行政院國家科學委員會專題研究計畫 成果報告
會計師任期、實質盈餘管理與應計盈餘管理
研究成果報告(精簡版)
計 畫 類 別 ： 個別型
計 畫 編 號 ： NSC 99-2410-H-004-061-
執 行 期 間 ： 99 年 08 月 01 日至 100 年 07 月 31 日
執 行 單 位 ： 國立政治大學會計學系
計 畫 主 持 人 ： 戚務君
報 告 附 件 ： 出席國際會議研究心得報告及發表論文
處 理 方 式 ： 本計畫可公開查詢
中 華 民 國 100 年 08 月 16 日
Vol. 25, No. 2 DOI: 10.2308/acch-10025 2011
pp. 315–335
Is Enhanced Audit Quality Associated with
Greater Real Earnings Management?
Wuchun Chi, Ling Lei Lisic, and Mikhail Pevzner
SYNOPSIS:We examine whether firms resort to real earnings management when their ability to manage accruals is constrained by higher quality auditors. In settings involving strong upward earnings management incentives, i.e., for firms that meet or just beat earnings benchmarks and firms that issue seasoned equities, we find that city-level auditor industry expertise and audit fees are associated with higher levels of real earnings management. We find similar, albeit weaker, results for the Big N auditors. Our paper suggests an unintended consequence of higher quality auditors constraining accrual earnings management, namely, firms resorting to potentially even more costly real earnings management. We also find that longer auditor tenure is associated with greater real earnings management, which could suggest merits of mandating audit firm rotation. Keywords: real earnings management; audit quality; industry expertise; auditor tenure;
audit fees.
JEL Classifications: M40; M41.
INTRODUCTION
I
n this paper, we examine the association between audit quality and real earnings management. Prior literature suggests that higher quality auditors reduce the level of accrual earnings management (Becker et al. 1998;Johnson et al. 2002;Balsam et al. 2003). We argue that, as a consequence of constrained accrual earnings management, clients of higher quality auditors likely resort to more real activities manipulation. Thus, we expect that higher audit quality is associated with higher levels of real earnings management when firms have strong incentives to manage earnings.Prior research suggests that accruals and real activities are two alternative ways to manage earnings (Roychowdhury 2006; Cohen et al. 2008; Zang 2007). While earlier studies focus on
Wuchun Chi is a Professor at National Chengchi University. Ling Lei Lisic is an Assistant Professor and Mikhail Pevzner is an Assistant Professor, both at George Mason University.
We thank Dana Hermanson (the Editor), two anonymous reviewers, and the workshop participants at George Mason University and Peking University for their helpful comments and suggestions. Wuchun Chi acknowledges the financial support from National Science Council (99-2410-H-004-061) and Mikhail Pevzner acknowledges summer research grants from George Mason University’s Provost and Accounting Advisory Council. All errors are our own.
Submitted: March 2010 Accepted: November 2010 Published Online: June 2010 Corresponding author: Ling Lei Lisic
accrual earnings management (Jones 1991; Teoh et al. 1998), more recent papers suggest that firms
also engage in real earnings management (Roychowdhury 2006; Kim et al. 2010; Cohen and
Zarowin 2010). Real earnings management potentially imposes greater long-term costs on
shareholders than accrual earnings management because it has negative consequences on future cash flows and might hurt firm value in the long run (Roychowdhury 2006; Cohen et al. 2008; Cohen and Zarowin 2010). Such long-term costs are driven by temporary price discounts or more lenient credit terms that lower margins on future sales, reductions in valuable investments in research and development and SG&A activities, and/or increasing investments in unneeded inventories via inventory over-production (Roychowdhury 2006; Gupta et al. 2010). Cohen and Zarowin (2009) also find evidence that firms engaging in real earnings management over-invest, which could adversely affect firms’ long-term prospects.
However, managing real activities is less costly to managers because it is less likely to draw auditor or regulatory scrutiny (Cohen et al. 2008). Real earnings management, as long as it is properly disclosed in the financial statements, cannot influence auditors’ opinions or regulators’ actions (Kim et al. 2010). Hence, managers could prefer real earnings management to accrual earnings management (Roychowdhury 2006).
Zang (2007) documents that accrual earnings management and real earnings management
function as substitutes. Thus, we expect that firms are more likely to engage in more extensive real earnings management when their ability to manage accruals is constrained. We can also derive this prediction from the theoretical work of Ewert and Wagenhofer (2005), whose model shows that firms resort to real earnings management when their accounting flexibility is reduced. One way to reduce a firm’s accounting flexibility is to engage an auditor who is less agreeable to earnings management.1Prior research shows that higher quality auditors are more successful in constraining accrual earnings management, i.e., they constrain accounting flexibility of managers. Consequently, higher audit quality could be associated with higher levels of real earnings management among firms with incentives to manage earnings.
Recent work by Reichelt and Wang (2010) shows that auditor industry specialization is a critical indicator of audit quality. In particular, clients of such auditors have lower discretionary accruals and are less likely to just meet analyst expectations. Other studies also find that industry expert auditors are associated with lower likelihood of being involved in Securities and Exchange Commission (SEC) enforcement actions (Carcello and Nagy 2004) and lower probabilities of restatements (Romanus et al. 2008). Prior research also suggests that audit firm size (Big N versus non-Big N) is another indicator of audit quality. Studies find that the Big N auditors charge higher audit fees (Craswell et al. 1995), and they are associated with lower absolute value of discretionary accruals (Becker et al. 1998) and higher ERCs (Teoh and Wong 1993). Thus, we focus on these two auditor characteristics, namely, auditor industry expertise and audit firm size, and examine their association with levels of real earnings management.
Following Reichelt and Wang (2010), we measure auditor industry expertise as the audit fee market share of each auditor in each industry at both the national level and the city level. We measure audit firm size as a Big N versus non-Big N indicator. Following Roychowdhury (2006) and Cohen et al. (2008), our proxies for real earnings management are estimates of a firm’s abnormal cash flows, abnormal inventory production, abnormal discretionary expenditures, and a summary measure combining these three components.
We focus on a sample of 925 firm-year observations from 2001 to 2008 that likely have strong incentives to manage earnings upward (identifiedex post or ex ante), i.e., firms that meet/just beat one
1
Other possible ways are introductions of less flexible accounting standards or more stringent governance mechanisms.
of the earnings benchmarks (zero earnings, previous year’s earnings, and analyst forecasts) and firms that issue seasoned equity offerings. Our primary finding is that within that sample, city-level auditor industry expertise is associated with higher levels of the overall real earnings management index and each of the components of the real earnings management index, i.e., lower levels of abnormal cash flows, higher levels of abnormal production, and lower levels of abnormal discretionary expenditures. We also find that the Big N auditors are associated with higher overall levels of the real earnings management index and lower levels of abnormal cash flows. When we use audit fees as an alternative measure of audit quality in additional analyses, we find similar results, i.e., higher audit fees are associated higher levels of real earnings management. Collectively, our findings are consistent with our prediction that higher audit quality is associated with higher levels of real earnings management for firms that have strong incentives to manage earnings. In addition, we find that longer auditor tenure is associated with higher levels of real earnings management at both the overall level and the individual component level among firms with incentives to manage earnings.2Finally, we find that the positive association between city-level industry expertise and real earnings management measures is significantly stronger in the upward earnings management sample than in the sample lacking of such incentives (i.e., all samples excluding the upward earnings management sample).
Our paper contributes to the literature by demonstrating that firms adapt to the presence of more stringent levels of auditing by engaging in real earnings management. Past auditing research has exclusively focused on accrual earnings management when examining the impact of audit quality on the clients’ behavior. Our paper suggests that an unintended consequence of higher quality auditors constraining accrual earnings management is that clients resort to higher levels of real earnings management, which is potentially more costly to the shareholders in the long run. Furthermore, our findings regarding the positive association between auditor tenure and real earnings management shed additional insights into the long and heated debate of whether audit firm rotation should be mandated (American Institute of Certified Public Accountants [AICPA] 1978, 1992; U.S. House of Representatives Sarbanes-Oxley Act [SOX] 2002; General Accounting Office [GAO] 2003; Cox 2006). Past research has exclusively focused on accrual earnings management when analyzing the benefits/costs of mandatory auditor rotation (Johnson et al. 2002;Myers et al. 2003; Davis et al. 2009). Our results alert regulators and researchers that mandating audit firm rotation could potentially reduce real earnings management, a benefit that has not been documented in prior research.
Our paper proceeds as follows. The second section reviews the literature and develops our hypotheses. The third section discusses research design. The fourth section describes our sample. The fifth section presents empirical results. The sixth section contains additional analyses. Finally, the seventh section concludes.
LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT
Prior research suggests that firms manage both accruals and real activities to maximize their valuations, avoid negative contracting consequences such as violations of debt covenants, and/or
2
Our results complement Cohen and Zarowin (2010), who examine firms that issue seasoned equities only and, in one of the tests, find that the Big 8 auditors and auditor tenure are associated with the probability of clients’ overall levels of real earnings management index being above the sample median. However, we note that Cohen and Zarowin (2010) do not consider the impact of auditor industry expertise, which, according to the results in Reichelt and Wang (2010), is a dominant audit quality measure. In addition, our paper considers a broader set of incentives for real earnings management, including but not limited to seasoned equity offerings. Finally, we study individual components of real earnings management, in addition to the composite index studied in Cohen and Zarowin (2010). In addition, a contemporaneous study, Yu (2008), examines national-level auditor industry expertise only and finds a positive relation with real earnings management. We take a broader view and use multiple proxies for audit quality. We find that city, but not national, level industry expertise is associated with greater real earnings management.
avoid negative regulatory consequences. While earlier papers focus more on accrual management (e.g., Jones 1991; Teoh et al. 1998), more recent papers suggest real activities manipulation for objectives similar to accrual earnings management. In particular, Roychowdhury (2006) finds that firms manage real activities to avoid missing earnings targets. Cohen and Zarowin (2010) report that firms engage in real earnings management in the year of seasoned securities offerings (SEO) to avoid SEO under-pricing. Kim et al. (2010) find that firms engage in greater real activities manipulation when they are closer to debt covenant violations.
The departing assumption of our paper is that among firms with incentives to manage earnings, accruals and real earnings management are substitutes. When costs of accrual earnings management are higher, ceteris paribus, firms are more likely to engage in real earnings management. In particular, Zang (2007) and Cohen et al. (2008) suggest that the presence of more stringent litigation and regulatory regime drives firms to real earnings management. This happens because real earnings management does not involve direct violation of any laws or regulations, as long as the outcomes of real earnings management are properly disclosed in the financial statements. This reasoning suggests that firms might switch from accrual earnings management to real earnings management when opportunities of accrual earnings management are constrained. Consistent with this argument, Ewert and Wagenhofer (2005) show analytically that when accounting standards are tightened, i.e., when accounting flexibility is reduced, firms tend to resort to real earnings management. Cohen et al. (2008) provide initial empirical support to Ewert and
Wagenhofer’s (2005) model. SOX has imposed greater regulatory scrutiny on firms and,
potentially, reduced their accounting flexibility. Cohen et al. (2008) find that, consequently, firms engage in less accrual earnings management, but more real earnings management post-SOX. An alternative way to reduce accounting flexibility is to engage an auditor less agreeable to accrual earnings management. Hence, we examine whether firms, conditional on incentives to manage earnings, resort to real earnings management when their auditors are of higher quality.
We focus on two auditor characteristics that proxy for higher level of audit quality: auditor industry expertise and audit firm size. A wide literature suggests that auditor industry expertise enhances audit quality and, thus, credibility of financial reporting. Craswell et al. (1995) find that audit specialists command higher fees. Knechel et al. (2007) find that firms audited by specialists receive higher valuations, and Dunn and Mayhew (2004) find that these firms have better disclosure quality. Balsam et al. (2003) and Krishnan (2003) find that auditor industry expertise is associated with lower levels of accrual earnings management. In addition to lower accruals, Reichelt and Wang (2010) show that auditor industry specialization is associated with lower likelihood of clients just meeting analyst expectations. Griffin et al. (2009) suggest that industry expert auditors have a greater propensity to issue going-concern opinions. Carcello and Nagy (2004) find that industry expert auditors are less likely to be involved in SEC enforcement actions. Romanus et al. (2008) find clients of industry expert auditors have lower probabilities of restatements.
The literature also recognizes that the Big N auditors provide higher quality audits and offer greater credibility to clients’ financial statements than the non-Big N auditors. Nichols and Smith (1983) find that the stock market reacts more favorably when a client switches to a Big N auditor than when it switches to a non-Big N auditor. Lennox (1999) suggests that the Big N auditors give more accurate signals of financial distress in their audit opinions. Craswell et al. (1995) document that the Big N auditors charge an audit fee premium over the non-Big N auditors. Studies also show that clients of the Big N auditors have lower absolute values of discretionary accruals (Becker et al. 1998) and higher ERCs (Teoh and Wong 1993). Firth and Smith (1992) find that clients of the Big N auditors incur less IPO underpricing than clients of the non-Big N auditors.
Thus, based on prior findings that industry expert auditors and the Big N auditors constrain their clients’ ability to manage earnings via accruals, we expect that their clients will resort to more real earnings management given incentives to manage earnings. Hence, our prediction is:
H1:Audit quality, as operationalized by auditor industry expertise and the presence of a Big N audit firm, is associated with higher levels of real earnings management among firms with incentives to manage earnings.
RESEARCH DESIGN
We focus on contexts in which the literature has shown that firms have strong incentives to manage earnings upward. We identify these firmsex post as firms that meet or just beat earnings benchmarks (zero earnings, previous year’s earnings, and analyst forecasts) andex ante as firms that issue seasoned equity offerings. Following Roychowdhury (2006), we define firms as meeting or just beating zero earnings benchmarks if their net income scaled by total assets at the beginning of the year falls into the interval [0, 0.005]. We similarly define firms as meeting or just beating previous year’s earnings benchmarks if their change in net income scaled by total assets at the beginning of the year falls into the interval between [0, 0.005].3Finally, we define firms as meeting or just beating analyst forecasts if their actual annual EPS figures reported by I/B/E/S are larger than the most recent consensus analyst forecasts before earnings announcements by one cent or less (Roychowdhury 2006). Following Gupta et al. (2010), we define firms as issuing seasoned equity offerings if Compustat reports a nonzero data item SSTK.4 To maximize the sample size, we pool all firms that satisfy at least one of the four conditions for upward earnings management, i.e., firms meeting or just beating one of the three earnings benchmarks or issuing seasoned equity offerings. We follow Roychowdhury (2006) and Cohen et al. (2008) in defining our proxies for real earnings management. As in these two papers, we consider abnormally low levels of cash flow from operations and discretionary expenses, and abnormally high levels of production costs as indicators of upward real activities manipulations. Our estimations of abnormal cash flow (Abn_CFO), abnormal production costs (Abn_Prod), and abnormal discretionary expenses (Abn_Discexp) follow Cohen et al. (2008). Specifically, we calculate Abn_CFO as residuals of regression Model (A), which is estimated by year and industry identified using two-digit SIC code:
CFOit=Assetsi;t1= a1tð1=Assetsi;t1Þ þ a2tðSalesi;t=Assetsi;t1Þ
þ a3tðDSalesi;t=Assetsi;t1Þ þ eit ðAÞ
whereCFO is cash flow from operations.
We similarly calculateAbn_Prod as residuals of regression Model (B): Prodit=Assetsi;t1= b1tð1=Assetsi;t1Þ þ b2tðSalesi;t=Assetsi;t1Þ
þ b3tðDSalesi;t=Assetsi;t1Þ þ b4tðDSalesi;t1=Assetsi;t1Þ þ eit ðBÞ
whereProd is sum of cost of goods sold and change in inventory in year t. Finally, we calculateAbn_Discexp as residuals of regression Model (C):
Discexpit=Assetsi;t1 = c1tð1=Assetsi;t1Þ þ c2tðSalesi;t1=Assetsi;t1Þ þ vit ðCÞ
whereDiscexp is the sum of advertising expenses, R&D expenses, and SG&A expenses. Also, following Cohen et al. (2008), we develop a comprehensive measure of real earnings management by combining the three individual measures. Specifically, we computeREM_Index as
the sum of the three standardized individual components, i.e., standardized Abn_CFO þ
3
Widening the intervals for defining firms as meeting or just beating zero earnings and previous year’s earnings benchmarks to [0, 0.01] or [0, 0.02] generates very similar results.
4 Following Cohen and Zarowin (2010) and Gupta et al. (2010), we measure seasoned equity offerings in year t,
i.e., in the same year as our real earnings management measures. Our results are robust to measuring seasoned equity offering in yeartþ 1 to examine real earnings management in anticipation of seasoned equity offerings.
standardizedAbn_Prod standardized Abn_Discexp. Higher levels of REM_Index indicate higher levels of overall real earnings management. Because the three individual variables provide richer information regarding real earnings management than usingREM_Index alone, we report results corresponding to the comprehensive real earnings management index (REM_Index) as well as the three individual real earnings management proxies (Abn_CFO, Abn_Prod, and Abn_Discexp).
To test our hypothesis, we extend the models in Cohen et al. (2008) by including proxies for auditor industry expertise and an indicator variable for the Big N auditors. Earlier studies measure industry expertise at the national level and find that national-level industry experts charge higher audit fees and are associated with lower abnormal accruals (Craswell et al. 1995; Balsam et al. 2003). However, more recent studies (Ferguson et al. 2003; Francis et al. 2005; Reichelt and Wang 2010) argue that industry expertise should be measured locally at the city level because industry expertise derives from deep client knowledge of professionals working in local audit offices, which is not easily transferable nationwide. Consistent with this, Ferguson et al. (2003) and Francis et al. (2005) both find that national-level industry experts do not earn an audit fee premium when they are not city-level industry experts; however, city-level industry experts can charge an audit fee premium when they are not national-level industry experts. Similarly, Reichelt and Wang (2010) find that auditors that are designated as industry experts at the city level but not at the national level are associated with lower abnormal accruals, but not vice versa. These studies suggest that city-level industry expertise dominates national-level industry expertise. Thus, we follow Reichelt and Wang (2010) and measure industry expertise at both city and national level and capture industry expertise as the auditor’s audit fee market share in each two-digit SIC code industry.
Thus, to test our hypothesis, we run the following regression model for the sample of firms that we identify as having strong incentives to manage earnings upward, i.e., firms that meet or just beat one of the three earnings benchmarks (zero earnings, previous year’s earnings, and analyst forecasts) or issue seasoned equity offerings:5
REMt= a0þ a1 IndExp citytþ a2 IndExp nationaltþ a3 BigNtþ a4 Tenuret
þ a5 Levt1þ a6 LMVEt1þ a7 MTBt1þ a8 DEt1þ a9 ROAt1
þ a10 ExOptiontþ a11 UnOptiontþ a12 Ownertþ a13 Bonust
þ a14 Year Dummies þ et ð1Þ
where the variables are defined as follows:
REM = real earnings management variables defined based on Cohen et al. (2008): Abn_CFO = abnormal cash flows (negative measure of real earnings management); Abn_Prod = abnormal inventory over-production (positive measure of real earnings
management);
5
An alternative way to test our hypothesis is to use the full sample regardless of earnings management incentives and generate a dummy variableIncentive = 1 if firms meet or just beat earnings benchmarks or issue seasoned equity offerings, and 0 otherwise. Then, we can run the following regression model.
REMt= a0þ a1 Incentivetþ a2IndExp_citytþ a3IndExp_nationaltþ a4BigNtþ a5Tenuretþ a6Incentive
IndExp_citytþ a7Incentive IndExp_nationaltþ a8Incentive BigNtþ a9Incentive Tenuret
þ a10 Levt–1þ a11 LMVEt–1þ a12 MTBt–1þ a13 DEt–1þ a14 ROAt–1þ a15 ExOptiont
þ a16 UnOptiontþ a17 Ownertþ a18 Bonustþ a19 Year Dummies þ et
We can test the signs of the coefficients on the interaction terms (i.e., Incentive IndExp_city, Incentive IndExp_national, and Incentive BigN) to test our hypothesis. We do not choose this research design because there appear to be multicollinearity problems in the regression models. The collinearity condition index is 37 and the highest variance inflation factor is 43 (the rule of thumb is that collinearity condition index larger than 30 and/or variance inflation factor larger than 10 indicates multicollinearity).
Abn_Discexp = abnormal discretionary expenses (negative measure of real earnings management);
REM_Index = standardized Abn_CFO þ standardized Abn_Prod standardized
Abn_Discexp (positive composite score of real earnings management). Standardized measure for each variable = [variable mean(variable)]/standard deviation(vari-able);
IndExp_city = audit fee market share of the local office of auditor in the city-industry combination;
IndExp_national = audit fee market share of the auditor in the industry; BigN = 1 if auditor is a Big N audit firm, and 0 otherwise;
Tenure = number of years the auditor has audited the company’s financial statements; LMVE = natural log of market value of equity for a firm;
MTB = a firm’s market-to-book ratio;
DE = change in a firm’s annual earnings, deflated by prior year assets;
ROA = a firm’s return on assets, defined as the ratio of earnings before extraordinary items deflated by prior period assets;
ExOption = value of executive exercisable (i.e., vested) options at the end of the year from ExecuComp;
UnOption = value of executive un-exercisable (i.e., un-vested) options at the end of the year from ExecuComp;
Owner = the sum of restricted stock grants in the current period and the aggregate number of shares held by the executive at year-end (excluding stock options) scaled by total outstanding shares of the firm, computed using ExecuComp data; and
Bonus = average bonus compensation as a proportion of total compensation received by the CEO and the CFO of the firm from ExecuComp.
If auditor industry expertise constrains accrual earnings management and, consequently, clients resort to real earnings management, we would expect the coefficient on IndExp_city to be
positive when we use REM_Index as the dependent variable. When we use each of the three
components of REM_Index, i.e., Abn_CFO, Abn_Prod, and Abn_Discexp, as the dependent
variable, we expect the coefficients on IndExp_city to be negative, positive, and negative, respectively. Since we expect that industry expertise at city level dominates industry expertise at national level, we expect that the corresponding coefficients on IndExp_national will be insignificant. Similarly, if Big N auditors constrain accrual earnings management and, consequently, clients resort to real earnings management, we expect the coefficient on BigN to be positive when we use REM_Index as the dependent variable. When we use each of its three components,Abn_CFO, Abn_Prod, and Abn_Discexp, as the dependent variable, we expect the coefficients on BigN to be negative, positive, and negative, respectively. Since studies (e.g., Reichelt and Wang 2010) have shown that auditor industry specialization could subsume the effects of the Big N when both are included in the same regression model, our results onBigN could be weaker than onIndExp_city.6
We control for audit firm tenure (Tenure) in the regression. There is considerable debate in the literature regarding whether longer auditor tenure is associated with higher or lower audit quality. On the one hand, longer auditor tenure familiarizes auditors with clients’ operations and therefore helps auditors perform better audits. On the other hand, longer auditor tenure could lead
6 In Table 7 of Reichelt and Wang (2010), where they report the Logit regression results of meeting or beating
analyst earnings forecasts, their city-level industry expertise variables are all significantly negative in all specifications. In contrast, the Big 4 indicator variable is always insignificant.
to more friendly relationships with the management and therefore might impair auditor independence. Empirical evidence on whether longer or shorter auditor tenure indicates higher audit quality is also mixed. Johnson et al. (2002) and Myers et al. (2003) both show that longer auditor tenure is associated with lower discretionary accruals. However, Davis et al. (2009) find that, pre-SOX, longer audit firm tenure is associated with deteriorating audit quality in the form of a client’s ability to use discretionary accruals to meet or beat forecasts. They do not find an association between tenure and discretionary accruals post-SOX. If longer (shorter) auditor tenure indicates higher audit quality, auditors with longer (shorter) tenure would constrain accrual earnings management to a greater extent and, consequently, clients would resort to more real earnings management. Thus, we would expect the coefficient onTenure to be positive (negative) when we use REM_Index as the dependent variable. In this case, when we use each of its three components,Abn_CFO, Abn_Prod, and Abn_Discexp, as the dependent variable, we expect the coefficient on Tenure to be negative (positive), positive (negative), and negative (positive), respectively.
We argue that it is important to investigate the association between auditor tenure and real earnings management because the finding will shed additional light onto the long and heated debate of whether audit firm rotation should be mandated (AICPA 1978, 1992; Cox 2006). The Sarbanes-Oxley Act (SOX) even required the General Accounting Office (GAO) to conduct a study of the potential effects of mandating audit firm rotation. Prior empirical research exclusively focuses on accrual earnings management when performing the cost-benefit analysis of longer auditor tenure. We argue that real earnings management should be an important part of the cost-benefit analysis as well, and that has been missing in prior research.
We also control for other variables adapted from Cohen et al. (2008) and for fixed-year effects. To mitigate the influence of potential outliers, we winsorize all continuous variables at their respective 1st and 99th percentiles. Following Gow et al. (2010), we report test statistics based on the two-way cluster-robust standard errors (cluster by firm and by year) which adjust for both cross-sectional and time-series dependence in panel data.
SAMPLE SELECTION AND DATA DESCRIPTIONS
We calculate city-level and national-level auditor industry expertise using audit fee data from Audit Analytics. Since audit fee disclosures were first mandated in 2001, our sample ranges from 2001 to 2008. There are 104,588 such firm-year observations. Requiring Compustat coverage for nonfinancial and nonutilities firms results in a loss of 38,473 observations. Following Roychowdhury (2006), we require at least 15 observations in each industry-year group with available data for regression Models (A)–(C) to calculate real earnings management measures (Abn_CFO, Abn_Prod, and Abn_Discexp). Such data requirements reduce the sample by 40,882 observations. Requiring availability of the additional Compustat control variables (e.g., auditor tenure, leverage, firm size, earnings level and change, and market-to-book ratio) reduces our sample size by another 9,206 observations. We then merge with the ExecuComp database to calculate executive compensation variables as our control variables (i.e., executive option holdings, bonus, and ownership), which reduces our sample size by 12,123 observations, resulting in 3,904 observations. Finally, because we focus only on firms that have strong incentives to manage earnings upward, i.e., firms that meet or just beat earnings benchmarks (zero earnings, previous year’s earnings, and analyst forecasts) and firms that issue seasoned equities, we drop 2,979 firm-year observations. Our final sample consists of 925 firm-year observations. Table 1 presents the sample selection procedures.
Panels A–C of Table 2 provide descriptive statistics for the estimated coefficients and R2s from the industry-year regression results that estimate the components of real earnings management
(i.e.,Abn_CFO, Abn_Prod, and Abn_Discexp). These regressions are based on all observations in Compustat with available data for Models (A)–(C) before we implement additional data requirements for Model (1). There are 337 such industry-year groups from 2001 to 2008. Our statistics are similar to those reported in Roychowdhury (2006).
Panel D of Table 2 provides descriptive statistics for all variables in our regression models.
The meanREM_Index is0.230. The means of its three components, Abn_CFO, Abn_Prod, and
Abn_Discexp, are 0.055,0.048, and 0.026, respectively, consistent with those reported in Cohen et al. (2008). On average, auditors’ city-level industry market share (IndExp_city) is 0.488, suggesting that a local audit office, on average, holds 48.8 percent of the audit fee market share in a city-industry combination. In comparison, the mean national level industry market share of the auditor (IndExp_national) is smaller at 0.245, suggesting that an audit firm, on average, holds 24.5 percent of the national audit fee market share in an industry. Finally, 96.8 percent of our sample is audited by the Big N accounting firms, and the mean of auditor tenure (Tenure) is 12.872 years.
Table 3 represents the pairwise Pearson and Spearman correlations. By construction, Abn_CFO, Abn_Prod, and Abn_Discexp are negatively, positively, and negatively correlated with REM_Index, respectively. IndExp_city is positively correlated with REM_Index, positively correlated withAbn_Prod, and negatively correlated with Abn_Discexp. The Pearson correlation
between BigN and REM_Index is positive. Tenure is positively correlated with REM_Index,
negatively correlated with Abn_CFO, positively correlated with Abn_Prod, and negatively
correlated with Abn_Discexp. These correlations suggest that, overall, auditor industry expertise and tenure are correlated with greater levels of real earnings management. The correlation between Big N auditors and real earnings management is weaker. However, we acknowledge that all these are merely univariate associations and we should rely on the multiple regression analyses for our inferences.
EMPIRICAL FINDINGS
We first confirm, using our sample, the results found in the literature regarding the negative association between audit quality and accrual earnings management. We run the following regression Model (2) extending Cohen et al. (2008):
TABLE 1 Sample Selection
Firm-Year Obs. Audit fees coverage in Audit Analytics from 2001 to 2008 104,588 Financial institutions or utility firms, or not covered by Compustat (38,473) Missing data to calculate real earnings management measures (40,882) Missing data for additional Compustat control variables (e.g., auditor tenure, leverage,
firm size, earnings level and change, and market-to-book ratio)
(9,206) Missing data for ExecuComp control variables (i.e., option holdings, bonus, and
ownership)
(12,123) Observations with available data for all regression models 3,904 Observations without clear incentives to manage earnings upward (2,979) Final sample: Observations with upward earnings management incentives (i.e., firms
that meet or just beat zero earnings, previous year’s earnings, or analyst forecasts benchmarks, or firms that issue seasoned equities)
TABLE 2 Descriptive Statistics
Panel A: Distribution of Estimated Coefficients and R2s from Model (A) to Calculate Abn_CFO
Mean Std. Dev. Q1 Median Q3 1/Assetst1 0.902 2.038 0.689 0.482 0.260
Salest/Assetst1 0.063 0.059 0.032 0.053 0.079
DSalest/Assetst1 0.004 0.209 0.082 0.008 0.099
Adj. R2 0.535 0.243 0.373 0.555 0.714
Panel B: Distribution of Estimated Coefficients and R2s from Model (B) to Calculate Abn_Prod
Mean Std. Dev. Q1 Median Q3 1/Assetst1 0.649 4.252 0.227 0.004 0.047
Salest/Assetst1 0.708 0.111 0.647 0.717 0.788
DSalest/Assetst1 0.031 0.348 0.084 0.036 0.149
DSalest1/Assetst1 0.004 0.294 0.121 0.019 0.085
Adj. R2 0.935 0.088 0.933 0.965 0.984
Panel C: Distribution of Estimated Coefficients and R2s from Model (C) to Calculate Abn_Discexp
Mean Std. Dev. Q1 Median Q3 1/Assetst1 2.110 5.400 0.796 1.173 1.506
Salest1/Assetst1 0.226 0.127 0.132 0.210 0.314
Adj. R2 0.739 0.171 0.649 0.740 0.880
Panel D: Distribution of all Variables
Variable Mean Std. Dev. Q1 Median Q3 REM_Indext 0.230 1.584 1.155 0.227 0.539 Abn_CFOt 0.055 0.116 0.003 0.054 0.113 Abn_Prodt 0.048 0.210 0.168 0.052 0.046 Abn_Discexpt 0.026 0.245 0.078 0.021 0.137 IndExp_cityt 0.488 0.287 0.248 0.464 0.714 IndExp_nationalt 0.245 0.098 0.182 0.244 0.307 BigNt 0.968 0.175 1.000 1.000 1.000 Tenuret 12.872 8.945 6.000 10.000 17.000 Levt1 0.549 0.356 0.342 0.514 0.672 LMVEt1 7.326 1.650 6.251 7.193 8.434 MTBt1 3.283 5.208 1.573 2.410 3.952 DEt1 0.018 0.197 0.012 0.013 0.036 ROAt1 0.045 0.184 0.017 0.061 0.106 ExOptiont 6.467 7.560 1.490 4.037 8.457 UnOptiont 3.254 3.859 0.718 2.058 4.355 Ownert 17.070 37.335 0.797 3.262 13.292 Bonust 0.165 0.167 0.000 0.124 0.278
DAt= a0þ a1 IndExp citytþ a2 IndExp nationaltþ a3 BigNtþ a4 Tenuret
þ a5 Levt1þ a6 LMVEt1þ a7 MTBt1þ a8 DEt1þ a9 ROAt1
þ a10 ExOptiontþ a11 UnOptiontþ a12 Ownertþ a13 Bonust
þ a14 Year Dummies þ et ð2Þ
where:
DAt = modified Jones (1991) model of discretionary accruals with control for
contempora-neous accounting performance, as suggested in Kothari et al (2005).
All other variables are as defined before. We expect negative coefficients onIndExp_city and BigN. If city-level industry expertise dominates national-level industry expertise, we would not find the coefficient on IndExp_national to be significant. If longer (shorter) auditor tenure indicates higher audit quality by constraining accrual earnings management, we expect the coefficient on Tenure to be negative (positive). We report our findings in Table 4. Consistent with our expectations, we find a negative coefficient of0.020 (t = 3.30) on IndExp_City, an insignificant coefficient of0.014 (t = 0.67) on IndExp_national, and a negative coefficient of 0.019 (t = 1.65) on BigN. Our results are consistent with the literature, suggesting that city-level auditor industry expertise and the Big N auditors constrain accrual management. We also find a negative coefficient of0.001 (t = 4.63) on Tenure, suggesting that longer auditor tenure is associated with lower accrual earnings management for our sample.
Table 5 presents our main results.7 We find a positive coefficient of 0.947 (t = 3.54) on IndExp_city in the REM_Index regression, suggesting that city-level auditor industry expertise is associated with more overall real activity earnings management. We also present regression results
in the next three columns using each of the three components of REM_Index as dependent
variables. Consistent with the REM_Index results, we find a negative coefficient of0.040 (t = 3.02), a positive coefficient of 0.119 (t = 3.12), and a negative coefficient of 0.113 (t = 3.09) on IndExp_city in the Abn_CFO, Abn_Prod, and Abn_Discexp regressions, respectively. These results suggest that higher city-level auditor industry expertise is associated with lower abnormal cash flow, higher abnormal production, and lower discretionary expenses. Collectively, these results suggest that higher city-level auditor industry expertise is associated with more real earnings management, an unintended consequence of higher city-level auditor industry expertise constraining accrual earnings management. In all four regressions, we fail to find significant coefficients onIndExp_national, suggesting that higher national-level auditor industry expertise is not associated with more real earnings management. This result is consistent with the findings in prior studies (Ferguson et al. 2003; Francis et al. 2005; Reichelt and Wang 2010) that industry expertise should be measured at the city level, not at the national level.
Furthermore, we find a positive coefficient of 0.509 (t = 1.85) on BigN in the REM_Index regression, suggesting that the Big N auditors are associated with more overall real earnings management. Consistent with theREM_Index results, we find a negative coefficient of0.041 (t =
TABLE 2(continued)
The table summarizes descriptive statistics for our sample. Panels A–C present distributions of the estimated coefficients and R2s from the industry-year regression results that estimate the components of real earnings management (i.e.,
Abn_CFO, Abn_Prod, and Abn_Discexp). These regressions are based on all observations in Compustat with available data for Models (A)–(C) before we implement additional data requirements for Model (1). We require at least 15 observations in each industry-year group. There are 337 such industry-year groups from 2001 to 2008. Panel D provides descriptive statistics for all variables in our regression Model (1).
7
1.82) on BigN in the Abn_CFO regression, suggesting that the Big N auditors are associated with lower abnormal cash flows. Although the coefficients onBigN in the Abn_Prod and Abn_Discexp regressions are insignificant at conventional levels, their signs are consistent with our predictions. Our results onBigN are weaker than on IndExp_city; however, this is consistent with the evidence
TABLE 3 Correlation Matrix Panel A: Correlation Matrix Columns A–H
(A) (B) (C) (D) (E) (F) (G) (H) REM_Indext(A) 0.627* 0.965* 0.776* 0.081* 0.024 0.016 0.050* Abn_CFOt(B) 0.542* 0.508* 0.156* 0.051* 0.027* 0.015 0.044* Abn_Prodt(C) 0.971* 0.416* 0.759* 0.077* 0.028* 0.020 0.040* Abn_Discexpt(D) 0.774* 0.043* 0.760* 0.096* 0.020 0.022 0.040* IndExp_Cityt(E) 0.076* 0.020 0.069* 0.087* 0.337* 0.203* 0.063* IndExp_Nationalt(F) 0.033* 0.003 0.033* 0.038* 0.357* 0.415* 0.004 BigNt(G) 0.033* 0.002 0.031* 0.041* 0.215* 0.302* 0.050* Tenuret(H) 0.079* 0.042* 0.067* 0.077* 0.085* 0.011 0.063* Levt1(I) 0.099* 0.167* 0.072* 0.012 0.116* 0.064* 0.025 0.050* LMVEt1(J) 0.170* 0.275* 0.148* 0.005 0.207* 0.153* 0.144* 0.153* MTBt1(K) 0.189* 0.211* 0.168* 0.074* 0.015 0.015 0.015 0.001 DEt1(L) 0.035* 0.021 0.039* 0.054* 0.017 0.010 0.004 0.048* ROAt1(M) 0.196* 0.604* 0.151* 0.233* 0.070* 0.047* 0.060* 0.011 ExOptiont(N) 0.042* 0.115* 0.056* 0.133* 0.095* 0.090* 0.079* 0.102* UnOptiont(O) 0.029* 0.066* 0.033* 0.087* 0.066* 0.052* 0.022 0.130* Ownert(P) 0.050* 0.029* 0.044* 0.044* 0.001 0.030* 0.072* 0.023 Bonust(Q) 0.043* 0.060* 0.036* 0.011 0.035* 0.046* 0.014 0.006
Panel B: Correlation Matrix Columns I–Q
(I) (J) (K) (L) (M) (N) (O) (P) (Q) REM_Indext(A) 0.198* 0.176* 0.361* 0.149* 0.369* 0.014 0.048* 0.041*0.050* Abn_CFOt(B) 0.134* 0.292* 0.430* 0.193* 0.569*0.069* 0.018 0.045* 0.083* Abn_Prodt(C) 0.171* 0.164* 0.330* 0.122* 0.327* 0.011 0.040* 0.032*0.036* Abn_Discexpt(D) 0.219* 0.001 0.165* 0.066* 0.045* 0.090* 0.097*0.033* 0.011 IndExp_Cityt(E) 0.186* 0.220* 0.022* 0.009 0.036*0.108* 0.074* 0.037* 0.029* IndExp_Nationalt(F) 0.076* 0.141* 0.003 0.023 0.005 0.097* 0.059* 0.070* 0.047* BigNt(G) 0.092* 0.135* 0.042* 0.013 0.019 0.068* 0.029*0.059* 0.021 Tenuret(H) 0.078* 0.142* 0.004 0.067* 0.016 0.097* 0.126* 0.051* 0.023 Levt1(I) 0.186* 0.058* 0.035* 0.051* 0.103* 0.090* 0.021 0.092* LMVEt1(J) 0.082* 0.507* 0.032* 0.354*0.380* 0.285* 0.265* 0.018 MTBt1(K) 0.025 0.225* 0.113* 0.537*0.141* 0.048* 0.114* 0.028* DEt1(L) 0.046* 0.048* 0.041* 0.417*0.051* 0.012 0.002 0.138* ROAt1(M) 0.268* 0.246* 0.146* 0.126* 0.107* 0.083* 0.005 0.072* ExOptiont(N) 0.022 0.359* 0.068* 0.019 0.123* 0.415* 0.228*0.014 UnOptiont(O) 0.030* 0.308* 0.037* 0.028* 0.087* 0.351* 0.067* 0.099* Ownert(P) 0.025 0.171* 0.037* 0.021 0.001 0.116* 0.041* 0.015 Bonust(Q) 0.077* 0.020 0.018 0.013 0.034*0.020 0.118*0.004
* Indicates significance at the 10 percent level.
See Appendix for variable definitions. Pearson correlations in the lower diagonal and Spearman correlations in the upper diagonal.
in Reichelt and Wang (2010) that the city-level industry expertise effect dominates the Big N effect for firms that meet or just beat earnings benchmarks. In addition, Panel D of Table 2 shows that 96.8 percent of our sample is audited by the Big N accounting firms. The lack of variation could also explain our weaker results on the Big N indicator variable. Collectively, these results provide some evidence that the Big N auditors are associated with more real earnings management, an unintended consequence of the Big N auditors constraining accrual earnings management.
Although empirical evidence on the association between auditor tenure and accrual earnings management is mixed, we find that longer auditor tenure is associated with higher levels of real earnings management at both the overall level and the individual component level. Specifically, we find a positive coefficient of 0.019 (t = 2.46), a negative coefficient of0.001 (t = 2.58), a positive coefficient of 0.002 (t= 2.42), and a negative coefficient of0.002 (t = 1.85) on Tenure in theREM_Index, Abn_CFO, Abn_Prod, and Abn_Discexp regressions, respectively. These results suggest that longer auditor tenure is associated with higher overall levels of real earnings management, lower abnormal cash flow, higher abnormal production, and lower discretionary expenses. Thus, collectively, these results suggest that longer auditor tenure is associated with more extensive real earnings management. This finding adds important insights into the debate regarding whether auditor rotation should be mandated. Past research has exclusively focused on whether longer or shorter auditor tenure is associated with lower accrual earnings management to infer whether mandatory auditor rotation is beneficial. Our paper adds an important piece in the literature by alerting regulators and researchers to the association between auditor tenure and real earnings
TABLE 4
Audit Quality and Accrual Earnings Management
DA t-stat Intercept 0.058* 1.90 IndExp_cityt 0.020*** 3.30 IndExp_nationalt 0.014 0.67 BigNt 0.019* 1.65 Tenuret 0.001*** 4.63 Levt–1 0.014 1.58 LMVEt–1 0.010*** 3.36 MTBt1 0.003*** 3.12 DEt1 0.087* 1.78 ROAt1 0.033 1.61 ExOptiont 0.000 1.30 UnOptiont 0.000 0.60 Ownert 0.000** 2.07 Bonust 0.044*** 2.75
Year Dummies Yes
n 925
Adj. R2 0.183
*, **, *** Denote 0.1, 0.05, and 0.01 significance levels, respectively.
The table summarizes the regression of modified Jones model abnormal accruals adjusted for contemporaneous accounting performance on audit quality proxies such as city- and national-level industry expertise variables, audit firm size, and associated control variables for the sample of firms that have strong incentives to manage earnings upward, i.e., firms that meet or just beat earnings benchmarks (zero earnings, previous year’s earnings and analyst forecasts), and firms that issue seasoned equity offerings. The model includes year fixed effects, and the t-statistics are based on the two-way cluster-robust standard errors (cluster by firm and by year), which adjust for both cross-sectional and time-series dependence in panel data. All continuous variables are winsorized at 1st and 99th percentiles. All variables are defined in the Appendix.
management. Our finding of a positive association suggests to the policy makers a potential benefit of mandating auditor rotation, which has been missing in past debates.
ADDITIONAL ANALYSES Audit Fees as an Alternative Proxy for Audit Quality
Prior work (DeFond et al. 2000;Francis 2004) suggests that audit fees could serve as an overall indicator of audit quality to the extent that audit fees capture higher level of auditor effort. Thus, as
TABLE 5
Audit Quality and Real Earnings Management
REM_Index Abn_CFO Abn_Prod Abn_Discexp Intercept 0.270 0.056** 0.072 0.002 (0.74) (2.00) (1.56) (0.03) IndExp_cityt 0.947*** 0.040*** 0.119*** 0.113*** (3.54) (3.02) (3.12) (3.09) IndExp_nationalt 0.206 0.010 0.018 0.037 (0.38) (0.28) (0.23) (0.45) BigNt 0.509* 0.041* 0.051 0.048 (1.85) (1.82) (1.45) (0.97) Tenuret 0.019** 0.001** 0.002** 0.002* (2.46) (2.58) (2.42) (1.85) Levt1 0.296 0.003 0.028 0.069*** (1.49) (0.44) (0.90) (2.64) LMVEt1 0.236*** 0.014*** 0.027*** 0.026*** (4.47) (4.52) (4.26) (3.11) MTBt1 0.065*** 0.005*** 0.007*** 0.006** (3.01) (3.16) (2.85) (2.08) DEt1 0.295 0.066** 0.072 0.130 (0.55) (2.46) (1.16) (1.04) ROAt1 0.536 0.223*** 0.058 0.299*** (0.85) (7.01) (0.73) (3.11) ExOptiont 0.019*** 0.000 0.002*** 0.004*** (3.00) (0.42) (2.67) (3.14) UnOptiont 0.024 0.001 0.002 0.003* (1.64) (1.59) (1.11) (1.77) Ownert 0.004 0.000*** 0.000 0.000 (1.31) (2.82) (1.15) (1.08) Bonust 0.797*** 0.051*** 0.093* 0.076 (2.69) (3.03) (1.93) (1.34)
Year Dummies Yes Yes Yes Yes
n 925 925 925 925
Adj. R2 0.196 0.392 0.145 0.112 *, **, *** Denote 0.1, 0.05, and 0.01 significance levels, respectively.
The table summarizes the regression of real earnings management measures on audit quality proxies such as city- and national-level industry expertise variables, audit firm size, and associated control variables for the sample of firms that have strong incentives to manage earnings upward, i.e., firms that meet or just beat earnings benchmarks (zero earnings, previous year’s earnings and analyst forecasts), and firms that issue seasoned equity offerings. All models include year fixed effects, and the t-statistics in parentheses are based on the two-way cluster-robust standard errors (cluster by firm and by year), which adjust for both cross-sectional and time-series dependence in panel data. All continuous variables are winsorized at 1st and 99th percentiles. All variables are defined in the Appendix.
before, we expect that firms will resort to more real earnings management when their ability to manage earnings via accruals is constrained by higher quality auditors that charge higher audit fees. We test our prediction with the following regression Model (3), which in essence replaces auditor industry expertise variables, the Big N indicator, and auditor tenure in Model (1) with an audit fee variable:
REMt = a0þ a1 LAudFeestþ a2 Levt1þ a3 LMVEt1þ a4 MTBt1
þ a5 DEt1þ a6 ROAt1þ a7 ExOptiontþ a8 UnOptiont
þ a9 Ownertþ a10 Bonustþ et ð3Þ
where:
LAudFees = natural logarithm of audit fees from Audit Analytics.
All other variables are as defined before. We expect the coefficient onLAudFees in Model (3) to be positive when we useREM_Index as the dependent variable. When we use each of its three components,Abn_CFO, Abn_Prod, and Abn_Discexp, as the dependent variable, we expect the coefficient onLAudFees to be negative, positive, and negative, respectively. Table 6 presents the regression results.8Consistent with our expectations, we find a positive coefficient of 0.257 (t= 3.37) onLAudFees in the REM_Index regression, suggesting that higher audit fees are associated with more overall real earnings management. We also find a negative coefficient of0.015 (t = 4.31), a positive coefficient of 0.031 (t = 2.83), and a negative coefficient of 0.026 (t = 2.61)
on LAudFees in the Abn_CFO, Abn_Prod, and Abn_Discexp regressions, respectively. These
results suggest that higher levels of audit fees are associated with lower levels of abnormal cash flow, higher abnormal production, and lower discretionary expenses. Thus, collectively, these results suggest that higher overall audit quality as proxied by higher audit fees is associated with more real earnings management, an unintended consequence of hiring a higher quality auditor to constrain accrual earnings management.
Individual Incentives for Upward Earnings Management
Our main analyses pool all firms that we identify as having at least one of the four incentives for upward earnings management, i.e., firms that meet or just beat the three earnings benchmarks (zero earnings, previous year’s earnings, and analyst forecasts) or firms that issue seasoned equities. In this analysis, we separately investigate each individual incentive: 54 (1.4 percent) of our 3,904 firm-year observations (see Table 1) meet or just beat the zero earnings benchmark, 221 (5.7 percent) meet or just beat the previous year’s earnings benchmark, 380 (9.7 percent) meet or just beat analyst forecasts, and 362 (9.3 percent) issue seasoned equity offerings.
For each of the four subsamples, we rerun Model (1). Due to the smaller sample sizes, the coefficients onIndExp_city and BigN are insignificant in some regressions. However, in all cases, their signs are consistent with our predictions. Furthermore, for the 221 observations that meet or just beat the previous year’s earnings benchmark, the coefficients onBigN are all significant at the
8
We first confirm using our sample that audit fees are negatively associated with accrual earnings management. We run the following regression model extending Cohen et al. (2008):
DAt= a0þ a1 LAudFeestþ a2 Levt1þ a3 LMVEt1þ a4 MTBt1þ a5 DEt1þ a6 ROAt1
þ a7 ExOptiontþ a8 UnOptiontþ a9 Ownertþ a10 Bonustþ et
We find a negative coefficient of0.009 (t = 2.89) on LAudFees. Our results are consistent with the notion that higher audit fees can serve as an overall proxy for higher audit quality, which constrains accrual management. Furthermore, our Model (2) results are robust to addingDA as a control variable.
10 percent level in all four real earnings management regressions (i.e., REM_Index, Abn_CFO, Abn_Prod, and Abn_Discexp regressions). For the 380 observations that meet or just beat analyst forecasts, the coefficients onIndExp_city are all significant at the 10 percent level in all four real earnings management regressions. For the 362 observations that issue seasoned equities, the coefficients on andIndExp_city and BigN are all significant at the 10 percent level in all four real earnings management regressions. Thus, overall, we find consistent evidence that higher quality auditors are associated with more extensive real earnings management when firms have incentives to manage earnings upward.
Comparing with Firms without Clear Incentives to Manage Earnings Upward
Although our proxies for real earnings management are widely used in the literature (Roychowdhury 2006; Cohen et al. 2008; Cohen and Zarowin 2010), these proxies may measure real earnings management with error. If these measurement errors are correlated with our proxies
TABLE 6
Audit Fees and Real Earnings Management
REM_Index Abn_CFO Abn_Prod Abn_Discexp Intercept 0.239 0.050** 0.073 0.007 (0.51) (2.15) (1.19) (0.10) LAudFeest 0.257*** 0.015*** 0.031*** 0.026*** (3.37) (4.31) (2.83) (2.61) Levt1 0.338* 0.004 0.034 0.076*** (1.96) (0.59) (1.23) (3.62) LMVEt1 0.298*** 0.018*** 0.034*** 0.032*** (5.39) (5.48) (5.03) (3.71) MTBt1 0.067*** 0.005*** 0.007*** 0.006** (3.26) (3.18) (3.13) (2.43) DEt1 0.271 0.034 0.004 0.189 (0.68) (1.45) (0.08) (1.59) ROAt1 0.135 0.200*** 0.010 0.341*** (0.24) (6.19) (0.15) (3.81) ExOptiont 0.024*** 0.000 0.003*** 0.004*** (3.75) (0.87) (3.34) (3.92) UnOptiont 0.017 0.001 0.002 0.003 (1.19) (1.20) (0.74) (1.41) Ownert 0.004 0.000*** 0.001 0.001 (1.42) (3.08) (1.28) (1.17) Bonust 1.043*** 0.065*** 0.122*** 0.102* (3.40) (3.38) (2.63) (1.81)
Year Dummies Yes Yes Yes Yes
n 925 925 925 925
Adj. R2 0.170 0.381 0.121 0.095 *, **, *** Denote 0.1, 0.05, and 0.01 significance levels, respectively.
The table summarizes the regression of real earnings management measures on natural log of audit fees and associated control variables for the sample of firms that have strong incentives to manage earnings upward, i.e., firms that meet or just beat earnings benchmarks (zero earnings, previous year’s earnings and analyst forecasts), and firms that issue seasoned equity offerings. All models include year fixed effects, and the t-statistics in parentheses are based on the two-way cluster-robust standard errors (cluster by firm and by year), which adjust for both cross-sectional and time-series dependence in panel data. All continuous variables are winsorized at 1st and 99th percentiles. All variables are defined in the Appendix.
for audit quality due to unobservable firm characteristics, the relation we document between audit quality and real earnings management could be spurious. We address this issue by demonstrating that the association between our real earnings management proxies and audit quality proxies is significantly stronger among firms that have strong incentives to management earnings upward (our main sample, Incentive = 1) than among firms that lack these incentives (Incentive = 0).9 The variableIncentive is an indicator variable that is coded as 1 for firms that have strong incentives to manage earnings upward, i.e., firms that meet or just beat earnings benchmarks (zero earnings, previous year’s earnings, and analyst forecasts) and firms that issue seasoned equities, and 0 otherwise.
Table 1 shows that we have 925 observations in the Incentive = 1 sample and 2,979
observations for theIncentive = 0 sample. We run Model (1) for the Incentive = 1 and Incentive = 0 samples, respectively, and report results of the comparison in Table 7. For brevity, we only show the coefficients of the audit quality proxies (IndExp_city, IndExp_national, and BigN). Note that the coefficients for theIncentive = 1 sample are replicates of those reported in Table 5.
We test whether the coefficients are statistically different for the two samples (Incentive = 1 versus 0) by performing the Chow test.10 Our results support our prior conclusion that city-level industry expertise is associated with a greater extent of real earnings management because the difference in the coefficients on IndExp_city for the Incentive = 1 and Incentive = 0 samples is significantly positive in theREM_Index regression (0.620, t = 2.31) and the Abn_Prod regression (0.083, t= 2.26), and significantly negative in the Abn_Descexp regression (0.088, t = 2.77). We do not find evidence that national-level industry expertise or the presence of a Big N audit firm is associated with real earnings management. A potential explanation for this lack of findings is that city-level industry expertise subsumes the effect of national-level industry expertise and the presence of a Big N audit firm, as suggested by prior research (e.g., Reichelt and Wang 2010). Besides, 97 percent of our pooled sample (Incentive = 0 plus Incentive = 1 samples) has Big N auditors, suggesting a lack of variation in theBigN variable. Taken together, our results provide strong evidence that audit quality, as proxied by city-level industry expertise, is associated with greater extent of real earnings management.
CONCLUSION
The literature suggests that higher audit quality constrains accrual earnings management. However, firms could resort to real earnings management when their opportunities for accrual earnings management are constrained. We examine whether higher audit quality has the unintended consequence of being associated with greater levels of real earnings management among firms with incentives to manage earnings. Our primary proxies for audit quality are auditor industry expertise and the presence of a Big N audit firm. For a sample of firms that manage earnings upward (i.e., firms that meet or just beat earnings benchmarks and firms that issue seasoned equities), we find that
9
We thank an anonymous reviewer for pointing out this issue and for suggesting this solution.
10
We perform the Chow test as detailed inhttp://www.stata.com/support/faqs/stat/chow3.html.Specifically, we pool theIncentive = 1 and Incentive = 0 samples together (n = 3,904), and create interactions of Incentive with each of the independent variables in Model (1) (i.e., 1, audit quality proxies, and control variables), and expand Model (1) by including all the interactions. The coefficients on the interactions between the audit quality proxies andIncentive (i.e., IndExp_city Incentive, IndExp_national Incentive, and BigN Incentive) are reported in the Difference rows, along with their associated t-statistics. However, we caution the readers when interpreting the statistical significance of the differences (i.e., the interaction terms) due to the presence of potential multicollinearity problems in the Chow test regressions. We find that the collinearity condition index is 46 and the highest variance inflation factor is 60. Thus, the multicollinearity problem could partially explain our failure to find significant differences of the impact ofIndExp_national and BigN on real earnings management between theIncentive = 1 and Incentive = 0 samples.
city-level auditor industry expertise and the presence of a Big N audit firm are both associated with greater overall real earnings management. City-level auditor industry expertise is also associated with each individual component of real earnings management, i.e., lower abnormal cash flow, higher over-production, and lower discretionary expenditures, whereas Big N audit firms are associated with lower abnormal cash flow. Using audit fees as an additional proxy for audit quality, our findings confirm our primary results that higher audit quality is associated with more real earnings management. We further find that the positive association between city-level industry expertise and real earnings management measures is significantly stronger for the upward earnings management sample than for the sample where such incentives are absent. Our results suggest that imposition of higher levels of audit quality could result in unintended consequences. Monitoring bodies, such as boards and audit committees, should consider implications of imposing higher quality auditing, which could drive firms to potentially value-decreasing real earnings management activities.
Regulators have been repeatedly debating whether to mandate audit firm rotation (AICPA 1978, 1992; SOX 2002; GAO 2003; Cox 2006). The main concern is that that longer auditor tenure would foster an overly friendly relationship between the management and the auditor, which would in turn impair audit quality. Prior empirical studies have exclusively focused on examining the association between auditor tenure and accrual earnings management (Johnson et al. 2002; Myers et al. 2003). When they find a negative association between auditor tenure and abnormal accruals, they conclude that their findings do not support regulator concerns. Our results suggest that there is another important side of the story potentially missing in prior research. We find that longer auditor
TABLE 7
Comparing with Firms without Clear Incentives to Manage Earnings Upward
REM_Index Abn_CFO Abn_Prod Abn_Discexp IndExp_city Incentive = 1 0.947*** 0.040*** 0.119*** 0.113*** (n= 925) (3.54) (3.02) (3.12) (3.09) Incentive = 0 0.327* 0.026*** 0.036 0.025 (n= 2,979) (1.78) (3.11) (1.47) (0.96) Difference 0.620** 0.014 0.083** 0.088*** (2.31) (0.87) (2.26) (2.77) IndExp_national Incentive = 1 0.206 0.010 0.018 0.037 (n= 925) (0.38) (0.28) (0.23) (0.45) Incentive = 0 0.110 0.009 0.016 0.001 (n= 2,979) (0.18) (0.33) (0.20) (0.01) Difference 0.316 0.019 0.034 0.036 (0.50) (0.46) (0.39) (0.49) BigN Incentive = 1 0.509* 0.041* 0.051 0.048 (n= 925) (1.85) (1.82) (1.45) (0.97) Incentive = 0 0.326 0.019 0.039 0.033 (n= 2,979) (1.17) (1.07) (1.27) (0.74) Difference 0.183 0.022 0.012 0.015 (0.58) (0.83) (0.38) (0.26) *, **, *** Denote 0.1, 0.05, and 0.01 significance levels, respectively.
The table compares the regression coefficients of real earnings management measures on audit quality proxies between the sample that has strong incentives to manage earnings upward (Incentive = 1) and the sample that does not have clear incentives to do so (Incentive = 0). See Table 1 for sample selection procedures. The coefficients on audit quality proxies for theIncentive = 1 sample replicate those presented in Table 5. Differences in coefficients are tested using the Chow test. t-statistics in parentheses are based on the two-way cluster-robust standard errors (cluster by firm and by year), which adjust for both cross-sectional and time-series dependence in panel data. All variables are defined in the Appendix.
tenure is associated with more extensive real earnings management. Our results suggest that there could be some merits to mandating audit firm rotation because shortened auditor tenure could be associated with lower levels of real earnings management. Hence, our results would provide important additional inputs to policy makers when they consider whether to mandate audit firm rotation.
Past research on real earnings management (Roychowdhury 2006; Cohen and Zarowin 2010) has exclusively focused on upward earnings management. An interesting question for future research is whether and how firms take real actions to manage earnings downward in certain contexts. Furthermore, what is the association between audit quality and downward real earnings management? Downward real earnings management seems a fruitful area for future research.
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APPENDIX Variable Definitions
REM = real earnings management variables defined based on Cohen et al. (2008): Abn_CFO = abnormal cash flows (negative measure of real earnings management); Abn_Prod = abnormal inventory over-production (positive measure of real earnings
management);
Abn_Discexp = abnormal discretionary expenses (negative measure of real earnings management);
REM_Index = standardized Abn_CFO þ standardized Abn_Prod standardized Abn_Discexp (positive composite score of real earnings management). Standardized measure for each variable= [variable mean(variable)]/standard deviation(variable); IndExp_city = audit fee market share of the local office of auditor in the city-industry
combination;
IndExp_national = audit fee market share of the auditor in the industry; BigN = 1 if auditor is a Big N audit firm, and 0 otherwise;
Tenure = number of years the auditor has audited the company’s financial statements; Lev = a firm’s leverage defined as the ratio of total liabilities to assets;
LMVE = natural log of market value of equity for a firm; MTB = a firm’s market-to-book ratio;
DE = change in a firm’s annual earnings, deflated by prior year assets;
ROA = a firm’s return on assets, defined as the ratio of earnings before extraordinary items deflated by prior period assets;
ExOption = value of executive exercisable (i.e., vested) options at the end of the year from ExecuComp;
UnOption = value of executive un-exercisable (i.e., un-vested) options at the end of the year from ExecuComp;
Owner = the sum of restricted stock grants in the current period and the aggregate number of shares held by the executive at year-end (excluding stock options) scaled by total outstanding shares of the firm, computed using ExecuComp data;
Bonus = average bonus compensation as a proportion of total compensation received by the CEO and the CFO of the firm from ExecuComp;
DA = modified Jones (1991) model of discretionary accruals, with control for contempora-neous accounting performance as suggested in Kothari et al (2005); and
Incentive = 1 for firms that have strong incentives to manage earnings upward (identified ex post or ex ante), i.e., firms that meet or just beat earnings benchmarks (zero earnings, previous year’s earnings, and analyst forecasts) and firms that issue seasoned equities, and 0 otherwise.
1
