financial statement disaggregation, auditor effort and ......audit effort and lower financial...
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Financial Statement Disaggregation, Auditor Effort and Financial Reporting Quality
Matthew J. Beck
Michigan State University [email protected]
Matt Glendening
University of Missouri-Columbia [email protected]
Chris E. Hogan
Michigan State University [email protected]
Current Version: September 2016
We would like to thank Randy Elder and Phil Lamoreaux for their comments and suggestions on earlier versions of the manuscript. The authors appreciate the helpful comments and suggestions from workshop participants at Arizona State University, National Chengchi University and Syracuse University.
Financial Statement Disaggregation, Auditor Effort and Financial Reporting Quality
Abstract
Financial reporting standards provide managers with discretion to choose the level of line item disaggregation within mandatory financial reports. We examine the consequences of differences in firms’ disaggregation choices for auditor effort and financial reporting quality. As increased disaggregation may change which accounts the auditor considers material and alter the thresholds used to evaluate potential financial misstatements, we predict auditor effort will increase with disaggregation. Consistent with our prediction, we document a significant positive association between financial statement disaggregation and audit fees, our proxy for auditor effort. We also examine the financial reporting quality effects of disaggregation and find that disaggregation forces managers to engage in more credible reporting thereby increasing financial reporting quality. Finally, we find some evidence that disaggregation is indirectly associated with lower audit fees through the improvement in financial reporting quality; however, the offset is minimal relative to the increase in fees directly associated with disaggregation. JEL classification: M41; M42 Keywords: Audit fees; financial statement disaggregation; materiality
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I. INTRODUCTION
This paper examines the implications of firms’ financial statement disaggregation choices on
auditor effort and financial reporting quality. While financial statements prepared in accordance
with U.S. Generally Accepted Accounting Principles (GAAP) necessarily aggregate and
condense accounting data to convey decision useful information to users in a cost effective
manner, standard setters also urge preparers to provide disaggregated accounting data to avoid
the exclusive attention on simplified accounting metrics, such as earnings (FASB 1984). Thus,
financial reporting standards acknowledge both the costs and benefits of disaggregated
accounting data, but provide no guidance on the appropriate level of disaggregation in financial
reports. Because managers are left considerable discretion when choosing the level of
disaggregation within mandatory reports, firms vary in the extent to which they provide
disaggregated accounting data.
Prior empirical work provides insight as to why managers choose to disaggregate certain
accounting items. Heitzman, Wasley, and Zimmerman (2010) and Riedl and Srinivasan (2010)
present evidence suggesting managers’ disaggregation decisions are partially driven by whether
the separately reported line item is material to the firm, and thus relevant to investors. In light of
this evidence, firms’ financial statement disaggregation choices have potential consequences for
auditors when making their materiality assessments because auditing standards generally define
material information as information that alters investors’ decision making process.1 Financial
statement disaggregation can impact auditors’ materiality assessments during the planning stage
of the audit and during the stages of identifying and evaluating misstatements, potentially
increasing the number of misstatements classified as material during the testing phase and
1 PCAOB (AS 11.2)
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requiring greater attention to uncorrected misstatements during the final evaluation stage of the
audit.
During the planning phase, auditors determine the materiality level for the financial
statements as a whole, and also determine materiality for individual accounts and disclosures.
Auditors are required to consider whether there are particular accounts or disclosures for which
misstatements of a lower level might be considered material, as compared to the materiality level
for the overall financial statements. When accounts of seemingly immaterial amounts are
individually presented on the face of the financial statements, and thus utilized by investors, the
auditor will need to alter the nature and extent of testing in order to audit these accounts that
would not normally be tested if they were aggregated together with similar accounts. In addition,
when a larger account is disaggregated into its smaller subaccounts in the financial statements,
additional audit objectives related to classification and presentation must be met, requiring
additional tests and evidence, further increasing auditor effort. Finally, an increase in the number
of accounts that are considered material would also result in more potential misstatements being
identified throughout the audit as significant. This increase in potential material misstatements
would increase the testing surrounding these particular accounts or disclosures as well as
increase the extent of negotiations with management during the completion phase of the audit
regarding the correction of those misstatements. Consistent with this argument, a recent
experiment by Libby and Brown (2013) finds that auditors reduce their tolerance for
misstatement as financial statement line items become more disaggregated. Based on these
predictions related to the impact of disaggregation during planning, and also when assessing
misstatements, we predict a positive association between financial statement disaggregation and
auditor effort (using audit fees as a proxy).
3
We also note that higher levels of disaggregation may increase financial reporting quality by
limiting managers’ ability to manage earnings. The findings of Hirst, Koonce and Venkataraman
(2007) suggest at least a perception of improved financial reporting quality as they document
experimental evidence that users believe managers’ ability and willingness to manage earnings
decreases with disaggregation. Consequently, we also examine the effect of financial statement
disaggregation on clients’ financial reporting quality. If financial statement disaggregation is
associated with higher financial reporting quality, this may reduce the assessed level of the
inherent risk of a material misstatement being present in the pre-audited financial statements and
therefore, indirectly reduce auditor effort.
To test the association between financial statement disaggregation and auditor effort, we
estimate a standard audit fee model that includes a measure of financial statement
disaggregation. Our use of audit fees to proxy for auditor effort is consistent with prior research
linking audit billing rates with auditor labor hours (Bell, Landsman, and Shackleford 2001;
Bedard and Johnstone 2004). We use the newly proposed measure of disaggregation quality by
Chen, Miao, and Shevlin (2015) to measure financial statement disaggregation. This measure
captures the fineness of data in the annual report based on a comprehensive set of GAAP
accounting line items in firms’ mandatory filings. Specifically, Chen et al. (2015) calculate
financial statement disaggregation through a count of non-missing Compustat items in the annual
income statement and the balance sheet. Firm differences in the disaggregation quality metric are
driven by discretionary disclosure choices made by managers when compiling external GAAP
reports.
Using a sample of 23,375 firm-year observations from 2002 to 2013, we find evidence of a
positive association between financial statement disaggregation and audit fees. This result is
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robust to controlling for a variety of client, auditor, and engagement characteristics that are
known determinants of audit fees. The positive association is significant when we separately
examine balance sheet and income disaggregation as well. To the extent higher audit fees reflect
higher auditor effort, our evidence suggests auditors respond to financial statement
disaggregation by increasing effort to maintain a desired level of overall audit risk. Consistent
with Libby and Brown (2013), auditors appear to increase the number of accounts they consider
material (i.e., the materiality thresholds used in the initial planning stages of the audit and the
materiality thresholds applied to errors discovered during the audit) as accounting information in
the financial statements becomes more disaggregated, thereby increasing auditor effort
throughout the course of the audit. We confirm this result through further analysis and show that
this change particularly affects auditors on engagements where more qualitative measures of
materiality are most likely, namely when the client reports net income just above zero.
We next consider the effect of financial statement disaggregation on clients’ financial
reporting quality, which we measure in this study using two types of abnormal accruals. Our
results utilizing the same firm-year observations noted above indicate that financial statement
disaggregation is significantly and positively associated with higher financial reporting quality.
As this increase in financial reporting quality may indirectly reduce auditor effort due to lower
assessed inherent risk as noted above, we next utilize path analysis to better understand the
association between disaggregation and auditor effort. We find that while financial statement
disaggregation continues to directly and significantly increase auditor effort, there is some
evidence that this increase is moderated slightly through the associated increase in financial
reporting quality; however the total effect continues to be significantly positive.
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Lastly, we examine how financial statement disaggregation influences the audit fee premium
associated with certain client risk attributes. We investigate whether increased audit fees due to
greater control risk, measured by internal control weaknesses, are moderated by financial
statement disaggregation. In addition, following Kim, Li, and Li (2015) who find executive vega
is perceived by auditors to be associated with greater earnings management risk (inherent risk),
we examine whether disaggregation reduces the higher audit fees associated with higher vega.
Our results indicate the positive association between internal control weaknesses and CEO vega
with audit fees becomes less pronounced as financial statement disaggregation increases. This
evidence suggests the extra auditor effort induced by financial statement disaggregation reduces
the auditor effort and fee premium stemming from increased control risk and executives’
incentives to misreport.
This study makes multiple contributions. First, we provide the first broad empirical evidence
on how auditor effort (as measured by fees) is associated with financial statement disaggregation.
Despite the posited role of information disaggregation in the external audit by academics and
audit standard setters, the empirical implications of financial statement disaggregation for
auditors’ risk assessments and pricing are not well understood. Our study provides empirical
evidence on this issue and provides insight into how the quality of mandatory disclosure
influences auditor perceptions. Second, our evidence informs the ongoing debate by standard
setters on whether to allow more netting of balances within the financial statements. To the
extent auditors lower their tolerance for misstatement when information provided to investors is
more disaggregated, then less netting may increase the overall reliability of the financial
statements. Because subtle differences in firms’ disaggregation choices within mandatory
reporting have potential consequences for audit decision making and financial reporting quality,
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future auditing and financial reporting guidance should give careful consideration to the role of
disaggregation within the financial statements.
This study also answers the call from Gaynor, Kelton, Mercer and Yohn (2016) for research
to better understand the recursive relationship between financial reporting quality and audit
quality. We show that disaggregation directly and positively affects both the level of audit effort
and the level of financial reporting quality for firms. Furthermore, this increase in financial
reporting quality then further indirectly affects the level of effort required of the external auditor
providing insight on the ways in which financial reporting quality and the audit process are
intertwined.
Finally, our findings have implications related to potential consequences of the FASB’s
proposed changes to the definition of materiality. If the FASB changes the definition of
materiality to be consistent with the legal definition as applied by the courts, then some argue
this would result in fewer items beings considered material, and as a result, less information
provided to investors (Morgensen 2016). To the extent our results indicate a benefit to lower
materiality levels associated with disaggregation, in terms of audit effort and financial reporting
quality, altering the definition of materiality may result in greater aggregation and therefore less
audit effort and lower financial reporting quality.
The paper proceeds as follows. Section II reviews relevant literature and develops our
hypotheses. Section III discusses the measure of financial statement disaggregation and describes
the sample and research design. Section IV presents the main empirical finding and additional
tests. Section V concludes the study.
II. BACKGROUND AND HYPOTHESIS DEVELOPMENT
Disaggregation in Financial Reporting
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Standard setters and regulators contend disaggregated accounting data increases the decision
usefulness of financial statements. SFAC No. 5 states “individual items, subtotals, or other parts
of a financial statement may often be more useful than the aggregate to those who make
investment, credit, or other decisions (FASB 1984)." Regulation S-X encourages all applicable
line items to appear on the face of the income statement (SEC 1995). From the auditing
perspective, the PCAOB considers a fact as material if the availability of the fact would likely
significantly alter the total information set to a reasonable investor.2 In guiding auditors’
materiality assessments, SEC guidance and auditing standards also suggest auditors use both
quantitative and qualitative factors in assessing materiality.3
While the decision to disaggregate financial information can be voluntary, Heitzman et al.
(2010) note that firms have an obligation to disclose information under FASB, SEC and
exchange rules, if it is considered material. Heitzman et al. (2010) provide evidence suggesting
managers do comply with these requirements as they find that managers are more likely to
separately disclose advertising costs if they are material, and therefore relevant to investors.
Riedl and Srinivasan (2010) find a similar result when examining managers’ decision to
disaggregate special items. Whether mandatory or voluntary, disaggregated financial information
can be more informative to investors.
Consistent with standard setters’ views, and the implications from Heitzman et al. (2010) and
Riedl and Srinivasan (2010), previous research investigates the capital market benefits of
disaggregation within the financial statements. Fairfield, Sweeney, and Yohn (1996) find that the
disaggregation of earnings (into operating earnings, non-operating earnings and taxes, and
special items) improves the predictability of future profitability. Chen et al. (2015) find that their
2 PCAOB (AS 2105.02) 3 SEC SAB No. 99 and PCAOB (AS 2810.17)
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disaggregation measure is negatively (positively) associated with the dispersion (accuracy) of
analyst forecasts, negatively associated with the information asymmetry component of the bid-
ask spread, and negatively associated with the cost of equity.4 Furthermore, studies on voluntary
disclosure report evidence suggesting management credibility increases with information
disaggregation (Hirst et al. 2007; D’Souza, Ramesh, and Shen 2010). Chen et al. (2015) suggest
their results provide evidence of an association between disaggregation and “information
quality,” though it is not clear how information quality is defined. Collectively, these studies
seem to suggest disaggregation is associated with greater predictability of future earnings;
however, the question remains whether disaggregation constrains management’s ability to
manage earnings, which would be the case if disaggregation makes it more difficult for
management to exercise discretion in financial reporting.
Financial Statement Disaggregation and Implications for Audit Effort
We discuss the implications of financial statement line item disaggregation on auditor effort
by reference to the audit risk model. Audit risk, defined as the risk of issuing an unqualified
opinion when in fact the financial statements are materially misstated, is a function of inherent
risk, control risk and detection risk and is assessed both at the overall financial statement level
and at the individual assertion level for accounts and transactions. Disaggregation can impact
audit effort in at least three ways: by altering the auditor’s determination of material accounts
and therefore audit risk at the individual account level, by altering auditor’s materiality
assessments at the individual account level, and/or by altering an auditor’s assessment of
inherent risk. Disaggregation may result in more accounts being assessed as material, and also
may result in a lower materiality threshold for particular accounts, both resulting in greater audit
4 Chen et al. (2015) are careful not to draw inferences about causality, but stress they are documenting an association.
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effort. On the other hand, disaggregation may result in a lower assessed inherent risk to the
extent disaggregation is associated with a lower likelihood of material misstatements in the pre-
audited financial statements, which would in turn reduce audit effort. In this section, we first
discuss the potential impact of disaggregation on the auditor’s determination of material accounts
and materiality levels and develop our hypothesis related to disaggregation and audit fees. In the
following section, we discuss the association between disaggregation and financial reporting
quality, how that association may impact the auditor’s assessment of inherent risk, and develop
the related hypotheses.
To the extent financial statement line items are more likely to be disaggregated when the
individual amounts are considered material to investors, additional audit procedures may be
planned that would not have been performed if the amounts were aggregated. According to
PCAOB standards, auditors should evaluate whether there are particular accounts or disclosures
for which misstatements of a lower level might be considered material, as compared to the
materiality level for the overall financial statements (PCAOB AS 2105.06-2105.07). When
individual accounts are considered material to investors, auditors may reduce the acceptable
level of audit risk at the account level, resulting in increased testing.
As an example, consider a client with several different types of intangible assets. The client
may choose to report only the aggregate amount of intangible assets, or may choose to separately
disclose certain intangible assets considered individually material. The auditor may assess a
lower acceptable audit risk given the relevance to financial statement users and therefore
increase the extent of testing on the subaccounts disclosed separately (i.e. valuation testing,
consulting with experts/specialists, or recalculations), relative to the extent of testing that would
have been performed if the amounts were aggregated. In addition, when financial statements
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items are more disaggregated, the classification and understandability as well as presentation and
disclosure assertions become relevant to the disaggregated sub-accounts and require additional
tests to ensure that the transactions and balances are properly classified, described and disclosed.
A higher level of disaggregation in an account might also suggest that the importance of that
account is greater to investors than aggregated accounts. The guidance in AS 2105 suggests that
a lower level of materiality might be appropriate in those circumstances. As there is an inverse
relationship between materiality and inherent risk as more misstatements would be considered
material, the auditor will need to decrease detection risk in order to maintain the appropriate
level of audit risk. Reducing detection risk might involve additional tests, utilizing larger sample
sizes for existing procedures, gaining more reliable evidence and/or utilizing more experienced
staff, all of which would increase audit effort.
Another key part of the auditing process where disaggregation may be a factor is determining
whether or not misstatements discovered from testing procedures are, in fact, material, and thus
require correction by management before financial reports are filed. Auditors’ tolerance for
misstatement decreases in the presence of disaggregation because disaggregated accounting
numbers are smaller in magnitude than the traditional materiality benchmarks (e.g., sales, assets,
or earnings (e.g., see Eilifsen and Messier, 2015)). Thus, discovered errors that would otherwise
be immaterial using aggregated data may become material when assessed using disaggregated
data. Audit effort may increase as a result of identifying material misstatements during the audit
engagement as auditors gather sufficient support to convince management of the materiality, and
also because of the indication of a potential material weakness in internal controls resulting from
a material misstatement identified by the auditor.
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Finally, disaggregated information may impact audit effort at the time of evaluating the
overall audit results. To the extent auditors identify misstatements during the audit that are not
deemed individually material, but are more than inconsequential, they will accumulate these
uncorrected misstatements and evaluate them collectively at the end of the audit. Thus, even if
misstatements are not deemed material and therefore are not initially corrected by management,
the uncorrected misstatements may be more likely to approach materiality in the presence of
disaggregated financial information. Experimental evidence consistent with this behavior is
provided by Libby and Brown (2013). Specifically, Libby and Brown (2013) conduct an
experiment to examine whether disaggregation influences auditor beliefs about whether a
discovered error should be corrected. Participants in their experiment include experienced audit
managers who evaluate an underaccrual, discovered towards the end of an audit, in occupancy
costs for a large retailer. The authors find that auditors require correction of smaller errors when
expense information is disaggregated on the face of the financial statements; however, this effect
is less pronounced when the disaggregation takes place in the notes to the financial statements
rather than on the face. Even though the focus of Libby and Brown (2013) was on financial
reporting quality rather than audit effort, their findings imply disaggregation could increase audit
effort as auditors negotiate the outcome of uncorrected misstatements with management.
To summarize, disaggregation can impact audit effort by altering audit risk assessments at
the individual account level, by altering materiality assessments during the planning process, and
by increasing the extent of testing for misstatements discovered during the audit process as well
as those evaluated at the end of the audit. These predictions are consistent with the audit risk
model which highlights the relation between audit risk, risk of material misstatement, and
detection risk. Therefore, we state our first hypothesis as follows.
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H1: Financial statement disaggregation is positively associated with auditor effort. Implications of Disaggregation for Financial Reporting Quality
We next consider whether financial reporting quality, defined as a lower degree of earnings
management through accruals, is partially conditioned on financial statement disaggregation.
Financial reporting quality of the pre-audited statements in turn has implications for an auditor’s
inherent risk assessment. Managers’ financial reporting decisions and use of accrual discretion
may depend on the extent to which financial statement information is disaggregated. Dutta and
Gigler (2002) provide the first theoretical analysis between managers’ voluntary disclosures and
the extent of earnings management. This study argues that managers engaging in greater amounts
of disclosure have less opportunity to manage earnings. Jo and Kim (2007) provide some
empirical evidence for this theory by showing that disclosure frequency is inversely related to
earnings management among a group of firms engaging in seasoned equity offerings. In addition,
Call, Chen, Miao and Tong (2014) find that companies who issue short-term quarterly earnings
guidance are associated with less earnings management, even when managers are known to have
strong short-term capital market incentives to manage earnings. To the extent greater disclosure
results in capital market benefits such as lower cost of equity as documented by Chen et al.
(2015), managers may be willing to constrain their ability to manage earnings. If disaggregation
can be considered an increase in disclosure of management information it may have a direct
impact on financial reporting quality to the extent managers have less opportunity to manipulate
disaggregated earnings.
D’Souza et al. (2010) provide additional evidence on extent of disclosure and management
behavior. They find evidence consistent with managers who regularly intervene in the earnings
reporting process—i.e., cater to market expectations, use accounting discretion to smooth
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earnings, or use discretionary accruals to alter the informativeness of earnings—limiting line-
item disclosures so that investors focus their attention on aggregated summary earnings. This
strategy may have negative consequences though as Hirst et al. (2007) find that users judge
disaggregated voluntary management forecasts to be more credible than aggregated management
voluntary forecasts, suggesting at least a perception that disaggregation limits earnings
management. In addition, they find that users are less concerned with managers’ incentives as a
driving force of credibility when disaggregated earnings forecasts are issued compared to when
aggregated earnings forecasts are issued. The main takeaway from these studies is that
disaggregated accounting forces managers to more credibly communicate information,
potentially enhancing the quality of financial reporting. As a result, we state the following
hypothesis (in alternative form):
H2: Financial statement disaggregation is positively associated with financial reporting quality.
Indirect Impact of Disaggregation on Auditor Effort
If H2 is true, it is possible that financial statement disaggregation may also affect auditor
effort indirectly, in addition to the direct path outlined above. Inherent risk is the probability of a
material misstatement being present in the financial statements prior to considering a client’s
internal controls. As inherent risk or the probability of a material misstatement decreases, the
auditor can reduce their audit effort and still maintain the same acceptable audit risk. If financial
statement disaggregation increases financial reporting quality and decreases the ability of
managers to manipulate accruals, the probability of a material misstatement in the pre-audited
financial statements decreases. Therefore financial statement disaggregation may indirectly result
in a reduction of auditor effort. As a result, we state the following hypothesis in (alternate form):
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H3: Financial statement disaggregation is indirectly negatively associated with auditor effort through the impact on financial reporting quality.
As financial statement disaggregation may be affecting auditor effort both directly as a result
of decreased materiality levels (H1) and indirectly through an increase in financial reporting
quality (H3), we perform a path analysis that examines both the direct and indirect effects.
Figure 1 outlines these predictions of both the direct and indirect effects of disaggregation on
audit fees. Also, further discussion of this analysis is provided in Section 3.
Finally, while the above discussion suggests a direct and possibly an indirect effect of
disaggregation on auditor effort, we acknowledge it is possible disaggregation does not have a
significant impact. First, SAB No. 99 urges auditors to rely on both quantitative and qualitative
indicators when determining materiality thresholds, but does not explicitly suggest auditors look
to financial statement disaggregation when making materiality judgments (SEC 1999).5 Second,
auditors often assess materiality for the financial statement as a whole using summary metrics,
such as earnings, revenues or assets (Eilifsen and Messier 2015), suggesting disaggregation may
have no impact on auditors’ materiality judgments and therefore no impact on audit effort. Along
these lines, Libby and Brown (2013) note, based on open-ended questions asked of participants
in their experiment, that there is little consensus in auditors use of disaggregation in judging
materiality, with “58 percent of participants indicating that disaggregating expenses will increase
the materiality of the error, while 42 percent indicate otherwise.” When asked why
disaggregation was not considered, typical participant responses included “materiality is based
on income before income taxes’’ or ‘‘auditors do not opine on separate line items in financial
statements . . . the error is material to the financials taken as a whole.’’ However, we view the
5 SAB No. 99 does provide guidance to auditors when aggregating and netting discovered misstatements, but the standards do not provide guidance related to using disaggregated information within the clients’ financial statements.
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direct and indirect effects as being more plausible and thus present the hypotheses in alternative
form.
In a concurrent working paper, Koh, Tong and Zhu (2016) also examine the association
between disaggregation and audit fees. Similar to our findings, they document that audit fees are
positively associated with disaggregation. However, Koh et al (2016) hypothesize the
incremental fees are due to litigation risk associated with disaggregated financial statements
because they provide plaintiffs with more detail and transparency to use against a firm in the
event of litigation. To support this hypothesis, Koh et al. document that disaggregation is
positively associated with the likelihood of litigation for the subsample of firms with a financial
statement restatement, i.e. those firms with a greater risk of litigation. Overall, they conclude the
incremental fees represent a risk premium due to increased litigation risk resulting from
disaggregation rather than incremental audit effort. Our study differs in that we argue
disaggregation will impact audit effort as well as financial reporting quality and that these two
outcomes are interrelated and need to be analyzed in combination with each other. We focus on
the interconnected “path,” or mechanism through which these results interact: (1) how
disaggregation directly impacts audit fees, (2) how disaggregation is associated with financial
reporting quality, and also (3) how disaggregation indirectly impacts audit fees through the
impact on financial reporting quality. Furthermore, per prior research (Pratt and Stice 1994;
Gramling, Schatzberg, Bailey and Zhang 1998; Bedard and Johnstone 2004) and conversations
with audit partners, it is unlikely that auditors, when faced with increased audit risk, charge
higher fees without a corresponding increase in audit testing and evidence required.
III. FINANCIAL STATEMENT DISAGGREGATION, SAMPLE, AND RESEARCH DESIGN
Measuring Financial Statement Disaggregation
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Chen et al. (2015) introduce a new measure of disaggregation quality (DQ) that captures the
fineness of accounting data on the face of the financial statements, namely the balance sheet and
income statement. Assuming finer information is of higher quality, their measure provides a new
way to quantify the quality of disclosure in mandatory reports. Given that much of the disclosure
literature has focused on managers’ voluntary disclosure actions (e.g., earnings forecasts),
analyst-based proxies (e.g., following, AIMR scores), or MD&A narratives (e.g., FOG index),
DQ measures a previously overlooked dimension of disclosure quality. Specifically,
discretionary aspects of line item disclosure choices made within mandatory reporting. Chen et
al. (2015) calculate the overall DQ measure as the simple average of the balance sheet DQ score
and the income statement DQ score.6
For the balance sheet score, the authors link 11 account groups to 25 second level (parent)
accounts, which are then linked to 93 subaccounts. The 11 account groups include account
groups from both the assets-side and claims-side of the balance sheet.7 For each of these 11
groups, the authors count the number of non-missing subaccount items and divide this number
by the total number of subaccounts in that group. For example, the current assets account group
includes 7 parent accounts linked to 20 subaccounts. If three of the subaccounts are missing, the
current assets group would have a non-missing items ratio of 0.85 (17/20).
6 The authors do not construct a DQ score for the statement of cash flows for two reasons. First, firms tend not to differ in the number of non-missing items provided in the statement of cash flows. Second, constructing and interpreting a DQ score for the statement of cash flows is difficult because the reporting format changed in 1989. 7 The 11 Compustat group accounts for the balance sheet DQ score include Current Assets – Total (ACT), Property Plant and Equipment – Total (Net) (PPENT), Investment and Advances – Other (IVAO), Intangible Assets – Total, Assets – Other – Total (AO), Current Liabilities – Total (LCT), Long-Term Debt – Total (DLTT), Deferred Taxes and Investment Tax Credit (TXDITC), Liabilities – Other (LO), Preferred/Preference Stock (Capital) – Total (PSTK), Common/Ordinary Equity – Total (CEQ). These 11 account groups are from the Compustat balancing model for the balance sheet. Two account groups, Investment and Advances – Equity (IVAEQ) and Noncontrolling Interest – Redeeemable – Balance Sheet (MIB), are excluded because they do not have subaccounts and exhibit no variation in their reporting.
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The non-missing items ratios are calculated for each of the 11 account groups, then each is
multiplied by the account group’s weighted value percentage (i.e., the dollar value of the account
group divided by the dollar value of total assets). The 11 value-weighted non-missing items
ratios are summed across all 11 groups. Since the balance sheet has two sides (i.e., assets-side
and claims-side), this summation has a theoretical range between 0 and 2. The sum is divided by
two so that the balance sheet DQ score varies between 0 and 1. This value weighting process
results in a balance sheet DQ score that places more emphasis on disaggregation within an
account group when that group constitutes a higher percentage of total assets. Using the above
example, assume two firms have a non-missing items ratio of 0.85 for the current assets account
group; however, the first firm relies more heavily on working capital assets in its operations and
has current assets equal to 75% of total assets, whereas the second firm has current assets equal
to 50% of total assets. The value-weighted non-missing items ratio for current assets would equal
0.6375 and 0.4250 for the first firm and second firm, respectively.
The DQ score for the income statement is constructed similarly to the balance sheet score.
For the income statement score, the authors link 7 account groups to 51 subaccounts.8 Note that
due to the structure of the income statement, there are no intermediate parent accounts. For each
of these 7 groups, the authors count the number of non-missing subaccount items and divide this
number by the total number of subaccounts in that group. For example, the total operating
expenses account group includes 13 subaccounts. If three of the subaccounts are missing, the
total operating expenses group would have a non-missing items ratio of 0.7692 (10/13).
8 The 7 Compustat group accounts for the income statement DQ score include Operating Expenses – Total (XOPR), Interest and Related Expense (XINT), Nonoperating Income (Expense) – Total (NOPI), Special Items (SPI), Income Taxes – Total (TXT), Extraordinary Items and Discontinued Operations (XIDO), and Comprehensive Income – Total (CITOTAL). These 7 account groups are from the Compustat balancing model for the income statement. One account group, Sales (SALE), is excluded because it has only one subaccount (Revenue – Total (REVT) and it is always non-missing.
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The non-missing items ratios are calculated for each of the 7 account groups, then an equal-
weighted income statement DQ score is created by averaging the ratios across the 7 account
groups. This income statement DQ score has in theoretical range between 0 and 1. Unlike the
value-weighted balance sheet DQ score, the income statement DQ score is equal-weighted. Chen
et al. (2015) point out that value-weighting within the income statement is problematic for two
reasons. First, since the income statement includes both positive items (e.g., revenues) and
negative items (e.g., expenses), value-weighting would have to be done according to absolute
values, making it difficult to interpret the meaning of the value-weighted measure. Second, since
total operating expenses make up about 90 percent of revenues, disaggregation outside of the
total operating expenses group account would be understated. Thus, the authors choose an equal-
weighting scheme for the income statement DQ score.9
Sample Selection and Data
When constructing our sample we begin with the intersection of Compustat Fundamentals
Annual and Audit Analytics. We calculate the degree of financial statement disaggregation (DQ)
at the firm-year level using Compustat data in line with the process used by Chen et al. (2015).
Our sample period begins in 2002 and ends in 2013. We exclude from our sample firms in the
financial services industry (SIC codes 6000-6999) or utilities industry (SIC codes 4900-4999)
because these regulated firms have unique financial reporting characteristics. We also remove
observations without necessary data to compute the dependent and independent variables in our
9 The DQ measure is meant to identify instances where the firm has the underlying subaccount but does not report it, and Compustat reports the subaccount as missing. Ideally, the DQ measurement process does not penalize a firm for not reporting an item when the firm’s operations do not support the particular subaccount, resulting in a zero balance. To mitigate the possibility that the firm does not have the underlying item and Compustat reports it as missing, we follow Chen et al. (2015) and employ two screening mechanisms based on the nesting feature of balance sheet accounts and, to a lesser extent, income statement accounts. First, subaccounts are excluded from the count of non-missing items if the parent account is zero. The second screening mechanism deals with instances where all but one of the subaccounts for a particular parent account are non-missing and the single missing subaccount can be indirectly calculated to be zero. In these instances, the single missing subaccount is not counted as missing.
19
regression analyses. Our final sample consists of 23,375 firm-year observations. We compare our
measure of DQ to that in Chen at al. (2015) and note a correlation of 0.9874.10 To mitigate the
effects of outliers, we winsorize all continuous variables throughout our analyses at the 1st and
99th percentiles by year at the firm-year level.
Research Design
Financial statement disaggregation and auditor effort
To test H1, we estimate a standard audit fee model that includes control variables for known
determinants of audit fees identified in the prior literature, including client size, complexity,
operating risk, and financial risk (Simunic 1980; Francis and Simon 1987; Raghunandan and
Rama 2006; Hogan and Wilkins 2008; Beck and Mauldin 2014). Our primary analyses estimate
the following pooled OLS regression, where the dependent variable (ln_FEES) is the natural
logarithm of audit fees (see Appendix A for a full list of variable definitions):
Our main variable of interest in Equation (1) is DQ, which represents the level of
disaggregation in the balance sheet and income statement in the prior year (Chen et al. 2015).11
We lag DQ for two reasons. First, DQ may influence initial materiality levels set during the
planning stage of the audit before current year’s DQ levels are known. Second, the idea that
10 We thank the authors of Chen et al. (2015) for providing their firm-year disaggregation score data so that we could validate our measure and compute a correlation between their measure and ours. 11 Model (1) is a levels specification rather than a changes specification because DQ is sticky over time. The untabulated mean (median) annual change in DQ for our sample equals 0.003 (0.002).
ln_FEESit =
β0 + β1DQit-1 + β2ln_ASSETSit + β3LEVERAGEit + β4LOSSit + β5ROAit + β6CFOit + β7ARINVit + β8GEO_SEGSit + β9OP_SEGSit + β10FOREIGNit + β11REPORTLAGit + β12ACC_FILERit + β13BIG4it + β14GCit + β15ICMWit + β16M&Ait + β17RESTRUCTUREit + β18SPECIALITEMSit + β19CFOVOLit + β20SALEGROWTHit + β21MTBit + β22NEW_FINANCINGit +β23lnOFFICEit + β24SHORTTENUREit + β25SPECIALISTit + βINDUSTRY + βYEAR + ε
(1)
20
disaggregation will cause auditors to lower the materiality threshold for discovered errors
assumes that investors have access to disaggregated data during the audit (Libby and Brown
2013). To test H1, we examine the coefficient on DQ. A statistically positive coefficient on DQ
would provide support for H1 indicating auditor effort increases with financial statement
disaggregation.
We include a number of client and auditor characteristics in Equation (1) to control for
potential determinants of audit fees. The control variable, ln_ASSETS, is the natural logarithm of
total assets. LEVERAGE is total liabilities scaled by total assets. LOSS is an indicator variable,
equal to 1 if the company reported a loss and zero otherwise. ROA equals income before
extraordinary items divided by total assets, CFO is operating cash flows scaled by total assets and
ARINV is accounts receivable plus inventory divided by total assets, which reflects operating
risk. GEO_SEGS and OP_SEGS are the number of geographic and operating segments,
respectively. FOREIGN is an indicator variable equal to 1 if foreign currency adjustments are
present and zero otherwise and defines firms with international operations (Whisenant,
Sankaraguruswamy, and Raghunandan 2003). REPORTLAG equals the number of days between
the company’s fiscal year end and the auditor’s signing date. ACC_FILER equals 1 if the
company is an accelerated filer and zero otherwise. BIG4 is a dummy variable equal to one if the
client is audited by one of the Big 4 auditors (PwC, Deloitte, EY, KPMG) and zero otherwise.
GC equals 1 if the company received a going concern modified opinion in the current year and
zero otherwise. ICMW equals 1 if the company discloses an internal control material weakness
and zero otherwise. Equation (1) also includes M&A, RESTRUCTURE, and SPECIALITEMS, as
these variables are predicted by Chen et al. (2015) to be important determinants of
21
disaggregation.12 Since we examine financial reporting quality in subsequent tests, we include
additional variables that are potentially correlated with both financial reporting quality and audit
fees (i.e., CFOVOL, SALEGROWTH, MTB, NEW_FINANCING, lnOFFICE, SHORTTENURE,
SPECIALIST).
To control for residual serial correlation, we employ standard errors that are clustered by
audit firm client because we expect larger firm client variations than year variations and the
number of year clusters is small (Petersen 2009; Thompson 2011).13 In addition, we include year
and industry fixed effects to control for differences between industries and any potential cross-
sectional dependence of residuals.
We also examine how the separate balance sheet and income statement components of DQ
influence auditor effort. To this end, we estimate a similar OLS model as in Equation (1) above,
but separate DQ into its two components. DQ_BS measures the level of disaggregation in the
balance sheet and DQ_IS measures the level of disaggregation in the income statement. Both
components are included in the model.
Financial statement disaggregation and financial reporting quality
To test H2 we utilize the following OLS model which regresses a measure of financial
reporting quality (FRQ) on DQ, along with several client and auditor characteristics (see the
Appendix A for a full list of variable definitions):
12 Appendix B reports an OLS regression of current period DQ on the vector of control variables specified in Equation (1). Appendix B reports that many of these client and auditor characteristics are significant determinants of DQ, underscoring the importance of controlling for these characteristics in Equation (1). 13 Thompson (2011) also demonstrates that when firm and time dimensions are extremely unbalanced, clustering only on the dimension with fewer variations, which is year in our case, can result in less bias. In sensitivity tests, we cluster by year and find qualitatively similar results to those reported.
FRQit =
α0 + α1DQit-1 + α2ln_ASSETSit + α3LEVERAGEit + α4LOSSit + α5ROAit + α6CFOit + α7ARINVit + α8GEO_SEGSit + α9OP_SEGSit + α10FOREIGNit + α11REPORTLAGit + α12ACC_FILERit + α13BIG4it + α14GCit + α15ICMWit + α16M&Ait + α17RESTRUCTUREit +
(2)
22
The dependent variable in Equation (2), FRQ, equals one of the following variables:
SIGNED_ABNACC_JONES or SIGNED_ABNACC_DD, multiplied by negative one so that
higher values of FRQ correspond to higher quality financial reporting. The variable
SIGNED_ABNACC_ JONES equals signed performance matched abnormal accruals from
estimating the modified Jones’ model (Jones 1991; Dechow, Sloan and Sweeney 1995; Kothari,
Leone and Wasley 2005). The variable SIGNED_ABNACC_DD equals signed abnormal accruals
measured by estimating the cross-sectional version of the Dechow and Dichev (2002) model as
modified by McNichols (2002) and Francis, LaFond, Olsson, and Schipper (2004).
We utilize abnormal accruals to proxy for financial reporting quality as these metrics outside
of statistical norms have been shown to provide insight into whether the client has failed to
follow GAAP. Feroz, Park and Pastena (1991) and Dechow, Hutton and Sloan (1996) have
shown that companies sanctioned by the SEC typically have large income increasing accruals
and Beneish (1997) and Dechow et al. (2011) show that these metrics have some predictive
ability in identifying firms receiving SEC sanctions for misreporting. In addition, even if these
metrics do not technically identify GAAP violations, high levels of these metrics lead to less
persistent earnings in the next period (Sloan 1996; Xie 2001) reducing the informativeness of
earnings for investors to predict future performance. We feel these two characteristics accurately
capture the level of financial reporting quality in line with the definition of Gaynor et al. (2016)
who define higher quality financial reports as those that “are more complete, neutral, and free
from error and provide more useful predictive or confirmatory information about the company’s
underlying economic position and performance.”
α18SPECIALITEMSit + α19CFOVOLit + α20SALEGROWTHit + α21MTBit + α22NEW_FINANCINGit +α23lnOFFICEit + α24SHORTTENUREit + α25SPECIALISTit + αINDUSTRY + αYEAR + ε
23
We utilize signed rather than unsigned accruals as our measures of financial reporting quality
as auditors are more likely to disagree with client accounting choices which are income-
increasing and require clients to adjust earnings downwards (DeFond and Jiambalvo 1994;
Kinney and Martin 1994; Caramanis and Lennox 2008). There is a greater chance of litigation
and loss of reputation when clients overstate earnings compared to when clients understate
earnings (Kellogg 1984). In addition, tests based on unsigned discretionary accruals are more
exposed to correlated omitted variables which can bias tests in favor of rejecting the null
hypothesis of no earnings management (Hribar and Nichols 2007).14 When testing H2 we
examine the coefficient on lagged DQ, which is predicted to be positive if investors’ access to
disaggregated accounting information forces managers to report more credible accounting
information.
Following prior research, Equation (2) includes controls for client and auditor attributes that
are potentially associated with measures of financial reporting quality. Specifically, we include
ln_ASSETS to control for client size (Becker, DeFond, Jiambalvo and Subramanyam 1998),
SALESGROWTH and MTB to control client growth (Menon and Williams 2004), CFO to control
for performance (Kothari et al. 2005), CFOVOL to control for operating volatility (Hribar and
Nichols 2007), LEVERAGE and LOSS to control for the effects of debt and financial distress
(DeFond and Jimbalvo 1994), ICMW to control for deficiencies in internal controls (Doyle, Ge
and McVay 2007), GEO_SEGS and OP_SEGS to control for firm complexity (Bushman, Chen,
Engel and Smith 2004), NEW_FINANCING to control for market-based incentives (Dechow, Ge,
Larson and Sloan 2011), BIG4 to control for auditor size (DeAngelo 1981), lnOFFICE to control
for auditor office size (Reynolds and Francis 2001; Francis and Yu 2009), SHORTTENURE to
14 However, we utilize unsigned accruals as a measure of financial reporting quality in our robustness tests and obtain similar results. See Section 4 for further discussion.
24
control for less client specific knowledge (Krishnan and Krishnan 1997; Stice 1991), and
SPECIALIST to control for auditor industry expertise (Reichelt and Wang 2010). To ensure
consistent control variables across the models used in our subsequent path analysis, we also
include in Equation (2) the additional control variables included in the audit fee model outlined
in Equation (1) (i.e., ROA, ARINV, FOREIGN, REPORTLAG, ACC_FILIER, GC, M&A,
RESTRUCTURE, SPECIALITEMS). Similar to Equation (1), when estimating Equation (2) we
cluster standard errors at the client level and include year and industry fixed effects.
Descriptive Statistics
Table 1 reports the summary statistics of variables used in the study. The mean (median)
level of DQ in our sample is 0.739 (0.755) with an interquartile range of 0.683 to 0.790 which is
slightly higher than the disaggregation levels found in Chen et al. (2015).15 The majority of our
firms are audited by a Big 4 auditor (0.753) and are accelerated filers (0.771). The mean and
median values of ln_FEES (13.452 and 13.446, respectively) along with the various client
characteristics are similar to recent studies examining audit fees (Beck and Mauldin 2014).
[INSERT TABLE 1 HERE]
Table 2 reports correlations for variables used in this study. Of note, our main variable of
interest DQ is significantly and positively correlated with client audit fees (ln_FEES), indicating
that financial statement disaggregation is associated with higher audit fees (H1). Table 2 also
reports that financial statement disaggregation varies with several client attributes. For example,
DQ is significantly positively correlated with foreign operations, reporting delay, merger and
acquisition activity, and special items. The variable DQ is significantly negatively correlated
with client size and leverage. Regarding financial reporting quality, DQ exhibits a statistically
15 This difference is likely due to our use of a different sample period than Chen et al. (2015). Chen et al. (2015) use a sample period of 1973-2011 and they document a sharp increase in DQ around 2003. DQ values after this period rise to between 0.70 and 0.80 after 2003, which is consistent with the time period and DQ values in our sample.
25
negative correlation with SIGNED_ABNACC_JONES and SIGNED_ABNACC_DD, which is
consistent with managers engaging in more credible financial reporting as financial statements
provide more disaggregated information (H2). While the univariate correlations provide some
support for H1 and H2, we rely on our multivariate analyses for making inferences.
[INSERT TABLE 2 HERE]
IV. RESULTS
Financial statement disaggregation and auditor effort – Main results
Table 3 presents the results from estimating Equation (1). Consistent with H1, Table 3
reports a statistically positive coefficient (coef. = 0.833, p < 0.01) on DQ in Model 1, suggesting
auditor effort increases as financial statements become more disaggregated. In terms of an
economic effect, we examine the coefficient on DQ in Model 1 and estimate that moving from
the 25th percentile to the 75th percentile of DQ increases audit fees by 0.089, which represents an
increase equal to 0.66 percent of the median value of ln_FEES. To put this economic effect in
perspective, the coefficient on BIG4 in Model 1 indicates the fee premium charged by Big 4
auditors is 0.077, or 0.57 percent of the median value of ln_FEES. In this light, we view our
documented effect of DQ on audit fees to be economically meaningful.
We next examine whether this relationship is due to disaggregation on the balance sheet,
income statement or both. This analysis allows us to consider the differential impact of financial
statement disaggregation on auditor effort that occurs at the planning stage versus the stages of
identifying and evaluating misstatements. Auditors typically allocate overall materiality to
balance sheet accounts rather than income statement accounts as auditors focus on testing
balance sheet accounts directly (and indirectly testing income statement accounts). This balance
sheet focus suggests disaggregation of the balance sheet accounts will impact materiality during
26
the planning process to a greater extent than disaggregation of the income statement accounts
(Arens, Elder and Beasley 2014).
During the identification and evaluation of misstatements, on the other hand, auditors will
also be concerned with individual income statement line items due to the relative importance of
the income statement to investors. Auditors will consider the likelihood of a material
misstatement on the income statement when evaluating identified misstatements as PCAOB
Auditing Standard 14 Appendix B provides several qualitative factors to consider when
evaluating the materiality of uncorrected misstatements, several of which reference effects to the
income statement. For both the balance sheet and the income statement, we expect a positive
association with disaggregation and audit effort.
Model 2 in Table 3 presents the results when estimating Equation (1) after separating
financial statement disaggregation into its balance sheet and income statement components. In
Model 2, we find a statistically positive coefficient on DQ_BS (coef. = 0.586, p < 0.01), which is
consistent with auditor effort increasing as the balance sheet becomes more disaggregated. In
addition, the coefficient on DQ_IS is positive and significant (coef. = 0.265, p < 0.01),
suggesting that the auditor increases effort when the income statement becomes more aggregated
as well. When testing whether the coefficients on DQ_BS and DQ_IS in Model 2 of Table 3 are
statistically different from each other, an untabulated F-test indicates that the coefficient on
DQ_BS is greater than the coefficient on DQ_IS (p = 0.075). This finding suggests that, while
balance sheet disaggregation and income statement disaggregation both affect auditor effort,
balance sheet disaggregation appears to play a more prominent role, perhaps due to materiality
thresholds set during the planning stage of the audit being based on the balance sheet rather than
the income statement.
27
[INSERT TABLE 3 HERE]
Financial statement disaggregation and financial reporting quality
The results reported in Table 3 lend empirical support for the idea that financial statement
disaggregation influences account-level audit risk and materiality levels set by auditors, and thus
auditor effort throughout the course of the audit. We now turn our focus to how disaggregation
influences financial reporting quality. Greater financial statement disaggregation potentially
increases financial reporting quality if disaggregation forces managers to engage in more
credible reporting (H2).
To test H2, we estimate Equation (2) and report results in Table 4. The dependent variable in
Model 1 (Model 2) equals SIGNED_ABNACC_JONES multiplied by negative one
(SIGNED_ABNACC_DD multiplied by negative one). Because we measure financial reporting
quality as signed abnormal accruals in Models 1 and 2, we assume in these models that poor
financial reporting quality is captured by upward bias in accruals (i.e., income-increasing
earnings management).
In both Models 1 and 2, we find a significantly positive coefficient on DQ, suggesting that
financial statement disaggregation results in higher quality financial reporting by constraining
managerial upward manipulation of accruals (H2). We examine the coefficient on DQ in Model
1 (Model 2) and estimate that moving from the 25th percentile to the 75th percentile of DQ
decreases SIGNED_ABNACC_JONES (SIGNED_ABNACC_DD) by 1.73 (1.56) percent of total
assets. Overall, the results reported in Table 4 suggest that more disaggregation in mandatory
financial reports lead to higher quality reporting. These findings are consistent with previous
studies finding disaggregation in voluntary disclosure forces managers to engage in more
credible reporting and constrains earnings management behavior (Hirst et al. 2007).
28
[INSERT TABLE 4 HERE]
Financial statement disaggregation and auditor effort – Path analysis
In this section we use path analysis to explore the indirect effect of financial statement
disaggregation on auditor effort (H3).16 Table 3 reports that financial statement disaggregation is
positively associated with auditor effort, yet Table 4 shows that more disaggregated reporting
increases financial reporting quality. To the extent auditors lower assessed inherent risk and
therefore reduce audit effort when pre-audited financial reporting quality is already high (e.g.,
because misstatement risk is reduced), then we expect financial statement disaggregation,
through its impact on reporting quality, to exhibit a negative indirect effect on auditor effort.
This argument provides the motivation for our path analysis which estimates the following
models:
where the vector of control variables (CONTROLS) is the same across all three models. Equation
(3a) is the same model outlined in Equation (1), Equation (3b) is the same model outlined in
Equation (2), and Equation (3c) is the same model outlined in Equation (1) but with the variable
FRQ included. Table 3 reports the results from estimating Equation (3a) and Table 4 reports the
results from estimating Equation (3b). We do not tabulate the full estimation of Equation (3b),
but the coefficients of focus (δ1 and δ2) are reported in Table 5.
16 Path analysis estimates a structural equation model to assess how an independent variable (e.g., DQ) affects a dependent variable (e.g., ln_FEES) by decomposing the association into its total effect, direct effect, and indirect effect (Baron and Kenny 1986). Our research design follows prior studies that use path analysis (e.g. Pevzner, Xie, and Xin 2015; DeFond, Lim, and Zang 2016)
ln_FEESt = β0 + β1DQt-1 + CONTROLS + εt (3a)
FRQt = α0 + α1DQt-1 + CONTROLS + εt (3b)
ln_FEESt = δ0 + δ1DQt-1 + δ2FRQt + CONTROLS + εt (3c)
29
As depicted in Figure 1, we use the estimated coefficients from the above models to
assess the total effect, direct effect, and indirect effect of financial statement disaggregation on
auditor effort. Table 5 reports the magnitude and t-statistics for each coefficient of interest for
both measures of FRQ. When measuring FRQ as the negative of SIGNED_ABNACC_ JONES
(see Model 1 of Table 5), we find the total effect of disaggregation on auditor effort (β1) equals
0.833 (p < 0.01), the direct effect (δ1) equals 0.833 (p < 0.01), and the indirect effect (α1*δ2)
equals -0.000 (p = 0.993). Even though the first indirect link (DQ to FRQ) is significantly
positive (α1 = 0.162, p < 0.01), the second indirect link (FRQ to ln_FEES) is insignificant (δ2 = -
0.000, p = 0.993), which results in an insignificant indirect effect (α1*δ2). Thus, while financial
statement disaggregation directly positively influences auditor effort by lowering materiality
thresholds, it does not indirectly influence auditor effort through financial reporting quality.
In Model 2 of Table 5, we measure FRQ as the negative of SIGNED_ABNACC_ DD. Since
the total effect of disaggregation on auditor effort (β1) does not depend on the FRQ measure, the
total effect of DQ remains equal to 0.833 (p < 0.01). When examining the direct and indirect
effects of DQ in Model 2, we find that the direct effect (δ1) equals 0.843 (p < 0.01) and the
indirect effect (α1*δ2) equals -0.010 (p = 0.013). When considering the two links comprising the
indirect effect, we find that the first indirect link (DQ to FRQ) is significantly positive (α1 =
0.145, p < 0.01), but the second indirect link (FRQ to ln_FEES) is significantly negative (δ2 = -
0.069, p = 0.010). This finding suggests that the mediator, FRQ, acts as a suppressor variable in
the association between DQ and ln_FEES. That is, financial statement disaggregation, through
its positive influence on financial reporting quality, exhibits an indirect negative association with
30
auditor effort.17 Overall, our path analysis reported in Table 5 provides some empirical support
for H3, but only when we measure FRQ as the negative of SIGNED_ABNACC_ DD.
[INSERT TABLE 5 HERE]
Additional Tests
Financial statement disaggregation and auditor effort – The role of small earnings
In developing our main hypothesis (H1), we argue that financial statement disaggregation
induces greater auditor effort by altering auditor risk assessments and lowering materiality levels
for individual accounts when investors rely on smaller, disaggregated amounts. While the results
reported in Table 3 lend support for this contention, we attempt to corroborate this idea by
examining whether the disaggregation-effort relation is intensified when other traditional
materiality benchmarks may be less desirable. Specifically, we expect that when reported
earnings are positive but small, more qualitative characteristics may be considered as a non-
material amount by standard quantitative measures may change a client’s earnings from positive
to negative, thereby leading to the greater use of disaggregated data by auditors in their
materiality assessments. To conduct this analysis, we re-estimate Equation (1) after including
SMALLPOS and the interaction term DQ*SMALLPOS, where SMALLPOS is an indicator
variable equal to one for firm-years where 0.00≤ROA≤0.05, zero otherwise. Table 6 reports the
results from this analysis. In Model 1 we include SMALLPOS without the interaction term and
find that auditor effort is unrelated to small positive earnings. Model 2 reports the results when
both SMALLPOS and the interaction term DQ*SMALLPOS are included. In Model 2, we find the
coefficient on DQ remains significantly positive, and more importantly, we find that the
coefficient on DQ*SMALLPOS is significantly positive. These findings suggest that financial
17 MacKinnon, Fairchild, and Fritz (2007) refer to instances when the indirect path is opposite of the direct path as inconsistent mediation. David Kenny notes that in these instances there is still mediation, but the mediator acts like a suppressor variable (see http://davidakenny.net/cm/mediate.htm#IM).
31
statement disaggregation matters more for auditor effort when more qualitative factors should be
considered when assessing materiality. Moreover, the results in Table 6 reinforce the idea that
disaggregation influences auditor effort through auditors’ materiality judgments.
[INSERT TABLE 6 HERE]
Financial statement disaggregation and the audit fee premium of client risk attributes
One unresolved issue pertains to the fact that providing disaggregated financial statements is
costly to clients in terms of audit fees, so aside from other capital market benefits of
disaggregation, it is unclear how clients could benefit (in terms of audit fees) by providing more
disaggregated reports. We contend that the additional auditor effort induced by disaggregation
can prove beneficial to the extent that the audit fee premium on other client risk attributes is
reduced. For example, we know that auditors increase effort and charge higher fees when faced
with certain client characteristics that increase the risk of a material misstatement. To the extent
auditors rely on disaggregation when setting materiality levels, and thus exert more effort, the
riskiness of these other client attributes will be attenuated.
One such client attribute is the presence of internal control material weaknesses (ICMW).
Ashbaugh-Skaife et al. (2008) find that internal control weaknesses are associated with lower
accruals quality, which suggests that internal control weaknesses signal to auditors that other
misstatement risks may exist. Consistent with this idea, Raghunandan and Rama (2006) and
Hogan and Wilkins (2008) find that audit fees are higher for clients with internal control
deficiencies. To the extent that financial statement disaggregation leads auditors to lower
materiality thresholds and exert more effort, we expect that greater disaggregation will attenuate
the audit fee premium relating to internal control material weaknesses. Our reasoning is that the
32
additional auditor effort induced by disaggregation will lessen the misstatement risk associated
with internal control problems.
CEO portfolio vega is another client risk attribute whose fee premium is potentially affected
by disaggregation. Kim et al. (2015) argue that CEO vega will increase client risk because CEOs
with higher portfolio vega have greater incentives to misreport earnings (Armstrong, Larcker,
Ormazabal and Taylor 2013). Because CEO vega increases misstatement risk, Kim et al. (2015)
predict and find that auditors increase effort in order to maintain a desired level of audit risk. We
argue that if auditors exert more effort when accounting data is disaggregated, then CEO vega
becomes less risky as financial statement disaggregation increases. That is, the disaggregation
effect on auditor effort lowers the chances that high-vega CEOs will be able to engage in
misreporting that goes undetected.
To test our above conjectures we re-estimate Equation (1) after including ICMW and
ICMW*DQ or ln(VEGA) and ln(VEGA)*DQ. The variable ICMW equals one if the company
discloses an internal control material weakness and zero otherwise. The variable ln(VEGA) is the
natural logarithm of CEO vega, defined as the dollar change (in $000s) in the CEO's wealth
associated with a 1 percent change in the standard deviation of the firm’s returns (see Core and
Guay (2002) and Coles, Daniel, and Naveen (2006)).18 Because Execucomp data is required to
compute vega, the sample size used when re-estimating Equation (1) is reduced to 7,843.
Table 7 reports the results from this analysis. In Model 1 we examine the effect of DQ on the
fee premium of ICMW. We document a significantly positive coefficient on ICWM, which is
consistent with Raghunandan and Rama (2006) and Hogan and Wilkins (2008). In addition, we
find that the coefficient on the interaction term ICMW*DQ is significantly negative. This finding
18 Our inferences are unchanged if we use raw CEO vega. We obtain data on vega from the following website: http://astro.temple.edu/~lnaveen/data.html. We thank Jeffrey Coles, Naveen Daniel, and Lalitha Naveen for making their data available.
33
suggests that financial statement disaggregation lessens the association between internal control
material weaknesses and audit fees. We estimate that an increase from the 25th percentile to the
75th percentile of DQ decreases the positive effect of internal control material weaknesses on
audit fees by approximately 9.7 percent.
Model 2 reports the results for vega. Consistent with Kim et al. (2015), we document a
significantly positive coefficient on ln(VEGA) in Model 2, suggesting auditors perform more
substantive testing procedures as CEO vega increases. Because misstatement risk increases with
CEO vega, this additional effort allows auditors to maintain an acceptable level of audit risk. We
also find a significantly negative coefficient on ln(VEGA)*DQ, indicating that the positive
association between CEO vega and audit fees becomes less pronounced as financial statement
disaggregation increases. We estimate moving from the 25th percentile to the 75th percentile of
DQ decreases the positive effect of CEO vega on audit fees by approximately 14.4 percent.
The results reported in Table 7 support our predictions and suggest that as accounting
information becomes more disaggregated, auditors lower their materiality thresholds and exert
more effort, thereby attenuating the effect of certain client attributes (i.e., internal control
material weaknesses and CEO vega) on audit risk. Hence, it appears the additional auditor effort
resulting from financial disaggregation does play a role in alleviating the risks associated with
client traits that are precursors to misreporting.
[INSERT TABLE 7 HERE]
Firm Fixed Effects
While we lag DQ in Equation (1) to mitigate concerns about reverse causality, this
specification does not resolve the time-invariant unobservable heterogeneity problem. As a
result, we conduct a robustness test that employs a firm fixed effects estimation of Equation (1)
34
to address omitted variable bias arising from time-invariant firm characteristics. Consistent with
our earlier results supporting H1, in untabulated results we continue to find a significantly
positive coefficient on DQ and on its components DQ_BS and DQ_IS. This finding suggests that
the documented positive effect of financial statement disaggregation on audit fees is robust to
differencing away firm-specific trends in audit fees.
Auditor Changes
In order to ensure that the auditor is not driving disaggregation choices at the client, we
examine disaggregation levels associated with auditor changes. First, we re-estimate the model in
Appendix B after including an indicator variable for an auditor change in the current year. The
coefficient on this variable was not significant at traditional levels with a p-value > 0.50. Next,
we re-estimated the model in Appendix B again but redefined the dependent variable in the
model as the absolute value of the change in DQ. The coefficient on the auditor change variable
had a p-value > 0.50. In addition, we performed a means test between the average yearly change
in DQ with the same auditor and the change in DQ when there was an auditor change. The
means were not statistically different. The results of these tests indicate that a change in auditor
does not produce any statistically significant change in the level of disaggregation indicating that
the auditor is not driving the level of financial statement disaggregation of its clients.
Unsigned Accruals
For completeness, we also utilized unsigned abnormal accruals as measures of financial
reporting quality. In untabulated results, we find that DQ increases financial reporting quality
when using UNSIGNED_ABNACC_JONES (p = 0.159) and UNSIGNED_ABNACC_DD (p <
0.05), as our dependent variables. In the path analysis utilizing these variables, while
disaggregation continued to significantly improve financial reporting quality similar to our main
35
results, the total indirect negative effect of DQ on audit fees through financial reporting quality
was not significant at p < 0.10 when utilizing UNSIGNED_ABNACC_JONES. However, when
using UNSIGNED_ ABNACC_DD as the measure of financial reporting quality, we find a
significant total indirect negative effect of DQ (p < 0.10).
Auditor Traits
We also examine whether financial statement disaggregation differentially affects auditors
with specific attributes. Specifically, we examine how larger auditors handle client financial
statement disaggregation. While DQ continues to remain positive and significant for all auditors,
in untabulated results we find that Big 4 auditors and auditors from larger offices (based on total
audit fees) increase their effort to a greater extent than smaller auditors. These results might
suggest that these types of auditors have greater expertise and experience and better understand
that financial statement disaggregation increases audit risk. On the other hand, the results could
simply suggest that these specific types of auditors are in a stronger negotiating position and
utilize increases in disaggregation as a reason to demand higher fees from their clients.
V. CONCLUSION
Mandatory financial reports provide varying levels of information disaggregation. In this
study, we examine whether and how auditor effort and financial reporting quality are influenced
by the disaggregation in a client’s financial statements. Using the disaggregation measure
developed by Chen et al. (2015), we document a positive association between financial statement
disaggregation and audit fees. This finding is consistent with auditors’ calibrating audit risk
assessments and materiality thresholds based on whether investors are provided disaggregated
data in financial reports. It appears that when investors have more line item detail, auditors lower
planning materiality and materiality used to evaluate detected errors, thereby exerting more
36
effort to ensure misstatement risk remains at an acceptable level. We also find that
disaggregation discourages managers from engaging in upward earnings management and forces
more credible financial reports. Our path analysis indicates however that any reduction in audit
effort due to the decreased earnings management is overwhelmed by the increase in effort due to
the disaggregation. We also find that the increased audit effort due to disaggregation mitigates
some of the increase in auditor effort required due to increased client risk, namely internal
control weaknesses and CEO vega.
Our study makes multiple contributions. First, we provide the first empirical evidence on the
consequences of clients’ disaggregation choices in financial reports for auditor effort and
financial reporting quality. Second, our evidence informs regulators as they continue the
discussion on the costs and benefits of allowing more netting of balances within the financial
statements. In addition, this study also answers the call from Gaynor et al. (2016) for research
into the intersection of financial reporting quality and audit quality by providing insight on the
ways in which financial reporting quality and the audit process are intertwined. Finally, our
results suggest there may be additional negative consequences to the FASB’s proposed changes
to the definition of materiality to the extent that change would result in less information being
disclosed to investors.
37
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ln_FEES = natural logarithm of audit feesDQ = financial statement disaggregation (i.e., average of DQ_BS and DQ_IS ) from Chen, Miao, and Shevlin
(2015)DQ_BS = balance sheet disaggregation from Chen, Miao, and Shevlin (2015)DQ_IS = income statement disaggregation from Chen, Miao, and Shevlin (2015)ln_ASSETS = natural logarithm of total assetsARINV = accounts receivable plus inventory scaled by total assetsLEVERAGE = total liabilities scaled by total assetsLOSS = 1 for firm-years reporting negative earnings, zero otherwiseROA = income before extraordinary items divided by total assetsCFO = operating cash flows scaled by total assetsGEO_SEGS = natural logarithm of a firm's total geographic segmentsOP_SEGS = natural logarithm of a firm's total operating segmentsFOREIGN = 1 for firm-years reporting foreign currency adjustments, zero otherwiseREPORTLAG = number of days between a firm's fiscal year-end and the auditor's signing dateACC_FILER = 1 for accelerated filer firms, zero otherwiseBIG4 = 1 for firm-years where the signing auditor is a Big 4 auditor, zero otherwiseGC = 1 for firm-years receiving a going concern modified opinion, zero otherwiseICMW = 1 for firm-years with a material weakness under SOX Section 302 or SOX Section 404, zero otherwiseM&A = 1 for firm-years engaged in merger and acquisitions (based on Compustat item SALE_FN), zero
otherwiseRESTRUCTURE = 1 if pretax restructuring costs are nonzero, zero otherwiseSPECIALITEMS = absolute value of special items divided by total assetsSHORTTENURE = 1 for firm-years where the length of the client-auditor relationship is less than or equal to three years, zero
otherwiselnOFFICE = audit office size, measured as the natural logarithm of aggregate audit fees for a particular audit officeSPECIALIST = 1 for firm-years where the auditor is defined as both a national and a city industry specialist following
Reichelt and Wang (2010), zero otherwiseSIGNED_ABNACC_JONES = signed value of abnormal accruals measured by estimating the cross-sectional version of the modified
Jones model (Jones 1991; Dechow, Sloan, and Sweeney 1995), where the residuals are performance-matched based on industry and ROA (Kothari, Leone, and Wasley 2005)
SIGNED_ABNACC_DD = signed value of abnormal accruals measured by estimating the cross-sectional version of the Dechow and Dichev (2002) model as modified by McNichols (2002) and Francis, LaFond, Olsson, and Schipper (2005)
SALEGROWTH = current year's sales divided by prior year's salesMTB = market value of equity divided by book value of equityCFOVOL = standard deviation of CFOt, CFOt-1, CFOt-2, where CFO is operating cash flows scaled by total assetsNEW_FINANCING = an indicator variable equal to 1 if a client has a new issuance of debt or equity during the fiscal year (i.e.,
a value for Compustat item DLTIS or CSHI greater than 5 percent of total assets), zero otherwise
VEGA = dollar change (in $000s) in the CEO's wealth associated with a 1% change in the standard deviation of the firm’s returns
Appendix AVariable Definitions
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Variable Coefficient t-statisticIntercept 0.704*** 71.05ln_ASSETS -0.001* -1.82LEVERAGE -0.083*** -18.64LOSS 0.006*** 4.34ROA 0.007 1.51CFO 0.001 0.18ARINV -0.015*** -2.60GEO_SEGS 0.004*** 4.57OP_SEGS -0.002** -2.10FOREIGN 0.013*** 8.72REPORTLAG 0.000 0.74ACC_FILER 0.006*** 2.74BIG4 0.003 1.38GC -0.003 -1.08ICMW 0.004** 2.50M&A 0.003** 2.38RESTRUCTURE 0.016*** 7.20SPECIALITEMS 0.026*** 3.53CFOVOL 0.010 1.42SALEGROWTH -0.003*** -3.89MTB 0.001*** 5.34NEW_FINANCING 0.000 0.19lnOFFICE 0.002*** 2.59SHORTTENURE 0.001 0.67SPECIALIST 0.000 0.23
Adjusted R2 0.585No. of Observations 23,375
Appendix BContemporanous Determinants of DQ
This table reports the results from estimating an OLS regression examiningdeterminants of financial statement disaggregation. Year and industry (atthe 2-digit SIC level) fixed effects are included but not tabulated. Standarderrors are clustered at the firm-level.
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ln_FEESt = δ0 + δ1DQt-1 + δ2FRQt + CONTROLS + εt
The coefficient β1 (H1) is the magnitude of the total effect of disaggregation on auditor effort.The coefficient δ1 is the magnitude of the direct effect of disaggregation on auditor effort. Thecoefficients α1 (H2) and δ2 capture the indirect path between disaggregation and auditor effort,where the magnitude of the indirect effect of disaggregation on auditor effort equals α1*δ2 (H3).
Figure 1Direct and Indirect Paths between DQ and Audit Fees
This figure is adapted from DeFond, Lim, and Zang (2016). The figure depicts the direct andindirect paths through which financial statement disaggregation is expected to affect auditoreffort. We expect disaggregation to directly positively affect auditor effort by loweringmateriality thresholds, and indirectly negatively affect auditor effort through its effect on financial reporting quality. The following models are estimated in the path analysis:
ln_FEESt = β0 + β1DQt-1 + CONTROLS + εt
FRQt = α0 + α1DQt-1 + CONTROLS + εt
DQδ1 (+)
AUDIT FEES
α1 (+) δ2 (-)
FRQ
45
Variable N Mean Median Std Dev Q1 Q3ln_FEES 23,375 13.452 13.446 1.304 12.510 14.318DQ 23,375 0.739 0.755 0.081 0.683 0.790DQ_BS 23,375 0.874 0.902 0.087 0.865 0.924DQ_IS 23,375 0.603 0.620 0.116 0.508 0.670ln_ASSETS 23,375 5.911 5.807 2.017 4.450 7.282ARINV 23,375 0.258 0.227 0.184 0.110 0.365LEVERAGE 23,375 0.436 0.430 0.214 0.260 0.592LOSS 23,375 0.347 0.000 0.476 0.000 1.000ROA 23,375 -0.036 0.033 0.229 -0.049 0.076CFO 23,375 0.043 0.080 0.178 0.015 0.133GEO_SEGS 23,375 1.775 1.946 0.928 1.386 2.485OP_SEGS 23,375 1.694 1.386 0.700 1.386 2.303FOREIGN 23,375 0.297 0.000 0.457 0.000 1.000REPORTLAG 23,375 63.501 62.000 20.893 53.000 74.000ACC_FILER 23,375 0.771 1.000 0.420 1.000 1.000BIG4 23,375 0.753 1.000 0.431 1.000 1.000GC 23,375 0.021 0.000 0.143 0.000 0.000ICMW 23,375 0.069 0.000 0.254 0.000 0.000M&A 23,375 0.194 0.000 0.395 0.000 0.000RESTRUCTURE 23,375 0.022 0.000 0.146 0.000 0.000SPECIALITEMS 23,375 0.029 0.003 0.117 0.000 0.017SHORTTENURE 23,375 0.209 0.000 0.407 0.000 0.000lnOFFICE 23,375 16.911 17.316 1.742 15.799 18.201SPECIALIST 23,375 0.057 0.000 0.232 0.000 0.000SIGNED_ABNACC_JONES 23,375 -0.008 -0.005 0.182 -0.084 0.073SIGNED_ABNACC_DD 23,375 0.007 -0.002 0.195 -0.087 0.080SALEGROWTH 23,375 1.138 1.079 0.428 0.971 1.213MTB 23,375 3.186 2.139 3.485 1.327 3.631CFOVOL 23,375 0.068 0.041 0.083 0.021 0.080NEW_FINANCING 23,375 0.844 1.000 0.362 1.000 1.000VEGA 7,843 167.174 63.625 343.638 20.790 171.608
Sample and Descriptive Statistics
This table reports descriptive statistics for the variables used in the empirical analyses. The sample includes 23,375 firm-years during 2002-2013. Due to data availability, the sample for VEGA is reduced to 7,843 firm-years. Appendix A provides variable definitions.
Table 1
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(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)(1) ln_FEES 1.000(2) DQ 0.188 1.000(3) ln_ASSETS 0.843 -0.026 1.000(4) ARINV -0.101 -0.013 -0.161 1.000(5) LEVERAGE 0.332 -0.209 0.353 0.115 1.000(6) LOSS -0.238 -0.002 -0.352 -0.119 0.008 1.000(7) ROA 0.252 0.012 0.385 0.158 -0.017 -0.654 1.000(8) CFO 0.259 -0.029 0.394 0.094 0.041 -0.533 0.804 1.000(9) GEO_SEGS 0.371 0.120 0.299 0.124 0.028 -0.141 0.207 0.210 1.000(10) OP_SEGS 0.146 -0.021 0.149 0.078 0.075 -0.108 0.095 0.082 0.137 1.000(11) FOREIGN 0.290 0.183 0.199 0.068 0.036 -0.080 0.104 0.096 0.365 0.015 1.000(12) REPORTLAG -0.110 0.119 -0.283 0.074 0.000 0.155 -0.134 -0.139 -0.087 -0.039 -0.016 1.000(13) ACC_FILER 0.507 0.062 0.560 -0.178 0.071 -0.247 0.241 0.243 0.187 0.071 0.142 -0.252 1.000(14) BIG4 0.457 -0.061 0.479 -0.159 0.133 -0.122 0.111 0.125 0.166 0.040 0.105 -0.245 0.447 1.000(15) GC -0.125 -0.030 -0.188 -0.014 0.066 0.184 -0.332 -0.300 -0.086 -0.039 -0.044 0.134 -0.165 -0.109 1.000(16) ICMW 0.015 0.024 -0.076 0.038 0.019 0.076 -0.043 -0.046 0.001 -0.008 0.015 0.338 -0.078 -0.098 0.053(17) M&A 0.162 0.029 0.160 -0.051 0.047 -0.081 0.071 0.070 0.070 0.088 0.041 -0.013 0.118 0.080 -0.035(18) RESTRUCTURE 0.043 0.001 0.032 -0.009 0.031 -0.007 0.018 0.009 0.040 0.004 0.028 -0.013 0.015 0.055 -0.008(19) SPECIALITEMS -0.023 0.014 -0.071 -0.028 0.025 0.224 -0.401 -0.146 -0.006 -0.011 -0.007 0.032 -0.052 -0.013 0.083(20) SHORTTENURE -0.243 -0.108 -0.202 0.077 -0.023 0.069 -0.061 -0.056 -0.052 -0.016 -0.042 0.073 -0.156 -0.252 0.034(21) lnOFFICE 0.561 0.085 0.498 -0.166 0.116 -0.114 0.119 0.117 0.207 0.058 0.162 -0.150 0.436 0.722 -0.112(22) SPECIALIST 0.123 -0.053 0.145 0.006 0.070 -0.042 0.036 0.021 0.029 0.026 0.009 -0.054 0.076 0.141 -0.017(23) SIGNED_ABNACC_JONES -0.052 -0.026 -0.063 0.032 -0.009 -0.008 -0.006 -0.238 -0.030 -0.011 -0.012 0.024 -0.060 -0.041 0.051(24) SIGNED_ABNACC_DD -0.165 -0.037 -0.225 -0.031 -0.008 0.190 -0.252 -0.580 -0.129 -0.041 -0.067 0.078 -0.171 -0.090 0.169(25) SALEGROWTH -0.034 -0.030 -0.021 -0.080 -0.021 -0.045 0.018 -0.046 -0.077 -0.024 -0.033 0.030 0.056 -0.013 -0.019(26) MTB 0.008 0.031 -0.057 -0.143 0.226 0.066 -0.173 -0.171 -0.096 -0.095 -0.031 -0.042 0.071 0.035 0.067(27) CFOVOL -0.317 -0.009 -0.420 -0.044 -0.093 0.315 -0.482 -0.511 -0.208 -0.115 -0.120 0.102 -0.225 -0.152 0.228(28) NEW_FINANCING -0.303 0.028 -0.366 -0.063 -0.168 0.192 -0.158 -0.137 -0.124 -0.133 -0.089 0.100 -0.180 -0.160 0.052(29) VEGA 0.359 -0.044 0.485 -0.159 0.085 -0.127 0.122 0.114 0.079 0.025 0.027 -0.127 0.082 0.098 -0.022
Table 2Correlations
This table provides the Pearson correlation coefficients. Correlations with significance with p < 0.10 are bolded. Due to data restrictions the correlations for VEGA are based on a reduced sample size of 7,843.
47
(16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29)(1) ln_FEES(2) DQ(3) ln_ASSETS(4) ARINV(5) LEVERAGE(6) LOSS(7) ROA(8) CFO(9) GEO_SEGS(10) OP_SEGS(11) FOREIGN(12) REPORTLAG(13) ACC_FILER(14) BIG4(15) GC(16) ICMW 1.000(17) M&A 0.019 1.000(18) RESTRUCTURE 0.011 0.006 1.000(19) SPECIALITEMS 0.032 0.012 -0.010 1.000(20) SHORTTENURE 0.069 -0.030 0.011 0.026 1.000(21) lnOFFICE -0.048 0.078 0.051 -0.018 -0.214 1.000(22) SPECIALIST -0.013 0.021 0.011 -0.015 -0.050 0.150 1.000(23) SIGNED_ABNACC_JONES 0.003 -0.032 0.002 -0.132 0.009 -0.042 -0.003 1.000(24) SIGNED_ABNACC_DD 0.015 -0.063 0.016 -0.164 0.036 -0.084 -0.028 0.417 1.000(25) SALEGROWTH 0.024 0.109 -0.007 -0.046 0.015 -0.002 0.004 0.017 -0.006 1.000(26) MTB -0.014 -0.037 0.017 -0.015 -0.026 0.049 0.022 0.001 0.036 0.152 1.000(27) CFOVOL 0.016 -0.088 -0.006 0.123 0.079 -0.161 -0.038 0.081 0.269 0.119 0.224 1.000(28) NEW_FINANCING 0.042 0.025 -0.017 0.054 0.066 -0.169 -0.069 -0.011 0.052 0.044 0.091 0.165 1.000(29) VEGA -0.042 0.022 0.000 -0.047 -0.036 0.160 0.030 -0.026 -0.070 0.006 0.154 -0.133 -0.088 1.000
Table 2 continuedCorrelations
This table provides the Pearson correlation coefficients. Correlations with significance with p < 0.10 are bolded. Due to data restrictions the correlations for VEGA are based on a reduced sample size of 7,843.
48
Variable Coefficient t-statistic Coefficient t-statisticIntercept 7.255*** 51.18 7.213*** 48.78DQ 0.833*** 6.07DQ_BS 0.586*** 4.28DQ_IS 0.265*** 3.24ln_ASSETS 0.499*** 76.02 0.500*** 75.69LEVERAGE 0.311*** 7.41 0.309*** 7.35LOSS 0.056*** 4.23 0.057*** 4.29ROA -0.354*** -9.03 -0.352*** -9.00CFO -0.087* -1.78 -0.088* -1.82ARINV 0.412*** 7.43 0.407*** 7.29GEO_SEGS 0.109*** 11.74 0.109*** 11.75OP_SEGS 0.025** 2.42 0.024** 2.30FOREIGN 0.111*** 6.91 0.114*** 7.01REPORTLAG 0.004*** 12.30 0.004*** 12.31ACC_FILER 0.076*** 3.43 0.075*** 3.39BIG4 0.077*** 2.96 0.077*** 2.95GC 0.089*** 2.60 0.090*** 2.61ICMW 0.176*** 8.51 0.175*** 8.49M&A 0.045*** 3.88 0.043*** 3.77RESTRUCTURE 0.096*** 3.81 0.098*** 3.90SPECIALITEMS -0.001 -0.02 0.001 0.02CFOVOL 0.347*** 4.99 0.358*** 5.11SALEGROWTH -0.052*** -5.52 -0.052*** -5.50MTB 0.007*** 3.50 0.007*** 3.56NEW_FINANCING 0.013 0.71 0.014 0.77lnOFFICE 0.091*** 14.96 0.091*** 15.00SHORTTENURE -0.112*** -8.05 -0.112*** -8.06SPECIALIST 0.059** 2.39 0.060** 2.43
Adjusted R2 0.838 0.838No. of Observations 23,375 23,375
Table 3The Effect of DQ on Audit Fees
This table reports the results from estimating an OLS regression examining the effect offinancial statement disaggregation on audit fees. Year and industry (at the 2-digit SIClevel) fixed effects are included but not tabulated. Standard errors are clustered at the firm-level.
Model 1 Model 2
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DV = FRQ:
Variable Coefficient t-statistic Coefficient t-statisticIntercept -0.188*** -6.24 -0.229*** -9.55DQ 0.162*** 5.86 0.145*** 6.81ln_ASSETS -0.000 -0.01 0.005*** 5.25LEVERAGE -0.004 -0.49 -0.031*** -4.26LOSS 0.013*** 3.53 -0.002 -0.78ROA -0.413*** -23.27 -0.545*** -25.93CFO 0.724*** 41.07 1.260*** 60.69ARINV -0.025** -2.27 0.018* 1.94GEO_SEGS -0.003 -1.60 -0.001 -0.76OP_SEGS 0.003 1.31 -0.001 -0.52FOREIGN -0.004 -1.36 -0.005** -2.02REPORTLAG 0.000 0.42 0.000 1.56ACC_FILER 0.007 1.52 0.009** 2.43BIG4 -0.004 -0.72 -0.006 -1.42GC -0.027** -2.02 -0.053*** -4.29ICMW 0.003 0.65 0.008* 1.80M&A 0.008*** 2.92 0.013*** 5.82RESTRUCTURE -0.000 -0.00 -0.013** -2.37SPECIALITEMS 0.036* 1.78 0.161*** 4.08CFOVOL -0.009 -0.42 -0.085*** -4.24SALEGROWTH 0.004 1.33 0.023*** 7.08MTB 0.001 0.91 0.001*** 2.93NEW_FINANCING 0.007** 2.10 0.006** 2.27lnOFFICE 0.002* 1.80 -0.001 -0.68SHORTTENURE 0.001 0.39 0.001 0.49SPECIALIST -0.001 -0.13 0.006 1.55
Adjusted R2 0.171 0.566No. of Observations 23,375 23,375
Table 4The Effect of DQ on Financial Reporting Quality
This table reports the results from estimating an OLS regression examining the effect offinancial statement disaggregation and on financial reporting quality. Year and industry (atthe 2-digit SIC level) fixed effects are included but not tabulated. Standard errors areclustered at the firm-level.
SIGNED_ABNACC_DDSIGNED_ABNACC_JONES
Model 2Model 1*-1*-1
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FRQ:
Variable Coefficient t-statistic Coefficient t-statisticTotal Effect:
β1 0.833*** 6.07 0.833*** 6.07Direct Effect:
δ1 0.833*** 6.06 0.843*** 6.14Indirect Path:
α1 0.162*** 5.86 0.145*** 6.81δ2 -0.000 -0.01 -0.069** -2.12
Indirect Effect:α1*δ2 -0.000 -0.01 -0.010** -2.47
Controls Yes YesNo. of Observations 23,375 23,375
Path Analysis for the Effect of DQ on Audit FeesTable 5
This table reports the results a path analysis that examines the effect of financial statement disaggregationon audit fees. The table reports the path coefficients of interest. The significance of the indirect effect isestimated using the Sobel (1982) test statistic.
SIGNED_ABNACC_JONES SIGNED_ABNACC_DD*-1 *-1
Model 1 Model 2
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Variable Coefficient t-statistic Coefficient t-statisticIntercept 7.250*** 51.27 7.329*** 53.73DQ 0.834*** 6.09 0.711*** 5.56SMALLPOS 0.006 0.44 -0.325*** -2.67DQ*SMALLPOS 0.450*** 2.78ln_ASSETS 0.499*** 76.10 0.499*** 76.25LEVERAGE 0.308*** 7.33 0.310*** 7.40LOSS 0.059*** 3.79 0.058*** 3.72ROA -0.351*** -8.89 -0.349*** -8.83CFO -0.085* -1.75 -0.088* -1.83ARINV 0.413*** 7.42 0.413*** 7.47GEO_SEGS 0.109*** 11.75 0.109*** 11.76OP_SEGS 0.025** 2.42 0.026** 2.47FOREIGN 0.111*** 6.91 0.111*** 6.89REPORTLAG 0.004*** 12.28 0.004*** 12.29ACC_FILER 0.076*** 3.43 0.076*** 3.44BIG4 0.077*** 2.97 0.077*** 2.98GC 0.091*** 2.63 0.089*** 2.59ICMW 0.176*** 8.49 0.176*** 8.48M&A 0.044*** 3.86 0.044*** 3.81RESTRUCTURE 0.096*** 3.80 0.095*** 3.78SPECIALITEMS 0.001 0.03 0.002 0.05CFOVOL 0.349*** 5.02 0.350*** 5.05SALEGROWTH -0.052*** -5.51 -0.052*** -5.52MTB 0.007*** 3.61 0.007*** 3.61NEW_FINANCING 0.012 0.71 0.013 0.73lnOFFICE 0.091*** 14.96 0.091*** 14.97SHORTTENURE -0.112*** -8.06 -0.112*** -8.03SPECIALIST 0.059** 2.40 0.057** 2.34
Adjusted R2 0.838 0.839No. of Observations 23,375 23,375
Table 6The Role of Small Earnings in the Effect of DQ on Audit Fees
This table reports the results from estimating an OLS regression examining the role of small earnings in the effect of financial statement disaggregation on audit fees. SMALLPOS isan indicator variable equal to one for firm-years where 0.00≤ROA ≤0.05, zero otherwise.Year and industry (at the 2-digit SIC level) fixed effects are included but not tabulated.Standard errors are clustered at the firm-level.
Model 1 Model 2
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Variable Coefficient t-statistic Coefficient t-statisticIntercept 7.230*** 50.65 6.088*** 19.41DQ 0.869*** 6.21 2.279*** 6.58ICMW 0.541*** 3.03DQ*ICMW -0.489** -2.07ln(VEGA) 0.238*** 4.34ln(VEGA)*DQ -0.320*** -4.42ln_ASSETS 0.499*** 76.00 0.525*** 45.87LEVERAGE 0.311*** 7.43 0.494*** 6.55LOSS 0.056*** 4.20 0.034 1.52ROA -0.355*** -9.06 -0.111 -1.00CFO -0.084* -1.74 -0.290** -2.23ARINV 0.413*** 7.45 0.780*** 7.49GEO_SEGS 0.109*** 11.74 0.110*** 6.28OP_SEGS 0.025** 2.42 0.034** 2.37FOREIGN 0.111*** 6.92 0.110*** 4.77REPORTLAG 0.004*** 12.26 0.004*** 7.24ACC_FILER 0.076*** 3.40 -0.068 -0.98BIG4 0.077*** 2.98 -0.088 -1.57GC 0.089** 2.57 -0.229* -1.73ICMW -- -- 0.281*** 7.94M&A 0.044*** 3.88 0.041** 2.36RESTRUCTURE 0.096*** 3.81 0.052 1.44SPECIALITEMS -0.000 -0.00 0.189 1.50CFOVOL 0.348*** 5.01 0.331* 1.65SALEGROWTH -0.052*** -5.54 -0.050 -1.56MTB 0.007*** 3.46 0.000 0.08NEW_FINANCING 0.013 0.71 0.006 0.26lnOFFICE 0.091*** 14.96 0.087*** 8.18SHORTTENURE -0.112*** -8.04 -0.129*** -4.54SPECIALIST 0.058** 2.38 0.057* 1.82
Adjusted R2 0.838 0.787No. of Observations 23,375 7,843
Table 7The Role of DQ in the Association between Client Risk and Audit Fees
This table reports the results from estimating an OLS regression examining the role offinancial statement disaggregation in the association between client risk and audit fees.Year and industry (at the 2-digit SIC level) fixed effects are included but not tabulated.Standard errors are clustered at the firm-level.
Model 1 Model 2