Discretionary Accruals, Audit-Firm Tenure and Audit-Partner Tenure: Empirical Evidence from Taiwan

Discretionary Accruals, Audit-Firm Tenure and Audit-Partner Tenure: Empirical Evidence from Taiwan Wuchun Chi"*tand Huichi Huangbt National Chengchi University National Taiwan University

Received September 2003; Accepted January 2005

Abstract This paper examines how audit tenure affects earnings quality by investigating the effect of audit-firm and audit-partner tenure on the level of discretionary accruals. We find that familiarity helps to produce higher earnings quality, but excessive familiarity results in lower earnings quality. Besides, Big 5 auditors are superior in obtaining learning experience in the initial period of engagement, which implies that the negative effect on earnings quality is more serious for clients of non-Big 5 auditors if audit-firm rotation is mandated. These empirical results are valuable to the regulator even though they are analysed on a non-mandatory auditor rotation regime. E L Ctassifcation: KO, M4 Keywords: audit-fmn tenure; audit-partner tenure; earnings quality; mandatory rotation Data Availability: All data used in this study is commercially available

1. Introduction

Auditor's independence is the cornerstone of the audit function. However, the effectiveness of auditing has been viewed much more suspiciously since the Enron audit failure.

' Correspondence to Wuchun Chi. Department of Accounting, National Chengchi University, 64,Zhi-nan Road, Section 2, Wenshan, 11605, Taipei, Taiwan, Republic of China. Tel: (886) 2-29393091 ext. 81031; Fax (886) 2-29387 113; E-mail: [email protected].

'

We gratefully acknowledge the comments and suggestions of Dan A. Simunic (co-editor), Anges Cheng (discussant), Suming Lin, Samuel Tung, and participants at the 2004 Asia-Pacific Journal of Accounting and Economics Symposium, as well as workshop participants at National Chengchi University, National Taiwan University, and National Sun Yat-sen University. Special thanks also go to the anonymous referee of this paper.

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Given the diminishing credibility of financial reporting, Congress and regulators in the United States have called for many reforms to the auditing profession. Recent reforms to oversee the audit function are focused on three main issues: 1) to strengthen the functioning of independent audit committees, 2 ) to restrict the scope of non-audit services, and 3) to require mandatory auditor rotation. This study investigates how audit tenure affects earnings quality in light of the debate on mandatory auditor rotation. The Sarbanes-Oxley Act of 2002 (hereafter the Act) treats audit services as unlawful if the lead or the coordinating audit partner provides services for a certain client for five consecutive years.’ Previous studies which empirically examined the association between the audit-firm-client relationship and discretionary accruals (e.g., Johnson et al., 2002; Myers et al., 2003; Ghosh and Moon, 2005), defined audit tenure as the number of years that a client has retained its “audit firm” rather than the lead or coordinating “audit partner” which is stated in the Act. But “audit-partner tenure” should be distinguished from “audit-firm tenure” in the debate on mandatory auditor rotation. In fact, unless audit-firm tenure and audit-partner tenure are perfectly correlated, the results from previous studies contain measurement error and cannot be used as evidence for or against mandatory auditor rotation. There is a growing body of literature indicating that short audit-firm tenure has negative effects on the quality of financial reporting (Johnson et al., 2002; Myers et al., 2003; Ghosh and Moon, 2005). These studies interpreted their empirical findings as resulting from a lack of client-specific knowledge in the early years of an audit and inferred that mandatory auditor rotation between audit firms may result in lower quality of earnings owing to short audit-firm tenure.2 These findings indicate a potential weakness of mandatory auditor rotation of audit firms. However, the loss of learning experience may not take place if the rotation requirement is aimed at auditors within an audit firm. In this study, we distinguish between audit-firm tenure and audit-partner tenure. This enables us to explore the potential effect of mandatory auditor rotation on earnings quality while considering the impact of rotation on auditors’ ability to gain client-specific experience with the passage of time. The trade-off between the incremental effect of learning experience and excessive familiarity has been a cost-benefit consideration in the rotation debate. If the positive impact of additional learning experience diminishes at a certain tenure level, excessive familiarity would impair audit quality in a lengthy auditor-client relationship. Accordingly, it is worthwhile to investigate whether the incremental effect of auditors’ learning experience diminishes with extended audit tenure. In addition, whether the assimilation of client-specific knowledge and the development of learning experience with tenure is the same at the “audit-firm’’ level and “audit-partner’’ level is an important issue and provides a valuable reference for legislators.

’

See section 203 of the Act. In addition, in its Final Report (2002), the Public Oversight Board calls for mandatory auditor rotation every seven years. Another reason mentioned by opponents of mandatory auditor rotation is that a new engagement needs significant start-up costs as well as firm-specific knowledge.

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Our study contributes to the debate on mandatory auditor rotation in at least two respects. First, our analysis provides new empirical evidence related to the Specifically, we use annual financial reports in Taiwan to obtain data on audit-firm tenure and auditpartner tenure. Since an audit report in Taiwan has to be signed by two audit partners in addition to a signature representative of an audit firm, the data in Taiwan provides us with a unique opportunity to investigate how audit-partner tenure and audit-firm tenure affect earnings quality. We believe that an analysis considering both of these effects separately and jointly provides more direct evidence to evaluate the potential impact of mandatory auditor rotation on the quality of financial reporting. Second, this paper explores the effect of mandatory auditor rotation from the point of view of learning experience. We posit it inefficient to mandate auditor rotation if extended auditor tenure has a positive effect on the learning experience.We further examine whether lower earnings quality are associated with excessive familiarity in the auditor-client relationship. Finally, we investigate whether the ability to develop client specific expertise through learning experience is better in Big 5 auditor^.^ Our empirical results indicate that, for either audit-partner tenure or audit-firm tenure, familiarity indeed helps in the auditing process and produces higher quality of earnings, but excessive familiarity impairs audit quality. The cut-off point of positive and negative effects of familiarity is around five years. Furthermore, when we jointly incorporate the two tenure definitions into the model, we find that audit-firm tenure is more important to the quality of earnings than audit-partnertenure. This finding is easily explained when we consider the effect of switching auditors on the learning experience. Specifically,a change in audit-partner tenure (due to switching audit-partners within a firm) does not influence the learning experience since the client specific information is transmitted between auditors within a firm. However, a change in audit-firm tenure (due to switching audit-firms) effectively ends the learning experience since the client specific knowledge that is acquired over time is not transmitted to the new audit firm. We also find that Big 5 auditors are prominent in auditing expertise because they gain client-specific knowledge more quickly. This implies that the threat that mandatory auditor rotation will impair the learning experience is more serious for non-Big 5 auditors because they are less specialised in building client-specific knowledge as compared to Big 5 auditors. Therefore, regulators should pay more attention to the potential negative effects of mandatory rotation on nonBig 5 auditors. Although our findings are based on a real world without mandatory auditor rotation, we believe the evidence still provides useful insights for policy makers. We organise the rest of this paper as follows: section 2 reviews prior literature on audit tenure and formulates our hypotheses, section 3 describes the research methodology, section 4 presents our major findings and we conclude the paper in section 5.

To our knowledge, Ferguson et al. (2003) is the only paper that distinguishes between firm-wide industry expertise and office-level expertise to examine how auditor industry expertise affects audit pricing. Nonetheless, it is different from our study, which focuses on the auditor-client relationship. The term “Big 5” is used throughout this study to refer to auditors affiliated with large international accounting firms.

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2. Literature Review and Hypothesis Development 2.1 Literature review There are pros and cons to mandatory auditor rotation. Brody and Moscove (1998) assert that auditor rotation enhances greater independence and improves audit quality through a reduction of clients’ inadequate influence on auditors. Auditors could be inadequately influenced if they perceive a risk of losing the client when they do not agree with managers’ financial reporting preferences (Farmer et al., 1987). Further, Knapp (1991) finds that audit committee members’ perceptions of audit quality are positively correlated with auditor tenure if auditor tenure with a given client is five years and that they become negatively correlated with auditor tenure when this period exceeds five years. In its July 2003 report, Rebuilding Public Confidence in Financial Reporting, the International Federation of Accountants (IFAC) treats familiarity in the auditing process as helpful in producing greater understanding and improved ability to identify and evaluate risks. However, the IFAC also recognises that familiarity is one of the significant threats to auditor independence. The main concern of the IFAC is that excessive familiarity may result in auditors’ complacency or hesitancy to challenge appropriately and thereby reduce the level of scepticism necessary for an effective audit (IFAC, 2003). Louwers (1998) also finds that the length of the auditor-client relationship affects auditors’ propensity to issue a going-concern disclosure to a distressed client. Hence, proponents of the Act suggest that rotation could significantly improve the overall quality of an audit and enhance the quality of the financial reporting process (e.g., Imhoff, 2003; Dopuch et al., 2001). However, a new engagement constitutes an especially challenging audit job because auditors have less information about these firms (AICPA, 1992). From the point of view of auditor expertise, auditors gain more experience and build client-specific assets from the ongoing relationship which improves their ability to detect accounting irregularities (Arrunada and Paz-Ares, 1997). Geiger and Raghunanda (2002) also demonstrate that there were significantly more audit failures in the earlier years of the auditor-client relationship. To assess the potential effectiveness of auditor rotation, recent studies examined the association between the audit-firmxlient relationship and the quality of earnings using discretionary accruals as a proxy for earnings quality (e.g., Johnson et al., 2002; Myers et al., 2003; Ghosh and Moon, 2005). Their empirical evidence suggests that short audit tenure leads to lower earnings quality. Since its introduction in the Jones Model (Jones, 1991), prevailing accounting studies have used discretionary accruals as the variable of choice to proxy for earnings quality5 (e.g., Defond and Jiambalvo, 1994; Subramanyam, 1996;Defond and Subramanyam, 1998; Becker et al., 1998; Teoh et al., 1998a, 1998b; Klein, 2002; Matsumoto, 2002; Nelson et al., 2002). Discretionary accruals may be calculated using various models such as the

In addition to discretionary accruals, other proxies that are used to examine the relationship between audit-firm tenure and earnings quality include the persistence of accruals into future earnings (Johnson et al., 2002), current accruals (Myers et al., 2003), special items, and market-based measures like earnings response coefficients (Ghosh and Moon, 2005).

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modified Jones model (Teoh et al., 1998a, 1998b), the revised modified Jones models (Dechow et al., 2003), or the performance-matcheddiscretionary accruals model (Kothari et al., 2005). Numerous prior studies on audit quality have also adopted discretionary accruals to explore how other factors (e.g., Big 5 and non-Big 5 , board of directors, audit services and non-audit services) affect audit quality (e.g., Becker et al., 1998; Klein, 2002, Nelson et al., 2002).6 2.2 Hypothesis development Although general knowledge and industry knowledge are important factors that enable auditors to detect accounting irregularities, a great deal of client-specific experience or knowledge is more critical to producing a report with higher audit quality. Armnada and Paz-Ares (1997) pointed out that knowledge and experience obtained from different clients might not be perfect substitutes. In addition, some empirical studies found that earnings quality is worse at the initial stage of new audit engagements because of a lack of client-specific knowledge (Johnson et al., 2002; Myers et al., 2003). Therefore, we postulate the following hypothesis in the alternative form: H,: (Learning Effect Hypothesis): Earnings quality is an increasing function of audit tenure in the initial period of an audit engagement. Regarding potential problems in a lengthy auditor-client relationship, IFAC (2003) mentioned that excessive familiarity leads to auditors’ complacency or hesitancy to challenge appropriately and hence reduces the level of scepticism. If this concern is valid and the incremental learning effect disappears with the passage of time, we anticipate the opposite of hypothesis H,to occur after a certain audit tenure level is reached. This leads to our second hypothesis, which predicts that audit tenure is inversely related to earnings quality in the later stage of the auditor-client relationship: H,: (Excessive-FamiliarityEffect Hypothesis): Earnings quality is a decreasing function of audit tenure after the learning effect diminishes. Because prior literature indicates that Big 5 auditors have more auditing and industrial expertise, we expect that they are able to acquire client specific knowledge more quickly than non-Big 5 auditors. Our third hypothesis tests whether Big 5 auditors expedite the learning effect:

H,: (Learning Differentiation Hypothesis): The learning effect via tenure occurs faster in Big 5 auditors.

Our paper implicitly assumes that earnings quality is lower with a higher level of discretionary accruals. However, other characteristics of accounting earnings, such as earnings conservatism (Basu, 1997), value relevance (Barth et al., 2001), or the earnings response coefficient (Teoh and Wong, 1993) also reflect the quality of earnings. Thus, we hesitate to claim that discretionary accruals is the only variable suitable for measuring the quality of earnings.

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Since audit quality (AQ) is a function of auditor expertise (AE) and auditor independence (AI), our research hypotheses clarify the relationship between audit tenure (T) and audit quality by describing how audit tenure affects auditor expertise and auditor independence. Specifically, we decompose the effect of audit tenure on audit quality, aAQ/aT, into two parts: 1) the effect of audit tenure on auditor expertise, aAE/aT; and 2 ) the effect of audit tenure on auditor independence, aAUaT. H, suggests that longer audit tenure improves the auditor’s ability to obtain client-specific expertise and in turn improves audit quality. That is aAE/aT is positive. H, addresses the potential problems that are likely to occur in a lengthy auditor-client relationship. Particularly, it predicts that earnings quality will decrease when excessive familiarity leads to auditors’ complacency or hesitancy to challenge appropriately and therefore reduces the level of scepticism. Thus, we expect aAI/aT to be negative. Since the previous literature indicates that Big 5 auditors have more auditing and industrial expertise, it is reasonable to suppose that they gain client specific expertise more quickly than non-Big 5 auditors. Therefore, H, states that (aAE/aT),ig > (aAE/aT)non-Big 5 . The next section describes how we use audit-firm tenure and audit-partner tenure to test our hypotheses.

3. Methodology and Sample Description 3.1 Dependent variable Following Johnson et al. (2002), Myers et al. (2003), and the conventional accounting literature, which interpret abnormal accrual adjustments as indication of lower quality of earnings, we use discretionary accruals (DAC) as a proxy for earnings quality to examine the effect of audit tenure on the quality of financial reporting. We measure DAC based on theforward-looking modeE proposed by Dechow et al. (2003)where accruals may be positively related to sales g ~ o w t h . ~ We measure DAC in the following sequence. First, since Hribar and Collins (2002) show that the errors in estimated accruals using the balance-sheet approach are correlated with firms’ economic characteristics, we use the cash-flow statement approach, following Phillips et al. (2003), to calculate the total accruals (TAC) of a firm by subtracting operating cash flows from net income: TAC, = EBEI,, - (CFO, - EIDO,,) where TACit EBE’,,

’

(1)

= firm i’s total accruals in year t; = firm i’s income before discontinued operations and extraordinary items in year t;

Dechow et al. (2003) proposed three revised cross-secfionalmodifedJones models, namely the “adopted”, “lagged”, and “forward-looking” models. Our findings are not sensitive to the choice of model used to measure DA C.

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CFO, EZDO,

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= firm i’s cash flows from operations in year t; = firm i’s extraordinary items and discontinued operations from the state-

ment of cash flows in year t. Second, we scale all variables by beginning-of-year total assets to estimate the following regression for each industry and each year as classified by the Taiwan Stock Exchange Corporation, ARECit = a, + k, . Asalesir+ E,, where ARECit

(2)

= the change in firm i’s accounts receivable from year t-1 to t, scaled by

beginning-of-year assets; Malesit = the change in firm i’s sales from year t-1 to t, scaled by beginning-ofyear assets; the error term. ‘it Finally, we derive discretionary accruals as the residuals from the following regression based on the forward-looking model proposed by Dechow et al. (2003):

firm i’s total accruals in the year t scaled by year t-1 total assets; firm i’s total accruals in the year t-1 scaled by year t-2 total assets; firm i’s year t gross property, plant, and equipment (land excluded); The growth rate of firm i’s sales from year t to t + 1; The error term. Following prior work such as Johnson et al. (2002), we also perform our tests using the absolute value of DAC as an alternative measure of earnings quality. 3.2 Measurement of audit tenure

To measure our primary variable of interest, audit tenure, we use two definitions for tenure in order to reflect the auditor-switch effect within an audit f i i and between audit f i s . We define TenureF as the number of years where an audit firm has provided audit services for a certain client and 7’enureA as the longest number of consecutive years where either incumbent auditor has attested to an engagement.8 To illustrate how we measure TenureA, define the vector (x,y),to represent auditors x and y attesting to a financial report in , z),-*,b, z)~.~,..., year t. If the following series of vectors occurs overtime, (x,y),, (x,Y ) ~ - ,(x,

In the case of mergers of accounting firms, we treat the original two firms as the same firm in the computation of audit-firm tenure.

12

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then TenureAis equal to 3.9In the empirical work that follows, we use the variable TenureA to represent the auditor-switch effect within an audit fm and the variable TenureF to investigate the auditor-switch effect between audit f m s . To simplify our description of the empirical model, we broadly refer to the variable Tenure as the length of the auditor-client relationship regarding both auditor-switch effects (i.e., the within and between auditor rotation effects). 3.3 Control variables We add several variables to the model to control for other cross-sectional factors with systematic effects on the distribution of DAC. In particular, we include Big5 since auditors with large accounting firms generally have higher audit quality (Becker et al., 1998), h u e because companies with seasoned equity offerings tend to record larger accruals (Teoh et al., 1998b), Age to control for the variation of accruals during different stages of the firm’s life cycle (Anthony and Ramesh, 1992), and Size to control for the fact that large firms have larger and more stable accruals (Dechow and Dichev, 2002). Finally, we also incorporate indicator variables for each year and industry to mitigate the potential confounding effects from different years and across industries. 3.4 The empirical model Our hypotheses suggest that there is a curvilinear relation between DAC and Tenure, where DAC first decreases in the early stage and then increases in the later stage of the auditor-client relationship. There are three commonly adopted approaches to empirically model such a relationship: the quadratic form approach (e.g., McConnell and Servaes, 1990), the dummy variables approach (e.g., Johnson et al., 2002), and the piecewise linear approach (e.g., Morck et al., 1988). We exclude the piecewise linear approach since there are only ten discrete integers in our sample (i.e., Tenure = 1, 2, ..., or 10).I0 We focus on the quadratic form approach to test our hypotheses but also present results from the dummy variables approach to ensure robustness of our results. Specifically, we use the following equation to test our hypotheses:

As previously mentioned in the text, an audit report in Taiwan has to be attested to by two audit partners along with a signature representative of an audit firm. At times, one auditor remains in the engagement but the other one quits, or both auditors quit the engagement but the successive auditors are both in the same audit firm. In fact, we once measured audit tenure as the “shortest” number of years for which either incumbent auditor has attested to an engagement. However, this definition of tenure does not support our hypotheses since the ability to gain learning experience should be attributed to the incumbent auditor who has worked with the client the longest. We believe that the measurement of tenure given by TenureA is appropriate for testing our hypotheses. ‘“FollowingCarcello and Neal (2003), we truncate audit tenure at ten years to reduce the potential effects of outliers (see the description of sample selection). Since our variable Tenure is constrained to a limited number of integers, we exclude the piecewise approach, which Morck et al. (1 988) use in their study to model the high dispersion of management ownership.

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where:

= a dummy variable equal to 1 if the company employs a Big 5 auditor and 0 otherwise; Tenure,, = the length of the auditor-client relationship calculated by audit-partner or audit-firm, which are traced to the firms since they were listed in Taiwan Stock Exchange Corporations; Issuei, = a dummy variable equal to 1 if the company issued new shares for cash during the year or in the previous two years and 0 otherwise;’’ the number of years since listing date; Age*, - firm size measured by the natural logarithm of total assets (in NT Sizel, dollars).

Bigs,,

If our learning effect hypothesis is supported by the data, we expect the coefficient p, to be negative because it implies that an increase in audit tenure will cause a decrease in discretionary accruals in the initial period of new engagements. Given a negative coefficient p,, we anticipate p, being positive to support our excessive-familiarity hypothesis. This is because a negative p, along with a negative p, implies that the learning effect goes on indefinitely.Therefore,the evidence to supportthe excessive-familiarityhypothesis while the learning effect hypothesis is sustained is that p, must be positive. In other words, if the benefit from longer tenure is greater than the threat to auditor independence in the initial period of an audit engagement, i.e., ldAE/dTI > IdAUdTI, p, would be negative, which supports the learning effect hypothesis. A positive p, indicates that the learning effect diminishes as time goes by since after a certain tenure level, the benefit to auditor expertise due to tenure is outweighed by the decrease in auditor independence, i.e., ldAE/dTI < IdAVdTI, which conforms to the excessive-familiarityhypothesis. The point where ldAE/dTI = ldAI/dTl occurs at the tenure level where the benefits to auditor expertise are offset by the costs of longer tenure in the form of impaired audit quality. Therefore, a negative 0, along with a positive p, is necessary to support our main hypotheses.12 If the evidence supports the first two hypotheses, we can further investigate the learning differentiation hypothesis. To test H,, we calculate the first derivative of Equation (4) with respect to Tenure to find the “optimal” audit tenure (denoted by R) and partition the sample into “below-optimal” and “above-optimal” sub-samples by the integer nearest to R. Then, we estimate the following regression for the two sub-samples separately:

‘I To control for the incentive of earnings management due to seasoned equity offerings, we use the variable Issue as an indicator when a company issues new shares for cash during the year or in the prior two years, similar to Firth (1997) and Johnson et al. (2002), rather than the amount of new shares which was used in Teoh et al. (1998b).

Note that a positive p, implies that either the learning effect does not exist or the learning effect is strictly dominated by the excess-familiarity effect (i.e., ldAE/aTI < IaAUdTI) in the early years of an audit engagement.

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If Big 5 auditors are prominent in auditing expertise in the initial period of a new engagement due to their ability to learn faster, we anticipate that the cross-term coefficient P, in Equation (5) will be negative in the below-optimal sample. We further explore the DAC behavior of the above-optimal sample and leave the corresponding results and explanations to the next section. To check the robustness of our results, we perform another test on audit tenure similar to the analysis in Johnson et al. (2002). We define audit tenure as Short when the client has retained its auditor or audit firm for three years or less (i.e., Tenure = 1,2, or 3), and define audit tenure as Long when the client has retained its auditor or audit firm for eight or more We then estimate the following regression: years (i.e., Tenure = 8 , 9, or

where Shortit

= a dummy variable set to 1 if TenureF or TenureA is equal to 1, 2, or 3,

Longir

= a dummy variable set to 1 if TenureF or TenureA is equal to 8,9, or 10,

and 0 otherwise; and 0 otherwise;

In Equation (6), we predict a positive P, based on the learning effect hypothesis and a positive P, based on the excessive-familiarity hypothesis. 3.5 Sample selection Our sample, obtained from the Taiwan Economic Journal Database, consists of yearend data for all listed firm-years from 1998 to 2001. We measured audit tenure as the number of consecutive years that the client has retained its auditor. We traced audit tenure up to ten consecutive years to ensure that our data covers a period reasonably longer than the five years that is required by the Act. Following Carcello and Neal (2003), we truncate audit tenure at ten years to reduce the potential effects of outliers. We further rule out banking and insurance industries for their unique industry characteristics, and exclude industries with less than eight firm-year observations in order to obtain a reasonable estimate of DAC.14 This process yielded 1,337 firm-year observations. The sample selection procedure and its effect on sample size are summarised in Table 1. We collected data from 1989 to 2002 for the purpose of calculating the variables Tenure and DAC in the forward-looking model and retrieved the data from 1998 to 2001 to perform our empirical analysis.

l 3 Since our results (see section 4) demonstrate that the “optimal” audit tenure, R, is close to five years, we define the two variables Short and Long accordingly to examine the learning effect hypothesis and the excessivefamiliarity hypothesis. l4

In Taiwan, companies in the utility industry are state-owned.

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Table 1 Sample Selection Selection Mode (by firm-years)

Number of Companies

Listed companies from year 1998 to 2001 Banking and Insurance Industries Non-calendar year firms Audit tenure selection Missing data while computing DAC Industry with less than eight firm-years

Total available data

4. Empirical Results

4.I Univariate analysis Table 2 presents descriptive statistics on discretionary accruals and all independent variables grouped by auditor types as well as audit tenure definitions. Table 2 Descriptive Statistics Panel A: Categories by Big5 or Non-Big5; Mean (Median) [Standard Deviation]

Variables

Full sample (n=1337)

Big5 (n=1063)

Non-Big5 (n=274)

t-test of Differences Mean (t-statistic)

DAC

-0.0003 (-0.OOOl) [O. 10051

-0.0028 (-0.0025) [0.1038]

0.0092 (0.0053) [0.0860]

-0.0120 (-1.7668)'

Big5

0.795 1 (1.0000) [0.4038]

TenureF

5.6522 (5.OOOO) [3.5078]

5.4741 (5.0000) [3.4870]

6.343 1 (7.OOOO) [3.5091]

-0.8689 (-3.6732)""

TenureA

5.1473 (4.0000) [3.2903]

5.0329 (4.0000) [3.2763]

5.5912 (5.OOOO) [3.3129]

-0.5583 (-2.5094)""

Issue

0.4921 (0.0000) [0.5001]

0.5 174 (1.OOoo)

0.1232 (3.6539)"'

[0.4999]

0.3942 (0.0000) [0.4896]

Age

9.4368 (7.0000) [9.0832]

9.1496 (6.0000) [9.10441

10.5511 (8.0000) [8.9295]

-1.4015 (-2.2810)**

Size

15.7910 (15.6816) [ 1.07961

15.8283 (15.6920) [ 1.12341

15.6466 (15.6431) [0.8764]

0.1817 (2.4886)""

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Table 2 (cont.) Descriptive Statistics Panel B: Categories by TenureF and TenureA

Variables

Short (n=495)

TenureF Middle (n=266)

Long (n=576)

Short (11342)

TenureA Middle (n=322)

Long (n=473)

DAC

-0.0021

-0.0167

0.0088

-0.0009

-0,0101

0.007 1

Big5

0.8384

0.8195

0.7465

0.8267

0.7950

0.7590

Issue

0.6444

0.5639

0.3281

0.6107

0.5124

0.3425

Age

4.1919

6.5602

15.2726

5.1310

8.3665

15.0994

Size

15.3137

15.9093

16.1467

15.3943

15.9190

16.1586

Variables

Difference of TenureF (Short - Middle) (Long - Middle)

Difference of TenureA (Short - Middle) (Long - Middle)

DAC

0.0146'

0.0255'"'

0.0092

0.0171*"

Big5

0.1089

-0.0730^'

0.0315

-0.0360

Issue

0.0805"'

-0.2358""*

0.0983""

-0.1700"""

Age

-2,3683"'

8.7 124"'

-3,2355"'

6.7329""

Size

-0.5956***

0.2374*"*

-0.5247*"

0.2396""

*, *' and *** indicate significance a t p c 0.1, p

< 0.05 and p c 0.01, respectively, on two-tailed tests.

= discretionary accruals computed according to the forward-looking model proposed by Dechow et al. (2003); Big5 = a dummy variable equal to 1 if the company employs a Big 5 auditor and 0 otherwise; Tenure = the length of the auditor-client relationship calculated by audit-partner and audit-firm respectively, defined as: TenureF= the number of years for which an audit firm has provided audit services, TenureA = the longest number of years for which either incumbent audit partner has attested to an audit, = a dummy variable equal to I if the company issued new shares for cash during the year or in the Issue previous two years and 0 otherwise; Age = the number of years since listing date; Size = firm size measured by the natural log of total assets (in NT dollars); Grouping Short = The subsample of TenureF or (TenureA) where tenure equals 1, 2, or 3 years; Middle = The subsample of TenureF or (TenureA) where tenure equals 4, 5 , or 6 years; Long = The subsample of TenureF or (TenureA) where tenure equals 7,8,9, or 10 years.

DAC

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Panel A shows that, similar to the market share of higher-quality auditors found in Becker et al. (1998) and Frankel et al. (2002), Big 5 auditors comprise the majority of listed companies in our sample (Big5 = 79.51%).15We find that the mean of the dependent variable, DAC, is significantly lower in the Big 5 subsample (the difference in means equals -0.0120 with a p-value < 0.1). This is also true of the variable Age (the difference in means is -1.4015 with a p-value < 0.05).16 The fact that the Big 5 subsample contains younger-age firms, suggests that newly listed companies are prone to choosing Big 5 auditors as a signal at the time of initial public offering. This in turn leads to the result that TenureF and TenureA are significantly shorter in the Big 5 subsample. In addition, the fact that the percentage of companies issuing new shares is larger in the Big 5 subsample (Issue = 5 1.74% as compared to 39.42% in the non-Big 5 subsample) shows that firms that employ Big 5 auditors either have a propensity to or are capable of issuing new stocks. Finally, we find that larger firms are more likely to employ a Big 5 auditor (size is significantly larger in the Big 5 subsample with p < 0.05). We further partition the sample according to audit tenure. For the Short-TenureF (-TenureA) group, audit-firm (audit-partner) tenure is equal to one, two, or three years; for the Middle-TenureF (-TenureA) group, audit-firm (audit-partner) tenure equals four, five, or six years and for the Long-TenureF (-TenureA) group, audit-firm (audit-partner) tenure equals seven, eight, nine, or ten years. The upper part of Panel B in Table 2 shows descriptive statistics for each group (i.e., Short, Middle, and Long) in each category (i.e., TenureF and TenureA). The lower part of Panel B shows the differences in the variables using the Middle group as a benchmark. We can see that both the Middle-TenureF and MiddleTenureA groups have the lowest DAC, consistent with our first two hypotheses presented in section 2. The Long-TenureF and Long-TenureA groups have the smallest Big 5 market shares and lowest frequency of issuing new shares. Finally, firm age and size are both smallest in the Short-TenureF and Short-TenureA groups. Based on these univariate results, we portray the relationship between audit tenure and discretionary accruals in Figure 1.

Is Based on data from 1989 to 1992, Becker et al. (1998) found the market share of Big 6 auditors to be 83% (10,397 firms employed Big 6 auditors and 2,179 firms employed non-Big 6 auditors). Frankel et al. (2002) gathered data on auditor fees disclosed in proxy statements filed between 5 February 2001 and 15 June 2001. They found the market share of Big 5 auditors to be 90% (2,780 firms employed Big 5 auditors and 294 non-Big 5 auditors). l6 Because the variable Age is truncated at ten years, the mean and median of Age reported in Table 2 are smaller than their real value.

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Figure 1 The Quadratic Relation between Audit Tenure and Discretionary Accruals Panel A: TemureF vs DAC

DAC

0.04

0 -0.01

TenureF

-0.02 -0.03

Panel B: TenureA vs DAC

DAC 0.04 I 0.03 0.02 0.01 0 -0.01

-0.02 -0.03

DAC

I

w 6 * *

8

TenureA

I

= discretionary accruals computed according to the forward-looking model proposed by Dechow et al. (2003);

Tenure = the length of the auditor-client relationship calculated by audit-partner tenure and audit-firm tenure respectively, defined as: TenureF = the number of years for which an audit firm has provided audit services, TenureA = the longest number of years for which either incumbent audit partner has attested to an audit.

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Figure 1 shows that there is an upward parabolic relationship between DAC and Tenure. This clearly suggests that the coefficients of Tenure and Tenure' in Equation ( 4 ) (p, and p,) should be negative and positive, respectively. In other words, DAC initially decreases with audit tenure and subsequently increases reaching a minimum at a tenure level of around five years. Finally, Table 3 presents the Pearson correlation matrix for all variables according to Big 5 and non-Big 5 subsamples. We find that DAC has a weak linear relationship with TenureF and TenureA in each subsample. In addition, TenureF and TenureA are positively correlated. Table 3 Correlation Matrix" ~

DAC DAC

TenureF

TenureA

0.029

-0.010 0.701"'

Issue

Age

Size

0.110'

-0.058

0.147"

TenureF

0.057'

-0.326"'

0.625"'

TenureA

0.049

0.873"'

0.373"'

-0.210"'

0.416"'

0.283"'

Issue Age

0.027 0.016

-0.284"" 0.592'"'

-0.250**' 0.555***

-0.249""

Size

0.041

0.366"*

0.356""

0.123"'

-0.144

0.04 I 0.362""

0.366"'

Lower triangular contains Pearson correlation coefficients for the clients of Big 5 (n = 1,063) while upper triangular belongs to that of non-Big 5 (n = 274). *. ** and *** indicate significance a t p c 0.1, p c 0.05 a n d p < 0.01, respectively, on two-tailed tests. a

DAC

=

Big5 Tenure

=

=

TenureF = TenureA = Issue = Age Size

= =

discretionary accruals computed according to theforward-looking model proposed by Dechow et al. (2003); a dummy variable equal to 1 if the company employs a Big 5 auditor and 0 otherwise; the length of the auditor-client relationship calculated by audit-partner and audit-firm respectively, defined as: the number of years for which an audit firm has provided audit services, the longest number of years for which either incumbent audit partner has attested to an audit, a dummy variable equal to 1 if the company issued new shares for cash during the year or in the previous two years and 0 otherwise; the number of years since listing date; firm size measured by the natural logarithm of total assets (in NT dollars).

4.2 Multivariate analysis As described in previous sections, we test our hypotheses by estimating Equations (4) and (6) while controlling for important factors that have systematic effects on the crosssectional variation in accruals. The regression results for various audit tenure definitions are shown in Table 4. The coefficient of Big5 in Equation (4)is negative and significant at p < 0.05 for both tenure definitions ( p ,= -0.0125 for TenureF and p, = -0.0138 for TenureA). This indicates that in Taiwan, Big 5 auditors provide better quality service as compared to non-Big 5 auditors, similar to the results found by Becker et al. (1998) for US data.

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Table 4 Effect of Measurementsof Audit Tenure on DiscretionaryAccruals Panel A: Regressor Tenure measured by its quantitative value

By TenureF (n=1337)

By TenureA (n=1337)

Coefieient (&statistic)

Coefficient (t-staristic)

Intercept

-0.0557 (-1.1539)

-0.0609 (-1.2570)

Big5

-0.0125 (-2.0663)"

-0.0138 (-2.2809)*'

Tenure

-0.0110 (-2,4284)"

-0.0069 (-1.5924)

Variable

0.0012

(3.1538)***

Issue

0.0104

(1.8620)'

Age

-0.0008 (-2.5592)""

Tenure2

0.0049

Size

(1.4539)

0.0143

Adjusted R2

0.0007

(2.1010)"

0.008 1

( 1.4918)

-0.0004 (-1.4068) 0.0050

(1.4913)

0.0073

Panel B: Regressor Tenure measured by its qualitative value

~

Variable

~

By TenureF (n=1337)

By TenureA (n=1337)

Coefficient (t-statistic)

Coeficient (t-statistic)

-0.0822 (-1,8354)' -0.0126 (-1.8462)'

Intercept Big5

-0.0506 (-0.9876) -0.0162 (-2.3594)**

Short

0.0122

(1.6594)'

0.0139

(1.7670)'

Long

0.03 15

(4.0057)""

0.0258

(3.6582)""

0.0100

(1.6796)'

0.0161

(2.7637)*'"

Issue Age

-0.0007 (-1.8790)'

-0.0006 (-1.7738)'

Size

0.0049 (1.6796)' 0.0142

0.0027 (0.7909) 0.0121

Adjusted R2

', and '** indicate significance a t p c 0.1, p < 0.05 andp c 0.01, respectively, on two-tailed tests. Fixed effects of years and industries are included but not reported. We report asymptotic t-statistics based on White (1980) standard errors in parentheses if necessary. I'

DAC

-

Big5

-

Tenure

=

TenureF TenureA Short Long Issue

= =

Age Size

-

-

--

discretionary accruals computed according to theforward-looking model proposed by Dechow et al. (2003); a dummy variable equal to 1 if the company employs a Big 5 auditor and 0 otherwise; the length of the auditor-client relationship calculated by audit-partner tenure and audit-firm tenure respectively, defined as: the number of years for which an audit firm has provided audit services, the longest number of years for which either incumbent audit partner has attested to an audit, a dummy variable set to 1 if TenureF or TenureA equals 1,2, or 3, and 0 otherwise; a dummy variable set to 1 if TenureF or TenureA equals 8, 9, or 10, and 0 otherwise; a dummy variable equal to 1 if the company issued new shares for cash during the year or in the previous two years and 0 otherwise; the number of years since listing date; firm size measured by the natural log of total assets (in NT dollars).

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Next, we report the results on the nonlinear relationship between DAC and Tenure in Panel A of Table 4. Beginning with audit firms, the coefficient of TenureF is significantly negative (p, = -0.0110, p < 0.05) and the coefficient of TenureP is significantly positive (p,= 0.0012, p < 0.01). This means that, ceterisparibus, audit tenure initially lowers DAC but later raises DAC if audit tenure is extended up to a certain number of years. Specifically, the negative coefficient of TenureF supports our learning effect hypothesis, and the positive coefficient of TenureP supports our excessive-familiarityhypothesis. In addition, the absolute value of p, is significantly larger than p, (p < 0.01, not tabulated), which guarantees that the decrease in the learning effect from an engagement is a concave function. We take the first derivative of DAC with respect to TenureF and find that “optimal” tenure (i.e., the tenure level associated with the lowest D A Q is 4.6 years.I7 For auditpartners (TenureA), the coefficients of Tenure-related variables weakly support our hypotheses ( p, = -0.0069, p = 0.1115 not tabulated and p, = 0.0007, p < 0.05). However, the relationship between DAC and TenureA is similar to the one we found for TenureF and “optimal” tenure is also equal to 4.6 years consistent with the findings in Figure 1.” Turning our attention to the estimates of Equation (6) shown in Panel B of Table 4, we can see evidence that the quality of financial reports issued by a company is lower when audit tenure is either short or long. In particular, the coefficients on the Short and Long dummy variables are both positive and significant at p < 0.1. The positive coefficient on the Short dummy variable supports our Learning Effect Hypothesis and the positive coefficient on the Long dummy variable supportsour Excessive-FamiliarityEffect Hypothe~is.’~ To test the hypothesis that there exist learning effect differences between Big 5 auditors and non-Big 5 auditors (HJ, we estimate Equation (5) using the two subsamples, below-optimal sample and above-optimal sample, and report the results in Table 5.

l7

If we let -0.0110

+ 2 x 0.0012 X TenureF equal zero, we find that TenureF = 4.6.

We will explain why the significance of TenureF-related variables is greater than that of TenureArelated variables in our subsequent findings reported in Tables 6 , 7 and 8. In particular, we find that the effect of Tenure on earnings quality is mainly due to audit-firm tenure rather than audit-partner tenure. l 9 Following Johnson et al. (2002), we also use the absolute value of DAC to test our hypotheses. Untabulated results show that although the signs of the coefficients of all significant control variables do not vary from Table 4, the results for Big5 and Tenure do not support our hypotheses. We also duplicated the methodology proposed by Kim et al. (2003) and separated the sample according to cash flows (CFO) of a firm as compared to other firms in the same industry. The evidence was inconclusive as compared to Kim et al. (2003). However, when we replace the comparative benchmark with CFO of the full sample across industries, our findings (not tabulated) are consistent with those in Kim et al. (2003). In addition, we also partitioned the full sample with respect to the sign of DAC (i.e., DAC > 0 and DAC < 0) and re-estimated Equation (4).We expect the coefficients on the tenure related variables to be more significant for the DAC > 0 sub-sample because both auditors and managers will, in general, have a stronger incentive to boost reported earnings when DAC > 0. The results show that, although there is no apparent difference in the significance level of TenureF-related coefficients between the full sample and the DAC > 0 sub-sample, the TenureA-relatedvariables do become more significant in the DAC > 0 sub-sample as compared with those in the full sample.

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Table 5 Marginal Effects of Big5 and Audit Tenure on Discretionary Accruals Panel A: Below-optimal sample

By TenureF (n=682)

By TenureA (n=778)

Variable

Coefficient (t-statistic)

Coefficient (t-statistic)

Intercept

-0.0876 (-1.2620)

-0,0327 (-0.4502)

Big5

-0.0193 (-1.7511)*

-0.0227 (-2,4088)"

Issue

0.0151 (1.8322)*

Age Size

-0.0012 (-2.1823)" 0.0060

0.0147

(1.3043)

0.0025

0.0109

Adjusted RZ

(1.9287)'

-0.0003 (-0.6488) (0.5202)

0.0051

Panel B: Below-optimal sample

By TenureF (n=682)

By TenureA (n=778)

Variable

Coefficient (t-statistic)

Coefficient (f-statistic)

Inremepi

-0.1327 (-1.741 l)*

-0.0749 (-1.0043)

Big5

0.0106

(0.4906)

Tenure

0.0033

(0.4781)

Big5 x Tenure

-0.0121 (-1.6490)'

0.0140

(0.6631)

0.0055

(0.9414)

-0.0136 (-2.0578)**

Issue

0.0126 (1.5892)

0.0133 (1,7719)"

Age

-0.0010 (-1.8184)'

-0.0000 (-0,1326)

0.0085

Size

0.0042

(1.6540)*

0.0107

0.0170

Adjusted RZ

(0.8393)

Panel C: Above-optimal sample DAC,r= Do + p,Big5,,+ p2Issues+ &SizeL,+ &,,Industry

Variable

+ P,,,,,,,Year

+ E~,

By TenureF (n=655)

By TenureA (n=559)

Coefficient (t-statistic)

Coefficient(t-statistic)

Intercept

-0.0084 (-0.1234)

-0.0853 (-1.3632)

Big5

-0.0051 (-0.7799)

-0.0030 (-0.4220)

Issue

0.0025

(0.3434)

Age

0.0001

(0.2295)

Size

0.0010

(0.2259)

Adjusted R2

-0.0050

0.0004

(0.0557)

-0.0004 (-1.1106) 0.0062

(1.5061)

-0.0003

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Table 5 (cont.) Marginal Effects of Big5 and Audit Tenure on DiscretionaryAccruals

Bv TenureF fn=655)

Bv TenureA 1n=559)

Variable

Coefficient (t-statistic)

Coefficient (t-statistic)

Intercept

-0.0348 (-0.4084)

-0.0592 (-0.7223)

Big5

-0.0393 (-0.7135)

-0.0581 (-1.1336)

Tenure

0.0035

(0.6487)

Big5 x Tenure

0.0038

(0.6389)

0.0064

(1.1355)

Issue

0.0060

(0.7703)

0.0017

(0.2158)

-0.0005 (-1.1395)

Age Size

0.0012 (0.2626)

Adjusted R2

0.0025

-0.0022 (-0.4231)

-0.0006 (-1.5552)

0.0059

(1.4345)

0.0014

*, **and*** indicate significance at p < 0.1, p < 0.05 andp < 0.01, respectively, on two-tailed tests. Fixed effects

of years and industries are included but not reported. We report asymptotic t-statistics based on White (1980) standard errors in parentheses if necessary. DAC

-

Big5

-

Tenure

=

TenureF TenureA Issue

=

Age Size

=

--

discretionary accruals computed according to theforward-looking model proposed by Dechow et al. (2003); a dummy variable equal to 1 if the company employs a Big 5 auditor and 0 otherwise; the length of the auditor-client relationship calculated by audit-partner tenure and audit-firm tenure respectively, defined as: the number of years for which an audit firm has provided audit services, the longest number of years for which either incumbent audit partner has attested to an audit; a dummy variable equal to 1 if the company issued new shares for cash during the year or in the previous two years and 0 otherwise; the number of years since listing date; firm size measured by the natural logarithm of total assets (in NT dollars).

In Table 5, Panels A and B show results using the below-optimal sample (i.e., for observations where audit-firm tenure and auditor tenure is at or below five years); Panels C and D show results using the above-optimal sample (i.e., for observations where auditfirm tenure and auditor tenure is above five years). Panel A shows the results of excluding the variable Tenure in the below-optimal sample. Consistent with Table 4 and prior literature, the coefficients of Big5 are all significantly negative in Panel A ( p, = -0.0193, p < 0.10 for TenureF and p, = -0.0227, p < 0.05 for TenureA).In Panel B, however, the coefficients of Big5 are both insignificant (PI= 0.0106 for TenureF and p, = 0.0140 for TenureA), which implies that Big 5 auditors are not better than non-Big 5 auditors in enhancing earnings quality in the initial period of an engagement. Furthermore, Panel B shows that the coefficients of TenureF and TenureA are both insignificant (p, = 0.0033 for TenureF and p, = 0.0055 for TenureA)while the coefficients of the cross term of Big5 and Tenure arebothsignificantlynegative (p,=-O.O121, p
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Wuchun Chi and Huichi Huang Journal of Contemporary Accounting & Economics 1 (2005)65-92

p < 0.05 for Big5 x TenureA). These findings indicate that the prominent role of Big 5 auditors with a new client can be attributed to their auditing expertise rather than to a pure Big 5 brand name effect. Finally, the results for the control variables are similar to those reported in Table 4. These results support our learning effect differentiation hypothesis, which states that Big 5 auditors are prominent in auditing expertise in the initial period with a new client due to their ability to gain client specific experience faster. Panels C and D in Table 5 show the results for the above-optimal sample. Interestingly, we find that the coefficient of Big5 is insignificant independent of whether we include the Tenure-related variables. In addition, the coefficients of Tenure and Big5 x Tenure are both insignificant. The results indicate that the major determinant of audit quality is that Big 5 auditors gain auditing expertise quickly in the initial period of an audit. However, during the later period of the engagement, the audit quality of Big 5 auditors loses its prominence as the non-Big 5 auditors catch up with the learning effect. Our empirical findings imply that the difference in learning ability between Big 5 and non-Big 5 auditors diminishes when audit tenure reaches a certain length. Therefore, whether opportunities to obtain learning experience will exist for non-Big 5 auditors after the enforcement of the Act is an open question. This concern is especially important if the requirement of mandatory rotation focuses on between audit firms rather than within an audit firm because the ability to obtain client-specific knowledge is different between audit firm types. In summary, the results indicate that the potential damage to earnings quality due to mandatory rotation between audit firms will be smaller for clients of Big 5 auditors and more serious for clients of non-Big 5 auditors. 4.3 Further analysis Since to a certain degree, there is an audit-firm effect embedded in the measurement of TenureA, we examine the joint effects of audit-firm tenure and audit-partner tenure by estimating Equation (7):

The results presented in Table 6 show that the coefficient of Big5 remains significantly negative ( p, = -0.0124, p < 0.05) and the signs of the coefficients of three of the control variables are all the same as those reported in Table 4. Furthermore, the significant coefficients of TenureF-related variables and insignificant coefficients of TenureA-related variables jointly indicate that the learning and excessive-familiarity effects are mainly due to audit firms rather than audit partners (P,= -0.0139, p < 0.05 and p,= 0.0014, p < 0.01; P, = 0.0033 and P, = -0.0003, both insignificant). In other words, the “positive” learning effect and the “negative” excessive-familiarity effect we find in the study imply that regulators should pay more attention to audit firms. Therefore, the expectation that audit quality would improve through a “moderate reform” in the Act, which requires auditors to rotate within an audit firm, might be overly optimistic because the effect on earnings quality mainly exists when audit firms are rotated rather than audit partners. Note that the insignificant results on the coefficients of TenureA-related variables in Table 6 may be related to a multicollinearity problem (the correlation between TenureF and TenureA is 0.873 in

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Table 6 Analysis of the Combined Effects of Audit-Firm Tenure and Audit-PartnerTenure

Variable

Coeftlcient

(t-statistic) (n=1337)

Intercept

-0.0568

(-1.1720)

Big5 TenureF

-0.0 124

(-2,0584)"

-0.0139

(-2.2680)"

TenureF2

0.0014

TenureA

0.0033

(0.5820)

TenureA2

-0.0003

(-0.6064)

Issue

0.0104

(1.8615)'

Age Size

-0.0008

(-2,5788)"'

0.0049

(1.4542)

Adjusted R2

0.0129

(2,8957)"'

*, "and *** indicate significance a t p c 0.1, p c 0.05 andp c 0.01, respectively, on two-tailed tests. Fixed effects of years and industries are included but not reported. We report asymptotic t-statistics based on White (1980) standard errors in parentheses if necessary.

DAC Big5 Tenure TenureF TenureA Issue Age Size

discretionary accruals computed according to theforward-looking model proposed by Dechow et al. (2003); a dummy variable equal to 1 if the company employs a Big 5 auditor and 0 otherwise; the length of the auditor-client relationship calculated by audit-partner tenure and audit-firm tenure respectively, defined as: the number of years for which an audit firm has provided audit services, the longest number of years for which either incumbent audit partner has attested to an audit, a dummy variable equal to 1 if the company issued new shares for cash during the year or in the previous two years and 0 otherwise; the number of years since listing date; firm size measured by the natural logarithm of total assets (in NT dollars).

Table 3). As a crude solution to this potential problem, we estimated Equation (7) by replacing TenureA with the residual from a regression of TenureF on a constant and TenureA and found that the results are unchanged (untabulated). We further estimate Equation (8) to re-examine the learning differentiationhypothesis by "below-optimal" and "above-optimal" samples, while taking into account the joint effects of audit-fiim and audit-partner tenure.

Table 7 reports that the auditing expertise of Big 5 auditors generates a significant coefficient on the cross-term Big5 x TenureF (P, = -0.0120, p c O.lO), consistent with the results of Panel B in Table 5. In addition, the insignificant coefficient of the cross-term

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Wuchun Chiand Huichi Huang Journal of Contemporary Accounting & Economics 1 (2005) 65-92

Table 7 Analysis of the Combined Effects of Audit-Firm Tenure and Auditor Tenure in the below- and aboveoptimal samples

Variable

Below-optimalsample (n -778) Coefficient (t-statistic)

Above-optimal sample (n = 559) Coefficient (t-statistic)

Intercept

-0.1315

(-1.7146)'

-0.0348

(-0.4086)

Big5

0.0102

(0.4624)

-0.0384

(-0.7070)

TenureF

0.0039

(0.5746)

0.0034

(0.6334)

TenureA

-0.0008

(-0.3185)

0.0001

(O.lOS8)

Big5 x TenureF

-0.0120

(- 1.7 141)"

0.0037

(0.6314)

Issue

0.0131

(1.5970)

0.0060

(0.7706)

Age Size

-0.0009

(- 1.5677)

-0.0004

(-1.1214)

0.0011

(0.2516)

Adjusted RZ

0.0084 0.0156

(1.6941)*

0.00 10

', "and *** indicate significance a t p < 0.1, p < 0.05 andp < 0.01. respectively, on two-tailed tests. Fixed effects of years and industries are included but not reported. We report asymptotic t-statistics based on White (1980) standard errors in parentheses if necessary. DAC Big5 Tenure TenureF TenureA Issue Age Size

discretionary accruals computed according to theforward-looking model proposed by Dechow et al. (2003); a dummy variable equal to 1 if the company employs a Big 5 auditor and 0 otherwise; the length of the auditor-client relationship calculated by audit-partner tenure and audit-firm tenure respectively, defined as: the number of years for which an audit firm has provided audit services, the longest number of years for which either incumbent audit partner has attested to an audit, a dummy variable equal to 1 if the company issued new shares for cash during the year or in the previous two years and 0 otherwise; the number of years since listing date; firm size measured by the natural logarithm of total assets (in NT dollars).

Big5 x TenureF (p, = 0.0037) in the "above-optimal'' sample is also consistent with the findings in Panel D of Table 5.

While Table 4 shows that auditor tenure does have certain effects on discretionary accruals, the results in Table 7 suggest that the effect of the auditor-client relationship on discretionary accruals is mainly due to audit firms rather than audit partners. Finally, to ascertain the effectiveness of the Act, we simulate a quasi-auditor-rotation sample to calculate T e n u r e A . Specifically, we gather the data in our sample which satisfy the following two conditions: (1) for a specific client, both audit partners changed once when we trace the observations from the sample year, and (2) before the auditpartner change, the audit firm had served the client for exactly five years. This results in 26 observations for the quasi-auditor-rotation sample. The variable TenureA in the

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87

quasi-auditor-rotation sample is closer to the spirit of the Act, which requires auditors to rotate within an audit firm.2o We re-estimate Equation (4) using the quasi-auditor-rotation sample to examine the effect on earnings quality of audit-partner change within an audit firm when the same audit firm has provided audit services to the client for five years. The empirical results in Table 8 reconfirm our hypothesis that Big 5 auditors’ prominent auditing expertise arises from their better ability to assimilate client-specific knowledge. The evidence shows that the coefficient of Big5 becomes insignificant in the quasi-auditorrotation sample. This suggests that audit quality of both auditor types is the same once non-Big 5 auditors acquire client-specific experience in the later period of an engagement. However, non-Big 5 auditors would not have the opportunity to obtain this experience if legislators required audit f m s to rotate off rather than require partner rotation only. Table 8 Analysis of the Audit-Partner Tenure Effect on DiscretionaryAccruals using the Quasi-Auditor-Rotation Sample

(t-statistic) (n = 26)

Variable

CoeMicient

Intercept

-0.1939

(-0.7227)

Big5

-0.0046

(-0.1672)

TenureA

-0.0867

(-0.7333)

TenureA2

0.0092

(0.5817)

Issue

-0.0235

(-0.8283)

Age Size

0.0027

(1.3333)

0.0225

(1.5614)

Adjusted R2

-0.0615

The quasi-auditor-rotation sample satisfies (1) for a specific client, both auditors of the client changed once when we trace the observations from the sample year, and (2) before the auditor change, the audit firm has served the client for exactly five years. *, ** and *** indicate significance at p < 0.1, p < 0.05 and p < 0.01, respectively, on two-tailed tests. Fixed effects of years and industries are included but not reported. We report asymptotic t-statistics based on White (1980) standard errors in parentheses if necessary. DAC

=

Big5 TenureA Issue

= = =

Age Size

= =

discretionary accruals computed according to theforward-looking model proposed by Dechow et al. (2003); a dummy variable equal to 1 if the company employs a Big 5 auditor and 0 otherwise; the longest number of years for which either incumbent audit partner has attested to an audit, a dummy variable equal to 1 if the company issued new shares for cash during the year or in the previous two years and 0 otherwise; the number of years since listing date; firm size measured by the natural logarithm of total assets (in NT dollars).

Because the insignificant results of Table 8 can be attributed to insufficient observations of the quasiauditor-rotation sample, we also gather a data set that satisfies the following conditions: (1) for a specific client, both audit partners of the client changed once when we trace the observations from the sample year, and (2) before the auditor change, the audit firm has served the client for at least five years. We obtained 90 observations and all results are qualitatively the same as those reported in Table 8.

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4.4 Robustness analysis

To test the robustness of our results with respect to the method used to calculate DAC, we perform the same empirical analysis using alternative measures of DAC. Specifically, we compute DAC using the following two approaches proposed by Kothari et al. (2005), where the return on assets is used as the performance index: (1) the performance-included modified Jones model, and (2) the performance-matched modified Jones model.

Performance-includedmodified Jones model: TACir= 6,

6, + 6,ASales,, + 4PPEi, + 6$0Aif + I),, + Assetsir-, ~

(9)

The variables in Equation (9) are change in sales, ASaleslt,and net property, plant, and equipment, PPE,I,both scaled by lagged total assets, Assetslcl.The return on assets, ROA,,, is computed as net income divided by lagged total assets. Following prior studies and Kothari et al. (2004), we estimate Equation (9) for each year and each industry, obtain the estimated coefficients, and subtract the change in accounts receivable (AREC,) from ASaleslt prior to estimating DAC.

Performance-matchedmodified Jones model: TAC,, = p,, +-

4 Assets,,.,

+ @ S a l e s j t+ p3PPEi,+ p4PPEit+ E,,

Here we follow a similar approach to get DAC from the modified Jones model for firm i in year t (DACfl).Match each firm-year observation with another from the same year and industry with the closest ROA in the current year. Finally, define the DAC of the performance-matched modified Jones model for firm i in year t (DACrTMM) as the firm i’s DAC from the modified Jones model in year t (i.e., DACi:) minus the matched firm’s DAC from the modified Jones model (DACYsmaIch). That is, DACzTMM = DACi:-- DACM,r,maIch . We re-examine our hypotheses using DAC as calculated by the performance-included modified Jones model and report the results in Table 9. Columns (1) and (4) show the results for the full sample. We can see that audit quality of Big 5 auditors is better than that of non-Big 5 auditors. Our learning effect hypothesis is still supported because both the coefficients of TenureF and TenureA are negative and significant at the conventional level. In addition, our excessive-familiarity effect hypothesis is also validated since the coefficients of TenureFL and TenureA2are positive and significant. Columns (2), (3), (5) and (6) show the results of the below-optimal sample (TenureF or TenureA is at or below five years). The significant negative coefficients of Big5 in columns ( 2 ) and ( 5 ) suggest that Big 5 auditors have better audit quality. However, the negative significant coefficients of Big5 x TenureF in column (3) and Big5 x TenureA in column (6) coupled with the insignificant coefficients of Big5 in columns ( 3 ) and (6) support our learning differentiation hypothesis. Finally, we use the performance-matched modified Jones model to measure DAC (not tabulated). Although all of the empirical results are still consistent with our conjectures in terms of the predicted signs of the estimated coefficients, some of the coefficients are insignificant.This implies that our findings might be sensitive to the discretionary accruals measurement.

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89

Table 9 Robustness Test Dependent Variable DAC calculated using the Performance-IncludedModified Jones Model

-

TenureF

Category Variables

TenureA

(1) n=1337

(2) n=682

(3) n=682

(4)

(5)

n=1337

n=778

(6) n=778

0.0400 (1.001)

-0.0135 (-0.213)

-0.0684 (-0.996)

0.0397 (0.9910)

0.0504 (0.847)

0.0081 (0.131)

Big5

-0.0134 (-2,095)"

-0.0193 (-1.722)'

0.0123 (0.629)

-0.0140 (-2.181)"

-0.0179 (-1.795)'

0.0151 (0.709)

Tenure

-0.0 109 (-2.834)***

0.0013 (0.211)

-0.0063 (-1.678)'

Intercept

0.0035 (0.546)

0.0006 (1.925)'

Tenure'

0.0010 (3.120):"

Issue

0.0102 (1.823)'

0.0184 (2.063)"

0.0156 0.0094 (2.0384)** (1.705)'

0.0178 (2.171)"

0.1063 (1.990)'*

Age

0.0003 (0.8915)

0.0005 (0.826)

0.0008 (2.001)"'

0.0005 (1.544)

0.0007 (1.295)

(1.744)*

0.004 (0.084)

0.0038 (0.822)

-0.0022 (-0.807)

-0.0039 (-0.992)

-0.0018 (-0.454)

Size

-0.0017 (-0.632)

Adjusted R2

-0.0123 (-1.755)'

-0.0127 (-1.917).

BigSxTenure

0.0116

0.0050

0.0189

0.0009

0.0071

0.0056

0.0132

', '* and '** indicate significance at p < 0.1, p < 0.05 andp < 0.01, respectively, on two-tailed tests.

DAC Big5 Tenure TenureF TenureA Issue Age Size

discretionary accruals computed according to the forward-looking model proposed by Dechow et al. (2003); a dummy variable equal to 1 if the company employs a Big 5 auditor and 0 otherwise; the length of the auditor-clientrelationship calculated by audit-partnerand audit-fm respectively, defined as: the number of years for which an audit firm has provided audit services, the longest number of years for which either incumbent audit partner has attested to an audit; a dummy variable equal to 1 if the company issued new shares for cash during the year or in the previous two years and 0 otherwise; the number of years since listing date; firm size measured by the natural logarithm of total assets (in NT dollars).

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5. Conclusion This study investigates the effects of audit-firm tenure and audit-partner tenure on discretionary accruals. Our work contributes to the mandatory auditor rotation literature by providing empirical evidence on the effects of auditor-switch within an audit firm. We select the sample in Taiwan to conform to the provisions of the Sarbane-Oxley Act of 2002 because the legislation requires audit partners to rotate within an audit firm. Prior empirical studies have only examined the effect of audit-firm tenure, which does not provide sufficient evidence related to the legislation. Our sample allows us to compute two measurements of audit tenure, TenureF and TenureA, representative of “audit-firm tenure” and “audit-partner tenure”, respectively. This provides for a unique opportunity to directly test the arguments of the Sarbanes-Oxley Act of 2002. We postulate three hypotheses: the learning effect hypothesis, the excessive-familiarity hypothesis, and the learning differentiation hypothesis. Our results support the learning effect hypothesis, which predicts that the ability to investigate accounting irregularities is a positive function of audit tenure, either audit-partner tenure or audit-firm tenure. We also find evidence consistent with the excessive-familiarity hypothesis, which anticipates lower earnings quality to be associated with lengthy audit tenure. Our empirical results show that the cut-off point of the positive and negative effects of familiarity is nearly five years. For the learning differentiation hypothesis, we obtain evidence to support that Big 5 auditors gain learning experience and build client-specific assets more quickly than non-Big 5 auditors. Furthermore, the difference in audit quality between Big 5 auditors and non-Big 5 auditors diminishes with the passage of time. By comparing the relative effects of auditfirm tenure and audit-partner tenure, we find that audit-firm tenure plays a key role in determining the quality of earnings. Although the evidence from our study and prior literature is based on data from non-mandatory auditor rotation regimes, we believe that our findings provide regulators with useful insights. More specifically, we suggest that legislators focus on regulating “audit firms” rather than “audit partners” if the final goal is to improve the quality of earnings. In addition, regulators should take into consideration the potential negative effects of mandatory auditor rotation of audit firms on non-Big 5 auditors. We believe that our findings provide valuable policy implications since this is the first paper aimed at examining the role of audit-partner rotation within an audit firm. Nonetheless, since earning opacity (Bhattacharya et al., 2003), accounting and auditing practice in corporate governance (Francis et al., 2003), and audit markets (Choi and Wong, 2004) are in essence different around the world, our findings may not fully reflect audit tenure effects in other countries. Furthermore, since both discretionary accruals and audit tenure are chosen by corporate managers or firms, the results of this paper are subject to the self-selection bias problem. There is also an issue of self-selection bias since managers choose Big 5 or non-Big 5 auditors. These econometric issues limit our contributions. Finally, our evidence is less powerful when we use the performance-matched modified Jones model.

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