审计外文文献

Do Nonaudit Services Compromise Auditor Independence? Further Evidence

Hollis Ashbaugh

University of Wisconsin – Madison

Ryan LaFond

University of Wisconsin – Madison

Brian W. Mayhew*

University of Wisconsin – Madison

February 20, 2003

* Corresponding Author

Brian W. Mayhew

University of Wisconsin – Madison

975 University Avenue

Madison, WI 53706

608-262-2714

bmayhew@https://www.wendangku.net/doc/4f4521020.html,

We acknowledge the financial support in collecting the data for this study provided by the Department of Accounting at the University of Wisconsin – Madison and its many supporters. We appreciate the comments received at the 2002 American Accounting Association’s Annual Meeting, 2002 University of Illinois Audit Symposium, University of Missouri, University of Wisconsin – Madison, and from Jere Francis, Karla Johnstone, Bill Kinney, Joel Pike and Terry Warfield.

Do Nonaudit Services Compromise Auditor Independence? Further Evidence ABSTRACT: This paper challenges the findings of Frankel, Johnson and Nelson (FJN) (2002). The results of our discretionary accruals tests differ from FJN’s when we adjust discretionary current accruals for firm performance. In our earnings benchmark tests, in contrast to FJN we find no statistically significant association between firms meeting analyst forecasts and auditor fees. Our market reaction tests also provide different results than those reported by FJN. Overall, our study indicates that FJN’s results are sensitive to research design choices, and we find no systematic evidence supporting their claim that auditors violate their independence as a result of clients purchasing relatively more nonaudit services.

Key Words: independence, audit fees, discretionary accruals, biased financial reporting

Data Availability: Data collected from public sources. Authors will make sample firms available upon request.

Do Nonaudit Services Compromise Auditor Independence? Further Evidence

I. INTRODUCTION

Regulators, financial statement users, and researchers are concerned that auditors compromise their independence by allowing high fee clients more financial statement discretion relative to low fee clients. Frankel, Johnson and Nelson (2002) (hereafter referred to as FJN) use the association between audit firm fees and two measures of biased financial reporting - firms’ discretionary accruals and the likelihood of firms meeting earnings benchmarks - to draw inferences on auditor independence. In addition, they conduct an event study to investigate whether the market reacts to the disclosure of auditor fees. FJN suggest that their results provide evidence that auditor independence is compromised when clients pay high nonaudit fees relative to total fees. We conduct the same three sets of empirical tests as FJN to investigate the sensitivity of FJN’s results to research design choices.

The motivation for FJN’s study and our own is based on the U.S. Securities and Exchange Commission’s (SEC) concern about the growth of nonaudit fees relative to audit fees during the 1990s (e.g., see Levitt 2000). The SEC’s concern that the growth in the provision of nonaudit services compromises audit firm independence is based on the premise that the provision of nonaudit services increases the fees paid to the audit firm thereby increasing the economic dependence of the audit firm on the client. Prior research agrees that it is the strength of the economic bond between the audit firm and its client that reduces auditor independence (DeAngelo 1981; Beck et al. 1988; Magee and Tseng 1990). Yet, there is dissention on how to measure the economic bond. Much of the contemporaneous research investigating whether the provision of nonaudit services decreases auditor independence uses the ratio of nonaudit fees to total fees (hereafter referred to as the fee ratio) as the measure of the economic bond.1 However, the fee ratio

does not capture the economic importance of the client to the audit firm when the total client fees are immaterial to the audit firm.

Consider the following. Two firms in our sample have a fee ratio of 73%. One firm reports total fees of $71,000 and the other firm reports total fees of $5.7 million. Based on their fee ratios, both firms are considered threats to independence, whereas only the latter is economically significant to the auditor. Thus, we posit that the total fee, i.e., the sum of audit and nonaudit fees, rather than the fee ratio is the more appropriate measure of the economic dependence of the auditor on a client, although, we include the fee ratio in our empirical analyses to provide a complete assessment of FJN’s results.

Our first set of tests examine whether FJN’s discretionary accruals results are sensitive to the measurement of discretionary accruals. Recent research suggests that the discretionary accrual measure used by FJN systematically rejects the null hypothesis of no-biased financial reporting at much higher than stated rates because the measure does not account for the impact of performance on accruals (Kothari et al. 2002). We calculate two measures of discretionary current accruals that control for the impact of firm performance in the estimation of discretionary accruals. The first method measures performance adjusted discretionary current accruals as the difference between a firm’s estimated discretionary current accruals and the median discretionary current accruals from an industry and performance matched portfolio. The second approach follows Kothari et al. (2002) and includes a control for firm performance in the regression model used to estimate nondiscretionary current accruals.

Consistent with FJN, we document a positive association between the absolute value of firms’ discretionary current accruals and fee ratio, regardless of which performance adjusted accrual measure is used. We also find, like FJN, no evidence that firms’ total fees are associated with firms’ discretionary current accruals. However, in contrast to FJN, when we partition sample firms based

on whether they report income-increasing or income-decreasing discretionary accruals, and we employ our performance adjusted measures of current discretionary accruals, we find no association between the fee ratio and income-increasing discretionary accruals. Further analysis suggests the measurement error caused by not controlling for firm performance in the estimate of income increasing discretionary accruals is associated with the fee ratio. Thus we provide an explanation for why FJN find an association between the fee ratio and income-increasing discretionary accruals whereas we do not.

Our results indicate that the association between the fee ratio and the absolute value of discretionary accruals is driven by income-decreasing discretionary accruals. While income-decreasing accruals can be interpreted as a form of biased financial reporting, income-decreasing accruals also reflect a conservative application of generally accepted accounting principles (GAAP). Typically regulators and financial statement users are more concerned with the opportunistic application of GAAP than the conservative application of GAAP, with the opportunistic application of GAAP more likely signaling problems with auditor independence (Becker et al. 1998).2 FJN also use the relation between audit firm fees and two earnings benchmarks – small earnings increases and meeting analyst forecasts - to draw inferences on audit firm independence. Our second set of tests replicate FJN’s earnings benchmark tests. Consistent with FJN, as well as with Francis and Ke (2001), we find no association between fee ratio and the likelihood of firms reporting small earnings increases. In addition, we, like FJN, document a negative association between total fees and the likelihood of firms reporting small earnings increases. However, unlike FJN, we find no statistically significant association between either the fee ratio or total fees, and firms meeting analyst forecasts.

In summary, based on the use of discretionary accruals and earnings benchmarks as proxies for biased financial reporting, we find little evidence supporting the claim that auditors violate their

independence as the result of clients paying high fees or having high fee ratios. Our discretionary accrual analyses appear consistent with concurrent research examining audit firm fees and measures of earnings management. Chung and Kallapur (2003) find no association between their audit fee metrics and the absolute value of discretionary accruals measured with the modified Jones model.3 They also explore four different sub-sets of firms where they expect firms to have greater incentives to manage earnings (i.e., firms with high market-to-book ratios, high leverage, low audit firm industry specialization, and high insider influence on the Board of Directors) and find no evidence of an association between audit or nonaudit fees and earnings manipulation. The inferences drawn from our discretionary accrual and earnings benchmark tests are also consistent with those reported by DeFond et al. (2002) who show no association between firms’ going concern opinions and magnitude of nonaudit services, and a positive association between the likelihood of issuing a going concern opinion and audit fees.

In addition to supporting the findings of concurrent audit research, the results of our discretionary accrual analysis extend the contemporaneous literature that addresses the estimation of firms’ discretionary accruals. Our results indicate that controlling for performance is important when estimating managers’ discretionary reporting practices. We also provide empirical evidence that measurement error in discretionary accruals can lead to erroneous inferences when the measurement error is correlated with the test variable.

The last set of tests that we conduct investigates the market’s reaction to the disclosure of auditor fees in firms’ proxy statements. FJN report that they find a significant negative association between share prices and the disclosure of higher than expected nonaudit fees, albeit they state the association is small in economic terms and insignificant when measured over long event windows. We have two primary concerns related to FJN’s market reaction analysis. First, when we calculate abnormal returns on firms’ proxy filing dates, we find that the 1-day market adjusted return is less

than a half of one-percent, and is not significantly different from zero. Our second concern is that firms are required to make a substantial number of disclosures in their proxy statements. Efforts to examine the relation between fee metrics and changes in market value without controlling for the other information contained in proxy statements can result in incorrect inferences being drawn on whether the market reacts to the disclosure of auditor fees. When we conduct market reaction tests examining the difference in abnormal returns on the filing of a firm’s 2000 versus 1999 proxy statements, unlike FJN, we find no evidence that the market, on average, reacts to the fee ratio. We posit that it is difficult to draw reliable conclusions about the market’s reaction to auditor fee disclosures without controlling for the firm-specific disclosures contained in proxy statements.

The paper proceeds as follows. The next section discusses the alternative ways to combine fee data to capture threats to auditor independence. Section III describes our sample and identifies the determinants of auditor fees that we need to control for in our empirical tests. Section IV presents the research design and results. The last section summarizes our findings relative to those of FJN.

II. MEASURING THREATS TO AUDITOR INDEPENDENCE DeAngelo (1981) models that as the economic bond between the audit firm and client increases the audit firm’s dependence on the client increases.4 Nonaudit fees further increase the client auditor bond by increasing the portion of audit firm wealth derived from a client (Simunic 1984; Beck et al. 1988). Nonaudit fees can also threaten independence when clients use them as contingent fees. Magee and Tseng (1990) note that while contingent fees are explicitly prohibited by audit standards, clients can create contingent fees by withholding profitable nonaudit services when the auditor does not allow the client to report its preferred financial condition. We argue that the sum of audit and nonaudit fees, i.e., total fees, best captures the explicit economic bond between the audit firm and client. We do not advocate the use of the fee ratio because the fee ratio does not

necessarily capture the economic importance of the client to the audit firm. The fee ratio does, however, capture the relative monetary value of the audit versus nonaudit services provided by the audit firm to a client, which may have an impact on the perception of independence held by regulators, as well as the general public. As stated earlier, we include the fee ratio in our empirical analyses to be comparable to FJN and contemporaneous auditor independence research.

Our research design, like FJN, investigates whether the degree of client auditor bonding is associated with evidence that the financial statements reflect a biased view of the client’s financial condition. We both use the magnitude of discretionary accruals and the likelihood of meeting earnings benchmarks as evidence of biased financial statements. Discretionary accruals and earnings benchmarks can be viewed as evidence of auditor independence violations based on three assumptions. The first assumption is that an independent auditor requires his/her client to file objective (i.e. unbiased) financial statements. Second, discretionary accruals provide a measure of the degree of bias inserted into the financial statements by management and allowed by the auditor. Third, that just meeting or beating earnings benchmarks such as prior years’ earnings or current analyst forecasts is not random, suggesting that managers bias financial statements to meet earnings targets (see Burgstahler and Dichev 1997). An objective auditor will not allow clients to bias earnings to achieve these benchmarks even as his economic dependence on the client increases.

III. AUDITOR FEES

Sample and Descriptive Statistics

We collected fee data for 4959 firms from U.S. registrants’ 2000 proxy statements that were available on Edgar or Global Access during November and December of 2001. We deleted 761 financial firms and 1028 firms that lacked the necessary financial data on Compustat to estimate our fee models arriving at a final sample of 3,170 firms.

[Insert Table 1 here.]

Panel A of Table 1 reports descriptive statistics for the four audit fee metrics that we use in our empirical tests. AUDIT and NONAUDIT are the audit and nonaudit fees reported in sample firms’ proxy statements, respectively.5 TOTAL is the sum of AUDIT and NONAUDIT. FEERATIO is defined as NONAUDIT divided by TOTAL. We also report fee metrics partitioned by Big5 and non-Big5 clients, where Big5 is defined as the audit firms of Arthur Andersen, Deloitte & Touche, Ernst & Young, KPMG, or PriceWaterhouseCoopers. We partition the sample by auditor choice because our fee data indicate that the magnitude of audit and nonaudit fees differ between Big5 and non-Big5. The mean (median) of AUDIT for Big5 clients is $562,420 ($212,000) and the mean (median) of NONAUDIT is $1,332,408 ($240,100). In contrast, the mean (median) of AUDIT for non-Big5 clients is $149,371 ($88,750) and the mean (median) of NONAUDIT is $136,483 ($33,360), resulting in TOTAL for non-Big5 auditors being smaller than for Big5 auditors. The descriptive statistics also indicate that the mean (median) FEERATIO is significantly smaller for non-Big5auditors [30.32 percent (27.87 percent)] than Big 5 auditors [49.60 percent (51.90 percent), p< .001].

To better understand the relation between client size and the demand for audit related services, we report in Panel B of Table 1 the mean (median) values of FEERATIO,

TOTAL/ASSETS, BIG5, ASSETS, and MVE across TOTAL deciles. MVE is equal to a firm’s market value of equity defined as price per share at fiscal year end (Compustat data item 199) multiplied by the number of shares outstanding (Compustat data item 25) measured in millions of dollars. TOTAL is directly related to client size. FEERATIO, ASSETS, and MVE are increasing in TOTAL, and the proportion of firms contracting with a Big5 auditor is increasing in TOTAL.

Panel C of Table 1 reports the distribution of our sample firms across the industry classifications reported in FJN, and shows that our sample’s industry composition is closely aligned

to the industry composition in the Compustat database. Our sample’s descriptive statistics are

similar, in all respects, to FJN’s.

Fee Determinants

We investigate the determinants of auditor fees for two reasons. First, 2000 is the first year

auditor fee data has been widely available for U.S. publicly traded companies. We want to establish

that the determinants of auditor fees identified by prior research using fee data from foreign

countries, as well as U.S. data collected via surveys, are valid for our sample. Second, we want to

determine whether variables that prior research documents are correlated with discretionary accruals

are also correlated with auditor fees. Identification of such variables is necessary in order to

properly assess the association between discretionary accruals and auditor fees. Failure to control

for these variables can result in a correlated omitted variables problem.

Prior research models auditor related fees as a function of a firm’s auditor choice, audit

complexity, audit risk, and the demand for consulting services (e.g., Firth 1997). We estimate the

following regression:

ε

ββββββββββα∑=++++++++++++=13110987654321___ln 5i i MMIES INDUSTRYDU ITEM SPECIAL ROA NEGATIVE IN

AR ROA LEVERAGE MB FINANCING MERGER

MVE BIG FEE (1)

where:

FEE is set to FEERATIO, AUDIT, NONAUDIT, or TOTAL . With the exception of FEERATIO,

we take the natural log of each fee variable to normalize these variables’ distributions, allowing

the cross-sectional aggregation of observations;6

BIG5 is coded one if a firm is audited by Arthur Andersen, Deloitte & Touche, Ernst & Young,

KPMG, or PriceWaterhouseCoopers, and zero otherwise;

lnMVE is the natural log of MVE;

MERGER is equal to one if the firm is engaged in a merger or acquisition (as reported in AFTNT 1

of Compustat), and zero otherwise;

FINANCING is set to one if MERGER is not equal to one and any of the following conditions

apply: long term debt increased by 20 percent or more, number of shares outstanding increased

by 10 percent or more after controlling for stock splits, and/or the firm first appeared on the

Center for Research in Securities Prices monthly returns database during the fiscal year, and zero otherwise;

MB is the firm’s market-to-book ratio at its fiscal-year-end, which is defined as its MVE divided by stockholders’ equity of common shareholders (Compustat data item 60); LEVERAGE is equal to a firm’s total assets less stockholders’ equity of common shareholders divided by total assets (Compustat data item 6);

ROA is defined as net income before extraordinary items (Compustat data item 18) divided by beginning of the year total assets;

AR_IN is equal to the sum of a firm’s receivables (Computstat data item 2) and inventory (Computstat data item 3) divided by its total assets;

NEGATIVE_ROA is equal to one if the firm’s ROA is negative, and zero otherwise;

SPECIAL_ITEM is equal to one if the firm reports special items in year 2000 (Computstat data item 17), and zero otherwise.

We include industry dummies to control for cross industry differences in fees.7

BIG5 is in our model because the descriptive statistics reported in Panel B of Table 1 indicate that the proportion of firms audited by the Big5 is increasing in TOTAL. lnMVE and MB proxy for audit complexity. MERGER and FINANCING capture the demand for additional audit and consulting services associated with business combinations and obtaining capital. LEVERAGE, ROA, AR_IN, NEGATIVE_ROA, and SPECIAL_ITEM proxy for audit risk. MB, ROA, and NEGATIVE_ROA are also proxies for firm performance.

[Insert Table 2 here.]

Table 2 reports the results of estimating the fee regressions. In general, we find a positive relation between BIG5, lnMVE, MERGER, FINANCING, LEVERAGE, and SPECIAL_ITEM, and the four fee variables. We also find, in general, a negative relation between ROA and MB, and the four fee variables. The AUDIT and TOTAL models’ adjusted R2 s are 66 percent and 68 percent, respectively. In contrast, the NONAUDIT and FEERATIO models’ R2 s are 34 percent and 28 percent, respectively. The relatively low R2 s of the NONAUDIT and FEERATIO models is partially due to the 138 sample firms that do not purchase nonaudit services from their auditors. When we eliminate the firms that report zero NONAUDIT from the analysis, we find the R2 of the NONAUDIT model to be similar to the R2’s of the audit and total fee model. We include these

firms in our empirical tests because we want our sample to be representative of the population of publicly traded firms, thereby increasing the external validity of our results.8

The results of our fee analysis indicate that poorly performing firms, as proxied by MB, ROA, and NEGATIVE_ROA, pay higher audit and nonaudit fees. The payment of higher fees by these firms may threaten auditor independence. Alternatively, auditors may be pricing the additional risks associated with poorly performing clients.

IV. EMPIRICAL ANALYSES

The Association Between Discretionary Accruals and Auditor Fees

FJN examine the relation between estimated discretionary accruals and auditor fees to provide evidence on whether the provision of nonaudit services is associated with earnings management. FJN investigate three specifications of auditor fees; (1) a client’s FEERATIO, (2) the percentile rank, by audit firm, of a client’s nonaudit (RANKNON) and audit (RANKAUD) fees, and (3) the percentile rank, by audit firm, of a client’s total fees (RANKTOT). In their primary analysis, FJN estimate discretionary accruals with a cross-sectional modified Jones model that controls for the variation in accruals across industry classifications (see FJN for details on the cross-sectional modified Jones model). FJN report a positive and significant association between discretionary accruals and FEERATIO after controlling for factors that are correlated with firms’ discretionary accruals (see the Appendix for details on FJN’s OLS model). They also report a positive relation between RANKNON and discretionary accruals, and a negative relation between RANKAUD and discretionary accruals. FJN find no significant relation between RANKTOT and discretionary accruals. When we replicate FJN’s discretionary accrual analysis, we find similar results (not tabled).9 It appears that total fees have a different relation with discretionary accruals than the proportion of nonaudit fees paid by a client.

Alternative Discretionary Accrual Measures

Prior research documents that discretionary accrual estimates are correlated with firm

performance (Dechow et al. 1995; Kasznik 1999; Kothari et al. 2002). We investigate the

robustness of FJN’s discretionary accrual tests by employing two alternative measures of

discretionary accruals that control for firm performance in the estimation of discretionary accruals.

We label our first measure of discretionary accruals Portfolio Performance Adjusted Discretionary

Current Accruals (PADCA), where we control for firm performance through a portfolio technique.

Our second measure of discretionary accruals adjusts for firm performance by including a variable

for firm performance in the regression model used to estimate discretionary accruals. We label this

measure of discretionary accruals as ROA in Estimation Discretionary Current Accruals (REDCA).

Both measures focus on current accruals because prior research suggests that management has the

most discretion over current accruals (Becker et. al. 1998).

PADCA is calculated as follows. We partition the entire population of Compustat firms,

excluding firms operating in the financial sector, by two-digit SIC codes. We then estimate the

parameters of the following regression for each two-digit SIC code partition:

)Re ()1/1(21v asset lag CA ?+=αα (2)

where current accruals (CA) is net income before extraordinary items (Compustat data item 123)

plus depreciation and amortization (Compustat data item 125) minus operating cash flows

(Compustat data item 308) scaled by beginning of year total assets. Lag1assets is total assets at the

beginning of the fiscal year and v Re ? is equal to net sales (Compustat data item 12) in year t less

net sales in year t-1 scaled by beginning of year total assets.

We use the parameter estimates from equation (2) to calculate expected current accruals

(ECA):

)Re (?)1/1(?21AR v asset lag ECA ???+=αα (3)

where AR ? is equal to accounts receivable (Compustat data item 2) in year t less accounts

receivable in year t-1 scaled by beginning of year total assets. A firm’s Discretionary Current

Accrual (DCA) is equal to CA minus ECA.

Finally, we partition firms within each two-digit SIC code into deciles based on their prior

year’s ROA. PADCA is the difference between a sample firm’s DCA and the median DCA for each

ROA portfolio, where the median value excludes the firm of interest. We implement this portfolio

adjustment rather than a matched-paired design because our sample cannot be classified into

treatment versus control groups since all sample firms are required to be audited and the vast

majority of firms disclose that they purchase some nonaudit services from their auditor (e.g., 95.65

percent of our sample firms). This is in contrast to other discretionary accrual studies, which focus

on non-random samples that are driven by some economic event or management choice. The

portfolio approach controls for relative firm performance across random samples.10

Our second measure of discretionary accruals, REDCA, follows Kothari et al. (2002) by

including lagged ROA in the accruals regression to control for firm performance. Kothari et al.

(2002) report that matching on lagged ROA, as opposed to contemporaneous ROA, eliminates any

mechanical relation between current period’s discretionary accrual estimate and the performance

metric. The calculation of REDCA begins by estimating the following cross-sectional current

accrual regression by each two-digit SIC code partition:

εγγγ++?+=ROA Lag v asset lag CA 1)Re ()1/1(321 (4)

The parameters from equation (4) are used to calculate expected current accruals estimated with a

performance control (ECAPC)11:

ROA Lag AR v asset Lag ECAPC 1?)Re (?)1/1(?321γγγ+???+= (5)

REDCA is equal to CA minus ECAPC. In contrast to PADCA, which controls for relative firm

performance within two-digit SIC classes, the REDCA estimate of discretionary current accruals

controls for performance on a firm-specific basis.12

[Insert Table 3 here.]

Table 3 reports descriptive statistics on the variables used in our empirical tests for the 3069

sample firms having data to calculate PADCA and REDCA.13 The mean (median) values of

PADCA and REDCA, both of which are deflated by ASSETS, are -1.08 percent (0.08 percent) and

–0.66 percent (0.53 percent), respectively. The means of PADCA and REDCA are significantly

different at the .001 level. Panel B of Table 3 reports the correlations between the alternative

measures of discretionary accruals. While highly correlated, the correlations between DCA,

PADCA, and REDCA are significantly less than one, indicating each variable is a distinct estimate

of a firm’s financial statement bias.

Discretionary Accrual Model

We use the following OLS regression model to test the stability of FJN discretionary accrual

results:

ε

ββββββββββββα+++++++++++++=CFO LOSS HOLDING INST LITIGATION MB LEVERAGE FINANCING MERGER MVE ACCRUAL

L BIG FEE PA DCA 121110987654321_ln 15_

(6)

where:

DCA_PA is the discretionary current accrual measure adjusted for firm performance, which is set

equal to PADCA or REDCA;

FEE is set to FEERATIO, or the natural log of TOTAL, AUDIT or NONAUDIT;

L1ACCRUAL is last years total current accruals equal to net income before extraordinary items

(Compustat data item 123) plus depreciation and amortization (Compustat data item 125) minus

operating cash flows (Compustat data item 308) scaled by beginning of year total assets;

LITIGATION is set to one if the firm operates in a high litigation industry, and zero otherwise.

High litigation industries are industries with SIC codes of 2833-2836, 3570-3577, 3600-3674,

5200-5961, and 7370-7370;

INST_HOLDING is equal to the percentage of shares held by institutional owners at the beginning

of 2000 calendar year;

LOSS is equal to one if the firm reports a net loss in fiscal year 2000, and zero otherwise;

CFO is cash flow from operations, defined as Compustat data item 308 scaled by beginning of year total assets;

All other variables are as previously defined.

Equation (6) is different from the model that FJN use to test the association between auditor fees and discretionary accruals in several ways. First, FJN use total accruals and the absolute value of total accruals in their regression model to control for firm performance. We do not include total accruals or the absolute value of total accruals in our model because these variables are used to derive discretionary accruals, which results in an overstatement of the explanatory power of the model.14 We do, however, add the prior year’s current accruals (L1ACCRUAL) to the model to capture the reversal of accruals over time.

Second, FJN use one variable to capture firms’ business combinations and financing activities. We use two separate variables, MERGER and FINANCING, because the two types of transactions may have different consequences for firms’ accruals, and the results of our fee analyses indicate different relations between audit related fees and these two economic events. Third, we drop audit tenure from the FJN model,as this variable does not add any explanatory power to our model.15 We also exclude the absolute value of cash flows from operations from the model, as including this variable induces multicollinearity with the cash flow from operations variable. Finally, we transform the dependent variable, PADCA or REDCA, by taking the natural log of the absolute value to correct for violations of normality (Warfield et al. 1995).

Discretionary Accruals Results

[Insert Table 4 here.]

Table 4 reports the results of our unsigned discretionary accruals tests. We find a positive relation between FEERATIO and the absolute value of PADCA and the absolute value of REDCA. We find no significant association between TOTAL and either unsigned estimate of discretionary accruals. The results on AUDIT and NONAUDIT are conditional on the discretionary accrual

estimate used. The coefficient on NONAUDIT is positive and significant only in the PADCA model, whereas the coefficient on AUDIT is negative and significant only in the REDCA model. The coefficients on the control variables are, in general, significant and stable regardless of fee metric or discretionary accruals measure. The sign and significance of the coefficients on FEERATIO are consistent with FJN’s results regardless of the discretionary accrual estimate used. In contrast to FJN, however, we find the significance of the coefficients on NONAUDIT and AUDIT to be sensitive to the estimate of discretionary accruals used.

[Insert Table 5 here.]

To provide evidence on whether there is any differential relation between the fee metrics and our measures of discretionary accruals conditional on whether discretionary accruals are income-increasing or income-decreasing, we partition the sample into two groups based on the sign of firms’ discretionary accruals. Panel A of Table 5 reports the regression results estimated using sample firms that report PADCA or REDCA greater than or equal to zero. We find no significant association between FEERATIO and positive PADCA or REDCA measures. Likewise, our results indicate no significant association between TOTAL and positive PADCA or REDCA measures. Furthermore, we find no evidence that AUDIT or NONAUDIT is significantly related to positive discretionary accruals. These findings are in contrast to FJN’s results, where they report FEERATIO, NONAUDIT and TOTAL to be positively related to positive discretionary accruals and AUDIT to be negatively related to positive discretionary accruals.

Panel B of Table 5 reports the regression results estimated using sample firms having PADCA or REDCA values less than zero. We find a negative relation between FEERATIO and both negative PADCA and negative REDCA. We also find a marginally significant negative relation between NONAUDIT and negative PADCA. These results are similar to FJN’s findings.

However, unlike FJN, we find no evidence that AUDIT is related to firms’ negative discretionary accruals.

[Insert Table 6 here.]

Prior research indicates that the U.S. clients of the Big5 report higher levels of total accruals and smaller amounts of estimated discretionary accruals than the clients of smaller audit firms (Francis et al. 1999). Table 6 reports the results of the discretionary accrual regressions where we partition sample firms based on the sign of their accruals conditioned by their auditor choice. The results of the positive discretionary accrual analyses reported in Panel A indicate that our earlier findings of no relation between positive discretionary accruals and the alternative fee metrics is stable across firms’ auditor choice. These results are different from FJN who report a positive relation between FEERATIO and positive discretionary accruals reported by non-Big5 clients. Similar to FJN, however, the results of the negative discretionary accrual analyses reported in Panel B indicate that the negative association between FEERATIO and negative discretionary accruals documented in the full sample does not hold for non-Big5 clients. 16 Overall, the results of our discretionary accrual analyses indicate that the relation between auditor fees and biased financial reporting is sensitive to the estimate of discretionary accruals and the audit fee metric used to capture the economic bond between the auditor and its client.

Klein (2002) and Kothari et al. (2002) state that measurement error in discretionary accruals can lead to erroneous inferences when the measurement error is correlated with the test variable. To investigate whether this issue explains why our FEERATIO results differ from FJN’s in the positive discretionary accrual analyses, we do the following. First, we calculate DIFFERENCE as being equal to DCA, i.e., the estimate of discretionary current accruals used by FJN, minus PADCA.17 DIFFERENCE represents the bias in the measurement of discretionary current accruals when the estimate of discretionary current accruals does not control for performance. Second, we correlate

DIFFERENCE with FEERATIO, i.e., the test variable of interest, conditional on firms reporting income increasing or income decreasing DCA.

We find that for positive (negative) DCA firms DIFFERENCE is positively (negatively) correlated with FEERATIO. The positive correlation between DIFFERENCE and FEERATIO for firms reporting income-increasing discretionary current accruals indicates that as FEERATIO increases the measurement error in FJN’s dependent variable increases. Thus, the measurement bias in the dependent variable likely contributes to FJN’s findings of a positive and significant coefficient on FEERATIO in their positive DCA analysis. For firms reporting income-decreasing accruals, as FEERATIO increases the difference between FJN’s estimate and our estimate of discretionary current accruals decreases, which likely explains why our results are similar to FJN’s results for negative accrual firms.

It appears that FJN’s finding of a positive relation between FEERATIO and the magnitude of income-increasing accruals is driven by systematic bias in their discretionary accrual measure that is correlated with FEERATIO.

Earnings Benchmark Tests

FJN examine the relation between earnings benchmarks and auditor fees to provide further evidence on whether the provision of nonaudit services is associated with earnings management. Specifically, FJN investigate the association between auditor fees and the likelihood of firms reporting small earnings increases (INCREASE) and the likelihood of firms meeting or beating analyst earnings forecasts (SURPRISE). We estimate the following logit regression to replicate FJN’s earnings benchmark results:

ε

ω

ω

ωω

ω

ω

ω

ω

+

+

+

+ +

+

+

+

=

PACDA

LOSS

HOLDING

INST

MVE MB

LITIGATION

FEE

Benchmark

7

6

5

4 3

2

1

_ln

(8)

where:

BENCHMARK is equal to INCREASE or SURPRISE. INCREASE is equal to one when the difference between a firm’s 2000 and 1999 net income (Compustat data 172),scaled by

beginning of year MVE, falls in the interval [0.00, 0.02), and zero otherwise. SURPRISE is equal to one when a firm meets or beats by one cent the mean consensus analysts forecast, as reported by FirstCall, for fiscal year 2000, and zero otherwise;

All other variables are as previously defined.

Our benchmark regression model is different from FJN’s in that we exclude audit tenure, CFO, MERGER, FINANCING, ROA, and annual market adjusted return from our regression model because FJN report insignificant coefficients on these determinants. We add PADCA to the BENCHMARK regression model to control for firms’ discretionary accruals, which likely is one mechanism that firms use to meet earnings benchmarks.18

[Insert Table 7 here.]

Panel A of Table 7 reports descriptive statistics for the sample used in the SURPRISE analysis. Requiring firms to have an analyst following reduces the sample to 1,666 firms, with the average sample firm being relatively larger than the average firm of the full sample. Panel B of Table 7 reports the SURPRISE regression results. We find no statistically significant association between any of the audit fee metrics and SURPRISE. These results also hold after partitioning firms by their auditor choice. Our results differ from FJN, who find a positive association between SURPRISE and FEERATIO. FJN also report a positive (negative) association between RANKNON (RANKAUD) and SURPRISE. We are unable to provide an explanation as to why our results differ from those of FJN.

[Insert Table 8 here.]

Panel A of Table 8 provides descriptive statistics for the variables used in the INCREASE benchmark regression. The descriptive statistics on the explanatory variables are similar to those of the discretionary accrual analysis. Panel B of Table 8 reports the INCREASE regression results. We find negative and significant coefficients on TOTAL and AUDIT, which is consistent with the findings of FJN, but is inconsistent with the claim that greater auditor fees increases the likelihood

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[1] 孙光国，杨金凤，郑文婧?财务报告质量评价：理论框架、关键概念、运行机制J].会计研究.2013(03) [2] 孙光国，杨金凤?财务报告质量评价研究：文献回顾、述评与未来展望J].会计研究.2012(03) [3] 王颖.高等学校内部审计运行模式研究[D].北京林业大学2008 [4] 张宁.关于中国电信战略转型的内部审计研究[D].南京理工大学2007 [5] 谢涤宇.利益相关者共同治理与企业内部审计的演进[D].湘潭大学2007 [6] 王玉兰，简燕玲?上市公司内部审计机构设置及履行职责情况研究J].审计研究.2012(01) [7] 程新生，孙利军，耿祎雯?企业内部审计制度改进了财务控制效果吗?--来自中国上市公司的证据[J].当代财经.2007(02) [8] 程娟.内部审计机构在我国上市公司中的定位问题研究[D].首都经济贸易大学2009 [9] 庄江波.内部审计职业化建设与发展[D].厦门大学2001 [10] 张欣?我国企业内部审计主要问题探析[D].江西财经大学2006 [11] 傅黎瑛?公司治理的重要基石：治理型内部审计[J].当代财经.2006(05) [12] 王光远，瞿曲.公司治理中的内部审计--受托责任视角的内部治理机制观[J].审计研究.2006(02) [13] 耿建新，续芹，李跃然.内审部门设立的动机及其效果研究--来自中国沪布的研究证据[J].审计研究.2006(01) [14] 刘国常，郭慧.内部审计特征的影响因素及其效果研究--来自中国中小企业板块的证 据[J].审计研究.2008(02) [15] 戴耀华，杨淑娥，张强.内部审计对外部审计的影响：研究综述与启示[J].审计研究.2007(03) [16] 王光远.现代内部审计十大理念[J].审计研究.2007(02) [17] 屈耀辉，时现.企业内部审计人员胜任能力评估(一)--基于上海市深圳市44家企业的调查数据[J].中国内部审计.2011(06)

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