Auditores e Nivel de Accrual

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Fees Paid to Audit Firms, Accrual Choices and Corporate Governance*

David F. Larcker Ph 215 898 5424

Email: [email protected]

Scott A. RichardsonPh: 215 898 2063

Email: [email protected]

The Wharton SchoolUniversity of Pennsylvania

Philadelphia, PA 19104-6365

Revised: October 28, 2003

*We appreciate the comments of Stanley Baiman, Jan Barton, Sudipta Basu, George Benston, Jeff Coulton,Richard Leftwich, Linda Myers, Grace Pownall, Stephen Taylor, an anonymous reviewer and seminar participants at Emory University. The financial support of The Wharton School and Ernst & Young LLP isgratefully acknowledged.



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Fees Paid to Audit Firms, Accrual Choices and Corporate Governance

Abstract

We examine the relation between the fees paid to auditors for audit and non-audit

services and the choice of accrual measures for a large sample of firms. Similar toFrankel et al. (2002), we find that the ratio of non-audit fees to total fees has a positiverelation with the absolute value of accruals. However, using latent class mixture modelsto identify clusters of firms with a homogenous regression structure reveals that this positive association only occurs for about 8.5 percent of the sample. In contrast to thisresult, we find consistent evidence of a negative relation between the level of fees paid toauditors and accruals (i.e., higher fees are associated with smaller accruals). The latentclass analysis also indicates that this negative relation is strongest for client firms withweak governance. Overall, our results are most consistent with auditor behavior beingconstrained by the reputation effects associated with allowing clients to engage inunusual accrual choices.



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Fees Paid to Audit Firms, Accrual Choices and Corporate Governance

1. Introduction

The purpose of this paper is to examine the relation between the fees paid to audit

firms for audit and non-audit services and the behavior of accounting accruals. This

relation (if it exists) is an important input into the ongoing debate in the regulatory and

academic communities about the structure of the accounting profession and the

appropriateness of providing non-audit services by accounting firms. Critics contend that

the extensive fees paid to auditors, especially fees for non-audit services, increase the

financial reliance of the auditor on the client (e.g., Becker et al., 1998 and Magee and

Tseng, 1990). As a result, independence may be compromised because the auditor

becomes reluctant to raise issues with the preparation of the financial statements at the

risk of foregoing lucrative fees. In contrast, DeAngelo (1981), Simunic (1984), and

others argue that the auditor faces substantial economic costs when audit failures are

observed. Thus, the relation between audit fees and auditor behavior is theoretically

ambiguous.

Prior research has examined many facets of this research question, but there is

little evidence that the level of audit fees or the provision of non-audit services is

associated with earnings quality. Frankel et al. (2002) claim that there is a positive

relation between the provision of non-audit services and accrual measures, which implies

that non-audit services impair earnings quality. However, more recent work by Antle et

al. (2002), Ashbaugh et al. (2003), Kinney, Palmrose and Scholz (2003) and Chung and

Kallapur (2003) have cast serious doubt on the findings and interpretations in Frankel et

al. (2002).



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There are at least three economic or econometric explanations for conflicting

results in the prior literature. First, the role of corporate governance has been largely

ignored in the research to date. The auditor is only one of many potential monitoring

mechanisms designed to mitigate the inherent agency problems in a publicly traded firm.

Examining the auditor in isolation of alternate governance mechanisms provides an

incomplete analysis of the determinants of earnings quality. Second, there are many

ways to measure the financial connection between the auditor and client. Prior research,

such as Frankel et al. (2002), has tended to focus on the provision of non-audit services

(e.g., ratio of non-audit fees to total fees). However, the total fees paid to the auditor are

an equally plausible measure for the dependence of the auditor on the client (DeAngelo,

1981, Reynolds and Francis, 2001, and Chung and Kallapur, 2003). Finally, different

models are likely that describe the relation between audit fees and earnings quality across

a large sample of firms. For example, the importance of the monitoring role served by

the auditor should vary depending on the strength of the client’s governance structure.

Hence, using a single (pooled) regression model across a sample that is composed of

different models is unlikely to provide an adequate assessment for the relation between

audit fees and accrual choices.

We address these limitations in prior research by using latent class mixture

models to examine the relation between several audit fee measures (our proxies for

auditor independence) and accrual measures (our proxies for earnings quality) for a large

sample of firms for fiscal years 2000 and 2001. We find that a positive association

between audit fees and unexpected accruals occurs only when audit fees are measured

using the ratio of audit fees to total fees paid to the auditor and unexpected accruals are

transformed using the absolute value (i.e., a non-directional measure). Moreover, the



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statistically positive association between non-audit fees and accrual behavior only occurs

for approximately 8.5 percent of the total sample. This small cluster of firms, relative to

the remaining clusters, has a smaller market capitalization, lower book-to-market ratio,

lower institutional holdings, and higher insider holdings. Thus, concurrent weakness in

corporate governance appears to be an important determinant of the relation between

auditor independence and earnings quality.

Although these results are provocative, we find that the relation between auditor

independence and earnings quality is highly sensitive to the specific measures used in the

analysis. Rather than simply using the ratio of non-audit fees to total fees, we consider

four alternate measures of auditor independence. Specifically, we use the ratio of dollar

(both audit and total) fees paid by the client to the auditor scaled by total fee revenue by

the auditor and abnormal audit (and total) fees based on the expectation models

developed by Simunic (1984) and Craswell et al. (1995).

In contrast to our initial results and those reported in Frankel et al. (2002), we find

a statistically negative relation between auditor independence (using the four alternate

measures described above) and earnings quality. Moreover, the cluster of firms with the

most pronounced negative association is characterized by low market capitalization, high

growth prospects, less independent boards, low institutional holdings, and high insider

holdings. For these firms the auditor appears to be playing a key role in the governance

process to limit abnormal accrual choices. Collectively, our results suggest that auditors

are less likely to allow abnormal accrual choices for firms where they have the greatest

financial interest. Overall, our results are most consistent with reputation concerns being

the primary determinant of auditor behavior with respect to limiting unusual accounting



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choices of client firms (similar to the discussion in Reynolds and Francis, 2001 and

Chung and Kallapur, 2003).

The remainder of the paper is divided into four sections. Section 2 provides a

review of the prior research examining the relation of payments to auditors and

accounting choices. Section 3 describes our sample, provides descriptive statistics and

justifies the use of latent class mixture models. The results are presented in Section 4 and

interpretations and conclusions are provided in the Section 5.

2. Prior Research

Early research examined the relation between audit fees and non-audit fees in an

attempt to identify the economies of scale that existed from the joint provision of these

services (e.g., Simunic, 1984 and Palmrose, 1986). Recent research, however, has shifted

focus onto the potentially detrimental aspects of the provision of non-audit services.

Frankel et al. (2002) find that the provision of non-audit services is associated with (i) the

likelihood of reporting earnings that meet or slightly exceed analyst expectations and (ii)

the magnitude of the absolute value of abnormal accruals. Frankel et al. (2002) interpret

these results as strong evidence that the provision of non-audit services reduces auditor

independence and lower quality financial information.

Subsequent research, however, has questioned the appropriateness of the

conclusions in Frankel et al. (2002). Ashbaugh et al. (2003) find that after controlling for

firm performance there is no longer a positive relation between the provision of non-audit

services and measures of unexpected or abnormal accruals for a sample of 3,170 firms.

Similarly, for a sample of 1,871 firms, Chung and Kallapur (2003) also fail to find any

evidence of a relation between measures of unexpected accruals and measures of auditor



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independence (using a measure of client dependence as opposed to simply examining the

provision of non-audit services). In addition, Ashbaugh et al. (2003) find no statistically

significant association between firms meeting analyst forecasts and auditor fees, and

Francis and Ke (2003) find that the association between firms meeting analyst forecasts

and auditor fees is very sensitive to the choice of comparison group.

Most prior research estimates the relation between the provision of non-audit

services and accruals using a relatively simple regression model between these two

variables. One notable exception, however, is contained in Antle et al. (2002) who

examine the relations among audit fees, non-audit fees and abnormal (or unexpected)

accruals in a simultaneous equations framework. Utilizing 2,443 firm-year observations

from the United Kingdom for the period 1992-2000, they find that the relation between

abnormal accruals and non-audit fees is negative after simultaneously estimating the

determinants of audit and non-audit services and accruals. They also find similar results

using a restricted sample of 1,430 U.S. firms for the year 2000.

Other measures have also been used to examine the impact of the relationship

between the client firm and auditor on earnings quality. DeFond et al. (2002) using a

sample of 944 financially distressed firms for the year 2000 find no evidence of an

association between the issuance of a qualified audit opinion and the provision of non-

audit services. Ruddock et al. (2003) using a sample of 4,708 Australian firm-year

observations from 1993-2000 find no association between measures of accounting

conservatism and the provision of non-audit services. Their argument is that if the

provision of non-audit services encourages income increasing earnings management this

will manifest via a reduction in observed accounting conservatism.



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Recent work by Kinney et al. (2003) examines the relation between earnings

restatements and the provision of non-audit services. Using proprietary data for auditor

fees for a matched sample of 174 firms for the period 1995-2000, they find no consistent

evidence of a positive association between audit firm fees for non-audit services and

restatements. Rather they find a negative association between the provision of tax

services and restatements. Observing an earnings restatement, however, is open to

several interpretations. The traditional view is that the earnings restatement is indicative

of severe earnings management by the firm and carelessness by the auditors.

Alternatively, it could be the result of an effective auditor imposing their will on the firm

and forcing the restatement.

A related stream of research examines the financial dependency of the auditor-

client relationship. DeAngelo (1981) develops a model where the auditor faces a conflict

of interest. The auditor has to choose whether to compromise independence in return for

retaining quasi-rents from key clients. The incentives to compromise independence

depend on client importance (typically measured as the ratio of fee revenue from a

particular client deflated by total fee revenue for the audit firm). One outcome of the

financial dependency is that auditors may sacrifice their independence for more important

clients. Reynolds and Francis (2001) test this prediction using client size as a proxy for

audit fees and find no evidence that economic dependence impacts the audit outcome.

Specifically, for a sample of 6,747 firms in 1996 they find that abnormal accruals are

lower when client dependence is greater and there is a marginally significant greater

likelihood of receiving a qualified audit opinion. These results suggest that litigation and

reputation risk prompt auditors to curb aggressive reporting practices of firms.



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Finally, accrual behavior and corporate governance has been examined in several

recent papers. Klein (2002) documents for a sample of 692 U.S. firm-years covering

1992-93 that the presence of independent outside directors on the board and audit

committee is associated with lower levels of unexpected or abnormal accruals (in

absolute terms). Other examples of this type of research include Xie et al. (2002) and

Jenkins (2002) who examine, among other things, the relation between board and audit

committee composition and measures of unexpected or abnormal accruals. Similar to

Klein (2002), these papers find that the presence of outside directors on the board and

audit committee is associated with lower levels of unexpected or abnormal accruals (in

absolute terms). This research highlights the importance of incorporating corporate

governance in the research design. In particular, corporate governance has an impact on

the demand for auditing quality and payment of audit and non-audit fees to the auditor,

and can have an important impact on financial reporting quality.

In summary, the literature examining the relation between audit fees and/or non-

audit services with accrual behavior finds virtually no statistical evidence for a relation

between auditor independence and earnings quality. Moreover, the results and

interpretations in prior research are statistically fragile and quite sensitive to changes in

research design and variable measurement. We extend the existing literature by

examining a more complete set of measures for both earnings quality and auditor

independence, relaxing the assumption that a single regression model describes the

relation between auditor independence and earnings quality, and explicitly analyzing the

role of corporate governance on the auditor-client relation.



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3. Methodological Approach

3.1 Sample

Our initial sample consists of 5,815 firm-years that report data on audit and non-

audit fees paid for fiscal years 2000 and 2001. These data were obtained directly from

Standard & Poors.1

In order to reduce the impact of very small firms and auditors, we

restrict our sample to clients of Arthur Andersen, Ernst & Young, Deloitte & Touche,

KPMG, and PricewaterhouseCoopers (i.e., the “Big Five”) plus Grant Thornton and BDO

Seidman (reducing the sample by 355 observations). Firms are retained for subsequent

analysis if they have sufficient Compustat data for computing the accrual measures used

in our analysis. We also exclude financial institutions from the sample (SIC codes

between 6000 and 6999). This reduces the sample by 357 observations. The 5,103 firm-

years (3,424 firms) in our final sample span many sectors of the economy (Table 1, Panel

A). The industries most represented in our sample are business services (16.46 percent of

the sample), chemicals (9.21 percent), electrical (9.21 percent) and industrials (6.75

percent). These percentages are similar to the breakdown for the Compustat population.

The mean (median) of operating cash flow is equal to four (seven) percent of

assets, for book-to-market ratio is equal to 0.78 (0.52) and for market capitalization of

about $2,806 ($271) million. These numbers compare with a mean (median) book-to-

market ratio of 0.92 (0.58) and a mean (median) market capitalization of $2,910 ($139)

million for all firms on Compustat for the 2000-2001 period.

3.2 Measurement of Auditor Independence

1 It is important to note that the SEC disclosures with respect to audit fees are limited. Total fees and auditspecific fees are disclosed but the remaining “other fee” category is not well defined.



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We employ five different measures of the fees paid to auditors. First, we

calculate the fee ratio as the ratio of non-audit fees to total fees paid to the auditor (the

sum of audit and non-audit fees). Similar to prior research (e.g., Frankel et al., 2002), the

mean (median) firm pays roughly the same amount for non-audit and audit services (see

Table 2). However, there is considerable cross-sectional variation in this measure

(denoted as RATIO). Although RATIO has some intuitive appeal for measuring the

financial linkage between an auditor and a client, the size of payments to the auditor is

not captured by this measure. That is, a client with one dollar of audit and non-audit

payments produces the same score as a client with ten million dollars of audit and non-

audit payments.

In an effort to incorporate payment size into our analysis, we focus on the

importance of a particular client to the audit firm. In particular, we measure client

importance as the ratio of fees paid by the client firm to the total revenue of the auditor

for that year. As in Chung and Kallapur (2003), we obtain the total fee revenue for each

auditor from Accounting Today. We calculate two basic measures of client importance.

NONAUDFEE , uses non-audit fees to compute client importance, and TOTFEE uses total

fees (both audit and non-audit). We use both measures because the quasi rents could be

greater for non-audit services than audit services. The mean (median) firm pays total fees

that are approximately three (one) percent of total revenue for their auditor and non-audit

fees that are approximately two (one-half) percent of total revenue for their auditor

(Table 2, Panel A). As would be expected, these three measures of payments to the

auditor exhibit large, positive correlations (Table 2, Panel B). In section 4.3 we introduce

our final two measures of auditor independence that focus on abnormal fee levels.



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3.3 Measurement of Accruals

Measuring earnings quality is a difficult and controversial task because there are

divergent views as to what constitutes “high quality” earnings. We follow prior research

and examine accrual measures as a proxy for issues of earnings quality (e.g., Frankel et

al., 2002, and Myers et al., 2003). The accrual component of earnings contains estimates

and forecasts, and is therefore easier to manipulate than cash flows. Thus, the flexibility

offered via accruals makes it a useful measure for examining the quality of financial

reports.

Rather than simply examining total accruals, we are interested in identifying the

“unexpected” component of total accruals. A large research has attempted to identify the

“unexpected” (also called discretionary or abnormal) accrual component. Jones (1991) is

the standard technique used for this decomposition. Total accruals are regressed on

variables that are expected to vary with “normal” accruals. These models have been

estimated using a time-series approach for each firm (e.g., Jones, 1991), or they have

been estimated in the cross-section for each industry (e.g., DeFond and Subramanyam,

1998). Both approaches have their limitations. The time series approach assumes

temporal stationarity of parameter estimates whereas the cross-sectional approach

assumes homogeneity across firms in the same industry. Consistent with the claims in

Bartov, Gul and Tsui (2001) that the cross-sectional models are better specified for a

sample of audit qualifications, we adopt a cross-sectional model. Furthermore, the cross-

sectional model maximizes our sample size.

Attempts to decompose total accruals into expected and unexpected components

can always be criticized for misclassifying expected accruals as unexpected because the



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model of expected accruals is incomplete (e.g., Bernard and Skinner, 1996). To address

this issue, we use a more advanced model that attempts to mitigate the misclassification

issue. The advanced model is similar to that employed in Dechow et al. (2003). Dechow

et al. show that this model (i) has far greater explanatory power than the cross-sectional

modified Jones model, (ii) identifies unexpected accruals that are less persistent than

other components of earnings, (iii) identifies unexpected accruals that detect earnings

manipulation identified in SEC enforcement actions, and (iv) identifies unexpected

accruals that are associated with lower future earnings and lower future stock returns.

However, we acknowledge that attempts to decompose total accruals are still subject to

the limitation of model mis-specification.

Our accrual model builds on the cross sectional modified Jones model discussed

in Defond and Subramanyam (1998). This model assumes that the change in revenues

less the change in accounts receivable is free from managerial discretion (i.e., credit sales

are assumed to be abnormal) and that capital intensity drive normal accruals. We include

two additional independent variables that have been shown to be correlated with

measures of unexpected accruals. First, we include the book-to-market ratio ( BM ). BM

is measure as the ratio of the book value of common equity (Compustat item 60) to the

market value of common equity (Compustat item 25 x item 199). BM is included as a

proxy for expected growth in the firm’s operations. We expect to see large accruals for

growing firms (see also McNichols 2000, 2002). Investment in inventory and other

assets are likely to accompany growth phases of the firm’s life cycle. Observing an

increase in inventory in this circumstance is not necessarily due to opportunistic

managerial behavior. However, the modified Jones model classifies such increases as

“unexpected”. Second, we include a measure of current operating performance.



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Previous research has shown that measures of unexpected accruals are more likely to be

mis-specified for firms with extreme levels of performance (Dechow et al., 1995). We

therefore include current operating cash flows, CFO (Compustat item 308), as an

additional independent variable. The advanced model is estimated as follows:

TA = α + β 1( ∆Sales-∆ REC) + β 2 PPE + β 3 BM + β 4CFO + ε (1)

Total Accruals (TA) is the difference between operating cash flows (Compustat

item 308) and income before extraordinary items (item 123) as reported on the statement

of cash flows. ∆Sales is the change in sales (item 12) from the previous year to the

current year, ∆ REC is the difference in accounts receivable (item 302) from the start to

the end of the year, and PPE is the end of year gross property, plant and equipment (item

7). All variables are scaled by the average of total assets using assets from the start and

end of the fiscal year (item 6). The residual value from this model is labeled Accruals,

the estimate of unexpected or abnormal accruals from our extended Jones model.

Inclusion of both BM and CFO is not without issue. It is likely that incentives to manage

earnings vary in response to growth opportunities and current operating performance.

Specifically, market expectations of future growth can place greater pressure on

management to engage in earnings management (Dechow and Skinner, 2000). In

addition, current performance can create incentives to engage in earnings management.

Including these additional variables may be controlling for some of the variation in total

accruals that we are seeking to identify.2

2 It is appropriate to include all controls in the accrual model rather than include them as additionalindependent variables in subsequent empirical analyses. Including variables such as book-to-market andoperating performance in the accrual model is beneficial as we are able to identify industry-year specificcoefficients. Including these variables as controls in subsequent tests examining the relation betweenabnormal accruals (without book-to-market and operating performance) and audit fees fails to capture these potentially important industry-year effects.



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The model in equation (1) was estimated using all firm-years from 1988-2001 on

Compustat that have the required information to calculate an estimate of total accruals

from the statement of cash flows along with estimates of property, plant and equipment

and book-to-market. The model was estimated individually for each two digit SIC code

with at least 8 observations, or a total of 648 industry-year regressions. The mean

coefficient estimates for the parameters for our model and distributional properties of the

resulting accrual measures are presented in Table 3.

Consistent with prior research we find a positive coefficient on ( ∆Sales-∆ REC)

and a negative coefficient on PPE (the traditional parameters in the modified Jones

model). We also find that BM and CFO are both negatively associated with total

accruals. The mean adjusted R 2 is about 30 percent in our extended accrual model.

For the pooled sample of 62,766 firm-year observations used to generate our

measures of unexpected accruals, the mean value of unexpected accruals is zero by

construction (unexpected accruals are the residual from a regression model). Hence,

finding zero unexpected accruals suggests we have random sample drawn from the

Compustat population.

In the tests that follow, we examine both the raw values for abnormal accruals

along with their absolute values.3

However, there is little consensus on the precise

accrual measure for earnings quality. If the earnings management is directional, the

appropriate metric is the raw value. For example, if payments to auditors creates an

incentive to engage in income increasing earnings management then the research design

3 In unreported results we also replicated all results using alternative measures of abnormal accruals using amore limited set of independent variables. Our conclusions are unaffected by these alternate measures of abnormal accruals.



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WorldScope. Data on board characteristics were obtained from Institutional Shareholder

Services ( ISS ). ISS classifies each director on the board as an (i) insider, (ii) affiliated, or

(iii) independent outsider. We measure Board Composition as the number of

independent outsiders on the board divided by Board Size.

The descriptive statistics for the governance variables are presented in Table 4.

Because our governance data are drawn from several sources we do not require complete

data availability for all measures to maximize the sample size. We have data available on

insider and institutional holdings for 2,742 of our 3,424 firms. Data are available for

board composition for 1,986 firm observations. The mean (median) firm in our sample

has approximately 44 (43) percent of its outstanding shares held by institutions and 16 (8)

percent held by corporate insiders. The mean (median) percentage of outside directors on

the board is 60 (62) percent. Table 4 (Panel B) reports the correlations between our

selected governance measures. Pearson (Spearman) correlations are reported below

(above) the diagonal and correlations. There is a strong negative correlation between

institutional holdings and holdings by corporate insiders. Institutional holdings are

greater for firms with larger and more independent boards. These governance data are

consistent with descriptive statistics reported in previous research (e.g., Klein, 2002).

Prior research suggests that corporate governance is enhanced by higher

institutional holdings (Shleifer and Vishny, 1997 describe the external monitoring

benefits) and a higher proportion of independent directors (internal monitoring). A large

stream of research has documented some benefit to the presence of independent outsiders

on the board. Directors have a fiduciary duty to exercise care in monitoring management

on behalf of shareholders (Fama and Jensen, 1983). A firm with an outside director

majority is more likely to replace a CEO following poor firm performance (Weisbach,



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1988), make better acquisitions (Byrd and Hickman, 1992), adopt poison pills to improve

shareholder value (Brickley et al., 1994) and have higher quality reported earnings

(Dechow et al., 1996).

A larger proportion of insider holdings can also improve corporate governance if

standard agency problems between stockholders and managers are mitigated. However,

as insider holdings increase, it is possible that insiders de facto assume control of the

firm, and this exacerbates agency problems (Morck et al., 1988, describe how insiders

can exploit the firm for intermediate levels of ownership).

3.5 Econometric Approach

A common assumption that is made in the prior research is that one structural

model is appropriate for the entire sample. However, if different models characterize

subsets of observations, the pooled estimation results can be highly misleading.4

An

alternative to using a pooled estimation approach is to classify the sample into

homogenous clusters of observations with similar regression models. Moreover, once

these clusters are identified, it is possible to determine what distinguishing factors are

associated with the observations in each cluster. For example, payment for non-audit

services can have positive relation with accrual behavior for some firms, negative for

some firms, and zero for still other firms. Given these clusters, standard statistical

methods can be used determine whether cluster membership (which corresponds to

different structural models) is related to differences in corporate governance.

4 Although a somewhat extreme example, it is possible for the regression model for 50% of the sample tohave a positive regression coefficient and the regression model for the remaining 50% of the sample tohave a negative coefficient with the same absolute value. Under this scenario, the estimated regressioncoefficients for the pooled sample (assuming equal error variances) will be zero. Clearly, this erroneousinterpretation occurs because of an inappropriate pooling of the data.



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There is an extensive literature in statistics, econometrics, and marketing that

provides the methodological basis for univariate mixtures of distributions (e.g.,

Newcomb, 1886 and Pearson, 1894) and switching regressions (e.g., Quandt and

Ramsey, 1978). In addition, DeSarbo and Cron (1988), Wedel and DeSarbo (1995), and

others have developed maximum likelihood methods for clusterwise regressions or latent

class mixture models. We employ latent class models in our empirical analysis.

In formal terms, the latent class model (or finite mixture model) assumes that the

sample of N observations can be characterized with K clusters. Each observation is yi =

XiΒ + ει, where yi is the dependent variable for the ith observation, Xi is the set of

independent variables for the ith observation, Β is the corresponding set of regression

coefficients for the K clusters, and ει is the normally distributed error term for the

regression model.5 If yi is distributed as a finite mixture of normal distributions, a sample

of N independent observations produces the following likelihood expression:

∏

σ

Β−−πσ∑λ= =

−=

N

1i2

k

2

k ii2/1k

K

1k k ]

2

)Xy(exp[)2(L , (2)

where λk is the unknown proportion of the sample that is contained in cluster k and σk is

the standard deviation of the error term in cluster k. The unknown parameters are λk , σk ,

and Βk . Estimates of these unknown parameters are obtained by maximizing the above

likelihood function subject to 10 k ≤λ≤ , 1K

1k k =∑λ

=, and 2

k σ > 0.

One difficulty in applying latent class mixture models is the determination of the

number of clusters (i.e., K). As with many multivariate procedures, formal statistics for

5 See DeSarbo and Cron (1988), Wedel and DeSarbo (1995), and references contained in these papers for the precise technical optimization details of clusterwise regressions and latent class modeling. Thestatistical discussion in the text is adapted from these papers.



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this choice are not available and heuristic fit statistics based on the logarithm of the

likelihood function (adjusted for the number of parameters used in the estimation) are

used. As suggested by Akaike (1974) and Bozdogan (1987), a reasonable fit statistic is

the Consistent Akaike Information Criterion (CAIC). The CAIC is equal to

-2 ln L + [P·K + 2K -1]·(ln(N + 1), L is the maximum likelihood for the K cluster

solution, P is the number of independent variables in the regression model, K is the

number of clusters, and N is the sample size. The number of clusters is determined for

that value of K that minimizes the CAIC.

Given the number of clusters and the parameter estimates, the posterior

probability that each observation belongs to a specific cluster can be computed using

Bayes theorem (which is referred to as a “fuzzy” clustering). A natural classification rule

is to assign each observation to the cluster where it has the largest relative posterior

probability. The estimatedλk (and the proportion of firms assigned to each cluster) is an

important parameter because it will identify whether a relation between audit fees and

accrual behavior characterizes the entire sample (i.e., λ = 1) or a smaller subset of the

observations (i.e., λ < 1). After firms are assigned to one specific cluster, the differences

in corporate governance across clusters (where a different relation between accruals and

payments for non-audit services characterizes each cluster) can be completed using

standard generalized linear model methods.

Rather than using latent class mixture modeling, prior research estimates a single

(pooled) regression model and then the model is extended to incorporate (usually

exploratory) interactions for subsets of interest (e.g., ownership variables). Although the

use of interactions (e.g., fees • institutional ownership) can provide useful insights into

the conditional relation between accruals and audit fees, this approach has several



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inherent limitations. First, there are numerous governance variables of interest and the

analysis will require many interactions in the regression model. These numerous

interactions will almost certainly result in high levels of multicollinearity, and this will

make the interpretation of the statistical significance for the coefficients problematic

(e.g., Yi, 1989). Second, typical interaction models allow the slope terms to vary, but

constrain the intercept (the conditional mean level for accruals) to be identical for all

firms in the pooled sample. However, Klein (2002), Xie et al. (2002) and Jenkins (2002)

find that the level of accruals varies with respect to selected governance variables. Thus,

it is questionable whether the implicit constraint on the intercept is appropriate.6

Finally,

interactions between continuous variables assume that the incremental impact on the

slope is either always increasing or decreasing. Unfortunately, if the impact of a

governance variable on the slope is nonlinear, the structure imposed by the interaction

will be inappropriate.7

Latent class mixture models do not impose this type of interaction structure on the

relation between accruals and audit fees. In contrast, this approach explicitly allows for

the possibility that there are different models linking accruals to audit fees (involving a

different intercept and/or slope). Once clusters of firms with a homogenous relation

between accruals and audit fees are identified, it is then possible to uncover whether

governance factors differ among the clusters. Although our approach has a very different

theoretical orientation than pooled regression models with interactions, interaction

6 It is, of course, possible to have a separate intercept for each group of firms using interactions. However,the groups (or clusters) of firms must be known a priori in order to implement this approach.Unfortunately, researchers do not typically have such strong a priori knowledge.7 One solution to this problem is to shift from interactions between continuous variables to an interaction between audit fees and a categorical governance variable (e.g., high, medium and low institutionalownership). This type of interaction can uncover nonlinear features in the data. However, the conversionof a continuous variable into a categorical variable is almost completely arbitrary and it is not clear how toassess model misspecification.



21

structures are simply special cases of latent class mixture models. Given the controversy

surrounding the actual relation (if any) between audit fees and earnings quality, a more

general analysis approach is an appropriate choice.

4. Results

4.1 Pooled Sample

In order to link our results to prior research, we first report the estimation results

for each of our accrual and payment measures with the basic regression model:

Accrual_Measurei = α + β Audit Fee Measurei + ε i (3)

Consistent with Frankel et al. (2002), RATIO has a marginally statistically positive

association with the non-directional accrual (Table 5, Panel B), but no statistical

association with any of the other accrual measures. TOTFEE and NONAUDFEE have a

statistically positive relation with directional accruals, which suggests that accruals are

higher in firms that pay larger fees to their auditors. However, TOTFEE and

NONAUDFEE have a statistically negative relation with non-directional accruals,

negative relation when accruals are restricted to values greater than zero, and positive

relation when accruals are restricted to values less than or equal to zero. These results

indicate that firms making larger payments to their auditors exhibit smaller accruals.

These contradictory results are likely to be caused by the extremely low level of

explanatory power of the regressions in Table 5. The weak statistical and substantive

relation for the pooled sample results is consistent with the conflicting evidence reported

in papers such as Frankel et al. (2002), Antle et al. (2002) and Ashbaugh et al. (2003).

One important assumption underlying the results in Table 5 is that a single

regression model is appropriate for the entire sample of firms. That is, similar to prior



22

research, the results assume that all of the sample observations are homogenous with

respect to the relation between the fees paid to auditors and accrual activity. In the next

section, we relax this assumption and analyze the same set of data using latent class

mixture models.

4.2 Latent Class Mixture Analysis

The regression model in equation (3) was estimated using a latent class mixture

approach for each combination of accrual and audit fee measures. The minimum CAIC

statistic (unreported) was achieved with three clusters for each combination. The

parameter estimates, t-statistics, and proportion of the sample that is contained in each of

the three clusters are presented in Tables 6, 7 and 8 for RATIO, TOTFEE and

NONAUDFEE , respectively.

There is no statistical association with between RATIO and directional accruals or

accruals that are constrained to be positive (Table 6, Panels A and C).8 However, Cluster

I exhibits a positive relation with non-directional accruals (Panel B) and a negative

relation with accruals that are constrained to be less than or equal to zero (Panel D).

Although these results are consistent with Frankel et al. (2002), it is important to note that

this association only applies to 8.5 percent of the sample in Panel B and 14.8 percent of

the sample in Panel D. Thus, if RATIO is a valid measure for describing the relation

between the auditor and a client, accounting quality is only sacrificed for a small subset

of firms.

As discussed in Section 3.4, the estimation results can be used to compute the

posterior probability that each observation belongs to a specific cluster. Based on these



23

posterior probabilities, we assign each observation to the cluster where it has the largest

estimated posterior probability. Given these classifications, it is possible to use standard

analysis of variance (ANOVA) methods to determine whether there are differences in

corporate governance and other variables across the three clusters.9

In Table 6 (Panels B and D), we find that firms in the cluster exhibiting a positive

relation between abnormal accruals and RATIO have smaller book-to-market ratios (i.e.,

high growth prospects) and market capitalizations than the remaining sample firms. In

addition, these firms have lower institutional and higher insider holdings. These results

are consistent with a firm that is difficult to monitor and where insiders have effective

control of the organization. Thus, concurrent weakness in corporate governance appears

to be an important determinant of the relation between auditor independence and earnings

quality.10

The results for TOTFEE and NONAUDFEE are presented in Tables 7 and 8,

respectively. Given the substantial correlation between these two measures (Table 2,

Panel B), it is not surprising that the results for TOTFEE and NONAUDFEE are virtually

identical. There are no statistically significant results for the slope coefficients when

directional accruals are used as the measure for earnings quality. However, large and

statistically significant results are observed with the remaining accrual measures. The

slope coefficient (β) is consistently negative for the non-directional accruals, negative for

8 In the analyses where the slope term is not statistically significant, the clusters differ only in terms of theintercept (which is equivalent to examining differences in the mean level of accrual across clusters).9 In each table we report an F-test that tests the null hypothesis of no difference across all three clusters. Inunreported tests we have also performed pair-wise tests using a Tukey HSD test. Our discussion of significant differences is based on these pair-wise differences.10 Prior research by Frankel et al (2002) and Ashbaugh et al. (2003) documents that firms with greater institutional holdings report smaller accruals. These papers, however, examine institutional holdings as acontrol variable and not as part of the overall governance structure that impacts the relation between



24

the accruals that are constrained to be greater than zero, and positive for accruals that are

constrained to be less than or equal to zero. These results indicate that accruals are

smallest for firms where the auditor has the greatest financial interest. This result is

completely inconsistent with the very modest results obtained using RATIO. The

economic significance of this relation is not trivial. For example, using the estimates for

cluster I from panel B of table 7 a change in TOTFEE equal to its inter-quartile range

(0.021) corresponds to a 0.0034 reduction in the value of |Accruals|. The median firm in

our sample reports operating earnings equal to six percent of its asset base. A 0.0034

reduction in accruals translates to a 6 percent (0.0034/0.06) reduction in ROA.

The largest statistically significant slope coefficient is observed for cluster of

firms that has a relatively low book-to-market ratio (i.e., high growth prospects), low

market capitalization, fewer external board members, low institutional holdings, and high

insider holdings. Thus, the relation between payments to auditors and earnings quality is

the most stringent when the client has weak governance. Overall, our results are most

consistent with reputation concerns being the primary determinant of auditor behavior

with respect to limiting unusual accounting choices of client firms and inconsistent with

payments to auditors causing a decrease in accounting quality.

4.3 Analysis of “Abnormal” Fees

One concern with the use of total and non-audit fees is that we use the overall

level of fees as the measure for the importance of the client to the auditor without any

controls for the source of these fees. In an attempt to address this limitation, we develop

two additional measures of client importance that focus on “abnormal” non-audit fees and

accrual measures and the provision of non-audit services. We are not aware of prior research that hasexamined the provision of non-audit services as part of a larger governance issue.



25

“abnormal” total fees. Our estimates of the abnormal fees are generated from the

following regression specification (see Simunic 1984 and Craswell et al., 1995 for model

details):11

Log(Fee) = φ 0 + φ 1 Log(Assets) + φ 2 Log(Segments) + φ 3 Inventory + φ 4 Receivables +

φ 5 Debt + φ 6 Income + φ 7 LOSS + φ 8Opinion + ε (4)

The variables are defined as follows. Log(Fee) is the natural logarithm of either

total fees or non-audit fees paid to the auditor. Log(Assets) is the natural logarithm of

total assets (Compustat data item 6). Log(Segments) is the natural logarithm of the

number of business segments reported on the Compustat Segment Data File. Inventory is

the ratio of the dollar value of inventory (item 3) to total assets (item 6). Receivables are

the ratio of the dollar value of accounts receivable (item 2) to total assets (item 6). Debt

is the sum of short term debt (item 34) and long term debt (item 9) to total assets (item 6).

Income is the ratio of operating income after depreciation (item 178) to average total

assets (item 6). LOSS is coded as an indicator variable that is equal to one if the firm

reports negative Income in any of the previous three years and zero otherwise. Opinion is

an indicator variable equal to one if the firm receives a qualified audit opinion and zero

otherwise. A qualified audit opinion is defined as anything other than the standard

unqualified audit opinion coded as one by Compustat.

Equation (4) is estimated for each of the 54 industry groups (two-digit standard

industrial classification) in our sample. The estimated residual ( ε̂ ) from equation (4) is

our proxy measure for “abnormal” fees. To transform this to a dollar amount we raise

exp to the power of the predicted value of Log(Fee) and then subtract this value from the

11 The model in equation (4) has been developed for audit fees, and not non-audit fees. To our knowledge, prior research has not developed a model for non-audit fees. In the absence of such a model, we simplyapply the same set of independent variables to total and non-audit fees. We acknowledge that our analysis



26

dollar fee. The result (after deflating by auditor firm revenue) is denoted as ABTOTFEE

( ABNONAUDFEE) for total fees and non-audit fees.

The explanatory power from this specification for fees is very high with the mean

adjusted R 2 of about 75 percent for the total fee model and 60 percent for the non-audit

fee model (Table 9). The mean coefficient estimate and mean t-statistics across the 54

industry groups is also presented in Table 9.12 As expected, fees are positively associated

with firm size, number of segments (a measure of audit complexity), extent of inventory

(a measure of audit complexity), existence of a loss and a qualified opinion (which

requires more audit effort). In general, the estimated coefficients and explanatory power

of our model are very similar to prior research (e.g., Simunic 1984), with firm size being

the key independent variable.

We expect auditor behavior to vary depending on whether the auditor is being

paid more or less than the economic benchmark for a specific client.13 When the

“abnormal” fee is less than or equal to zero (denoted as either LowABTOTFEE or

LowABNONAUDFEE ), the auditor has little to lose if they impose stringent accounting

requirements on the client that result in lower levels of accruals. If this action causes the

auditor to lose this client, we assume that there are other more profitable uses for the staff

previously assigned to this client. However, if the client remains with the auditor, there is

considerable incentive for the auditor to be aggressive with this client in order to

minimize any reputation loss due to an audit failure. Obviously, the case where the

of non-audit fees may be confounded by the use of an inappropriate model for computing “abnormal” non-audit fees.12 We acknowledge that the 54 regressions are likely to exhibit positive cross-sectional correlation.Although we do not know the extent of cross-sectional correlation, the z-statistic that results from theaggregation of individual t-statistics will range from the mean t-statistic (if the correlation is equal to one)to approximately 54 times the mean t-statistic (if the correlation is equal to zero). The latter computation

is labeled as the maximum t-statistic in Table 9.



27

auditor would be most susceptible to client pressure is when the “abnormal” fee is greater

than zero (denoted as either HighABTOTFEE or High ABNONAUDFEE ). If reputation

concerns are not relevant to the auditor, we should observe lower earnings quality for

firms that pay a positive premium to the auditor for audit and non-audit work.

The results of the latent class mixture analysis using the “abnormal” fee measures

are presented in Table 10. For the “abnormal” total fee measures (Panels A to D), there

are no statistically significant slope coefficients for directional accruals. However, we

find consistent results across the three clusters for the other three accrual measures.

Positive “abnormal” fees are associated with lower non-directional accruals and smaller

(closer to zero) negative and positive accruals. The same results are observed with

negative “abnormal” fees (after incorporating the necessary shift in the sign of the

coefficient). In addition, we find virtually the same results with “abnormal” non-audit

fees. The only difference is that there is a statistically positive coefficient on

LowABNONAUDFEE the directional accrual measure (which indicates that as the

“abnormal” fee becomes more negative, the accrual becomes more negative). Overall,

the results in Table 10 are consistent with reputation being an important determinant of

earnings quality. We find no evidence that large “abnormal” payments to auditors cause

a decline in earnings quality. In fact, our results indicate precisely the opposite outcome.

5. Summary and Conclusions

The relation between the fees paid to auditors and earnings quality has been the

focus of considerable scholarly, institutional and regulatory debate. Assumptions and

conjectures about this relationship have been instrumental in shaping the regulatory

13 In a recent paper Raghunandan, Read and Whisenant (2003) find no evidence of abnormally high fee payments to restatement firms compared to a matched sample.



28

debate concerning the forced divestiture of the consulting function in audit firms and the

general structure for the auditing industry. Unfortunately, prior research by Frankel et al.

(2002), Antle et al. (2002) and Ashbaugh et al. (2003) have found mixed results on this

important empirical association. Therefore, the role of fees for audit and non-audit

services on accounting choices is an unresolved issue.

Similar to some prior research, we find very little evidence of a positive relation

between the fees paid to auditors and measures of accruals for a large pooled sample of

firms. We find a positive association between audit fees and unexpected accruals only

when audit fees are measured using the ratio of audit fees to total fees paid to the auditor

and unexpected accruals are transformed using the absolute value (i.e., a non-directional

measure). Moreover, the statistically positive association between non-audit fees and

accrual behavior only occurs for approximately 8.5 percent of the total sample. This

small cluster of firms, relative to the remaining clusters, has a smaller market

capitalization, lower book-to-market ratio, lower institutional holdings, and higher insider

holdings. Thus, corporate governance is a key factor for understanding accrual choices,

as opposed to these choices simply being a function of fees paid to auditors.

Using alternate measures of audit fees which capture the extent of financial

dependence of a given client to an auditor, we find a statistically negative relation

between auditor independence and earnings quality. The cluster of firms with the most

pronounced negative association is characterized by low market capitalization, high

growth prospects, less independent boards, low institutional holdings, and high insider

holdings. For these firms the auditor appears to be playing a key role in the governance

process to limit abnormal accrual choices. Similar to Francis and Reynolds (2001), our



29

results are consistent with reputation concerns being the primary determinant of auditor

behavior with respect to limiting unusual accounting choices of client firms.

Our study has several limitations, and it is important to make these problems

explicit. First, auditor choice, purchase of non-audit services, and the governance

structure are endogenous variables. This endogeneity is ignored in our analysis and our

results are subject to the traditional econometric problems caused by endogeneity. With

the exception of the structural modeling approach in Antle et al. (2002), this limitation is

inherent in all prior research examining the relation between non-audit services and

accrual behavior. It is important for future research to develop a more complete set of

structural models with a sophisticated selection of exogenous (or instrumental) variables.

Second, our results are based only on two years of data, and this limits the ability

to generalize the results to other time periods. With the exception of the research by

Kinney et al. (2003) that uses proprietary data, and data from other countries (e.g., U.K.

data used in Antle et al., 2002 and Australian data used in Ruddock et al., 2003), this is

also a limitation to prior research. Ignoring issues of self-selection, the data used by

Kinney et al. are especially intriguing because it is collected during the time period prior

to the auditing controversies. In contrast, the self-reported fee data for the time periods

used in this study may be “manipulated” by the firms in response to shareholder and

legislative inquiries.

Finally, similar to prior research, we use accrual measures as indicators for

earnings quality or “bad behavior” by managers. Measures of unexpected accruals are

criticized because they incorrectly classify “expected” accruals as unexpected. Hence,

any association between payments to auditors and measures of unexpected accruals could

be due to measurement error in unexpected accruals and not “bad managerial behavior.”



30

To help mitigate this problem we employ a model of unexpected accruals that has greater

explanatory power and is less likely to misclassify expected accruals as unexpected

(Dechow et al., 2003). Nevertheless, there is in an unknown degree of measurement error

inherent in our accrual metrics.



31

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Table 1

Descriptive Statistics for the Sample

The sample includes 5,103 firm-year observations for the fiscal years 2000 and 2001 for which we are able to calculate measures of abnormal accruals and non-audit services.

Panel A: Industry Composition

2 digit SIC Industry NumberPercent of

Sample

Compustat

Composition

1 Crops 13 0.25 0.2910 Ores 20 0.39 1.1213 Oil & Gas 160 3.14 3.6114 Quarry 13 0.25 0.2915 Building - Light 26 0.51 0.4616 Building - Heavy 16 0.31 0.3417 Construction 15 0.29 0.3220 Food 101 1.98 2.26

22 Textile Mill 22 0.43 0.4023 Apparel 47 0.92 0.9324 Lumber 30 0.59 0.5125 Furniture 37 0.73 0.5326 Paper 60 1.18 1.0427 Printing 67 1.31 1.3328 Chemicals 470 9.21 8.5629 Petroleum 21 0.41 0.6230 Rubber 56 1.10 1.1731 Leather 18 0.35 0.3632 Stone 35 0.69 0.67

33 Metal Work - Basic 89 1.74 1.5234 Metal Work – Fabrication 87 1.70 1.4435 Industrial 357 7.00 6.8436 Electrical 479 9.39 8.8837 Transport – Equipment 116 2.27 1.7938 Instruments 372 7.29 7.0339 Misc. Manufacturing 47 0.92 1.1440 Railroad 12 0.24 0.2742 Motor freight 44 0.86 0.7244 Water Transport 27 0.53 0.4445 Air Transport 42 0.82 0.57

47 Transport - Services 19 0.37 0.3548 Communications 151 2.96 4.2249 Utilities 187 3.66 3.0850 Durables – wholesale 137 2.68 2.5451 NonDurables - wholesale 77 1.51 1.5852 Garden 15 0.29 0.2053 General Stores 42 0.82 0.5854 Food Stores 26 0.51 0.56



Table 1 (continued)

2 digit SIC Industry NumberPercent of

Sample

Compustat

Composition

55 Auto Dealers 30 0.59 0.4456 Apparel - Retail 72 1.41 0.8857 Home Equipment 34 0.67 0.4558 Eating 71 1.39 1.5259 Misc. Retail 103 2.02 2.0870 Hotels 24 0.47 0.4872 Personal Services 17 0.33 0.3173 Business Services 840 16.46 17.6975 Auto Repair 7 0.14 0.2478 Movies 14 0.27 0.6579 Amusements 56 1.10 1.1580 Health 88 1.72 1.7282 Educational 21 0.41 0.4283 Social 15 0.29 0.2687 Engineering – Retail 147 2.88 2.4699 Nonclassifiable 11 0.22 0.70

Panel B: Distributional statistics for firm characteristics

Variable Mean Std. Dev. Q1 Median Q3

Earnings -0.048 0.229 -0.097 0.020 0.072

CFO 0.041 0.196 -0.014 0.070 0.140

Book-to-Market 0.784 0.856 0.283 0.520 0.944

Log(Market Value) 5.641 2.038 4.240 5.602 6.933

CFO is the operating cash flows (item 308). Earnings is income before extraordinary items (item 123) as reported on the statement of cash flows. Book-to-Market ( BM ) is the book to market ratio calculated as the book value of common equity (item 60)divided by market capitalization at the end of the fiscal year (item 25 * item 199).CFO and Earnings are scaled by average total assets using assets from the start and end of the fiscal year.



Table 2

Descriptive statistics for the audit fee variables.

Analysis of various audit fee measures for our sample of 5,103 firm year observations

with available Compustat and Standard and Poors audit fee data in 2000 and 2001.

Panel A: Distributional statistics


RATIO 0.483 0.222 0.317 0.496 0.660

TOTFEE 0.032 0.067 0.005 0.011 0.026

NONAUDFEE 0.020 0.047 0.002 0.005 0.015

Panel B: Correlations

Audit Fee Variable

RATIO TOTFEE NONAUDFEE RATIO - 0.550** 0.764**

TOTFEE 0.321** - 0.940**

NONAUDFEE 0.391** 0.978** -

** (*) Significant at the 1% (5%) level.Pearson correlation coefficients are reported below the diagonal and Spearman above.

RATIO is the ratio of fees paid to auditors for non-audit services divided by the total fees (the sum of auditand non-audit fees) paid to auditors.TOTFEE is the ratio of total fees (the sum of audit and non-audit fees) paid to the auditor, to the totalrevenue received that year by the auditor.

NONAUDFEE is the ratio of non-audit fees paid to the auditor, to the total revenue received that year bythe auditor.



Table 3

Analysis of model of abnormal accruals using data from 1988-2001.

Panel A: Mean coefficient estimates for accrual models based on 648 two-digit SIC-

year regressionsIndependent Variables

∆ Sales-∆ REC PPE BM CFOAdj. R 2

0.060(11.62)

-0.014(-3.40)

-0.010(-5.63)

-0.376(-31.71)

0.301

Panel B: Adjusted R 2

Across 648 industry-year regressions

Distribution Statistics

Mean Std. Dev. Q1 Median Q3

0.301 0.249 0.109 0.256 0.477

Panel C: Distributional statistics for 5,103 firm year observations


Accruals 0.00 0.13 -0.03 0.01 0.06

|Accruals| 0.08 0.10 0.02 0.05 0.10

Parameter estimates are averages from the respective 648 two-digit SIC-year regressions. T-statistics arereported in parentheses below parameter estimates. Standard errors are based on the distribution of two-digit SIC-year parameter estimates.

The accrual model is estimated using the Jones (1991) technique of decomposing total accruals into anormal (expected) and abnormal (unexpected) component. The method of decomposition is as follows:


TA is the difference between operating cash flows (item 308) and income before extraordinary items (item123) as reported on the statement of cash flows.

∆Sales is the change in sales (item 12) for the year.

∆ REC is the change in receivables reported on the statement of cash flows (item 302) for the year. PPE is the gross amount of property, plant and equipment (item 7).CFO is the operating cash flows (item 308).

Book-to-Market ( BM ) is the book to market ratio calculated as the book value of common equity (item 60)divided by market capitalization at the end of the fiscal year (item 25 * item 199).

Accruals is the residual from equation (1) above, | Accruals| is the absolute value of the residual fromequation (1) above. This is our estimate of abnormal accruals.All variables used in the abnormal accrual model (except BM ) are scaled by average total assets usingassets from the start and end of the fiscal year (item 6).



Table 4

Descriptive statistics for the governance variables.

Analysis of various corporate governance measures for our sample of 5,103 firm year

observations with available Compustat and Audit Fee data in 2000 and 2001.

Panel A: Distributional statistics


Institutional Holdings 0.436 0.262 0.218 0.427 0.655

Insider Holdings 0.164 0.213 0.012 0.076 0.233

Board Composition 0.601 0.186 0.333 0.615 0.750

Panel B: Correlations

Governance Variable

Institutional

Holdings Insider Holdings Board Composition

Institutional

Holdings- -0.307** 0.248**

Insider Holdings -0.313** - -0.300**

Board Composition 0.239** -0.272** -

** (*) Significant at the 1% (5%) level.Pearson correlation coefficients are reported below the diagonal and Spearman above.

Institutional holdings are the fraction of outstanding shares that are held by institutions (as reported byWorldScope). Insider holdings are the fraction of outstanding shares that are held by insiders (as reported byWorldScope).

Board Composition is the fraction of directors serving on the board who are independent frommanagement.



Table 5

Pooled regression analysis.

Analysis of the relation between various audit fee and accrual measures for our sample of 5,103 firm year observations with available Compustat and Audit Fee data in 2000 and

2001.

Accrual Measurei = α + β Audit Fee Measurei + ε i (3)

Panel A: Directional Accrual Measure ( Accruals)

Audit Fee Measure

RATIO TOTFEE NONAUDFEE

α 0.002(0.45) -0.001(-0.58) -0.001(-0.36)

β -0.002(-0.29)

0.063(2.31)

0.077(1.99)

R 2

0.000 0.001 0.001

Panel B: Non-Directional Accrual Measure (|Accruals|)

Audit Fee Measure


α 0.077(23.20)

0.087(56.17)

0.086(56.84)

β 0.011(1.69)

-0.130(-6.21)

-0.172(-5.81)

R 2 0.000 0.007 0.006



Panel C: Positive Abnormal Accruals Only ( Accruals+)

Audit Fee Measure


α 0.068(19.26) 0.075(45.99) 0.075(46.72)

β 0.010(1.43)

-0.075(-3.52)

-0.104(-3.41)

R 2 0.000 0.004 0.004

Panel D: Negative Abnormal Accruals Only ( Accruals-)

Audit Fee Measure


α -0.091(-14.74)

-0.102(-35.92)

-0.101(-36.23)

β -0.011(-0.97)

0.205(5.09)

0.265(4.66)

R 2 0.000 0.011 0.009

Parameter estimates are for the pooled sample of 5,103 observations form the fiscal year ended 2000 and2001. T-statistics are reported in parentheses below parameter estimates.

RATIO is the ratio of fees paid to auditors for non-audit services divided by the total fees (the sum of auditand non-audit fees) paid to auditors.

TOTFEE is the ratio of total fees (the sum of audit and non-audit fees) paid to the auditor, to the totalrevenue received that year by the auditor. NONAUDFEE is the ratio of non-audit fees paid to the auditor, to the total revenue received that year bythe auditor.


TA is the difference between operating cash flows (item 308) and income before extraordinary items (item123) as reported on the statement of cash flows.


∆ REC is the change in receivables reported on the statement of cash flows (item 302) for the year. PPE is the gross amount of property, plant and equipment (item 7).CFO is the operating cash flows (item 308).

Book-to-Market ( BM ) is the book to market ratio calculated as the book value of common equity (item 60)divided by market capitalization at the end of the fiscal year (item 25 * item 199). Accruals is the residual from equation (1) above, | Accruals| is the absolute value of the residual fromequation (1) above. Accruals+ is equal to Accruals when Accruals>0 and zero otherwise. Accruals- isequal to Accruals when Accruals<0 and zero otherwise.All variables used in the abnormal accrual model (except BM ) are scaled by average total assets usingassets from the start and end of the fiscal year.



Table 6

Latent Class Mixture Analysis – Using RATIO.

Panel A: Accruals = α + β RATIO

Cluster α β % SampleBook-to-Market

Log (MarketValue)

BoardComposition

I0.019(4.58)

-0.008(-0.96)

37.1 0.810 5.922 0.610

II0.019(2.80)

0.006(0.46)

44.3 0.771 5.462 0.586

III-0.079(-3.63)

0.001(0.02)

18.6 0.708 4.952 0.607

Model R 2

= 0.086883 F-test 3.54* 65.52** 6.09**

Panel B: |Accruals| = α + β RATIO


Log (MarketValue)

BoardComposition

I0.282

(13.25)0.081(2.21)

8.5 0.697 5.025 0.609

II0.126

(17.60)0.007(0.61)

21.9 0.736 5.177 0.582

III0.039

(22.08)0.001(0.02) 69.6 0.806 5.829 0.605

Model R 2 = 0.675570 F-test 4.58* 60.15** 3.74*



Panel C: Accruals+ = α + β RATIO

Cluster α β % Sample

Book-to-

Market

Log (Market

Value)

Board

Composition

I0.310(7.14)

0.061(0.80)

3.5 0.597 5.310 0.578

II0.122

(14.30)0.010(0.71)

24.3 0.708 5.370 0.594

III0.042

(18.47)-0.001(-0.01)

72.2 0.797 5.897 0.599

Model R 2 = 0.617301 F-test 4.16* 18.45** 0.35

Panel D: Accruals-= α + β RATIO


Log (MarketValue)

BoardComposition

I-0.281

(-10.72)-0.081(-1.82)

14.8 0.724 4.944 0.613

II-0.137

(-10.42)-0.002(-0.07)

18.6 0.765 4.839 0.574

III-0.034

(-12.25)-0.001(-0.17)

66.6 0.812 5.703 0.612

Model R 2 = 0.709783 F-test 1.63 37.19** 3.73*

** (*) Significant at the 1% (5%) level.

Model R 2 is computed as 1 – L/L0 , where L is the maximum for the log-likelihood for the threecluster solution and L0 is log-(intercept only).



RATIO is the ratio of fees paid to auditors for non-audit services divided by the total fees (the sum of audit and non-audit feesTOTFEE is the ratio of total fees (the sum of audit and non-audit fees) paid to the auditor, to the total revenue received that ye

NONAUDFEE is the ratio of non-audit fees paid to the auditor, to the total revenue received that year by the auditor. Book-to-Market ( BM ) is the book to market ratio calculated as the book value of common equity (item 60) divided by marketfiscal year (item 25 * item 199).

Board Composition is the fraction of directors serving on the board who are independent from management. Institutional Holdings is the fraction of outstanding shares that are held by institutions (as reported by WorldScope). Insider Holdings is the fraction of outstanding shares that are held by insiders (as reported by WorldScope).

The accrual model is estimated using the Jones (1991) technique of decomposing total accruals into a normal (expected) and

The method of decomposition is as follows: TA = α + β 1( ∆Sales-∆ REC) + β 2 PPE + β 3 BM + β 4CFO + ε

TA is the difference between operating cash flows (item 308) and income before extraordinary items (item 123) as reported on


∆ REC is the change in receivables reported on the statement of cash flows (item 302) for the year. PPE is the gross amount of property, plant and equipment (item 7).CFO is the operating cash flows (item 308).All variables used in the abnormal accrual model (except BM ) are scaled by average total assets using assets from the start an

Accruals is the residual from equation (1) above, | Accruals| is the absolute value of the residual from equation (1) above. Acc

Accruals>0 and zero otherwise. Accruals- is equal to Accruals when Accruals<0 and zero otherwise.



Table 7

Latent Class Mixture Analysis – Using TOTFEE

Panel A: Accruals = α + β TOTFEE


Log (MarketValue)

BoardComposition

I0.022(6.98)

-0.002(-0.03)

44.7 0.770 5.475 0.588

II0.016(8.37)

-0.015(-0.58)

36.6 0.811 5.923 0.609

III-0.086(-8.00)

0.326(1.35)

18.7 0.709 4.944 0.606

Model R 2

= 0.090106 F-test 3.71* 65.31** 5.09**

Panel B: |Accruals| = α + β TOTFEE


Log (MarketValue)

BoardComposition

I0.135

(33.29)-0.163(-4.02)

22.2 0.727 5.218 0.583

II0.040

(43.31)-0.028(-2.27)

69.4 0.805 5.799 0.604

III0.331

(25.08)-0.342(-1.44) 8.3 0.699 5.048 0.608

Model R 2 = 0.674633 F-test 4.91** 47.07** 3.18*



Panel C: Accruals+ = α + β TOTFEE

Cluster α β % Sample

Book-to-

Market

Log (Market

Value)

Board

Composition

I0.131

(26.97)-0.114(-2.47)

24.5 0.709 5.399 0.593

II0.043

(36.37)-0.024(-1.62)

72.2 0.797 5.889 0.599

III0.354

(11.71)-0.258(-0.43)

3.3 0.616 5.282 0.559

Model R 2 = 0.619948 F-test 3.73* 16.44** 1.02

Panel D: Accruals-= α + β TOTFEE


Log (MarketValue)

BoardComposition

I-0.144

(-20.85)0.286(2.66)

18.9 0.758 4.862 0.574

II-0.326

(-21.96)0.349(1.30)

14.8 0.722 4.997 0.617

III-0.036

(-24.56)0.030(1.36)

66.3 0.819 5.688 0.611

Model R 2 = 0.707190 F-test 1.95 32.76** 3.73*


Model R 2 is computed as 1 – L/L0 , where L is the maximum for the log-likelihood for the three cluster solution and L0 is log-(intercept only).




NONAUDFEE is the ratio of non-audit fees paid to the auditor, to the total revenue received that year by the auditor. Book-to-Market ( BM ) is the book to market ratio calculated as the book value of common equity (item 60) divided by marketfiscal year (item 25 * item 199).

Board Composition is the fraction of directors serving on the board who are independent from management. Institutional Holdings is the fraction of outstanding shares that are held by institutions (as reported by WorldScope). Insider Holdings is the fraction of outstanding shares that are held by insiders (as reported by WorldScope).


The method of decomposition is as follows: TA = α + β 1( ∆Sales-∆ REC) + β 2 PPE + β 3 BM + β 4CFO + ε

TA is the difference between operating cash flows (item 308) and income before extraordinary items (item 123) as reported on



Accruals is the residual from equation (1) above, | Accruals| is the absolute value of the residual from equation (1) above. Acc

Accruals>0 and zero otherwise. Accruals- is equal to Accruals when Accruals<0 and zero otherwise.



Table 8

Latent Class Mixture Analysis – Using NONAUDFEE.

Panel A: Accruals = α + β NONAUDFEE


Log (MarketValue)

BoardComposition

I0.022(7.19)

-0.004(-0.06)

45 0.771 5.476 0.588

II0.016(8.43)

-0.021(-0.57)

36 0.810 5.923 0.609

III-0.084(-7.95)

0.396(1.05)

19 0.711 4.949 0.607

Model R 2

= 0.089009 F-test 3.51* 64.90** 4.72**

Panel B: |Accruals| = α + β NONAUDFEE


Log (MarketValue)

BoardComposition

I0.133

(33.06)-0.206(-3.65)

22.2 0.726 5.231 0.582

II0.039

(44.06)-0.038(-2.25)

69.4 0.806 5.797 0.605

III0.330

(25.68)-0.529(-1.46)

8.4 0.702 5.033 0.608

Model R 2 = 0.674277 F-test 4.99** 46.07** 3.42*



Panel C: Accruals+

= α + β NONAUDFEE

Cluster α β % Sample Book-to-Market

Log (MarketValue)

BoardComposition

I0.130

(26.88)-0.151(-2.25)

24.5 0.710 5.392 0.591

II0.042

(36.96)-0.033(-1.60)

72.2 0.797 5.890 0.599

III0.357

(11.61)-0.646(-0.46)

3.3 0.599 5.298 0.568

Model R 2

= 0.620519 F-test 3.91* 16.68** 0.75

Panel D: Accruals

-

= α + β NONAUDFEE


Log (MarketValue)

BoardComposition

I-0.142

(-20.42)0.334(2.65)

18.9 0.754 4.879 0.574

II-0.325

(-22.41)0.470(1.26)

14.8 0.726 4.982 0.617

III-0.036

(-25.05)0.043(1.40)

66.3 0.819 5.687 0.611

Model R 2 = 0.706307 F-test 1.94 32.12** 3.82*


Model R 2 is computed as 1 – L/L0 , where L is the maximum for the log-likelihood for the three cluster solution and L0 is log-(intercept only).




NONAUDFEE is the ratio of non-audit fees paid to the auditor, to the total revenue received that year by the auditor. Book-to-Market ( BM ) is the book to market ratio calculated as the book value of common equity (item 60) divided by market

fiscal year (item 25 * item 199). Board Composition is the fraction of directors serving on the board who are independent from management. Institutional Holdings is the fraction of outstanding shares that are held by institutions (as reported by WorldScope). Insider Holdings is the fraction of outstanding shares that are held by insiders (as reported by WorldScope).


The method of decomposition is as follows: TA = α + β 1( ∆Sales-∆ REC) + β 2 PPE + β 3 BM + β 4CFO + ε TA is the difference between operating cash flows (item 308) and income before extraordinary items (item 123) as reported on



Accruals is the residual from equation (1) above, | Accruals| is the absolute value of the residual from equation (1) above. Acc Accruals>0 and zero otherwise. Accruals- is equal to Accruals when Accruals<0 and zero otherwise.



Table 9

Audit fee regression models for our sample of firm observations with available

Compustat and Standard and Poors audit fee data in 2000 and 2001. Regressions

are run separately for each industry group.

Log(Fee) = φ 0 + φ 1 Log(Assets) + φ 2 Log(Segments) + φ 3 Inventory + φ 4 Receivables +φ 5 Debt + φ 6 Income + φ 7 LOSS + φ 8Opinion + ε (4)

Panel A: Total Fees

Independent

Variable

Mean

Estimate

T Statistic

(minimum)

T Statistic

(maximum)

Intercept 2.154 6.19 45.49 Log(Assets) 0.626 13.15 96.63

Log(Segments) 0.156 0.97 7.13 Inventory 1.187 1.05 7.72

Receivables 1.250 0.04 0.29

Debt 0.120 0.19 1.40 Income -0.286 -0.67 -4.92 LOSS 0.155 0.72 5.29

Opinion 0.187 1.02 7.50

Mean Adjusted R2 0.749

Panel B: Non-audit Fees

Independent

Variable

Mean

Estimate

T Statistic

(minimum)

T Statistic

(maximum)

Intercept 0.042 0.38 2.79 Log(Assets) 0.835 9.13 67.09

Log(Segments) 0.155 0.31 2.28 Inventory 0.837 0.52 3.82

Receivables -0.537 -0.25 -1.84 Debt -0.002 0.03 0.22

Income 0.143 -0.20 -1.47 LOSS 0.188 0.41 3.01

Opinion 0.072 0.28 2.06

Mean Adjusted R2 0.587

Mean coefficients are based on industry level regressions, reported T-statistics (minimum) are the mean t-statistics across the industry level regressions, and T-statistics (maximum) are the mean t-statistics acrossthe industry level regressions multiplied by the square root of the number of industries used to compute themean ( 54 ).

The estimated residual from equation (4) is our proxy measure for “abnormal” fees. To transform this to adollar amount we raise exp to the power of the predicted value of Log(Fee) and then subtract this valuefrom the dollar fee. The result (after deflating by auditor firm revenue) is denoted as ABTOTFEE ( ABNONAUDFEE) for total fees and non-audit fees.



Log(Assets) is the natural logarithm of total assets (Compustat data item # 6). Log(Segments) is the natural logarithm of the number of business segments reported on the CompustatSegment Data File.

Inventory is the ratio of the dollar value of inventory (item 3) to total assets (item 6). Receivables is the ratio of the dollar value of accounts receivable (item 2) to total assets (item 6). Debt is the sum of short term debt (item 34) and long term debt (item 9) to total assets (item 6).

Income is the ratio of operating income after depreciation (item 178) to average total assets (item 6). LOSS is an indicator variable equal to one if the firm reports negative Income in any of the previous threeyears and zero otherwise.Opinion is an indicator variable equal to one of the firm receives a qualified audit opinion and zerootherwise. A qualified audit opinion is defined as anything other than the standard unqualified auditopinion coded as “one” by Compustat.



Table 10

Latent Class Mixture Analysis - Abnormal audit fees.

Panel A: Accruals = α + β 1 HighABTOTFEE + β 2 LowABTOTFEE

Cluster α β1 β

2

%

Sample

I-0.084(-7.63)

0.584(0.82)

-0.946(-0.94)

19.1

II0.021(7.23)

0.031(0.29)

-0.134(-0.37)

48.2

III0.016(7.57)

0.014(0.23)

0.186(0.91)

32.7

Model R 2 = 0.086791

Panel B: |Accruals| = α + β 1 HighABTOTFEE + β 2 LowABTOTFEE

Cluster α

β1 β2

%

Sample

I0.136

(73.48)-0.228(-4.20)

1.301(6.63)

22.1

II0.040

(65.45)-0.044(-2.43)

0.184(3.06)

69.6

III0.331

(39.33)-0.689(-2.02)

0.413(0.47)

8.3

Model R 2

= 0.676026

Panel C: Accruals-= α + β 1 HighABTOTFEE + β 2 LowABTOTFEE

Cluster α β1 β2

%

Sample

I-0.147

(-21.47)0.398(2.08)

-1.854(-3.12)

19.9

II-0.036

(-25.03)0.069(1.30)

-0.210(-1.40)

66.4

III-0.331

(-20.92)0.558(0.74)

0.739(0.50)

13.7

Model R 2

= 0.717128

Panel D: Accruals+

= α + β 1 HighABTOTFEE + β 2 LowABTOTFEE

Cluster α β1 β2 %

Sample

I0.131

(55.06)-0.175(-2.75)

0.721(2.80)

24.2

II0.043

(53.78)-0.032(-1.50)

0.154(1.90)

72.4

III0.359

(17.57)-0.691(-0.86)

2.048(1.02)

3.4

Model R 2 = 0.622091



Panel E: Accruals = α + β 1 HighABNONAFEE + β 2 LowABNONAFEE


Sample

I0.017

(16.46)-0.026(-0.66)

0.325(3.24)

32.9

II -0.084(-9.66)

0.799(1.71)

-1.167(-1.04)

19.1

III0.022

(11.39)0.015(0.18)

0.026(0.12)

48.0

Model R 2 = 0.087216

Panel F: |Accruals| = α + β 1 HighABNONAFEE + β 2 LowABNONAFEE


Sample

I0.135

(33.18)-0.299(-2.29)

1.453(4.00)

22.1

II 0.040(43.80)

-0.086(-2.36)

0.242(2.32)

69.6

III0.330

(24.96)-0.950(-1.08)

0.474(0.40)

8.3

Model R 2

= 0.674661

Panel G: Accruals-= α + β 1 HighABNONAFEE + β 2 LowABNONAFEE


Sample

I-0.146

(-21.96)0.562(2.21)

-2.219(-2.19)

19.2

II -0.036(-25.11)

0.103(1.45)

-0.210(-1.20)

66.5

III-0.328

(-22.28)0.877(1.00)

0.361(0.29)

14.3

Model R 2

= 0.712791

Panel H: Accruals+

= α + β 1 HighABNONAFEE + β 2 LowABNONAFEE


Sample

I0.043

(37.24)

-0.080

(-1.89)

0.254

(1.90)72.5

II0.131

(26.58)-0.227(-1.45)

0.800(2.00)

24.2

III0.360

(11.14)-0.945(-0.31)

1.703(0.32)

3.3

Model R 2 = 0.621562



Model R 2 is computed as 1 – L/L0 , where L is the maximum for the log-likelihood for the three cluster solution and L0 is log-likelihood for the null model (intercept only).

Log(Fee) = φ 0 + φ 1 Log(Assets) + φ 2 Log(Segments) + φ 3 Inventory + φ 4 Receivables + φ 5 Debt + φ 6 Income +

φ 7 LOSS + φ 8Opinion + ε (4)

The estimated residual ( ε̂ ) from the above equation is our proxy measure for “abnormal” fees. To

transform this to a dollar amount we raise exp to the power of the predicted value of Log(Fee) and thensubtract this value from the dollar fee. The result (after deflating by auditor firm revenue) is denoted as

ABTOTFEE ( ABNONAUDFEE) for total fees and non-audit fees. Log(Assets) is the natural logarithm of total assets (Compustat data item # 6). Log(Segments) is the natural logarithm of the number of business segments reported on the CompustatSegment Data File.

Inventory is the ratio of the dollar value of inventory (item 3) to total assets (item 6). Receivables is the ratio of the dollar value of accounts receivable (item 2) to total assets (item 6). Debt is the sum of short term debt (item 34) and long term debt (item 9) to total assets (item 6). Income is the ratio of operating income after depreciation (item 178) to average total assets (item 6). LOSS is an indicator variable equal to one if the firm reports negative Income in any of the previous threeyears and zero otherwise.Opinion is an indicator variable equal to one of the firm receives a qualified audit opinion and zero

otherwise. A qualified audit opinion is defined as anything other than the standard unqualified auditopinion coded as “1” by Compustat.

HighABTOTFEE ( HighABNONAUDFEE ) is equal to ABTOTFEE ( ABNONAUDFEE ) when ABTOTFEE ( ABNONAUDFEE ) is greater than zero, and zero otherwise.

LowABTOTFEE ( LowABNONAUDFEE ) is equal to ABTOTFEE ( ABNONAUDFEE ) when ABTOTFEE ( ABNONAUDFEE ) is less than or equal to zero, and zero otherwise.

Book-to-Market ( BM ) is the book to market ratio calculated as the book value of common equity (item 60)divided by market capitalization at the end of the fiscal year (item 25 * item 199).

Board Composition is the fraction of directors serving on the board who are independent frommanagement.

Institutional Holdings is the fraction of outstanding shares that are held by institutions (as reported byWorldScope).

Insider Holdings is the fraction of outstanding shares that are held by insiders (as reported by WorldScope).

The accrual model is estimated using the Jones (1991) technique of decomposing total accruals into anormal (expected) and abnormal (unexpected) component. The method of decomposition is as follows:

TA = α + β 1( ∆Sales-∆ REC) + β 2 PPE + β 3 BM + β 4CFO + ε (2)TA is the difference between operating cash flows (item 308) and income before extraordinary items (item123) as reported on the statement of cash flows.


∆ REC is the change in receivables reported on the statement of cash flows (item 302) for the year. PPE is the gross amount of property, plant and equipment (item 7).CFO is the operating cash flows (item 308).All variables used in the abnormal accrual model (except BM) are scaled by average total assets usingassets from the start and end of the fiscal year.

Auditores e Nivel de Accrual

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