supply chain specialization and audit...

Supply Chain Specialization and Audit Fees*

Hsihui Chang

Drexel University

Philadelphia, PA 19104

[email protected]

Hsin-Chi Chen

I-Shou University

Dashu Township, Taiwan 840

[email protected]

Jengfang Chen

National Cheng Kung University

Tainan, Taiwan 701

[email protected]

Sungsoo Kim

Rutgers University

Camden, NJ 08102

[email protected]

First draft: August 2010

Last revised: December 2011

* The authors acknowledge the comments of Steve Balsam, Carl Hertrich, James

Karan, Zenghui Liu, Michael Paz, Kateryna Polozkova, Mark Vargus, Beth Vermeer,

Eddie Werner and seminar participants at Drexel University, Rutgers University,

National Chung Hsin University, and National Taiwan University. The paper is

previously titled “The Effects of Supply-Chain Knowledge Spillover on Audit

Pricing”.

Supply Chain Specialization and Audit Fees

Abstract: This study examines the association between auditor supply chain

specialization and audit fees. Analyzing data from Audit Analytics for the seven

year period from 2003-2009, we find that audit fees are negatively associated with the

joint supplier-buyer- related supply chain specialization and buyer-related supply

chain specialization. This indicates that the auditor offers reduced audit fees to its

clients within a supply chain when the auditor also audits its client’s suppliers and

major buyers or its client’s major buyers, but not its client’s suppliers alone. In

addition, we find that these audit fee discounts are greater when the auditor has supply

chain expertise at the office level than when the auditor has supply chain expertise at

the national firm level. Our findings are robust to the use of alternate measures of

supply chain expertise as well as the analysis of the restricted sample comprising only

supply chain companies.

Keywords: Supply chain specialization, Audit fees, Suppliers, Major buyers

Data Availability: Data are publicly available.

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I. INTRODUCTION

Audit research has extensively studied the topic of audit pricing. Earlier studies

investigate the effect of auditor’s brand name (Francis 1984; Francis and Simon 1987;

Palmrose 1986), non-audit services provision (Palmrose 1986; Simunic 1984) and

new engagements (Simon and Francis 1988) on audit fees. Later, numerous studies

focus on the effect of industry specialization on audit fees in order to separate the

industry specialist premium from the general brand name premium (Craswell et al.

1995; Ferguson and Stokes 2002). These studies argue that auditors have incentives

to make costly investments in industry-specific knowledge to become industry

specialists because these investments could prevent competitors from easily achieving

comparable degrees of specialization and hence enable the auditors to earn rents on

their specialization (Craswell et al. 1995; Ferguson and Stokes 2002). By using a

national “firm-wide” market share measure to infer industry expertise, some studies

document that auditor firms with industry expertise earn a fee premium (Ferguson et

al. 2003; DeFond et al. 2000) while other studies find little evidence of a fee premium

for industry specialists (Palmrose 1986; Ferguson and Stokes 2002).

Recently, this line of research has been extended to examine the industry

specialist premium measure at the national (firm-wide) level vs. the office

(city-specific) level. Ferguson et al. (2003) document that the industry specialist

premium exists only when the auditor is both the national and the city-specific

industry leader. Similarly, Francis et al. (2005) find that Big 5 auditors charge

industry specialist premiums when they are jointly the national industry leaders and

the city-specific industry leaders. However, while Francis et al. (2005) observe no

evidence of national industry leaders earning an industry specialist premium without

also being city-specific industry leaders, they find inconclusive results with respect to

firms which are solely city-specific industry leaders.

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The central issue relating to firm-wide and office-specific industry expertise

research is whether an auditor being an industry specialist extracts an industry

specialist premium and disseminates it to other audit offices within the national

network. In contrast, we in this study evaluate whether an auditor, being a supply

chain specialist who audits a client and that client’s supply chain partners such as its

suppliers, major buyers, or both, shares cost savings from knowledge spillover and

expertise transferability with the client in such a supply chain. While knowledge

spillover and expertise transferability that lead to improved audit efficiencies and cost

savings (e.g., less efforts and lower billable hours and, thus, lower audit fees) are

possible within a supply chain, whether the auditor indeed shares cost savings with

the clients in a supply chain is an important empirical issue which is not yet being

studied. To fill in this void, we investigate whether the audit firm charges lower fees

when the auditor is a supply chain specialist who possesses knowledge and expertise

developed from simultaneously auditing significant partners within a supply chain.

A supply chain is a network of associated organizations that work together, and

in competition with other such networks, to produce value for its end-user customers.

By sharing resources and information and eliminating duplications, firms linked in a

supply chain facilitate the rapid flow of information enabling a smooth product flow

through the chain (Agrawal and Pak 2001). Firms such as Wal-Mart and Boeing

actively work with their suppliers to improve the processes and methodologies

followed by the suppliers and reduce inefficiencies within the supply chain (Chang et

al. 2009). This use of common information resources and sharing of knowledge

along the supply chain may have implications for audit fees to the extent that partners

within a particular supply chain are audited by the same audit firm. However, the

extant audit fees research exclusively considers the impact of the auditor’s expertise

on audit pricing horizontally among industry peers, thus far ignoring the potential

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impact of the auditor’s knowledge spillover and expertise transferability in the client’s

vertical relationship with its supply chain partners on audit pricing. We empirically

examine this issue. Specifically, we contend that an auditor accumulates and

develops its supply chain expertise by auditing suppliers, major buyers, or both within

the supply chain, and that the resulting improved efficiency from knowledge spillover

and expertise transferability enables the auditor to share cost savings with its supply

chain clients via reduced audit fees. Therefore, the first objective of our study is to

examine whether an auditor with supply chain (i.e. supplier-related, buyer-related, or

joint supplier-buyer-related) expertise offers audit fee discounts to clients within such

a supply chain.

As described earlier, recent audit fee research investigates city (office-specific)

reputation as a distinct value that is separate from national (firm-wide) industry

expertise. For instance, Ferguson et al. (2003) find that there is an average audit fee

premium of 24 percent associated with industry expertise when an auditor is both the

city-specific industry leader and one of the top two firms nation-wide in the industry.

However, they do not find any evidence supporting that the top two firms nationally

earn a premium in cities where they are not city leaders. Similarly, Francis et al.

(2005) find that national leaders do not earn a premium, but that joint national and

city-specific leaders as well as city-specific leaders do.1 Viewed in its entirety, the

city-level analyses seem to suggest that the pricing of industry expertise is primarily

based on office-level industry leadership in city-specific audit markets. Under the

office-level perspective of expertise, an auditor’s industry knowledge is indelibly tied

1 In a different context, Reichelt and Wang (2010) report that auditors who are both city

leaders and national leaders provide higher quality audits, measured by abnormal accruals,

the likelihood of meeting and beating forecasted EPS by one penny, and issuance of a going

concern audit opinion. These findings are consistent with industry expertise based on city-

and national-level metrics yielding higher quality audits and fee premiums.

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to unique personnel and is acquired through experience working with individual

clients at the local office level (Ferguson et al. 2003). Following the same logic, the

second objective of our study is to investigate whether firm-wide and/or city-specific

supply chain expertise impact audit fees.

Analyzing audit fee disclosure data between 2003-2009, after controlling for

audit quality, industry specialist premiums and other common determinants of audit

fees, we find that audit fees are negatively associated with the joint

supplier-buyer-related supply chain specialization and buyer-related supply chain

specialization. This indicates that the auditor offers reduced audit fees to its clients

within a supply chain when the auditor also audits its client’s suppliers and major

buyers or its client’s major buyers, but not its client’s suppliers alone. In addition,

we find that these audit fee discounts are greater when the auditor has supply chain

expertise at the office level than when the auditor has supply chain expertise at the

national firm level. This finding is in part conceptually parallel, but in opposite

direction, to that of Francis et al. (2005) in that they find fee premiums for auditors

that are both national and city-specific industry specialists and we observe fee

discounts for auditors with the joint supplier-buyer-related supply chain expertise.

Taken together, the evidence suggests that the joint supplier-buyer-related supply

chain expertise and buyer-related supply chain expertise at the office level are key

drivers of discounted audit fees.

The remainder of the paper is organized as follows. The next section provides a

brief review of the related literature and develops the hypotheses. Section III

describes our empirical estimation including the sample selection and model

specifications. Section IV presents and discusses empirical results. Finally,

Section V concludes the study by summarizing research findings, discussing

managerial implications to accounting firms as well as other service professions, and

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offering some directions for future research.

II. Literature Review and Hypotheses Development

As auditors accumulate knowledge and develop expertise in certain industries it

is also common for them to engage their clients’ suppliers and buyers as well (Levinthal

and Fichman 1988). Drawing upon the prevalent research in the supply chain context,

we test whether an auditor offers reduced fees to a client when the client’s suppliers,

major buyers, or both are also audited by the auditor. Auditors accumulate knowledge

and develop expertise about a supply chain through audit engagements with their

clients and their clients’ supply chain partners. It is expected to be mutually beneficial

to both auditors and clients as they can share the cost savings from the improved audit

efficiency stemming from supply chain knowledge spillover and expertise

transferability.

Specifically, as auditors learn and accumulate relationship-specific knowledge

among clients and clients’ suppliers or major buyers, they can enhance the efficiency

of audit planning, substantive testing and risk assessment due to their better

understanding of the client’s operations environment. Having been engaged with

clients’ supply chain partners the auditor would also have enhanced capabilities to

gauge the client’s business model including operation processes and practices. In

addition to this relationship-specific knowledge the auditors would gain

transaction-specific knowledge relating to product financing arrangements, long-term

lease contracts, or joint ventures or consortiums among supply chain members.

Knowledge spillover and expertise transferability gained from such relationship- and

transaction-specific experiences from the client’s supply chain partners are expected

to produce scale economies in audit engagements and result in lower audit fees as the

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auditors pass on cost savings to their clients. Therefore, for an auditor having supply

chain expertise who provides attestation services to the financial statements of a client

in that supply chain, the auditor could improve its audit efficiency by capitalizing on

its supply chain knowledge and expertise. The knowledge spillover and expertise

transferability are expected to result in less audit work and lower audit fees.

Accordingly, we specify our first set of hypotheses as follows:

H1a: Ceteris paribus, audit fees are lower for a client having the same audit firm

as its suppliers.

H1b: Ceteris paribus, audit fees are lower for a client having the same audit firm as its major buyers.

H1c: Ceteris paribus, audit fees are lower for a client having the same audit firm as both its suppliers and major buyers.

Auditing is a knowledge-based professional service. Under the office-level

perspective of auditor expertise, an auditor’s industry knowledge is considered to

reside in unique office personnel and is acquired through experience working with

individual clients at local offices (Solomon et al. 1999; Ferguson et al. 2003).

Therefore, such an auditor’s expertise is less likely to be transferable across offices

because the audit firm's investment in expertise is in fact an investment in the human

capital of its professional staff. In other words, the expertise derived from the

human capital investment tends to be both client- and office-specific (Francis et al.

2005). Although some aspects of expertise can be captured by the audit firm and

distributed to its other local offices through the audit firm’s network (e.g. general

audit programs and databases and internal benchmarking of best practices), in-depth

client knowledge is indelibly tied to the audit firm's individual experts, and is limited

to the specific engagements on which these professionals work. Further, auditors

typically continue to service clients in one locale over time (Francis et al. 2005).

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Following the local office viewpoint from prior literature, we posit that if the client’s

suppliers, the client’s major buyers, or the client’s suppliers and major buyers are also

audited by the same local office of the audit firm, the extent of the supply chain

knowledge spillover and expertise transferability will be larger than when the client’s

suppliers, the client’s major buyers, or the client’s suppliers and major buyers are

audited by different local offices of the audit firm. This could result in less audit

efforts and lower billable hours and, thus, lower audit fees. Therefore, we expect the

auditor with supply chain expertise at the office level to offer greater audit fee

discounts to its clients as stated in the following hypotheses:

H2a: Ceteris paribus, audit fee discounts are greater for a client having the same

audit office as its suppliers than for a client having different audit offices

from its suppliers.

H2b: Ceteris paribus, audit fee discounts are greater for a client having the same

audit office as its major buyers than for a client having different audit

offices from its major buyers.

H2c: Ceteris paribus, audit fee discounts are greater for a client having the

same audit office as both its suppliers and major buyers than for a client

having different audit offices from both its suppliers and major buyers.

III. Empirical Estimation

Sample Selection

Our sample is restricted to U.S. publicly listed companies audited by the Big 4

auditors. We obtain financial statement data from Compustat, and auditor fee data

from annual proxy statements filed with the Securities and Exchange Commission

(SEC) and available electronically from Audit Analytics. As shown in Panel A of

Table 1, we start with 57,635 firm-year observations having audit fee data and the

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location of the auditor’s city from Audit Analytics for the seven year period from

20032 to 2009.

[Inset Table 1 about here]

After merging the Audit Analytics sample with Compustat, we remove 19,438

firm-year observations that have audit fee data from Audit Analytics but could not be

matched by CIK to the Compustat annual files. We remove another 9,347 firm-year

observations with missing Compustat data for model variables and/or observations

related to foreign-based firms. We exclude 10,420 financial institutions (SIC codes

6000 to 6999) because of their dissimilar nature to other industries (Francis et al. 2005;

Reichelt and Wang 2010). In addition, we delete 2,150 firm-year observations for

which the auditors are not located in one of 280 metropolitan statistical areas (MSAs)

defined in the U. S. 2003 Census. To ensure that city-level industry specialization is

not determined by a single observation in a city-industry-fiscal year combination, we

exclude 2,062 city-level industry observations in those city-industry-year

combinations in which there are fewer than two companies (Francis et al. 2005;

Reichelt and Wang 2010). This results in a total of 13,218 unique firm-year

observations including both supply chain and non-supply chain companies in our final

sample.

In order to examine the effect of supplier-buyer expertise on audit pricing, we

further identify a firm's suppliers and main buyers. Financial Accounting Standard

Board (FASB)3 mandates that public firms disclose information about their major

buyer(s) to enhance the decision-usefulness of financial reporting. Under ASC

2 Our test period starts from 2003 to remove the possible effect of the Arthur Andersen

collapse on the results. 3 FASB did not specify the materiality threshold, although a 10 percent guideline has gained

the support of practice.

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280-10-54-42 of FASB, if a buyer accounts for 10 percent or more of a firm’s total

sales, the firm is required to report the name and the total sales made to that buyer in

its 10-K statements. This information is compiled in the Compustat Customer

Segment Database. However, ASC 280-10-54-42 does not specify how the name

should be declared and hence there is no uniformity in the way these buyer firms are

named.4 Additionally, there is no identifier code. Thus, the information cannot be

used without significant processing and data cleaning. Following Fee and Thomas

(2004), we first compare the firm names given under the customer column in the

Customer Segment Database with the exact firm name obtained from the Compustat

database. We compare the two sets of names by using a word matching software

code which analyzes the individual letters of the firm names in the two datasets and

arrives at possible matches. The strength of the match is indicated in terms of

percentages, with 100% being a perfect match. Due to the algorithm used, it is

possible for one firm’s name from the Customer Segment Database to have several

matches in the Compustat database. When this happens, we go through the list

visually to identify the best possible match using online resources. Once the list is

complete, we do a further visual check to ensure that the matches are as accurate as

possible. After identifying the supply chain relationship from the 13,218 firm-year

observations, we find 1,683 (=1,246+437) and 3,455 (=3,018+437) firm-year

observations that have at least one supplier and one major buyer. Of those, 458,

1,032, and 56 firm-year observations are audited by the same audit firms as their

suppliers only, their major buyers only, and both their suppliers and major buyers,

respectively.

Estimation Models

4 We work backward from buyer data to identify suppliers.

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We specify a cross-sectional audit fee regression model, similar to that used in

prior audit fee studies, to estimate the association between supply chain specialist and

audit fees (Simunic 1980; Francis 1984; Ferguson and Stokes 2002). In this

regression model, we consider a set of variables to control for cross-sectional

differences in factors that affect audit fees such as industry specialization, client size,

audit complexity, audit-client risk sharing, and audit quality. Prior studies indicate

that audit fee regression models have good explanatory power and have been robust

across different samples, time periods, countries and sensitivity analyses for model

misspecification (Chan et al. 1993). Specifically, we estimate the following

regression model to evaluate hypotheses H1a, H1b and H1c:5

LNFEE = α0 + α1 S_Audit_F + α2 B_Audit_F + α3 SB_Audit_F + α4 SPEC_F

+ α5 SPEC_O + α6 SPEC_FO + α7 LNTA + α8 EMPLOY+ α9 LNBS

+ α10 LNGS + α11 INVREC +α12FOREIGN + α13 EXORD + α14 LOSS

+ α15 LEV + α16 ROA + α17 ISSUE + α18 BTM + α19 ABSPMA

+ ∑year dummy + ∑ industry dummy + ε (1)

where:

LNFEE = natural logarithm of audit fees6

S_Audit_F = 1 if the company has the same audit firm as its suppliers; 0 otherwise

B_Audit_F = 1 if the company has the same audit firm as its major buyers; 0

otherwise

SB_Audit_F = 1 if the company has the same audit firm as both its suppliers and major

buyers; 0 otherwise

SPEC_F = 1 if the firm is audited by an auditor that is defined as a national

industry specialist but not a city industry specialist.

SPEC_O = 1 if the firm is audited by an auditor that is defined as a city industry

specialist but not a national industry specialist.

5 To mitigate the effect of potential outliers, we winsorize continuous variables at the 1st and

99th percentiles. 6 We use log of audit fees to be consistent with the previous literature, e.g., Fields et al.

(2004), Abbott et al. (2003), and Mayhew and Wilkins (2003).

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SPEC_FO = 1 if a company is audited by an auditor that is defined as both a national

and a city industry specialist.

LNTA = natural log of total assets

EMPLOY = square root of the number of employees

LNBS = natural log of one plus number of business segments

LNGS = natural log of one plus number of geographic segments

INVREC = inventory and receivables divided by total assets

FOREIGN = 1 if the firm pays any foreign income tax, 0 otherwise

EXORD = 1 if the firm reports any extraordinary gains or losses, 0 otherwise

LOSS = an indicator variable taking the value of 1 if net income is negative and

0 otherwise

LEV = leverage measured as the total liabilities divided by total assets

ROA = return on assets (income before extraordinary items divided by average

total assets)

ISSUE = 1 if the sum of debt or equity issued during the year is more than 5

percent of the total assets, 0 otherwise

BTM = book-to-market ratio

ABSPMA = absolute value of abnormal accruals. The abnormal accruals is

measured by adjusting for firm-performance

To evaluate hypotheses H2a, H2b and H2c, we specify the following model for

estimation:

LNFEE = α0 + β1 S_Audit_O + β2 S_Audit_nO + β3 C_Audit_O

+ β4 C_Audit_nO + β5 SB_Audit_O + β6 SB_Audit_nO + β7 SPEC_F

+ β8 SPEC_O + β9 SPEC_FO + β10 LNTA + β11 EMPLOY + β12 LNBS

+ β13 LNGS + β14 INVREC+ β15FOREIGN + β16 EXORD + β17 LOSS

+ β18 LEV+ β19 ROA + β20 ISSUE + β21 BTM + β22 ABSPMA

+ ∑year dummy + ∑ industry dummy + ε (2)

where

S_Audit_O = the percentage of purchases by the firm that were made from its

suppliers having the same audit office as the firm.

S_Audit_nO = the percentage of purchases by the firm that were made from its

suppliers having the same audit firm but not at the same audit office as

the firm

B_Audit_O = the percentage of sales by the firm that were sold to its major buyers

having the same audit office as the firm.

B_Audit_nO = the percentage of sales by the firm that were sold to its major buyers

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having the same audit firm but not at the same audit office as the firm

All other variables are as defined earlier in (1).7

Note that all of our models control for the determinants of audit fees previously

found in the audit fee literature. These determinants include proxies for auditors’

industry expertise, client size, audit complexity, audit-client risk sharing, and audit

quality. We define an auditor as a national (city) industry specialist if it has the

largest annual market share in an industry, based on the two-digit SIC category

(Reichelt and Wang 2010). Following Simunic (1980) and Choi et al. (2010), we

control for client size (LNTA and EMPLOY), the scope of business (LNBS and LNGS),

and the operating complexity of the client (INVREC, FOREIGN, and EXORD). We

also include LOSS, LEV and ROA to control for client-specific risk to be undertaken

by auditors since previous literature indicates that auditors charge higher fees for risky

clients (Simunic and Stein 1996). In order to capture the effect of a client’s growth

ability on audit fees, we include ISSUE and BTM in our model. Highly growing firms

are more likely to raise capital by external financing activities such as issuing equity

or bonds. These external financing activities require that such firms solicit more

audit and non-audit services than low-growth firms (Reynolds et al. 2004). Finally,

we include the absolute value of abnormal accruals ABSPMA in our estimation

models as a control variable for audit quality since Choi et al. (2010) indicate that

auditors may charge those firms with abnormally high discretionary accruals higher

audit fees.

VI. Empirical Results

Univariate Analysis

7 Variable definitions are summarized in the Appendix.

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Table 2 provides descriptive statistics for the variables used in our regression

analyses. Mean and median values of audit fee are almost the same, suggesting that

audit fees are normally distributed. The number of firm-year observations for firms

having the same auditor as its major buyers (B_Audit_F) is more than twice as large

as that for firms having the same auditor as its supplier (S_Audit_F). All other

control variables show descriptive statistics which are qualitatively consistent with

prior literature.


In Table 3 we present the Pearson correlation coefficients between audit fees and

the independent variables in our empirical estimation models. As we observe from

Table 2, the correlation coefficient between LNFEE and each of the control variables

is statistically significant, suggesting that audit fees are significantly associated with

all the control variables. However, we note that the pair-wise correlation among our

explanatory variables is not very high in its magnitude with the correlation between

LNTA and EMPLOY of 0.696 being the highest. This high correlation is expected

because large firms tend to hire more employees.


Multiple Regression Results

We first examine the association between supply chain specialist and audit fees

at the audit firm level. Regression results are reported in Table 4. The model has

an adjusted R2 of 0.79, which is in line with prior research, and indicates statistically

significant explanatory power for the models. As expected, we find a positive

association between audit fees and industry specialist. Specifically, the auditor

charges a premium when it is a city-level industry specialist or when it is a joint

national-level and city-level industry specialist. This later result is consistent with

the finding of Francis et al. (2005). We also find that audit fees increase with the

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size and risk of the audit client as well as the complexity of the audit, that is, both

LNTA and EMPLOY are positive and significant. INVREC is positive and significant.

Except LNBS, all proxies for audit complexities and client risks (i.e., LNGS, EXORD,

LOSS, LEV, and ISSUE) are positive and significant, consistent with prior literature.

ROA is negative and significant, which means that profitable audit clients (ROA) pose

less audit risk to the auditor. The absolute value of performance adjusted

discretionary accruals (ABSPMA) variable is statistically significant, suggesting that

the quality of earnings is a factor in the audit fee context after controlling for other

previously found audit fee determinants.


From Table 4 we see that the coefficient for S_Audit_F is positive (0.0194), but

not statistically significant at a conventional level. This suggests that the auditor

does not offer discounted audit fees when its client’s suppliers are also audited by the

auditor at the firm level. Therefore, our hypothesis H1a is not supported. In

contrast, the coefficient of B_Audit_F is negatively significant at p=0.01 (coefficient

is -0.0395), indicating that the auditor indeed offers discounted audit fees if its client’s

major buyers are also audited by the auditor. Evidently, this confirms our hypothesis

H1b. Likewise, the coefficient of SB_Audit_F is -0.1620 which is statistically

significant at p=0.00, suggesting that the auditor offers reduced audit fees to its client

when the auditor also audits both that client’s suppliers and major buyers. This

supports hypothesis H1c. Collectively, our results indicate that auditors who possess

the joint supplier-buyer-related supply chain expertise and who have major

buyer-related supply chain expertise charge lower audit fees to their clients in the

supply chain. However, auditors having only supplier-related supply chain expertise

do not offer fee discounts to their clients in the supply chain. One explanation for

this finding is that knowledge spillover and expertise transferability is greater for

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buyer-related supply chain expertise than for supplier-related supply chain expertise.8

Next, we evaluate the pricing effect of supply chain expertise at the national vs.

the city-specific level. Results in Table 5 show that the coefficient on the

supplier-related supply chain expertise at the audit firm level (B_Audit_nO) and at the

office level (B_Audit_O) is insignificantly positive (coefficient is 0.0217 and 0.0098,

respectively). This indicates that the auditor does not offer reduced fees even when

the auditor also performs audit services at the office level to its client’s suppliers. In

contrast, the coefficient for the buyer-related supply chain expertise at the audit firm

level (B_Audit_nO) and at the office level (B_Audit_O) is statistically significant at 5

% levels, indicating that auditors with buyer-related supply chain expertise charge

lower fees to their clients in the supply chain. Using the procedure described in

Craswell et al. (1995, p.307), the coefficient on the variable B_Audit_O (0.1146)

represents an average audit fee discount of 11.46%. Similarly, the coefficient on the

variable B_Audit_nO is -0.0318, representing an average fee discount of only 3.18%.

Taken together, this suggests an office-level fee discount of 8.28% (=11.46%-3.18%),

almost 2.5 times as much as the firm-level effect of 3.18%. Further, the coefficient

on the joint supplier-buyer-related supply chain specialization at the audit firm level

(SB_Audit_nO) and at the office level (SB_Audit_O) is significantly negative

(coefficient is -0.1491 and -0.5061, respectively), indicating that the auditor offers

reduced fees when the auditor also audits both the client’s suppliers and major buyers

in the supply chain. The difference in coefficients is -0.3570 (=-0.5061+0.1491),

indicating an additional office level fee discount of 35.7% when the auditor retains

joint supplier-buyer-related supply chain expertise.

8 Major buyers are generally large relative to their suppliers because suppliers typically

produce a limited number of products, whereas buyers purchase multiple products from a

number of suppliers (e.g. Retail giants like WalMart).

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Results of F-tests reported at the bottom of Table 5 indicate that there is no fee

difference (p=0.41) between a client having the same audit office as its suppliers and a

client having a different audit office from its suppliers. This evidence does not

support hypothesis H2a. However, the audit fee discount is significantly larger

(p=0.04) when the client’s major buyers are audited by the same audit office as

opposed to when they are not. This suggests that the audit fee discount is larger

when the client’s auditor maintains a buyer-related supply chain specialization at the

office level (B_Audit_O) compared to such a specialization at the firm level

(B_Audit_nO). Therefore, our hypothesis H2b is supported. Similarly, F-test

results indicate that the auditor charges significantly lower (p=0.01) fees when the

auditor audits its client’s suppliers and major buyers at the same office compared to

that at different offices. This supports our hypothesis H2c. Collectively, our results

imply that the auditor’s buyer-related supply chain expertise at the office level plays a

more important role in audit pricing than such expertise at the firm-wide level.

Alternative Measures of Supply Chain Expertise

To evaluate the robustness of our results to alternative definitions of supply chain

expertise, we replace our indicator variables, S_Audit_O, S_Audit_nO,

B_Audit_O,B_Audit_nO,SB_Audit_O and SB_Audit_nO, with continuous variables

aS_Audit_O, aS_Audit_nO, aB_Audit_O, aB_Audit_nO, aSB_Audit_O and

aSB_Audit_nO, respectively. We define aB_Audit_O (aB_Audit_nO)9 as the sum of

the percentage of firm sales that are made to its major buyers if the firm and its major

9 If major buyer 1 and major buyer 2 have the same audit office as the firm, and the firm sells

10 percent and 20 percent of its sales to buyer 1 and buyer 2 separately, then the B_Audit_O is

equal to 0.3 (=0.1+0.2).

17

buyers are audited by the same (different) audit office, aS_Audit_O (aS_Audit_nO)10

as the sum of the percentage of firm purchases that are made from its suppliers if the

firm and its suppliers are audited by the same (different) audit office, and

aSB_Audit_O (aSB_Audit_nO) as the sum of the percentage of firm purchases and

sales that are made from its suppliers and major buyers if the firm and its suppliers

and major buyers are audited by the same (different) audit office. By using the

percentage of sales (purchases) as our proxy for supply chain expertise, we

incorporate the magnitude of sales (purchases) volume between a client and its

suppliers (major buyers). As can be observed from Table 6, the results are consistent

with those reported earlier in Table 5, indicating the robustness of our empirical

results to the use of alternative measures of supply chain expertise. In fact, these

results are, as expected, even stronger than those reported in Table 5, possibly

ascribed to the refinement of the supply chain expertise measurement.


Reduced Sample Tests

To ensure our inferences are reasonable, we limit our sample to those companies

in supply chains. Since the characteristics of firms with supply chain relationships

might be different from those without supply chain relationships, we retain only those

4,701 (=1,246+3,108+437) firm-year observations that belong to a supply chain from

our final sample. Next, we run our estimation models again on the reduced sample

to re-evaluate our hypotheses. The results are presented in Table 7. Clearly, these

results are very similar to those discussed earlier in Table 5 and Table 6 when the full

sample is included in our estimation, indicating the robustness of our results.

10 If supplier 1 and supplier 2 have the same audit office as the firm, and the sales by supplier

1 and supplier 2 to the firm are $10 and $20, respectively, then S_Audit_O is the ratio of $30

divided by the cost of goods sold of the firm.

18


Finally, we examine the variance inflation factors (VIFs) for all the regressions.

While a variance inflation factor in excess of 10 indicates that multicollinearity may

be unduly influencing least squares estimates, we find none of the VIFs approach this

level. In fact, none of the VIFs approach the conservative threshold of 3, the highest

being 2.80. Thus, it does not appear that our results are influenced by

multicollinearity among independent variables.

V. Summary and Conclusion

A large stream of research in the audit literature has investigated the association

between auditor industry expertise and audit fees. Empirical evidence indicates that

auditors charge industry specialist premiums especially when the auditor is both the

national and city-specific industry leader. Extending the notion of audit specialist to

the supply chain context, we examine the association between supply chain

specialization and audit fees for U.S. firms over the 2003-2009 period. Unlike the

mixed findings of prior studies regarding the association between industry specialist

and audit fees, we find that auditors with supply chain expertise charge discounted

audit fees to their clients when auditors also audit their clients’ suppliers and major

buyers or their clients’ major buyers alone. These audit fee discounts are

significantly greater for the supply chain expertise derived from the office level. Our

findings are robust to the use of alternative measures of supply chain expertise as well

as to the analysis of a restricted sample comprising only supply chain companies.

Our findings imply the existence of knowledge spillover and expertise transferability

of auditors with expertise in a specific supply chain at the office level. We propose

that supply chain specialists share their cost savings resulting from improved

19

efficiencies with their clients by charging lower audit fees to these clients within such

a supply chain.

To date, accounting researchers have focused on the effect of auditors’

expertise on audit pricing among industry peers (horizontal relationship). In contrast,

we examine the impact of auditor expertise on audit fees among client supply chain

partners (vertical relationship). Specifically, we investigate whether the auditor’s

supply chain knowledge and expertise derived from auditing its client’s suppliers

and/or major buyers affects audit fees charged to the client. Therefore, our study

contributes to the extant literature by examining the effect of auditor supply chain

specialization on audit fees. We report the impact of the client’s supplier-buyer

relationship on the audit fees charged by the auditor after controlling for the auditor’s

firm-wide and city-specific industry specialist premiums, audit quality and other

contextual variables. Our results inidicate that the audit fee discount is largely

attributable to buyer-related supply chain expertise derived from audits with the client’s

major buyers rather than to supplier-related supply chain expertise resulted from

engagements with the client’s suppliers. Our findings have important managerial

implications for fee determination in other professions such as consulting services and

legal services.

Future research can extend this study to further investigate whether the audit fee

discount differs when all supply chain partners are in the same industry as opposed to

when all or some of them are in different industries. For instance, the automobile

industy involves parts suppliers, automobile manufacturers, and dealerships which

sellautomobiles to the customer. As a result, industry specialist audtiors are more

likely to be auditors of the auto parts suppliers (eg. AC Delco), the automobile

manufacturers (e.g. Ford), and possibly the automobile dealer. However, other supply

chain partners may be in different industries, such as those from manufacturing and

20

retailing (e.g. Proctor and Gamble, and WalMart have supply chain partners in various

industries). Thus, industry specialist audtiors may be less likely to audit supply chain

partners in different industries. In addition, future research can investigate how

auditor supply chain expertise affects client selection as well as audit quality.

21

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23

Appendix

Variable Definitions

LNFEE = natural log of audit fees paid to the auditor

S_Audit_F = 1 if the company has the same audit firm as its suppliers; 0

otherwise

B_Audit_F = 1 if the company has the same audit firm as its major buyers; 0

otherwise

SB_Audit_F = 1 if the company has the same audit firm as both its suppliers

and major buyers; 0 otherwise

S_Audit_O = 1 if the company has the same audit firm at the office level as

its suppliers; 0 otherwise

S_Audit_nO = 1 if the company has the same audit firm at the firm level, but

not at the office level, as its suppliers; 0 otherwise

B_Audit_O = 1 if the company has the same audit firm at the office level as

its major buyers; 0 otherwise

B_Audit_nO = 1 if the company has the same audit firm at the firm level, but

not at the office level, as its major buyers; 0 otherwise

SB_Audit_O = 1 if the company has the same audit firm at the office level as

both its suppliers and major buyers; 0 otherwise

SB_Audit_nO = 1 if the company has the same audit firm at the firm level, but

not at the office level, as both its suppliers and majorbuyers; 0

otherwise

SPEC_F = 1 if the company’s auditor is an industry specialist only at the

firm level; 0 otherwise. The auditor is an industry specialist at

the firm level if it has the largest annual market share in an

industry in a national audit market

SPEC_O = 1 if company’s auditor is an industry specialist only at the

office level; 0 otherwise. The auditor is an industry specialist

at the office level if it has the largest annual market share in an

industry in a city audit market

SPEC_FO = 1 if company’s auditor is an industry specialist both at the firm

level and at the office level. The auditor is an industry

specialist at both the firm level and the office level if it has the

largest annual market share in an industry, in both the national

and the city audit market

LNTA = natural log of total assets

EMPLOY = square root of the number of employees

LNBS = natural log of one plus number of business segments

24

LNGS = natural log of one plus number of geographic segments

INVREC = inventory and receivables divided by total assets

FOREIGN = 1 if the company pays any foreign income tax; 0 otherwise

EXORD = 1 if the company reports any extraordinary gains or losses; 0

otherwise

LOSS = 1 if the company reports a loss; 0 otherwise

LEV = leverage, measured as total liabilities divided by total assets

ROA = return on assets (income before extraordinary items divided by

average total assets)

ISSUE = 1 if the sum of debt or equity issued during the year is more

than 5% of the total assets; 0 otherwise

BTM = book-to-market ratio

ABSPMA = absolute value of the performance-matched abnormal accruals

25

TABLE 1: Sample Composition

Panel A: Sample selection

Big 4 observations with positive audit fees 56,635

Less:

Companies unavailable on Compustat -19,438

Missing Compustat data or foreign firms -9,347

Financial sector -10,420

Unmatched city (MSA) code -2,150

City-industry-fiscal year combinations less than 2 observations -2,062

Final sample size 13,218

Panel B: Industry distribution

SIC Description All

Obs with

suppliers

only

Obs with

buyers

only

Obs with

both suppliers

& buyers

Obs having

same

auditor as

its suppliers

only

Obs having

same auditor

as its buyers

only

Obs having same

auditor as both its

suppliers &

buyers

10-14 Mining 568 64 196 30 8 66 3

15-17 Construction 55 5 8 0 0 3 0

20-39 Manufacturing 6,964 565 1,977 333 276 720 41

40-49 Transportation, Comm., Electronics.

1441 230 166 9 53 61 0

50-51 Wholesale Trade 435 67 58 32 30 11 3

52-59 Retail Trade 820 191 23 5 55 4 2

70-89 Service 2,887 121 579 28 35 165 7

99 Others 48 3 11 0 1 2 0

Total 13,218 1,246 3,018 437 458 1,032 56

26

TABLE 2: Descriptive Statistics for Variables (N=13,218)

Variable Mean Std.

1% 25% Median 75% 99% Dev.

LNFEE 13.7811 1.1298 11.3022 12.9923 13.7645 14.5261 16.6067

S_Audit_F 0.0347 0.1829 0 0 0 0 1

B_Audit_F 0.0781 0.2683 0 0 0 0 1

SB_Audit_F 0.0043 0.0650 0 0 0 0 0

S_Audit_O 0.0064 0.0795 0 0 0 0 0

S_Audit_nO 0.0283 0.1658 0 0 0 0 1

B_Audit_O 0.0072 0.0845 0 0 0 0 0

B_Audit_nO 0.0709 0.2566 0 0 0 0 1

SB_Audit_O 0.0002 0.0123 0 0 0 0 0

SB_Audit_nO 0.0041 0.0638 0 0 0 0 0

SPEC_F 0.1164 0.3208 0 0 0 0 1

SPEC_O 0.3231 0.4677 0 0 0 1 1

SPEC_FO 0.1867 0.3897 0 0 0 0 1

LNTA 6.3051 1.8389 2.2004 4.9965 6.2766 7.5721 10.5873

EMPLOY 1.9442 1.9775 0.1 0.6099 1.2578 2.4968 10.502

LNBS 1.5465 0.7583 0 1.0986 1.3863 2.1972 3.0445

LNGS 1.5779 0.9232 0 1.0986 1.6094 2.3026 3.3673

INVREC 0.2289 0.1725 0 0.0894 0.1975 0.3294 0.7618

FOREIGN 0.5403 0.4984 0 0 1 1 1

EXORD 0.0381 0.1913 0 0 0 0 1

LOSS 0.3237 0.4679 0 0 0 1 1

LEV 0.4646 0.2237 0.0574 0.2802 0.4658 0.6325 0.9547

ROA -0.0231 0.2044 -0.9561 -0.0343 0.035 0.0784 0.2713

ISSUE 0.4865 0.4998 0 0 0 1 1

BTM 1.1515 5.5587 -28.5428 0.6073 0.9781 1.7715 29.7638

ABSPMA 0.0834 0.0983 0.0009 0.0223 0.0511 0.1039 0.5765

Variable definitions appear in the Appendix.

27

TABLE 3: Pearson Correlation Matrix

LNFEE S_Audit_F B_Audit_F SB_Audit_F SPEC_F SPEC_O SPEC_FO LNTA EMPLOY LNBS LNGS INVREC FOREIGN EXORD LOSS LEV ROA ISSUE BTM

S_Audit_F 0.045a

B_Audit_F -0.066a -0.015c SB_Audit_F 0.049a -0.003 -0.011 SPEC_F -0.057a -0.001 0.036a -0.003 SPEC_O 0.094a 0.008 0.015c 0.031a -0.251a SPEC_FO 0.147a 0.01 -0.012 -0.013 -0.174a -0.331a LNTA 0.801a 0.054a -0.065a 0.073a -0.063a 0.079a 0.171a EMPLOY 0.616a 0.057a -0.078a 0.04a -0.061a 0.063a 0.175a 0.696a LNBS 0.116a 0.013 -0.018b -0.003 -0.02b 0.002 0.061a 0.163a 0.13a LNGS 0.285a 0.015c 0.004 0.026a -0.017b 0.021b 0.028a 0.203a 0.171a 0.246a INVREC 0.019b 0.017c -0.035a -0.008 -0.006 -0.046a 0.032a -0.072a 0.095a 0.104a 0.224a FOREIGN 0.422a 0.011 -0.014 0.022b 0.004 0.031a -0.009 0.288a 0.256a 0.014 0.477a 0.203a EXORD 0.074a 0.019b 0.016c -0.002 0.01 0.017c 0.019b 0.132a 0.082a 0.086a 0.034a -0.041a -0.02b LOSS -0.222a -0.014 0.069a -0.014c 0.017c -0.001 -0.083a -0.369a -0.28a -0.13a -0.137a -0.159a -0.156a -0.019b LEV 0.36a 0.039a -0.063a -0.002 -0.034a 0.035a 0.119a 0.41a 0.313a 0.099a 0.003 0.087a -0.001 0.1a -0.038a ROA 0.261a 0.01 -0.048a 0.026a 0.011 -0.002 0.069a 0.423a 0.281a 0.115a 0.242a 0.228a 0.252a 0.03a -0.682a 0.036a ISSUE 0.053a 0.012 -0.017b -0.007 -0.018b 0.016c 0.024a 0.081a 0.027a 0.018b -0.078a -0.081a -0.093a 0.019b 0.049a 0.23a -0.13a BTM 0.025a -0.002 -0.016c -0.006 0.005 -0.002 0.013 0.036a 0.015c 0.015c 0.025a 0.008 0.004 0.026a -0.043a -0.005 0.029a -0.009 ABSPMA -0.185a 0.002 0.054a -0.009 0.002 -0.009 -0.063a -0.285a -0.21a -0.073a -0.078a -0.073a -0.099a -0.033a 0.276a -0.076a -0.379a 0.055a -0.033a

a,b and c represents 1%, 5% and 10% significance levels, respectively; Variable definitions appear in the Appendix.

28

TABLE 4: Supply Chain Specialist and Audit Fees – Firm Level Analysis

Dependent variable: LNFEE

Variables Pred. Sign Coef. p-value

INTERCEPT ? 10.9556 0.00

S_Audit - 0.0194 0.20

B_Audit - -0.0395 0.01

SB_Audit - -0.1620 0.00

SPEC_F + 0.0325 0.02

SPEC_O + 0.0853 0.00

SPEC_FO + 0.1068 0.00

LNTA + 0.4151 0.00

EMPLOY + 0.0729 0.00

LNBS + -0.0003 0.48

LNGS + 0.0769 0.00

INVREC + 0.3264 0.00

FOREIGN + 0.2744 0.00

EXORD + 0.1355 0.00

LOSS + 0.1023 0.00

LEV + 0.2827 0.00

ROA - -0.4438 0.00

ISSUE + 0.0185 0.03

BTM - 0.0002 0.38

ABSPMA + 0.1112 0.02

Industry dummies Yes

Year dummies Yes

Adj. R-square 0.79


29

TABLE 5: Supply Chain Specialist and Audit Fees – Office Level Analysis

Dependent variable: LNFEE.


INTERCEPT ? 10.9559 0.00

S_Audit_O - 0.0098 0.42

S_Audit_No - 0.0217 0.19

B_Audit_O - -0.1146 0.01

B_Audit_nO - -0.0318 0.04

SB_Audit_O - -0.5061 0.00

SB_Audit_nO - -0.1491 0.00

SPEC_F + 0.0322 0.02

SPEC_O + 0.0857 0.00

SPEC_FO + 0.1070 0.00

LNTA + 0.4150 0.00

EMPLOY + 0.0729 0.00

LNBS + -0.0001 0.49

LNGS + 0.0769 0.00

INVREC + 0.3268 0.00

FOREIGN + 0.2747 0.00

EXORD + 0.1354 0.00

LOSS + 0.1025 0.00

LEV + 0.2822 0.00

ROA - -0.4440 0.00

ISSUE + 0.0187 0.03

BTM - 0.0002 0.39

ABSPMA + 0.1115 0.02

Industry dummies ? Yes

Year dummies ? Yes

F-tests for Hypotheses H2a, H2b & H2c:

S_Audit_O < S_Audit_nO -0.0119 0.41

B_Audit_O < B_Audit_nO -0.0828 0.04

SB_Audit_O < SB_Audit_nO -0.3570 0.01

Adj. R-square 0.79


30

TABLE 6: Alternative Measure of Supply Chain Specialist and Audit Fees –

Office Level Analysis

Dependent variable: LNFEE


INTERCEPT ? 10.9552 0.00

aS_Audit_O - -0.2644 0.36

aS_Audit_nO - -0.1874 0.19

aB_Audit_O - -0.4206 0.00

aB_Audit_nO - -0.1988 0.00

aSB_Audit_O - -1.2098 0.00

aSB_Audit_nO - -0.5166 0.00

SPEC_F + 0.0336 0.01

SPEC_O + 0.0865 0.00

SPEC_FO + 0.1074 0.00

LNTA + 0.4152 0.00

EMPLOY - 0.0735 0.00

LNBS + -0.0005 0.47

LNGS + 0.0767 0.00

INVREC + 0.3249 0.00

FOREIGN + 0.2736 0.00

EXORD - 0.1367 0.00

LOSS + 0.1025 0.00

LEV + 0.2812 0.00

ROA + -0.4439 0.00

ISSUE + 0.0182 0.03

BTM + 0.0002 0.39

ABSPMA + 0.1159 0.02

Industry dummies yes

Year dummies yes


aS_Auditor_O < aS_Auditor_nO -0.0770 0.48

aB_Auditor_O < aB_Auditor_nO -0.2218 0.07

aSB_Auditor_O < aSB_Auditor_nO -0.6932 0.00

Adj. R-squares 0.79

aS_Audit_O =

the percentage of purchases by the firm that were made from its suppliers


aS_Audit_nO = the percentage of purchases by the firm that were made from its suppliers

having the different audit office from the firm.

31

aB_Audit_O = the percentage of sales by the firm that were sold to its major buyers


aB_Audit_nO = the percentage of sales by the firm that were sold to its major buyers


aSB_Audit_O = the percentage of (the sum of firm’s sales sold to its major buyers having

the same audit office as the firm and its cost of goods sold bought from its

suppliers having the same audit office as the firm)/(the sum of sales and

cost of goods sold of the firm)

aSB_Audit_nO = the percentage of (the sum of firm’s sales sold to its major buyers having

the different audit office from the firm and its cost of goods sold bought

from its suppliers choosing the different audit office from the firm)/(the

sum of sales and cost of goods sold of the firm)

Other variable definitions appear in the Appendix.

32

TABLE 7: Alternative Measure of Supply Chain Specialist and Audit Fees–

Office Level Analysis (Supply Chain Companies only, N=4,701)

Dependent variable: LNFEE.


INTERCEPT ? 10.2086 0.00

aS_Audit_O - 0.0136 0.39

aS_Audit_nO - 0.0155 0.28

aB_Audit_O - -0.1054 0.01

aB_Audit_nO - -0.0241 0.12

aSB_Audit_O - -0.5041 0.00

aSB_Audit_nO - -0.1359 0.01

SPEC_F + 0.0913 0.00

SPEC_O + 0.0914 0.00

SPEC_FO + 0.119 0.00

LNTA + 0.3899 0.00

EMPLOY + 0.1024 0.00

LNBS + -0.0113 0.16

LNGS + 0.0835 0.00

INVREC + 0.2814 0.00

FOREIGN + 0.2349 0.00

EXORD + 0.1297 0.00

LOSS + 0.0727 0.00

LEV + 0.3069 0.00

ROA - -0.363 0.00

ISSUE + 0.017 0.14

BTM - 0.0006 0.33

ABSPMA + 0.1904 0.02

Industry dummies ? yes

Year dummies ? yes


aS_Auditor_O < aS_Auditor_nO -0.0019 0.49

aB_Auditor_O < aB_Auditor_nO -0.0813 0.05

aSB_Auditor_O < aSB_Auditor_nO -0.3682 0.00

Adj. R-square 0.82

aS_Audit_O =

the percentage of purchases by the firm that were made from its suppliers


aS_Audit_nO = the percentage of purchases by the firm that were made from its suppliers


33

aB_Audit_O = the percentage of sales by the firm that were sold to its major buyers


aB_Audit_nO = the percentage of sales by the firm that were sold to its major buyers


aSB_Audit_O = the percentage of (the sum of firm’s sales sold to its major buyers having

the same audit office as the firm and its cost of goods sold bought from its

suppliers having the same audit office as the firm)/(the sum of sales and

cost of goods sold of the firm)

aSB_Audit_nO = the percentage of (the sum of firm’s sales sold to its major buyers having

the different audit office from the firm and its cost of goods sold bought

from its suppliers choosing the different audit office from the firm)/(the

sum of sales and cost of goods sold of the firm)

Other variable definitions appear in the Appendix.

supply chain specialization and audit...

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