Rev Account Stud (2013) 18:414–442 DOI 10.1007/s11142-012-9218-3
Qualitative audit materiality and earnings management Joseph Legoria • Kevin D. Melendrez J. Kenneth Reynolds
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Published online: 22 December 2012 Springer Science+Business Media New York 2012
Abstract This study investigates auditors’ propensity to rely on quantitative materiality thresholds to the exclusion of qualitative materiality thresholds. Specifically, we examine whether auditors are more likely to allow earnings management that is less than typical quantitative materiality thresholds but that nonetheless is qualitatively material. We use changes in tax expense as a proxy for earnings management. Our results indicate that companies with pre-managed earnings that would have missed the consensus analyst forecast are more likely to decrease their tax expense when the magnitude of the decrease is less than quantitative audit materiality thresholds. The results also indicate that firms are more likely to meet or beat the forecast when the amount of earnings management necessary to meet the analyst forecast is less than quantitative materiality. These results are consistent with auditors relying on quantitative materiality thresholds to the exclusion of qualitative materiality thresholds, i.e., the importance of meeting or beating the analyst forecast. Finally, we find that the ability to use tax expense reduction within quantitative materiality to meet or beat analysts’ consensus forecasts was significantly reduced by the SEC’s guidance on materiality in SAB-99 and by the passage of the Sarbanes–Oxley Act. J. Legoria Department of Accounting, Louisiana State University, Business Education Complex, Room 2800, Baton Rouge, LA 70803, USA e-mail:
[email protected] K. D. Melendrez Department of Accounting & IS, New Mexico State University, MSC 3DH, P.O. Box 30001, Las Cruces, NM 88003, USA e-mail:
[email protected] J. K. Reynolds (&) Department of Accounting, Florida State University, 408 Rovetta Business Annex, 821 Academic Way, Tallahassee, FL 32306, USA e-mail:
[email protected]
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Keywords
415
Audit materiality Earnings management
JEL Classification
M40 M42 L84
1 Introduction In his 1998 ‘‘Numbers Game’’ speech, former Securities and Exchange Chairman Arthur Levitt expressed his concern that managers and auditors were abusing the concept of ‘‘audit materiality’’ to manage earnings to meet important earnings benchmarks, such as analysts’ forecasts. Specifically, Chairman Levitt stated: Some companies misuse the concept of materiality. They intentionally record errors within a defined percentage ceiling. They then try to excuse that fib by arguing that the effect on the bottom line is too small to matter. If that’s the case, why do they work so hard to create these errors? Maybe because the effect can matter, especially if it picks up that last penny of the consensus estimate. When either management or the outside auditors are questioned about these clear violations of GAAP, they answer sheepishly … ‘‘It doesn’t matter. It’s immaterial.’’ In markets where missing an earnings projection by a penny can result in a loss of millions of dollars in market capitalization, I have a hard time accepting that some of these so-called non-events simply don’t matter. The danger of emphasizing quantitative over qualitative aspects of materiality has long been recognized in the conceptual definition of materiality. FASB Concept Statement No. 2 (paragraph 132) defines materiality (italics ours) as the ‘‘… magnitude of an omission or misstatement of accounting information that, in the light of surrounding circumstances, makes it probable that the judgment of a reasonable person relying on the information would have been changed or influenced by the omission or misstatement.’’ (FASB 1980) The statement further emphasizes that the magnitude ‘‘… by itself, without regard to the nature of the item and the circumstances in which the judgment has to be made, will not generally be a sufficient basis for a materiality judgment’’ (p. 7). In addition to the profession’s long-standing definition of materiality and the former Chairman’s admonitions, the US Supreme Court (1976), the Securities and Exchange Commission (SEC 1999), and the AICPA (2006, SAS 107) have issued guidance concerning the qualitative aspects of materiality. Despite regulatory and professional guidance, anecdotal evidence suggests auditors rely mainly on quantitative materiality estimates. Our study examines whether firms manipulate earnings within quantitative audit materiality guidelines to meet analysts’ forecasts. Thus the study addresses a timely research question of importance to standard setters, regulators, financial statement users, and audit practitioners. We use accrual-based changes in tax expense as a specific measure of earnings management. We examine whether firms manage earnings by changing their effective tax rates from the third to fourth quarter (Dhaliwal et al. 2004; Cook et al. 2008; Gleason and Mills 2008) to meet earnings targets. Our tests are conducted on
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a sample of 11,829 firm-year observations for the sample period from 1990 to 2009 that could meet their forecasted earnings only through tax expense management. The results indicate that companies for which the analysts’ consensus forecast exceeds earnings before tax expense management (‘‘pre-managed earnings’’) by an amount less than quantitative audit materiality have a greater probability of decreasing tax expense to meet the forecast. We also find that firms are more likely to meet or beat the analysts’ consensus forecast when the amount of tax management needed to achieve the forecast is less than quantitative materiality. Finally, we examine the impact of the Securities and Exchange Commission’s Staff Accounting Bulletin 99 (SAB-99), which emphasized the fact that materiality has qualitative implications rather than just quantitative. We also examine the impact of the passage of the Sarbanes–Oxley Act and find that the likelihood of meeting the forecast was significantly reduced by both acts. These results suggest that auditors have not simply ignored potential manipulations to the financial statements but also that they have tended to emphasize the quantitative elements of materiality at the expense of the qualitative elements. Dhaliwal et al. (2004) show that firms are more likely meet or beat analysts’ forecasts when the magnitude of earnings management needed to meet or beat analysts’ forecasts is smaller. Our study differs from theirs in at least two ways. First, our emphasis is on the investigation of audit materiality, and our primary contribution is in furthering our understanding of audit materiality. We use the tax accrual as a tool in that investigation. Second, we contribute to the findings of Dhaliwal et al. and the related literature stream (e.g., Cook et al. 2008; Gleason and Mills 2008) by showing that this relation is conditional on whether the magnitude of earnings management needed to meet or beat analysts’ forecasts is within quantitative audit materiality guidelines. Our findings regarding audit materiality are consistent with recent experimental studies by Libby and Kinney (2000), Ng and Tan (2003), and Nelson et al. (2005). The results also support the concerns expressed by regulators (i.e., Levitt 1998) concerning the use of materiality estimates. Our findings also provide support for recent regulatory focus on qualitative factors to be considered by auditors when determining materiality (SEC 1999; AICPA 2006, SAS No. 107). The remainder of our study proceeds as follows. The next section discusses the prior literature and develops our hypotheses. Section 3 describes the methodology and sample selection, and Sect. 4 presents our results. Section 5 provides sensitivity analysis, and Sect. 6 concludes.
2 Prior literature and hypotheses development 2.1 Audit materiality research Messier et al. (2005) indicate that the concept of materiality has mattered to accounting and auditing practitioners and regulators dating back to the 1970s. At that time, the Financial Accounting Standards Board (FASB) issued a discussion
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memorandum related to materiality (FASB 1975, 3), which culminated with the formal definition in the FASB’s Statement of Financial Accounting Concepts No. 2 (SFAC 2).1 Associated with this, a large body of academic research developed in the 1970s and early 1980s study materiality. Holstrum and Messier (1982) provide a detailed review of that literature, finding that the percentage effect on net income was the single most important factor used in computing materiality, while a distant second was the effect on the trend of earnings. However, they concluded that the literature up to 1982 is difficult to integrate into any set of comprehensive implications for both audit practice and policy making, due to the divergent research topics investigated and the different views of materiality among financial statement users, preparers, and auditors. Messier et al. (2005) note that, while there was little research related to materiality in the mid 1980s, the integration of the audit-risk model into auditing standards and public accounting firms’ audit methodologies resulted in a renewed interest in the topic in the late 1980s. The research conducted since the late 1980s includes archival studies using audit-related sources such as audit manuals (Steinbart 1987; Friedberg et al. 1989; Martinov and Roebuck 1998), audit working papers (Robinson and Fertuck 1985; Icerman and Hillison 1991; Wright and Wright 1997) and other publicly available data (Chewning et al. 1989; Wheeler et al. 1993). In addition, experimental research has studied materiality by examining the materiality judgments of financial statement users (Haka et al. 1986; Fisher 1990), auditors (Krogstad et al. 1984; Carpenter and Dirsmith 1992), and other users such as judges and lawyers (Jennings et al. 1987). The archival and experimental studies over this period are consistent with the research from the earlier periods with respect to net income being the most common factor used in determining materiality. In recent years, there has been a renewed interest among regulators, standard setters, and audit practitioners with respect to audit materiality. For example, the SEC (1999) issued Staff Accounting Bulletin (SAB) No. 99, Materiality, which stated that auditors should not strictly rely on quantitative measures when assessing materiality. Rather, auditors should also consider qualitative factors, such as the effect of an item on meeting earnings forecasts, when determining audit materiality. The major accounting firms formed the Big Five Audit Materiality Task Force and issued a report in October 1998 (Big Five Audit Materiality Task Force 1998). The task force’s conclusions were then considered by the Auditing Standards Board (ASB) and resulted in the issuance of Statement on Auditing Standards (SAS) No. 89 and 90 (AICPA 2004). These two standards require that management sign off on waived adjustments and that any waived adjustment be reported to the firm’s audit committee. In addition, guidance requiring auditors to consider qualitative as well as quantitative factors in determining materiality has long been a component of the professional auditing standards and was repeated and emphasized in Statement on 1
Statement of Financial Accounting Standards No. 162 (FASB 2008) describes the hierarchy of Generally Accepted Accounting Principles (GAAP). The FASB’s Concept Statements are not listed in the GAAP hierarchy. Rather, they provide a theoretical structure to evaluate current accounting standards and help in the formulation of future accounting standards. Thus the formal definition of materiality as stated in the FASB’s Concepts Statement No. 2 does not constitute GAAP. Nevertheless, SFAC 2 did have an impact on the how the concept of materiality has developed in both accounting practice and academia.
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Auditing Standards No. 107, Audit Risk and Materiality in Conducting an Audit (AICPA 2006). Messier et al. (2005) indicate that, given the renewed interest of materiality by standard setters and regulators, academic research needs to integrate the prior research on materiality and identify the implications of this renewed interest and prior research on future research. Their suggestions for future research included evaluating materiality decisions. Three recent experimental studies (Libby and Kinney 2000; Ng and Tan 2003; Ng and Tan 2007) indicate that auditor decisions to waive an adjustment are associated with whether booking the adjustment will cause the firm to miss analysts’ forecasts of earnings. Nelson et al. (2005) investigate how auditors apply the cumulative approach versus the current-period approach when assessing materiality misstatements.2 They note that either approach can result in a higher quantitative materiality threshold. Analyzing auditors decisions across various experimental contexts, they find that auditors are less likely to require clients to book the misstatement under whichever approach makes the misstatement appear less quantitatively material. The study concludes that standard setters should mandate that auditors apply both approaches and only waive adjustments that are not material under both approaches for quantitative materiality assessments. The findings of these studies are consistent with the regulatory concerns that quantitative factors may dominate qualitative factors in determining materiality. We contribute to this literature stream by providing archival evidence of auditors’ behavior in determining materiality. In the next two sections, we review the literature on earnings benchmarks and earnings management and link those streams of research to the audit materiality literature to formulate our hypotheses. 2.2 Earnings benchmarks The ability to meet an earnings target is considered to be among the most important factors in assessing whether an amount is qualitatively material. Dechow and Skinner (2000, p. 248) conclude that ‘‘managers have incentives to ‘beat benchmarks’, implying that firms beating benchmarks are potentially more likely to be engaging in earnings management’’—a view consistent with Levitt (1998). Prior research has documented that managers manipulate earnings to meet or exceed earnings benchmarks (Burgstahler and Dichev 1997a; Degeorge et al. 1999). Additional studies (Bartov et al. 2002; Kasznik and McNichols 2002; Lopez and Rees 2002) have shown that the market rewards (penalizes) firms for meeting (missing) earnings benchmarks. Recent research has also shown that meeting analysts’ forecasts has emerged as the most important earnings benchmark (Brown and Caylor 2005) and that the relative frequency of meeting or beating analysts’ forecasts of earnings has increased over time (Bartov et al. 2002; Matsumoto 2002). Abarbanell and Lehavy (2003) find that firms with buy recommendations are more 2
Under the cumulative approach, the auditor compares the amount of the misstatement at the end of the accounting period to net income, whereas with the current-approach, the auditor compares the amount of the misstatement added in the current period to net income.
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likely to manipulate earnings to meet analysts’ forecasts. Hence, in this study we define qualitative materiality as the ability to meet or beat the consensus analyst forecast. We assess the materiality of an expense-reducing accrual, discussed in the next section. 2.3 Earnings management and accruals We measure earnings management by use of accruals. General accrual models tend not to perform very well and create substantial noise when attempting to assess measures where the magnitude is critical. In addition, Healy and Wahlen (1999) note that, while academic research has documented earnings management when using broad measures (e.g., accruals), and samples where motivation for earnings manipulation exists, much of this research is of little value to standard setters and regulators. They state that identifying specific accruals used to manage earnings would be of greater value to standard setters and regulators. Therefore we concentrate on specific accruals rather than general accrual models. Nelson et al. (2003) point out that, other than a few exceptions, academic research is lacking with respect to specific methods used to manipulate earnings. Studies that do employ specific accruals have tended to concentrate on industryspecific measures, such as loan loss provisions (Beaver et al. 1989; Moyer 1990), provision for bad debts (McNichols and Wilson 1988), and property-casualty insurance claim reserves (Petroni 1992). Nelson et al. (2003) also find that tax expense is among the specific accruals that used by firms to manipulate earnings. For reasons discussed in the following two paragraphs, we use tax expense to test whether firms manage their earnings within audit materiality to meet analysts’ forecasts. Dhaliwal et al. (2004) examine whether firms manage earnings by changing their effective tax rates (i.e., use tax expense to manage earnings) between the third and fourth quarters to meet earnings targets. They indicate that the computation of tax expense creates the necessary information asymmetry between management and financial statement users and is a specific accrual that is material to a broad range of firms. Also, since tax expense is one of the last accounts closed before the financial statements are released, it represents the final tool that firms can use to manage earnings to meet earnings benchmarks. The study finds that fourth quarter decreases in tax expense are used to manipulate earnings to meet earnings targets. Cook et al. (2008) extend Dhaliwal et al. (2004) and find that firms use effective tax rates (i.e., tax expense) to manage earnings and that the model used by Dhaliwal et al. (2004) is robust to including abnormal accruals and deferred taxes. In addition, they find that Sarbanes–Oxley did not eliminate the opportunity for firms to use tax expense to manage earnings. Gleason and Mills (2008) examine how market participants view firms that change their effective tax rates (ETR) in the fourth quarter to meet analyst forecasts and whether the market reacts differently when firms meet analyst forecasts by using tax expense compared with not using it. They argue that, since market participants can see changes in effective tax rates and compare them with prior period effective tax rate changes and determine whether the change in ETRs would
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allow firms to meet analyst forecast, there may be a different market reaction between those two groups of firms. The study finds that the firms that managed earnings to meet earnings targets using effective tax rates had a smaller positive market reaction compared with firms that used other accruals to do so. Taken as a whole, the findings of Gleason and Mills (2008) are consistent with firms having incentives to use tax expense as ‘‘last chance’’ earnings management to meet analyst forecast given the rewards the market gives firms for meeting earnings targets. 2.4 Hypotheses The research reviewed above indicates firms have incentives to meet earnings benchmarks (Degeorge et al. 1999; Dechow and Skinner 2000) and that analysts’ forecasts are among the most important of those benchmarks (Brown and Caylor 2005; Graham et al. 2005).3 The literature also indicates that firms manipulate earnings to meet those forecasts (Abarbanell and Lehavy 2003) and use tax expense to do so (Dhaliwal et al. 2004). If auditors act to constrain earnings management that exceeds materiality, then decreasing tax expense should be more likely when the amount of the decrease is less than the auditor would consider material. Thus we formulate our first hypothesis as follows: H1 Controlling for the distance between pre-managed earnings and analysts’ consensus forecasts, firms with earnings before tax expense management that miss analysts’ forecasts by an amount less than quantitative materiality are more likely to decrease their effective tax rate in the fourth quarter relative to the third quarter. The above discussion suggests that auditors may emphasize quantitative factors (i.e., a percentage of income) rather than qualitative factors (i.e., meeting earnings forecasts) in determining materiality. Being able to reduce tax expense sufficiently to hit a forecast that otherwise would have been missed characterizes a situation that qualitatively is material. If auditors focus on quantitative rather than qualitative materiality factors, then we expect that firms decreasing their tax expense are more likely to meet the forecast if the amount of the adjustment is less than quantitative materiality. This leads to our second hypothesis: H2 Controlling for the distance between pre-managed earnings and analysts’ consensus forecasts, firms with earnings before tax expense management that miss analysts’ forecasts by an amount less than quantitative materiality are more likely to meet or beat the analysts’ consensus forecast than firms that miss analysts’ forecasts by an amount more than quantitative materiality. Finally, if auditors have tended to rely on quantitative factors in assessing materiality (H2), then that emphasis may have been curtailed by two regulatory actions. The SEC issued SAB-99 in 1999, emphasizing that auditors should not strictly rely on quantitative measures when assessing materiality. Two years later, Congress passed the Sarbanes–Oxley Act, which increased regulatory oversight of 3
For instance, Graham et al. (2005) surveyed more than 400 CFOs and found that the analyst forecast and meeting the prior year’s quarterly earnings are the most important benchmarks.
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the public accounting profession and also added significant sanctions to management. If either SAB-99 or Sarbanes–Oxley were effective in pushing auditors to consider qualitative materiality factors, then a company’s ability to meet or beat the forecast should decrease significantly after the regulation. This leads to our final set of hypotheses: H3a Controlling for the distance between pre-managed earnings and analysts’ consensus forecasts, firms with earnings before tax expense management that miss analysts’ forecasts by an amount less than quantitative materiality are less likely to meet or beat the analysts’ consensus forecast after Staff Accounting Bulletin No. 99. H3b Controlling for the distance between pre-managed earnings and analysts’ consensus forecasts, firms with earnings before tax expense management that miss analysts’ forecasts by an amount less than quantitative materiality are less likely to meet or beat the analysts’ consensus forecast after passage of the Sarbanes–Oxley Act.
3 Methodology We adapt a model from Dhaliwal et al. (2004) to test our first hypothesis. Specifically, we test whether a reduction in tax expense is associated with firms meeting their earnings targets within quantitative audit materiality thresholds. The model is specified as follows (see ‘‘Appendix’’ for definitions of variables): ProbðETR DECREASEt Þ ¼ a0 þ a1 QUALITATIVE EMt þ a2 INDUCED CHANGEt þ a3 TAX OWEDt þ a4 ETR3t þ a5 PROFITt þ a6 SIZEt þ a7 DEBT=ASSETt þ a8 MBVt þ a9 MISS AMOUNTt þ a10 BIG4t þ a11 TENUREt þ YEAR dummies þ et
ð1Þ
ETR_DECREASE is a proxy for tax expense management. We set ETR_DECREASE equal to one if the firm decreased its annualized tax expense from the third quarter to fourth quarter and zero otherwise. While Dhaliwal et al. (2004) use the magnitude of the change in effective tax rate, we do not try to predict the amount by which firms will change their effective tax rate. Therefore we examine whether the firm decreased its effective tax rate. Earnings management within qualitative audit materiality (QUALITATIVE_EM) is our variable of interest. In theory, firms that miss the analyst forecast by a small percentage may be more likely to decrease tax expense to meet or beat the forecast rather than firms that miss the forecast by a small amount (i.e., magnitude), or vice versa. Our test variable, QUALITATIVE_EM, captures both concepts and thus both possibilities. The variable does not simply measure the magnitude by which a firm would have missed the forecast by without tax expense management. Instead, it captures whether this amount is less than 5 % of NI (the normal rule of thumb materiality amount). Thus, by construction the variable captures whether the miss amount is some percentage of total earnings, within audit materiality. To measure QUALITATIVE_EM in such a manner to capture the concepts described in the previous paragraph requires us to estimate three items: the amount
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by which the forecast would have been missed absent tax earnings management (MISS_AMOUNT), quantitative audit materiality (MATERIALITY_NI), and whether the firm would have missed or met the benchmark without the change. MISS_AMOUNT is measured as the IBES consensus forecast of earnings (Burgstahler and Eames 2006) minus the firm’s pre-managed earnings, divided by weighted average shares used to compute basic EPS. Following Dhaliwal et al. (2004), pre-managed earnings are computed as pre-tax income less a proxy for unmanaged tax expenses (actual pre-tax earnings times the annual ETR reported for the third quarter). Since MISS_AMOUNT is the difference between the analysts’ consensus forecast and pre-managed earnings, it captures a firm’s incentive to manage earnings. We estimate audit materiality quantitatively based on income, MATERIALITY_NI, as 5 % of net income divided by weighted average shares. The 5 % of income ‘‘rule of thumb’’ is the most widely used quantitative threshold for audit materiality for public companies (Holstrum and Messier 1982; SEC 1999; Brody et al. 2003; Messier et al. 2005; Nelson et al. 2005).4 Using MISS_AMOUNT and MATERIALITY_NI, we set QUALITATIVE_EM equal to one if MISS_AMOUNT is less than MATERIALITY_NI (i.e., where the difference between pre-managed earnings and the analysts’ consensus forecast is less than quantitative audit materiality) and zero otherwise. Finally, we restrict the analysis only to companies whose earnings would have fallen below the forecast without use of tax expense management. These firms have the incentive, and may have the ability, to manipulate tax expense to meet earnings targets. They have the incentive since failing to meet analysts’ forecasts results in negative stock market reactions (Bartov et al. 2002; Kasznik and McNichols 2002; Lopez and Rees 2002; Skinner and Sloan 2002). They may have the ability to manipulate tax expense (i.e., decrease tax expense) within quantitative materiality thresholds to meet those targets if the auditors rely on the quantitative materiality estimation. Although MISS_AMOUNT is a component of our definition of QUALITATIVE_EM, we also include it as a control variable in our models. We wish to avoid making false attributions to materiality, if in fact quantitative materiality is simply correlated with the amount by which the company would have missed the forecast and if the magnitude of that miss is the primary determinant of whether or not the company will meet the forecast. In other words, firms with premanaged earnings that miss the forecast by a smaller amount might be more likely to hit the forecast simply because it may be more difficult to manage earnings by large amounts. Including MISS_AMOUNT controls for the possibility that the likelihood of a firm ultimately achieving the forecast might vary with how close earnings before tax expense management are to the forecast. We include additional control variables for induced tax change (INDUCED_CHANGE), taxes owed (TAX_OWED), and the firm’s annual tax expense reported at 4
Gleason and Mills (2002) investigate the factors that cause firms to disclose and record contingent liabilities. They find that firms also use the ‘‘5 % rule of thumb’’ as firms tended to not disclose and record contingent liabilities when the amount was greater than 5 % of pre-tax income or 5 % of total assets. Since these firms are SEC filers, they would require an audited financial statement, and thus the ‘‘5 % income rule of thumb’’ used by Gleason and Mills (2002) provides additional support for its use as a quantitative materiality threshold.
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the third quarter (ETR_Q3). We measure INDUCED_CHANGE by first computing unexpected earnings, measured as the difference between IBES actual and forecasted earnings per share. We then convert the difference per share to total dollars and gross it up using the applicable U.S. statutory tax rate to obtain an estimate of unexpected pre-tax earnings. Next, we multiply this estimate by the difference between ETR_Q3 and the statutory tax rate to obtain the unexpected tax. Finally, we divide the unexpected tax by pre-tax accounting income to produce our measure of induced tax change (INDUCED_CHANGE). As Dhaliwal et al. (2004) note, INDUCED_CHANGE controls for changes in tax expense due to exogenous factors such as unanticipated changes in earnings or errors in the prediction of earnings. Since the direction of the unanticipated earnings surprises and misestimation of earnings is difficult to predict, we do not predict a sign on INDUCED_CHANGE. Taxes Owed (TAX_OWED) is measured as the difference between taxes payable and tax refunds, scaled by pre-tax income. We expect a negative coefficient on TAX_OWED. Finally, we include the firm’s annual tax expense at the third quarter (ETR_Q3) to control for the amount by which a firm could decrease its tax expense. The predicted sign on ETR_Q3 is positive since, the higher the third quarter effective tax rate is for a firm, the more likely it is that the firm will be associated with a decrease in tax expense in the fourth quarter. We also include control variables to capture the differential earnings management incentives related to profitability, firm size, debt, and growth. Prior findings (Hayn 1995; Burgstahler and Dichev 1997b; Brown 2001) suggest that profitreporting firms have greater market based incentives, compared with loss-reporting firms, to manipulate earnings to meet analysts’ forecasts. To control for these incentive differences, we include the variable PROFIT, which is an indicator variable set equal to one if the firm reports a profit and zero otherwise.5 We predict a positive sign on PROFIT. Larger firms are followed by more analysts (Bhushan 1989) and have less optimistic bias in analysts’ forecasts (Brown 1997; Das et al. 1998; Matsumoto 2002), which suggests that these firms may have greater incentives to manipulate earnings to meet the forecasts. We measure firm size (SIZE) as the natural log of the market value of equity. Consistent with the prior literature, we predict a positive coefficient on SIZE. Watts and Zimmerman (1986) find that firms with high debt ratios have incentives to manipulate earnings upward to avoid violating debt covenants. We include the debt to asset ratio (DEBT/ASSET), measured as total debt divided by total assets, to control for this incentive and predict a positive coefficient. Skinner and Sloan (2002) and Brown (2001) document that growth firms have greater incentives to avoid negative earnings surprises. We control for firm growth by using the firm’s market-to-book ratio (MBV). We predict that MBV will be positively related to ETR_DECREASE. Finally, we include BIG4 and TENURE as measures of audit quality. BIG4 is an indicator variable equal to 1 if the firm is audited by a Big N auditor and zero otherwise. TENURE is measured as the number of continuous years that the auditor audits the firm. We expect that both
5
Excluding loss firms does not qualitatively change the results.
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BIG4 and TENURE will be negatively related to ETR_DECREASE because higher audit quality should restrict earnings management. Our second hypothesis relates to meeting or beating the consensus analysts’ forecasts. We test H2 with the following logistic model (see ‘‘Appendix’’ for definitions of variables): ProbðBEATt Þ ¼ a0 þ a1 QUALITATIVE EMt þ a2 PROFITt þ a3 SIZEt þ a4 MBVt þ a5 MISS AMOUNTt þ a6 BIG4t þ a7 TENURETt þ a8 CFOt þ a9 ROAt þ a10 NUM ANALYSTt þ a11 INDUSTRYt þ YEAR dummies þ et
ð2Þ
BEAT, the dependent variable used to test H2, is a dichotomous variable equal to one if the firm just meets or beats analysts’ expectations forecast by one-cent and zero otherwise (Frankel et al. 2002; McVay et al. 2006; Reichelt and Wang 2010).6 We examine clients that just meet or beat the analysts’ earnings forecast within one cent since these firms are more likely to have missed their consensus earnings forecast by an amount within quantitative audit materiality and also reduced tax expense to just meet or beat the consensus forecast. On the other hand, firms that beat their analyst forecast by larger amounts are more likely to have done so due to firm effort and performance rather than earnings management. As we did for H1, we add controls to prevent drawing materiality inferences on what might simply be an artifact of the magnitude by which the benchmark is missed. We use ROA, PROFIT, and CFO to control for firm performance (Brown 2001; Frankel et al. 2002). We use the natural log of market value of equity (SIZE) and the market to book ratio (MBV) to control for firm size and growth (Matsumoto 2002) and audit firm size (BIG4) and the length of the auditor–client association (TENURE) to control for audit quality (Reichelt and Wang 2010). We expect that profitable firms, larger firms, and firms with more growth opportunities are more likely meet or beat analyst forecasts. We also expect that, if auditor quality restricts earnings management, BIG4 and TENURE will be negatively related to the probability of meeting or beating the analyst forecast. Finally, we expect that better performing firms are more likely to meet or beat analyst forecasts. We also control for analyst following (NUM_ANALYST) and industry using Fama and French (1997) industry classifications. Our final hypotheses relate to the impact of SAB-99 and Sarbanes–Oxley on quantitative materiality. We test the impact of SAB-99 and Sarbanes–Oxley on the probability of meeting or beating the forecast. Since the post-SAB 99 and postSarbanes–Oxley eras overlap, we include both regulatory changes in a single model to avoid wrongly attributing results to either of the two effects. We modify model 2
6
We use the last consensus forecast from IBES before the annual earnings announcement to measure the forecast error. We examined the forecasts, and, for observations with a third quarter earnings announcement date in IBES, 98 % of the forecasts used in our analyses are after the third quarter earnings are announced. Excluding those observations for which the forecast was prior to the third quarter earnings announcement date and those observations for which the third quarter earnings announcement date was not available does not affect our results.
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by including intercept terms for both SAB 99 and Sarbanes–Oxley and interacting each with QUALITATIVE_EM. Our focus is on the interaction terms. A significant negative interaction between SAB 99 and QUALITATIVE_EM would indicate an attenuation in the probability of using a reduction in tax expense to meet or beat the analysts’ forecasts. A significant negative interaction between SOX and QUALITATIVE_EM would indicate the same for Sarbanes–Oxley. 3.1 Sample selection Table 1 summarizes the sample selection process. Our sample consists of firms for which we could obtain financial data from the Compustat Full Coverage and Research Database (COMPUSTAT) and actual and forecasted earnings data from the Institutional Brokers’ Estimate System (IBES) detailed database from 1990 to 2009. We require that all firm-year observations meet the following conditions: (1) Compustat data is available to measure all financial statement variables (2) IBES data is available to measure consensus analysts’ forecast and the analysts’ forecast errors, and (3) pre-managed earnings are less than the consensus analysts’ forecast. These data limitations result in 69,079 firm-year observations between 1990 and 2009 with available Compustat data. We delete 32,876 firm-year observations without the necessary IBES data. Finally, we delete 24,374 firm year observations with pre-managed earnings greater than the consensus analysts’ forecast. By deleting these observations, our sample includes only those firms that had premanaged earnings below the forecast and were able to meet the forecast only by using tax expense to manage earnings. In other words, our sample only includes observations for which analysts’ forecasts exceed pre-managed earnings. After all data screens are imposed, 11,829 firm-year observations remain in the sample. Our sample selection process ensures that the sample excludes firms that have already met or beaten consensus analyst earnings forecasts without having to use tax expense to manage earnings. For these firms, there is no incentive to use tax expense to manipulate earnings. As a result, the firms that remain in the sample consist of those firms that have pre-managed earnings below the consensus analyst forecast. For these remaining firms, the only way for them to meet or beat analysts’ forecast is to manage tax expense. Thus the variable of interest, QUALITATIVE_EM, is measured to capture those firms that have pre-managed earnings that are below the consensus analyst forecast
Table 1 Sample selection: sample year 1990–2009 Number of firms Firm-year observations with necessary Compustat data
69,079
Less: Firm-year observations without necessary IBES data
32,876
Less: Firm-year observations with pre-managed earnings greater than the most recent mean analyst forecast before earnings announcement
24,374
Total observations
11,829
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and less than quantitative materiality. Thus firms with reported earnings that achieved analyst earnings forecast managed their tax expense to do so.
4 Results 4.1 Quantitative audit materiality thresholds In Table 2, we profile four common bases used to compute quantitative audit materiality thresholds (Brody et al. 2003, p. 158). Those bases are total assets (MATERIALITY_TA), total sales (MATERIALITY_SALES), net income (MATERIALITY_NI) and a conservative blend of the three bases (MATERIALITY_BLEND). MATERIALITY_TA and MATERIALITY_SALES are computed as 1 % of total assets and 1 % of total sales divided by weighted average shares used to compute basic EPS, respectively. MATERIALITY_NI is computed as 5 % of net income divided by weighted average shares used to compute basic EPS. MATERIALITY_BLEND is computed by averaging MATERIALITY_TA, MATERIALITY_SALES, and MATERIALITY_NI. We provide these statistics for the various bases for two reasons: (1) to provide descriptive statistics for a large sample firms for common audit materiality levels and (2) to compare the various materiality levels to determine the most restrictive materiality threshold to use in measuring QUALITATIVE_EM, which is our variable of interest. Panel A profiles the 36,203 observations with the necessary Compustat and IBES data (Table 1). The mean (median) per share amounts for MATERIALITY_TA, MATERIALITY_SALES, MATERIALITY_NI, and MATERIALITY_BLEND are 0.413 (0.200), 0.289 (0.180), 0.080 (0.059), and 0.260 (0.165), respectively. These data indicate that firms on average could manipulate earnings by as much as 41 cents per Table 2 Descriptive statistics of audit materiality rules of thumb N
Mean
SD
Quartile 1
Median
Quartile 3
Panel A Descriptive statistics for all observations for period 1990 to 2009 using common materiality rules of thumb MATERIALITY_TA
36,203
0.413
0.765
0.102
0.200
0.415
MATERIALITY_SALES
36,203
0.289
0.389
0.091
0.180
0.343
MATERIALITY_NI
36,203
0.080
0.124
0.032
0.059
0.099
MATERIALITY_BLEND
36,203
0.260
0.337
0.089
0.165
0.310
N
Quartile 1
Median
Quartile 3
Panel B Materiality distributions of sample firms used in regression based on quartiles from Panel A MATERIALITY_TA
11,829
3,228
6,316
MATERIALITY_SALES
11,829
3,432
6,616
9,509
MATERIALITY_NI
11,829
4,651
7,674
10,094
MATERIALITY_BLEND
11,829
3,520
6,658
9,494
Sample period is from 1990 to 2009
123
9,315
Qualitative audit materiality and earnings management
427
share using total assets and 29 cents per share using total sales and still be within typical quantitative materiality thresholds. In contrast, using net income as the materiality base results in a threshold of only 8 cents per share. Panel B profiles the quantitative materiality thresholds reported in Panel A for the 11,829 sample firms used in our multivariate analysis. Using the quartiles for MATERIALITY_NI reported in Panel A, 4,651 firms have a quantitative materiality threshold at the 25th percentile (0.032 per share), 7,674 have a quantitative materiality threshold at the median (0.059), and 10,094 have a quantitative materiality threshold at the 75th percentile (0.099). The distributions of the other three materiality bases (MATERIALITY_SALES, MATERIALITY_TA, and MATERIALITY_BLEND) are similar, but MATERIALITY_NI has the highest cumulative number of observations for each quartile of quantitative materiality thresholds. After reviewing Panels A and B, we concluded that quantitative materiality based on net income (MATERIALITY_NI) is the most appropriate choice for our multivariate analysis. Not only is MATERIALITY_NI the most common quantitative materiality estimate used in practice for publicly traded firms, but it also imposes the most restrictive materiality threshold of the four bases. 4.2 Descriptive statistics for sample observations We report descriptive statistics related to our sample observations and regression variables in Table 3. Panel A shows that the mean for ETR_Q3 and ETR_Q4 are 0.354 and 0.347, respectively, which are similar to the means reported in Dhaliwal et al. (2004) for their 4,656 sample observations over the period 1986 to 1999 (0.363 and 0.360, respectively). The means for ETR_Q3 and ETR_Q4 mirror the current U.S. statutory tax rate of 35 %. Firms in our sample missed their forecast on average by approximately five cents per share (mean forecast error = -0.047) compared with -0.005 for the sample observations used in Dhaliwal et al. (2004).7 MISS_AMOUNT, the difference between pre-managed earnings and the analysts’ consensus forecast, has a mean (median) of 0.249 (0.118), indicating that on average our sample firms needed to manage earnings almost twenty-five cents per share to meet analysts’ forecasts. MATERIALITY_NI, our quantitative materiality threshold, has a mean (median) of 0.057 (0.042), indicating that on average firms could manipulate earnings by almost six cents per share and be within quantitative materiality levels used in auditing practice. The mean (median) for QUALITATIVE_EM is 0.280 which indicates that 28 % of sample firms have pre-managed earnings before tax expense that are within quantitative audit materiality. The means for BIG4 and TENURE are 0.888 and 9.046, respectively, which indicates that almost 89 % of sample firms use a Big 4 auditor and the average auditor tenure is 9.05 years. The mean for BEAT is 0.193, indicating that just over 19 % of the observations with pre-managed earnings less than the forecast were able to attain the forecasted earnings through tax expense management. 7
Dhaliwal et al. (2004) use a sample that excludes firms with a forecast error greater than or less than five cents per share. Our results are robust to excluding these firms. We winsorize the top and bottom 1 % of observations for forecast error.
123
Mean
123
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
11,829
QUALITATIVE_EM
INDUCED_CHANGE
TAX_OWED
ETR_Q3
ETR_Q4
PROFIT
SIZE
DEBT/ASSET
MBV
MISS_AMOUNT
FORECAST_ERR
MATERIALITY_TA
MATERIALITY_SALES
MATERIALITY_NI
MATERIALITY_BLEND
ASSETS
SALES
CFO
BIG4
TENURE
ROA
NUM_ANALYSTS
BEAT
0.193
6.670
0.055
9.046
0.888
0.096
1,910.93
3,568.11
0.221
0.057
0.242
0.364
-0.047
0.249
2.863
0.512
6.331
0.977
0.347
0.354
0.105
0.004
0.280
Panel A Univariate statistics for sample observations for period 1990 to 2009
N
Table 3 Descriptive statistics for sample observations
0.395
6.070
0.051
8.170
0.316
0.090
5,665.41
19,644.07
0.268
0.061
0.296
0.649
0.158
0.338
22.816
0.245
1.828
0.149
0.124
0.116
0.997
0.390
0.449
SD
0.000
2.000
0.021
3.000
1.000
0.046
128.48
145.77
0.078
0.021
0.081
0.096
-0.070
0.038
1.328
0.325
4.982
1.000
0.301
0.318
0.000
-0.001
0.000
Quartile 1
0.000
5.000
0.044
6.000
1.000
0.091
408.00
522.11
0.142
0.042
0.158
0.183
-0.010
0.118
1.937
0.513
6.258
1.000
0.361
0.366
0.007
0.000
0.000
Median
0.075
9.000
0.075
13.000
1.000
0.141
1,375.29
1,898.30
0.267
0.075
0.292
0.362
0.020
0.316
3.026
0.675
7.549
1.000
0.395
0.397
0.120
0.002
1.000
Quartile 3
428 J. Legoria et al.
QUALITATIVE_ EM
1
MBV
MISS_ AMOUNT
1
0.074
0.049
20.075
20.029
PROFIT
0.040
0.014
0.026
0.001
0.002
-0.013
0.074
0.000
-0.001
-0.001
INDUCED_ CHANGE
TAX_OWED
-0.007
20.040
CFO
1
-0.005
-0.007
20.020
0.053
0.019
-0.018
0.009
0.009
BIG4
INDUCED_ CHANGE
20.401
ETR_Q3
0.003
-0.009
ETR_ DECREASE
QUALITATIVE_EM
Panel B Pearson correlation coefficients for regression variables
ROA
BEAT
TENURE
BIG4
CFO
MISS_ AMOUNT
MBV
DEBT/ASSET
SIZE
PROFIT
ETR_Q3
TAX_OWED
INDUCED_ CHANGE
QUALITATIVE_EM
ETR_ DECREASE
Panel B Pearson correlation coefficients for regression variables
ETR_ DECREASE
Table 3 Descriptive statistics for sample observations
0.064
0.018
-0.011
20.038 20.024
-0.001
-0.004
0.115
0.038
BEAT
1
0.025
0.045
0.010
-0.008
-0.010
TENURE
1
0.064
0.059 20.027
-0.015
ETR_ Q3
20.026
TAX_ OWED
0.271
20.106
-0.003
-0.016
0.225
0.051
ROA
0.007
20.038
0.035
-0.001
0.036
0.008
NUM_ ANALYST
1
0.469
0.012 1
20.062
20.147
-0.007 -0.003 20.054
0.036
-0.002
-0.006
DEBT/ASSET
-0.014
-0.001
20.064 20.031 1
0.028
0.002
SIZE
0.069
0.035
PROFIT
Qualitative audit materiality and earnings management 429
123
123
0.001 1 1
0.088
0.009 0.048
0.036
0.171 0.000
20.046 20.206 20.022
BIG4
CFO
Correlations in bold represent significant correlations at the 0.05 level or better
1
1
0.264
0.128
-0.004
0.015
MISS_ AMOUNT
MBV
Sample period is from 1990 to 2009
ROA
BEAT
TENURE
BIG4
CFO
MISS_ AMOUNT
MBV
DEBT/ASSET
SIZE
Table 3 continued
1
0.185
0.096
0.109
0.005
0.032
0.275
TENURE
1
0.009
0.033
0.044
20.076
0.018
20.065
0.020
BEAT
1
0.073
0.032
-0.011
0.437
20.150
0.025
20.346
20.231
ROA
0.07
0.121
0.198
0.203
0.163
0.109
0.017
0.034
0.589
NUM_ ANALYST
430 J. Legoria et al.
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431
Panel B of Table 3 reports the Pearson correlations between our regression variables. ETR_DECREASE is significantly negatively correlated with QUALITATIVE_EM, our test variable. ETR_DECREASE is also significantly correlated with ETR_Q3, PROFIT, SIZE, MISS_AMOUNT, BEAT, and ROA. Additionally, BEAT is significantly positively correlated with QUALITATIVE_EM. BEAT is also significantly correlated with PROFIT, SIZE, DEBT/ASSET, MISS_AMOUNT, CFO, TENURE, ROA, and NUM_ANALYST. 4.3 Logistic regression results Tables 4, 5, and 6, present the logistic regression results. As discussed above, all analyses are based on a sample that only includes observations for which analysts’ forecasts exceed pre-managed earnings. Table 4 provides the results of estimating Eq. 1. Recall that QUALITATIVE_EM is an indicator variable set equal to one if the firm has pre-managed earnings less than the consensus analyst forecast and the difference between pre-managed earnings and the forecast (MISS_AMOUNT) is less than quantitative materiality (MATERIALITY_NI). Supporting H1, the coefficient on QUALITATIVE_EM is positive and significant with a p value of\0.001 (v2 33.54), indicating that firms having earnings before tax expense management that miss the forecast by an amount lower than quantitative materiality (MATERIALITY_NI) are associated with a greater probability of decreasing tax expense (ETR_DECREASE), after controlling for other factors related to changes in tax expense. For the control variables, ETR_Q3, PROFIT, SIZE, and DEBT/ASSET are all significantly different from zero. The coefficient on ETR_Q3 is positive and significant (p \ 0.001, v2 53.636) and suggests that firms with higher tax expense at the end of quarter 3 lowered their tax expense before issuing their financial statements. Alternatively, it could indicate the firm’s tax expense reverted to its historical mean (Dhaliwal et al. 2004). The coefficient on PROFIT is positive and significant (p \ 0.001, v2 16.444) and suggests that profit-reporting firms were more likely to decrease tax expense. The coefficient on SIZE (p \ 0.001, v2 20.595) is positive and statistically significant, indicating that large firms were more likely to decrease tax expense. The coefficient on DEBT/ASSET is negative and significantly different from zero (p \ 0.05, v2 5.163) and suggests that firms with high debt ratios were less likely to decrease tax expense. The control variables INDUCED_ CHANGE, TAX_OWED, MBV, MISS_AMOUNT, BIG4, and TENURE are not significant. With respect to MISS_AMOUNT, the results from Table 4 also indicate that, when QUALITATIVE_EM is not included in Eq. 1, the coefficient on MISS_AMOUNT is negative and significantly different from zero (p \ 0.001; v2 20.728), consistent with Dhaliwal et al. (2004). On the other hand, when QUALITATIVE_EM is included in Eq. 1, MISS_AMOUNT is no longer significant. This finding suggests that it is not the magnitude in cents that matters but whether the magnitude of the miss is within quantitative audit materiality. Overall, the results of our analysis in Table 4 provide evidence that firms whose earnings before tax expense management miss the consensus analyst forecast by an amount
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J. Legoria et al.
less than quantitative materiality thresholds are more likely to decrease tax expense. The evidence suggests that firms may be able to manipulate earnings within quantitative materiality thresholds used in practice and meet or beat analysts’ forecasts.8 We test this hypothesis in H2. Table 5 presents the results of the estimation of Eq. 2 to test H2. Supporting H2, the coefficient on QUALITATIVE_EM is positive and significant (p \ 0.001, v2 93.320). While the coefficient on MISS_AMOUNT is negative and significantly different from zero (p \ 0.001, v2 16.617), the positive and significant coefficient on QUALITATIVE_EM indicates that, after controlling for the distance between premanaged earnings and the analyst’s consensus forecasts, firms are more likely to meet or beat the consensus forecast by reducing tax expense when the magnitude of the reduction is less than quantitative materiality (MATERIALITY_NI).9 The coefficients on SIZE, TENURE, and NUM_ANALYST are all significantly different from zero. The positive coefficient on SIZE indicates that larger firms are more likely to manage earnings to meet or beat the consensus analyst forecast. The negative coefficient on TENURE suggests that longer auditor tenures lower the probability that a firm will be able manage earnings to meet or beat its analyst forecast. The coefficient on BIG4, which is the other proxy for auditor quality, is not significant and is likely the result of the fact that our sample has only 11 % of firms that do not use a Big 4 auditor, and thus there is very little variation in that variable. Finally, the positive coefficient on NUM_ANALYST indicates that firms that have more analysts following them are more likely to manage earnings to meet or beat analyst forecasts, which is consistent with managers viewing analyst forecasts as an important benchmark. Davis et al. (2009) also find a positive association between meeting or beating the consensus forecast and analyst following. As noted above, our results are robust to controlling for the magnitude by which firms needed to manage earnings to meet or beat the forecasts (MISS_AMOUNT). This suggests that our results are in fact capturing the effect of qualitative materiality and are not driven by the magnitude of the earnings management needed to meet or beat the forecast. The results in Table 5 provide additional insight into the associations in Table 4. Not only are firms whose pre-managed earnings miss the forecast by an amount less than quantitative materiality more likely to decrease 8
To measure economic significance, we examine the odds ratio for the coefficient on QUALITATIVE_EM in Table 4, which is 1.32. This implies that, for firms with pre-managed earnings less than quantitative materiality, the odds of decreasing the effective tax rate in the for quarter are 1.32 times greater than the odds of firms with pre-managed earnings greater than quantitative materiality decreasing their effective tax rate. The predicted probability of decreasing the fourth quarter ETR when QUALITATIVE_EM is zero (and all other variables are measured at their mean) is 60.7 %. On the other hand, when QUALITATIVE_EM is one the probability increases to 67.2 %, or a 10.8 % increase in the probability.
9
To measure economic significance, we examine the odds ratio for the coefficient on QUALITATIVE_EM in Table 5, which is 1.78. Therefore the odds of a firm with pre-managed earnings less than quantitative materiality meeting or just beating the analysts’ forecast is almost two times greater than the odds of a firm with pre-managed earnings greater than quantitative materiality meeting or just beating the analysts’ forecast. The predicted probability of meeting or just beating analysts’ consensus forecast when QUALITATIVE_EM is zero (and all other variables are measured at their mean) is 15.2 %. On the other hand, when QUALITATIVE_EM is one, the probability increases to 24.2 %, or a 59.2 % increase in the probability.
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Qualitative audit materiality and earnings management
433
fourth quarter tax expense (Table 4), but those firms are also more likely to hit the forecast by doing so.10 Table 6 shows the results of tests for our final set of hypotheses. In this model, we control for both SAB-99 and Sarbanes–Oxley.11 The coefficient on QUALITATIVE_EM is positive and significantly different from zero (p \ 0.001, v2 193.645), consistent with the results presented for H1 and H2, which were reported in Tables 4 and 5, respectively. The results show a significant effect for the pre- and post-SAB-99 and Sarbanes–Oxley eras as the coefficients on QUALITATIVE_EM*SAB and QUALITATIVE_EM*SOX. Both are significantly negative (p \ 0.001, v2 12.898 and p \ 0.001, v2 113.687, respectively), supporting both H3a and H3b, respectively. These findings indicate that firms are significantly less likely to meet or beat the analysts’ consensus forecasts through tax expense reduction within quantitative materiality thresholds in the post SAB-99 and postSarbanes–Oxley eras. With respect to the control variables, the coefficients on SIZE, MISS_AMOUNT, TENURE, and NUM_ANALYST are significantly different from zero and exhibit the same signs and level of statistical significance previously reported for those same variables in Table 5. Cook et al. (2008) found mixed support for the effectiveness of Sarbanes–Oxley in altering firms’ incentives to use their effective tax rates to manipulate earnings. Taken together, our results suggest that both SAB-99 and Sarbanes–Oxley had some impact on auditor behavior. We believe that the stronger results for Sarbanes–Oxley in our study likely result from our study having more post- Sarbanes–Oxley data than Cook et al. (2008). In addition, SAB-99 was also in effect and thus both SAB99 and Sarbanes–Oxley together seemed to have increased regulatory oversight of auditors. Our results are consistent with Li et al. (2008), who show that investors anticipated that Sarbanes–Oxley would constrain accrual-based earnings management, and Cohen et al. (2008), who report evidence of a decline in accrual-based earnings management post- Sarbanes–Oxley.
5 Sensitivity tests 5.1 Tax aggressiveness The decrease in effective tax rates from the third-to-fourth quarter (ETR_DECREASE) observed in this study and others (Dhaliwal et al. 2004; Cook et al. 2008) may be the result of effective tax planning by firms rather than earnings management. To control for potential tax planning, we use the tax aggressiveness variable (DTAX) developed by Frank et al. (2009, pp. 471–473). Frank et al. (2009) regress permanent tax differences on various nondiscretionary items (e.g., intangible 10 The American Jobs Creation Act of 2004 permitted a one-time dividend repatriation deduction of 85 % but increased effective tax rates. Thus we exclude observation from 2004 and 2005, and our results are unaffected. 11 SAB 99 was issued by the SEC in 1999 and thus the indicator variable SAB99 is set to one if the year is greater than or equal to 1999 and zero otherwise. Sarbanes–Oxley was passed in 2002, and therefore the indicator variable SOX is coded one if the year is greater than or equal to 2002 and zero otherwise.
123
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J. Legoria et al.
Table 4 Test of fourth quarter decrease in effective tax rate Predicted sign
Estimate
v2
-0.962
26.414***
Intercept
?
QUALITATIVE_EM
?
INDUCED_CHANGE
?
-0.062
0.579
Estimate
v2
-0.965
26.591***
0.279
33.540***
-0.064
0.600
TAX_OWED
-
-0.032
2.332
-0.031
2.124
ETR_Q3
?
1.302
54.481***
1.289
53.636***
PROFIT
?
0.557
18.591***
0.524
16.444***
SIZE
?
0.078
29.925***
0.066
20.595*** 5.163**
DEBT/ASSET
?
-0.215
5.720*
-0.204
MBV
?
0.000
0.255
0.000
0.301
MISS_AMOUNT
-
-0.266
20.728***
-0.112
3.035
BIG4
-
-0.034
0.280
-0.033
0.256
TENURE
?
-0.003
1.050
-0.003
1.116
Number of observations
11,829
11,829
Psuedo R
1.85 %
2.23 %
Model v2
161.37
195.20
2
This table reports the results of a logistic regression testing whether firms with earnings before tax expense management within audit materiality thresholds and less than the analysts’ forecasts decreased tax expense in the fourth quarter to increase earnings (Decrease = 1; Increase = 0). The sample consists of 11,829 observations from 1990 to 2009. The model is specified as follows: ProbðETR DECREASEt Þ ¼ a0 þ a1 QUALITATIVE EMt þ a2 INDUCED CHANGEt þ a3 TAX OWEDt þ a4 ETR3t þ a5 PROFITt þ a6 SIZEt þ a7 DEBT=ASSETt þ a8 MBVt þ a9 MISS AMOUNTt þ a10 BIG4t þ a11 TENUREt þ YEAR dummies þ et * Denotes significance at the 0.05 level using a two-tailed test ** Denotes significance at the 0.01 level using a two-tailed test *** Denotes significance at the 0.001 level using a two-tailed test
assets, state taxes) that cause permanent tax differences but that are not likely to be the result of aggressive tax planning. The residual from that estimation (DTAX) represents discretionary permanent differences that are likely to be result of aggressive tax planning. We include DTAX in each of our regression specifications and re-estimate each model. When we add Frank et al.’s (2009) proxy for tax aggressiveness to our models, our results (untabulated) and inferences remain the same. In addition, DTAX is not statistically significant in any of the specifications, and QUALITATIVE_EM, our test variable, remains significant in all cases. 5.2 Auditor quality Both auditor size and auditor tenure have been employed in prior research to capture audit quality. We examine whether auditor quality affects the relation between QUALITATIVE_EM and the probability of meeting or beating the analyst forecasts by
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Qualitative audit materiality and earnings management
435
Table 5 Test of meeting or beating analysts’ forecasts Predicted sign
Estimate
v2
-2.278
81.245***
Intercept
?
QUALITATIVE_EM
?
PROFIT
?
0.151
0.654
Estimate
v2
-2.352
85.623***
0.575
93.320***
0.168
0.802
SIZE
?
0.095
15.470***
0.069
7.915**
MBV
?
0.001
1.447
0.001
1.867
MISS_AMOUNT
-
-0.800
75.280***
-0.391
16.617***
BIG4
-
-0.043
0.237
-0.057
0.416
TENURE
?
-0.009
7.034**
-0.009
7.315**
CFO
?
-0.442
1.988
-0.431
1.825
ROA
?
1.809
11.376***
0.823
2.576
NUM_ANALYST
?
0.031
31.101***
0.034
35.238***
Number of observations
11,829
11,829
Psuedo R2
8.54 %
9.73 %
649.24
742.10
2
Model v
This table reports the results of a logistic regression testing whether firms are more likely to meet or beat the consensus analysts’ forecast (BEAT = 1; else 0) when firms can manage earnings within audit materiality thresholds. The sample consists of 11,829 observations from 1990 to 2009. The model is specified as follows: ProbðBEATt Þ ¼ a0 þ a1 QUALITATIVE EMt þ a2 PROFITt þ a3 SIZEt þ a4 MBVt þ a5 MISS AMOUNTt þ a6 BIG4t þ a7 TENURETt þ a8 CFOt þ a9 ROAt þ a10 NUM ANALYSTt þ a11 INDUSTRYt þ YEAR dummies þ et * Denotes significance at the 0.05 level using a two-tailed test ** Denotes significance at the 0.01 level using a two-tailed test *** Denotes significance at the 0.001 level using a two-tailed test
including the interaction terms QUALITATIVE_EM and BIG4 and QUALITATIVE_EM and TENURE in Eq. 2. The results (untabulated) show that the interaction between QUALITATIVE_EM and BIG4 and QUALITATIVE_EM and TENURE are not significant. Thus it seems that auditor quality does not affect the relation between QUALITATIVE_EM and the probability of meeting or beating the analyst forecasts. 5.3 Alternative estimates of quantitative materiality In practice, some auditors apply less restrictive constraints than the common 5 % rule. We thus repeat the tests of H1 and H2 using 10 % of net income as an estimate of quantitative materiality. The results (untabulated) for a reduction in fourth quarter tax expense are weaker but consistent with Table 4. In addition, the results for meeting the forecast (Table 5) are also consistent using 10 % of income as the materiality threshold. Collectively, these findings suggest that these clients still reduce fourth quarter tax expense and use the reduction to hit the forecast even when the amount of the reduction is within a broader 10 % of income rule used to assess quantitative materiality.
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J. Legoria et al.
Table 6 Test of the impact of SAB-99 and Sarbanes–Oxley on qualitative materiality v2
Predicted sign
Estimate
Intercept
?
-2.445
89.633***
QUALITATIVE_EM
?
1.165
193.645***
SOX
?
0.341
4.662*
SAB
?
0.986
34.877***
QUALITATIVE_EM*SOX
-
-1.200
113.687***
QUALITATIVE_EM*SAB
-
-0.597
12.898***
PROFIT
?
0.128
0.459
SIZE
?
0.069
7.982**
MBV
?
0.001
1.850
MISS_AMOUNT
-
-0.459
22.433***
BIG4
-
-0.046
0.267
TENURE
?
-0.009
7.178**
CFO
?
-0.483
2.240
ROA
?
0.791
1.968
NUM_ANALYST
?
0.034
35.058***
Number of observations
11,829
Psuedo R2
11.20 %
Model v2
858.60
This table reports the results of a logistic regression testing whether firms’ propensity to meet or beat analysts’ consensus forecasts changed after the passage of Staff Accounting Bulletin 99 and the Sarbanes–Oxley Act. The sample consists of 11,829 observations from 1990 to 2009. The model is specified as follows: ProbðBEATt Þ ¼ a0 þ a1 QUALITATIVE EMt þ a2 SOX þ a3 SAB99 þ a4 QUALITATIVE EMt SOXt þ a5 QUALITATIVE EMt SAB99t þ a6 PROFITt þ a7 SIZEt þ a8 MBVt þ a9 MISS AMOUNTt þ a10 BIG4t þ a11 TENUREt þ a12 CFOt þ a13 ROAt þ a14 NUM ANALYSTt þ INDUSTRY dummies þ YEAR dummies þ et * Denotes significance at the 0.05 level using a two-tailed test ** Denotes significance at the 0.01 level using a two-tailed test *** Denotes significance at the 0.001 level using a two-tailed test
In addition, practicing auditors sometimes prefer to exclude extraordinary and discontinued items and income tax expense when computing materiality. As an additional sensitivity test, we replace net income with earnings before extraordinary and discontinued items and also with earnings before taxes. The results (untabulated) are consistent with the results reported in Tables 4 and 5. 5.4 Magnitude of miss amount In Sect. 3, we discussed it is possible that some firms may be more likely to meet or beat analysts’ consensus forecasts simply because they need to manage earnings by a smaller amount than other firms. To prevent such incorrect inferences, our models
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Fig. 1 The percentage of firms that meet/beat the analyst consensus forecast within a given amount partitioned on QUALITATIVE_EM. For example, 41 % of firms that have a MISS_AMOUNT less than or equal to 1 cent (that is, within the 5 % rule of thumb quantitative materiality threshold) meet/beat the consensus analyst forecast. In contrast only 26 % of firms that have a MISS_AMOUNT less than or equal to 1 cent (that is, greater than the 5 % rule of thumb quantitative materiality threshold) meet/beat the consensus analyst forecast
control for the amount by which pre-managed earnings would have missed the earnings forecast, as previously discussed in constructing models 1 and 2. To provide additional evidence that results are not driven by the magnitude of the miss, we examine the percentage of firms that meet or beat the analysts’ forecasts for varying magnitudes of MISS_AMOUNT partitioned on QUALITATIVE_EM. Figure 1 shows that the percentage of firms that meet or beat and would have missed within quantitative materiality is consistently greater than the percentage of firms that meet or beat and would have missed outside quantitative materiality (with the exception of 0.02, for which there is essentially no difference). Thus our results are not simply a function of the magnitude by which the firm would have missed the forecast, but rather are driven by whether the magnitude of the miss was within or outside of the common quantitative materiality estimate of 5 % of income.
6 Discussion Professional standards, regulators, and the courts are clear in their statements that qualitative aspects of materiality must be considered along with quantitative aspects. A misstatement that would not necessarily be sufficient to impact a reasonable financial statement user based on magnitude alone may nevertheless
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affect that user’s decision if it leads to a qualitatively different characterization of the company’s financial position or performance. Among the most important of those qualitative aspects of materiality is meeting or beating earnings benchmarks. Despite standards and regulatory emphasis, prior experimental research has provided evidence that auditors rely on the quantitative aspects of materiality. Using archival data and focusing on fourth quarter tax adjustments, we also find evidence that materiality estimates rely on quantitative computations rather than qualitative characteristics. Specifically, we find that firms are more likely to reduce tax expense when pre-managed earnings miss the analysts’ consensus forecast by an amount less than quantitative materiality. In addition, we find that firms reducing tax expense are more likely to meet the analysts’ consensus forecast with the reduction if the amount of the reduction needed to meet expectations is less than quantitative materiality. Finally, we find that the ability to use tax expense reduction within quantitative materiality to meet or beat analysts’ consensus forecasts was significantly reduced by the SEC’s guidance on materiality in SAB-99 and the regulatory impact of Sarbanes–Oxley. Acknowledgments The authors would like to express their gratitude to Marsha Keune, Lillian Mills, and workshop participants at Florida State University, Indiana University, Michigan State University, the University of Missouri at Columbia, Louisiana State University, and the Annual Meeting of the American Accounting Association for comments on prior versions of this study.
Appendix See Table 7.
Table 7 Definition of variables used in the study Variable name
Definition
Assets
Total assets in $millions (Compustat annual item #6)
BEAT
An indicator variable equal to one if a just firm meets or beats the analysts’ consensus forecast of earnings (i.e., forecast error of 0 or 1 cents) and zero otherwise
BIG4
An indicator variable equal to one if the firm is audited by a Big N auditor and zero otherwise
CFO
Cash flow from operations (Compustat annual item #308) divided average total assets (Compustat annual item #6)
DEBT/ASSET
Ratio of total debt to total assets (Compustat annual data items #181/#6)
ETR_DECREASE
An indicator variable equal to one if fourth quarter ETR (ETR_Q4) less the third-quarter ETR (ETR_Q3), where ETR is defined as year-to-date tax expense (Compustat quarterly data item #6) divided by accumulated pre-tax income (Compustat quarterly item #23) is negative (i.e., lowered tax expense) and zero otherwise
ETR_Q3
The year-to-date tax expense up through the third quarter (Compustat quarterly data item #6) divided by accumulated pre-tax income (Compustat quarterly data item #23)
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Table 7 continued Variable name
Definition
ETR_Q4
The year-to-date tax expense through the fourth quarter (Compustat quarterly data item #6) divided by accumulated pre-tax income (Compustat quarterly data item #23)
FORECAST_ERR
Actual earnings per share per IBES less the last consensus forecast per share
INDUCED_CHANGE
Induced tax change divided by pre-tax income (Compustat annual data item #170), where induced tax change equals (the statutory tax rate less ETR_Q3) * unexpected pre-tax income. Unexpected pre-tax income is measured as (the difference between IBES actual versus consensus forecast per share) * IBES split factor * weighted average shares (Compustat annual data item #54) divided by (1—the statutory tax rate)
MATERIALITY_BLEND
(1 % of total assets ? 1 % of total sales ? 5 % of net income divided by 3) divided by weighted average shares used to compute earnings per share
MATERIALITY_NI
5 % of net income divided by weighted average shares used to compute earnings per share
MATERIALITY_SALES
1 % of total sales divided by weighted average shares used to compute earnings per share
MATERIALITY_TA
1 % of total assets divided by weighted average shares used to compute earnings per share.
MBV
Market-to-book ratio [Compustat annual data item #199/(#60/#25)]
MISS_AMOUNT
IBES consensus forecast less pre-managed earnings (where pre-managed earnings are defined as earnings before tax expense management), divided by weighted average shares used to compute basic EPS (Compustat annual data item 54)
PROFIT
An indicator variable equal to one if the firm is profitable (net income [ 0) and zero otherwise (Compustat annual data item #172)
QUALITATIVE_EM
An indicator variable set equal to one if MISS_AMOUNT is less than MATERIALITY_NI
SAB99
An indicator variable set equal to one if year is greater than or equal to 1999 and zero otherwise
Sales
Total sales (Compustat annual item #12), in millions of dollars
SIZE
Natural logarithm of market value of equity (Compustat annual items 199*25)
SMALL
An indicator variable equal to one if MISS_AMOUNT is less than or equal to five cents, four cents, three cents, two cents, or a cent, depending on the model specification
SOX
An indicator variable set equal to one if year is greater than or equal to 2002 and zero otherwise
TAX_OWED
Taxes payable (Compustat annual data item # 71) less tax refunds (Compustat annual data item # 161) divided by pre-tax income (Compustat annual data item # 170)
TENURE
The number of continuous years that the auditor audits the firm
ROA
Net income divided by average total assets
NUM_ANALYST
The number of analysts providing earnings estimates for the firm
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