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Erasmus University RotterdamErasmus School of EconomicsAccounting, Auditing & ControlAccounting & AuditingMaster Thesis
The Impact of Audit Client Complexity on the Audit Fee: the Role of Auditor Industry Specialization
Date: 28 July 2014
Name: Jian Guo (328226)
Supervisor: Mr. E.A. de Knecht RA
Co-reader: Mr. R. van der Wal RA
AcknowledgementsThis thesis is written for the completion of the master program Accounting, Auditing and
Control at the Erasmus University of Rotterdam. It can be seen as the final assignment of my
scientific education and a starting point for my professional education to become a certified
public auditor in the Netherlands. Therefore, the research subject has been carefully chosen
that is embedded within the auditing theory: audit fee determination. The main motivation for
this topic is that it is highly relevant to the practice. Since there is not a clear guideline to
determine the level of audit fee, academic contribution of examining of audit fee determinants
can be really helpful towards existing literature about audit fee determination.
It has been an intensive period to conduct the research and to write the master thesis,
especially since the first time that I met my supervisor was eight weeks after handing in the
application form and thesis proposal, what normally takes around three weeks. Nevertheless, I
was able to finish the thesis and want to thank to several people who have supported me
during the time of writing the thesis.
First of all, I want to thank my supervisor, Mr. E.A. de Knecht RA, who has provided me lots
of valuable feedbacks and useful insights on a timely manner. The discussions with Mr. de
Knecht were very helpful to obtain a questioning mind and critical view. This was not only
helpful for my thesis, but also for my future career and life. Secondly, I want to thank EY
Rotterdam for providing me a stimulating environment to write my thesis. I especially want to
thank my supervisor at EY, Ms. Marloes de Vries RA, for giving me useful supports from
practical aspects of the audit professions in relation to my thesis subject. Last but not least, I
want to thank my parents, my girlfriend and my friends for their tremendous support during
the final period of my master program.
Jian Guo
Delft, July 2014
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AbstractThis thesis examines whether the auditor industry specialization would influence the impact
of the audit client complexity on the audit fee. By using 10136 firm-years observations of
U.S. listed companies from 2007 up to and included 2013, the findings indicate that the audit
industry specialist fee premium exists for complex clients. In the supplemental tests, the
difference in the audit fee the among complex and noncomplex companies has been
confirmed to be statistically significant when controlling other fee determinants, consistent
with the conclusion that industry specialists require a higher fee for compensating the
education costs. When considered in conjunction with the prior research, this thesis provides
another approach to measure the audit client complexity. The results suggest that companies
of certain industries are more complex to audit due to their characteristic operations, and
thereby require more efforts from auditors to perform the audit and ask for a higher audit fee.
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Table of Contents
1. Introduction 61.1 Background 61.2 Relevance 81.3 Objective 91.4 Research question 91.5 Methodology 101.6 Demarcation and Limitation 101.7 Structure 11
2. Theoretical Background 122.1 Introduction 122.2 Agency Theory 12 2.2.1 Information Asymmetry and Agency Costs 12 2.2.2 Financial Statements Audit and the Costs 132.3 The Role of the Public Auditor 14 2.3.1 Audit Services 14 2.3.2 Certified Public Accounting Firms 15 2.3.3 The BIG 4 Network and Its Impact 152.4 Audit fee 16 2.4.1 Audit Pricing Model 162.5 The Determinants of the Audit Fee 17 2.5.1 Client Size 17 2.5.2 Client Business Risk 18 2.5.3 Client Complexity 18 2.5.4 Auditor Specialization 192.6 Summary 20
3. Prior Research 223.1 Introduction 223.2 Common Determinants of Audit Fee 223.3 Audit Client Complexity 263.4 Auditor Industry Specialization 293.5 Hypotheses Development 313.6 Summary 32
4. Research Design 344.1 Introduction 344.2 Research Approaches in Accounting 34 4.2.1 Quantitative Researches 354.3 Research Methodology 354.3.3 Control Variables 384.4 Sample Collection 40
5. Results 43
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5.1 Introduction 435.2 Assumptions for Regression Analysis 435.3 Mean Differences of Client Complexity and Industry Specialists 465.4 Association of Client Complexity and Industry Specialists 475.5 Analysis of the Investigation Concerning the Audit Client Complexity and the Auditor Industry
Specialization 48 5.5.1 Descriptive Statistics of the Regression Components 485.6 Interaction of Client Complexity and the Industry Specialists on the Audit Fee 505.7 Discussion 525.8 Summary 54
6. Conclusion 566.1 Introduction 566.2 Research Summary 566.3 Conclusions 566.4 Limitations 576.5 Suggestions for Further Research 58
References 59
Appendix 62
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1. Introduction
1.1 Background
Nowadays, public auditors are accomplishing an indispensable role in the worldwide financial
markets. They ensure the quality of the company’s annual financial statements and enhance the
confidence of the public and the investors in the capital market. It is worth noting that, over the last
decades, several well-known financial scandals exist in which public auditors are jointly responsible.
Because of these scandals investors and stakeholders have paid huge prices. Consequently,
maintaining a high level of audit quality to prevent such a scandal from happening again in the future
should be the top priority to all audit firms.
Audit service provided by public auditors ‘is a professional service that improves the quality of
information for decision makers’ (Arens, Elder, & Beasley, 2013 p.8). High quality information
indicates that the firms’ annual financial statements are free of material misstatements, consequently,
presenting a firm’s financial position in a true and fair view. In return to the service, companies are
charged by the audit firms to compensate the effort from the public auditors, this is qualified as the
audit fee. Because every company has its unique settings and industry environment, among firms the
amount of audit fee varies.
In order to perform an audit, a certain amount of professional knowledge from public auditors is
required. It is not only the knowledge about how to design and implement the audit procedures, but
also specific knowledge regarding to the industry in which the client firm is operating. This industry
specific knowledge is particularly valuable for understanding their client business and its business
environment. Consequently, this specific knowledge can be qualified as the specialization of an audit
firm, which is not required by any regulations but which is essential for maintaining a high quality
audit engagement.
When preventing audit failures, the public auditor’s knowledge is crucial. Audit failure occurs when
the public auditors stated that a firm’s annual financial statement is free of material misstatements
when it is still contains material misstatements. Audit failure is one of the causes of the recent
financial scandals. Financial scandals like Enron, WorldCom, Ahold and Vestia had a huge negative
impact on the public confidence in the security markets. The involvement of public auditors in these
incidents creates both by the regulators and by the public concerns about the audit quality.
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In response to the financial scandals of Enron and WorldCom, the U.S. government in 2002 passed the
Sarbanes-Oxley Act (SOx) with enhanced standards introduced to restore the confidence in the
security market. One essential implementation of SOx is included in Section 404 (hereafter: SOx 404):
Issuers are required to publish information in their annual reports concerning the scope and adequacy of the internal control structure and procedures for financial reporting. This statement shall also assess the effectiveness of such internal controls and procedures.
The registered accounting firm shall, in the same report, attest to and report on the assessment on the effectiveness of the internal control structure and procedures for financial reporting.
The main interpretation of SOx 404 is the requirement of the corporate managers and the public
auditors to report on the adequacy of the company’s internal control. Managers, as a part of the annual
report, are required to publish an internal control report and the public auditors need to express their
opinions about the effectiveness of the internal control structure of the firm. Consequently, in order to
assess a firm’s internal control, public auditors have to be equipped with even more specific
knowledge which enables them to have a better understanding about the firm and its industry.
Concerning some industries, performing a high quality audit only have audit procedure knowledge is
not sufficient. Due to the complexity of an industry, to judge the line items in the annual financial
statement it requires more specific knowledge. Typical operations within an industry or the
decentralization of operations to complete the audit would require significant more efforts from the
public auditors (Hay, Knechel, & Wong, 2006). For instance, it is well-known that the pharmaceutical
industry requires a high research and development intensity. Statement of Financial Accounting
Standards 2 suggested that ‘All research and development costs encompassed by this Statement shall
be charged to expense when incurred’. However, International Accounting Standard 38 ‘Intangible
Assets’ allows companies to capitalize development costs when it is able to produce probable future
economic benefits under certain criteria. Consequently, in order to judge whether the expectation of a
company is justified it requires the public auditors having a large amount of industry specific
knowledge. If auditors are not educated in judging the company’s expectation, for the evaluation to
maintain the level of the audit quality a third party should be involved. Alternatively, if public auditors
in this industry are specialized, they would perform this audit in a more efficiently way. Consequently,
to a certain extent, the complexity of an audit engagement relies on the specialization of the public
auditors.
When a group of public auditors are more specialized than the other, it is not surprising that the
charged fees are different as well. As signaled before, the audit fee is the compensation paid by firms
for the audit effort by the public auditors. A problem with the determination of audit fee is that no
clear guideline exists to follow. Concerning the determination of the audit fee, academicians have
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developed a theoretical model. This model separated the fee into two components: audit production
and the present value of potential future costs associated with the current audit (Simunic, 1980). The
audit production is a function of the audit hours and the audit cost per hour. Concerning the specialists,
because they have an extra amount of knowledge to improve the efficiency of the audit the assumption
is that the needed audit hours are less than non-industry specialists. However, to compensate their
training investment specialists charge a higher fee per hour. Consequently, not is known whether an
increase in the specialization to ease the industry complexity could create an increase or decrease in
the audit fee.
Consequently, in improving the efficiency of an audit engagement particularly within relatively
complex industries, it is essential to realize that specialization can be highly valuable. Specialized
industry knowledge helps the public auditors ease the complexity of an audit and reduce their effort in
the audit production. Because it is not clear whether audit clients pay a fee premium for the audit
specialist, this thesis will investigate the correlated effect of specialization, generated to ease industry
complexity, on the audit fee.
1.2 Relevance
Audit fee is mostly determined prior an audit engagement. At the time of contracting, public auditors
should estimate the nature and the magnitude of the evidence needed to mitigate the uncertainty of an
audit failure. In the U.S, public firms report the expenses paid for auditing their financial statement in
their annual reports. However, it is still unclear what factors were used to determine the level of audit
fee. Besides, the determinants of the audit fee are not clear-cut in addition due to the corresponding
duty of the professional confidentiality within the public audit profession. Because of this feature, a
number of researches have been performed to determine what factors are systematically associated
with the audit fee.
Among the studies conducted, researchers confirmed that several factors with the audit fee are
systematically correlated. For instance, audit client size, complexity, the public auditor’s size, brand
names and specialization all showed a significant impact on the audit fee (Hay et al., 2006).
Complexity and specialization are both confirmed to be positively associated with the audit fee. In
prior researches, the proxy for complexity is mostly the number of subsidiaries (Hay et al., 2006). It is
suggested that subsidiaries would increase the complexities of transactions and consequently requires
more audit effort. However, this measurement of complexity is not associated with a particular
industry. A limitation of this approach is that it assumes that the complexity among industries is equal.
Since the complexity in addition varies due to the nature of an industry, this could create biased
results. The contribution of this thesis is an alternative way to measure the complexity. In addition,
when an industry is complex due to its required specialized knowledge, this complexity term becomes
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a subjective matter. Additional tests in this thesis are going to investigate whether specialization could
influence the positive association of the complexity and the audit fee.
The approach is that the complexity of an audit due to the industry characteristics is subjective. The
level of complexity would decrease if public auditors do have industry specific knowledge comparing
with public auditors who do not have this knowledge. Specialized public auditors in addition are able
to perform the audit differently than non-specialists do. Consequently, the difference in audit effort
could cause a difference in the audit fee.
1.3 Objective
The purpose of this thesis is to determine whether specialization influences the correlation between the
complexity and the audit fee, if so, in which direction. As signaled in the previous section, the public
auditor’s specialization could be useful for certain complex industries. However, not is known whether
this amount of specialization could create an increase or a decrease in the audit fee.
Alternatively, because it serves as a cost concerning the public audit firms for training and educating
their staffs the audit fee may increase. Consequently, they may charge a specialist premium to
compensate these expenses. Besides, these specialized public auditors are more likely to provide
services in their specialized industry consequently acquiring expertise due to hands-on experiences.
Eventually, they may become the only provider of the high quality audit services within a particular
industry thereby enjoying a monopolistic position and provides it price-setting power which in
addition creates an increase in audit fee. Alternatively, when a public audit firm owns a large amount
of market share, the training costs can be spread to more clients. Since the non-specialists are facing
higher costs, at the same time, they are able to keep increasing their market share. Consequently, the
audit firm achieves economies of scales within this certain industry. To keep its competitive position
among other public audit firms it will reduce its audit fee eventually.
The results in this thesis would be useful for several groups. First, an alternative approach will use to
measure a firm’s complexity. This approach assumes that industry characteristics would increase the
complexity of a specific firm. This alternative measurement could create different implications for
prior researches. Second, to understand the development of their audit fee the outcome of the thesis
could be useful for corporate managers. Third, the audit firms could use the results as a suggestion to
whether or not develop a differentiation strategy with specialization.
The main purpose of this thesis is to provide researchers, auditors and audit clients more insights in the
determination of the audit fee.
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1.4 Research question
In this study, the next research question will be answered:
Does the public auditor’s industry specialization influence the association between the audit fee and
the audit client complexity?
In order to provide thorough understanding of this topic, the next sub questions will be answered:
1. What is the function of the public auditor?
2. What is the content of the term client complexity?
3. What is the content of the term auditors industry specialization?
4. Why does the complexity drive up the audit fee?
5. Is auditor’s specialization really needed to perform an audit?
6. Is the audit fee for complex industry higher than for others?
1.5 Methodology
The level of the complexity would be measured as a dummy variable. Companies from a complex
industry received 1, otherwise 0. The level of specialization is measured as percentage of an industry
that is audited by a particular public audit firm; the audit firm with the market share of 15% or more
will be recognized as specialist.
Firstly, to ascertain that the audit fee is higher for complex industries, the audit fee for complex
industries and regular industries will be compared. Secondly, a test will be performed to assess
whether companies who used industry specialists are significantly paying more fee than
nonspecialists. Finally a regression test will perform with the audit fee as independent variable, the
complexity as dependent variable and the specialization as moderating variable.
During the research, other variables that could be a driver of the audit fee will be controlled. These
variables are: audit client size measured by total assets and total turnover, business risk as the
combination of certain financial ratios, geographical dispersion and previous auditor’s opinion.
1.6 Demarcation and Limitation
The sample is selected from the U.S. Audit Analysis database and Compustat; the results may
consequently be insignificant for other regions. Additionally, the audit fee data is only available for
public companies; the relation may only appear for public companies instead of all companies
including private companies. In addition, due to the availability of the data the time period is selected
after 2001. However, the implementation of SOx could drive up the audit fee.
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The measurement of auditor specialization has its limitations as well. The specialization is measured at
the national level. Since it could be that one local office is much more specialized in a specific
industry than the other establishment from the same audit firm, this could create biased results The
reason behind this could be that due to the geographical locations of clients, it is more efficient to set
up a specialized office nearby the client. When measured at the national level, only the aggregated
specialization is taken into account.
1.7 Structure
Chapter 2 describes the theoretical backgrounds of the main concepts used in this thesis. The
theoretical background includes the role of the public auditor, the audit client complexity, the auditor’s
industry specialization and the confirmed determinants of the audit fee. The concepts will be explained
and their theoretical association will be presented.
Chapter 3 concentrated on the prior researches conducted in this topic. The most influential papers in
this topic will be presented. The thesis is mainly an extension of these papers. In addition in this
chapter the hypotheses will be developed.
Chapter 4 describes the research design and the sample selection. The research design mainly includes
the model construction and the way to measure the selected variables. Additionally, a description will
be given in which manner the sample was obtained.
Chapter 5 will analyze the results obtained from the sampling. This chapter focuses on the findings of
the results and to test the hypotheses.
Chapter 6 will present a summary of the thesis and the main conclusion derived from the results. And
the limitations and the suggestions will be given.
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2. Theoretical Background
2.1 Introduction
In order to have a better understanding concerning the research question this chapter mainly aims at
introducing the theoretical frameworks used in this thesis. To clarify the role of public auditors
particularly to capital providers, the rise of the public audit profession will first be analyzed from the
perspective of the agency theory. This is fundamental for understanding the origination of the audit
fee. Besides, a brief description of the certified public auditing accounting firms will be presented
which is followed by a short description of the current audit market domination by the Big4 firms.
Since the Big4 firms are able to deliver high quality audits which are appreciated by most of the
clients, the level of audit fees may be not determined solely from a price competition. More
specifically, a differentiation strategy in specialization provides audit firms opportunities to increase
the fee. Finally y, the determination of audit fee is revealed from an academic view: the audit pricing
model of Simunic (1980). Based on this model, determinants of interests for this thesis will be
explained. These are the client size, the client business risks, the client complexity and the auditor
specialization.
2.2 Agency Theory
The agency theory is raised based on principal-agent relationship. The principal-agent relationship is a
widely applied theory. This thesis focuses only on the effects on stock exchange quoted companies.
As what the name suggested, the principal-agent relationship exists between two parties: the principal
and the agent of the principals. Concerning stock exchange quoted companies, the agent is the board
of management and the principals are shareholders who are the actual owners of the company. Agents
are recruited to represent the principals and perform on the principals’ behalf. Since the managers are
the leaders of the company, they, as agents, have more superior information than the actual owners
have. To a large extent, the revenue for the shareholders is dependent on the performance of the
managers. However, it happens quite frequently that managers have conflicting objectives and/or goals
than the shareholders have.
2.2.1 Information Asymmetry and Agency Costs
The main objective for the shareholders is to maximize their revenue. This could be achieved by hiring
managers with great managerial capacity. A company’s performance can be largely dependent on the
talents of the managers. Motivated managers with good management skills and rich experiences help
to increase the revenue for shareholders and maximize the company’s profit.
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However, managers often have their own objectives that are different from the ones of the
shareholders and due to the information asymmetry they have the ability to act for their own interest.
When doing so, the shareholders’ value is not maximized. For instance, in order to enable shareholders
to receive more dividends, it is managers’ task to maximize the net income. However, academic
economic literature showed evidence that managers are only likely to increase the company’s net
income when their compensation will increase as well (Healy, 1985).
For shareholders, due to the information asymmetry between he managers and the shareholders in
general it is difficult to determine whether or not the managers are acting in their own interests.
Concerning the shareholders, several sources of information exist to obtain information of their
company. The most important information source remains the financial statements, which consist of a
balance sheet, income statement, cash flow statement and statement of owner’s equity. The purpose of
publishing financial statements is to present to the stakeholders the company’s financial information as
clearly and precisely as possible. The most important group of stakeholders is the company’s creditors
and the shareholders. They support the company by providing capitals and receive interests or
dividends as returns. Not surprisingly, to this group of capital providers it is essential to evaluate a
company’s potential to achieve its objectives and repay them in the future. When a company through
its financial statements communicates unreliable information, for capital providers it becomes even
more difficult to obtain real information. In that case they have to acquire reliable information on their
own. Consequently, they may require more return on their capital, which can be qualified as an
expenditure arisen from the agent-principal relation, and consequently increase the company’s
expenditures. This in turn will decrease a company’s ability to attract more capital and achieve its
objectives.
By providing reliable information by the company, because they do not need to acquire reliable
information on their own, the capital providers will use the presented information in full extend, which
could lower the required return. Additionally, regulators have established divers accounting guidelines
to improve the reliability of the published financial statements. Consequently, all published financial
statements have to adhere to standard guidelines provided like the General Accepted Accounting
Principles (GAAP) or the International Financial Reporting Standards (IFRS). To maintain the quality
of the published financial statements like has been signaled in the introduction chapter, regulators of
these standards are constantly updating their rules.
2.2.2 Financial Statements Audit and the Costs
As signaled before, it is crucial that the reported information on the financial statements are reliable
and accurately concerning the companies’ financial position. This is exactly the responsibility of the
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public auditors: to ensure with reasonable assurance that a company’s financial statements present its
financial position in a true and fair view. It is commonly accepted and proved by researches that the
auditors’ name reputation (e.g. Big 4) is positively associated with the audit quality (Becker, DeFond,
Jiambalvo, & Subramanyam, 1998; Francis & Yu, 2009).
Companies invite public auditors to investigate their financial statements and in compensation pay
them the audit fees. It is essential to note that, even though the audit fee for the companies remains as
an expense the major concern when choosing an auditor (firm) is still the quality according to what is
suggested in the agent-principle theory. In other words, the amount of audit fee is not the main focus
of the company. Since it states that the level of the audit fee is not fully determined by a price
competition among audit firms, this assumption is crucial in this thesis. Suppose it was the case, the
main driver for the audit fee would be the competiveness within the audit industry, which would
decrease the validity of the determinants that in the academic literature are relevant to investigate the
audit fee.
2.3 The Role of the Public Auditor
This thesis is about financial statement auditing, consequently the definitions in n this paragraph are
applied for the financial statements audits.
2.3.1 Audit Services
2.3.1.1 Nature of Auditing
Arens et al., (2013, p.4) defines auditing as “the accumulation and evaluation of evidence about
information to determine and report on the degree of correspondence between the information and
established criteria”. In this definition two key terms exist: information and established criteria. To
ascertain the reported information is in accordance with the established criteria, auditors need to
collect evidence. The information that is being audited can be quantifiable information (e.g. financial
statements numbers) or qualified information (e.g. the efficiency of a firm’s operation). Based on the
collected evidence, auditors should be able to draw a conclusion about the quality of the reported
information based on their understanding of the established criteria, in other words, whether the
company’s financial statements present its company’s financial position in a true and fair view. The
results of that investigation are included in the audit report with the auditor’s opinion. Concerning
public auditors it is essential to remain independent from their client even though an economic bond
exists between the auditors and their clients, which is the audit fee.
2.3.1.2 Auditor’s Responsibilities
The main responsibility of the public auditors can be described as
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‘Obtain reasonable assurance about whether the financial statements as a whole are free from
material misstatements, whether due to fraud or error, thereby enabling the auditor to express an
opinion on whether the financial statements are presented fairly, in all material respects, in
accordance with an applicable financial reporting framework.’ (Arens et al., 2013, p144)
The level of assurance can be qualified as a level of certainty that auditors have upon completion of
an audit. Auditing standards requires auditors to have reasonable assurance instead of absolute
assurance. The main reason an absolute assurance is not applicable is that auditors are not issuers of
the published financial statements. Consequently, they are not able to provide fully assurance. In
order to provide a reasonable assurance, a certain amount of professional knowledge is required. For
instance, in order to draw conclusions about the correctness of an account, auditors often have to
investigate samples. The sample selection procedures and its sizes are fully dependent on the
judgment of the auditors. An inadequate decision concerning the sample increases the associated risk
of the presence of a material misstatement in the published financial statements. Besides, certain
industries often have to deal with complex estimations, which form the auditor requires certain
industrial knowledge. Consequently, in order to realize a correct judgment auditors are required to
have a well-educated background.
2.3.2 Certified Public Accounting Firms
A certified public accounting firm (CPA firm) provides assurance and attestation services. Audit
services belong to the assurance services and are mostly the core business activities concerning a CPA
firms.
Concerning the most CPA firms, the organizational structure is Limited Liability Partnership (LLP),
which states that the firm is owned by one or more partners. The advantage of this organizational
structure is that partners are only personally liable for debt and obligations of their own acts and not
for the liabilities arising from other partners.
In the U.S., financial statements audits of all general companies are performed by CPA firms. In 2012,
in the US more than 45000 CPA firms exists with a range in size from 1 to 40000 staff (Arens et al.,
2013). In general, audit firms are categorized based on their revenue. The categories are: Big 4,
National, Regional and Large Local (Arens et al., 2013). Concerning this thesis, due to the availability
of data, the audit fee and the auditor’s specialization are mainly derived from Big4 CPA firms.
2.3.3 The BIG 4 Network and Its Impact
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Currently, the audit market is predominant by the Big 4 firms. This has raised a much concerns about
the competition within the market which would in turn influences the audit quality. Consequently, in
order to maintain the audit quality, regulators have enhanced/set new regulations. For instance, audit
independence rules are set to limit the audit firms to provide other services to its audit client. Although
the intention of these rules is to maintain and improve the audit quality, they could potentially reduce
the number of available CPA firms for a client to choose from. Concerning companies that demand
high quality auditing services from Big4 firms, the limited choices available could create a lower
bargaining power (Porter, 1979). Consequently, the CPA firms may ask a higher compensation for
their audit effort. Consequently, a less competitive audit market increases the audit fee.
2.4 Audit fee
2.4.1 Audit Pricing Model
Much scientific research exists about various aspects of the audit market, of the audit pricing and of
the audit production. Current studies about the audit fee determination are derived from the seminal
work of Simunic (1980). It serves as a foundation to all later studies in this field. It basically assumed
that the audit fee is a function of the auditor’s effort. This function contains two components: the
direct production costs and the expected future costs that arise from the current audit (Simunic, 1980).
Based on this assumption, to explain the relation between the audit production and the audit pricing an
audit pricing model is developed (Simunic, 1980):
E (~C )=cq+E (~d|a , q ) E (~Ɵ)
[1]
This formula illustrates the audit pricing in a mathematical way. The first component included is the
pricing for audit production, where c is the cost per unit resource and q is the quantity of the used
resources. The second component is the present value of the possible future losses represented by ~d,
and a denotes internal resources a client devotes to the audit-related activities. This present value
amount is then multiplied by the likelihoodE(~Ɵ) that the costs will indeed occur. The first component
is the costs purely related to the audit production itself and the second component can be qualified as
an allowance for the possible future costs associated with the current year audit.
This audit pricing model provides a foundation in determining the audit fees. It calculates the audit fee
based on its costs. However, when using this model two limitations are related.
1) The cq part is only valid to predict the costs for the audit production if the audit quality of the
conducted audits performed would be at the same level as the assumed quality of the audit. This
assumed audit quality is mostly associated with the brand name of the audit firm. If it is not the
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scenario, cost per unit resource is not able to capture the unit cost in a reliable way. Besides, the
E (~d|a ,q ) E(~Ɵ) part in addition would predict a wrong value if the perceived audit quality is not
the same as the assumed audit quality.
2) In order to determine the right amount of per unit cost the audit market should be competitive. If
the audit market is formed by monopolistic market players, they are able to ignore the pressure
from the competitors and consequently present to them the price-setting power.
These two limitations were confirmed by later researches. The quality of an audit can be affected by
many factors. It is nearly impossible for an audit firm to perform every audits at the same quality level
as assumed by their brand name (Francis, 2004). Besides, the audit market is predominating by the
Big4 accounting firms. The large market share would reduce the impact of a competitive market on
the audit fee. Consequently, a priori reasoning exists to believe that the audit costs model provided a
valid and reliable framework to predict the audit fee Simunic (1980). However, in practice it is
inappropriate.
Consequently, Simunic (1980) and other researchers had to found several factors that would
systematically influence the audit fee. They have chosen to link some client characteristics directly to
the audit fee and found that a certain amount of client characteristics are effective proxies to determine
the audit fee (Hay et al., 2006). Besides, researchers in addition found that auditor characteristics and
engagement attributes could influence the audit fee (Hay et al., 2006). Essentially, since these
characteristics would affect the audit fee either by the total audit hours or the audit cost per hour, the
audit pricing model by researchers is still used as a foundation to determinate the drivers for the audit
fees.
2.5 The Determinants of the Audit Fee
Several client characteristics exist that could influence the audit fee. Since they have a direct
association with the audit effort and the audit engagement risk, which will increase the total audit
hours, these determinants are essential. The most important characteristics are presented in the next
paragraphs.
2.5.1 Client Size
In determining the audit fee, the audit client size by far was confirmed to be one of the most
significant explanatory variables (Hay et al., 2006). The reason behind this is that in order to mitigate
the increased audit risk larger clients typically require more effort from the auditors. Based on this
argue, the audit fee per hour would remain at the same level regardless the size of the auditees. In the
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study conducted by O’Keefe, Simunic, & Stein (1994) for investigating CPA firm’s use of different
grade of professionals and various client characteristics, they have found clear evidence that the audit
effort and the client size has a concave relation. The explanation for this concave relationship is that
the use of a relatively larger proportion of low-level professional auditors, consequently the
inexpensive personnel costs would create a little increase in the audit fee. However, the total audit
hours is still increasing with the size of the client which will create an increase in the audit fee.
The size of an auditee firm can be measured by multiple ways. The most used proxy is the amount of
the total asset (Hay et al., 2006). Another widely used variable is total sales. Because the lawsuits
against the auditors mostly arise from improper valuations, the amount of the total assets in addition
has consequences on the responsibilities of the auditors. Consequently, a firm with larger assets
requires more efforts from the auditors, consequently possessing a higher audit fee.
2.5.2 Client Business Risk
Besides the company size, business risk in addition is one of the major factors affecting the audit fee.
For instance, when a company endured financial losses a higher possibility will exists of bankruptcy,
which in turn creates a larger possibility of legal actions against the client and in addition against the
auditor. To prevent such a situation from happening, auditors have to perform more effort to mitigate
the risk and to avoid any lawsuits in the future (Arens et al., 2013). Francis & Simon (1987) found a
positive correlation between the audit risk and the audit fee. A survey study conducted by Bell et al.
(2001) further confirmed that such a correlation was caused by the related audit hours.
The client business risk can be measure in two ways, namely operational risk or financial risk. The
most used proxies to measure business risk are financial ratios to measure a company’s ability to pay
off its debt.
2.5.3 Client Complexity
Audit client complexity in addition could affect the audit fee. Since auditors need to have a good
understanding of their client’s company in order to develop an appropriate audit strategy (Arens et al.,
2013), complex clients would requires more efforts from auditors to fully understand their business
operations than less complex clients. In other words, the more complex a client, the more time-
consuming the audit procedure would be that could create a higher audit fee. Existing scientific
literature showed a positive correlation between the audit client complexity and the audit fee (Hay et
al., 2006).
18
The most used measurement for client complexity is the number of subsidiaries that a firm locally and
internationally owns. The argument for this proxy is that if a firm is complex, it in addition has more
diversified operations. Sandra & Patrick (1996) showed that auditors are likely to charge a higher fee
for complex clients. They have examined the Hong Kong audit market, and found that a company that
has more foreign subsidiaries is facing a variety of legislative requirements of disclosures.
Consequently, this requires more audit testing than companies with less foreign subsidiaries. When
more audit testing is needed, audit firms would add more manpower to complete the audit
engagement, which implies that the clients have to pay an additional charge for the audit engagement
(Simunic, 1980).
A company’s complexity is mostly due to its business, which is industrial specific. Consequently,
instead of the number of subsidiaries, the industry sector is another way of specifying company
complexity. Craswell & Taylor (1991) followed this method, examined the correlation between the
complexity of audit clients and the corresponding audit fees. They classified all companies into the
following groups
(1) natural resources, which have unique accounting problems with respect to the valuation of
mineral/oil reserves, income determination, and complex forward sales and hedging contracts;
(2) building suppliers and engineering firms, both of which are involved in multi-period
contracting which creates special accounting problems relating to cost capitalization and
income recognition;
(3) retailers, which have elaborate inventory systems and special revenue recognition issues
associated with sales returns and various types of customer financing and
(4) investment and financial services, which have complex contracts for financial instruments and
derivatives, large-scale EDP systems, and special regulatory accounting requirements.
This classification was based on the industry-level measurements of the systematic risk and the total
risk. In addition, Craswell, Francis, & Taylor (1995) used a similar classification for the complexity
and found that specialized auditors charge the complex clients a fee premium of 18% on average than
less complex industries.
2.5.4 Auditor Specialization
Based on the Porter's (1985) competitive strategy, an audit firm may choose for a differentiation
strategy to gain a sustainable competitive advantage over its competitors. For audit firms, one way to
achieve a competitive advantage is to obtain specializations in particular industries. This strategy is
used by many audit firms aiming to attract more clients (Mayhew & Wilkins, 2003). Because they
19
believe it increases the audit quality indeed this is appreciated by the audit clients (Klein & Leffler,
1981; Low, 2004; Owhoso, Messier, & Lynch, 2002).
The choice and the ability for audit firms to apply the specialization strategy is dependent on the type
of industry (Cahan, Godfrey, Hamilton, & Jeter, 2008; Cairney & Young, 2006). Cahan et al. (2008)
assert that the key driver for firms performing such a choice is the industry specific investment
opportunity. They showed that exploiting investment opportunity requires industry specific
knowledge, which an auditor may increase his competitive position.
Specialization in a particular industry enables a firm to increase its bargaining power against its
current and potential audit clients and in addition increases the premium charged comparing to their
less specialized competitors (Klein & Leffler, 1981; Porter, 1979). Besides, a specialized audit could
enjoy an increase in its reputation (DeAngelo, 1981), which permits them possessing a more
competitive advantage position and consequently increases its power, allowing them to charge a
higher premium. On the other hand, specialized auditors are able to work more efficiently and in
addition reduce the working hour needed. In addition, it is easier for them to achieve economic of
scales particularly in their specialized industry. Consequently, increased specialization in specific
industries may reduce a firm’s cost and provide the possibility of discounts in charging the audit fee
(Cairney & Young, 2006; Eichenseher & Danos, 1981). Summarizing, industry specialists have the
incentive and the ability to charge either a fee premium or discounts.
2.6 Summary
In the introduction of this thesis, several questions were formulated to provide a better understanding
of this thesis. This chapter has answered the first four questions. A brief summary of the answers is
provided below.
1. What is the function of a public auditor?
The role of a public auditor is to provide reasonable assurance that the published financial statements
of a company are free of material misstatements. Since an important group of financial statements
users is capital providers of the company, this is essential concerning the effectiveness of the capital
market.
2. What is the content of the term client complexity?
Client complexity can be qualified as an attribute from clients’ side to influence the audit fee.
Typically, client complexity is measured using the number of subsidiaries. Since the complexity
creates a more time-consuming audit, auditors usually charge a higher fee for complex clients.
20
In this thesis, client complexity will be measured based on the industry characteristics, which can be
implemented that some industries are systematically more difficult to audit than other industries.
3. What is the content of the term auditors’ industry specialization?
In order to increase its competitive position, audit firms can choose for a differentiation strategy.
Auditors’ specialization is one way to achieve it. When an auditor is specialized in a particular
industry, it will gain the ability to charge either a fee premium or discounts.
It is essential to note that this specialization is not mandatory in completing an audit engagement.
4. Why does the complexity will drive up the audit fee?
Because auditors need to spend more time to have a good understanding of the business, basically, a
complex company requires more efforts from the auditors. This process is essential concerning the
development of the audit strategy. Consequently, to compensate their efforts due to the complexity,
auditors will charge a fee premium.
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3. Prior Research
3.1 Introduction
This thesis investigates the influence of the auditor’s industry specialization on the audit fee among
listed firms in the United States of America (U.S). In order to have a better understanding of the topic
in this chapter the determinants of the audit fee will be analyzed. Consequently, this will provide a
context in which the results of this thesis can be referred.
In this chapter, seven academic articles published during 1986 up to and included 2013 in great detail
will be presented. These articles possess either comparable research objective or methodology with
this thesis. By comparing with previous studies, it provides the possibility to explanation the
implications of the results in this thesis. A short summary of all articles is provided in appendix 1.
Simunic (1980) suggested investigating the direct determinants that significantly influences the audit
fee. This approach was applauded by researchers and services as the foundation concerning the study
of the audit fee. The next passage summarizes in which way this was applied in most audit fee studies.
“Typically, an estimation model is developed by regressing fees against a variety of measures
surrogating for attributes that are hypothesized to relate to audit fees, either negatively or positively”
(Hay et al., 2006, p. 146). This was illustrated in the next equation:
ln f i=b0+b1 ln A i+∑ bk gik+∑ be g ie+e i [2]
WhereLn fi = the audit fee (natural log)b0 = the interceptbn = the coefficientsLn Ai = the audit client size measure (natural log)ei = the error term
The other two groups of variables gik and gie are the potential fee drivers to be tested. All the articles
which will be presented in this chapter in their researches have used this.
3.2 Common Determinants of Audit Fee
3.2.1 Audit Client Size
Based on the audit pricing model, the audit client size is considered as the most important influential
factor on the audit fee. In fact, to ensure the validity of the results in the audit fee regression it is
22
always included as a control variable. In this section, two major articles studying the effect of the
client size on audit fees will be presented.
3.2.1.1 Palmrose (1986)
This paper by Palmrose (1986) was one of the earliest studies in the audit fee determinants. The main
research objective of Palmrose’s study was to detect whether the auditor’s size has a systematically
correlation with the audit fee. Additionally, Palmrose believes that a large sized client requires more
audit procedure and the increased audit effort would push up the audit fee to a higher level.
To test the assumption the study adopted a regression analysis. In this regression equation, the
dependent variable was the audit fee and the independent variables contain the client size, the number
of reports, the number of the audit locations, and the percentage reduction in the fees from the auditee
inputs, the ownership indicator variable, the report modification indicator, the industry specialist
indicator and the client industry indicator. However, the author did not specifically emphasize the
hypotheses in this paper.
The data was obtained from questionnaires which aimed at individuals ‘who were considered most
likely to be knowledgeable about the services of the public accounting firms’ (Palmrose, 1986, p.
102). The questionnaires were delivered to 1186 domestic public and non-public companies in the
U.S. during early November and mid-December 1981. Overall, there were 361 usable responses, at a
response rate of 30%, which is similar to the other survey studies performed regarding to audit fee
(Miller, 1978; Simunic, 1980).
The term LnAssets had a coefficient of .470 which was significant at α = .01. Among the independent
variables, the major explanatory variable in the pricing of the audit fee was the clients’ assets. This
result is in conformity with the prediction that the size of the audit client is significant positively
correlated with the audit fee.
3.2.1.2 Carson & Fargher (2007)
This study examined whether larger clients are charged with a higher fee. Their intuition was that
larger clients usually require more specialists to complete an audit engagement. Consequently, since
previous studies have showed that a specialization increases the premium charged (Ferguson, Francis,
& Stokes, 2003), increase the audit fee. Consequently, they expected that, to compensate the auditor’s
specialization, large clients would pay more audit fee. In this paper no hypotheses are signaled.
23
The data was collected based on the audit engagements performed in the year 1998, 1999 and in 2004
in Australia. The data from 1998 were used to test the research question. Since Price Waterhouse
Coopers and Lybrand merged in late 1997, which could potentially impacts their reputation and
pricing, the data from 1999 and 2004 was used as sensitivity analysis. The samples of 1998 and of
1999 were drawn from the Who Audits Australia database. This database contains the fees for audit
services and for non-audit services of the Australian listed companies. In addition, the sample
concerning 2004 was drawn from the University of New South Wales audit fee database.
The finding of this study showed that the clients located in the upper quintiles regarding to firm size
requires a significant higher audit fee. One explanation presented by the authors was that since their
auditors are designated the industry specialist larger clients are charged with a higher fee.
Consequently, the result was in conformity with the prediction that the audit fees are positively
correlated with the audit client size.
3.2.2 Auditors’ Business Risk
When determining the amount of audit fee, auditors should take the behavior of their client into
consideration. For instance, clients might be tempted to perform risky conducts to achieve their
objectives. These acts might create a wrong estimation of the accounting numbers. Since it is the
auditors’ responsibility to ensure that the annual financial statements are free of material
misstatements, risky conducts may increase the audit risk of the auditors. Consequently, auditors
would ask a compensation for the potential work needed to mitigate the audit risk. A number of
researches have examined whether a risky conduct of the audit clients could create a higher audit fee
(Bell et al., 2001; O’Keefe et al., 1994; Seetharaman, Gul, & Lynn, 2002). Since no consistent
measure exists of the term business risk, the results until now are mixed. In general, two possible
business risks exist which may affect the auditors. One is the current business risk that could cause
potential legal actions against the auditors in the future. For instance, auditors of risky clients face a
higher possibility of liability payments. However, a problem with this approach is that not a common
way exists to measure this possibility. Researchers exist who used a survey study to directly obtain the
auditors’ opinion about the influence of their perceived business risk on the audit fee (Bell et al.,
2001). The other possible business risk arises from misconducts that are not by definition illegal.
Based on the U.S. law those misconducts are totally legal and will not result in a misstated financial
statement (Lyon & Maher, 2005). However, they could harm the reputation of the audit firm
consequently decreases its attractiveness to existing and potential clients. To measure this type of
business risk researchers mostly use financial ratios (Seetharaman et al., 2002). In this section, based
on existing researches both types will be signaled.
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3.2.2.1 Seetharaman, Gul and Lynn (2002)
Seetharaman, Gul and Lynn studied the effect of the litigation risks of auditors on the audit fee. They
assume that auditors would be in serious trouble if they underestimate their legal liability exposure.
Legal actions against auditors do not only create liability payments, it represents the poor quality of
the services (Palmrose, 1988). Litigation claims would reduce an audit firm’s reputation which could
cause current or potential clients leave the audit firm. Consequently,
“the threat from litigation makes it incumbent upon auditors to continually assess their exposure to
lawsuits and to incorporate that assessment into the planning and pricing of audit services”
(Seetharaman et al., 2002, p. 92).
A reason exists to assume that auditors will charge a premium for the potential consequences of the
client’s litigation risk. The authors have not specifically formulated any hypotheses.
As a proxy to measure the auditors’ litigation risks, Seetharaman et al. chose to use the number of
legal environments in which audit clients are operating. .With the increase of the legal environments,
the company faces variant financial reporting standards, which increases the possible litigation
exposure. For instance, the “antifraud provision of the Securities Exchange Act of 1934 have a
transnational jurisdiction applied, in particular, to non-US auditors (Seetharaman et al., 2002, p. 92).
In other words, when Non-US companies are willing to issue equity in the US stock exchanges, they
are exposed to the liabilities based on the American securities laws. Consequently, it is expected that if
the clients are facing different legal environments an increase in the audit fee exists.
In order to test the hypotheses stated before, the authors used a regression model to examine whether
the auditors of U.K. firms charge a fee premium for their clients accessing the U.S. capital markets.
The data used in this study was obtained from the Financial Times Extel Company Analysis database,
the Bank of New York ADR database and the Disclosure’s Global Access database. After excluding
banks concerning the period 1996 up to and included 1998 they collected in total 3666 observations.
The results of the study showed that the UK auditors indeed charge a higher fee when their non-US
oriented clients have accessed the American market. However, it is worth noting that the measure of
the business risk used in this study is similar to the complexity measures in other audit fee studies,
namely the number of subsidiaries. The only difference is that the subsidiaries used in this study have
to face different legal environments. Consequently, it cannot be determined whether the increase in the
audit fee is due to the complexity, e.g. number of subsidiaries, or due of the different legal
environments.
25
3.2.2.2 Lyon and Maher (2005)
Lyon & Maher (2005) studied another type of business risk: the legal misconducts and its association
with the audit fee. As a proxy for the client misconduct, they employed clients’ bribery payments to
high level government officials. A company bribes governmental officials to achieve its own business
objectives. In a country where bribery is common, companies have to follow the rule of the game no
matter whether they are willing too. Based on a legal perspective, in 1974 in the U.S. bribery
payments were not illegal In fact, the SEC included a voluntary disclose program to request companies
to report their questionable payments to the governmental officials. Consequently, nearly 200
companies did report their bribery payments which are mostly paid to government officials in
developing countries. Based on the financial reporting perspective, the act of paying these bribes was
not an accounting issue if the amount was reported correctly. Hence, since bribery did not create any
misstatements auditors have the choice to accept this item. However, the bribery scandals attract
widespread media attention and may harm the reputation of the audit firm. To cover themselves in this
situation, auditors may need to spare more efforts in their audit to ensure their opinions about the
company are correct. This consequently will drive up the audit fee.
The authors developed the next hypothesis to test their assumption:
Hypothesis 1
To clients who pay bribes to foreign government official’s auditors charge higher fees than to clients
who do not pay bribes to foreign government officials.
To test the hypothesis a cross-sectional audit fee regression model was employed. A dummy variable
was introduced which is equal to 1 if the company did report bribery, otherwise 0. Besides generally
used control variables including clients’ size, audit complexity and auditor-client risk sharing, several
variables in addition that are correlated with bribe and might directly affect the cost of an audit are
included. These are geographical dispersion, percentage of assets in developing countries and
corruption index.
Data from 82 companies registered with the SEC were obtained from the Paton Accounting Center at
the University of Michigan, SEC 10-K reports, Moody’s, and Compustat in 1974.
The results showed that auditors, even though the misconduct is not illegal and does not cause any
financial misstatements, do allege corporate misconduct as risky business, and ask a higher audit fee.
3.3 Audit Client Complexity
26
Among the signaled determinants before, researchers in addition expect that audit client complexity is
correlated with the audit fee. The reason is that a complex client to complete the audit procedures
concerning the auditor will be more time-consuming (Hackenbrack & Knechel, 1997; Simunic, 1980).
Consequently, a complex client should be charged with a higher amount of audit fee. However, the
measurement of the client complexity across studies is different. In general, two streams of complexity
measure exist. On one hand, researchers assume that the client company structure can cause the
complexity. They used the number of subsidiaries as the proxy to represent it, a company with more
subsidiaries has decentralized operations and consequently is more complex concerning the audit (Hay
et al., 2006). On the other hand, researchers assume that complexity can be qualified as an industry
characteristic, which implies that some industries are more complex to the audit than others.
Consequently, to assess the complexity they used the Standard Industrial Classification (SIC) codes. In
the next section, two articles of each stream will be presented.
3.3.1 Sandra & Patrick (1996)
To have a better understanding of the components of the audit fee , Sandra & Patrick (1996) studied
the audit market in Hong Kong. Client complexity was one of their major interests, which was
measured by the number of subsidiaries. They believe that a company with more subsidiaries has more
diversified operations hence requiring more hours to complete the audit.
The authors in total formulated 10 hypotheses to confirm the audit fee drivers found in U.S. and in
U.K. studies in addition are valid to Hong Kong markets. Since the hypothesis 6 has been developed to
test the influence of the client complexity on the audit fee. This hypothesis will be given:
Hypotheses 6
Audit fee is positively associated with the number of principal subsidiaries in the group.
In order to test these hypotheses, the authors adopted a regression model with audit fee as the
dependent variable and several fee drivers as independent variables. Unfortunately, the authors in their
paper did not provide their regression equation.
The sample used concerns companies listed on the Stock Exchange of Hong Kong Limited from the
year 1992 and 1993. Since it is required for all listed companies in Hong Kong to disclose the amount
of auditors’ fee, the authors are able to obtain the data from the annual financial reports included in the
database Hong Kong City Hall Library. Companies were excluded if (1) no audit fee was disclosed,
some foreign companies are not required to report the audit fees; and (2) if they are from the banking
industry. Consequently, there were 313 companies for the year 1992 and 396 companies for year 1993,
which represent 76% and 83% of the population concerning the two years, respectively.
27
This study has proved that a significant correlation exists between the number of subsidiaries and the
audit fee required which suggests that the complexity in the audit pricing process is relevant.
3.3.2 Craswell, Francis and Taylor (1995)
One of the purposes of the study by Craswell, Francis and Taylor is to investigate whether the audit
specialization premium asked are industry-specific. Their motivation is that some industries require
different accounting technologies than other general industries. The accounting technology refers to a
company’s accounting system and the selection and the application of accounting policies to report
their activities.
“Crucial accounting policy issues concern the recognition and measurement of assets, liabilities, and
income arising from the firm’s economic activity. To the extent accounting technology is industry-
specific rather than generic, the firm’s agency/contracting problems and their method of resolution
via accounting will also have unique industry features” (Craswell et al., 1995, p. 300)
Based on that concerning the audit some industries are relatively more complex. When auditing in
these industries auditors need to have more industry specific knowledge. Hence, to compensate their
training and education investment they will charge a higher fee. The authors in total have developed
three hypotheses. The three hypotheses are formulated as followed:
Hypothesis 1
In those industries not having specialist auditors, Big 8 auditors will have higher audit fees than non-
Big 8 auditors.
Hypothesis 2
In industries having specialist auditors, non-specialist Big 8 auditors wilt have higher audit fees than
non-Big 8 auditors.
Hypothesis 3
In those industries having auditor specialists, specialist Big 8 auditors will have higher audit fees than
non-specialist Big 8 auditors.
The sample used in this study was hand-collected from the annual reports of 1484 companies listed on
the Australia Stock Exchange in the year 1987. The authors have classified 23 industry groups
whereby 9 of them are labeled as complex which require industry specialists to perform the audit. Out
of the 1484 listed companies 911 companies are regarded as complex.
28
The results showed that the auditors of complex industries in comparing with other general industries
indeed charge a premium.
3.4 Auditor Industry Specialization
Auditor industry specialization in the scientific auditing literature is considered as a major subject.
These specialists are important due to their contribution to a higher audit quality. For instance, an
experiment by Solomon, Shields, & Whittington (1999) showed that relative to other industries the
industry specialists have more accurate predictions of the potential financial statement errors in their
industries. In addition, industry specialized auditors are able to constrain the use of earnings
management and consequently increase the quality of the published earnings. Balsam, Krishnan, &
Yang (2003) found that companies that are audited by industry specialists have lower discretionary
accruals and a higher earnings response coefficient, which implies that the industry specialists are able
to improve the quality of the earnings. Additionally, Carcello & Nagy (2004) reported that a negative
correlation exists between the industry specialists and financial fraud. Based on the additional
trainings needed and the potential benefits of the specialized auditors, a reason exists to assume that
specialization would create a higher audit fee.
Although many researches on the auditors’ specialization exist, the definition and the designation of
the specialized auditors among the researchers are still not unified. Most researchers followed the
definition communicated by Palmrose (1986) which stated that the largest, the second and the third
largest suppliers can be qualified as specialists in each industry. This definition basically relies on the
within-industry market share approach which considered that an auditor is to be industry specialists if
he has a significant part of market share in that industry. An alternative approach is based on the
within-firm portfolio, which stated that an auditor can be qualified as a specialist for industries that
have the largest portfolio shares within the audit firm. The rationale behind this approach is that to an
audit firm, industry with the largest portfolio share generates the most revenues hence attracts the most
attention as well as efforts within the audit firm.
Moreover, in addition studies exist that provided evidence that auditor industry specialization does not
increase the audit fee. For instance, to improve their bargaining position audit firms are able to choose
for a differentiation strategy however, this does not necessarily creates higher audit fee. Casterella,
Francis, Lewis, & Walker (2004) provided explanation to this issue. Additionally, to increase the cost
efficiency, industry specialist are able to achieve economies of scale, which in turn could decrease the
audit fee (Bills, Jeter, & Stein, 2013).
3.4.1 Casterella, Francis, Lewis, & Walker (2004)
29
Casterella, Francis, Lewis, & Walker (2004) used Porter (1985) analysis of the competitive strategy to
understand an auditor’s choice of becoming industry specialists. They assessed industry specialization
as a differentiation strategy of audit firms which provides them sustainable competitive advantage
over other auditors; hence permit them a stronger bargaining position. However, because of their size
and the related revenues this bargaining power is much weaker particularly to large clients.
Consequently, large firms have a stronger influence in negotiating their audit fees. Hence, the authors
of this paper expected that small clients are more likely to pay a fee premium for industry specialists
than large clients.
The authors did not provide the development of the hypotheses. In order to test their assumption, they
adopted a regression model with audit fee as dependent variable and several fee drivers as independent
fee variables. Companies audited by Big 6 auditors and have a SIC code below 6000 excluding
financial institutions were selected and a survey questionnaire was sent in which were requested the
total audit fee, the auditor tenure, the number of subsidiaries and the comments of the nonrecurring
events that might influence the audit fee. At a usable response rate of 21%, information from 651
companies was collected. In addition, auditors with 20 percent or more market share within an
industry are labeled as industry specialist and clients with total assets less than $ 123 million are
labeled as small clients.
The result was in conformity with their prediction: compare to non-specialists specialists do require a
higher audit fee, and this is only applicable to smaller companies. For large clients, companies not
only do not pay an industry premium, in addition when comparing with smaller firms their audit fee is
relatively less.
3.4.2 Bills, Jeter & Stein (2013)
The authors of this article assumed that the auditors’ industry specialization could create a decrease in
the audit fee. Their argument was that companies of certain industries contain stable operations over
time, which creates industry homogeneity and consequently are more easily to audit than other
industries. Consequently, to obtain cost based competitive advantages concerning auditors it is more
attractive to stay within those industries with a greater homogeneity. In addition to benefit from
economies of scale for auditors it is more likely to stay within these industries. Consequently, industry
homogeneity enables industry specialists requiring lower audit compensation. An essential limitation
is that, since the audit firm cost data are not publicly available, the cost based competitive advantages
cannot be observed. The authors are only able to test whether the industry homogeneity creates a
decrease in audit fee for industry specialists.
30
In order to test their argumentation, the authors developed the next hypotheses:
Hypothesis 1
As evidenced by relatively lower fees for audits in industries with homogeneous operations, industry
specialization results in cost efficiencies.
Hypothesis 2
As evidenced by relatively lower fees for audits in complex industries with homogenous operations
industry specialization results in cost efficiencies.
The term homogeneity is classified following the study by Cairney & Young (2006) which measured
homogeneity by using the correlation of the differences in the year-to-year operating expenses for
companies within an specific industry. Since concurrent economic conditions are resulting in a
homogeneous reported financial impact in a relative way this measure reflects the underlying
similarity of the companies’ operations (Cairney & Young, 2006).
The sample was collected using the Audit Analytics and the Compustat. After excluding financial
industries and observations with only one company within an industry, 23852 firm-year observations
exist between fiscal years 2004 up to and included 2009.
The results suggested that the industry specialist require a fee premium for clients from non-
homogenous industries, but charged an incrementally lower fee to clients from homogenous industries.
In the sensitivity test, the authors found no significant differences in the audit quality concerning both
industries.
3.5 Hypotheses Development
Based on the examined prior research, the next hypotheses are formulated.
Audit client complexity in this thesis will be used as a determinant for the audit fee. This can be
caused by many factors. One of them is the industry in which the audit clients are operating. For
instance, the virtue of some industries requires special accounting policies, which increases the
difficulty and the audit hours to complete the audit. Consequently, clients in a typical complex
industry would be charged with a higher audit fee than clients in other general industries. In order to
test this assumption, the next hypothesis is developed:
Hypothesis 1
Higher complexity of the auditee would cause higher audit fee
31
The amount of industry specific knowledge could create either higher or lower audit fee. Because they
provide their personnel with additional trainings and educations in order to obtain industry specific
knowledge, to cover the extra costs, the audit firms may charge their clients a higher audit fee. In
addition, by their valuable industry specialized knowledge, auditors could gain a competitive
advantage over their competitors, hence charging a higher fee. On the other hand, industry specific
knowledge in addition could create a lower audit fee. Since the auditors have more knowledge about
the client’s industry, they are able to recognize the industry specific audit risks at an earlier stage.
Additionally, industry specialists are more likely to attract potential clients from their specialized
industry and in the end achieve economies of scales, which comparing with non-specialists could
create a fee discount. Hence to test which one is the case, and which correlation is significant the next
hypotheses are developed.
Hypothesis 2a
Industry specific knowledge would cause a higher audit fee
Hypothesis 2b
Industry specific knowledge would cause a low audit fee
Based on the approach that audit clients’ complexity could create a higher audit fee, the industry
specific knowledge of the auditors could reduce the relative complexity and increase the audit
efficiency hence create a decreased audit fee particularly for complex clients. Consequently, the next
hypothesis was developed:
Hypothesis 3
Industry specific knowledge decreases the effect of auditee’s complexity on the audit fee
3.6 Summary
The chapter provided a short analysis of several studies concerning the audit fee determinants. All
articles used a similar regression analysis to examine their assumed fee drivers and the most of them
are confirmed to have a statistically significant influence on the audit fee. These confirmed
determinants are also used in this thesis as control variables to eliminate their effect with the audit fee.
However, there are several differences between the articles which will be highlighted subsequently.
These differences did not cause opposite conclusions but are worthwhile to mention in order to
prevent a wrong implementation. First, the sample selection method is different since only a few
countries require a disclosure of the financial statements audit fee. Researchers of other countries need
to collect fee data by themselves, which consequently decrease the sample size and external validity.
Second, the differences of legal environments are not taken into consideration as well; different legal
32
environments may lead to a different approach for auditor in the planning phase regarding the client
business risk assessment. The conclusion then should be implemented more carefully. Last, the studies
mostly used a Big N term as a control variable since Big N audit firms mostly require a higher audit
fee due to their high audit quality. However, there have been series of mergers taken place whereby
the number of Big N firms reduced from eight to four. The existing studies are not able to capture this
effect into their analysis.
Concerning the auditor industry specialization, previous researches presented mixed results regarding
the effect of the auditor industry specialization on the level of the audit fee. All possible outcomes are
found: positive correlation (Casterella et al., 2004; Craswell et al., 1995) and no correlation (Palmrose,
1986). The conflict results are mainly due to the differences in periods, in the locations and especially
in the measurements of the specialization. It is not surprising if they use different standards for
measuring the specialization of the auditor that researchers have distinct findings. Because most
researches in studying the audit specialization follows the approach stated by Palmrose (1986), this
thesis will adapt the same definition: the auditors with large market shares are industry specialists.
In addition, although many researches have studied the association between the auditor industry
specialization and the audit fee using the U.S. data, the results of their studies do not represent the full
range of industries served by audit firms. Only particular industries were selected. For instance, the
survey studies conducted by Casterella et al., (2004) and Palmrose (1986) had a very limited coverage
even the response rate was quite high or the studies focused solely on certain industries. Since
companies were not obligated to report the fee for auditors this was the reason that they were not able
to obtain the audit fee data. Thanks to the Audit Analytics database, this thesis is going to extend the
range of the industries and study the impact of the auditor industry specialization on the association
between the audit client complexity and the audit fee.
The next chapter further comments the research design and the selected sample that are used to test the
hypotheses. The expectation is that the hypotheses will be verified by using statistical methods,
assuring that the auditor specialization will affect the association between client complexity and audit
fees.
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4. Research Design
4.1 Introduction
In order to test the developed hypotheses, this chapter will first present the research approach that is
adopted in this thesis. It starts with an overview of all the research approaches available for examining
the determinants of the audit fee and further elaborates on the methodology utilized in this thesis. To
demonstrate the casual relations between the dependent and the independent variables, a predictive
validity framework called Libby boxes are presented. Concerning the clarification and the
repeatability for further researches at the end, the method of data collection is presented. It is essential
to note that the theoretical background and the prior research in previous chapters will be applied and
as foundation service for this chapter.
4.2 Research Approaches in Accounting
Some educational books from the accounting field describe accounting researchers as ‘parasites’ who
prey on the work of existing scientific literature to generate their own findings (e.g. Brownwell, 1995,
p. 2). This is obviously an overstatement; however it is not completely false. The reason why
accounting researchers to a great extent rely on prior researches is that they have little theory or
specific research method of their own and only a few instruments exist to rely on within this scientific
topic (Smith, 2011).
Researches concerning accounting are mostly conducted to provide explanations and prediction for the
real market. In this field two major research approaches exist, quantitative and qualitative research.
The major difference between these two types is the likelihood to apply the results to real scenarios.
Quantitative researches use numerical evidence which permits researchers to construct complex
predictive models to test the correlation between two or more variables. However, one drawback is
that in most cases the quantitative data are not able to include the motives behind the correlation. In
other words, quantitative researches face the threats of the external validity. On the other hand, by
asking the actors for their intuitions and motives directly qualitative researches can mostly be used to
fill this gap. As a starting point to development a research question and a research methodology this
approach often requires a theory. Since there are limited theories for them to reply on, concerning
accounting researchers this creates difficulties. Consequently, the most accounting researches remain
quantitative researches.
Following the past studies, this thesis adopts a quantitative research method and examines the
association between the auditor industry specialization and the audit fee. The data are collected solely
from the databases Compustat and Audit Analytics.
34
4.2.1 Quantitative Researches
Several types of quantitative researches exist that can be performed. One way to distinguish them is
the data collection process. To ask the motive of the participants, researchers of survey studies mostly
use a questionnaire. Obviously, concerning this thesis, it is not possible to ask companies and their
audit firms in which way they determined the audit fee. When a large research sample is needed
additionally, survey research is costly and time consuming. Consequently, direct measurement of the
audit fee determinants through a survey in this thesis is not applicable. Alternatively, performing an
experiment is a way to conduct quantitative research. The nature of an experiment allows researchers
to manipulate diverse variables to test their correlation with the subject which is randomly assigned to
different groups (Abdel-Khalik & Ajinkya, 1979). Experiments are designed to establish the causal
relations between the dependent variable and the independent variables. The major advantage of an
experiment is having a high internal validity, which relies heavily on the researchers’ experiment
design ability by controlling the effect of all the possible influencing factors. Due to its high-control
environment, a laboratory experiment will achieve the highest level of internal validity; however, they
have an extremely low external validity which implies that the results are mostly not applicable in the
real world.
Till so far, to generate the outcome researches focused on the determinants of the audit fee mostly
used quantitative evidence. In addition studies exist that combined experiments together with evidence
partly collected by using questionnaires (Bell et al., 2001; Casterella et al., 2004; Palmrose, 1986). The
reason they combined different research methods is mostly due to the fact that some data were not
publicly available, and using questionnaire was the only possibility for obtaining such data. A major
disadvantage of a survey is the low response rate which significantly decreases the size of the sample.
Another method to conduct quantitative research, which in addition in this thesis will adopt, is desk
research. In order to perform causal analyses, desk research uses existing groups of subjects and data
that have been collected by databases. Thanks to the large databases like Compustat and Audit
Analytics, recent audit fee researchers are able to obtain a large sample size within a relatively short
time that h significant increases the reliability of the outcome.
4.3 Research Methodology
To better understand the development of the research methodology the research question is recalled:
Does the public auditor’s industry specialization influence the association between the audit fee and the audit
client complexity?
35
To answer the research question, to establish the degree of association between the variables in order
to develop an explanatory relationship between those variables in this thesis an OLS regression model
is adopted (Smith, 2011). An OLS regression measures the vertical deviation of points away from a
trend line and ensures that the sum of the squares of the distances to the trend line is as small as
possible. The trend line is in a linear form
Y=a+bX [3]
Which contains only one explanatory variable and a represents the Y-intercept and the corresponding
parameter of b is the slope of the line. Since b characterizes the relationship between the two variables,
it is the coefficient of interest.
Obviously concerning determining the audit fee more factors are involved. Consequently, this thesis
uses a multiple regression in which more than one explanatory variable exists. This equation has the
following form:
Y=a0+b1 X1+c2 X2+e [4]
In which the dependent variable Y is the audit fee and other explanatory variables are potential fee
determinants. In addition, the ‘interaction’ term in the regression model will be added that represents
the moderating effect of the auditor industry specialization on the association between the client
complexity and the audit fee. By adding the interaction term the regression equation is changed into
the next form:
Y=a0+b1 X1+c2 X2+d3 ( X1∗X2 )+e [5]
This product term ( X 1∗X2 ) represents the moderating effect of X2, which is the auditor industry
specialization, on the correlation between X1, client complexity, and Y, the audit fee. When the
coefficient d3 is statistically significant, X2 then has a significant moderating effect. In other words, the
correlation between X1 and Y is conditional on the value of X2.
The next section will provide the detailed components of the regression equation used in this study.
4.3.1 Measuring Client Complexity
Researchers labeled client complexity as a major factor that could influence the level of the audit fee.
A problem with the client complexity is that it cannot be observed directly. Past researchers often use
36
the number of subsidiaries to represent the complexity, which can be understood as the more
subsidiaries a client has the more time-consuming the audit will be. A drawback of this measurement
is that the researchers assume that the industry characteristics are not influencing a company’s
complexity, since some risks are typically associated with a certain industry, this is not the case (Arens
et al., 2013).
This thesis uses another approach explained by Bills, Jeter, & Stein (2013) that explains the
complexity of a client by its industry. An industry differs from others by its operation structures, and
consequently creating more difficulties to auditors to understand the business. The American Institute
of Certified Public Accountants (AICPA) periodically issues specified guidance to help auditors
handling complex audits across a variety of institutes. These guidance can be qualified as accounting
profession’s assessment of industries that increase the accounting complexities in the financial
reporting and the need of auditors for guidance as a supplement to the current accounting standards
(Bills et al., 2013). Consequently, the variable COMPLEX equals 1 for industries, based on two-digit
SIC codes, with a specific auditing and accounting guides, otherwise 0.
4.3.2 Measuring Auditor Industry Specialization
As signaled in chapter 3, this thesis follows the definition presented by Palmrose (1986) that used the
size of auditor’s within-industry market share as an indicator for the auditor industry specialization.
The market share is measured at a national level based on the audit fee within a two-digit SIC
category. The market share is calculated as follows:
MARKETSHAREk , i=∑j=1
J
TA kij
∑i=1
I
∑j=1
J
TAkij
[6]
Where,
MARKETSHAREk , i = the market share of auditor i of industry k.
TAkij = the size of client company j measured in total assets in the industry k audited by
auditor i.
J = the number of clients that auditor i serve in industry k and I is the number of
audit firms in industry k.
37
Following the approach by (Craswell et al., 1995), auditors with market share of more than 15% can
be labeled as industry specialists, and the variable SPEC equals 1. The assumption is that industry
specialists are able to differentiate themselves from non-specialists by receiving education for a
particular industry, and they are more likely to increase their market share to spread the investment
costs to more clients.
4.3.3 Control Variables
The regression model measures the effect of the auditor industry specialization on the audit fee. As
stated in the chapter 3, various factors can influence the level of the audit fee including the client size,
the business risk, the auditor identity and the geographic diversification. To examine their correlation
with the audit fee these factors in the test will be included as control variables. This section will focus
on these control variables.
Client Size (TA and TURNOVER)
In determining the level of the audit fee the client size is consistently confirmed to be the most
significant explanatory variable. To collect representative evidence, to review their business
operations and perform more audit procedures, auditors need to devote more time to large companies.
Consequently, it is reasonable to expect that client size would create an increase in the audit fee.
Most researchers used the total assets as a proxy to measure the client size. This measure is suitable
for auditors who adopt a balance sheet audit approach, which relies on the fact that verification of the
balance sheet items in addition indirectly verifies the net income (Simunic, 1980). However, when
companies have different age profiles of assets or the used accounting policy is different, this measure
could significantly vary between companies even with comparable sizes. To conquer the problem
signaled before, a measure of size based on the net turnover may be a better variable. This measure is
suitable for auditors who employ a transactions based audit approach, which verifies the turnover and
the net income directly. However, the turnover definition may vary between companies and industries.
For instance, the turnover for manufacturing companies may differ in concept from the definition used
by a financial institution. The regression model in this thesis as the measure for client size includes
both total assets (TA) and turnover (TURN). Doing consequently will eliminate the possibility of
multicollinearity of this two, consequently increases the predicting power of the coefficients of the
variables.
In addition, the economies of scale of the audit production and more sophisticated internal control
procedures, especially after SOX 404, within a large company suggest that the relationship between
the audit fee and the clients’ size is not likely to be linear. Consequently, this thesis follows other
38
researches (Bills et al., 2013; Palmrose, 1986; Seetharaman et al., 2002) by applying a log
transformation to the total assets and to the turnover to reflect the likelihood that proportional increase
function in the audit fees would create a decreasing function of the audit intensity.
Client Business Risk (QRATIO, DE, ROA and OPI)
In determining the level of the audit fee, auditors consider client business risk as an essential element.
Increased lawsuits against auditors for negligence upon business failure could create reputational
damages for the audit firms which could decrease the attractiveness to potential and to existed clients.
Consequently, to eliminate the likelihood of potential audit failures auditors of risky companies would
increase their efforts. Additionally, to compensate the longer working hours on performing extra audit
procedures on riskier areas they may charge a higher audit fee.
To represent the business risk, this thesis measures business risk by assessing the level of financial risk
of the client firm. To test the company’s financial ability following the research by Seetharaman, Gul,
& Lynn (2002) several variables are included. The next variables are included: quick ratio (QRATIO),
debt ratio (DE) and the return on assets (ROA). To measure a company’s ability to use its quick assets,
e.g. cash, cash equivalent, marketable securities and accounting receivable, to pay off its current
liability immediately, the quick ratio is adopted. The debt ratio is calculated as the total debt divided
by the total assets. Since the firm has high obligated payment a high debt ratio is associated with a
high financial risk. The return on assets (ROA) represents the profitability of a company relative to its
total assets. This represents how well the management is employing the company’s total assets in
realizing profit. Consequently, if auditors are taking the financial risks into consideration when
determining the audit fee, the coefficient on the DE is expected to be significantly positive and the
coefficients of QRATIO and ROA are expected to be significantly negative.
Additionally, the audit opinion (OPI) from previous fiscal year into the regression model is included.
An unqualified audit opinion represents that the company’s financial statements are free of material
misstatements. A departure from an unqualified opinion will definitely attract the auditor’s attention
thereby increasing their attention and their audit efforts. Consequently, a dichotomous variable is used
to represent the auditor’s opinion from the last fiscal year. Companies that received a qualified or an
adverse opinion (value = 1) last year are expected to have a higher fee this year.
Geographical Diversification (GeoDiv)
Geographical diversification is likely to be correlated with the level of the audit fee. Subsidiaries in
different countries have to comply with a variety of professional requirements for financial reporting
and disclosures which entails additional audit procedures. Consequently, clients with subsidiaries
across different countries are expected to pay a higher fee. This variable is presented with GeoDiv
39
which measures the number of geographical subsidiaries of the audit client. To conclude, the
regression model is summarized as follows:
LnAF=β0+ β1 LnTA+β2 LnTURNOVER+β3QRATIO+β4 DE+β5 ROA+β6 GeoDiv+β7 OPI+ β8COMPLEX +β9 SPE+β10(COMPLEX∗SPE)+e [7]
WhereLn AF = natural log of audit fee,Ln TURNOVER = natural log of turnover,QRATIO = the ratio of current assets less inventories to current liabilities,DE = the ratio of long-term debt to total assets,ROA = the ratio of net income to total assets,GeoDiv = number of geographical locations,OPI = indicator variable, 1 = qualified audit report, adverse opinion or disclaimer
of opinion,COMPLEX = indicator variable, 1 = complex industry,SPE = indicator variable, 1 = auditors are industry specialist, and e = error term.
To visualize the constructs of the research concepts and the underlying proxies used for the
measurement of the variables, the predictive framework of Libby (1981) is provided.
Figure 1 Libby Boxes
This framework provides several links between different variable which are explained subsequently.
Link 1 represents the external validity, which can be implied as the extent the results of this study can
be generalized for other studies. Link 2 and 3 represent the construct validity, which is the validity of
the proxies that can be used to measure the unobservable latent variables. Link 4 is the statistical
conclusion validity that reports whether a statistical conclusion can be drawn. Link 5 and link 6
40
represents the internal validity, which reflects the confidence about the inferences regarding the causal
relationship.
4.4 Sample Collection
The sample of U.S. publicly traded companies will be obtained from Compustat and from Audit
Analytics between 2007 up to and included 2013. The Compustat database will be used concerning the
annual financial information, e.g. balance sheet and income statement numbers, and the Audit
Analytics will used concerning the audit fee data. It is essential to note that the audit fee is solely the
fee paid for the financial statements audits.
Since the amount of data for geographical dispersion is limited, this data was obtained at first. To
collect the maximum amount data of segments, a search in the entire database of Compustat Historical
Segments was performed. At the same time, to use as a reference the Central Index Key (CIK) a code
of each selected company was included. By using the CIK codes, additional information was retrieved
from Compustat Fundamentals and from Audit Analytics. Hereafter, because they are missing
necessary data unusable firm observations are dropped in addition industries with less than 10 firm-
year observations are dropped as well. In addition, firms from financial industry are excluded (SIC
codes 6000-6999). Originally 13259 firm-year observations were collected, after the eliminations
10136 observations are kept for the thesis, see the next table.
Firm years observations collected from Compustat and Audit Analytics for the period 2007 - 2013 13259
-Missing necessary data (e.g. amongst others total assets and current liabilities) -1919
-Less than 10 firm-year observation within an industry -51
-Exclude SIC 6000-6999 -1153
Total sample 10136
Table 1 Data elimination procedure
Following the client complexity classification (Bills et al., 2013), companies from industries with
complicated operations or accounting requirements are labeled as complex. These industries are
presented in the next table; the industry specialists in addition are included.
SIC Codes (2-digits) Industry Description Specialized audit firms
1 Agricultural Production Crops PwC, EY, Deloitte
7 Agricultural Services KPMG
13 Oil And Gas Extraction PwC, EY, KPMG
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15 Building Construction EY, Deloitte
16 Heavy Construction EY
17 Construction Special PwC, EY, Deloitte
37 Transportation Equipment PwC, Deloitte, KPMG
45 Air Transportation EY
73 Business Services EY, Deloitte, KPMG
79 Amusement Recreation PwC, EY, KPMG
80 Health Services PwC, EY, Deloitte, KPMG
87 Engineering and Accounting Research PwC, EY, Deloitte, KPMGTable 2 Industry complexity classification and auditor specialization
This chapter describes the development of the research method that will be used to answer the research
question. Additionally, the procedure of sample selection and further classifications are provided. The
next chapter will analyze the selected sample in order to test the hypotheses and eventually answer the
research question.
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5. Results
5.1 Introduction
This chapter presents the results of the statistical investigation conducted to determine the impacts of
the auditor industry specialization on the association between the audit client complexity and the audit
fee among U.S. listed firms. First, to examine if the sample fulfills the requirements, the necessary
assumptions of the sample are tested. Second, the evidence of the regression analyses of the
determinants for the audit fee will be presented. Additionally, the evidence is interpreted to determine
whether the hypotheses should be accepted or rejected.
5.2 Assumptions for Regression Analysis
Several assumptions of multiple regressions exist that have to be fulfilled. A failure in one of the
assumptions will disturb the validity and the correctness of the regression model. All of these
assumptions are tested and necessary corrections are performed. The procedures are described in the
paragraph below.
5.2.1 The Assumptions
First, in order to ensure the validity of the research outcome, outliers should be detected and
eliminated. An outlier is an observation that does not follow the usual pattern of points, e.g. they are
far away from the predicted value. As they can overstate the coefficient of the determination (R2), the
linear regression technique is highly sensitive to outliers. The outliers are detected by observing the
standardized residuals which are greater than 3 times the standard deviation and by using statistical
test of Cook’s distance measure. Second, the sample should follow a normal distribution. To assess the
sample distribution this thesis uses a histogram and a Q_Q plot. Third, the assumption of
homoscedasticity will be checked. This assumption suggests that the residuals are equal for all values
of the dependent variable. The homoscedasticity is checked by using a scatter plot. Fourth, the
multicollinearity should be investigated. Multicollinearity occurs when two or more determinants are
highly correlated to each other. Multicollinearity is assessed by the inspection of the Pearson
correlations and independence of the residuals is assessed by using Durbin-Watson statistic. Finally,
the independent variables should be collectively linearly related to the dependent variables and each
independent variable should be particular linearly related to the dependent variable as well. This will
be tested by inspecting the partial regression plots. The categorical independent variables are ignored.
43
5.2.2 The Test of Assumptions
Observations with standardized residuals greater than ±3 standard deviation are treated as an outlier.
In total 36 observations exists with the standard deviation is more than 3 times large and consequently
should be labeled as outliers, however they are kept for a further investigation of the Cook’s Distance.
The Cook’s Distance estimates the influence of an observation when performing the regression
analysis. The outlier will be dropped if it is too influential as assessed by the Cook’s Distance.
Consequently, the maximum value of Cook’s Distance is 0,647, which suggests that no need exists to
perform any corrections as they are below the cutoff guideline of 1.
In order to be able to run inferential statistics, the sample should be normally distributed. This research
used two ways to check for this assumption, a histogram with a superimposed normal curve and a P-P
plot. The histogram is presented below.
Figure 2 Normality histogram LnAF
Based on the histogram can be observed that the standardized residuals appear to be normally
distributed. However, since it could be deceptive because their appearance can be largely dependent
on the selection of column width a solely inspection of the histogram for normally distribution might
not be enough. Since it is really essential that the residuals are normally distributed, as well to assess
the distribution a P-P plot is used. The P-P plot is presented on the next page.
44
Figure 3 Q_Q plot to assess normality
When the residuals are normally distributed, the points will be aligned along the diagonal line. Based
on this P-P plot, enough evidence exists to approve that the residuals are normally distributed.
The homoscedasticity is assessed by plotting the regression standardized residuals against the
regression standardized predicted values and inspect whether the residuals are equally spread over the
predicted values of the dependent variable. The scatterplot is showed below.
Figure 4 Scatterplot to assess homoscedasticity
Based on the scatterplot is visible that the points in the scatterplot are not equally spread, which
suggests that heteroscedasticity exists. This implies that the OLS estimations are not the Best Linear
Unbiased Estimators (BLUE), although the estimates of variance of coefficients can be biased,
however, it does not cause unbiased coefficients estimates Consequently, the results from this OLS
45
will still be able to provide an unbiased estimation of the relationship between the audit fee
determinants and the audit fee.
Next, the multicollinearity is assessed by an inspection of the Pearson correlation coefficients, which
are showed in the next table.
Table 3 Correlations among independent variables
Concerning this research design, a concern of multicollinearity exists among the total assets and the
turnover. It is natural to expect a company with high level of total assets will achieve a high amount of
turnover as well. Consequently, it is essential to perform multicollinearity test to understand their
correlations. A correlation above 0, 9 will normally be considered as highly correlated and corrections
should be performing. However, the correlation between total assets and turnover is 0,865, which is
below the cutoff guideline. Consequently, since all other variables are not highly correlated with each
other as well, no actions are needed. Additionally, independence of residuals exists, as assessed by a
Durbin-Watson statistic of 2,025, which belongs to the acceptable range of 1,5 until 2,5.
Since it is essential that the determinants of the audit fee should be collectively linearly related to the
audit fee and in addition each specific determinant is linearly related to the audit fee, the linear
relationship in addition is investigates. This is performing by the investigation of the scatterplot for
overall and partial regression plots. All of them are showing a linear relationship with the audit fee.
Categorical variables in this test are not included.
5.3 Mean Differences of Client Complexity and Industry Specialists
Independent-sample t tests are conducted to examine if a statistical difference exists in the means of
the audit fee between the complex and the noncomplex companies and between the specialists and the
nonspecialists. The outputs of the tests are provided subsequently.
Table 4 Independent t test complexity output
46
Table 5 Independent t test complexity output
Complex companies are paying more audit fee (M = 13,77, SD = 1,424) than noncomplex companies
(M = 13,65, SD = 0,999). However, as assessed by Levene’s test for equally of variances (p < ,0005),
the assumption of the homogeneity of variances was violated. Consequently, concerning further
analysis the results for unequal variances will be used. In average, the audit fee for complex
companies was 0,120, 95% [0,068 to 0,172] higher than the audit fee for noncomplex companies and a
statistically significant difference in the audit fee in average exists between complex and noncomplex
companies, t (5844,193) = 4,521, p < .0005. The output of the test can be found in Appendix 1.
In addition the same independent-samples t test was performed for the industry specialist group and
concerning the non-industry specialist group.
Table 6 Independent t test auditor specialization output
Table 7 Independent t test auditor specialization output
More audit fee was paid to industry specialists (M = 14,07, SD = 0,929) than to nonspecialist (M =
13,03, SD = 1,266). However, the Levene’s test shows that the assumption of homogeneity of
variances was violated (p < .0005). Consequently, the results for the unequal variances for the further
analysis are used. In average, the audit fee for industry specialists was 1,037, 95% [0,990 to 1,084]
higher than the audit fee for non-industry specialist and a statistically significant difference in the audit
fee in average exists between the industry specialists and the nonspecialist, t (5905,326) = 43,368, p
< .0005.
5.4 Association of Client Complexity and Industry Specialists
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The association between the client complexity and the industry specialists is measured by using the
chi-square test. This test determines whether the two dichotomous variables, e.g. complexity and
specialization, are statistically independent. The output of the test is provided subsequently.
Table 8 Chi-Square test complexity vs. specialization
Table 9 Chi-Square test complexity vs. specialization
All expected cell frequencies were greater than five which can be implied that a statistically significant
association exists between the client complexity and the auditor industry specialization when
determining the audit fee χ2 (1) = 321,137, p < ,0005.
5.5 Analysis of the Investigation Concerning the Audit Client Complexity and the
Auditor Industry Specialization
To determine the effect of the audit client complexity on the audit fee the next regression model will
be used.
ln A F=β0+β1 LnTA+β2 LnTURNOVER+β3QRATIO+β4 DE+ β5 ROA+ β6 GeoDiv+β7 OPI +β8COMPLEX+β9 SPE
5.5.1 Descriptive Statistics of the Regression Components
48
Table 10 Descriptive statistics regression components
WhereLn AF = natural log of audit fee,Ln TURNOVER = natural log of turnover,QRATIO = the ratio of current assets less inventories to current liabilities,DE = the ratio of long-term debt to total assets,ROA = the ratio of net income to total assets,GeoDiv = number of geographical locations,OPI = indicator variable, 1 = qualified audit report, adverse opinion or disclaimer
of opinion,COMPLEX = indicator variable, 1 = complex industry,SPE = indicator variable, 1 = auditors are industry specialist
The sample used consisted of 10136 firm-year observations between 2007 up to and included 2013.
The demonstrated features involve the basic statistical terms that are explained subsequently. “Mean”
presents the average value of the variables, while “Median” presents the median value of the variables.
“Std. Deviation” presents the deviation from the average value of the examined variables, “Min”
presents the lowest value of the variables, and “Max” is the maximum existing value of the variables.
The interested term is the LnAF with a mean of 13,69, which is amounted approximately to $ 882046.
3709 (36,6%) observations in the sample are considered as complex companies and the remaining
6427 (63,4%) is qualified as noncomplex companies. In addition, 6481 (63,9%) financial statements
audits are performed by an industry specialist and the remaining 3655 (36,1%) was perform by non-
specialist auditors.
The coefficient of determination, or the R2 value, is 0,751, which represents the proportion of variance
in the audit fee that can be explained by the assumed determinants. Consequently, the used
determinants explain 75% of the audit fee variability. However, since it is the expectation of the
population the R2 is based on the sample and the adjusted R2 is mostly qualified as more valuable. In
this case, the adjusted R2 is the same as the R2 value, which is equal to 0,750. Additionally, the F-ratio
shows that the determinants statistically significant predict the audit fee, F (9,10126) = 3384,491, p
< ,0005.
49
A table containing the estimated coefficients is presented below.
Table 11 Coefficients analysis without the interaction term
All coefficients are statistically significant at a confidence level of 95% except the QRATIO and DE.
The other coefficients are as expected to be either positively or negatively related with the audit fee.
The general form of the equation to predict audit fee is:
Ln audit fee = 10,659 + 0,299 × Ln total assets + 0,129 × Ln turnover + 0,012 × Debt Ratio + -0,001 ×
ROA + 0,001 × number of geographical subsidiaries + 0,222 × previous auditor’s opinion + 0,147 ×
client complexity + 0,234 × auditor industry specialization.
As expected, it is the client size that is the major determinant of the audit fee. This is consistent with
conclusions in the prior research. Since the coefficients are statistically significant positively
correlated with the audit fee, the results in addition proved evidence to accept hypothesis 1 and 2a.
Because the output is not providing any evidence that a negative correlation exists between the
auditor’s specialization and the audit fee, hypothesis 2b is rejected.
5.6 Interaction of Client Complexity and the Industry Specialists on the Audit Fee
To expand the understanding of the relationships among the variables in the model an interaction term
of complexity and industry specialists in the regression is added. This interaction term establishes the
relationship between the levels of complexity on the amount of the audit fee, whether or not with the
industry specialists auditor change. Consequently, this regression model is used:
LnAF=β0+ β1 LnTA+β2 LnTURNOVER+β3QRATIO+β4 DE+β5 ROA+β6 GeoDiv+β7 OPI+ β8COMPLEX +β9 SPE+β10(COMPLEX∗SPE)
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Where,Ln AF = natural log of audit fee,Ln TURNOVER = natural log of turnover,QRATIO = the ratio of current assets less inventories to current liabilities,DE = the ratio of long-term debt to total assets,ROA = the ratio of net income to total assets,GeoDiv = number of geographical locations,OPI = indicator variable, 1 = qualified audit report, adverse opinion or disclaimer
of opinion,COMPLEX = indicator variable, 1 = complex industry,SPE = indicator variable, 1 = auditors are industry specialist.
The coefficient of determination, or the R2 value, is 0,751, which was the same as the previous
regression outcome without the interaction term and represents the proportion of variance in the audit
fee that can be explained by the assumed determinants. And the adjusted R2 value is also 0,750 for the
population. The coefficients are presented in the table below:
Table 12 Coefficients analysis with the interaction term
All coefficients are statistically significant at a confidence level of 95% except the QRATIO and DE.
It is still the client size that is the major determinant of the audit fee and the changes in coefficients are
minimal except for the coefficients of COMPLEX and SPEC. In the table below a more detailed table
of the comparison will be presented. According to the outcome of the coefficients analysis, the general
form of the equation to predict audit fee is:
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Ln audit fee = 10,681 + 0,297 × Ln total assets + 0,130 × Ln turnover + 0,012 × Debt Ratio + -0,001 ×
ROA + 0,001 × number of geographical subsidiaries + 0,222 × previous auditor’s opinion + 0,110 ×
client complexity + 0,210 × auditor industry specialization + 0,062 × (client complexity × auditor
industry specialization).
The changes in the coefficients are presented in the table below:
Variableswithout interaction term with interaction term change
LnTA 0,299 0,297 -0,002LnTURNOVER 0,129 0,130 0,001QRATIO 0,000 0,000 0,000DE 0,012 0,012 0,000ROA -0,001 -0,001 0,000GeoDiv 0,001 0,001 0,000OPI 0,222 0,222 0,000COMPLEX 0,147 0,110 -0,037SPEC 0,234 0,210 -0,024COMPLEX × SPEC 0,062 0,062Constant 10,659 10,681 0,022
Unstandardized Coeffi cients
Table 13 Comparison of the changes after including the interaction term
After including the interaction term, a change in the interpretation of the coefficients exists. In the first
regression model no interaction term included, COMPLEX and SPEC would be interpreted as having
a unique effect on audit fee by respectively 0,147 and 0,234. However, the interactions imply that the
effect of client complexity on the audit fee is different for the different values of the auditor industry
specialization. Consequently, the unique contribution is not limited to complexity only; it depends on
the coefficient of the interaction term and the presence of the industry specialists. The unique effect of
complexity is now represented by 0,110 + 0,062 × specialization.
Consequently, for complex firms that do not have industry specialists, e.g. SPEC = 0, the effect of
complexity on audit fee will be 0,110 + 0,062 × 0 = 0,110. If complex firms do use specialized
auditors for their financial statements audit, which is situated by hypothesis 3, the effect of complexity
will be 0,110 + 0,062 × 1 = 0,172. This implies that the auditor industry specialization does not
decrease the effect of complexity on the audit fee; consequently hypothesis 3 will be rejected.
5.7 Discussion
The results from the regression analysis indicate that the audit client size is still the major audit fee
determinant as suggested by prior research. Interestingly, the auditor’s opinion seems to be an
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important fee determinant as well, which was not highlighted in the prior research. This may occur due
to the timing of the sample selection. The sample was selected between 2007 up to and included 2013,
where the financial crisis was taking place. This consequently leads to increased business risks and
auditors may possibly more sensitive to the outcome of the previous auditor opinion.
Next, there were several ways to assess the complexity and the most researches used the number of
subsidiaries to determine the level of complexity. However, this thesis takes the approach that
companies with complex operation routines are more complexity and consequently requires a higher
audit fee. The results show a statistic significant positively correlation between the complexity and the
audit fee. This result is in line with the study of Bills, Jeter, & Stein (2013), which used the same way
to measure the complexity.
Additionally, the measurement of industry specialization is a cutoff of 15% market share within a
particular industry, which was used by Craswell, Francis, & Taylor (1995). The difference with this
study (Craswell et al., 1995) is that thesis uses a larger amount of sample. Moreover, the range of the
sample was not only limited to particular industries thanks to the available databases. Consequently,
the established relationship between auditor industry specialization on the audit fee has more external
validity than prior research.
The outcome of the analysis confirms the positive association between the client complexity and the
audit fee. This association shows that companies with complex operations do require more efforts
from auditors. Additionally, a positive association is found between the auditor industry specialization
with the audit fee. These two associations are essential to answer the research question since they both
showed their statistically significant contribution to the audit fee. Otherwise it would be less
worthwhile to examine the impact of the auditor industry specialization on the audit fee. Furthermore,
since the auditor specialization does not lead to a decrease of the audit fee, it means that industry
specialists are not likely to provide fee discounts. This can be understood from two perspectives. First,
auditor industry specialists are not able to provide fee discounts since they are not able to achieve the
economies of scale. Second, it could also be that industry specialists are not willing to provide fee
discounts since they do have a sustainable competitive advantage over their competitors and they do
not need to provide any discounts to attract more clients. This positive correlation is in line with the
prior research stating the industry specialists are requiring a fee premium and is once again confirmed
from this thesis when evidence showed that auditor industry specialization do not decrease the positive
association between the client complexity and the audit fee. One possible explanation is that due to the
auditor industry specialization, auditors may provide audit service with a higher quality comparing
with nonspecialists. Since the audit quality is seen as more important than the amount of the audit fee,
53
which is explained based on the agency theory, companies are still likely to appoint an industry
specialists to perform their financial statements audit.
This thesis contributes to the existing literature in several aspects. First, due to the database Compustat
and Audit Analytics, this thesis is able to make use of a relatively large sample size comparing with
prior research, which will greatly increase the external validity of the results. Second, this thesis takes
another approach to measure a company’s complexity, namely by its industry auditing guidance. As
expected, this measurement has been proved to be positive statistically significant with the audit fee.
This measure of complexity together with the number of subsidiaries can be more validly to assess a
company’s complexity. Last, the previous auditor opinion showed up as one of the major positive fee
determinants. This could be the effect of the financial crisis during the sample period. During the
financial crisis, it is understandable that auditors are more sensitive when assessing a client business
risk. A departure from unqualified opinion from the last period would more quickly to attract the
current auditor’s attention.
5.8 Summary
This chapter was dedicated to provide empirical evidence and investigation to provide analysis of the
developed hypotheses in the previous chapter. In order to present scientific relevant results, two
statistical models were regressed. Additionally, several other analyses are performed to have a
thoughtful understanding of the variables of interests, e.g. client complexity and the auditor industry
specialization.
In order to explain the variability of the audit fee the regression model presented was test concerning
the validity of the chosen determinants. In this regression model, the underlying assumption is that
each determinant has its unique contribution regarding the audit fee. Descriptive statistics of the
sample of U.S. listed firms between 2007 up to and included 2013 were presented. Additionally, to
observe the relation between client complexity and auditor industry specialization the independent-
samples t tests and the chi-square test were conducted. The evidence showed that a significant
difference in the average audit fee exists among complex and non-complex companies. Moreover,
financial statements audited by industry specialists are in average more expensive than financial
statements audits by nonspecialists. In addition, the association between complexity and specialization
is examined and proved that a statistical significant association exists between complexity and
specialization. This provides a statistical argument to examine the interaction effect of the complexity
and of the specialization on audit fee.
The first regression model was generated without the interaction term. The most coefficients are
significantly correlated with the audit fee. The chosen determinants can be used as reliable predictors
54
of audit fee as the adjust R square value was as high as 0,750. As expected based on scientific prior
research the most influential factor is the client size. Based on this regression, can be determine that
both client complexity and auditor industry specialization are statistically significant positively
correlated with the audit fee. This implies that a complex company can drive up the audit fee and in
addition it is more expensive to appoint industry specialists to conduct the financial statements audit.
Since the coefficients of these variables are statistically significant, hypothesis 1 and 2a can be
accepted. Hypothesis 2b, which predicts a negative correlation between the industry specialist and
audit fee, consequently need to be rejected.
The second regression model was generated including the interaction term. This term is included to
examine whether the effect of the client complexity on the audit fee is different for different values of
auditor industry specialization. This can be implied as follows: based on the first regression it is
obvious that client complexity will drive up the audit fee, and the interaction term is included to
determine if an industry specialized auditor could affect the proportional contribution of complexity
on the audit fee as analyzed by the first regression. This is incorporated in hypothesis 3 that states that
the auditor industry specialization would decrease the association between the audit client complexity
and the audit fee. The second regression with the interaction has rejected this hypothesis. Conversely,
with the presence of an industry specialist, complex companies are required even to pay more fees for
their financial statements audit. This can be implied as an allocation of their training and education
costs by audit firms for developing industry specific knowledge to their clients.
55
6. Conclusion
6.1 Introduction
This chapter presents the final part of this thesis. First, a summary of the research is provided, which
includes the motivation, the research design. Additionally, the conclusion of the research is
emphasized and in order to prevent an incorrect understanding of the research results the limitations
are highlighted. The final paragraph provides recommendations for further research.
6.2 Research Summary
Auditors should estimate the nature and the magnitude of the evidence needed to mitigate the
uncertainty of an audit failure, a large amount of evidence is requiring more efforts from auditors and
would drive up the audit fee. However, it is not clear which factors, e.g. fee determinants, are directly
influencing the expected audit efforts and in which proportion. In order to have a good understanding
how the audit fee should be set, it is essential to determine the determinants of the audit fee, which are
relevant to both audit firms and their clients.
To indicate which factors as determinants can be qualified that systematically affect the level of the
audit fee, an extensive body of scientific researchers have been performed. Among these determinants,
the audit client complexity and the auditor industry specialization knowledge are confirmed to be the
determinants of the audit fee. The audit client complexity is mostly measured by the number of
subsidiaries and the specialization is measured by the market share of an audit firm within a particular
industry. This thesis uses the same measure for the auditor specialization and takes another approach
to measure the complexity by using a dichotomous variable, namely companies within industries with
specified audit guidance can be qualified as complex.
The first regression model confirms the positive correlation between the client complexity and the
auditor specialization with the audit fee. Next, to determine the interaction effect of the auditor
industry specialization on the association between the audit client complexity and the audit fee a
regression model with an interaction term is used. The coefficient analysis proved evidence that
industry specialists are requiring a higher fee for complex industries, which confirms the impact of
auditor industry specialization on the association between the audit client complexity and the audit fee.
6.3 Conclusions
This thesis is set out to determine the correlations of the audit fee determinants on the audit fee based
on the audit pricing model (Simunic, 1980). This study was designed to investigate whether the
investigation of auditor industry specialization will affect the association of the client complexity on
56
the audit fee. There are arguments that industry specialization will either increase or decrease the audit
fee. Specialization could increase the audit fee when audit firms choose to allocate the education costs
to their clients. On the other hands, specialization enables audit firms to obtain a sustainable
competitive position and achieve economies of scale within the particular industry and eventually
provide clients with a cost advantage of the audit fee.
The empirical evidence suggests that the level of auditor industry specialization is affecting the
association between the client complexity and the audit fee. More specifically, the presence of the
industry specialists will not reduce the contribution of the client complexity to the audit fee.
Consequently, it is more costly to appoint an industry specialist as their auditor for complex
companies. This can be interpreted that audit firms are likely to let their clients pay for the education
investment or they are not able to achieve economies of scale yet.
6.4 Limitations
One limitation of this thesis is that the selected sample only consisted of US publicly listed companies.
Since US publicly listed companies are required to publish an audited report of internal control after
the implementation of Sarbanes-Oxley Section 404. This could influence the external validity. Since
the external financial auditors are required to perform more tests of controls, this might increases the
audit fee. However, since the entire sample companies are US publicly listed, the effect of this in this
cannot be controlled. A proper comparison with private companies could determine the contribution of
SOX 404 to the level of the audit fees.
The next limitation is that the assumption of homoscedasticity has been violated. This consequently
leads to a model uncertainty that varies from observation to observation. Since the omitted effects are
not represented by included independent variables, e.g. fee determinants, the heteroscedasticity error
may be caused by one or more omitted variables. A solution to solve this problem may be to find and
include the omitted variables in the regression model.
Another limitation occurs from the timing of the sample selection. The sample was obtaining between
2007 up to and included 2013. During this period, the financial crisis would impact the economic
environment as a whole; this might have its influence on the audit fee. However, due to the availability
of the data, it was not possible to control for this effect.
The next limitation is the reduced external validity due to the measure of complexity. This thesis
assessed the complexity of a company based on the published auditing guidance for particular industry
by the AICPA. Because the operations of these industries are complicated the AICPA guidance is
helpful. One of the reasons of this complication can be the special financial reporting requirements set
by the government. However, these requirements may not be required by other countries and complex
57
companies in the U.S. do not necessarily have to be complex in other legal environments.
Consequently, the results of this thesis should be carefully implemented to other countries than the
U.S.
6.5 Suggestions for Further Research
Several recommendations for further research follow based on the limitations presented in the
previous paragraph.
As the implementation of SOX 404 might have impact on the audit fee. To highlight its effect
private companies could be included in the sample selection.
In order to avoid a violation of the heteroscedasticity assumption in further research the
independent variables should be chosen with great care.
In order to determine whether a financial crisis in addition impacts the audit fee, the sample
could include both the financial crisis and the non-financial crisis periods. This might deliver
usable information to both the companies and the audit firms concerning determining their
audit fee.
When determining a company’s complexity the accounting policies should be considered
carefully.
58
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Appendix
1.
Year
Author(s) Object of the study
Sample Methodology Outcome
1986 Palmrose To detect whether the auditor’s size has a systematically correlation with the audit fee. Palmrose believes that a large sized client requires more audit procedure, symbol by a larger audit team. The increased audit effort would push up the audit fee to a higher level.
361 firms from a questionnaire in the U.S.in 1981.
Regression model
The term LnAssets had a coefficient of .470 which was significant at α = .01. Among the independent variables, the major explanatory variable in the pricing of the audit fee was the clients’ assets. This result is in conformity with the prediction that the size of the audit client is significant positively correlated with the audit fee.
1995 Craswell, Francis & Taylor
To investigate whether the audit specialization premium asked are industry-specific.
1484 companies listed on Australia Stock Exchange in 1987
Regression model
The auditors of complex industries in comparing with other general industries indeed charge a premium.
1996 Sandra & Patrick
To test whether a company with more subsidiaries has more diversified operations hence requiring more hours to complete the audit.
709 firm observations from companies in Hong Kong in 1992 and 1993
Regression model
A significant correlation exists between the number of subsidiaries and the audit fee required which suggests that the complexity in the audit pricing process is relevant.
2002 Seetharaman, Gul & Lynn
To test whether auditors will charge a premium for the potential consequences of client’s litigation risk.
3666 firm-year observation of UK firms which are listed on the U.S. stock exchanges in 1996-1998
Regression model
UK auditors indeed charge a higher fee when their non-US oriented clients have accessed the American market.
2004 Casterella, Francis, Lewis, & Walker
To prove evidence that small clients are more likely to pay a fee premium for industry specialists than large clients.
Information from 651 companies was collected by questionnaire in 2002
Regression model
Compare to non-specialists specialists do require a higher audit fee, and this is only applicable to smaller companies. For large clients, companies not only do
62
not pay an industry premium, in addition their audit fee is relatively less when comparing with smaller firms.
2005 Lyon & Maher
Bribery scandals attract widespread media attention and may harm the reputation of the audit firm. To cover themselves in this situation, auditors may need to spare more efforts in their audit to ensure their opinions about the company are correct. This consequently will drive up the audit fee.
82 companies registered with the SEC in the U.S. in 1974
Regression model
Auditors do allege corporate misconduct as risky business, and ask them for a higher audit fee, even though the misconduct is not illegal and does not cause any financial misstatements.
2007 Carson & Fargher
To test the assumption that larger clients usually require more specialists to complete an audit engagement consequently increase the audit fee since previous studies have showed that a specialization increases the premium charged.
1712 firm-years between 1994 and 2004 from Australia obtained from Who Audits Australia database.
Regression model
The clients located in the upper quintiles regarding to firm size requires a significant higher audit fee. One explanation presented by the authors was that since their auditors are designated the industry specialist larger clients are charged with a higher fee. Consequently, the result was in conformity with the prediction that the audit fees are positively correlated with the audit client size.
2013 Bills, Jeter, & Stein
To confirm that auditor industry specialization could lead to a decrease in audit fee.
23852 firm-year observation between fiscal years 2004 and 2009
Regression model
Industry specialist requires a fee premium for clients from non-homogenous industries, but charged an incrementally lower fee to clients from homogenous industries.
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