if the auditor issued a going concern opinion and theissuer did not declare bankruptcy the following year.

Control Variables:
ForeignPifo

Absolute value of pretax income from foreign operations (PIFO) divided by the absolutevalue of pretax income (PI).

Logat

Natural logarithm of the issuer’s assets.

GeosegNumber of geographic segments, from GEOSEG in Compustat SEGMENTS.

33

Variable

Definition

BussegNumber of business segments, from BUSSEG in Compustat SEGMENTS.

Decye

An indicator variable equal to one if the issuer’s fiscal year ends in December.

StdCFOat

Standard deviation of the issuer’s cash flows from operations deflated by beginning
assets, computed from year t − 3 to year t.

CFOat

Issuer’s cash flows from operations deflated by beginning assets.

Leverage

Total debt (short-term plus long-term) divided by the sum of total debt and equity.

BTM

Book-to-market ratio.

Litigation

An indicator variable equal to one if the issuer is in a higher litigation industry (SIC code
between 2833 and 2836, 8731 and 8734, 3570 and 3577, 7370 and 7374, 3600 and 3674,
or 5200 and 5961).

Big 4

Indicator variable equal to one if the audit firm is a Big 4, and zero otherwise.

SalegrowthYear-on-year sales growth of the issuer.

Weaknesses

Number of material internal control weaknesses in a fiscal year as reported by Audit
Analytics.

HiTechAn indicator variable equal to one when the firm is in a hi-tech industry (three-digit SIC
code equal to 272, 283, 355, 357, 360, 361, 362, 363, 364, 365, 366, 367, 369, 381, 382,
386, 481, 484, 489, 573, 596, 621, 679, 733, 737, 738, or 873).

Inspection Variable:
Inspection

An indicator variable equal to one if the engagement is selected for inspection by the
PCAOB.

34

Appendix B: Job Requirements for PCAOB Inspection Team Members – Sample of Job
Posting from the PCAOB Website
12/18/2014
Audit Regulator, Inspector - Chicago
Division of Registration and Inspections
Full Time, Chicago, IL
Our Mission
The PCAOB is a nonprofit corporation established by Congress to oversee the audits of public companies in order toprotect the interests of investors and further the public interest in the preparation of informative, accurate andindependent audit reports. The PCAOB also oversees the audits of broker-dealers, including compliance reports
filed pursuant to federal securities laws, to promote investor protection.
Responsibilities
As an Inspections Specialist you will have the opportunity to use your skills and experience in auditing to participate
in a rigorous program of inspections of registered public accounting firms including:








Inspecting portions of audit workpapers to assess the degree of compliance with the Sarbanes-Oxley Act, the rulesof the Board, the Securities and Exchange Commission, and professional standards in connection with theperformance of audits, issuance of reports, and related matters involving issuers;
Assessing the firms' quality control structures and the related impact these structures have on audit quality;
Meeting and interviewing firm personnel to (1) understand the firms' quality control structures, (2) understand theaudit approach to audit engagements, and (3) further develop and/or identify quality control and/or audit
deficiencies;
Gathering and organizing information to support identified inspection issues;
Researching and consulting with others on technical issues;
Communicating findings to firm personnel in meetings and in comment forms; andPreparing comment forms and assisting with drafting inspection reports.
As an Inspections Specialist, you will also be able to:






Enhance your technical skills through inspection activities and collaboration with highly skilled Inspection teams;
Participate in inspection projects aimed at enhancing the effectiveness of the Inspection program;Participate in the development and/or facilitation of training courses; andParticipate in the PCAOB's recruiting process.
Qualifications










Current CPA (Certified Public Accountant) license required;
Six to Fifteen years of progressively responsible experience having attained the Senior Manager or Manager levelin the audit of companies traded on the U.S. markets;
Bachelor's degree in Accounting; MBA/Master's degree is a plus;
Strong knowledge of PCAOB standards, generally accepted accounting principles and/or international financial
reporting standards;
Strong communication skills, both written and oral;
Ability to work independently and as a member of a team;
Ability to travel 20-30% of time (including some non-U.S. travel); andFluency in a foreign language is a plus.

35

Table 1: Sample Statistics
This table presents a year-by-year description of the inspections data sample. Panel A shows the number ofinspected engagements in the sample, while Panel B shows the split between Big 4 and non-Big 4 auditors.

Panel A: Inspections by year in the sample
Year

Non Inspected

Inspected

Total Engagements

2003

5,811

370

6,181

2004

5,742

256

5,998

2005

5,627

271

5,898

2006

5,427

360

5,787

2007

5,177

420

5,597

2008

4,997

556

5,553

2009

4,905

465

5,370

2010

4,803

407

5,210

2011

4,663

454

5,117

2012

4,587

438

5,025

Total

51,7393,997

55,736

Panel B: Split Big 4 non-Big 4Year

Non Big 4

Big 4

Total

2003

69

301

370

2004

55

201

256

2005

65

206

271

2006

83

277

360

2007

183

237

420

2008

241

315

556

2009

197

268

465

2010

172

235

407

2011

198

256

454

2012

186

252

438

Total

1,449

2,548

3,997

36

Table 2: Descriptive Statistics
This table presents descriptive statistics for the inspected sample. There are 3,997 observations in the inspected
sample, except for the analyses for AbsDD, where the sample is reduced to 3,774 due to the requirement to obtainprior and future year cash flow from operations data. See Appendix A for variable definitions.

Variable

MeanStandard Deviation

25th percentile

50th percentile

75th percentile

Part I Finding

0.27

0.44

0.00

0.00

1.00

GC

0.06

0.23

0.00

0.00

0.00

GCNoBankruptcy

0.06

0.23

0.00

0.00

0.00

AbsDD

0.04

0.06

0.01

0.03

0.05

SmallProfit

0.14

0.35

0.00

0.00

0.00

SmallBeat

0.11

0.32

0.00

0.00

0.00

DA

-0.02

0.22

-0.08

-0.02

0.04

AbsDA

0.12

0.21

0.03

0.06

0.12

AbsAccruals

0.14

0.41

0.03

0.07

0.12

AbsAccrualsCFO

1.70

4.04

0.34

0.63

1.25

Restatement

0.11

0.32

0.00

0.00

0.00

ForeignPifo

0.23

0.43

0.00

0.00

0.28

Logat

6.36

2.42

4.93

6.44

7.97

Geoseg

2.49

2.37

1.00

2.00

4.00

Busseg

2.05

1.68

1.00

1.00

3.00

Decye

0.70

0.46

0.00

1.00

1.00

StdCFOat

0.16

0.71

0.03

0.05

0.10

CFOat

0.04

0.43

0.02

0.09

0.15

Leverage

0.31

0.52

0.03

0.26

0.49

BTM

0.56

1.18

0.26

0.49

0.82

Litigation

0.24

0.43

0.00

0.00

0.00

Big4

0.64

0.48

0.00

1.00

1.00

Salegrowth

0.19

0.72

-0.03

0.08

0.23

Weakness

0.05

0.21

0.00

0.00

0.00

HiTech

0.38

0.49

0.00

0.00

1.00

37

Table 3: Sample Split Between Part I Findings and no Part I Findings and Correlations
Panel A of this table presents a split of the sample between inspections that received a Part I Finding and those that
did not. 2,923 inspections did not result in a Part I Finding, whereas 1,074 inspections did. A t-test (Wilcoxon test) isused to test the differences in means (distribution). Significance levels are * 10%, ** 5% and *** 1%. In Panel B,
correlations among the main variables of interest are shown. Spearman correlations are above the diagonal, while
Pearson correlations are below. Correlations significant at 5% or better are shown in bold.

Panel A: Sample split between No Part I Finding and Part I Finding
No Part I Finding
VariablePart I Finding

Tests of Difference

MeanMedianMeanMedian

GC

0.05

0.00

0.09

0.00

5.66

***

5.64

***

GCNoBankruptcy

0.04

0.00

0.09

0.00

6.06

***

6.03

***

AbsDD

0.04

0.03

0.05

0.02

0.44

SmallProfit

0.13

0.00

0.17

0.00

3.21

SmallBeat

0.11

0.00

0.11

0.00

-0.33

-0.33

DA

-0.02

-0.02

-0.02

-0.02

-0.13

0.70

AbsDA

0.11

0.06

0.14

0.06

3.86

***

2.08

**

AbsAccruals

0.13

0.06

0.19

0.07

4.21

***

2.09

**

AbsAccrualsCFO

1.57

0.60

2.04

0.71

3.29

***

4.81

***

Restatement

0.10

0.00

0.15

0.00

4.66

***

4.65

***

ForeignPifo

0.23

0.00

0.23

0.00

-0.46

-2.10

**

Logat

6.42

6.48

6.18

6.30

-2.76

-1.89

*

Geoseg

2.51

2.00

2.43

2.00

-0.89

-1.66

*

Busseg

2.05

1.00

2.04

1.00

-0.28

-0.43

Decye

0.70

1.00

0.71

1.00

0.32

0.32

StdCFOat

0.13

0.05

0.23

0.05

3.75

***

-0.85

CFOat

0.05

0.09

-0.01

0.08

-4.02

***

-4.47

***

Leverage

0.30

0.25

0.33

0.29

1.58

2.90

***

BTM

0.54

0.47

0.62

0.56

1.93

*

4.97

***

Litigation

0.25

0.00

0.21

0.00

-2.91

***

-2.90

***

Big4

0.67

1.00

0.56

1.00

-6.31

***

-6.28

***

Salegrowth

0.19

0.09

0.19

0.07

0.18

-2.22

**

Weakness

0.05

0.00

0.04

0.00

-0.51

-0.51

HiTech

0.39

0.00

0.36

0.00

-1.52

-1.52

38

t-testWilcoxon

-0.56
***

***

3.20

***

Panel B: Correlations among the main variables of interest

Part I Finding

Part I FindingAbs
Accruals
CFO

Restatement

GC

GCNoBankruptcy

AbsDD

SmallProfit

SmallBeat

DA

AbsDA

Abs
Accruals

0.09

0.10

-0.01

0.05

-0.01

0.01

0.03

0.03

0.08

0.07

0.97

0.19

-0.08

-0.08

0.03

0.23

0.24

0.14

0.01

0.19

-0.08

-0.08

0.03

0.23

0.24

0.14

0.01

-0.11

-0.13

0.01

0.35

0.24

0.09

0.04

0.10

0.05

-0.16

-0.14

0.11

0.02

0.02

-0.15

-0.17

-0.09

-0.04

-0.22

-0.29

-0.10

0.03

0.44

0.19

0.01

0.62

0.02

GC

0.09

GCNoBankruptcy

0.10

0.97

AbsDD

0.01

0.35

0.34

1.00

SmallProfit

0.05

-0.08

-0.08

-0.09

SmallBeat

-0.01

-0.08

-0.08

-0.09

0.10

DA

0.00

-0.04

-0.04

-0.04

0.02

0.00

AbsDA

0.06

0.39

0.39

0.48

-0.12

-0.10

-0.22

AbsAccruals

0.07

0.42

0.43

0.43

-0.08

-0.08

-0.53

0.71

AbsAccrualsCFO

0.05

0.16

0.16

0.08

-0.08

-0.08

-0.17

0.20

0.21

Restatement

0.07

0.01

0.01

0.04

0.02

-0.04

0.03

0.00

-0.01

39

0.03
0.02

Table 4: Results of the Part I Findings Prediction Model
This table presents the results of Model (3). The dependent variable, Part I Finding, equals one if the inspection
results in the issuance of a Part I Finding. Each column shows the regression results for a different measure of audit
quality, Measure of Audit Quality, shown at the top of each column. Panel A presents the regression results for
accruals-based measures of audit quality, while Panel B presents the regression results for the other measures ofaudit quality. Variable definitions are provided in Appendix A. The z-statistic (in parenthesis) is below thecoefficient. Standard-errors are clustered at the issuer-level. Significance levels are * 10%, ** 5% and *** 1%.

Panel A: Accruals-based measures of audit quality
Dependent variable: Part I Finding
Measure of Audit Quality
ForeignPifo
Logat
GeosegBussegDecye
StdCFOat
CFOat
Leverage
BTM
Litigation
Big4
Salegrowth
Weakness
HiTechConstant

Observations
Pseudo R-squared
AUC

DA

AbsDA

AbsDD

AbsAccruals

AbsAccrualsCFO-0.077[-0.422]0.019[0.199]0.027[1.170]0.012[0.694]-0.003[-0.118]0.001[0.010]0.083[1.317]-0.238**[-2.407]0.149*[1.833]0.066*[1.768]-0.212**[-2.113]-0.510***[-5.312]-0.029[-0.539]-0.085[-0.491]-0.046[-0.516]-0.903***[-6.305]0.530***[2.671]
0.025[0.272]0.038*[1.674]0.012[0.684]-0.002[-0.099]-0.005[-0.064]0.068[1.125]-0.177*[-1.791]0.176**[2.168]0.076**[2.056]-0.211**[-2.094]-0.513***[-5.322]-0.044[-0.833]-0.072[-0.417]-0.054[-0.612]-1.044***[-6.890]-0.657[-0.962]0.008[0.081]0.028[1.209]0.013[0.742]-0.007[-0.305]
0.017[0.196]0.064[0.898]-0.288***[-2.583]
0.120[1.358]0.076*[1.795]-0.222**[-2.153]-0.505***[-5.089]-0.002[-0.032]-0.047[-0.267]-0.038[-0.420]-0.898***[-5.684]0.276***[2.633]0.021[0.229]0.034[1.517]0.013[0.760]-0.002[-0.086]-0.007[-0.086]
0.065[1.014]-0.142[-1.373]0.170**[2.115]0.079**[2.127]-0.215**[-2.139]-0.515***[-5.344]-0.036[-0.680]-0.073[-0.421]-0.041[-0.464]-1.004***[-6.822]0.023***[2.792]
0.022[0.235]0.033[1.433]0.012[0.672]-0.002[-0.101]-0.005[-0.056]
0.088[1.428]-0.230**[-2.347]0.135*[1.672]0.064*[1.821]-0.209**[-2.085]-0.510***[-5.317]-0.028[-0.532]-0.097[-0.560]-0.052[-0.585]-0.970***[-6.734]

3,997
0.01530.586

3,997
0.01680.590

3,774
0.01460.585

3,997
0.01680.590

3,997
0.01680.589

40

Panel B: Other measures of audit quality
Dependent variable:
Part I Finding
Measure of Audit Quality
ForeignPifo
Logat
GeosegBussegDecye
StdCFOat
CFOat
Leverage
BTM
Litigation
Big4
Salegrowth
Weakness
HiTechConstant

Observations
Pseudo R-squared
AUC

SmallProfit

SmallBeat

GC

GCNoBankruptcy

Restatement

0.317***[3.074]-0.011[-0.117]0.020[0.890]0.016[0.947]-0.005[-0.203]
0.000[0.003]0.089[1.434]-0.225**[-2.287]
0.143*[1.772]0.056[1.518]-0.203**[-2.025]-0.505***[-5.273]-0.023[-0.421]-0.088[-0.506]-0.042[-0.475]-0.910***[-6.359]-0.031[-0.273]
0.018[0.194]0.028[1.233]0.012[0.681]-0.003[-0.116]0.002[0.021]0.086[1.373]-0.237**[-2.392]
0.147*[1.818]0.064*[1.729]-0.211**[-2.107]-0.511***[-5.334]-0.030[-0.567]-0.085[-0.491]-0.046[-0.515]-0.906***[-6.321]0.706***[3.865]0.025[0.263]0.049**[2.140]0.012[0.685]-0.004[-0.161]-0.010[-0.120]0.060[1.009]-0.138[-1.399]
0.132*[1.723]0.086**[2.401]-0.215**[-2.144]-0.507***[-5.227]-0.039[-0.742]-0.084[-0.490]-0.026[-0.287]-1.094***[-7.482]

0.786***[4.229]0.026[0.276]
0.051**[2.229]0.012[0.693]-0.004[-0.163]-0.011[-0.130]
0.055[0.919]-0.137[-1.408]
0.134*[1.752]0.085**[2.373]-0.219**[-2.178]-0.507***[-5.223]-0.040[-0.748]-0.076[-0.448]-0.025[-0.283]-1.109***[-7.548]

0.528***[4.890]0.020[0.217]0.031[1.371]0.013[0.741]-0.003[-0.129]0.004[0.055]0.090[1.452]-0.233**[-2.342]0.135*[1.661]
0.063*[1.680]-0.233**[-2.326]-0.530***[-5.522]-0.036[-0.673]-0.133[-0.777]-0.042[-0.476]-0.977***[-6.744]

3,997
0.01730.592

3,997
0.01520.586

3,997
0.01880.597

3,997
0.01950.599

3,997
0.02030.595

41

Table 5: Results of the “Kitchen Sink” Prediction Model
This table presents the results of Model (3) when using “kitchen sink” regressions, including all the variables thatwere predictive of Part I Findings in Table 4. Variable definitions are provided in Appendix A. Standard-errors are
clustered at the issuer-level. The z-statistic (in parenthesis) is below the coefficient. Significance levels are * 10%,** 5% and *** 1%.
Dependent variable: Part I Finding
AbsDA

(1)

(2)
0.456**[2.331](3)
0.391**[1.981]

AbsAccruals
AbsAccrualsCFO
Restatement

0.523***[4.822]0.324***[3.140]0.740***[3.963]0.004[0.042]
0.056**[2.434]0.017[0.989]-0.005[-0.226]-0.013[-0.159]
0.048[0.834]-0.078[-0.788]
0.144*[1.857]0.086**[2.365]-0.231**[-2.290]-0.527***[-5.407]-0.047[-0.927]-0.118[-0.698]-0.028[-0.316]-1.297***[-8.350]
3,997
0.02770.6121

SmallProfit
GCNoBankruptcy
ForeignPifo
Logat
GeosegBussegDecye
StdCFOat
CFOat
Leverage
BTM
Litigation
Big4
Salegrowth
Weakness
HiTechConstant
Observations
Pseudo R-squared
AUC

0.018
[0.196]0.027[1.195]0.012[0.697]-0.003[-0.125]0.001[0.016]0.086[1.375]-0.236**[-2.389]0.148*[1.820]0.064*[1.726]-0.211**[-2.104]-0.509***[-5.311]-0.030[-0.564]-0.084[-0.486]-0.045[-0.509]-0.905***[-6.314]3,997
0.01520.5856420.018**[2.123]0.521***[4.799]0.336***[3.250]0.708***[3.790]0.005[0.053]0.058**[2.499]0.017[0.980]-0.005[-0.209]-0.016[-0.199]
0.053[0.926]-0.085[-0.857]
0.131*[1.696]0.084**[2.377]-0.229**[-2.272]-0.527***[-5.409]-0.043[-0.851]-0.130[-0.760]-0.033[-0.369]-1.324***[-8.495]
3,997
0.0286
0.6133(4)0.185*[1.779]0.018**[2.123]0.524***[4.826]0.329***[3.184]0.706***[3.752]0.002[0.022]0.055**[2.370]0.018[1.026]-0.005[-0.200]-0.017[-0.207]
0.052[0.879]-0.063[-0.605]
0.124[1.626]0.084**[2.400]-0.232**[-2.307]-0.528***[-5.426]-0.038[-0.734]-0.131[-0.768]-0.024[-0.268]-1.286***[-8.462]
3,997
0.02840.6131

Table 6: Robustness Tests on Discretionary Accruals
This table provides robustness tests on the discretionary accruals analysis. The sample is partitioned between
positive and negative discretionary accruals in the first two columns, respectively. In the third column, the sample isrestricted to issuers that report small profits (SmallProfit equal to one), and small profit increases (SmallBeat equal
to one) in the fourth. Variable definitions are provided in Appendix A. Standard-errors are clustered at the issuerlevel. The z-statistic (in parenthesis) is below the coefficient. Significance levels are * 10%, ** 5% and *** 1%.
Dependent variable:
Part I Finding
AbsDA

Positive Accruals
subsample

Negative Accruals
subsample

0.744**[2.158]0.424*[1.719]DA
ForeignPifo
Logat
GeosegBussegDecye
StdCFOat
CFOat
Leverage
BTM
Litigation
Big4
Salegrowth
Weakness
HiTechConstant

Observations
Pseudo R-squared
AUC

SmallProfit
subsample

SmallBeat
subsample

0.321[0.203]-0.305[-0.900]
0.301***[4.260]0.028[0.466]-0.092[-1.475]-0.076[-0.279]-1.897[-1.027]
0.390[0.299]0.121[0.244]0.288[1.351]-0.754**[-2.187]-1.607***[-4.652]-0.087[-0.305]
0.218[0.398]0.301[1.040]-2.108***[-3.589]450
0.08620.7034-0.155[-0.999]
0.032[0.952]-0.025[-0.936]
0.015[0.407]0.083[0.652]0.047[0.454]-0.158[-1.020]0.098[0.722]0.143**[1.977]-0.191[-1.252]-0.330**[-2.200]-0.103[-1.613]0.047[0.167]-0.014[-0.102]-1.153***[-4.890]0.118[0.988]0.039[1.253]0.036[1.556]-0.013[-0.429]-0.066[-0.620]0.081[1.059]-0.166[-1.159]0.240**[2.403]0.042[0.961]-0.233*[-1.767]-0.633***[-5.085]0.038[0.439]-0.162[-0.741]-0.098[-0.851]-0.966***[-4.721]3.504***[2.694]-0.173[-1.012]0.173***[3.058]0.050[1.133]-0.022[-0.409]
0.074[0.336]-0.775[-0.936]
0.263[0.220]-0.327[-0.907]0.086[0.772]-0.197[-0.713]-0.618**[-2.363]0.126[0.488]0.208[0.477]0.305[1.314]-1.607***[-3.726]

1,588
0.0176
0.59792,409
0.02200.6002575
0.03400.623743

Table 7: Selection Model
This table shows the second stage regression of a bivariate probit with selection model, with Part I Finding as thedependent variable. The first stage regression is not reported to preserve confidentiality. Standard errors are
clustered at the firm level. The z-statistic (in parenthesis) is below the coefficient. Significance levels are * 10%, **5% and *** 1%.
Dependent Variable: Part I Finding
AbsDA

(1)
0.262**[2.33](2)0.226**[2.01]

AbsAccruals
AbsAccrualsCFO
Restatement
SmallProfit
GC No Bankruptcy
ForeignPifo
Logat
GeosegBussegDecye
StdCFOat
CFOat
Leverage
BTM
Litigation
Big4
Salegrowth
Weakness
HiTechConstant
Observations
Chi-Square
R-square0.340***[5.50]
0.158**[2.33]
0.463***[4.28]
0.037[0.65]
0.050***[3.20]
0.019*[1.71]
0.008[0.52]-0.012[-0.26]0.030[0.91]-0.021[-0.34]0.073[1.61]
0.047**[2.27]-0.102[-1.57]-0.319***[-5.63]-0.025[-0.87]0.005[0.05]-0.005[-0.09]-1.671***[-2.82]
3,997
117.82***0.031
440.010*[1.96]
0.339***[5.46]
0.165***[2.42]
0.445***[4.12]
0.037[0.65]
0.051***[3.23]
0.019*[1.69]
0.008[0.52]-0.014[-0.30]0.033[0.99]-0.026[-0.41]0.067[1.49]
0.046**[2.29]-0.102[-1.56]-0.319***[-5.64]-0.022[-0.79]-0.002[-0.02]-0.007[-0.15]-1.672***[-2.80]3,997
121.07***
0.032

(3)

0.124**[2.09]
0.010*[1.88]
0.341***[5.50]
0.161**[2.37]
0.439***[4.02]
0.036[0.63]
0.049***[3.15]
0.020*[1.76]
0.008[0.54]-0.014[-0.31]0.031[0.90]-0.009[-0.14]0.065[1.47]
0.048**[2.37]-0.103[-1.59]-0.320***[-5.65]-0.020[-0.69]-0.001[-0.01]-0.002[-0.04]-1.662***[-2.84]
3,997
122.44***0.031