Journal of accounting, auditing finance tập 27, số 03, 2012 7

Reexamining the Relationship Between Audit and Nonaudit Fees: Dealing With Weak Instruments in Two-Stage Least Squares Estimation Journal of Accounting, Auditing & Finance 27(3) 299–324 Ó The Author(s) 2012 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0148558X11409154 http://jaaf.sagepub.com Lilian Chan1, Tai-Yuan Chen2, Surya Janakiraman3, and Suresh Radhakrishnan3 Abstract The authors introduce some new econometric tests and techniques for identifying and overcoming the problem of weak instruments in the context of joint provision of audit and nonaudit fees The authors use this context because identifying appropriate instruments is difficult due to the lack of theoretical guidance as well as due to the difficulty in intuitively identifying instruments that satisfy the econometric requirements The authors introduce a battery of empirical tests based on recent developments in econometrics to test for the appropriateness of the instruments The authors then illustrate two approaches of using instruments from existing data: the size industry average portfolio approach and the synthetic-instrument approach Although the approach using synthetic instruments sidesteps issues of identifying proxies with desirable properties, it requires some stringent assumptions that cannot be directly tested However, as a methodological alternative, this approach can be used for robustness tests The authors find that when the instruments are not weak, audit and nonaudit fees are positively associated This relationship holds for audit and tax-related nonaudit fees as well Overall, the evidence suggests the existence of economies of scope benefits from the joint supply of audit and nonaudit services Methodologically, the authors illustrate the importance of testing the appropriateness of the instruments utilized when accounting for endogeneity Keywords audit fees, nonaudit fees, instruments, two-stage least squares estimation University of Hong Kong, Hong Kong Hong Kong University of Science and Technology, Clearwater Bay, Hong Kong University of Texas at Dallas, USA Corresponding Author: Suresh Radhakrishnan, School of Management, SM41, The University of Texas at Dallas, Richardson, TX 75083, USA Email: sradhakr@utdallas.edu Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 300 Journal of Accounting, Auditing & Finance A number of studies show a positive association between audit and nonaudit fees, suggesting that knowledge spillovers occur from one service to the other (see, for example, AbdelKhalik, 1990; Bell, Landsman, & Shackelford, 2001; Davis, Ricchiute, & Trompeter, 1993; DeBerg, Kaplan, & Pany, 1991; Hay, Knechel, & Wongm, 2006; Palmrose, 1986; Simunic, 1984) These studies use the ordinary least squares (OLS) procedure to estimate the association between audit and nonaudit fees However, Whisenant, Sankaraguruswamy, and Raghunandan (2003) consider the joint determination of audit and nonaudit fees using the two-stage least squares (2SLS) procedure and show no association between audit and nonaudit fees In this study, we reexamine the association of audit and nonaudit fees and reconcile the ‘‘seemingly’’ conflicting results Reconciling the conflicting results on the existence of knowledge spillover effects between audit and nonaudit services is important both for gaining theoretical insights as well as guiding policy on the provision of nonaudit services On the theoretical front, Simunic (1980) considers an analytical model of joint provision of audit and nonaudit services and shows that audit and nonaudit fees should be positively associated when there are economies of scope, that is, knowledge spillovers.1 The intuition is that with economies of scope providing audit and nonaudit services, only some of the savings of auditor’s costs will be passed onto clients This in turn will enhance the profitability of both segments of the auditor’s business.2 The empirical audit- and nonaudit-fee models relate the auditors’ supply of effort to the fees Based on the knowledge spillover hypothesis, in the audit-fee models, a positive association between audit and nonaudit fees would imply the existence of a spillover effect, and no association would imply that the audit and nonaudit efforts are independent and as such imply no knowledge spillover effects Accordingly, Whisenant et al (2003) finding of no association between audit and nonaudit fees lead them to conclude that, The findings are not consistent with the existence of economies of scope for the joint performance of audit and non-audit services Given the ongoing debate over the level of allowed non-audit services by auditors, the argument for joint provision of audit and non-audit services is less justified than if the joint-supply benefits had been documented (p 722) In other words, if there are no benefits to the supply of nonaudit services, but only potential costs in terms of lower audit quality due to loss of audit independence, then limiting the nonaudit services would be a reasonable policy.3 Thus, reconciling the seemingly conflicting findings of the association between audit and nonaudit services is important Reconciling the conflicting results is also important in light of the recent developments with respect to nonaudit services.4 Although nonaudit fees from management advisory services have been curtailed since 2002, recently the Public Company Accounting Oversight Board (PCAOB) has initiated efforts to curtail auditors of public companies from supplying certain types of nonaudit tax services Reacting to the extensive media coverage and congressional actions pertaining to tax shelters promoted by auditors for their public company clients, the PCAOB conducted a roundtable in July 2004 and stated that ‘‘the Board has determined that it is appropriate to consider the impact of tax services on auditor independence’’ (see PCAOB, 2004a) In December 2004, the PCAOB (2004b) proposed rules to curb certain types of nonaudit tax services for their public company clients.5 To summarize, although knowledge spillover is the benefit, loss of auditor independence is the cost of Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Chan et al 301 providing nonaudit services It is therefore important to document whether there is a knowledge spillover benefit at all Audit and nonaudit services/fees are jointly determined because factors such as agency cost, complexity, size, risk, performance, and auditor characteristics influence the demand for both (see Whisenant et al., 2003) It is well known that when audit and nonaudit fees are jointly determined, using the OLS procedure leads to inconsistent/biased coefficient estimates (Wooldridge, 2002) Accordingly, 2SLS procedure should be used Recent developments in econometrics show that using weak instruments in 2SLS can lead to inappropriate conclusions (see Ebbes, Wedel, & Bockenholt, 2009, for an excellent summary) As such, in the context of joint provision of audit and nonaudit fees, we reconcile the mixed findings by assessing the appropriateness of instruments, that is, whether the instruments are weak We estimate the audit- and nonaudit-fee model using 2SLS with the instruments used in prior research, that is, reporting lag and new financing activity for audit and nonaudit fees, respectively We this for three separate periods: 2000, 2001, and 2002 to 2006 We consider year 2000 separately so as to provide a benchmark of our sample with that of prior research that also considers the same year We consider 2002 onward separately because of the auditor reforms that went into effect from 2002 We find that audit and nonaudit fees are not associated with each other, that is, the findings in prior research that use 2SLS are robust for each period However, the tests on the strength of the two instruments reveal that the instruments are ‘‘weak.’’ We thus consider additional instruments to examine the joint determination of audit and nonaudit fees with 2SLS We use the industry average audit and nonaudit fees as additional instruments for audit and nonaudit fees, respectively There is a rich tradition of using industry averages as instruments in the financial economics literature: for instance, Lev and Sougiannis (1996), Bertrand and Mullainathan (2001), Murphy (2000), Hanlon, Rajagopal, and Shevlin (2003) In particular, we use the mean/median of the 10 closest firms in terms of total assets operating in the same Fama–French industry group as the instrument We show that these instruments are not weak, and with these instruments, audit and nonaudit fees are positively associated with each other This suggests that knowledge spillover effects between audit and nonaudit services exist, that is, there are benefits to the joint provision of audit and nonaudit services Although our research design does not consider the cost of such provision of joint services in terms of conflicts of interests and poor accounting quality, our results suggest that the benefits should be compared with the costs in arriving at policy decisions In other words, it is not the case that there are no benefits and only costs of allowing such joint provision of audit and audit-related services We also consider synthetic instruments as additional instruments for audit and nonaudit fees—synthetic instruments are instruments derived from the transformation of endogenous and exogenous variables, that is, the instruments are synthetically derived (see Lewbell, 1997) We this because even though there is a rich tradition of using industry averages as instruments in the financial economics literature, recently Larcker and Rusticus (2010) argue that such methods may not be appropriate.6 It is widely recognized in the econometrics literature that finding suitable instruments may be difficult if not impossible; thus, creating such synthetic instruments are important for obtaining consistent and efficient estimates Ebbes et al (2009) and Lewbell (1997) show that candidates for synthetic instruments are (a) the product of the endogenous and exogenous variables and (b) the second moment of the endogenous variables We consider additional synthetic instruments to show that if instruments are weak then we again obtain the result of no association between audit Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 302 Journal of Accounting, Auditing & Finance and nonaudit fee We find that for the calendar year when the synthetic instruments based on the product of the exogenous and endogenous variables are weak, the positive association between the audit and nonaudit fee is also not statistically significant When we consider the second moment of the endogenous variables as additional instruments, the instruments are not weak and the audit and nonaudit fees are positively associated This provides indirect evidence on the importance of considering instruments that are not weak in assessing the relationship between audit and nonaudit services Finally, we examine the relationship between audit fees and tax-related nonaudit fees by considering the industry average–based instruments for these endogenous variables for the period of 2002 to 2006 We find that audit and tax-related nonaudit fees are positively associated This shows the existence of knowledge spillover effects across audit and tax services As discussed earlier, the PCAOB is considering the relationship between the nonaudit, tax service, and audit independence Kinney, Palmrose, and Scholz (2004) use accounting restatements as a proxy for audit quality and show that the provision of nonaudit services is not positively related to accounting restatements Although our research design/test does not provide evidence on the relationship between the joint provision of audit and tax-related services, and auditor independence/quality, our evidence provides insights into whether there are economies of scope in the provision of such services Overall, our objective is to introduce some new econometric techniques using the context of joint provision of audit and nonaudit fees We use the ‘‘joint provision of audit and nonaudit’’ service setting because identifying appropriate instruments is difficult due to the lack of theoretical guidance as well as intuitively identifying instruments that satisfy the econometric requirements We use a battery of empirical tests, developed recently in the econometric literature, to test for the appropriateness of the instruments We then illustrate two approaches of using instruments from data that are already available: the industry average approach, which has been used in previous research, and the synthetic instrument approach Although the approach using synthetic instruments sidesteps issues of identifying proxies with desirable properties, it requires some stringent assumptions that cannot be directly tested.7 However, as a methodological alternative, this approach can be used for robustness tests More importantly, we use this approach to demonstrate the importance of identifying weak instruments The rest of the article is organized as follows: Section titled ‘‘Summary of Econometric Issues with Instruments’’ provides a summary of the econometric issues with weak instruments, section titled ‘‘Empirical Analysis’’ presents the empirical analysis, and section titled ‘‘Concluding Remarks’’ contains some concluding remarks Summary of Econometric Issues With Instruments In this section, we summarize the issues with instrumental variable (IV) methods drawing on literature from statistics and econometrics To highlight the issues with IV methods, consider the following equation y5a0 1a1 x1v; ð1Þ where y is the dependant variable, x is the explanatory variable, and v is the structural error term If y is the audit fee and x is the nonaudit fee, and audit and nonaudit fees are jointly determined, then the nonaudit fee and the structural error are potentially correlated: the explanatory variable x is not independent of the structural error term v It is well known Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Chan et al 303 that estimating Equation using OLS leads to an inconsistent estimate of a1 One approach to mitigate this problem is to choose an IV, z, that is not correlated with the structural error term, v, and is correlated with the explanatory variable, x The 2SLS uses the IV approach.8 In the first step, the endogenous variable x is regressed on an instrument z as follows: x5b0 1b1 z1u: ð2Þ In the second stage, Equation is estimated with x being replaced by the predicted value of the explanatory variable from Equation 2, x^ The probability limit (plim) of the estimator is given by (see Wooldridge, 2002) plimð^ a1 Þ5a1 1½covðz; vÞ=covðx; zފ where cov(a, b) is the covariance of a and b It follows that if the instrument z is correlated with x but not with v then the second term converges to zero for sufficiently large samples In other words, we can obtain consistent estimates of a1, if cov(z, v) is close to zero and cov(x, z) is nonzero and sufficiently large Correspondingly, an inappropriate instrument would have large cov(z, v) and/or sufficiently small cov(x, z), and such weak instruments will not provide consistent estimates The probability limit of the coefficient estimate provides the basis for establishing the appropriateness of the instruments For this purpose, recently, various tests have been developed in the econometrics literature to validate the appropriateness of the instruments We provide a brief discussion of these tests Relevance of Instruments Partial R2 The bias introduced in the estimate using the 2SLS procedure depends on the covariance of the IV and the endogenous variable (see Equation 1) Specifically, Larcker and Rusticus (2007) show that the IV method is valid only if the following inequality is satisfied: R2zv\R2xz Á R2xv , where R2ij is the squared population correlation between variables i and j It follows that a necessary condition for good instruments is R2xz should be sufficiently large The R2 obtained from the regression of the endogenous variable x on the IV z provides a measure of the potential improvement that the IV method provides over the OLS procedure in correcting the bias and is called partial R2 In the case of one endogenous variable and one instrument, the corresponding measure is the correlation coefficient In the case of a multivariate version of Equation with many exogenous variables, the corresponding measure is the partial R2 It follows that if the partial R2 is small, then the instruments are not appropriate.9 Partial F test Estimates based on the 2SLS procedure are likely to have larger standard errors than estimates obtained from the OLS procedure (Bartels, 1991) The asymptotic standard error of the estimates obtained from the 2SLS procedure is greater than that obtained from the OLS procedure: the difference in the asymptotic standard error increases with decreases in the correlation between the endogenous variable x and the instrument z In other words, as the correlation between x and z approaches zero, the standard error using the 2SLS procedure approaches infinity.10 To see this, consider the following equation in place of Equation 1: y5ao 1a1 x1 1a2 x2 1ak xk 1u, where xk is the endogenous variable Let a ^ to be the vector of 2SLS estimators using instruments Z The asymptotic var^ k , where SSR ^ k is the sum of iance (Avar) of the 2SLS estimator is Avarð^ aÞ’ s2 =SSR residuals of the first-stage regression, that is, the regression of the endogenous variable on ^ k 5SST ^ k ð1 À R^2 Þ where all exogenous variables (Wooldridge, 2002) The denominator SSR k 2 ^ k is the total sum of squares of x^k and R^ is the R of the regression of the predicted SST k value of the endogenous variable x^k on the exogenous variables in the second stage It Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 304 Journal of Accounting, Auditing & Finance follows that if (1 À R^2k ) is small, Avar can be very high (Wooldridge, 2002) Simply put, if the endogenous variable is only weakly associated with the instruments over and above the ^ k is small leading to higher standard errors in the other exogenous variables, then SST second-stage regression Weak instruments lead to small (1 À R^2k ) and, most importantly, a nonzero correlation between the endogenous variable, and the instruments alone is not sufficient to ensure that (1 À R^2k ) is not small The partial correlation in the first-stage regression between the endogenous variable and the IV, that is, the correlation between the endogenous variable and the instruments, after accounting for the correlation between the endogenous variable and all other exogenous variables in the model needs to be sufficiently large Thus, good instruments need to have a sufficiently large partial F statistic in the first-stage regression: the partial F statistic measures the strength of the correlation between the endogenous variable and the instruments over and above that of all other exogenous variables The partial R2 and partial F tests not provide an indication of how much correlation is good enough Although Staiger and Stock (1997) provide F values that are considered desirable for different sizes of finite samples, recently there have been various test statistics developed for validating the appropriateness of the instruments These tests are described briefly below Underidentification Test The underidentification test provides a canonical correlation version of the partial R2 and F statistic tests That is, the endogenous variable x must be correlated with the IV z, when there are more than one endogenous and IVs The canonical correlations represent the correlations between the endogenous variable x and linear combinations of z The squared canonical correlations with multiple endogenous variables and instruments are calculated as the eigenvalues, and the underidentification test requires that all canonical correlations are different from zero Conversely, if one or more of the canonical correlations is zero, then the model is underidentified For the case where the errors are independent and identically distributed (i.i.d.), the Anderson’s (1951) canonical correlation LM statistic is computed For the case where the errors are non-i.i.d., Kleibergen and Paap (2006) propose a robust error correction for the Anderson’s statistic A failure to reject the null of zero correlation indicates that the model is underidentified Weak Identification Tests When the correlation between the endogenous variable and the IV is nonzero but small, the weak instrument problem arises (see discussion above) Staiger and Stock (1997) show that for large samples, the weak instrument problem can arise even when the correlation between x and z is significant at conventional levels of 5% and 1% Stock and Yogo (2005) develop an F statistic based on the Cragg and Donald (1993) F statistic for i.i.d errors, and Kleibergen and Paap (2006) extend this, if the i.i.d assumption is violated The test statistic is based on the rejection rate of various percentages, if the true rejection rate is 5% Stock– Yogo’s critical values are available for a range of the number of endogenous variables and instruments If the computed F statistic is below the critical value, then the instruments are weak In our reported results, we use the rejection rate of 20% Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Chan et al 305 Weak Identification Robust Inference Tests The Anderson and Rubin (1949) test is used by substituting the relationship between the endogenous variable and the IV from Equation in Equation Thus, Equation is estimated in its reduced form, and if the test fails to reject that the coefficient estimate on the IV is zero, then the instrument is likely to be weak The idea with this test is that as the instruments become weak, the relationship between the endogenous variable and the IV must be weak, and thus, the relationship between the IV and dependant variable must also be weak The beauty of this test is that it is robust to implicit weakness of the instruments The Anderson and Rubin F statistic and the Stock and Wright (2000) S statistic provide tests for the weak identification tests, when errors are non-i.i.d Overidentifying Restrictions Test In choosing instruments, we need to be reasonably assured that the covariance of the IV z with the structural error term v in Equation is zero In general, demonstrating this is difficult because the structural error term is unobservable When the Generalized Method of Moments (GMM) estimation is used, the Hansen’s J test can be used The null hypothesis is that the instruments are not correlated with the structural error term in the second-stage regression That is, rejection of the null hypothesis provides evidence that the instruments are correlated with the error term In general, for large samples this test would lead to rejection of the null hypothesis (see Larcker & Rusticus, 2007) The Hausman Test Hausman (1978) provides a formal test to assess whether the OLS estimates are significantly different from the 2SLS estimates: the Hausman test The Hausman test is a test for the difference in OLS and 2SLS coefficients under the maintained assumption that the instruments are not weak From the discussion of the partial R2 and partial F statistic above, it follows that the Hausman test could reject the null or no joint determination of the endogenous variables if the instruments are weak Thus, the Hausman test should be conducted only if the instruments are not weak (also see Larcker & Rusticus, 2007) In summary, we compute the following test statistics when we use the 2SLS estimation: (a) the partial R2, (b) the partial F statistic, (c) Anderson’s canonical correlation LM statistic for underidentification, (d) Cragg and Donald’s (1993) Wald F statistic for weak identification, (e) Anderson–Rubin F statistic and Stock and Wright’s S statistic for weak instrument robust inference, and (f) Hansen’s J statistic for the overidentification We use the GMM estimation when we consider additional instruments: We substitute the Kleibergen and Paap’s (2006) rk LM statistic and F statistic for Anderson’s canonical correlation LM statistic and Cragg and Donald F statistic, respectively (see Stock & Wright, 2000; Stock, Wright, & Yogo, 2002) Empirical Analysis Following Whisenant et al (2003), we consider estimate Equations and 4: These models draw on a rich literature on the determinants of audit fees.11 Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 306 Journal of Accounting, Auditing & Finance Empirical Model LogðAudit feeÞ5a1a1 LogðNonAudit feeÞ1aD Determinants1e; ð3Þ LogðNonaudit feeÞ5b1b1 LogðAudit feeÞ1bD Determinants1e; ð4Þ where Log(Audit fee) is the natural logarithm of fees paid to the auditor for audit and auditrelated services, and Log(Nonaudit fee) is the natural logarithm of fees paid to the auditor for nonaudit services (fee data are obtained from Audit Analytics databases) Lag is the number of days between current fiscal year-end and earnings announcement date (Compustat report date of quarterly earnings) New Finance is an indicator variable that equals one if the firm issues equity (Compustat Item 108 US$10 million) or long-term debt (Compustat Item 111 US$1 million) in either the current or subsequent fiscal year and is zero otherwise Assets is the natural logarithm of total assets (Compustat Item 6) Segments is the square root of the number of segments (Compustat Segment database) Employees is the square root of the number of employees (Compustat Item 29) DA ratio is total debt (Compustat Item 181) divided by total assets (Compustat Item 6) Liquidity is the ratio of current assets (Compustat Item 4) divided by current liabilities (Compustat Item 5) Inventory and AR turnover is inventory (Compustat Item 3) plus accounts receivable (Compustat Item 2), divided by total assets (Compustat Item 6) ROA is return on assets defined as operating income (Compustat Item 178) divided by total assets (Compustat Item 6) Institutional ownership is the percentage of institutional holdings at the beginning of the year Initial is an indicator variable that equals one if the auditor has been the firm’s auditor for less than years (Compustat Item 149) and is zero otherwise BIG5 is an indicator variable that equals one when the auditor is a member of the Big 5, and zero otherwise Foreign Operations is an indicator variable that equals one if the firm has foreign operations as indicated by foreign currency adjustments to income (Compustat Item 150) and is zero otherwise Loss is an indicator variable that equals one if the firm reported negative net income (Compustat Item 172) in either of the two previous years and is zero otherwise Sales growth is the growth rate in sales (Compustat Item 12) compared with the previous year Volatility is variance of the residual from the market model over the current fiscal year Opinion is an indicator variable that equals one if the firm received a modified goingconcern audit opinion in either the current or previous fiscal year (Audit Analytics) and equal to zero otherwise Employee plans is an indicator variable that equals one if the firm’s pension or postretirement plan assets or cost is greater than US$1 million and is zero otherwise Book to market is the book-to-market ratio at the beginning of the year Discontinued operations is an indicator variable that equals one if the firm reports extraordinary items or discontinued operations (Compustat Item 48) and is zero otherwise Distress probability is the 1-year change in Zimjewski’s (1984) probability of bankruptcy score Stock return is the raw return for the fiscal year Restate is an indicator variable that is one if net income or assets were restated and zero otherwise (data obtained from Government Accountability Office [GAO] list of restatements’’) Based on the knowledge spillover, we expect that a1 and b1 in Equations and will be positive, and based on Whisenant et al.’s (2003) results of no knowledge spillover across audit and nonaudit fees, we expect a1 and b1 to be statistically no different from zero (the null hypothesis) Note that if only a1 (b1) is positive then theoretically speaking, Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Chan et al 307 if we have the ‘‘correct and complete’’ model, the knowledge spillover is only from nonaudit to audit (audit to nonaudit) This of course is a conclusion of tall order to draw from our empirical analysis The instruments are Lag for audit fee and New Finance for nonaudit fee As discussed in the previous section, the instruments Lag and New Finance should be correlated with audit and nonaudit fees, respectively, and should not be correlated with the structural error term in the audit and nonaudit fee models, respectively.12 Sample and Initial Results The sample contains all firms not in the financial services industry with data available in Compustat and Center for Research in Security Prices for fiscal years 2000 to 2006 We stop at 2006 because the instrument New Finance requires 1-year-ahead data The audit and nonaudit fees data are from Audit Analytics We consider three subsamples: year 2000, year 2001, and years 2002 to 2006 In particular, we consider year 2000 separately to provide a benchmark for comparing our results with those of prior research We consider 2002 to 2006 together because there were regulatory changes involving nonaudit fees in 2002 (see Sarbanes-Oxley Act [SOX] of 2002 and Securities and Exchange Commission Release No 33-8183).13 As such, year 2001 is also considered separately There are 2,768; 3,812; and 20,173 observations for the subsamples 2000, 2001, and 2002 to 2006, respectively Table 1, Panel A provides the descriptive statistics for the three subsamples Compared with the prior research that is typically based on hand collected data, the companies in our sample pay lower audit and nonaudit fees in 2000 This could be due to Audit Analytics coverage of many smaller firms compared with the sample of earlier studies The mean audit fee increases from about US$0.5 million in 2001 to about US$1.2 million in 2005; the median shows a similar substantial increase This suggests that the SOX requirements have substantially increased audit fees in the later years of our sample Correspondingly, the nonaudit fees have substantially decreased The audit fees have increased over time, whereas the nonaudit fees have decreased reflecting changes in the regulatory environment It is also interesting to note that even though the sample of firms covered in Audit Analytics has increased over time, the fundamental firm characteristics are roughly stable, that is, the mean and median total assets, lag, segments, employees, debt to asset ratio, liquidity, turnover ratios, and institutional ownership are similar over the years, whereas profitability, growth, and financial distress measures exhibit macroeconomy-wide trends Table contains the estimation of Equations and Panel A reports the results of the OLS estimation: similar to prior research, we find that the coefficients of Log(Audit fee) and Log(Nonaudit fee) are positive and significant, suggesting the presence of knowledge spillover effects Although statistically speaking the results are qualitatively similar to those of prior research, compared with Whisenant et al (2003), in our 2000 sample, for Equation the coefficient estimate is a little more than twice as large; these differences are clearly attributable to differences in the sample Table 2, Panel B reports the results of using 2SLS estimation for Equations and For the 2002-to-2006 sample, we use the robust standard errors to compute the t statistic, with Fama–French 48 industry groups as the cluster.14 Here again, as with the discussion of Table 2, Panel A above, the results for year 2000 are qualitatively similar to those of prior research Most importantly, the OLS results and the 2SLS results together are consistent with the conclusion that audit and nonaudit fees are not associated when we consider their joint determination This conclusion extends to the later years: 2001 and 2002 to 2006 Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 424 Journal of Accounting, Auditing & Finance Auditing Standard to report on internal control over financial reporting in accordance with Sarbanes-Oxley Section 404, and the identification of companies that had material weaknesses in internal controls As such, we have included variables to control for the effect on fees for these events and the impact on audit fee The variable AUDCH_AA, for firms that switched from Andersen after January 11, 2002, is significant in the 2002 year (untabulated result) but not in the pooled regression over the entire period In all models, the other three variables (IC, ADVS, and SOX) that we use to control for changes in fees over time are significant in the pooled regression but become nonsignificant as we add more control variables, and we test with the Fama and MacBeth (1973) procedure The IC variable indicates whether a company that had a report on internal controls over financial reporting was highly significant in 2004, the year that regulation began to be phased in, and in the pooled regression ADVS, an indicator variable for firms that had an adverse opinion on internal controls, was highly significant in 2004 and in the pooled regression over the sample period This result is consistent with the results of Raghunandan and Rama (2006) and Hoitash et al (2008) The variable SOX, representing any company that had a fiscal period after the passage of the Sarbanes-Oxley Act, is highly significant in the pooled regression We believe that controlling for these events that impact fees over time allows us to draw better inferences from the composite fee model All of the control variables in the model are significant except for percentage of institutional investors, whether a firm had a Big auditor and whether a firm issued stock or debt during the year, none of which were constantly significant across the different models One of the size proxies, natural log of sales, is not significant in the pooled regressions in Table but becomes highly significant in the Fama and MacBeth (1973) regressions Finally, the coefficient of the length of time between the fiscal year-end and the audit opinion signature (AUDITLAG) is positive and significant in the pooled regression (Tables 3, 4, and 5) confirming the findings reported in Hay (Hay et al., 2006) of a positive association between audit report lag and audit fees Managerial Ownership and Audit Fees We model managerial ownership under three models In Model 1, we use the value of managerial ownership MGR as the main treatment variable The results of Model indicate that fees are consistently negatively related to managerial ownership In Model 2, we use the value of managerial ownership and the squared managerial ownership to control for a presumable nonlinear relationship between managerial ownership and audit fees, up to certain ownership threshold Again, the results show a consistent negative and significant correlation between managerial ownership and audit fees The MGR2 term is positive across all models, suggesting a nonlinear (i.e., concave) relationship between the ownership and the fees In Model 3, we run piecewise regressions to account for the nonlinearity using various cutoffs in managerial ownership In Table 5, we present the results of Model 3, which allows three different slope coefficients for managerial ownership, depending on whether the ownership level falls below 5%, between 5% and 25%, or above 25% Accordingly, this method enables us to document potentially nonlinear effect of managerial ownership on fees in the three different ownership levels Results for this model, again, confirm the strong negative relationship between managerial ownership and audit fees in the low level of managerial ownership Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 425 Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Count 7,214 7,214 7,214 7,214 7,214 7,214 7,214 7,214 7,214 7,214 7,214 7,214 7,214 7,203 7,174 7,213 7,214 7,214 7,214 7,214 7,214 7,214 7,214 7,214 7,214 7,214 7,214 7,165 4,689 4,666 4,683 4,683 6,683 3,595 Variables AFEE MGR MGR_L MGR_M MGR_H GIDX ANACOV INST AUDCH AUDCH_AA IC ADVS MNA lnTA lnSale BM ROA LEV LOSS ISSUE EMPL INVREC FOROPS BIG4 N_BIZ N_GEO SOX GC BOARDSIZE AUDITLAG INDSIZE PCT_IND RESTATEMENT CEOPRICE Panel A: Descriptive Statistics of Selected Variables 0.1499 0.0004 0.5944 0.0049 8.9646 55.0034 6.3098 0.7014 0.8639 3.2873 0.2461 0.3478 0.9317 1.2859 1.3705 7.4323 5.4816 0.5153 0.0798 0.2276 0.2088 1.9569 46.0072 0.0689 0.0338 0.4275 0.0373 0.4530 14.0414 8.0291 2.7134 3.3445 1.9712 5.9344 M Table Descriptive Statistics and Pearson Correlations 0.3570 0.9977 0.4910 0.0697 2.1932 41.7438 2.1856 0.1633 0.3429 2.9456 0.1656 0.4763 0.2523 0.7478 0.8021 1.4640 2.0738 0.5658 0.1133 0.1859 0.4065 0.8783 34.0806 0.2533 0.1808 0.4948 0.1895 0.4978 1.1390 15.0292 1.9900 6.3298 9.5846 2.4075 SD 0.0000 20.3125 0.0000 0.0000 7.0000 34.0000 5.0000 0.6000 1.0000 1.5120 0.1106 0.0000 1.0000 0.6931 0.6931 6.3793 4.0763 0.2700 0.0448 0.0603 0.0000 1.3863 0.9922 0.0000 0.0000 0.0000 0.0000 0.0000 13.2516 0.6800 0.6800 0.0000 0.0000 4.0000 25% 0.0000 20.0822 1.0000 0.0000 9.0000 52.0000 6.0000 0.7273 1.0000 2.4098 0.2228 0.0000 1.0000 1.3863 1.3863 7.2978 5.4529 0.4284 0.0839 0.2213 0.0000 2.0794 56.0976 0.0000 0.0000 0.0000 0.0000 0.0000 14.0144 2.4800 2.4800 0.0000 0.0000 6.0000 Median (continued) 0.0000 0.1645 1.0000 0.0000 10.0000 65.0000 8.0000 0.8333 1.0000 3.9749 0.3386 1.0000 1.0000 1.7918 1.9459 8.3266 6.9501 0.6291 0.1326 0.3450 0.0000 2.6391 75.5556 0.0000 0.0000 1.0000 0.0000 1.0000 14.7870 8.1800 5.0000 3.1800 0.0000 8.0000 75% 426 Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 10 11 14 15 16 17 18 19 20 21 22 23 24 Note: See Table for definition of variables Correlation coefficients that are statistically significant at the 5% level are shown with an asterisk 0.0197 0.0558* 0.3189* 0.0476*20.0479*20.0618* 0.1052* 0.3171* 0.3608*20.1617* 0.0069 0.0615* 0.0813*20.0368* 0.2277* 0.1768* 0.2272* 0.2753*20.2265* 0.7138* 0.1626* N_BIZ 0.2316*20.0186 20.0353*20.1103*20.3743*20.0112 0.1239* 0.0866* 0.2473*20.0113 0.0437*20.0078 20.0696* 0.0077 0.0027 0.0485*20.0246* N_GEO 0.3486*20.0138 0.0593* 0.0233*20.3935*20.01 0.0906* 0.0529* 0.3845*20.0774* 0.0815*20.1343*20.0401*20.0101 0.0102 0.1040* 0.3041* AA_CG 20.0926*20.0017 20.0156 20.0051 0.1045* 0.6878*20.0008 20.0119 20.0958* 0.0539*20.0380* 0.0343* 0.0416* 0.0161 20.0156 20.0242*20.0062 IC 0.3953* 0.0955* 0.0566* 0.0389*20.6422*20.0603* 0.0805* 0.0283* 0.4858*20.1330* 0.0684*20.0405*20.1136*20.0132 20.0169 20.0192 0.0191 ADVS 0.1225* 0.0329*20.0559* 0.0537*20.1089*20.0044 0.0061 20.0415* 0.0610*20.0157 20.0385*20.0043 0.0573*20.0350*20.0270* 0.0005 0.0498* SOX 0.3650* 0.0568* 0.0380* 0.0332*20.4518*20.1097* 0.0423* 0.0083 0.3771*20.1603* 0.0338*20.0538*20.0967*20.0282*20.0399*20.0418* 0.0579* 13 19 20 21 22 23 24 12 LEV 0.1705*20.0248*20.0003 20.1009* 0.0285* 0.0253* 0.1035* 0.3084*20.0552*20.0387*20.1030* LOSS 20.1114* 0.0032 20.1668* 0.0909* 0.0273* 0.0313*20.0488*20.1914*20.1052* 0.2091*20.5725* 0.1089* ISSUE 0.1022*20.0560* 0.1142*20.0520* 0.0484*20.0037 0.0835* 0.1458*20.0115 20.0021 20.0336* 0.2804* 0.0269* EMPL 0.5089*20.0670* 0.3575*20.0924* 0.0240*20.0234* 0.0616* 0.6474* 0.3838*20.0735* 0.1718* 0.1069*20.1448* 0.0687* INVREC 20.0026 0.0148 20.2104*20.0604* 0.0192 20.0224 20.0443*20.1302*20.0527* 0.0652* 0.1919*20.1483*20.1070*20.0807* 0.0727* FOROPS 0.1547*20.0430* 0.0024 0.0125 0.0361* 0.0025 0.0073 20.0319* 0.0797*20.0561*20.0127 20.1261* 0.0288*20.0343*20.0424* 0.0614* BIG4 0.1411*20.0473* 0.0844*20.0329*20.0349*20.0088 0.0048 0.0768* 0.1316*20.0453*20.0035 20.0066 20.0109 0.0463* 0.0381*20.0351* 0.0340* 1 0.1833* 0.1001* 0.5303* 0.0039 20.0460*20.1713* 0.0464* 0.2080* 0.1413*20.2083* 12 13 14 15 16 17 18 ADT_CG 20.0604* 0.0066 20.0361*20.0062 0.0235* MNA 0.2154*20.0344* 0.0604*20.0672*20.0795* 0.0042 lnTA 0.7142*20.1380* 0.5616*20.1554* 0.0196 20.0188 lnSale 0.6009*20.0091 0.4105* 0.0169 20.4929*20.0393* BM 20.1119*20.0053 20.2358* 0.0037 0.0225 0.0319* ROA 0.1195*20.0126 0.2125*20.0803* 0.0353*20.0479* AFEE MGR 20.1068* ANACOV 0.3627*20.0879* GIDX 20.1301* 0.1896*20.0013 INST 20.2093*20.1770* 0.0648*20.0586* 10 11 Variables Panel B: Pearson Correlations Table (continued) Gotti et al 427 Overall, our results are consistent with the alignment hypothesis rather than the entrenchment hypothesis With regard to economic significance of the impact of managerial ownership on audit fees, the reported coefficient on MGR of 20.002 in Table translates into a decrease in audit fees by 2% if MGR increases by 2.48% (i.e., the median MGR as reported in Table 2) As shown in Table 5, the coefficient on MGR_L is 20.014, and thus audit fees decrease by 1.4% if MGR increases by 2.48% This finding confirms not only that the alignment effect of share ownership on audit fees is more pronounced when managerial ownership level is low, which is the case for most U.S companies, but also that the effect is economically significant In addition, these results complement prior research that examines the effects of managerial ownership on firm value (e.g., McConnell & Servaes, 1990; Morck et al., 1988) For instance, it is practically difficult to identify whether managerial ownership increases firm value or whether managers simply hold more shares when firm value is high Himmelberg et al (1999) note that endogeneity in corporate ownership poses a challenge in testing and establishing a relationship between ownership and firm value, and claim that after controlling for both observed firm heterogeneity, they are unable to conclude that managerial ownership relates to firm performance Our audit fee setting is less susceptible to this endogeneity problem as (a) it is unlikely that managers change their equity holdings based on the level of audit fees (which means that the direction of causality likely runs from ownership to fees), and (b) the audit fee model includes an extensive list of control variables, which represents various aspects of firm characteristics that could possibly relate to managers’ decisions to hold the shares Analysts Monitoring and Audit Fees As shown in Tables 3, 4, and 5, we obtain similar results for ANACOV, that is, a negative relationship between audit fees and analyst coverage The coefficient is negative and significant whether we run a pooled regression or a Fama and MacBeth (1973) regression Because auditors assess the risk of the firm and plan audits accordingly (Bedard & Johnstone, 2004; Dickins et al., 2008), the results imply that auditors implicitly perceive analysts’ coverage as a form of monitoring that reduces information risk This result is consistent with Yu (2008), who finds that analyst monitoring constraints opportunistic earnings management by managers The impact of analyst coverage on audit fee also appears economically significant For instance, one more analyst following a company reduces audit fees by 8.3%, on average Robustness Checks We perform a variety of sensitivity checks to ensure the robustness of our results First, as we discussed in ‘‘Measurement of Audit Fees,’’ we combine audit and audit-related fees for years after 2003, whereas prior to that we only use audit fee As the amount of auditrelated fees are sometimes quite significant,13 it is possible that the audit fees under this new definition introduce some bias toward the hypothesized relationships To make sure that our result is not sensitive to the definition of audit fees, we run the same regressions using the same audit fees variable for the whole sample period We find that the results are virtually identical Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 428 Journal of Accounting, Auditing & Finance Table Linear Regressions (With MGR) Variables MGR ANACOV GIDX INST AUDCH MNA lnTA lnSale BM ROA LEV LOSS ISSUE EMPL INVREC FOROPS BIG4 N_BIZ N_GEO AUDCH_AA IC ADVS SOX GC (1) Pooled (2) Pooled (3) Fama–MacBeth (4) Fama–MacBeth 20.002*** (24.066) 20.083*** (27.280) 20.009** (23.199) 0.001 (1.875) 20.109** (23.206) 0.083*** (6.295) 0.554*** (45.814) 0.014* (2.417) 20.028 (21.559) 20.463*** (26.340) 0.159*** (3.517) 0.110*** (5.391) 0.031 (1.697) 0.046*** (8.090) 0.826*** (12.867) 0.142*** (9.889) 0.051 (1.707) 0.026** (2.749) 0.150*** (13.476) 0.020 (0.371) 0.565*** (15.600) 0.329*** (8.977) 0.327*** (8.085) 20.002*** (24.677) 20.097*** (26.647) 20.000 (20.081) 0.001 (1.278) 20.103* (22.292) 0.092*** (5.155) 0.547*** (32.797) 0.008 (0.997) 20.041 (21.787) 20.407*** (23.721) 0.203** (3.209) 0.096*** (3.767) 0.010 (0.420) 0.040*** (5.321) 0.719*** (8.501) 0.132*** (7.077) 0.037 (1.016) 0.033** (2.704) 0.160*** (11.356) 20.060 (20.813) 20.169 (21.871) 0.284*** (5.239) 20.192 (21.529) 0.080 (0.481) 20.002* (23.437) 20.102*** (27.812) 20.014** (24.311) 0.041 (1.359) 20.182* (22.902) 0.123*** (5.757) 0.473*** (13.725) 0.070** (4.803) 20.123* (23.450) 20.630** (23.907) 20.034 (20.477) 0.130*** (6.009) 0.051* (2.703) 0.037*** (5.898) 0.474** (3.927) 0.214*** (39.989) 0.108** (3.821) 0.038* (3.021) 0.238*** (22.448) 20.024 (20.427) 0.208 (1.518) 0.147* (2.509) 0.127 (1.527) 20.002* (23.402) 20.118*** (27.246) 20.002 (20.394) 0.064 (1.800) 20.102 (21.813) 0.144*** (6.393) 0.467*** (13.822) 0.069** (4.487) 20.166* (22.602) 20.584 (22.319) 20.008 (20.097) 0.128*** (11.523) 0.042* (2.472) 0.032* (3.698) 0.368* (3.430) 0.195*** (13.411) 0.074 (1.739) 0.047*** (6.891) 0.239*** (21.198) 0.027 (1.000) 0.119 (0.793) 0.107 (1.957) 20.015 (21.000) 20.106 (20.479) (continued) Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Gotti et al 429 Table (continued) Variables (1) Pooled BOARDSIZE AUDITLAG INDSIZE PCT_IND RESTATEMENT Constant Observations R2 Adjusted R2 Number of groups 8.623*** (54.346) 7,174 0.793 0.790 (2) Pooled 0.023 (1.372) 0.001* (2.299) 0.011 (0.467) 0.113 (0.508) 20.066* (22.312) 8.332*** (34.270) 4,119 0.784 0.779 (3) Fama–MacBeth 9.218*** (31.991) 7,174 0.721 0.722 (4) Fama–MacBeth 20.006 (20.289) 0.001* (2.455) 0.042 (1.719) 20.097 (20.358) 20.044 (20.847) 9.297*** (19.760) 4,119 0.710 0.711 Note: Robust t-statistics are in parentheses Variable definitions are in Table Year and industry dummies are included in the regressions but not reported for simplicity The R2 and adjusted R2 for the Fama–MacBeth regressions are the means of the statistics during the sample years *p \ 05 **p \ 01 ***p \ 001 Second, we employ various cutoff points in defining the low/medium/high level of managerial ownership such as 3% and 20% of ownership and top 3rd and 4th quintile of ownership Our inference does not change Finally, we ensure that our inference is not affected by several other additional controls For instance, we control for the association between CEO compensation to stock price by including the residuals from a regression of firms’ returns over total CEO compensation We also control for the presence of entrenchment using a selection of corporate governance provisions used to compute the Gomper’s Index (Gompers et al., 2003) Untabulated results confirm the negative association between managerial ownership, analysts’ coverage, and audit fees Other Due to data limitations, we, as in several previous large sample studies, have not collected detailed information on audit committee characteristics other than audit committee size (Carcello et al., 2002) Although this might induce an omitted variable problem, as of 2004, companies are relatively homogeneous in that they all have audit committees comprised of outside directors with a financial expert Thus, we expect less variation in audit committee characteristics starting in 2004 Even then, differences in the level of financial expertise, capabilities, and effort levels of audit committee members will likely remain However, for the other audit committee characteristics to bias our inference, they must be negatively correlated with the governance variables we examine, given the positive relationship between audit committee strength and audit fees (Abbott et al., 2003) But it is not clear from the extant literature how the governance variables we examine relate to audit Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 430 Journal of Accounting, Auditing & Finance Table Nonlinear Regressions (With MGR and MGR2) Variables MGR MGR2 ANACOV GIDX INST AUDCH MNA lnTA lnSale BM ROA LEV LOSS ISSUE EMPL INVREC FOROPS BIG4 N_BIZ N_GEO AUDCH_aa IC ADVS SOX (1) Pooled (2) Pooled (3) Fama–MacBeth (4) Fama–MacBeth 20.004*** (23.878) 0.000** (2.827) 20.082*** (27.247) 20.009** (23.070) 0.001 (1.555) 20.109** (23.181) 0.083*** (6.297) 0.552*** (45.517) 0.014* (2.378) 20.029 (21.593) 20.459*** (26.288) 0.157*** (3.477) 0.111*** (5.402) 0.031 (1.697) 0.046*** (8.057) 0.824*** (12.821) 0.142*** (9.867) 0.050 (1.674) 0.026** (2.741) 0.149*** (13.414) 0.019 (0.338) 0.567*** (15.673) 0.331*** (9.012) 0.326*** (8.078) 20.005*** (23.339) 0.000* (2.045) 20.097*** (26.631) 20.000 (20.007) 0.001 (1.058) 20.103* (22.283) 0.092*** (5.176) 0.545*** (32.583) 0.008 (0.948) 20.042 (21.841) 20.404*** (23.703) 0.201** (3.171) 0.097*** (3.779) 0.010 (0.413) 0.040*** (5.291) 0.717*** (8.471) 0.132*** (7.059) 0.036 (0.988) 0.034** (2.742) 0.159*** (11.302) 20.061 (20.819) 20.131 (21.254) 0.283*** (5.231) 20.190 (21.505) 20.006** (24.542) 0.000** (4.734) 20.099*** (27.959) 20.014** (24.163) 0.032 (1.066) 20.185* (22.908) 0.124*** (5.842) 0.467*** (13.666) 0.071** (4.844) 20.124* (23.461) 20.626** (23.830) 20.031 (20.429) 0.129*** (5.837) 0.052* (2.640) 0.038*** (5.916) 0.481** (3.921) 0.214*** (39.250) 0.105** (3.755) 0.037* (2.939) 0.237*** (22.334) 20.024 (20.431) 0.211 (1.553) 0.148* (2.507) 0.127 (1.527) 20.007* (23.225) 0.000* (2.885) 20.115*** (27.265) 20.002 (20.322) 0.061 (1.664) 20.105 (21.842) 0.144*** (6.533) 0.463*** (13.713) 0.069** (4.373) 20.168* (22.647) 20.576 (22.255) 20.006 (20.065) 0.129*** (10.780) 0.043 (2.383) 0.032* (3.676) 0.379* (3.392) 0.196*** (12.908) 0.071 (1.711) 0.049*** (7.199) 0.238*** (21.888) 0.026 (1.000) 0.128 (0.878) 0.107 (1.963) 20.014 (21.000) (continued) Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Gotti et al 431 Table (continued) Variables (1) Pooled GC BOARDSIZE AUDITLAG INDSIZE PCT_IND RESTATEMENT Constant Observations R2 Adjusted R2 Number of groups 8.668*** (54.129) 7,174 0.793 0.790 (2) Pooled 0.082 (0.497) 0.024 (1.417) 0.001* (2.311) 0.011 (0.448) 0.107 (0.482) 20.065* (22.269) 8.384*** (34.170) 4,119 0.784 0.779 (3) Fama–MacBeth 9.282*** (31.629) 7,174 0.721 0.722 (4) Fama–MacBeth 20.100 (20.449) 20.004 (20.186) 0.001* (2.483) 0.039 (1.569) 20.083 (20.302) 20.039 (20.797) 9.330*** (20.073) 4,119 0.710 0.711 Note: Robust t-statistics are in parentheses Variable definitions are in Table Year and industry dummies are included in the regressions but not reported for simplicity The R2 and adjusted R2 for the Fama–MacBeth regressions are the means of the statistics during the sample years *p \ 05 **p \ 01 ***p \ 001 committee characteristics, hence, the existence of bias excluding these variables from the models Conclusion This article answers the call of Hay et al (2006) to address the need to further explore the relationship between audit fees and governance We use a large sample (more than 7,000 firm-year observations) during the period from 2000 to 2007 to explore the relationship between audit fees and managerial ownership and corporate monitoring by financial analysts We find that managerial ownership is associated with lower fees, consistent with the alignment hypothesis, which suggests that managerial equity ownership aligns management and shareholder interests We also find that analyst following is associated with lower audit fees Both these associations are both statistically and economically significant These results may assist board compensation committees in understanding the relationship in management compensation through stock ownership and firm risk Finally, our results are subject to the following caveat First, like any other association studies, our evidence does not enable us to establish a causal relationship between firms’ governance characteristics and audit fees However, our extensive set of control variables, our robustness checks, and high explanatory power of the audit fee regressions mitigate this concern Second, the readers should read our study with the caveat that our sample draws from six different data sources, and thus, the generalizability of the results might be Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 432 Journal of Accounting, Auditing & Finance Table Piecewise Regressions (With MGR_L, MGR_M, and MGR_H) Variables MGR_L MGR_M MGR_H ANACOV GIDX INST AUDCH MNA lnTA lnSale BM ROA LEV LOSS ISSUE EMPL INVREC FOROPS BIG4 N_BIZ N_GEO AUDCH_AA IC ADVS (1) Pooled (2) Pooled (3) Fama–MacBeth (4) Fama–MacBeth 20.014** (23.268) 20.001 (20.653) 20.001 (21.071) 20.081*** (27.125) 20.009** (23.139) 0.001 (1.561) 20.109** (23.207) 0.084*** (6.363) 0.548*** (44.989) 0.014* (2.269) 20.029 (21.582) 20.462*** (26.326) 0.163*** (3.615) 0.111*** (5.412) 0.032 (1.762) 0.046*** (8.037) 0.828*** (12.901) 0.141*** (9.795) 0.051 (1.726) 0.027** (2.808) 0.150*** (13.477) 0.019 (0.338) 0.569*** (15.678) 0.330*** (9.011) 20.026*** (24.341) 0.000 (0.214) 20.002* (22.431) 20.094*** (26.442) 20.001 (20.165) 0.001 (0.979) 20.101* (22.288) 0.094*** (5.294) 0.537*** (31.854) 0.007 (0.778) 20.042 (21.826) 20.406*** (23.720) 0.212*** (3.363) 0.096*** (3.776) 0.013 (0.508) 0.040*** (5.257) 0.727*** (8.605) 0.130*** (6.962) 0.042 (1.134) 0.034** (2.779) 0.161*** (11.440) 20.065 (20.889) 20.149 (21.476) 0.280*** (5.230) 20.021** (24.462) 20.001 (20.688) 0.000 (0.830) 20.098*** (27.632) 20.014** (24.215) 0.048 (1.272) 20.188* (22.899) 0.125*** (5.806) 0.461*** (13.412) 0.069** (4.843) 20.124* (23.449) 20.627** (23.795) 20.025 (20.342) 0.131*** (6.014) 0.054* (2.751) 0.038*** (5.992) 0.491** (4.017) 0.212*** (37.359) 0.107** (3.922) 0.038* (3.028) 0.238*** (23.007) 20.024 (20.447) 0.209 (1.552) 0.147* (2.505) 20.029** (24.614) 20.000 (20.050) 20.000 (20.313) 20.113*** (26.997) 20.003 (20.438) 0.077 (1.680) 20.109 (21.930) 0.147*** (6.546) 0.452*** (13.672) 0.069** (4.536) 20.166* (22.677) 20.584 (22.343) 0.001 (0.017) 0.129*** (12.826) 0.045* (2.479) 0.032** (3.771) 0.388* (3.456) 0.193*** (13.398) 0.077 (2.008) 0.049*** (6.730) 0.240*** (22.393) 0.026 (1.000) 0.123 (0.857) 0.106 (1.949) (continued) Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Gotti et al 433 Table (continued) Variables SOX (1) Pooled 0.325*** (8.042) GC BOARDSIZE AUDITLAG INDSIZE PCT_IND RESTATEMENT Constant Observations R2 Adjusted R2 Number of groups 8.704*** (54.170) 7,174 0.793 0.790 (2) Pooled (3) Fama–MacBeth (4) Fama–MacBeth 20.185 (21.453) 0.085 (0.529) 0.027 (1.618) 0.001* (2.412) 0.007 (0.280) 0.118 (0.534) 20.068* (22.354) 8.460*** (34.267) 0.127 (1.527) 20.014 (21.000) 20.100 (20.452) 20.001 (20.045) 0.001* (2.578) 0.036 (1.392) 20.087 (20.305) 20.040 (20.835) 9.419*** (20.533) 4,119 0.784 0.779 9.344*** (31.775) 7,174 0.722 0.723 4,119 0.712 0.712 Note: Robust t-statistics and in parentheses Variable definitions are in Table Year and industry dummies are included in the regressions but not reported for simplicity The R2 and adjusted R2 for the Fama–MacBeth regressions are the means of the statistics during the sample years *p \ 05 **p \ 01 ***p \ 001 somewhat limited Despite these facts, our findings should be of interest to both regulators and academics in today’s regulatory environment Authors’ Note JEL Classification Code: M4 Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/ or publication of this article Funding The author(s) received no financial support for the research, authorship, and/or publication of this article Notes Auditor/client realignments may result from either party choosing to exit the audit contract DeFond (1992) and Johnson and Lys (1990) provide evidence that demand side realignments are the result of cost minimization Krishnan and Krishnan (1997) conclude that auditors resign from clients to lower litigation risk Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 434 Journal of Accounting, Auditing & Finance Examples of the some of the items included the following: The board of directors is not provided 10 11 12 13 sufficient timely information about sensitive issues, or the audit committee does not hold frequent timely meetings with the Chief Financial Officer (CFO) or other accounting officers Exceptions are Palmrose (1986), who discusses and documents that auditors charge higher fees for public companies than for nonpublic companies due to greater risk exposure, and Han, Kang, and Rees (2009), who show that short-term institutional ownership relates positively to audit fees Here, the bias refers to the fact that it is not clear whether good corporate governance leads to higher firm value or firms with higher value adopt better governance, as those firms likely have more resources available to improve governance Clients with a market capitalization in excess of US$75 million are also required to get an audit of internal controls over financial reporting under the provisions of Section 404 of the SarbanesOxley Act For simplicity, we focus the remainder of this discussion on the auditor’s opinion on financial reporting Although we present a directional hypothesis, we recognize that studies on behavioral biases in analyst coverage, which includes investment banking affiliation and optimism (e.g., Clarke & Subramanian, 2006; Lin & McNichols, 1998; O’Brien, McNichols, & Lin, 2005), suggest possible conflicts of interest associated with analyst coverage As Lin and McNichols (1998) note, analysts’ independence might be compromised in the presence of other interests such as an investment banking relationship with the firm To the extent that auditors price this factor, the predicted negative association between analyst coverage and audit fee in Hypothesis will be attenuated Please refer to the Security and Exchange Commission’s (SEC) rules on Strengthening the Commission’s Requirements Regarding Auditor Independence (Release No 33-8183, January 28, 2003) for more details Specifically, stock ownership by officers, directors, and beneficial owners are included in measuring managerial ownership if they hold at least 1,000 shares One concern in measuring analyst coverage is that it is endogenously determined so that the analysts’ preference for following more transparent firms with better corporate governance may drive the result (e.g., Healy, Hutton, & Palepu, 1999; Lang & Lundholm, 1996) To address the potential endogeneity problem, we use alternative analyst coverage measures such as the residuals from the regressions of analyst coverage (ANACOV) on firm size, as in Hong, Lim, and Stein (2000) The results are qualitatively similar with those reported Some of these variables may only apply for some years For example, ADVS only apply after 2004 since audits on internal controls over financial reporting were not required until that year SOX only appears after 2002 This assumption may introduce the measurement errors in our empirical analysis However, Gompers, Ishii, and Metrick (2003) claim that the error is likely to be small because of the stability of the G-Index over time We control for audit committee size in the robustness section During our sample period, audit committee composition was changing because of the changing regulatory environment Prior to the adoption of the Sarbanes-Oxley Act, research indicated that variation in audit committee characteristics was priced by auditors (Abbott et al., 2003; Carcello, Hermanson, Neal, & Riley, 2002) By the last year of our sample horizon, 2004, all companies had to have audit committees with independent directors and were required to designate one financial expert For example, according to General Electric’s 2007 proxy statement, audit fees paid to KPMG were US$85.8 million whereas audit-related fees were US$20.6 million in the year 2006 References Abbott, L J., Parker, 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2012 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0148558X12453457 http://jaaf.sagepub.com Gotti, G., Han, S., Higgs, J L., & Kang, T (2012) Managerial stock ownership, analyst coverage, and audit fee Journal of Accounting, Auditing & Finance, 27, 412-437 (Original DOI: 10.1177/0148558X11409158) The fourth author’s name was altered in error The correct spelling is Tony Kang Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 [...]... 2.4016 38.02 0.4152 2. 074 6 0.4586 3. 275 7 0.2 477 0.9895 0. 278 0 0 .79 13 0.0269 1 .70 03 0. 272 5 0 .79 05 1,405,892 4,151,136 1.9514 3.8989 866,296 1, 4 27, 3 72 392, 877 2,439,960 SD 5.3234 69.10 1.2360 1.5601 0.8335 2.16 67 0.25 67 20.1134 0. 373 8 0.0 876 0.0268 20. 874 3 20.0082 0.1656 1,191,363 523,539 12.3656 9.9360 195,412 6,939 1,119 97, 402 M 2.9206 57. 27 0.4339 2.2980 1.9668 3.6536 0.2455 3.16 07 0.3150 0.8843 0.0224... 522,096 5.3 171 54.98 1.2124 1.43 87 0. 570 1 2.2088 0.2826 20.3584 0.3386 0. 072 4 0.0392 0.3526 0.0382 0.0864 2. 272 4 32.46 0.3984 1.9388 0.3515 3.8302 0.2500 0.6098 0.2659 1. 674 5 0.0299 1.0 278 0.2 872 0 .72 62 M 1, 570 , 272 4,366,506 2. 271 6 4.0000 551,064 1,482,143 84,2 57 3,3 67, 652 SD 5.3485 46.00 1.0000 0 .70 32 0.5303 1.2985 0.2262 0.0184 0.2911 0.00885 0.03 27 0.4928 0.0000 0.0194 153,225 101,000 11.93 97 11.5229... 255,8 37 59,565 12.4523 10.9948 6,500 0 0 0 Median SD (continued) 5.5 878 58.00 1.0000 0 .73 08 0.5 372 1.2493 0.18 57 0.0249 0.3168 0.0514 0.0211 0.3436 0.0000 0.0414 3,953 ,77 5 2,268,502 2.2159 4.2 076 1,001, 379 378 ,590 43, 670 926,998 Years = 2002 to 2006 309 Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 M Median Year = 2000 0. 375 5 0.3211 0.4966 0.2 478 0.3654 0.4323 0. 377 1 0.3 971 ... 0.2152*** (13. 57) 0. 077 9 (0.69) 0.0288 (0.31) 0.0801 (1 .72 ) Sales growth 0.0032 (0.18) 20.0541* (–2.25) 20.0139 (–1.36) 20.0421 (–1. 47) 0.1140* (2.11) 20.00 17 (–0.06) Volatility 20.3182 (–0.21) 2.2311* (2.34) 21.1819** (–3.12) 20.50 47 (–0.21) 0.4814 (0.23) 23.4965** (–3.16) Opinion 0.1686 (1.13) 0. 177 7* (2.19) 20.1652*** (–4.50) 20.1111 (–0. 47) 20.23 17 (–1.29) 20.1913 (–1 .78 ) Employee plans 0. 171 1 (1 .79 ) 0.3044***... ( 7. 13) 20.3651 (–1.89) 0.2350 (1.34) 20.0181 (–0.16) Stock Return 0. 071 3 (1. 67) 0.0083 (0.35) 0.0128 (1.66) 20.2231*** (–3.30) 0.0984 (1.89) 0.0394 (1 .74 ) Restate 0.2320 (0.83) 0.0 278 (0.25) 0.2015*** (5. 57) 0.5691 (1.28) 0.3982 (1. 57) 20.0 470 (–0.45) Number of observations 2 ,76 8 3,812 20, 173 2 ,76 8 3,812 20, 173 4894 5 271 6399 5326 470 7 3135 Adjusted R2 2000 Equation 3 Dependant variable = Log(Audit fees)... (–2. 47) 20. 975 7*** (–4.83) 20.2058 (–1.36) 20.1883** (–2.61) Liquidity 20.0310*** (–3.62) 20.0219*** (–3 .74 ) 20.0199*** (–8.42) 0.0086 (0.63) 0.0155 (1.18) 20.00 27 (–0.39) Inventory and AR turnover 0.4251* (2.51) 0.3 973 *** (4.05) 0.3604*** (8. 97) 0.4133 (1.54) 0.3859 (1 .75 ) 0.9083*** (7. 71) ROA 20.2024 (–1.59) 0.0132 (0.21) 20.3383*** (–12.36) 20.4042* (–2.01) 20.6 579 *** (–4 .71 ) 20. 070 2 (–0. 87) Institutional... Institutional ownership 0.01 07 (0.08) 0.0128 (0.19) 0.38 87* ** (15. 97) 0.5120* (2.55) 0.6041*** (4.11) 0. 175 9* (2.48) Initial 0. 072 4 (0.82) 0.0 977 * (1.98) 0.0462** (2.64) 20.4010** (–2.89) 20.4830*** (–4. 37) 20.5 579 *** (–10.92) BIG5 20.0101 (–0.09) 20.0032 (–0.05) 0.2883*** (13.96) 0.5253** (3.16) 0. 973 2*** (7. 39) 0.1943** (3.21) Foreign operations 0. 070 9 (0.83) 0.1653*** (3.44) 0.1 472 *** (9. 37) 20.0552 (–0.41)... estimation of financial distress prediction models Journal of Accounting Research, 22, 59-82 Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Do Takeover Defenses Impair Equity Investors’ Perception of ‘‘Higher Quality’’ Earnings? Journal of Accounting, Auditing & Finance 27( 3) 325–358 Ó The Author(s) 2011 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1 177 /0148558X11409155... (48.15) 0.90 37* ** (66.84) 0.8039*** ( 47. 13) Tests for relevance of instruments First-stage F statistic; p values in parenthesis 4.4 67* ** (.00) 2.384*** (.00) 13.865*** (.00) 7. 995*** (.00) 3.094*** (.00) 2. 275 *** (.00) Partial R2 8329 79 07 7949 8839 74 27 5 479 Underidentification test Kleibergen–Paap rk LM statistic (x2); p values in parenthesis 14.12** (.00) 19.05*** (.00) 16.44***(.00) 11 .74 *** (.00)... Lewbell, 19 97) In Panel B, the instruments for Log(Audit fee) are Lag, product of the demeaned log of total asset times demeaned Log(Audit fee), square of demeaned log of total assets, and square of demeaned square root of segments, and the instruments for Log(Nonaudit fee) are New Finance, square of demeaned log of total assets, and square of demeaned square root of segments (see Lewbell, 19 97) Significance