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RESEARCH PAPER SERIES
GRADUATE SCHOOL OF BUSINESS
STANFORD UNIVERSITY
RESEARCH PAPER NO. 1696R
Auditor Independence and Earnings Quality
Richard M. Frankel Marilyn F. Johnson
Karen K. Nelson
August 2001
Auditor Independence and Earnings Quality
Richard M. Frankel MIT
Sloan School of Business 50 Memorial Drive, E52.325g Cambridge, MA 02459-1261
(617) 253-7084 [email protected]
Marilyn F. Johnson Michigan State University
Eli Broad Graduate School of Management N270 Business College Complex
East Lansing, MI 48824-1122 (517) 432-0152
Karen K. Nelson Stanford University
Graduate School of Business Stanford, CA 94305-5015
(650) 723-0106 [email protected]
August 2001 We thank Bill Beaver and participants at the 2001 Stanford Summer Accounting camp for helpful discussions and comments. Karen Nelson acknowledges the support of the Financial Research Initiative at Stanford University Graduate School of Business. We thank First Call for providing the analyst forecast data. Waqas Nazir and Nora Richardson provided valuable data collection assistance.
Auditor Independence and Earnings Quality Abstract: We examine whether the provision of non-audit services by auditors is negatively correlated with firm value and the quality of earnings. Because of concerns regarding the effect of non-audit services on auditor independence, the Securities and Exchange Commission recently issued revised auditor independence rules requiring firms to disclose in their annual proxy statement the amount of fees paid to auditors for audit and non-audit services. Using data collected from over 4,000 proxies filed between February 5, 2001 and June 15, 2001, we find a significant negative market reaction to proxy statements filed by firms reporting higher than expected non-audit fees. Our evidence also indicates an inverse relation between the magnitude of non-audit fees and earnings management. Firms purchasing more non-audit services from their auditor are more likely to just meet or beat three earnings benchmarks – analysts’ expectations, prior year earnings, and zero earnings – and to report large discretionary accruals. Keywords: Auditor independence, Auditor fees, Earnings management, Discretionary accruals Data Availability: The data used in this study are from the public sources identified in the text.
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I. INTRODUCTION
The purpose of this paper is to examine the relation between non-audit services and
earnings quality. On November 15, 2000, the Securities and Exchange Commission (SEC)
adopted revised auditor independence rules requiring proxy statement disclosure of all audit and
non-audit fees paid by a firm to its auditor. Motivating the new rules is the SEC’s belief that the
high percentage of revenue auditors derive from the provision of non-audit services poses a threat
to auditor independence (e.g., SEC 2000). The audit industry disputes this claim, in part because
it is not supported by empirical evidence (Antle, Griffin, Teece, and Williamson 1997).1
We examine two issues relevant to the ongoing debate over the provision of non-audit
services by a firm’s financial auditor. First, we measure the market reaction to the disclosure of
auditor fees to determine if they convey value-relevant information to investors. Second, we
investigate whether the magnitude of non-audit fees explains cross-sectional variation in earnings
management. Our analyses are based on audit and non-audit fee data collected from over 4,000
proxies filed with the SEC between February 5, 2001, and June 15, 2001.
We predict that the market will react negatively to proxy statements disclosing higher
than expected non-audit fees. We measure unexpected non-audit fees using two proxies: (i) the
ratio of non-audit to total fees, which assumes uniform prior expectations, and (ii) the residual
from a model explaining cross-sectional variation in the non-audit fees ratio, which assumes that
the publicly available data items in our model are those used by investors to form expectations.
Our results indicate that market-adjusted announcement day returns are significantly negative for
1 The audit industry’s views are shared by incoming SEC Chairman Harvey L. Pitt. In a 1997 White Paper submitted to the Independence Standards Board, Pitt writes that “the overwhelming economic interest of accounting firms in their reputational capital provides a powerful incentive to safeguard independence. Non-audit services increase the firm’s investment in reputational capital, contribute importantly to the quality of audit services, and provide other benefits to clients and the public” (Pitt and Birenbaum 1997, 4).
2
the quartile of firms with the highest unexpected non-audit fees. This finding is robust across
both of our proxies for the unexpected component of non-audit fees.
Our second set of tests examines whether there is a positive relation between the
provision of non-audit services and earnings management. Consistent with predictions, we find
that firms with relatively higher non-audit fees are more likely to just meet or beat three earnings
benchmarks – analysts’ expectations, prior year earnings, and zero earnings – and to report larger
income-increasing and income-decreasing discretionary accruals. These findings are robust to
several specification checks. In addition, we find no difference in earnings management behavior
associated with the two categories of non-audit services for which firms are required to disclose
fees, i.e., financial information systems design services and all other types of non-audit services.
This evidence is important given ongoing debate about the effect of non-audit services on
auditor independence, and ultimately the quality of earnings. Resolution of this debate has been
hindered by a lack of direct, large-sample empirical evidence. Our study provides evidence that
the recently required auditor fee disclosures are useful to investors in assessing auditor
independence and its financial statement effects.2
This study also contributes to two streams of accounting research. Prior research
measures audit quality using an indicator variable for Big Five versus non-Big Five auditors (e.g.,
DeAngelo 1981; St. Pierre and Anderson 1984; DeFond and Jiambalvo 1991,1993; Teoh and
Wong 1993; Becker, DeFond, Jiambalvo, and Subramanyam 1998). All audits by a given auditor
are assumed to be of equal quality, and audit quality is assumed to be increasing in the size of the
auditor. We extend this line of research by showing that the magnitude of non-audit fees relative
to total fees is able to differentiate cross-sectional variation in audit quality. Indeed, our results
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indicate that the distinction between Big Five and non-Big Five auditors is not significant after
controlling for the relative size of non-audit fees. Our study is also related to the earnings
management literature. Although considerable prior research examines earnings management
incentives, few studies investigate factors that limit earnings management (Healy and Wahlen
1999). We show that there is less earnings management when non-audit fees are relatively low.
The remainder of the paper is organized as follows. Section II discusses the SEC’s new
rules on auditor independence and develops our hypotheses. Section III describes the sample and
data. Section IV presents the empirical results. Section V summarizes and concludes.
II. BACKGROUND AND HYPOTHESIS DEVELOPMENT
SEC disclosure requirements
The SEC has long recognized the importance of auditor independence. For example,
Accounting Series Release (ASR) 296 requires that auditors not have “any financial or other
interest which would create the perception” of impaired independence. A primary focus of the
SEC’s independence rules has been the provision of non-audit services. In 1978, concerns about
the expansion of non-audit services provided by public accounting firms led the SEC to issue
ASR 250 requiring disclosure of information about non-audit services. However, the Public
Oversight Board (POB) of the American Institute of Certified Public Accountants (AICPA)
subsequently concluded that “available empirical evidence does not reveal any actual instances
where the furnishing of MAS [management advisory services] has impaired independence” (POB
1979, 35), and the rule was rescinded in 1982.
2 Related evidence in Gore, Pope, and Singh (2001) for a sample of U. K. firms suggests that earnings management activity is generally positively associated with the provision of non-audit services for firms with non-Big Five auditors, but not for firms with Big Five auditors.
4
In the 1990’s, a dramatic increase in the proportion of revenues derived from non-audit
services renewed concerns about auditor independence. For example, former SEC Chairman
Arthur Levitt (2000) states that:
auditing no longer dominates the practices of the largest firms. It accounts for just 30 percent of total revenues – down from 70 percent in 1977. Consulting and other management advisory services now represent over half – up from 12 percent in 1977… As the firms expand their product lines, consulting and other services may shorten the distance between the auditor and management. Independence – if not in fact, then certainly in appearance – becomes a more elusive proposition. In response to this trend, the SEC issued revised auditor independence rules on November
15, 2000 after extensive public comment and debate. The rules require companies to disclose in
proxy statements filed on or after February 5, 2001, information regarding fees billed by the
auditor for the most recent year.3 Specifically, the rules require companies to disclose:
(1) under the caption “Audit Fees” the aggregate fees billed for professional services rendered for the audit of the annual financial statements and for the reviews of the quarterly financial statements;
(2) under the caption “Financial Information Systems Design and
Implementation Fees” the aggregate fees billed for professional services rendered for (i) directly or indirectly operating, or supervising the operation of, the company’s information system or managing the company’s local area network, or (ii) designing or implementing a hardware or software system that aggregates source data underlying the financial statements or generates information that is significant to the company’s financial statements taken as a whole; and
(3) under the caption “All Other Fees” the aggregate fees billed for all services
rendered by the auditor other than those described in (1) and (2). Hypothesis development
Audits are a device to enhance the credibility of financial statements. Independent
auditors facilitate contracting between investors and management, and hence the operation of
3 In addition, the rules liberalize restrictions regarding investments by auditors and their family members in audit clients and the employment of auditors and their family members by audit clients. The rules also identify certain non-audit services that, regardless of the size of fees they generate, impair independence.
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capital and labor markets. Because actual independence is not observable, shareholders rely on
signals such as an auditor’s reputation, the degree of oversight by professional and regulatory
bodies, and the degree of economic bonding between auditor and client to assess auditor
independence.4
Both regulators and academic researchers have posited that the provision of non-audit
services to an audit client can impair auditor independence by creating an economic bond
between auditor and client. For example, the POB (POB 2000, 119) states:
Of fundamental importance in understanding the conflict of interest that arises from the provision of non-audit services to audit clients is the fact that in so doing the audit firm is really serving two different sets of clients: management in the case of management consulting services, and the audit committee, the shareholders and all those who rely on the audited financials and the firm’s opinion in deciding whether to invest, in the case of the audit… It is obvious that in serving these different clients the firm is subject to conflicts of interest that tear at the fragile fabric of loyalty owed to one client or the other. And it is equally obvious that the existence of dual loyalties creates a serious appearance problem, regardless of whether, in particular cases, the fabric actually tears apart or not. Simunic (1984) models the joint demand for audit and non-audit services. He shows that
when both services are provided by the same auditor and the auditor retains a portion of the cost
savings that arise from “knowledge spillovers,” then the auditor will be economically bonded to
the client.5 The new auditor independence rules adopted by the SEC require firms to disclose
audit and non-audit fees to provide investors with the information “to evaluate for themselves
whether the proportion of fees for audit and non-audit services causes them to question the
auditor’s independence” (SEC 2000, III.c.5).
4 Antle (1984) notes the problems associated with defining auditor independence without reference to a formal theoretical context. In a principal-agent framework, he defines independence as the extent to which managers and auditors collude in pursuit of their own self-interests. Simunic (1984) describes independence as the likelihood that the auditor will distort his findings. Kinney (1999) discusses alternative approaches to defining auditor independence. 5 The multiperiod horizon of many audit engagements is another source of economic bonding. DeAngelo (1981) presents a model in which auditors have an incentive to “lowball” initial engagements with the intent of making up
6
Proponents of the SEC’s new disclosure requirements argue that the provision of non-
audit services results in lower audit quality. DeAngelo (1981) defines audit quality as the
probability that the auditor will both detect and report errors. The detection and reporting of
errors requires auditor independence. The provision of non-audit services can impair
independence in two ways. First, an auditor concerned about the possible loss of non-audit fee
revenue may be less likely to disagree with management’s accounting choices. Second, the
auditor may be reluctant to criticize the work of the firm’s consulting division. As a result, the
audit is less effective as a monitoring mechanism, causing contracting costs to increase and firm
value to decrease.
Opponents of the rule changes counter that the threat of litigation provides a strong
incentive to maintain independence.6 In addition, audit partners have large financial and
reputational investments in their firms that would be reduced or eliminated in the event of an
audit failure.7 Opponents also argue that economies of scope in the bundling of audit and non-
audit services would be eliminated by the SEC’s rule changes. Perhaps the most compelling
argument offered against the rule change is that there is no evidence to date that indicates an
independence problem exists. For example, in a report commissioned by the AICPA, Antle,
Griffen, Teece, and Williamson (1997, ii) conclude that:
There is no evidence that the supply of non-audit services threatens auditor independence. The supply of non-audit services is not a significant factor in auditors’ losses in litigation or in pricing their liability insurance. There is no evidence that investors are concerned that the supply of non-audit services impairs independence.
the initial losses in later periods. An independence problem arises if an auditor is unwilling to risk losing the future, higher fees by displeasing the client. 6 By one estimate, Big Five audit firms spent $2.4 billion on legal costs from 1990-1993, or approximately 10% of gross assurance service revenues. (Mayer, Brown & Platt, letter of June 3, 1994, to Mr. Walter Schuetze, Chief Accountant, Securities and Exchange Commission.) 7 In 1996, total partner capital invested in what was then the Big Six exceeded $3.5 billion (Antle, Griffin, Teece, and Williamson 1997).
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Specifically, Palmrose (1999) finds no evidence that the provision of non-audit services is
part of the basis of complaint in litigation against auditors, suggesting that investors do not share
regulators’ concerns about the provision of non-audit services. In addition, empirical evidence
that the provision of non-audit services creates an economic bond between auditor and client is
weak. Although Simunic (1984) finds that audit fees are higher when an auditor provides non-
audit services to its audit client, consistent with a “knowledge spillover” argument, Palmrose
(1986) shows that audit fees are also higher when non-audit services are provided by a party
other than the client’s auditor.
The new fee disclosures provide an opportunity to re-examine the effect of non-audit
services on auditor independence. If the provision of non-audit services impairs auditor
independence, then the disclosure of these fees will inform investors about the potential for noise
or bias in the reported financial statements. Thus, we predict there will be a negative stock price
reaction to news that the level of non-audit services is higher than expected:
H1: Ceteris paribus, stock prices will respond negatively to news that non-audit services are higher than expected.
Failure to observe a significant negative stock price reaction is consistent with the argument that
the provision of non-audit services does not impair auditor independence. However, it may also
indicate that our tests are not sufficiently powerful to detect a stock price effect. For example,
we are unlikely to detect a significant market reaction if our proxy for the news contained in the
disclosures regarding the level of non-audit services is a poor measure of investors’ expectations.
The latest revision of the auditor independence rules is part of the SEC’s effort to curb a
perceived increase in earnings management activity (Levitt 1998). Consolidation among the
largest accounting firms and the rapid growth of non-audit services have magnified the SEC’s
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concerns regarding the effect of auditor independence on earnings quality.8 Several highly
publicized accounting “irregularities” have focused additional attention on this issue.9
Underlying the SEC’s position is the belief that the provision of non-audit services impairs
independence and thus compromises audit quality. A higher quality audit increases the
likelihood that questionable accounting practices will be detected, that the auditor will object to
their use, and that the auditor will qualify the audit report if the practices are not modified by
management. High-quality audits deter earnings management (or, in the extreme, accounting
fraud). Therefore, if the provision of non-audit services impairs independence resulting in a
lower quality audit, then we expect earnings management activity to increase with the level of
non-audit services provided by the auditor:
H2: Ceteris paribus, there is a positive relation between the provision of non-audit services and earnings management.
III. DATA
Sample selection
Our initial sample consists of all definitive proxy statements on the SEC’s EDGAR
database that were filed between February 5, 2001 and June 15, 2001. We require that the proxy
statement disclose audit fees, financial information systems fees, other fees, and the firm’s
auditor. We exclude firms that changed auditors during the year. We also exclude financial
institutions (SIC codes 6000-6999) because estimation of discretionary accruals is problematic
for these firms. These procedures yield an initial sample of 4,225 observations. Because of the
8 These concerns echo a long history of SEC statements linking accounting quality and auditor independence. For example, ASR 296 states that “the quality of information disseminated in the securities markets and the continuing conviction of individual investors that such information is reliable are key to the formation and effective allocation of capital. Accordingly, the audit function must be meaningfully performed and the accountants’ independence not compromised.” 9 See, e.g., “SEC List of Accounting-Fraud Probes Grows,” Wall Street Journal, July 6, 2001, C1.
9
interest in the new auditor fee disclosures, we provide some preliminary descriptive statistics for
this broad sample before imposing any additional data requirements.
Table 1 reveals that audit fees comprise, on average, slightly more than half of all fees
billed by auditors to their audit clients. Less than 25 percent of firms engaged their auditor to
provide financial information systems design and implementation services. However, fees for
these services are relatively large. The smallest financial information systems fee reported by our
sample, $116,000, is not far below the median fee for either audit or other non-audit services. In
contrast to the infrequent occurrence of financial information systems fees, over 95 percent of
firms report fees for other non-audit services. The average fee for other services is nearly $1
million.10 Finally, mean (median) non-audit fees as a percent of total fees is 47 (48) percent.
Table 2 provides summary fee data for each of the Big Five audit firms, and for all other
audit firms combined. Non-audit fees comprise the majority of fees billed by Big Five auditors,
while audit fees comprise the majority of fees billed by non-Big Five auditors. Audit fees as a
percent of total fees are nearly twice as large for the non-Big Five auditors as for any of the Big
Five auditors. In addition, the composition of fees varies among the Big Five auditors.
PricewaterhouseCoopers, the largest auditor in terms of total fees billed, derives approximately
three-fourths of its total fee revenue from non-audit services, compared to approximately two-
thirds for the other Big Five auditors. Ernst & Young earns only six percent of its revenue from
financial information services, but bills more fees for other non-audit services (64 percent) than
any other Big Five auditor.
10 Approximately one-fourth of the firms in the initial sample provide information on the composition of other non-audit fees. Untabulated descriptive statistics indicate that firms with large fees for other services, as a percent of total fees, are more likely to disclose additional information on the composition of other fees. Controlling for the ratio of other fees to total fees, firms audited by Ernst & Young are more likely to disaggregate other fees, while firms audited by non-Big Five auditors are less likely to disaggregate other fees.
10
To determine the primary sample for our empirical tests, we merge the fee data with
Compustat. There are 2,506 firms that also have total assets for fiscal year 2000 on Compustat.
Panel A of Table 3 details the frequency of proxy filings by month for this sample. Firms must
file their proxy statement prior to the annual shareholders meeting which typically occurs three to
four months after the fiscal year end. The majority of the firms in our sample filed their proxy
statement in March and April, corresponding to a December or January fiscal year end. Despite
the time-period clustering of our sample, Panel B of Table 3 reveals that the industry
composition of our sample is similar to that of the 2000 Compustat database.
Variable measurement and descriptive statistics
Our hypotheses predict that the provision of non-audit services is negatively correlated
with firm value and earnings quality. We measure the provision of non-audit services using the
ratio of non-audit fees to total fees (FEERATIO). This variable is consistent with definitions
used by prior researchers (Scheiner and Kiger 1982; Glezen and Millar 1985; Parkash and
Venable 1993; Firth 1997), and reflects the SEC’s stated purpose in requiring these disclosures,
i.e., that investors will consider the proportion of fees for audit and non-audit services in
evaluating whether the auditor is independent (SEC 2000). The descriptive statistics reported in
Table 3, Panel C indicate that the mean (median) FEERATIO is 50 (53) percent, slightly higher
than the percentages reported in Table 1. Untabulated statistics reveal that median non-audit fees
as a percent of net sales (average total assets) is 0.11 (0.10) percent, indicating that non-audit fees
do not have a material direct effect on firms’ operations.
Our first hypothesis predicts that, certeris paribus, the market reaction to the public
disclosure of auditor fees is inversely related to the unexpected portion of non-audit services. To
identify the earliest date at which the fee information became public, we searched the EDGAR
11
database to determine if the firms in our sample filed a preliminary proxy statement disclosing
auditor fees.11 If we find a preliminary proxy containing the necessary fee data then we use the
release date of the preliminary proxy as our event date; if not, we use the release date of the
definitive proxy. We obtain returns data from Datastream, and calculate market-adjusted returns
(ARET) by subtracting either the S&P 500 Index return (for firms with a market capitalization
greater than $10 billion) or the S&P Midcap 400 Index (for firms with a market capitalization
less than $10 billion) from the firm-specific raw return on the date that the proxy statement was
filed.12 Table 3, Panel C reveals that mean (median) ARET is –0.09 (–0.17) percent.
Because stock prices will react to the new information contained in the auditor fee
disclosures, tests of our first hypothesis require a proxy for the unexpected component of the
non-audit fee ratio. Our first proxy, FEERATIO, assumes uniform prior expectations. This
proxy is appropriate if the non-audit fee ratio contains a shock that is large relative to its expected
firm-specific component. Anecdotal evidence suggests that investors were surprised by the
magnitude of non-audit fees (e.g., Weil and Tannenbaum 2001), consistent with the notion of a
large, positive unexpected shock. Our second proxy is the residual (FEERESIDUAL) from a
model that assumes that non-audit fees vary inversely with agency costs (Parkash and Venable
1993; Firth 1997). The Appendix provides details on the empirical estimation of the model.
This alternative proxy assumes that the publicly available data items in our model are those used
by investors to form expectations of non-audit fees.13
11 Approximately 9 percent (143 firms) of the 1,538 definitive proxies in our returns sample were preceded by the release of a preliminary proxy. 12 The proper cut-off is unclear given that the indices have some market capitalization overlap. Genzyme is the largest company in the S&P Midcap 400 Index with a market capitalization of $10.1 billion. American Greetings is the smallest firms in the S&P 500 Index with a market capitalization of $700 million. 13 Although firm-level data on audit and non-audit fees have not been publicly disclosed in the United States since ASR 250 was rescinded in 1982, it is likely that investors formed expectations on the magnitude of non-audit fees based on similar disclosures in the U.K. and Australia. Gore, Pope and Singh (2001) indicate that non-audit fees as a percent of total fees are not nearly as material in the U.K. as our evidence indicates they are in the U.S.
12
Our second hypothesis predicts that, certeris paribus, there is a positive relation between
the provision of non-audit services and earnings management. We use two complementary
approaches to identify firms managing earnings. Prior research (e.g., Burgstahler and Dichev
1997; Burgstahler and Eames 1998; Degeorge, Patel, and Zeckhauser 1999) documents a
significantly higher than expected frequency of firms with slightly positive earnings surprises,
earnings changes, and earnings levels, consistent with firms managing earnings to meet simple
benchmarks. Both the SEC and the POB expressed particular concern that the pressure for
public companies to meet analysts’ expectations and project a smooth earnings path creates
pressure on auditors to enable their clients to meet those objectives (SEC 2000; POB 2000).
Thus, our first test of the earnings management hypothesis examines whether firms that just meet
or beat three earnings benchmarks, i.e., (i) analysts’ expectations, (ii) prior period earnings, and
(iii) zero earnings, purchase more non-audit services than other firms.
Using data obtained from First Call, we calculate quarterly earnings surprises as the
difference between actual earnings per share and the last available consensus (median) forecast
calculated prior to the announcement of earnings. We identify firms that just meet or beat
analysts’ expectations with an indicator variable (SURPRISE) equal to one if the firm reports a
earnings surprise of 0.00 or 0.01 in at least three of the four quarters in fiscal 2000, and zero
otherwise. Similarly, following Burgstahler and Dichev (1997), we identify firms reporting small
increases in earnings (INCREASE) and small profits (POSITIVE) by scaling net income and the
annual change in net income by beginning of the year market value of common equity. We
consider firms with scaled changes in earnings in the interval [0.00, 0.02) as reporting a small
increase in earnings, and firms with scaled earnings in the interval [0.00, 0.04) as reporting a
13
small profit.14 The statistics in Table 3, Panel C indicate that 18 percent of the sample firms
report a small positive forecast error, 18 percent report a small increase in earnings over the prior
year, and 15 percent report a small profit.
We estimate the following logit model, where BENCHMARK indicates the three
earnings benchmarks, SURPRISE, INCREASE, or POSITIVE:
( )
++++
+++=
uLOGMVEβINST%βM/BβLITRISKβBIGFIVEβFEERATIO β
FBENCHMARKProb654
3210β (1)
Our tests control for other factors that are expected to influence whether a firm just meets
or beats earnings benchmarks. Prior research finds that Big Five auditors are less likely to allow
earnings management than non-Big Five auditors (e.g., DeFond and Jiambalvo 1991,1993;
Becker, DeFond, Jiambalvo, and Subramanyam 1998). Thus, we include a Big Five indicator
variable (BIGFIVE) to control for a relation between earnings management and auditor size.
Table 3, Panel C shows that Big Five firms audit 92 percent of our sample.15
Matsumoto (1999) reports that firms with high litigation risk, growth prospects, and
institutional ownership are more likely to be concerned about missing earnings benchmarks.
Poorly performing firms and firms with a sudden stock price drop due to a negative earnings
surprise are at higher risk of a shareholder lawsuit, and thus have greater incentives to meet
earnings benchmarks. We measure litigation risk (LITRISK) as an indicator variable equal to
one if the company operates in an industry at high risk of securities litigation (SIC codes 2833-
2836, 3570-3577, 7370-7374, 3600-3674, and 5200-5961). In our sample, 39 percent of the
firms operate in high litigation risk industries. Additionally, high growth firms are more likely to
14 We base the width of the intervals on the recommendation in Degeorge, Patel, and Zeckhauser (1999) that interval width be positively related to the variability of the data and negatively related to the number of observations. Specifically, they suggest an interval width of 2(IQR)n-1/3, where IQR is the sample interquartile range of the variable and n is the number of available observations.
14
be punished by the market for missing earnings benchmarks (Skinner and Sloan 1999). We use
the market-to-book ratio at the beginning of the year (M/B) to proxy for growth. Mean (median)
M/B is 6.78 (2.34). Because institutional investors are believed to focus on near-term earnings
performance, firms with high institutional ownership are expected to have greater incentives to
meet earnings benchmarks. We measure institutional ownership (%INST) as the percentage of
shares held by institutions at the beginning of the year (as reported by Spectrum), and find that
mean (median) %INST for our sample is 36 (31) percent. Finally, following Matsumoto (1999),
we control for firm size with the log of the market value of equity (LOGMVE). Table 3, Panel C
reveals that the distribution of market value is highly skewed, with the mean ($3,204 million)
nearly ten times larger than the median ($304 million).
Our second approach to identifying firms managing earnings uses discretionary accruals
estimated with a cross-sectional modified Jones model (Dechow, Sloan, and Sweeney 1995).
Specifically, discretionary accruals (DACC) are calculated as follows:
[ ]( )1 2ˆ ˆˆDACC TA REV REC PPEα β ∆ ∆ β= − + − + (2)
where all variables are scaled by total assets at the beginning of the year, and TA is total accruals,
defined as net income less cash from operations, ∆REV is the change in net revenues, ∆REC is
the change in net receivables, PPE is gross property, plant, and equipment, and the estimates of
α, β1, and β2 are obtained from the following model:
1 2TA REV PPE eα β ∆ β= + + + (3)
In his discussion of earnings management, Levitt (1998) identifies several techniques that
allow firms to either overstate or understate current earnings, including “big bath” charges,
15 The data in Table 2 indicate that 85 percent of firms are audited by a Big Five auditor. Thus, the additional data requirements for our primary sample exclude a disproportionate number of firms audited by non-Big Five auditors.
15
“cookie jar” reserves, and premature recognition of revenue. Ceteris paribus, independent
auditors should be a deterrent to both earnings overstatements and understatements. We use the
absolute value of discretionary accruals (ASDACC) to measure the combined effect of income-
increasing and income-decreasing earnings management decisions. Table 3, Panel C indicates
that mean (median) ABSDACC is 13 (7) percent of total assets.
We estimate the following model to examine the relation between discretionary accruals
and auditor independence:
εββββββ
ββββα
+++++++
++++=
LEVERAGEFINANCINGACQUISTIONLOGASSETABSACCACC
ABSCFOCFOBIGFIVEFEERATIO ABSDACC
109
8765
4321
(4)
Following Becker, DeFond, Jiambalvo, and Subramanyam (1998), we control for several
other potential determinants of discretionary accruals. Similar to the earnings benchmark model,
we include a Big Five indicator variable (BIGFIVE) to control for a relation between earnings
management and auditor size. In addition, prior research shows that discretionary accruals
models fail to completely extract non-discretionary accruals that are correlated with firm
performance.16 Thus, we include four measures of firm performance as control variables, all of
which are deflated by average total assets: cash from operations (CFO), the absolute value of
cash from operations (ABSCFO), total accruals (ACC) and the absolute value of total accruals
(ABSACC). The log of total assets (LOGASSET) is used to control for firm size. We include
indicator variables for whether the firm acquired another company during the year
(ACQUISTION) or issued debt or equity (FINANCING) because managers are expected to
manage earnings to influence the terms of these transactions (e.g., Erickson and Wang 1999;
Teoh, Welch, and Wong 1998a,b). The data for these variables were obtained from Securities
16
Data Corporation. In our sample, 45 percent of the firms acquired another company during 2000,
and 26 percent issued debt or equity. Finally, we control for leverage, measured as the ratio of
total liabilities to total assets (LEVERAGE), because managers of highly leveraged firms may
have incentives to overstate earnings to avoid violating debt covenants (DeFond and Jiambalvo
1994) or, alternatively, to understate earnings due to contractual negotiations by distressed firms
(DeAngelo, DeAngelo, and Skinner 1994). Mean (median) LEVERAGE is 0.52 (0.50).
IV. EMPIRICAL RESULTS
Market reaction to the disclosure of auditor fees
Table 4 presents summary statistics from an event study that examines the mean market
reaction to the release of proxy statements containing auditor fee disclosures. We assess the
statistical significance of the results using a t-test to determine whether the mean market reaction
in each portfolio is significantly negative (tµ<0), and a sign test to determine whether the
proportion of firms with positive abnormal returns in each portfolio is significantly less than 50
percent (sign test%<50%). Because the observations used in the tests are drawn from a four and
one-half month period in the beginning of 2001, our results may be biased due to cross-sectional
dependence in the returns data. However, Bernard (1987) finds that cross-sectional dependence
problems are less severe for estimations using daily returns data. Moreover, we attempt to
control for common factors that predict returns and are related to FEERATIO by including these
variables (size, market-to-book, and past returns) in the estimation of the unexpected non-audit
fee ratio discussed in the Appendix.17
16 For example, Dechow, Sloan, and Sweeney (1995) show that discretionary accruals are biased for extreme values of earnings and cash flows. For further discussion of related research design issues, see McNichols (2000). 17 The release of CRSP data for our sample period will allow us to adopt more sophisticated methodologies for analyzing stock price reactions.
17
We partition the sample into quartiles based on our proxies for unexpected non-audit
fees. The results for our first proxy, FEERATIO, are reported in Panel A of Table 4. The
median FEERATIO in the quartile containing firms with the highest unexpected non-audit fees is
0.77, compared to 0.20 in the quartile with the lowest. If the market is surprised by the
magnitude of non-audit fees revealed in the proxy statement and believes the provision of non-
audit services impairs auditor independence, then we expect abnormal returns to be most
negative in the quartile containing the firms with the highest unexpected non-audit fees.
As predicted, the results indicate that the market reaction is negative and significant in the
top quartile. The mean return on the date these firms filed proxies was –0.44 percent, significant
at the 0.05 level. In contrast, the mean return is positive, but statistically insignificant, in the
other three quartiles. Similarly, 43 percent of the returns in the top quartile are positive,
significant at the 0.01 level, compared to 47 to 51 percent of the returns in the other three
quartiles.
To examine the robustness of our results, we partition the sample into quartiles based on
our second estimate of unexpected non-audit fees (FEERESIDUAL) in Panel B of Table 4. The
median FEERESIDUAL in the top quartile is 0.21, compared to –0.24 in bottom quartile. The
results of the market reaction tests are similar to those reported in Panel A. Specifically, in the
top quartile, the mean market return is –0.40 percent, significant at the 0.05 level, and the
proportion of firms with positive returns is 45 percent, significant at the 0.01 level. None of the
results in the other three quartiles is significant.
Overall, the results reported in Table 4 support the hypothesis that investors responded
negatively to revelations that fees for non-audit services were higher than expected. Because
financial statements may be less reliable or unbiased for firms purchasing more non-audit
services, investors may reduce their expectations of discounted future cash flows. However, the
18
disclosed fee information also may have implications for the market’s expectations of future cash
flows unrelated to the quality of the audited financial reports. For example, unexpectedly high
fees for non-audit services may signal poor management quality or that the firm faces difficulties
previously unknown by investors.
Association between non-audit services and earnings management
Our first test of the earnings management hypothesis examines whether firms that just
meet or beat earnings benchmarks purchase more non-audit services. We predict a positive
relation between FEERATIO and firms that just meet or beat each of the three earnings
benchmarks, SURPRISE, INCREASE, and POSITIVE. Table 5 reports summary statistics from
the empirical estimation of equation (1). Consistent with predictions, the coefficient estimate on
FEERATIO is positive and significant in all three estimations. However, the result is relatively
less significant (p-value of 0.07) in the POSITIVE estimation compared to the SURPRISE and
INCREASE estimations (p-values < 0.01). These results suggest that firms paying relatively
more non-audit fees are more likely to meet earnings benchmarks, particularly analysts’
expectations and prior period earnings, consistent with the concerns expressed by the SEC (2000)
and POB (2000).
Regarding the control variables, BIGFIVE is insignificant in all estimations in Table 5.
The results for the remaining control variables are generally consistent with Matsumoto (1999).
Litigation risk, LITRISK, is positive and significant in the SURPRISE and POSITIVE models,
but not in the INCREASE model. These findings suggest that firms at high risk of securities
litigation have greater incentives to meet earnings benchmarks. In addition, LOGMVE and
%INST are positive and significant in all estimations, suggesting that larger firms and those with
greater institutional ownership are more likely to just meet or beat earnings benchmarks.
19
Our second test of the earnings management hypothesis examines the relation between
discretionary accruals and the purchase of non-audit services. Table 6 reports summary statistics
from the empirical estimation of equation (4). Consistent with predictions, the coefficient
estimate on FEERATIO is positive and significant. Firms purchasing more non-audit services
exhibit greater earnings management. Controlling for the level of non-audit fees, there is no
evidence that Big Five auditors limit discretionary accruals more than non-Big Five auditors.
Our results also indicate that discretionary accruals are correlated with firm performance, firm
size, and acquisition and financing activity.18
Sensitivity tests
Estimations using alternative specifications indicate that our findings are robust. First, to
further ensure that our results are not driven by cross-sectional variation between firms with a
Big Five and non-Big Five auditor, we repeat the analyses in Tables 5 and 6 using only the
sample of firms with a Big Five auditor. The untabulated results are similar to those reported
above. Second, we examine the association between the provision of non-audit services and the
direction of earnings management for this sample of firms. Prior research suggests that earnings
overstatements are of greater concern to auditors because investors frequently sue auditors when
a client overstates income, but rarely, if ever, sue when a client understates income (e.g., St.
Pierre and Anderson 1984).
Because it is inappropriate to partition the sample on the dependent variable, we use a
reverse regression procedure to examine the association between the purchase of non-audit
services and the direction of earnings management. We obtain a measure of discretionary
18 We also estimate equation (4) using the absolute value of discretionary working capital accruals, defined as total accruals less depreciation expense, and the absolute value of discretionary accruals obtained from a cross-sectional
20
accruals that is orthogonal to the control variables in equation (4), and then partition the
observations into firms with income-increasing discretionary accruals (DACC+) and firms with
income-decreasing discretionary accruals (DACC–). We regress FEERATIO on these two
variables and find a positive and significant association between FEERATIO and DACC+ and a
negative and significant association between FEERATIO and DACC–. These results confirm the
finding in Table 6 that firms purchasing more non-audit services report relatively higher income-
increasing and income-decreasing discretionary accruals. In addition, the magnitude of the
coefficient estimates on DACC+ and DACC– is not statistically different, suggesting that for a
given level of non-audit services, auditors are equally likely to prevent earnings overstatements
and understatements.
Third, we disaggregate FEERATIO into two components – the ratio of financial
information systems design fees to total fees (ISRATIO) and the ratio of all other non-audit fees
to total fees (OTHRATIO). Because of concerns that the provision of information technology
services poses a particularly strong threat to independence, the preliminary independence rules
issued by the SEC on June 27, 2000 prohibited auditors from providing these services to their
audit clients. The final rules relaxed the ban, but require separate disclosure of the information
technology services component of non-audit fees. To examine whether information technology
services pose a stronger threat to auditor independence than do other types of non-audit services,
we repeat the analyses in Tables 5 and 6 substituting ISRATIO and OTHRATIO for FEERATIO.
The untabulated results indicate that both fee variables are positive and statistically significant in
all estimations except for OTHRATIO in the POSITIVE earnings benchmark test. In addition,
the coefficient estimates on the two variables are statistically indistinguishable in all estimations.
Jones model (Jones 1991). Our untabulated findings are similar to those reported in Table 6.
21
These results indicate that the two categories of non-audit fees required to be disclosed in firms’
annual proxy statements are equally important to an assessment of earnings quality.
Fourth, the results in Table 4 indicate that the market reaction to the release of the auditor
fee information is only significant in the quartile with the highest unexpected non-audit fees. To
control for similar non-linearities in the relation between non-audit fees and earnings
management, we repeat the analyses in Tables 5 and 6 using an indicator variable equal to one if
the firm is in the highest quartile of FEERATIO, and zero otherwise. All results are robust to
this alternative specificiation. Finally, we repeat the analyses in Tables 5 and 6 using a common
sample of 1,209 firms with no change in inferences.
V. SUMMARY AND CONCLUSION
This study examines the implications of non-audit fees for shareholder wealth and the
quality of earnings. The SEC recently revised its auditor independence rules to protect investors
who rely on the integrity of financial statements audited by public accountants. Of particular
concern to the SEC is the effect of non-audit services on auditor independence.
We examine the market reaction to the disclosure of auditor fee data required by the new
independence rules. We find a significant negative stock price response for the quartile of firms
with the highest unexpected non-audit fees. We also study the relation between the provision of
non-audit services and two measures of earnings management – the likelihood of meeting
earnings benchmarks and the magnitude of discretionary accruals. We find that firms with a high
ratio of non-audit fees to total fees are more likely to report small positive earnings surprises,
small increases in earnings, and small profits. In addition, firms with a high ratio of non-audit
fees to total fees report larger income-increasing and income-decreasing discretionary accruals.
Disaggregating total non-audit fees into fees from information technology services and fees for
22
all other types of non-audit services reveals that the provision of both types of non-audit services
increases earnings management activity.
This study is a first step in examining the implications of non-audit services for auditor
independence and the quality of financial reporting. Although our evidence indicates that firms
purchasing non-audit services are more likely to engage in earnings management, we do not
address the related issue of whether the provision of non-audit services influences an auditor’s
incentives to detect and report financial statement fraud. Auditors’ legal liability as well as their
financial and reputational investments may serve as effective constraints in this setting. Nelson,
Elliot, and Tarpley (2000) examine several factors that affect auditors’ decisions to waive firms’
earnings management attempts, but do not consider the effect of the provision of non-audit
services on this decision.
The SEC disclosure requirements may also alter the existing equilibrium in the audit
services market. For example, the disclosure of fee data could increase the competitiveness of
the audit market by reducing the cost to firms of making price comparisons and negotiating fees.
In addition, firms may reduce the purchase of non-audit services from their auditor to avoid the
appearance of independence problems.
23
APPENDIX Estimation of Unexpected Non-Audit Fees
In this appendix, we develop a model to explain cross-sectional variation in FEERATIO.
The purpose of this model is to estimate the unexpected portion of non-audit fees for use in our
market reaction tests. We base our model on Parkash and Venable (1993) and Firth (1997) who
examine a company’s decision to purchase non-audit services from their auditor, conditional on
the level of auditing services purchased. They argue that companies facing high agency costs
will demand high quality audits and restrict the purchase of other services from their auditor.
We estimate the following model using all observations in our primary sample with the
necessary data:
υβββββββ
βββββα
++++++++
+++++=∑=
M/B_ENDVE_ENDMFINANCINGACQUISITONINVRECEV_ENDLNST_ENDI%
VARRETANNRETLOSSROABIGFIVEINDFEERATIOi
ii
121110
9876
543
13
121
(A1)
We include separate industry intercepts, INDi, for each of the i = 1 to 13 industries
identified in Panel B of Table 3. An indicator variable (BIGFIVE) controls for the possibility
that Big Five auditors are able to charge a premium for their services, whether audit or non-audit.
Consistent with agency incentives, Parkash and Venable (1993) and Firth (1997) find that non-
audit fees (relative to audit fees) are increasing in firm profitability and institutional ownership,
and decreasing in leverage. We include two measures of accounting performance, ROA, defined
as net income divided by average total assets, and LOSS, an indicator variable equal to one if the
firm reports a net loss. We also include two measures of stock return performance, ANNRET,
the compounded CRSP monthly return in calendar 2000, and VARRET, the variance of CRSP
monthly returns for calendar 2000. Our control for institutional ownership is %INST_END, the
24
percentage of shares held by institutions (as reported by SPECTRUM) at the end of calendar
2000. Finally, LEV_END is ratio of total liabilities to total assets at the end of fiscal 2000.
We include three measures to control for the extent and complexity of services performed
by the auditor. INVREC is inventory plus accounts receivable as a percentage of total assets at
the end of fiscal 2000, and two indicator variables for whether the firm acquired another
company (ACQUISITON) or issued securities (FINANCING) in 2000. Finally, we control for
firm size with the log of market value of equity at the end of fiscal 2000 (MVE_END), and
growth with the market to book ratio computed at the end of fiscal 2000 (M/B_END).
Summary statistics from the estimation of equation (A1) are reported in Table A1. The
model explains 30 percent of the cross-sectional variation in FEERATIO, comparable to the
explanatory power of the models in Parkash and Venable (1993) and Firth (1997). The results
indicate that firms in four industries, extractive, utilities, retail, and services, purchase fewer non-
audit services than other firms, and that firms in the computer industry purchase more non-audit
services than other firms. The results on the remaining variables are generally consistent with
Parkash and Venable (1993) and Firth (1997). Firms with Big Five auditors and poorly
performing firms, as measured by ANNRET, purchase more non-audit services. However,
neither institutional ownership nor leverage is significant. Acquisition and financing activities
both contribute to significantly higher non-audit fees. Finally, non-audit fees are increasing in
firm size and, contrary to expectations, decreasing in growth.
25
TABLE A1 Estimation of Unexpected Non-Audit Fees
Variable Coefficient Estimate t-statistic Agriculture 0.01 0.11 Mining & construction –0.05 –1.51 Food –0.06 –1.73 Textiles & printing/publishing –0.02 –0.84 Chemicals –0.03 –0.94 Pharmaceuticals –0.04 –1.90 Extractive –0.11 –4.34 Durable manufacturers –0.01 –0.65 Transportation 0.01 0.80 Utilities –0.05 –2.15 Retail –0.06 –2.09 Services –0.04 –2.06 Computers 0.16 5.84 BIGFIVE 0.08 4.79 ROA –0.01 –0.88 LOSS 0.02 1.65 ANNRET –0.03 –4.38 VARRET –0.14 –0.09 %INST_END 0.00 0.31 LEV_END 0.00 0.13 INVREC –0.02 –0.78 ACQUISTION 0.02 2.55 FINANCING 0.11 10.31 MVE_END 0.05 14.95 M/B_END 0.00 –4.88 Adjusted R2 0.30 N 2,149 Industry membership is determined by SIC code as follows: Agriculture (0100-0999), Mining & construction (1000-1999, excluding 1300-1399), Food (2000-2111), Textiles & printing/publishing (220-2799), Chemicals (2800-2824, 2840-2899), pharmaceuticals (2830-2836), Extractive (2900-2999, 1300-1399), Durable manufacturers (3000-3999, excluding 3570-3579 and 3670-3679), Transportation (4000-4899), Utilities (4900-4999), Retail (5000-5999), Services (7000-8999, excluding 7370-7379), and Computers (7370-7379, 3570-3579, 3670-3679). The variables are defined as follows: BIGFIVE = one if the firm’s auditor is a Big Five firm, and zero otherwise, ROA = net income divided by average total assets, LOSS = one if net income was less than zero, and zero otherwise, ANNRET = compounded CRSP monthly returns for the firm in calendar 2000, VARRET = variance of firm’s CRSP monthly returns for calendar 2000, %INST_END = percentage of shares held by institutions (as reported by SPECTRUM) at the end of 2000, LEV_END = ratio of total liabilities to total assets at the end of the year, INVREC = inventory plus accounts receivable as a percentage of total assets at the end of fiscal 2000, ACQUISTION = one if the firm acquired another company (as reported by SDC) in 2000, and zero otherwise, FINANCING = one if the firm issued debt or equity (as reported by SDC) in 2000, and zero otherwise, MVE_END = log of market value of equity at the end of fiscal 2000, and M/B_END = market to book ratio computed at the end of fiscal 2000.
26
REFERENCES
Antle, R. 1984. Auditor independence. Journal of Accounting Research 22: 1-20. Antle, R., P. A. Griffen, D. J. Teece, and O. E. Williamson. 1997. An economic analysis of
auditor independence for a multi-client, multi-service public accounting firm. Report prepared for the AICPA by The Law & Economics Consulting Group, Inc.
Becker, C. L. , M. L. DeFond, J. Jiambalvo, and K. R. Subramanyam. 1998. The effect of audit
quality on earnings management. Contemporary Accounting Research 15: 1-24. Bernard, V. L. 1987. Cross-sectional dependence and problems in inference in market-based
accounting research. Journal of Accounting Research 25: 1-48. Burgstahler, D., and I. Dichev. 1997. Earnings management to avoid earnings increases and
losses. Journal of Accounting and Economics 24: 99-126. Burgstahler, D., and M. Eames. 1998. Management of earnings and analyst forecasts. Working
paper, University of Washington. DeAngelo, L. 1981. Auditor size and audit quality. Journal of Accounting and Economics 3: 183-
199. DeAngelo, H., L. DeAngelo, and D. J. Skinner. 1994. Accounting choice in troubled companies.
Journal of Accounting and Economics 10: 193-225. Dechow, P. M., R. G. Sloan, and A. P. Sweeney. 1995. Detecting earnings management. The
Accounting Review 70: 193-225. DeFond, M. L., and J. Jiambalvo. 1991. Incidence and circumstances of accounting errors. The
Accounting Review 66: 643-655. DeFond, M. L., and J. Jiambalvo. 1993. Factors related to auditor-client disagreements over
income-increasing accounting methods. Contemporary Accounting Research 9: 411-431. DeFond, M. L., and J. Jiambalvo. 1994. Debt covenant violation and manipulation of accruals.
Journal of Accounting and Economics 17: 145-176. Degeorge, F., J. Patel, and R. Zeckhauser. 1999. Earnings management to exceed thresholds.
Journal of Business 72: 1-34. Erickson, M., and S. Wang. 1999. Earnings management by acquiring firms in stock for stock
mergers. Journal of Accounting and Economics 27: 149-176. Firth, M. 1997. The provision of nonaudit services by accounting firms to their audit clients.
Contemporary Accounting Research 14: 1-21.
27
Glezen, G. W., and J. A. Millar. 1985. An empirical investigation of stockholder reaction to disclosures required by ASR No. 250. Journal of Accounting Research 23: 859-870.
Gore, P., P. F. Pope, and A. K. Singh. 2001. Non-audit services, auditor independence, and
earnings management. Working paper, Lancaster University. Healy, P. M., and J. M. Wahlen. 1999. A review of the earnings management literature and its
implications for standard setting. Accounting Horizons 13: 365-383. Jones, J. J. 1991. Earnings management during import relief investigations. Journal of
Accounting Research 29: 193-228. Kinney Jr., W. R. 1999. Auditor independence: a burdensome constraint or core value?
Accounting Horizons 13: 69-75. Levitt, A. 1998. The Numbers Game. Remarks delivered at the NYU Center for Law and
Business, September 28. Levitt, A. 2000. Renewing the covenant with investors. Remarks delivered at the NYU Center for
Law and Business, May 10. Matsumoto, D. 1999. Management’s incentives to guide analysts’ forecasts. Working paper,
University of Washington. McNichols, M. F. 2000. Research design issues in earnings management studies. Journal of
Accounting and Public Policy, forthcoming. Nelson, M. W., J. A. Elliott, and R. L. Tarpley. 2000. Where do companies attempt earnings
management, and when do auditors prevent it? Working paper, Cornell University. Palmrose, Z-V. 1986. The effect of non-audit services on the pricing of audit services: further
evidence. Journal of Accounting Research 24: 405-411. Palmrose, Z-V. 1999. Empirical research on auditor litigation: considerations and data. Studies in
Accounting Research #33. American Accounting Association: Sarasota, FL. Parkash, M., and C. F. Venable. 1993. Auditee incentives for auditor independence: the case of
nonaudit services. The Accounting Review 68: 113-133. Pitt, H. L., and D. E. Birenbaum. 1997. Serving the public interest: a new conceptual framework
for auditor independence. American Institute of Certified Public Accountants: New York, NY.
Public Oversight Board. 1979. Scope of Services by CPA Firms. AICPA: New York, NY. Public Oversight Board. 2000. Panel on Audit Effectiveness: Report and Recommendations.
AICPA: Stamford, CT.
28
Scheiner, J. H., and J. E. Kiger. 1982. An empirical investigation of auditor involvement in non-audit services. Journal of Accounting Research 20: 482-496.
Securities and Exchange Commission. 2000. Final Rule: Revision of the Commission’s Auditor
Independence Requirements. Washington D.C. Simunic, D. 1984. Auditing, consulting, and auditor independence. Journal of Accounting
Research 22: 679-702. Skinner, D., and R. G. Sloan. 1999. Earnings surprises, growth expectations, and stock returns, or
don’t let a torpedo sink your portfolio. Working paper, University of Michigan. St. Pierre, K., and J. A. Anderson. 1984. An analysis of the factors associated with lawsuits
against public accountants. The Accounting Review 59: 242-263. Teoh, S. H., and T. J. Wong. 1993. Perceived auditor quality and the earnings response
coefficient. The Accounting Review 68: 346-367. Teoh, S. H., I. Welch, and T. J. Wong. 1998a. Earnings management and the long-run market
performance of initial public offerings. Journal of Finance 53: 1935-1974. Teoh, S. H., I. Welch, and T. J. Wong. 1998b. Earnings management and the underperformance
of seasoned public offerings. Journal of Financial Economics 50: 63-99. Weil, J., and J. Tannenbaum. 2001. Big companies pay audit firms more for other services. The
Wall Street Journal, April 10: C1.
29
TABLE 1 Descriptive Statistics of Auditor Fees Disclosed in Definitive Proxy Statements
Filed Between February 5, 2001 and June 15, 2001 (N = 4,225)
Variable
Mean StandardDeviation
First Quartile
Median
Third Quartile
Minimum
Maximum
Audit 499 1,667 85 166 370 2 48,000 Audit/Total 0.53 0.24 0.33 0.52 0.73 0.02 1.00 IS 176 1,701 0 0 0 0 46,800 IS/Total 0.02 0.09 0.00 0.00 0.00 0.00 0.95 Other 975 3,748 40 160 569 0 77,000 Other/Total 0.45 0.24 0.26 0.46 0.65 0.00 0.98 Non-audit 1,150 4,652 40 166 594 0 84,200 Non-audit/Total 0.47 0.24 0.27 0.48 0.67 0.00 0.98 Total 1,649 5,913 145 354 979 2 105,500 The sample consists of all definitive proxies filed between February 5, 2001 and June 15, 2001 that disclose information on auditor, audit fees, information systems fees, and other fees. Audit is the aggregate fees billed for professional services rendered for the audit of the annual financial statements and the reviews of the quarterly financial statements; IS is the aggregate fees billed for financial information systems design and implementation; Other is the aggregate fees billed for all services rendered other than the services covered by Audit and IS; Non-audit is the sum of IS and Other; Total is the total fees billed. All levels are in thousands of dollars.
30
TA
BL
E 2
D
escr
iptiv
e St
atist
ics o
f Aud
itor
Fees
Disc
lose
d in
Def
initi
ve P
roxy
Sta
tem
ents
Fi
led
Bet
wee
n Fe
brua
ry 5
, 200
1 an
d Ju
ne 1
5, 2
001,
By
Aud
itor
(N=4
,225
)
Aud
itor a
N
Audi
t Au
dit/T
otal
IS
IS/T
otal
O
ther
O
ther
/Tot
al
Tota
l A
A
717
40
7,02
0 0.
33
14
0,05
4 0.
11
67
5,39
0 0.
55
1,
222,
464
DT
486
34
3,25
3 0.
32
11
0,17
7 0.
10
61
0,47
8 0.
57
1,
063,
907
EY
833
41
4,45
3 0.
30
80
,927
0.
06
87
5,07
9 0.
64
1,
370,
459
KPM
G
650
29
4,39
5 0.
30
11
3,18
0 0.
12
56
3,35
7 0.
58
97
0,93
2 PW
C
804
58
0,22
4 0.
26
29
7,18
7 0.
13
1,
346,
421
0.61
2,22
3,83
2 A
ll O
ther
64
7
66,9
00
0.58
442
0.00
47,4
15
0.41
114,
757
The
sam
ple
cons
ists
of a
ll de
finiti
ve p
roxi
es fi
led
betw
een
Febr
uary
5, 2
001
and
June
15,
200
1 th
at d
iscl
ose
info
rmat
ion
on a
udito
r, au
dit f
ees,
info
rmat
ion
syst
ems f
ees,
and
othe
r fee
s. A
udit
is th
e ag
greg
ate
fees
bill
ed fo
r pro
fess
iona
l ser
vice
s ren
dere
d fo
r the
aud
it of
the
annu
al fi
nanc
ial s
tate
men
ts a
nd th
e re
view
s of
the
quar
terly
fina
ncia
l sta
tem
ents
; IS
is th
e ag
greg
ate
fees
bill
ed fo
r fin
anci
al in
form
atio
n sy
stem
s des
ign
and
impl
emen
tatio
n; O
ther
is th
e ag
greg
ate
fees
bill
ed
for a
ll se
rvic
es re
nder
ed o
ther
than
the
serv
ices
cov
ered
by
Audi
t and
IS; T
otal
is th
e to
tal f
ees b
illed
. A
ll le
vels
are
in th
ousa
nds o
f dol
lars
. a T
he a
udito
rs a
re A
rthur
And
erso
n (A
A),
Del
oitte
& T
ouch
e (D
T), E
rnst
& Y
oung
(EY
), K
PMG
(KPM
G),
and
Pric
ewat
erho
useC
oope
rs (P
WC
). T
here
are
225
se
para
te a
udito
rs in
clud
ed in
the
cate
gory
of “
All
Oth
er”,
incl
udin
g G
rant
Tho
rnto
n (1
11 o
bser
vatio
ns),
BD
O S
eidm
an (7
3 ob
serv
atio
ns),
Cro
w C
hize
k (6
0 ob
serv
atio
ns),
and
McG
ladr
ey &
Pul
len
(32
obse
rvat
ions
). N
o ot
her a
udito
r exc
eeds
15
obse
rvat
ions
.
31
TABLE 3 Sample Descriptive Statistics
Panel A: Distribution of observations by month
Month N % February 74 2.95 March 717 28.61 April 1,449 57.82 May 212 8.46 June 54 2.15 Total 2,506 100.00
Panel B: Distribution of observations by industry
Sample Compustat Industry Description N % %
Agriculture 7 0.28 0.44 Mining & construction 49 1.96 2.33 Food 38 1.52 2.41 Textiles & printing/publishing 130 5.19 5.31 Chemicals 66 2.63 2.73 Pharmaceuticals 212 8.46 6.49 Extractive 106 4.23 4.00 Durable manufacturers 600 23.94 23.57 Transportation 185 7.38 6.60 Utilities 91 3.63 3.45 Retail 237 9.46 9.88 Services 269 10.73 11.57 Computers 516 20.59 21.22 Total 2,506 100.00 100.00
32
TABLE 3 - continued Sample Descriptive Statistics
Panel C: Distribution of regression variables
Variable
N
Mean
Standard Deviation
First Quartile
Median
Third Quartile
Dependent variables: ARET 1,482 –0.09 5.08 –2.37 –0.17 1.97 SURPRISE 1,615 0.18 0.38 0.00 0.00 0.00 INCREASE 2,506 0.18 0.38 0.00 0.00 0.00 POSITIVE 2,506 0.15 0.36 0.00 0.00 0.00 ABSDACC 1,952 0.13 0.24 0.03 0.07 0.14 Test variable: FEERATIO 2,506 0.50 0.24 0.32 0.53 0.70 Control variables: BIGFIVE 2,506 0.92 0.28 1.00 1.00 1.00 LITRISK 2,506 0.39 0.49 0.00 0.00 1.00 M/B 2,153 6.78 25.38 1.20 2.34 5.88 %INST 2,072 0.36 0.26 0.13 0.31 0.56 CFO 2,391 –0.01 0.26 –0.06 0.05 0.12 ABSCFO 2,391 0.16 0.20 0.06 0.10 0.19 ACC 2,391 –0.08 0.26 –0.12 –0.05 –0.01 ABSACC 2,391 0.01 0.13 0.00 0.00 0.00 ACQUISTION 2,019 0.45 0.50 0.00 0.00 1.00 FINANCING 2,019 0.26 0.44 0.00 0.00 1.00 LEVERAGE 2,496 0.52 0.53 0.27 0.50 0.68 ASSET 2,501 1,914.69 8,335.34 45.06 189.55 842.91 MVE 2,256 3,204.29 15,062.51 70.28 304.11 1,252.13 Industry membership is determined by SIC code as follows: Agriculture (0100-0999), Mining & construction (1000-1999, excluding 1300-1399), Food (2000-2111), Textiles & printing/publishing (220-2799), Chemicals (2800-2824, 2840-2899), pharmaceuticals (2830-2836), Extractive (2900-2999, 1300-1399), Durable manufacturers (3000-3999, excluding 3570-3579 and 3670-3679), Transportation (4000-4899), Utilities (4900-4999), Retail (5000-5999), Services (7000-8999, excluding 7370-7379), and Computers (7370-7379, 3570-3579, 3670-3679). The variables are defined as follows: ARET = percent size-adjusted return, calculated by subtracting either the S&P 500 index return (for firms
with a market capitalization greater than $10 billion) or the S&P MidCap 400 index return (for firms with a market capitalization less than $10 billion) from the firm-specific raw return on the date the proxy statement was filed,
SURPRISE = one if the firm just meets or beats the consensus analyst forecast (i.e., forecast error of 0.00 or 0.01) in at least three of the four quarters in fiscal 2000, and zero otherwise,
INCREASE = one if the firm reports a small increase in earnings relative to the prior year, and zero otherwise, POSITIVE = one if the firm reports a small profit in the current year, and zero otherwise,
33
TABLE 3 - continued Sample Descriptive Statistics
ABSDACC = absolute value of discretionary accruals, estimated using a cross-sectional modified Jones model, FEERATIO = ratio of non-audit fees to total fees, BIGFIVE = one if the firm’s auditor is a Big Five firm, and zero otherwise, LITRISK = one if the firm is in a high litigation risk industry (SIC codes 2833-2836, 3570-3577, 7370-7374,
3600-3674, 5200-5961), and zero otherwise, M/B = market-to-book ratio at the beginning of the year, %INST = percent of shares held by institutions at the beginning of the year (as reported by Spectrum), CFO = cash from operations, deflated by average total assets, ABSCFO = absolute value of CFO, deflated by average total assets, ACC = total accruals, calculated as net income minus cash from operations, deflated by average total
assets, ABSACC = absolute value of ACC, deflated by average total assets, ACQUISTION = one if the firm acquired another company (as reported by SDC) in 2000, and zero otherwise, FINANCING = one if the firm issued debt or equity (as reported by SDC) in 2000, and zero otherwise, LEVERAGE = ratio of total liabilities to total assets at the beginning of the year, ASSET = book value of total assets at the beginning of the year (in millions), and MVE = market value of equity at the beginning of the year (in millions).
34
TABLE 4 Abnormal Returns to the Disclosure of Auditor Fee Information
by Quartiles Based on Unexpected Non-Audit Fees
Panel A: Partitioning sample into quartiles based on FEERATIO Highest
FEERATIO 3
2
Lowest FEERATIO
Mean –0.44 0.10 0.08 0.30 Standard deviation 4.86 5.17 5.47 5.91 25th percentile –2.44 –2.27 –2.19 –2.50 Median –0.58 0.08 –0.17 –0.26 75th percentile 1.61 2.00 2.35 2.11 % positive 43.23 50.91 47.79 46.88 tµ<0 –1.76* 0.38 0.27 1.01 Sign test%<50% –25.50** 4.00 –8.50 –12.00 Nobs. 384 385 385 384 Median FEERATIO 0.77 0.60 0.43 0.20 Panel B: Partitioning sample into quartiles based on FEERESIDUAL Highest
FEERESIDUAL 3
2
Lowest FEERESIDUAL
Mean –0.40 0.16 0.02 0.26 Standard deviation 4.63 6.25 5.19 5.25 25th percentile –2.44 –2.40 –2.20 –2.41 Median –0.50 0.08 –0.29 –0.21 75th percentile 1.74 2.08 1.88 2.44 % positive 45.05 50.39 46.75 46.61 tµ<0 –1.70* 0.49 0.11 0.97 Sign test%<50% –19.00** 2.00 –12.00 –13.00 Nobs. 384 385 385 384 Median FEERESIDUAL 0.21 0.07 –0.06 –0.24 Abnormal returns are size-adjusted returns, calculated by subtracting either the S&P 500 index return (for firms with a market capitalization greater than $10 billion) or the S&P MidCap 400 index return (for firms with a market capitalization less than $10 billion) from the firm-specific raw return on the date the proxy statement was filed. tµ<0 is the t-statistic from a test that the mean abnormal return less than zero. Sign test%>50% is the statistic from a test that the proportion of firms with positive abnormal returns is less than 50%. FEERATIO is the ratio of non-audit fees to total fees. FEERESIDUAL is the prediction error from the model estimated in Appendix A. * (**) indicates significance at or below the 0.05 (0.01) level, one-tailed.
35
TA
BL
E 5
L
ogit
Reg
ress
ions
Mod
elin
g th
e Pr
obab
ility
of M
eetin
g E
arni
ngs B
ench
mar
ks
(
)(
)uIN
ST%
βLO
GM
VEβ
M/B
βIT
RISK
Lβ
BIG
FIVE
βFE
ERAT
IOβ
FBE
NC
HM
ARK
rob
P+
++
++
++
=6
54
32
10β
BE
NC
HM
ARK
SURP
RISE
INC
REAS
E
POSI
TIVE
Vari
able
Pred
ictio
n
Coe
ffici
ent
Estim
ate
p-
valu
e
Coe
ffici
ent
Estim
ate
p-
valu
e
Coe
ffici
ent
Estim
ate
p-
valu
e In
terc
ept
? –2
.35
< 0.
01
–1.3
8 <
0.01
–1
.27
< 0.
01
FEER
ATI
O
+ 0.
71
< 0.
01
0.51
<
0.01
0.
28
0.07
B
IGFI
VE
– 0.
17
0.54
–0
.02
0.90
–0
.06
0.67
LI
TRIS
K
+ 0.
25
< 0.
01
0.05
0.
47
0.20
<
0.01
M
/B
+ 0.
01
0.82
0.
01
0.27
0.
01
0.34
LO
GM
VE
+ 0.
06
0.01
0.
61
< 0.
01
0.32
0.
02
%IN
ST
+ 0.
82
< 0.
01
0.01
<
0.01
0.
01
< 0.
01
Pseu
do R
2
0.05
0.
04
0.02
Nob
s.
1,40
3
2,
063
2,06
6
Th
e va
riabl
es a
re d
efin
ed a
s fol
low
s:
SUR
PRIS
E =
one
if th
e fir
m ju
st m
eets
or b
eats
the
cons
ensu
s ana
lyst
fore
cast
(i.e
., fo
reca
st e
rror
of 0
.00
or 0
.01)
in a
t lea
st th
ree
of th
e fo
ur q
uarte
rs in
fisc
al
2000
, and
zer
o ot
herw
ise,
IN
CR
EASE
=
one
if th
e fir
m re
ports
a sm
all i
ncre
ase
in e
arni
ngs r
elat
ive
to th
e pr
ior y
ear,
and
zero
oth
erw
ise,
POSI
TIV
E =
one
if th
e fir
m re
ports
a sm
all p
rofit
in th
e cu
rren
t yea
r, an
d ze
ro o
ther
wis
e,
FEER
ATI
O
= th
e ra
tio o
f non
-aud
it fe
es to
tota
l fee
s, B
IGFI
VE
= on
e if
the
firm
’s a
udito
r is a
Big
Fiv
e fir
m, a
nd z
ero
othe
rwis
e,
LITR
ISK
=
one
if th
e fir
m is
in a
hig
h lit
igat
ion
risk
indu
stry
(SIC
’s 2
833-
2836
, 357
0-35
77, 7
370-
7374
, 360
0-36
74, 5
200-
5961
), an
d ze
ro o
ther
wis
e,
M/B
=
mar
ket-t
o-bo
ok ra
tio a
t the
beg
inni
ng o
f the
yea
r, LO
GM
VE
= lo
g of
mar
ket v
alue
of e
quity
at t
he b
egin
ning
of t
he y
ear,
and
%IN
ST
= p
erce
nt o
f sha
res h
eld
by in
stitu
tions
at t
he b
egin
ning
of t
he y
ear (
as re
porte
d by
Spe
ctru
m).
36
TABLE 6 Summary Statistics from Discretionary Accruals Regressions
εββββββββββα
+++++++++++=
LEVERAGEFINANCINGACQUISTIONLOGASSETABSACCACCABSCFOCFOBIGFIVEFEERATIOABSDACC
10987
654321
Variable Coefficient Estimate t-statistic Intercept 0.16 8.15 FEERATIO 0.13 6.33 BIGFIVE 0.02 1.09 CFO –0.22 –8.04 ABSCFO –0.28 –8.18 ACC –0.63 –22.31 ABSACC 3.41 3.96 LOGASSET –0.03 –10.66 ACQUISTION 0.04 4.44 FINANCING 0.07 6.46 LEVERAGE –0.01 –0.15 Adjusted R2 0.38 N 1,872
The variables are defined as follows: ABSDACC = absolute value of discretionary accruals, FEERATIO = ratio of non-audit fees to total fees, BIGFIVE = one if the firm’s auditor is a Big Five firm, and zero otherwise, CFO = cash from operations, deflated by average total assets, ABSCFO = absolute value of CFO, deflated by average total assets, ACC = total accruals, calculated as net income minus cash from operations, deflated by average total
assets, ABSACC = absolute value of ACC, deflated by average total assets, LOGASSET = log of book value of total assets at the beginning of the year, ACQUISTION = one if the firm acquired another company (as reported by SDC) in 2000, and zero otherwise, FINANCING = one if the firm issued debt or equity (as reported by SDC) in 2000, and zero otherwise, and LEVERAGE = ratio of total liabilities to total assets at the beginning of the year.