Microsoft Word - 157-COPYEDIT-FINAL.docx http://journals.sfu.ca/abr ADVANCES IN BUSINESS RESEARCH 2016, Volume 7, pages 81-89 81 Auditor Bargaining Power and Audit Fee Lowballing Shaowen Hua La Salle University Zenghui Liu Western Washington University Xiaojie Christine Sun California State University, Los Angeles Ji Yu State University of New York at New Paltz Incoming auditors usually charge fewer audit fees to obtain the client (i.e., audit fee lowballing). Prior research shows that industry expert auditors have better expertise and resources to perform a higher quality audit than non-expert auditors. Consistent with this literature, we predict and find empirical evidence that the magnitude of lowballing will be significantly smaller for industry expert auditors compared with non-expert auditors. This result adds new evidence of the impact of auditors’ bargaining power to the audit fee lowballing literature. Keywords: Audit fees, lowballing, bargaining power, industry expert auditor Introduction This paper investigates the cross-sectional variances regarding the audit fee lowballing effect. Specifically, a study is conducted on whether the magnitude of the lowballing effect varies when levels of auditor bargaining power change. The audit fee lowballing refers to the practice whereby auditors charge lower audit fees for initial audit engagements (DeAngelo 1981). Regulators and legislators have expressed concerns that the lowballing practice could impair auditor independence, and therefore, decrease audit quality (Securities and Exchange Commission (SEC) 1978; AICPA 1978; SEC 2000; Healy 2005; Williams 2007). To address these concerns, several researchers use an analytical model to examine lowballing and its effect on auditor independence. For example, DeAngelo (1981) and Chan (1999) argue that the technology advantage and transaction costs allow the incumbent auditors to keep their clients, and therefore, auditors are able to charge higher audit fees in subsequent years (quasi-rents). Dye (1991) suggests that the quasi-rent is zero if clients have the ability to negotiate the audit fees to the level of audit costs, and therefore, lowballing only occurs when quasi-rents are not observable by the clients. In addition, Morgan and Stocken (1998) model the effects of business risk on auditors’ fee decisions and predict the lowballing practice in initial audit engagements for clients with higher business risks. Moreover, Kanodia and Mukherji (1994) use an analytical model to explore the conditions under which the magnitude of lowballing is related to the bargaining power between AUDITOR BARGAINING AND LOWBALLING 82 auditors and clients. In this study, we empirically test the impact of the auditor’s bargaining power on lowballing. Prior literature provides consistent empirical evidences on the lowballing practice in auditing profession. Simon and Francis (1988) and Ettredge and Greenberg (1990) document evidence that auditors offer abnormally low fees during the initial year of audit engagements. In addition, Sankaraguruswamy and Whisenant (2009) provide evidence that auditors’ lowballing practices continue after the required disclosure of audit fees. Moreover, Pong and Whittingtong (1994) and Gregory and Collier (1996) also find lowballing evidence in a U.K. setting. While prior studies focus on the evidence of lowballing behavior as well as its effect on auditor independence, few studies explore the systematic variance of the lowballing practice. Ettredge and Greenberg (1990) report evidence that fee cuts are related to the change of auditors as well as to the numbers of bidding auditors. Ghosh and Lustgarten (2006) document that big auditors have less competition, and therefore, use the lowballing practice less. Huang et al. (2009) suggest that there is less lowballing behavior among Big 4 firms in the post-SOX period than in the pre- SOX period. Casterella et al. (2004) report that audit industry specialists collect more audit fees from small clients. This study extends this literature by exploring whether an auditor’s lowballing behavior varies based on the auditor’s bargaining power. Auditor industry expertise is used as a proxy for bargaining power. Auditors with industry expertise have more resources and better technology to provide a high quality audit. Therefore, clients who need these resource and technology advantages are limited to these expert auditors. As a result, industry expert auditors have more bargaining power when negotiating the audit fees with their clients (Poter 1985). Auditor industry expertise could affect lowballing practice in two ways. On the one hand, auditors with industry expertise are more efficient at conducting audits in their specific industry, and thus have the advantage of economies of scale. As a result, auditors are more capable to lower their audit fees for initial engagements to attract more clients. On the other hand, industry expert auditors can provide higher quality audit work with advanced technology and industry knowledge. Therefore, they may be less likely to lower their audit fees because clients that need this high quality audit might not have other choices. Given that auditor industry expertise could either increase or decrease the likelihood of lowballing behavior, we do not offer a prediction of the association. Consistent with prior studies (Ferguson and Stokes 2002; Casterella et al 2004; Ghosh and Lustgarten 2006; Huang et al. 2009), we examine the effect of auditor industry expertise on audit fee lowballing by including the interaction of two indicator variables, initial-year audit and auditor industry expertise, in a regression model with the natural logarithm of audit fees as the dependent variable. By analyzing a sample of 21,255 firm-years during the years 2000 to 2012, our results indicate that auditor industry expertise has a negative effect on the lowballing practice. Specifically, we find evidence of lowballing behavior for the group of auditors who are not industry experts, and no evidence of the lowballing practice in the group of industry expert auditors. In summary, these results suggest that auditors with industry expertise have higher bargaining power than non-expert auditors, and therefore, are less likely to lowball their audit fees during the initial audit engagement. The rest of this paper is organized as follows. Section 2 shows the methodology used in this study. Section 3 discusses the empirical results, and section 4 presents the conclusion and discussion. Hua et al. 83 Methodology This section describes the research methods used, including how the sample was selected and the regression model. Sample Selection The sample selection process began by extracting audit fees and auditor-related information for fiscal years 2000 to 2012 from the Audit Analytics database. The sample is then merged with the COMPUSTAT database to acquire financial information of companies. Foreign companies (ADRs), companies in regulated industries (SIC 4000-4999) and in financial industries (SIC 6000-6999) were then removed. The final sample contains 21,225 firm-years. Regression Model We follow prior audit-fee studies (Ferguson and Stokes 2002; Francis 1984; Huang et al. 2009) to test the effect of auditor industry expertise on the relationship between audit fees and new audit engagements. In addition to the variables of interest, industry expert and new audit engagement, we also control for financial and auditor factors of companies in our sample. !"#$%&! = !! + !!!"#$%!!! + !!!"#!$%! + !!!"#_!"#! + !!!"#$%! + !!!"! + !!!"#$! + !!!"#! + !!!"#$%! + !!!"#"$%&"! + !!"!"##! + !!!!"#_!"#! + !!"!"#$%&! + !!"!"#$! + !!"!"#$! + !!"!"#$! + !!"!"#! + !!"!"#$!%_!"#! + !! Our dependent variable is a natural log of audit fees in thousands of dollars (LAUDIT), which is in line with prior literature (Abbott et al. 2003; Fields et al. 2004; Mayhew and Wilikins 2003). New audit engagement is surrogated by an indicator variable of LOWBALL, equaling to 1, if the company is a new client of the auditor, 0 otherwise. We define an auditor as an industry expert when the auditor is ranked top at both the national and local level in an industry (Reichelt and Wang 2010). We further interact new engagement and industry expert (LOW_EXP) to observe whether the discount of the initial engagement is attenuated by the premium of auditor industry expertise. Because the financial status of a company affects the scope of the audit work, we use a natural log of total assets (LOGAT) to control for the size of the company, and we use book-to-market ratio (BM) to control for the growth opportunity of the company. The labor hours of audit work is determined by a company’s operating income as well. We include return on assets (ROA), whether the fiscal year incurs a net loss (LOSS), the debt to asset ratio (LEVERAGE), and the quick ratio (QUICK) to control for the operating effectiveness of the company. As the company extends its operations to foreign countries and offers distinct products, the business becomes more complex and requires more auditor resources. Therefore, we control for number of segments (NSEG) and foreign operations (FORPS) of companies. Furthermore, inventory/receivables (INV_REC) and special items (SPITEM) are accounts that are easily subject to earnings management, and auditors need to spend more time testing these accounts. Hence, we control for these factors in the model. AUDITOR BARGAINING AND LOWBALLING 84 Finally, several auditor characteristics affect audit fees. Because most companies end their fiscal year in December, they will pay extra fees to compete for auditors’ time. As a result, we include an indicator variable, BUSY, to represent the December fiscal-end of companies. Auditors also consider the risk of the engagement when they price an audit project. We add the going-concern opinion from an auditor (GCM) in the model as another element that affects audit fees. Prior literature documents that Big4 (or Big5 prior to the demise of Arthur Andersen) auditors possess prestigious brand names that allow them to charge significantly higher audit fees than their peers. We created an indicator variable BIGN to denote Deloitte, PwC, Ernst and Young, or KPMG. When the audit report is issued at a date long after the fiscal year-end, it implies that the audit work is complicated. Therefore, we use the number of days between fiscal year-end and audit report date (REPORT_LAG) to control for the difficulty of the audit task. Definition of all variables can be found in Table 1. Table 1. Variable Definitions Dependent Variables AUDFEE = audit fees in thousand dollars; LAUDIT = log of audit fees in thousand dollars; Independent Variables LOWBALL = 1 if it is a new audit engagement, and 0 otherwise; EXPERT = 1 if an auditor is both national and city level industry expert, 0 otherwise LOW_EXP = the interaction of LOWBALL and EXPERT ASSET = total assets in millions of dollars; LOGAT = natural log of total assets; BM = book-to-market ratio; BUSY = 1 if fiscal year end is December, and 0 otherwise; ROA = income before extraordinary items deflated by total assets; QUICK = current assets divided by current liabilities; LEVERAGE = total debts deflated by total assets; LOSS = 1 if the firm report loss for current year, and 0 otherwise; INV_REC = sum of inventories and receivables, divided by total assets; SPITEM = 1 if the firm reports a special item, and 0 otherwise; BIGN = 1 if the firm is audited by a big 5 audit firm, and 0 otherwise; NSEG = the number of business segments; FOPS = 1 if firm has a foreign operation, and 0 otherwise; GCM = 1 if firm receives a going concern opinion, and 0 otherwise; REPORT_LAG = time in days from fiscal year end to the audit report date; Hua et al. 85 Table 2 reports descriptive statistics of the variables in our model. LAUDIT has a similar mean and median, suggesting that audit fees are normally distributed. The mean of LOWBALL is 0.07, which indicates that most of our observations are not first-time engagements. EXPERT shows that 27.6% of the sample audit reports are prepared by industry expert auditors. In addition, 74.2% of clients’ books are closed during busy season (BUSY), and 74.1% of the audits are done by Big4 auditors (BIGN). The companies in our sample are generally growing as the mean book-to-market ratio (BM) is smaller than one. Average ROA of our sample companies is -9.59%, and 35.68% of companies reported net loss (LOSS). Though being unprofitable in general, our sample companies have good liquidity as the mean quick ratio (QUICK) is 218.84%. The sample companies are not heavily indebted, because on average, 58.67% of companies’ total assets are financed through debts (LEVERAGE). Our variables generally show similar distribution to what prior audit literature documents. Table 2. Descriptive Statics (N = 21,225) Variable Mean Median Standard Deviation 25th Percentile 75th Percentile AUDFEE 1,640.02 607.00 3,391.94 199.67 1,616.00 LAUDIT 6.39 6.40 1.42 5.29 7.38 LOWBALL 0.07 0.00 0.26 0.00 0.00 EXPERT 0.28 0.00 0.44 0.00 1.00 LOGAT 5.60 5.72 2.31 3.99 7.24 BM 0.44 0.45 1.19 0.24 0.76 BUSY 0.74 1.00 0.44 0.00 1.00 ROA -0.09 0.03 0.48 -0.06 0.07 QUICK 2.19 1.48 2.23 0.95 2.52 LEVERAGE 0.59 0.48 0.66 0.29 0.66 LOSS 0.36 0.00 0.48 0.00 1.00 INV_REC 0.30 0.27 0.21 0.14 0.42 SPITEM 0.66 1.00 0.47 0.00 1.00 BIGN 0.74 1.00 0.44 0.00 1.00 NSEG 2.29 1.00 1.60 1.00 3.00 FOPS 0.55 1.00 0.49 0.00 1.00 GCM 0.07 0.00 0.25 0.00 0.00 REPORT_LAG 107.77 100.00 46.17 86.00 117.00 Table 3 provides the Pearson correlation matrix of our variables. Consistent with prior literature, the dependent variable, LAUDIT, is negatively correlated with new audit engagement (LOWBALL) and positively correlated with industry expert auditors (EXPERT). LAUDIT is significantly correlated with each of the explanatory variables. Most of the paired correlations are significant at the 0.10 level or lower. AUDITOR BARGAINING AND LOWBALLING 86 Bold indicates correlation significant at p< 0.10 level. See Table 1 for variable definitions. Results The regression results are reported in Table 4. We found a negative association between audit fees and the initial engagement (LOWBALL), which suggests that auditors give discounts on audit fees for first-time clients. The positive association between industry expert auditors (EXPERT) and audit fees suggests that industry experts charge higher prices than non-experts. Our chief variable of interest, industry experts (LOW_EXP), which indicates that new customers are audited by industry experts if equals to 1, exhibited a significantly positive coefficient. The results indicate that auditor industry expertise has a diminishing effect on the audit fee lowballing practice. Furthermore, the combined coefficient of LOW_EXP and LOWBALL is not significant, suggesting that expert auditors do not cut prices for their new clients. The model reports a high adjusted R- square (87%), which is consistent with prior audit literature. All other variables, controlling for the characteristics of financial performance of companies and the audit engagement, also demonstrate the same signs and similar significance as previous audit studies. We further separate our sample into companies audited by industry experts and non-experts and run regressions on these two subsamples (untablulated). The coefficient of LOWBALL of the expert group is not significant, whereas it is significantly negative for the non-expert group. The results from the subsamples support our primary finding that non-experts lowball the audit fees for initial engagements; we do not find evidence that industry expert auditors lower their audit fees for new audit engagements. Hua et al. 87 Table 4. Testing the moderating effect of auditor bargaining power on lowballing Variables Predicted Sign Coefficient t-Statistic p-value INTERCEPT ? 3.003 115.58 0.000 LOW_EXP ? 0.115 2.77 0.006 LOWBALL - -0.128 -8.59 0.000 EXPERT + 0.110 12.55 0.000 LOGAT + 0.495 197.44 0.000 BM - -0.031 -8.65 0.000 BUSY + 0.117 14.11 0.000 ROA - -0.186 -15.94 0.000 QUICK - -0.031 -17.05 0.000 LEVERAGE + -0.000 -0.08 0.937 LOSS + 0.143 15.86 0.000 INV_REC + 0.173 9.01 0.000 SPITEM + 0.132 16.08 0.000 NSEG + 0.063 23.88 0.000 FOPS + 0.303 33.55 0.000 BIGN + 0.362 17.16 0.000 GCM + -0.016 -0.89 0.376 REPORT_LAG + 0.001 15.78 0.000 N 21,225 Adjusted R2 0.87 Conclusion While the practice of lowballing audit fees attracts concerns from regulators and legislators (SEC 1978; AICPA 1978; SEC 2000; Healy 2005; Williams 2007), few empirical studies explore the moderating effect of lowballing. Kanodia and Mukherji (1994) analyze the theoretical model in conditions where bargaining power affects the magnitude of lowballing. This study provides the empirical evidence on the effects of the auditor’s bargaining relationship on the audit fee during the initial year of audit engagement. This paper documents a negative association between auditor industry expertise and lowballing. In addition, we only find evidence of lowballing for non-expert auditors. These results are consistent with the notion that industry expert auditors have high bargaining power when negotiating audit fees and do not engage in the lowballing practice. Our study is the first to consider the effect of auditor bargaining power on lowballing for new audit engagements. Our findings are of interest to regulators, professionals, and academic researchers. AUDITOR BARGAINING AND LOWBALLING 88 References Abbott, L.J., S. Parker, G. F. Peters, and K. Raghunandan. 2003. 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ASR No 250: Disclosure of Relationships with Independent Public Accountants. Washington, DC: Government Printing Office. Securities and Exchange Commission (SEC). 2000. Revision of the Commission’s Auditor Independence Requirements. Financial Reporting Release No. 56. Washington, D.C.: SEC. Simon, D., and J. Francis. 1988. The effects of auditor change on audit fees: Tests of price cutting and price recovery. The Accounting Review 63 (2): 255-269. Williams, P. 2007. Accounting: Playing lowball. Accountancy Age (July 12). Available at: http:// www.accountancyage.com / articles / print / 2193827 Shaowen Hua is Assistant Professor of Accounting in the School of Business at La Salle University. She holds a Ph.D. in Accounting from LeBow College of Business, Drexel University. Her research interests include analyst/management forecasts, corporate governance, and auditing. Zenghui Liu is Assistant Professor of Accounting in the College of Business and Economics at Western Washington University. He earned his Ph.D. degree in Accounting from Drexel University. His research specialty ranges from auditing, corporate governance, to financial accounting. He could be reached at: liuz3@wwu.edu. Xiaojie Christine Sun is Assistant Professor of Accounting at California State University, Los Angeles. She received her Ph.D. in Accounting from LeBow College of Business, Drexel University. Her research interests include auditing and financial reporting. Ji Yu is Assistant Professor of Accounting in the School of Business at State University of New York at New Paltz. He holds a Ph.D. in Accounting from the Fogelman College of Business and Economics, University of Memphis. His research interests include initial public offering underpricing, financial reporting quality and auditing.