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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 



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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.  

 
 
 
 
 



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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.  

 

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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.	
	

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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	
	

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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. 

 
 



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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.  

 



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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. 

 

 

 


