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The Association between Financial Reporting Risk and Audit Fees Before and After the Historic Events Surrounding SOX
Shannon L. Charles Brigham Young University
Steven M. Glover ([email protected])
Brigham Young University
Nathan Y. Sharp Texas A&M University
October 2009
SUMMARY: This study investigates whether the association between financial reporting risk and audit fees changed during 2000-2003: a time period marked by momentous and historic events for auditors. We find a positive statistically and economically significant relationship between financial reporting risk and audit fees paid to Big 4 auditors. More importantly, we predict and find that the relation between financial reporting risk and audit fees strengthened significantly in 2002 and 2003, consistent with a shift in the way auditors priced risk, likely in response to the events surrounding the Sarbanes-Oxley Act of 2002. Finally, we provide evidence that a commercially developed, comprehensive risk measure effectively proxies for an element of risk beyond what has traditionally been captured by various risk measures in audit fee models: namely, the risk that financial statements have been intentionally misstated. We believe this risk measure will be of interest to future researchers. Keywords: audit fees, financial reporting risk, audit risk, Sarbanes-Oxley Data Availability: Contact the authors Shannon L. Charles is an Assistant Professor, Steven M. Glover is a Professor, both at Brigham Young University, and Nathan Y. Sharp is an Assistant Professor at Texas A&M University. ________________________________ We are grateful for comments from Bill Kinney, Keith Houghton, Brian Mayhew, Doug Prawitt, Neil Schreiber, Mark Zimbelman, and David Wood. We are grateful to Jack Zwingli and Ophir Gottlieb of Audit Integrity LLP for assistance and to Audit Integrity for providing their financial reporting risk measure. We also appreciate comments from the editor, the associate editor, two anonymous reviewers, and workshop participants at Brigham Young University. We are grateful for financial support of Texas A&M University, the Mays Business School, and the Mary & Ellis Professorship.
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The Association between Financial Reporting Risk and Audit Fees Before and After the Historic Events Surrounding SOX
INTRODUCTION
Conceptual models of auditing (e.g., the audit risk model and the audit production
function) suggest that audit fees should increase with increased risk of a material financial
statement misstatement due to error or fraud (hereafter financial reporting risk). However, in the
late 1990s regulators began publicly expressing concern that audits were being considered a
commodity and perhaps priced at a loss—offset by other consulting services (Levitt 1998;
Bergsman 2000; Turner 2001; Brown 2002). In this study, we re-examine the relationship
between audit fees and financial reporting risk and investigate whether the association between
financial reporting risk and audit fees changed during the events surrounding the Sarbanes-Oxley
Act (SOX) in light of three research advantages: (1) publicly available audit fee data, (2) a study
period, 2000 to 2003, marked by historic events that increased audit firms’ business risk and thus
the likelihood that audit fees will be positively associated with financial reporting risk, and (3) a
commercially developed, comprehensive risk measure that is a better measure of financial
reporting risk than proxies previously employed.
Our study begins in 2000 because audit fee data became publicly available that year, and
we conclude in 2003 because fees for audits of internal controls, as mandated in Section 404 of
SOX, are indistinguishable from financial-statement audit fees beginning in 2004.1 Our primary
sample consists of 4,320 Big 4 client firm-years during the period 2000-2003.2 We predict and
1 Our inability to disentangle SOX 404 fees for internal-control audits after 2004 makes incorporating audit fees from 2004 and beyond unfeasible for our study. 2 We examine clients of the Big 4 auditors and do not include Arthur Andersen clients in our sample because of the large audit failures at Andersen in 2000 and 2001 and because Andersen effectively had no audit clients in 2002 or 2003. Our sample size is large compared to other audit-fee studies. Hay et al. (2006) review 88 published papers in the audit fee literature and report a median sample size of just 203 firms in these studies.
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find a statistically and economically significant positive relationship between audit fees and
financial reporting risk. More importantly, we also predict and find that the responsiveness of
audit fees to financial reporting risk increased during our study’s time period. Our results show
that the magnitude of the relationship between financial reporting risk and audit fees in 2002 and
2003 is more than double the magnitude of the relationship in 2000, suggesting a significant shift
in the way firms risk-priced audits during our sample period. Finally, we demonstrate that a
commercially developed, comprehensive risk measure serves as an effective proxy for financial
reporting risk that can be used in future audit research.
Understanding how the auditing profession’s risk management and pricing practices
changed in response to the significant events of recent years is important to researchers, audit
firms, investors, regulators, and audit clients. The significant association we find between audit
fees and financial reporting risk adds to the evidence in prior literature that audit fees are
adjusted in response to risks faced by the auditors. The increased responsiveness of fees to
financial reporting risk during our time period is a more interesting and important finding. This
historic time period is marked by increased business and litigation risk for auditors as a result of
highly publicized accounting scandals, the demise of Arthur Andersen LLP, regulatory changes,
and increased public scrutiny of financial reporting and auditing. The strengthened relation
between financial reporting risk and audit fees over time is consistent with a change in the way
the Big 4 audit firms priced audits in response to increased auditor business risk (i.e., reputation,
litigation, regulation) during our sample period. The events during this time period caused a
“back to basics” movement within the Big 4 regarding the performance of auditing services as
well as risk management practices. Finally, our finding that a commercially developed,
comprehensive measure of financial reporting risk effectively captures risk priced by auditors
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has potential implications for future research concerning audit fees. Future researchers may want
to incorporate this commercially based measure in addition to other risk proxies used previously.
BACKGROUND AND HYPOTHESIS DEVELOPMENT
Audit Fee Framework
Auditors perform risk assessment procedures to support their decisions regarding client
continuance, audit planning, and audit pricing (Messier et al. 2008). Important considerations in
assessing risk and planning and pricing an audit include, but are not limited to, management’s
competence, the “tone at the top” of an organization, and the susceptibility of accounts and
disclosures to misstatement. A useful conceptual framework when considering auditor risk
assessment and planning is the audit risk model as outlined in professional auditing standards
(AICPA, AU Section 312). In that model, inherent risks at the client increase with factors such as
lower quality and competence of management, a poor attitude toward fair and transparent
financial reporting, and weak corporate governance practices. According to the model, as
inherent risk increases, auditors should accept a lower detection risk, all else constant. Auditors
reduce detection risk by improving or increasing the level of audit evidence collected, which
increases the costs of an audit. Similarly, the production function model of audit fees (Simunic
1980) suggests that auditors will charge clients more for external audit services when the risks of
performing the audit are higher.
Relationship between Audit Fees and Financial Reporting Risk
Financial Reporting Activities and Corporate Governance: Findings in the accounting literature
based on experimental data suggest that auditors consider the aggressiveness of financial
reporting and the risk of earnings manipulation during audit planning and pricing stages (e.g.,
Houston 1999; Phillips 1999; Beaulieu 2001). In the archival literature, Gul et al. (2003) find a
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positive association between earnings management (as measured by discretionary accruals) and
audit fees. Similarly, Bedard and Johnstone (2004) use a proprietary dataset of audit firms’ actual
client risk assessments and report that heightened earnings management risk and corporate
governance risk are both associated with an increase in planned audit effort and increased billing
rates. Finally, Lyon and Maher (2005) examine firms in developing countries and find that audit
fees are higher for firms known to have paid bribes to government officials. Together, this
evidence is consistent with auditors’ assessing risk at the client level, and then passing higher
expected costs to risky clients in the form of higher audit fees.3
The attitudes, awareness, policies, and actions of management and the board of directors
concerning internal control and financial reporting set the tone of an organization and are
important components of corporate governance. Kaplan (1985) and Kruetzfeldt and Wallace
(1986) find that auditors increase effort for clients with significant internal control weaknesses.
Cohen and Hanno (2000) find that auditors planned substantive testing increases in the presence
of ineffective corporate governance. However, Carcello et al. (2002) and Abbott et al. (2003)
examine specific components of corporate governance (board independence, diligence, and
expertise) and find a significantly positive relationship between audit fees and corporate
governance measures (i.e., higher fees associated with higher-quality corporate governance).
Events Leading Up To and During Our Sample Period: As described in the following
paragraphs, a number of historic and significant events that impacted the auditing profession
occurred just before or during our sample period. We do not attempt to link any one event or date
to a structural shift in audit firms’ risk management or pricing practices due to the complexities
3 In contrast to these results, Frankel, et al. (2002) find a negative relation between audit fees and the likelihood of earnings management (as proxied by absolute discretionary accruals), and Larcker and Richardson (2004) find evidence of a negative relationship between fees paid to auditors (both audit and nonaudit) and unexpected accruals.
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of the audit fee environment (e.g., the presence of some pre-negotiated fees with fixed
components, time lag in the responsiveness of fees to events, client and firm differences).
However, the number and significance of the events described below does support a prediction
that the responsiveness of audit fees to financial reporting risk will strengthen during our time
period.
In the late 1990s regulators became increasingly concerned about the quality of financial
reporting and auditing. Former Securities and Exchange Commission (SEC) chairman Arthur
Levitt’s comments in 1998 identified a growing concern over aggressive earnings management:
“I fear that we are witnessing an erosion in the quality of earnings, and therefore, the quality of
financial reporting. Managing may be giving way to manipulation; integrity may be losing out to
illusion.” This speech triggered a joint statement on February 8, 1999 by the New York Stock
Exchange, the National Association of Securities Dealers, and the Blue Ribbon Committee on
Improving the Effectiveness of Corporate Audit Committees (BRC). Their report announced ten
recommendations for improving the quality of corporate financial reporting, including explicit
guidelines on audit committee membership, structure, and function.
In 1998, the SEC also requested that the Public Oversight Board establish a Panel on
Audit Effectiveness. The panel conducted a comprehensive review and evaluation of the way
independent audits of financial statements of publicly traded companies were performed and
assessed the effects of audit quality on the public interest. The Panel reviewed a sample of actual
audits to gather empirical data on the quality of auditing. The Panel’s final report was issued in
August 2000. The report provided a number of recommendations for improving the quality of
audits and identified areas where the Panel found evidence of shortcomings including situations
where auditors had gathered insufficient appropriate evidence to support their audit opinions.
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The Big 5 audit firms were in the process of responding to the Panel’s recommendations
when a number of high profile financial statement frauds and audit failures were reported in the
press (e.g., Waste Management, Enron, WorldCom, Tyco, Qwest, and others). As a result of the
Waste Management, Enron, WorldCom audits and an indictment on obstruction of justice,
Arthur Andersen LLP collapsed in 2002. These events and others (e.g., public congressional
hearings) dramatically increased media attention on auditing and financial reporting
In response to Public Oversight Board’s Panel recommendations and the high profile
financial frauds, the Auditing Standards Board announced it would revise the auditing standard
on the “Consideration of Fraud in a Financial Statement Audit.” The final revised standard,
issued in 2002, includes audit procedures related to fraud risk identification and detection. Also
in 2002, Congress passed the Sarbanes-Oxley Act (SOX), which imposed significant changes in
the financial reporting environment of public companies, including new requirements for
auditors and increased involvement and responsibility of the Audit Committee in the audit
function.
In 2001, the SEC issued new rules on auditor independence limiting certain services (e.g.,
systems implementation, internal audit services) and requiring registrants to publicly disclose the
fees they paid their audit firm for audit and nonaudit services. In 2001, the Auditing Standards
Board also issued new guidance on independence. In the wake of Enron, WorldCom and other
perceived audit failures, the Sarbanes-Oxley Act of 2002 and the Public Company Oversight
Board imposed additional restrictions on nonaudit services that audit firms can provide to their
public company audit clients. Three of the Big 4 firms sold off their consulting practices between
2000 and 2002.4
4 All of the Big 4 firms continue to provide nonaudit services to audit clients; however the scope and nature of those services changed significantly during this time period.
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In addition to increased regulation, the SEC also increased its enforcement activities
related to independence violations. For example, after an investigation prompted by SEC charges
of independence violations for the time period 1996 to 2001, PricewaterhouseCoopers was fined
and required to improve its systems. Ernst & Young was found guilty of improper conduct,
violating applicable professional standards, and engaging in conduct that was reckless and
negligent in association with business dealings with audit client PeopleSoft during the period
1994 to 1999. E&Y was fined $1.7 million, had to retain an independent consultant to help the
firm implement policies and procedures to remedy ethics and independence violations, and had
to refrain from accepting new public company audit engagements for six months.5
Collectively, the audit failures and regulatory actions of this period highlighted the
importance of audit quality. In the years leading up to and during the crisis, the accounting
profession faced a long list of criticisms including: focusing too much on non-audit services
provided to audit clients; rewarding partners more for new business and increased revenues than
for quality audits; lacking sufficient independence from audit clients; allowing clients to manage
earnings by abusing the concept of materiality; being insufficiently skeptical; and implementing
insufficient rigor into auditing standards setting and other aspects of self-regulation due to a lack
of oversight and accountability (Zeff 2003). In hindsight, leaders in the profession have
acknowledged that at least some of these criticisms were valid. For example, in a speech before
the US Chamber of Commerce, James S. Turley, Chairman and CEO of Ernst & Young said:
Certainly, the accounting profession, our firm included, has taken some shots from regulators and others over the last several years, and I’m here to tell you that we deserved some of those shots… The times have taught us the dangers of being arrogant—of not listening… We and others got caught up in a ’90s-era rush to become one-stop global shops, hoping to provide not only our core services, but also hoping to be the
5 See Schroeder and Burns (2000) for details on the push from regulators for additional auditor independence and accountability issues during our time period.
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biggest technology consulting firms and even the biggest law firms. Those days are over. (Turley 2005) In the wake of these events, the Big 4 renewed their focus on improving the quality of
financial statement audits (i.e., they invested heavily in audit quality initiatives including revised
audit methodologies, training, new audit tools, increased emphasis on fraud detection, etc.).
James Turley, indicated, “In many ways…recent events have carried us back into a world that is
focused on the basics—and rightly so” (Turley 2005). A recent survey conducted by the
International Federation of Accountants found that respondents were pleased with the
improvements in audit quality during the preceding five years, which was attributed to auditors
becoming more independent from companies and more focused on risk (Rappeport 2008).
Consistent with the conceptual models of audit fee pricing we expect to find evidence
that audit fees are associated with financial reporting risk. Of more interest, we also expect the
responsiveness of audit fees to financial reporting risk will strengthen during our study time
period given the momentous events affecting auditor business risk leading up to and during our
sample years.6 In addition to examining an historic time period, we also believe the
comprehensive measure of financial reporting risk we use increases the likelihood of observing a
shift towards a more positive association between financial reporting risk and audit fees.
The foregoing discussion leads to our hypotheses:
H1: Audit fees increase as financial reporting risk increases.
H2: The responsiveness of audit fees to financial reporting risk increases over our sample period (2000 to 2003).
MEASURES OF FINANCIAL REPORTING RISK
6 The predicted increase in sensitivity of audit fees in relation to financial reporting risk could be caused by an increase in audit work or an increase in a risk premium, or both. We do not attempt to distinguish between these potential causes in this study.
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While audit-fee regression models in the literature include numerous proxies for risks
faced by auditors, we add a proprietary comprehensive measure of financial reporting risk based
on an accounting and governance risk score produced commercially by Audit Integrity, LLP,
which we label AIFRR. Because AIFRR is relatively new to the accounting literature and is new
to audit-fee research, we discuss the components of the measure as well as other research using
the measure.
Audit Integrity provides an objective, comprehensive measure of the “overall risk of
potentially fraudulent or misleading” financial reporting at public companies (Audit Integrity
2005). Audit Integrity’s model evaluates public companies’ financial reporting and governance
activities and objectively assesses the risk of intentional misreporting. Their model uses detailed
historical data to identify illegal or unethical patterns of behavior, and the output is a numerical
measure of financial reporting fraud risk.7 Audit Integrity’s proprietary model has been
developed, in part, by a detailed analysis of the accounting and governance practices of firms
determined ex post to have committed financial statement fraud. Thus, compared with accruals-
based risk measures, AIFRR is focused more specifically on the risk of (outside-GAAP)
intentional misstatement of the financial statements.
The inputs to the Audit Integrity model are based on publicly available information from
SEC filings, financial statements (including footnotes), annual reports, company-specific press
releases, insider sales filings, executive compensation data, litigation proceedings, and other
sources.8 The metrics Audit Integrity uses to calculate the risk score are categorized at the
highest level into the following five risk groups: (1) Expense Recognition, (2) Revenue
Recognition, (3) High Risk Events, (4) Governance, and (5) Asset and Liability Valuation. Each
7 Beginning in 2006, the Audit Integrity’s risk scores are available on the Bloomberg Professional service. 8 Market pricing data and audit fee data are not used in the calculation of Audit Integrity’s risk measure.
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risk category is measured by multiple metrics (ratios) that are examined along three dimensions:
(1) the percentage change from the prior year; (2) the number of standard deviations from
industry average; and (3) the volatility over an eight-quarter period.
The weighting procedure that determines each metric’s contribution to the overall risk
profile of a company was developed by analyzing firms that were successfully sued by the SEC
for fraudulent behavior. Metrics which are shown by Audit Integrity’s model to be the most
highly predictive of the quality of accounting and governance are given the most weight in the
computation of risk rankings. For example, the Accounts Receivable over Sales metric is given a
high weighting in the model because accounts receivable buildup is highly related to accounting
problems in the database of known fraudulent companies. Flagged risk metrics are weighted and
summed into an overall risk value.
Concurrent academic research demonstrates that the Audit Integrity’s risk measures are
superior to accruals-based measures for predicting SEC enforcement actions, shareholder
lawsuits, and earnings restatements (Correia 2009). Furthermore, Daines et al. (2008) evaluate
several commercially produced governance ratings, including Audit Integrity’s measure, and find
that while all commercial ratings appear to have less predictive validity than their producers
claim, the Audit Integrity measure generally outperforms other ratings in its ability to predict
restatements, class-action lawsuits, future operating performance, and future excess stock
returns. Additionally, Bartov and Hayn (2007) demonstrate that Audit Integrity’s measure is an
effective proxy for financial reporting risk. Bartov and Hayn conclude that Audit Integrity’s risk
scores, “appear to be the best measure available” for capturing overall reporting transparency.
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They also conclude that an accruals-based measure of transparency reflects significantly more
noise than the Audit Integrity measure.9
To facilitate easier interpretation of the results in our paper, we reverse Audit Integrity’s
risk scores. Audit Integrity’s original measure is scaled 100 (least risky) to 0 (most risky) and our
AIFRR measure ranges from 0 (least risky) to 100 (most risky).10 In the audit-fee models
throughout our paper, we use the AIFRR score from the fourth fiscal quarter of the prior year.
Thus, for a calendar year 2001 audit (where audit report and 10K will be filed with the SEC in
2002), we use the rescaled 2000 Q4 Audit Integrity risk assessment score in our model. The prior
year’s fourth quarter assessment of AIFRR is likely to be the most recent assessment the audit
firms have made before setting audit fees for the coming year. Untabulated results indicate that
the mean (median) change in yearly AIFRR scores across our sample period is relatively small at
about 6 points (5 points) on the 100-point AIFRR scale.
SAMPLE SELECTION AND RESEARCH DESIGN
We obtain four years of audit fee data from the Compustat audit fee database and
combine these data with additional financial-statement data from Compustat and our measure of
AIFRR. The audit fee database also provides details about fees paid to auditors for services not
related to the financial statement audit, which we utilize in our models. The specific Compustat
data items used in our tests are listed in the Appendix.
The Compustat audit fee data represents 22,239 firm years from 2000-2003. The
intersection of additional Compustat financial-statement data, the AIFRR data, and the audit fee
9 Audit Integrity has performed extensive testing to validate its model. Test results indicate that Audit Integrity’s risk measure accurately separates low and high-risk firms for every range of risk with acceptable levels of Type I and Type II error rates. Out-of-sample testing indicates that Audit Integrity’s model is not over-fitted and performs well on hold-out samples. Audit Integrity has also compared their risk measure to accruals-based models of earnings manipulation and found that their model significantly outperforms the accruals-based models. 10 To reverse, we subtract the original Audit Integrity measure from 100.
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data creates a sample of 18,927 firm-year observations from 2000-2003. Requiring a Big 4
auditor further reduces the sample to 13,993 observations, and data requirements to estimate
discretionary accruals (for the current and prior year for each observation) reduces the sample to
10,646 observations. Finally, to assure our time-series results are not driven by differences in our
sample composition each year, we require that each registrant or auditee be present in all four
years of our sample, which reduces our final sample to 4,320 firm-year observations during the
period of 2000-2003.11
Because we are interested in measuring changes in the responsiveness of audit fees to
financial reporting risk over time, a period longer than four years is desirable. However, audit
fees became publicly available in 2000, and we are forced to exclude audit fee data for 2004 and
beyond because of the confounding effects of fees charged to audit clients for the audit of
internal controls over financial reporting as mandated by Section 404 of SOX. We are unable to
disentangle the portion of the audit fee that relates only to the financial statement audits after
2004 for comparison with the 2000-2003 data.
To test the association between our proxy for financial reporting risk, AIFRR, and fees
paid to auditors, we estimate the following regression model based on audit fee models from the
literature (e.g. Hay et al. 2006):
LOGAFj,t = β0 + β1 2001 + β2 2002 + β3 2003 + β4 AIFRR j, t-1 + β5 ABSDACC j, t-1 +
β6 LOGTAj,t + β7 N_SEGj,t + β8 FOREIGNj,t + β9 ROA,j,t + β10 LOSS,j,t +
β11 AR_INV j,t + β12 DEBTj,t + β13 SPECIAL j,t + β14 MB j,t + β15 MODIFY j,t +
β16 LOGNAF,j,t + β17 CHGAUD j,t + ε j, t
11 Note that since our portfolio of audit clients remains constant across our entire sample period, our results regarding the increased sensitivity of audit fees to financial reporting risk cannot be attributed to the audit firms picking up new, riskier clients later in our sample period (i.e., from Andersen or other non-Big 4 auditors).
(1)
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The Appendix contains a complete description of variable definitions. The dependent
variable in Equation 1 is the natural log of fees paid to auditors for audit services. We include
indicator variables for 2001-2003 to capture the time trend in audit fees, and we include many of
the variables that researchers have used in the prior literature to proxy for audit risk. As noted
above, higher AIFRR scores represent more risky financial reporting practices. Therefore, H1
predicts a significantly positive coefficient on AIFRR because a higher AIFRR score increases
the risk of fraud or misreporting, which should be associated with higher audit fees.
Since the literature suggests absolute discretionary accruals (ABSDACC) may indicate
aggressive financial reporting, we add ABSDACC to the model.12 We also include a measure of
firm size (the log of total assets, LOGTA), which has consistently dominated other determinants
of audit fees in the prior literature, with size generally explaining close to 70 percent of the cross-
sectional variation in audit fees (e.g., Simunic 1980, Hay et al. 2006). Similarly, researchers
have found consistent empirical evidence for the intuitive idea that the more complex a client is,
the higher the audit fees will be since the audit becomes more time-consuming and difficult to
complete (Simunic 1980; Hackenbrack and Knechel 1997). We measure complexity with both
the number of business segments reported on Compustat, N_SEG, and with a dummy variable
that represents whether the company paid taxes in a foreign country, FOREIGN.
Based on findings in the literature, we expect a positive relation between audit fees and
profitability (ROA) and recently reported losses (LOSS). For profitability, the worse the
performance of the firm, the more risk to the auditor and the higher the audit fee should be.
Profitability reflects the extent to which the auditor may be exposed to liability in the event that a
client becomes financially insolvent and eventually fails (Simunic 1980), and firms with recent
12 We note that discretionary accruals have been criticized repeatedly in the literature for being a noisy proxy for earnings management (Dechow et al. 1995; Guay et al. 1996; McNichols 2000).
15
losses present additional risk to the auditor. Auditors also consider factors such as inherent risk
and difficulty of auditing certain risky areas when setting audit fees. Simunic (1980) discusses
inventory and receivables as being the two most difficult areas to audit, so we measure inherent
risk and audit difficulty as receivables plus inventory deflated by assets, AR_INV.
Each of the following variables represents increased risk faced by auditors: leverage
(DEBT), the reporting of special items (SPECIAL), growth (MB), and modified audit opinions
(MODIFY). Leverage measures the risk of a client failing; firms reporting special items and
growth firms present special risks; and a modified audit opinion may signify problems
encountered during the audit. We also expect a positive relation between non-audit fees
(LOGNAF) and audit fees based on prior research (Hay et al. 2006). Finally, we expect a
negative relation between recent auditor changes (CHGAUD) and audit fees because companies
may decide to change auditors to obtain a lower audit fee.
In H2 we predict that the responsiveness of audit fees to financial reporting risk increased
during our sample period. To investigate how the relationship between AIFRR and audit fees has
changed over time, we estimate the following regression:
LOGAFj,t = β0 + β1 2001 + β2 2002 + β3 2003 + β4 AIFRR j, t-1 + β5 AIFRR j, t-1 x 2001 +
β6 AIFRRj, t-1 x 2002 + β7 AIFRR j, t-1 x 2003 + β8 ABSDACC j, t-1 + β9 LOGTAj,t +
β10 N_SEGj,t + β11 FOREIGNj,t + β12 ROA,j,t + β13 LOSS,j,t + β14 AR_INV j,t +
β15 DEBTj,t + β16 SPECIAL j,t + β17 MB j,t + β18 MODIFY j,t + β19 LOGNAF,j,t +
β20 CHGAUD j,t + εj,t
This regression provides for a separate slope coefficient on AIFRR for each year in the
sample period. The coefficient estimate for β4 represents the relationship between AIFRR and
audit fees in 2000; each of the other coefficients, β5 – β7, is the incremental slope coefficient on
(2)
16
AIFRRt-1 for the respective years. If audit fees have become more sensitive to AIFRR during our
sample period, then we expect a significantly positive coefficient on β5, β6, or β7.
RESULTS
Descriptive and Univariate Results
Table 1 contains descriptive statistics for our sample. Panel A illustrates that the average
firm in our sample has $4.98 billion in total assets (median of $512 million) and pays a mean
audit fee of about $1.26 million (median audit fee of $443,000). Because our sample consists of
only Big 4 auditor clients that are present in all four years of our sample period, these firms are
larger and more stable than the average firm in Compustat. On average, fees for nonaudit
services (NAF) are $1.3 million (median of $265,000).13 Mean ROA for our sample is -0.05
(median ROA is 0.02), which reflects the market conditions during our sample period of 2000-
2003. The average firm in our sample has 1.6 business segments as self-reported on the
Compustat business segments files and has mean discretionary accruals of -1.7 percent (median
1.3 percent) of total assets.14 The average financial reporting risk (AIFRR) ranking for firms in
our sample is 35, on a 0 – 100 scale, suggesting that firms in our sample fall, on average, toward
the more conservative side of the rankings of all public companies.
(Insert Table 1 here)
Table 1 panel B shows the Pearson and Spearman correlations between the variables in
our models. These results provide initial confirmation of our hypothesized positive relationship
between financial reporting risk (AIFRRt-1) and audit fees (LOGAF). Firm size (LOGTA) is the 13 Nonaudit services include tax preparation fees, information system consulting fees, and other fees. 14 Discretionary accruals are calculated using a cross-sectional Modified-Jones model regression estimated for each 2-digit SIC and year combination with at least 10 observations (Defond and Jiambalvo 1994, Dechow et al. 1995). Since auditors are likely to scrutinize both positive and negative discretionary accruals (Frankel, Johnson, Nelson 2002; Chung and Kallapur 2003; Myers, Myers, Omer 2003), we use absolute discretionary accruals in our regression models. Note that our inferences regarding AIFRR are unchanged if we use unsigned discretionary accruals in our tests.
17
most strongly correlated variable with audit fees, with firm size explaining more than 60 percent
of the variation in audit fees. Fees for non-audit services (LOGNAF) are also highly positively
correlated with audit fees, and absolute discretionary accruals (ABSDACC) are positively
correlated with AIFRR.
Table 1 panel C shows the trend in fees paid to auditors during our sample period. While
audit fees have increased consistently during our sample period, fees for nonaudit services have
declined significantly. Total fees paid to auditors have declined during our sample period, from a
mean of $2.7 million in 2000 to a mean of $2.5 million in 2003.
Table 2 panel A shows that firms in the most conservative quintile of AIFRR are more
profitable, pay lower audit and non-audit fees, have lower total assets, have a higher percentage
of their assets in accounts receivable and inventory, and are less financially levered than their
counterparts in the riskiest quintile of AIFRR. Table 2 panel B shows the results of testing for
differences in the means and medians of descriptive variables in the highest and lowest quintiles
of AIFRR. These differences are statistically significant for all variables tested, indicating the
importance of including these variables in our model to mitigate correlated omitted variable bias
when analyzing the relationship between financial reporting risk and audit fees. Untabulated
results indicate that there is no significant difference between the risk groups in industry
composition for industries that represent at least 5 percent of each group; however, since industry
effects may drive some differences in audit fees, we include industry-level fixed effects in all our
regression models.15
(Insert Table 2 here)
Multivariate Results
15 We also test our multivariate regression models after removing all observations in the financial services industry or public utility industry, and all results are qualitatively the same.
18
Tables 3 and 4 present the results of our multivariate tests of the relationship between
AIFRR and fees paid to auditors for financial-statement audits. For all regression models, we use
White-adjusted standard errors and firm-level clustering to account for the non-independence of
firm-level observations over time. Table 3 panel A presents the results of regressing the natural
log of audit fees (LOGAF) on financial reporting risk (AIFRRt-1) and other variables shown in
prior literature to be related to audit fees. Results indicate that our model explains about 77
percent of the variation in audit fees for our sample, which is comparable to other audit fee
studies (e.g., Gul et al. 2003 and Hay et al. 2006).
(Insert Table 3 here)
H1 predicts a positive relationship between financial reporting risk (AIFRR) and audit
fees. Table 3 reveals an economically and statistically significantly positive relationship between
AIFRR and audit fees, supporting H1. Economically, a one point increase in the prior-year
AIFRR ranking (higher AIFRR is more risky) equates to a 0.6 percent increase in the current
year’s audit fee. In terms of the fee elasticity of our AIFRR risk measure, our results indicate that
a 1 percent increase in financial reporting risk leads to a 0.20 percent increase in audit fees.16
The difference between the first and third quartiles of AIFRR rankings in our sample is 17
points; thus for our mean audit fee of $1.26 million, the difference in audit fees paid by
otherwise equal firms in the first and third quartiles of AIFRR is approximately $128,500
(approximately a 10 percent increase in audit fees). Our results provide strong evidence that
audit fees for Big 4 audit clients are higher when the financial reporting risk (i.e., risk of
fraudulent or misleading reporting) is higher. Furthermore, we find evidence that the
comprehensive risk measure, AIFRR, provides incremental information content beyond risk
16 The fee elasticity of financial reporting risk is obtained as the coefficient estimate on a log transformation of AIFRR in the same regression model (i.e., with log audit fees as the dependent variable).
19
measures typically included in audit fee research.17 Year indicator variables in Table 3 show an
increasing trend in audit fees over time and, for our control variables, we find significant
coefficients in the expected direction for all variables except DEBT and CHGAUD, which are
not statistically significant. These results are consistent with prior research (Hay et al. 2006). For
comparison purposes, Table 3 panel A also provides the results of our audit fee regression
excluding AIFRR.
In Table 3 panel B, we divide our continuous measure of financial reporting risk
(AIFRR) into indicator variables representing the quartiles of AIFRR in our sample. This allows
us to test whether there are thresholds in the effect of AIFRR on audit fees. Results indicate that
the effect of AIFRR on audit fees is stronger in the two higher quartiles of AIFRR than in the
lower two quartiles. The coefficient on the indicator variable for the highest quartile of AIFRR
(Q4_AIFRR) indicates that, on average, audit fees for firms in the highest quartile of AIFRR are
19.8 percent higher than audit fees for firms in the lowest quartile of AIFRR after holding
constant all the other determinants of audit fees in the model.18 This difference represents an
economically significant impact of financial reporting risk on clients’ audit fees, and this result
provides further evidence that AIFRR effectively captures an element of risk incremental to what
has traditionally been included in audit fee models.
17 In untabulated results, we also run the audit-fee model in Equation 1 with AIFRR, a comprehensive measure of financial reporting risk, but without the other traditional proxies for risk (e.g., ABSDACC, ROA, LOSS, AR_INV, DEBT, SPECIAL, MB, MODIFY). Not surprisingly, the coefficient on AIFRR in this simplified model is about 25 percent higher than the coefficient on AIFRR as reported in Table 3. When we regress AIFRR on these common risk measures, the coefficients on ROA and AR_INV are negative while the coefficients on LOSS and ABSDACC are positive. The other risk measures (DEBT, SPECIAL, MB, and MODIFY) are not significant in the model. The R-square on the model regressing AIFRR on common risk measures is about 0.09, suggesting AIFRR captures elements of financial reporting risk that are incremental to what is captured in traditional risk proxies. 18 Since the dependent variable in the model is the log of audit fees (LOGAF), the percent change in audit fees is found by eβ - 1 or e0.181 – 1.
20
Consistent with H2, Table 4 provides evidence that the responsiveness of audit fees to
financial reporting risk increased significantly in the latter part of our sample period.19 As shown
in Panel B of Table 4, in 2000, a one point increase in AIFRRt-1 results in a 0.3 percent increase
in audit fees. In 2002, the coefficient on AIFRRt-1 is more than double the magnitude of the
coefficient in 2000, and the difference between 2000 and 2002 is highly significant. A one-point
increase in AIFRR in 2002 results in a 0.8 percent increase in audit fees. Thus in 2002, after
controlling for the other determinants of audit fees in our model, the difference in mean audit
fees for the first and third quartiles of AIFRR is about $189,000 (nearly a 13 percent increase in
audit fees).20 In 2003, the coefficient estimate for AIFRR is statistically equal to the estimate for
2002, indicating that the heightened association of audit fees to AIFRR was sustained in 2003.
(Insert Table 4 here)
Overall, the results in Table 4 represent significant evidence that the responsiveness of
audit fees to financial reporting risk for clients of the Big 4 auditors increased significantly
during our sample period of 2000 to 2004. We believe that this strengthened relationship
between financial reporting risk and audit fees over time is due to changes in Big 4 audit firms’
pricing practices due to increases in audit firm business and litigation risk caused by the
significant events that occurred during our sample period. These events include the Report by
the Panel on Audit Effectiveness, concerns voiced by the SEC during this period about auditor
independence, high-profile frauds and audit failures, the collapse of Arthur Andersen, increased
media attention on the integrity of auditing practices, and the passage of the Sarbanes-Oxley Act.
19 Results of a chi-square test (not tabulated) reveal no significant difference in the distribution of risky and conservative firms (top and bottom quintile of AIFRR) across the years in our sample (i.e., the audit client risk remained relatively constant, while auditor business risk was increasing). Furthermore, multicollinearity diagnostic tests indicate that the only collinearity in our model (i.e., VIF > 10) is the expected collinearity between the year indicator variables and the interaction terms that are multiplied by the year indicator variables. 20 Mean audit fees in 2002 are $1.477 million, and the difference between the first and third quartiles of AIFRR in 2002 is 16 points.
21
ADDITIONAL ANALYSES
In this section we discuss the results of several additional tests designed to examine the
robustness of our results and rule out potential alternative explanations. First, given the positive
correlation between AIFRR and firm size (Table 1 Panel B), it is possible that the significant
interaction on AIFRR x 2002 and AIFRR x 2003 in Table 4 is attributable to correlated omitted
variables: namely the interactions of LOGTA x 2002 and LOGTA x 2003. To test this concern,
we rerun the regression model in Table 4 and include interactions between firm size (LOGTA)
and indicator variables for 2001, 2002, and 2003. In untabulated results, we find that our
inferences related to increased sensitivity to AIFRR in 2002 and 2003 are unchanged.
To test whether the association between audit fees and another risk measure (ABSDACC)
increases over time, we rerun the regression model in Table 4 and include interactions between
discretionary accruals (ABSDACC) and year indicator variables and find that the association
between ABSDACC and audit fees increases significantly in 2002 and 2003. Thus, the pattern
of results for discretionary accruals confirms the results for AIFRR: both risk measures are more
strongly associated with audit fees in the final two years of our sample than in the first year of
our sample period.
A potential alternative explanation for our results is that the SEC changed the audit fee
disclosure requirements effective for annual filings after December 15, 2003. These changes
resulted in more services being classified as audit-related services (see Asthana and Krishnan,
2006). We believe the evidence reported in Table 4 that the relation between AIFRR and audit
fees increased significantly beginning in 2002 makes it unlikely that our results are attributable
to changes in fee definitions that were not effective until 2003. However, we perform two
additional tests to rule out this alternative explanation. First, we perform a regression similar to
22
the regression in Table 4 except with the log of nonaudit fees (LOGNAF) as the dependent
variable. If our reported increase in the sensitivity of audit fees to AIFRR is related to a shift in
fee definitions, we should observe changes in the sensitivity of nonaudit fees to AIFRR over
time. However, we find that the relation between nonaudit fees and AIFRR in 2000 is not
different from the relation in 2002 or 2003. Furthermore, we also rerun the regression in Table 4
using only firms in the lowest quartile of nonaudit service fees because these firms would have
the least amount of shifting in fees due to the SEC’s changes. In this low nonaudit fee sample,
we still observe an increase in the sensitivity of audit fees to AIFRR in 2002 and 2003. Thus, a
shift in fee definitions in 2003 is not a plausible explanation for our results.
Limitations of the Study
Limitations of this study include issues related to the sample selection, the study time
period, and proxies included in the model. Our results are based on Big 4 audit clients with
available data in each of the years of our study period; thus, our findings may not generalize to
other firms or years. Some events during the study period had a direct impact on audit fees, such
as: (1) the SEC changed the audit fee disclosure requirements effective for annual filings after
December 15, 2003 resulting in more services classified as audit-related services (see Asthana
and Krishnan, 2006); and (2) the requirement to perform an integrated audit (i.e., SOX 404)
began in 2004, but there may have been audit-related work associated with 404 readiness in
2003. As discussed in the prior section, we believe the first event, the change in disclosure
definition, affected the level of reported audit fees, but not the sensitivity of audit fees to
financial reporting risk. Similarly, we believe the second event, 404 readiness, affected the level
of reported audit fees, but not the sensitivity of audit fees to financial reporting risk.
Furthermore, Raghunandan and Rama (2006) suggest that internal control testing did not
23
extensively increase until 2004 and we find increased sensitivity of audit fees to financial
reporting risk beginning in 2002. Finally, our results may be limited by measurement error in
AIFRR, our proxy for financial reporting risk. However, we believe the evidence in our paper
and the evidence in other independent research cited in our paper demonstrates that AIFRR is an
effective proxy for financial reporting risk with less measurement error than traditional accruals-
based proxies used in the academic literature.
CONCLUSION
In this paper, we examine a sample of 4,320 Big 4 client firm-years during the period
2000-2003 to examine the relation between audit fees and financial reporting risk (the risk of
misleading or fraudulent reporting). Around the beginning of our time period, regulators were
expressing concerns that audits were priced as a commodity or at a loss. We predict and find a
statistically and economically significant positive association between audit fees and financial
reporting risk. More importantly, we predict and find that the responsiveness of audit fees to
financial reporting risk increased during our study’s time period. We find that the relation
between audit fees and financial reporting risk increased significantly in 2002, which may
represent a response to the increased business and litigation risk auditors faced in the wake of
highly publicized accounting scandals and the additional rules and regulations proposed or
enacted during this time period. These results are consistent with risk management and pricing
changes at the firms in response to significant and historic events affecting the auditing
profession during our time period. Finally, we demonstrate that the commercially developed,
comprehensive measure of financial reporting risk provides incremental information content in
predicting audit fees over the traditional proxies used in audit fee research.
24
The results in this paper provide important insights into the Big 4 auditor fee pricing and
how the Big 4 responded to recent large-scale corporate accounting fraud and regulatory
changes. Future research may examine the impact of integrated audits on the relation between
audit fees and financial reporting risk. Our finding that the comprehensive measure for financial
reporting risk provides incremental information content over traditional measures has potential
implications for research models investigating audit fees and financial reporting risk. We believe
researchers will want to incorporate the AIFRR proxy for financial reporting risk in future
research, and they may want to further explore how and why a commercially based measure of
financial reporting risk is superior to the academic proxies used previously.
25
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29
APPENDIX – VARIABLE DEFINITIONS (ALPHABETICAL)
AF is the sum of audit fees and audit-related fees as reported in the Compustat audit fee database.
AIFRR is a measure of financial reporting risk based on Audit Integrity’s risk score determined by modeling accounting and governance variables associated with known financial frauds. AIFRR lies between 0 and 100, and we subtract the original scores from 100 so that 100 is the most risky ranking and 0 is the least risky ranking.
AR_INV is accounts receivable (Compustat item 2) plus inventory (Compustat item 3), scaled by total assets (Compustat item 6). We winsorize AR_INV at the top and bottom 1 percent.
CHGAUD is a dummy variable that takes a value of 1 if the auditor in the current year is different from the auditor in the past year, from the Compustat audit fee file.
DACC is discretionary accruals from a cross-sectional Modified-Jones model regression estimated for each 2-digit SIC and year combination with at least 10 observations (Defond and Jiambalvo 1994, Dechow et al. 1995), scaled by total assets (Compustat item 6). We winsorize DACC at the top and bottom 1 percent.
ABSDACC is the absolute value of discretionary accruals from a cross-sectional Modified-Jones model regression estimated for each 2-digit SIC and year combination with at least 10 observations (Defond and Jiambalvo 1994, Dechow et al. 1995), scaled by total assets (Compustat item 6). We winsorize ABSDACC at the top and bottom 1 percent.
DEBT is long-term debt (Compustat item 9) plus debt in current liabilities (Compustat item 34), scaled by total assets (Compustat item 6). We winsorize DEBT at the top and bottom 1 percent.
FOREIGN is a dummy variable that takes a value of 1 if the firm reported paying taxes in a foreign jurisdiction (Compustat item 64).
LOGAF is the natural log of the sum of audit fees and audit-related fees as reported in the Compustat audit fee database.
LOGNAF is the natural log of the sum of information systems consulting fees (ISCF), tax preparation fees (TPF), and other fees (OTHER) paid to the audit firm, as reported in the Compustat audit fee database.
LOGTA is the natural log of total assets (Compustat item 6). LOSS is a dummy variables that takes a value of 1 if the firm reported negative income before
extraordinary items (Compustat item 18) in the current year or either of the prior two years.
MB is market value of equity (Compustat item 25 multiplied by Compustat item 199) divided by the book value of equity (Compustat item 60). We winsorize MB at the top and bottom 1 percent.
MODIFY is a dummy variable that takes a value of 1 if the audit opinion code (Compustat item 149) has any value other than 1 (unqualified opinion).
N_SEG is the number of business segments firms self-reported on the Compustat business segments file.
ROA is income before extraordinary items (Compustat item 18) scaled by total assets (Compustat item 6). We winsorize ROA at the top and bottom 1 percent.
SPECIAL is a dummy variable that takes a value of 1 if the firm reported special items (Compustat item 17) in the current year.
TA is total assets (Compustat item 6). We winsorize TA at the top and bottom 1 percent.
30
TABLE 1 Descriptive Statistics for Audit Fee Sample
Panel A: Overall Descriptive Statistics for All Firms in Sample (2000-2003)
Variable N Mean Median Q1 Q3 Stdev AF ($ thous) 4,320 1,260 443 211 1,042 3,423 NAF ($ thous) 4,320 1,310 265 80 842 4,706 TA ($ mm) 4,320 4,983 512 156 1,994 25,090 ROA 4,320 -0.05 0.02 -0.04 0.06 0.27 N_SEG 4,320 1.60 1.00 1.00 1.00 1.34 AR_INV 4,320 0.24 0.21 0.09 0.34 0.18 DEBT 4,320 0.22 0.18 0.02 0.34 0.22 DACCt-1 4,320 -0.02 0.01 -0.07 0.10 0.35 AIFRRt-1 4,320 35 32 25 42 12
Panel B: Correlations among Variables of Interest – Pearson (above) / Spearman (below)
(1) (2) (3) (4) (5) (6) (7) (8) (9) (1) LOGAF 1 0.79 0.20 0.61 0.24 0.31 0.02 0.24 -0.10 (2) LOGTA 0.78 1 0.36 0.63 0.19 0.22 -0.08 0.32 -0.20 (3) ROA 0.17 0.30 1 0.19 -0.10 0.09 0.16 0.07 -0.25 (4) LOGNAF 0.63 0.66 0.22 1 0.18 0.06 0.05 0.17 -0.06 (5) AIFRRt-1 0.23 0.18 -0.10 0.19 1 0.04 -0.14 0.12 0.07 (6) N_SEG 0.26 0.19 0.06 0.03 0.04 1 0.05 0.06 -0.07 (7) AR_INV 0.07 -0.08 0.23 0.09 -0.13 0.09 1 -0.03 -0.12 (8) DEBT 0.33 0.43 0.03 0.24 0.11 0.08 0.00 1 -0.13 (9) ABSDACC t-1 -0.14 -0.29 -0.18 -0.09 0.06 -0.09 -0.06 -0.24 1
All correlations in bold are significant at p < 0.05 level. Panel C: Trends in Mean Fees Paid to Auditors over the Sample Period
2000 2001 2002 2003 Audit Fees ($ thousands) 738 979 1,477 1,845 Non-Audit Fees ($ thousands) 1,972 1,635 995 638 Total Fees Paid to Auditor ($ thousands) 2,710 2,614 2,471 2,483
See Appendix for definitions of all variables.
31
TABLE 2 Comparison of Variables across Quintiles of Financial Reporting Risk, 2000-2003
Panel A: Comparison of Means across Quintiles of Financial Reporting Risk (AIFRR)
Conservative Risky
Variable Quintile
1 Quintile
2 Quintile
3 Quintile
4 Quintile
5 ROA -0.01 -0.04 -0.05 -0.06 -0.08 LOGAF 5.88 5.97 6.16 6.34 6.70 LOGTA 6.04 6.02 6.27 6.55 7.03 LOGNAF 4.97 5.13 5.47 5.62 6.08 AR_INV 0.28 0.25 0.24 0.23 0.21 DEBT 0.20 0.19 0.22 0.23 0.26 AIFRR t-1 19.20 25.64 31.43 39.22 53.79
Panel B: Mean and Median Tests Across Top/Bottom Quintile of Financial Reporting Risk (AIFRR) AIFRR Quintile
Variable Most
Conservative Most Risky Mean Test Median Test
Log Audit Fees (LOGAF) Mean 5.88 6.70 -13.97*** Median 5.76 6.52 -12.34*** Observations 728 873
Log Total Assets (LOGTA) Mean 6.04 7.03 -10.41*** Median 5.96 6.88 -9.38*** Observations 728 873
Return on Assets (ROA) Mean -0.005 -0.08 5.90*** Median 0.03 0.01 6.16*** Observations 728 873
Log Non-Audit Fees (LOGNAF) Mean 4.97 6.08 -11.21*** Median 5.15 6.13 -11.19*** Observations 728 873
Number of Business Segments (N_SEG) Mean 1.52 1.70 -2.66*** Median 1.00 1.00 -2.18** Observations 728 873
Accounts Receivable & Inventory (AR_INV) Mean 0.28 0.21 8.22*** Median 0.26 0.18 7.78*** Observations 728 873
Debt (DEBT) Mean 0.20 0.26 -5.75*** Median 0.14 0.23 -5.46*** Observations 728 873
***, **, * indicate statistical significance at the 0.01, 0.05, and 0.10 levels respectively. See Appendix for all definitions of variables.
32
TABLE 3 Testing the Association between Audit Fees and Financial Reporting Risk
Panel A: Using a continuous measure of financial reporting risk (AIFRR) LOGAFj,t = β0 + β1 2001 + β2 2002 + β3 2003 + β4 AIFRR j, t-1 + β5 ABSDACC j, t-1 +
β6 LOGTAj,t + β7 N_SEGj,t + Β8 FOREIGNj,t + β9 ROA,j,t + β10 LOSS,j,t + β11 AR_INV j,t + β12 DEBTj,t + β13 SPECIAL j,t + β14 MB j,t + β15 MODIFY j,t + β16 LOGNAF,j,t + β17 CHGAUD j,t + ε j, t
Including AIFRR Excluding AIFRRa
Independent Variable Predicted
Sign Estimate t Value Estimate t Value Intercept ? 1.803 5.92*** 1.916 6.21*** 2001 + 0.074 4.21*** 0.070 4.01*** 2002 + 0.405 16.25*** 0.403 16.11*** 2003 + 0.625 23.33*** 0.619 23.05*** Financial Reporting Risk AIFRR j, t-1 + 0.006 5.85*** Client Attributes ABSDACC j, t-1 + 0.051 1.57* 0.063 1.91** LOGTA j,t + 0.425 24.65*** 0.432 24.72*** N_SEG j,t + 0.047 5.04*** 0.048 5.12*** FOREIGN j,t + 0.305 9.50*** 0.307 9.51*** ROA j,t - -0.332 -6.48*** -0.365 -7.13*** LOSS j,t + 0.136 4.82*** 0.150 5.22*** AR_INV j,t + 0.594 6.28*** 0.557 5.86*** DEBT j,t + -0.015 -0.19 0.004 0.05 SPECIAL j,t + 0.050 1.87** 0.052 1.93** MB j,t + 0.007 2.23** 0.007 2.27** Engagement Attributes MODIFY j,t + 0.167 6.25*** 0.171 6.40*** LOGNAF j,t + 0.116 6.32*** 0.119 6.58*** CHANGEAUD j,t - 0.046 0.58 0.061 0.74
Sample Size 4,320 4,320 Adjusted R-Square 0.770 0.767 <Continued on next page>
33
TABLE 3 (Continued)
Panel B: Using indicator variables for quartiles of financial reporting risk (AIFRR)
Independent Variable Predicted
Sign Estimate t Value Intercept ? 1.918 6.26*** 2001 + 0.074 4.18*** 2002 + 0.405 16.27***2003 + 0.625 23.27***Quartiles of Financial Reporting Risk Q2_AIFRR j, t-1 + 0.032 1.17 Q3_AIFRR j, t-1 + 0.089 3.07*** Q4_AIFRR j, t-1 + 0.181 5.20*** Client Attributes ABSDACC j, t-1 + 0.052 1.58* LOGTA j,t + 0.425 24.63***N_SEG j,t + 0.047 5.07*** FOREIGN j,t + 0.306 9.53*** ROA j,t - -0.337 -6.58*** LOSS j,t + 0.136 4.82*** AR_INV j,t + 0.590 6.22*** DEBT j,t + -0.010 -0.13 SPECIAL j,t + 0.050 1.87** MB j,t + 0.007 2.25** Engagement Attributes MODIFY j,t + 0.165 6.18*** LOGNAF j,t + 0.116 6.32*** CHANGEAUD j,t - 0.048 0.59 Sample Size 4,320 Adjusted R-Square 0.77
In all OLS regressions, robust standard errors are estimated using the Huber (1967) / White (1980) procedure, with firm-level clustering (Rogers 1993) for lack of independence of audit-fee observations by firm. All regressions include year and industry-level fixed effects, with industry definitions corresponding to the 17 groups of SIC codes on Ken French’s website (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html). a See footnote 17 for a description of additional untabulated analyses. ***, **, * indicate one-tailed statistical significance at the 0.01, 0.05, and 0.10 levels respectively. See Appendix for definitions of all variables.
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TABLE 4 Testing for Temporal Changes in the Relation between Audit Fees and Financial Reporting Risk
Panel A: Full Regression Results
LOGAFj,t = β0 + β1 2001 + β2 2002 + β3 2003 + β4 AIFRR j, t-1 + β5 AIFRR j, t-1 x 2001 + β6 AIFRRj, t-1 x 2002 + β7 AIFRR j, t-1 x 2003 + β8 ABSDACC j, t-1 + β9 LOGTAj,t + β10 N_SEGj,t + β11 FOREIGNj,t + β12 ROA,j,t + β13 LOSS,j,t + β14 AR_INV j,t + β15 DEBTj,t + β16 SPECIAL j,t + β17 MB j,t + β18 MODIFY j,t + β19 LOGNAF,j,t + β20 CHGAUD j,t + ε j, t
Independent Variable Predicted
Sign Estimate t Value Intercept ? 1.905 6.22*** 2001 + -0.007 -0.11 2002 + 0.213 3.29*** 2003 + 0.477 6.71*** Financial Reporting Risk AIFRR j, t-1 + 0.003 1.93** AIFRR j, t-1 x 2001 + 0.002 1.33* AIFRR j, t-1 x 2002 + 0.006 3.02*** AIFRR j, t-1 x 2003 + 0.004 2.21** Client Attributes + ABSDACC j, t-1 + 0.051 1.56* LOGTA j,t + 0.425 24.63*** N_SEG j,t + 0.045 4.90*** FOREIGN j,t + 0.305 9.47*** ROA j,t - -0.330 -6.42*** LOSS j,t + 0.137 4.85*** AR_INV j,t + 0.596 6.29*** DEBT j,t + -0.016 -0.21 SPECIAL j,t + 0.050 1.87** MB j,t + 0.007 2.27** Engagement Attributes MODIFY j,t + 0.166 6.21*** LOGNAF j,t + 0.116 6.31*** CHANGEAUD j,t - 0.042 0.53 Sample Size 4,320 Adjusted R-Square 0.77
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TABLE 4 (Continued) Panel B: Abbreviated Regression Results
Summarized Coefficients on
AIFRR 2000 0.003** 2001 0.005*** 2002 0.008*** 2003 0.007*** Tests F-Stat Equality of coefficients across years 3.39** 2000=2001 1.77 2000=2002 9.09*** 2000=2003 4.89** 2001=2002 4.20** 2001=2003 1.29 2002=2003 0.52
The regression model in Panel A examines the changing relation between financial reporting risk and audit fees over time. Panel B summarizes the coefficients on AIFRR by year. In all OLS regressions, robust standard errors are estimated using the Huber (1967) / White (1980) procedure, with firm-level clustering (Rogers 1993) for lack of independence of audit-fee observations by firm. Multicollinearity diagnostic tests indicate that the only collinearity in the model (i.e., VIF > 10) is the expected collinearity between the year indicator variables and the interaction terms that are multiplied by the year dummies. All regressions include year and industry-level fixed effects. ***, **, * indicate one-tailed statistical significance at the 0.01, 0.05, and 0.10 levels respectively. See Appendix for definitions of variables.
