non-audit fees, institutional monitoring, and audit quality
TRANSCRIPT
ORI GINAL RESEARCH
Non-audit fees, institutional monitoring, and auditquality
Chee Yeow Lim • David K. Ding • Charlie Charoenwong
Published online: 14 September 2012� Springer Science+Business Media, LLC 2012
Abstract We posit that the effect of non-audit fees on audit quality is conditional on the
extent of institutional monitoring. We suggest that institutional investors have incentives
and the ability to monitor financial reporting quality. Because of the reputation concerns
and potential litigation exposure, auditors are likely to provide high audit quality, when
they also provide non-audit services to clients, particularly when clients are subject to high
institutional monitoring. We find evidence that, as non-audit fees increase, audit quality
(measured by performance-adjusted discretionary current accruals and earnings-response
coefficients) reduces only for clients with low institutional ownership but not for clients
with high institutional ownership. Our results are robust after controlling for auditor
industry specialization, firms’ operating volatility, size effect, and potential endogeneity
between institutional ownership and audit quality.
Keywords Audit quality � Auditor independence � Discretionary accruals �Earnings return relation � Institutional investors
JEL Classification M41 � G14 � G38
C. Y. Lim (&)School of Accountancy, Singapore Management University, 60 Stamford Road Level 4,Singapore 178900, Singaporee-mail: [email protected]
D. K. DingSchool of Economics and Finance, Massey University, North Shore City 0745, New Zealand
D. K. DingLee Kong Chian School of Business, Singapore Management University, 50 Stamford Road,Singapore 178899, Singapore
C. CharoenwongNanyang Business School, Nanyang Technological University, Singapore 639798, Singapore
123
Rev Quant Finan Acc (2013) 41:343–384DOI 10.1007/s11156-012-0312-1
1 Introduction
In this study, we investigate whether the relation between non-audit fees and audit quality
is contingent on the extent of institutional ownership. We posit and provide evidence that
impairment of audit quality is contingent on institutional monitoring—audit quality is less
likely to be impaired with the provision of non-audit services when the extent of external
monitoring by institutional investors is high. In doing so, we add to prior research that
documents mixed findings on the relation between non-audit service provision and audit
quality (e.g., DeFond et al. 2002; Frankel et al. 2002; Ashbaugh et al. 2003; Srinidhi and
Gul 2007; Krishnan et al. 2005; Francis and Ke 2006; Higgs and Skantz 2006) by showing
that the effects of non-audit services on audit quality are not apparent without also jointly
accounting for the effects of institutional ownership. We complement prior studies that
investigate the moderators on the relation between non-audit fees and audit quality (Lim
and Tan 2008; Duh et al. 2009; Hoitash et al. 2005) by suggesting that institutional
monitoring is another important mechanism that may mitigate the independence issue
arising from the provision of non-audit services.
Prior research indicates that the provision of non-audit services creates economic bonds
that weaken an auditor’s independence, and therefore, audit quality (DeAngelo 1981;
Simunic 1984; Beck et al. 1988). Concerns about reputation (Benston 1975; Watts and
Zimmerman 1983) and litigation exposure (Palmrose 1988; Shu 2000) are likely to motivate
auditors to be more independent in carrying out their audit and non-audit works. We posit that,
in the presence of high institutional monitoring, auditors are likely to maintain independence
and preserve audit quality when they provide non-audit services to their clients.
Despite the voluminous research investigating the effect of non-audit fees on audit
quality, none has considered the potential monitoring role of institutional investors. Previous
studies (e.g., Grossman and Hart 1980; Shleifer and Vishny 1986; Huddart 1993) suggest
that large shareholders have incentives to undertake monitoring or other costly control
activities when the increased returns from such monitoring activities are sufficient to cover
their associated costs. Because of the magnitude of wealth invested, institutional share-
holders are likely to actively monitor corporate affairs and monitor the quality of financial
statements’ information. To the extent that institutional investors are effective monitors, we
expect auditors to be concerned about their reputation, and possible litigation exposure if
they provide low quality in their assurance services. We posit that the association between
provision of non-audit services and impairment of auditor quality is moderated by the extent
of institutional monitoring, and that failure to account for this moderating role of institu-
tional investors can mask the relation between non-audit fees and auditor quality.
There is little consensus on what is the most appropriate proxy for audit quality and its
empirical measures are usually noisy. Hence, we conduct empirical tests using two proxies
of audit quality that have been used by prior studies. We infer improved audit quality from:
(a) lower performance-adjusted discretionary current accruals; and (b) stronger market’s
response to quarterly earnings surprises, i.e., earnings return coefficients (ERC). The first
measure is a proxy for actual (as opposed to perceived) audit quality, while the second
measure is a proxy for investors’ perception of audit quality.1 We measure economic
bonding with a client by the magnitude of non-audit fees and the relative importance of the
1 Prior studies also use the issuance of going concern opinions and incidence of restatements as alternativeproxies for audit quality (e.g., DeFond et al. 2002; Kinney et al. 2004). We do not use these two proxiesbecause of the small sample size. For example, the study by DeFond et al. (2002) only includes 96 firms thatreceive going concern opinions while the study by Kinney et al. (2004) has only 187 restating firms.
344 C. Y. Lim et al.
123
client. To provide a more complete coverage of the total economic bonding between
auditors and their clients, we also include total fees, which is the sum of both audit and
non-audit fees.2 Our primary motivation is to document whether sophisticated institutional
investors are likely to affect audit quality when auditors provide non-audit services to their
clients. To the extent that institutional investors perform an effective monitoring role, we
expect auditors to provide high audit quality when they also provide non-audit services to
firms with high institutional ownership.
Our results provide consistent evidence that, with increased non-audit fees, audit quality
is higher for firms with a greater level of institutional ownership compared to firms with a
lower level of institutional ownership. In the first proxy of audit quality (absolute level of
performance-adjusted discretionary current accruals), our sample consists of 5,951 firm-
year observations from 2000 to 2001. Depending on the proxies used for fees, we find
mixed evidence regarding the association between non-audit fees and discretionary current
accruals. However, we find that magnitude of non-audit fees and percentile rank of a
client’s non-audit fees interact significantly with institutional ownership in explaining the
level of discretionary current accruals. Specifically, an increase in non-audit services is
positively and significantly associated (negatively or not associated) with discretionary
current accruals for clients with low (high) institutional ownership.
In the second proxy of audit quality (earnings response coefficients), we employ a
sample of 3,425 firm-year observations from 2000 to 2001 to estimate these coefficients.
Across all the three fee measures, we find no evidence that earnings response coefficients
are lower for clients that pay high non-audit fees to external auditors. More importantly, we
find that, for all three fee measures, earnings response coefficients are significantly lower
(not significantly lower) for clients with low (high) institutional ownership.
Our paper contributes to the literature on the effect of non-audit service provision on
audit quality by providing the empirical evidence that this effect is conditional on insti-
tutional monitoring. The paper complements recent research that investigates the moder-
ators on the relation between non-audit fees and audit quality. Lim and Tan (2008) provide
some evidence that auditor specialization mitigates the economic bonding between audi-
tors and auditees, while Duh et al. (2009) and Hoitash et al. (2005) report that changes in
regulatory environment affect the relation between non-audit fees and audit quality. Our
study is closely related to Lim and Tan (2008) where they document that auditor spe-
cialization moderates the relation between non-audit fees and audit quality when an
earnings return relation is used as a proxy for audit quality, but not when discretionary
accruals are used. In contrast, we find that institutional ownership moderates the relation
between non-audit fees and audit quality when either discretionary accruals or the earnings
return relation is used as a proxy for audit quality. Our results are robust after explicitly
controlling for auditor specialization as in Lim and Tan’s (2008) study. In addition, in the
earnings return test, we find that auditor specialization moderates the relation between non-
audit fees and audit quality only when institutional ownership is low, but not when it is
high. This result suggests that the moderating role of audit specialists is present only in
firms with low institutional ownership. Our study thus complements Lim and Tan (2008)
by documenting the important monitoring role of sophisticated institutional investors in
moderating the association between non-audit fees and audit quality. This evidence is
important because recent regulatory initiatives have been directed at reducing the per-
ceived and actual threats of high non-audit fees on auditor independence, and that failure to
2 For brevity, we refer to all three measures as proxies for non-audit fees, although total fees (which includenon-audit fees) is more related to total economic bonding.
Non-audit fees, institutional monitoring, and audit quality 345
123
account for this moderating role of external monitors can mask the relation between fee
dependence and auditor quality.
The remainder of this paper is organized as follows. We discuss prior literature and
develop our hypotheses in Sect. 2. Section 3 describes our sample and variable mea-
surement. We present empirical results for the discretionary current accruals and earnings
return coefficients tests in Sects. 4 and 5, respectively. Section 6 discusses the sensitivity
analyses performed. Section 7 summarizes and concludes.
2 Related literature and hypotheses development
The SEC, accounting practitioners, and academic researchers have expressed concern
about the effect of fee dependence (primarily non-audit fee revenues) on auditor inde-
pendence. The provision of non-audit services can strengthen an auditor’s economic bond
with a client, and this bond can affect the objectivity of an audit (Simunic 1984; Becker
et al. 1998).3 Frankel et al. (2002) find a positive association between non-audit fees and
the magnitude of discretionary accruals. Similarly, Srinidhi and Gul (2007) report a neg-
ative association between non-audit fees and accrual quality computed from Dechow and
Dichev’s (2002) model. However, Ashbaugh et al. (2003) and Chung and Kallapur (2003)
do not find any relation between non-audit fees and discretionary accruals while Larcker
and Richardson (2004) find that non-audit fees are associated positively with various
measures of accruals only for firms with weak corporate governance. DeFond et al. (2002)
find that firms that pay high non-audit fees are not associated with the incidence of going
concern opinions, suggesting that auditor independence is not compromised with high non-
audit fees. Kinney et al. (2004) examine restated financial statements and find either no or
negative association between restatements and major classes of non-audit services. Hence,
there is no pervasive evidence indicating that high non-audit fees erode actual audit quality
as measured by discretionary accruals, issuance of going concern opinions, and the inci-
dence of financial restatements.
However, more recent evidence suggests that a high degree of fee dependence may
impair auditor independence. For example, using client-specific ex ante cost of equity as a
proxy for investor perceptions of financial reporting reliability, Khurana and Raman (2006)
find that high non-audit and total fees are associated with lower financial reporting cred-
ibility. Using earnings response coefficients as proxies for perceived audit quality (and
hence earnings quality), Francis and Ke (2006) report that the market valuation of earnings
surprises following the initial proxy statement fee disclosure is significantly lower for firms
with high non-audit fees than for firms with low non-audit fees. Similarly, Krishnan et al.
(2005) report a negative association between non-audit fees and earnings response coef-
ficients in each of the three quarters following the public disclosure of fee information in
the proxy statements. In contrast, Higgs and Skantz (2006) document a positive association
between earnings response coefficients and total fees, and limited evidence that non-audit
fees are associated with lower earnings response coefficients.
The inconclusive results in prior research suggest a lack of evidence that auditors ‘‘cave
in’’ to the demand of clients when the economic bonds between auditors and clients are
high. On one hand, previous research indicates that auditor’s provision of non-audit
3 For example, auditors are more likely to agree with managers’ financial-reporting preferences when therisk of losing the client is high (Farmer et al. 1987) and when accounting standards require a greater level ofjudgment (Magee and Tseng 1990; Trompeter 1994).
346 C. Y. Lim et al.
123
services creates economic bonds on the auditor and may potentially cause the auditor to be
financially reliant on the client (DeAngelo 1981; Simunic 1984; Beck et al. 1988) and lose
objectivity. On the other hand, prior research also indicates several factors that counter
these incentives that dilute auditors’ objectivity—reputation concerns (Benston 1975;
Watts and Zimmerman 1983) and litigation exposure (Palmrose 1988; Shu 2000). Whether
auditors’ independence and audit quality are impaired when they provide non-audit ser-
vices is a function of the net balance of the economic dependency arising from non-audit
service provision, and the mitigating factors that promote auditor independence. We posit
that monitoring by institutional investors can act as a mitigating factor that promotes
auditor independence.
Institutional investors are often characterized as sophisticated investors with incentives
to monitor the quality of financial reporting. First, financial statements are an important
source of information about the firm that institutional investors mostly rely on. Second,
institutional investors are capable of analyzing financial statements more thoroughly and
proficiently than individual investors. For example, Hand (1990) finds that sophisticated
investors more accurately interpret information in earnings announcements. Bartov et al.
(2000) find that the pattern of post earnings announcement drift documented in the prior
literature (e.g., Bernard and Thomas 1990) is reduced as the level of institutional invest-
ment increases.
Third, institutional investors are not only sophisticated investors who use and under-
stand accounting information, but are capable monitors as well. Institutional monitoring
can occur explicitly through corporate governance practices or implicitly through infor-
mation gathering and by correctly pricing the impact of managerial decisions (Kao 2007).
Gillan and Starks (2000) note that shareholder activism can take several forms, such as
submitting a shareholder proposal to the proxy statement, direct negotiation with the
management, or public targeting of the firm. Shareholder activism is more likely when
institutional investors own a large portion of the firms’ shares. Consistent with this view,
Brickley et al. (1988) find that institutions tend to oppose managerial decisions that are
harmful to shareholders. Fourth, institutional investors improve the information environ-
ment and earnings quality of the firms they invest in. For example, Ajinkya et al. (2005)
find that firms with greater institutional ownership are more likely to issue more frequent,
more specific, and more accurate forecasts. Because of their access to timely and valuable
sources of firm-specific information, institutional investors recognize earnings manage-
ment more quickly and easily than individual investors (Balsam et al. 2002) and, corre-
spondingly, earnings quality is higher for firms with greater institutional ownership (Chung
et al. 2002, 2005; Mitra and Cready 2005; Jiambalvo et al. 2002; Kwak et al. 2009).
In summary, institutional investors have incentives to monitor the quality of financial
reporting and the power to discipline managers who report low quality accounting figures.
In the presence of high institutional monitoring, auditors are more likely to be independent
and more concerned about their reputation and the possible litigation exposure arising from
low audit quality performed. Velury et al. (2003) and Kane and Velury (2004) report that
institutional investors tend to influence managers of firms to hire high-quality auditors to
enhance the credibility of financial reporting. Prior studies show that high-quality auditors
provide high quality audits to protect their reputations (e.g., Becker et al. 1998; Teoh and
Wong 1993). The threat of litigation also provides auditors the incentive to provide high
quality audits for their audit clients (Francis and Krishnan 2002). Given the substantial
direct and indirect costs of potential litigation (i.e., settlements costs, damage to reputation,
and opportunity costs in terms of time away from more productive efforts), the higher the
risk of litigation, the greater will be the auditor’s incentives to perform diligently. Hence,
Non-audit fees, institutional monitoring, and audit quality 347
123
when client firms are closely monitored by influential institutional investors, auditors are
more likely to protect their reputation and, at the same time, avoid costly litigation in the
case of audit failure by providing higher audit quality.
Based on the discussions above, we posit that the presence of high institutional own-
ership should provide a countervailing force for the auditors to remain independent and
provide high audit quality when they also provide non-audit services to clients. We suggest
that the association between non-audit fees and audit quality is likely moderated by the
extent of institutional ownership. Specifically, the provision of non-audit services is less
likely to impair audit quality for firms with high institutional ownership compared to firms
with low institutional ownership. We test the following hypothesis (stated in alternative
form):
H1 Audit clients with low institutional ownership will have lower audit quality when
non-audit fees increase than clients with high institutional ownership.
This hypothesis is tested using two proxies for audit quality: performance-adjusted
discretionary current accruals and earnings return coefficient.
3 Research design
3.1 Data
Our initial sample consists of 13,789 firm-years with fee information from the Audit
Analytics database for fiscal years 2000–2001. We do not include year 2002 (and beyond)
because the year 2002 is associated both with the demise of Arthur Andersen and the
effective banning of auditors from performing various kinds of non-audit services by
the Sarbanes–Oxley Act. These two events may have undue influences on the firms, and
the audit and stock market during that year. We remove 3,855 firm-years without financial
information in the Compustat database. We restrict our study to clients of Big 5 auditors to
control for brand name (Chung and Kallapur 2003; Srinidhi and Gul 2007; Lim and Tan
2008). Accordingly, we remove 1,718 firm-years that are not audited by Big 5 auditors.
Given the fundamentally different operating characteristics associated with financial
institutions, we exclude 961 financial companies from the analyses (SIC Codes
6000–6999). We next remove 824 firms without institutional ownership data in Thomson
Financial database. The above screening procedure yields a remaining sample of 6,431
firm-year observations.4 We winsorize each of the continuous control variables used in the
regression at the top and bottom one percent. For our first proxy of audit quality, discre-
tionary current accruals, we have 5,951 firm-years over 2000–2001 with all available
financial data in Compustat. The sample for our second proxy of audit quality, earnings
return coefficients, contains 3,425 firm-year observations with complete information on the
control variables from Compustat, I/B/E/S detailed files, and CRSP databases.
Panels A and B in Table 1 report the distribution of sample firms by year and industry,
respectively, for the two sets of data used for the discretionary current accruals and
earnings return regression tests.
4 Bushee (2001) classifies institutions into three categories: transient institutions, which hold diversifiedportfolios with high turnover; dedicated institutions, which hold concentrated portfolios with low turnover;and quasi-indexers, which hold diversified portfolios with low turnover. We use aggregate institutionalownership due to data unavailability.
348 C. Y. Lim et al.
123
3.2 Non-audit fees
Prior studies (e.g., Frankel et al. 2002) use the ratio of non-audit fees to total fees to proxy
for fee dependence and, hence, auditor independence. However, the validity of this mea-
sure as a proxy of an audit firm’s economic bond has been questioned (e.g., Kinney and
Libby 2002). Some researchers argue that fee level (DeFond et al. 2002; Ashbaugh et al.
2003) or client importance (Chung and Kallapur 2003) are better measures. Hence, for
completeness, we use three measures to capture the economic bonding between the clients
and auditor: (1) the natural log of non-audit fees (LNAU), which captures the level of
Table 1 Sample size and industry description
Panel A: Distribution of sample firms by year
Years Accruals model ERC Model
N Percent N Percent
2000 2,585 43.44 1,508 44.03
2001 3,366 56.56 1,917 55.97
Total 5,951 100.00 3,425 100.00
Panel B: Distribution of sample firms by industry
Industry Accruals model ERC model
N Percent N Percent
Agriculture 24 0.40 9 0.26
Chemicals 144 2.42 89 2.60
Computers 1,226 20.60 620 18.10
Durable manufacturers 1,460 24.53 861 25.14
Extractives 231 3.88 137 4.00
Food 118 1.98 69 2.01
Mining & construction 119 2.00 54 1.58
Pharmaceuticals 402 6.76 268 7.82
Retail 680 11.43 400 11.68
Services 661 11.11 341 9.96
Textile & printing/publishing 299 5.02 193 5.64
Transportation 371 6.23 211 6.16
Utilities 216 3.63 173 5.05
Total 5,951 100 3,425 100
The sample period is from fiscal years 2000–2001, and the sample consists of non-financial firms audited byBig 5 public accounting firms. For the accruals model, the sample consists of 5,951 firm-year observationsfor the period 2000–2001 that have complete financial information in Compustat database. The sample forthe earnings return regression (ERC) model is 3,425 firm-year observations, with all information availablefrom Compustat, I/B/E/S detailed files, and CRSP. Panel A shows the sample distribution by year whilePanel B shows the detailed breakdown of sample size according to industry. Industry membership isdetermined by the SIC code as follows: agriculture (0100–0999), mining & construction (1000–1999,excluding 1300–1399), food (2000–2111), textiles & printing/publishing (2200–2799), chemicals(2800–2824, 2840–2899), pharmaceuticals (2830–2836), extractive (2900–2999, 1300–1399), durablemanufacturers (3000–3999, excluding 3570–3579 and 3670–3679), transportation (4000–4899), utilities(4900–4999), retail (5000–5999), services (7000–8999, excluding 7370–7379), computers (3570–3579,3670–3679, 7370–7379)
Non-audit fees, institutional monitoring, and audit quality 349
123
economic bonding resulting from the purchase of non-audit services, (2) the percentile rank
of a particular client’s non-audit fees given all total fees received by the audit firm
(PRNAU), which captures the relative significance of client non-audit fees to the total fees
revenue received by the auditor, and (3) the natural log of total fees (LTOT), which
captures the total economic bonding of the client to the auditor created by the provision of
both non-audit and audit services.5
4 Discretionary current accruals
4.1 Empirical model
Following prior studies (e.g., Ashbaugh et al. 2003; Lim et al. 2008), we compute perfor-
mance-adjusted discretionary current accruals (PA_DCA) based on the cross-sectional
modified Jones (1991) model for all firms recorded in Compustat. We define current accruals
(CA) as income before extraordinary items plus depreciation and amortization minus oper-
ating cash flows. To obtain the PA_DCA in a given year, we regress the following:
CAi;t
TAi;t�1
¼ k1
1
TAi;t�1
� �þ k2
DREVi;t
TAi;t�1
� �þk3
IBi;t�1
TAi;t�1
� �þ ei;t ð1Þ
where CAi,t is the current accruals for firm i in fiscal year t, TAi,t-1 is the total assets for
firm i in fiscal year t-1; DREVi,t measures the change in revenues for firm i in year t less
revenues in t-1; IBt-1 is the income before extraordinary items in year t-1; and ei,t is the
random residual term. Similar to previous studies, we estimate Eq. (1) cross-sectionally on
all firms recorded in Compustat with the same two-digit SIC industry. PA_DCA are then
estimated as:
PA DCAi;t ¼CAi;t
TAi;t�1
� �� k̂1
1
TAi;t�1
� �� k̂2
ðDREV � DTRÞi;tTAi;t�1
� �� k̂3
IBt�1
TAt�1
� �ð2Þ
where k̂j; for j = 1, 2, and 3, are the estimated parameters from Eq. (1) and DTRi,t is the
change in trade receivables for firm i in year t less the trade receivables in the previous year.
Consistent with Frankel et al. (2002), Ashbaugh et al. (2003) and Lim and Tan (2008),
we run the following model to test the association between non-audit fees and discretionary
current accruals:
APA DCA ¼ x0 þ x1FEEþ x2TENUþ x3CFOþ u4LEVþ x5LITIGþ x6MB
þ x7SIZEþ x8LOSSþ x9FINþ x10LAG ACAþ x11ZSþ x12SPEC
þ x13INSTþ x14Y00þ x15FEE � SPECþ x16FEE � INSTþ e ð3Þ
APA_DCA = absolute value of the performance-adjusted discretionary current accruals
estimated with lagged ROA in the cross-sectional Jones model;
5 The distribution of the non-audit fees and total fees are non-normal, and hence following prior studies(e.g. Frankel et al. 2002; DeFond et al. 2002), we log-transformed the non-audit and total fee variables. Ourmeasure for client importance (ratio of non-audit fees to total fees received by the auditor) is also non-normal and highly skewed. Hence, we follow Frankel et al. (2002) and Lim and Tan (2008) by rank-transforming the variable. The rank-transformation substantially reduced the skewness and kurtosis of thevariable.
350 C. Y. Lim et al.
123
FEE = fee metrics, LNAU, PRNAU, and LTOT as defined earlier;
TENU = number of years that the auditor has audited the firm’s financial statements;
CFO = cash flow from operations scaled by total assets at the beginning of the fiscal
year;
LEV = debt-to-equity ratio;
LITIG = 1 if the firm operates in a high-litigation industry, and 0 otherwise. High-
litigation industries are industries with SIC codes 2833–2836, 3570–3577, 3600–3674,
5200–5961, and 7370–7374 (as in Francis et al. (1994) and used by Frankel et al. (2002)
and Ashbaugh et al. (2003));
MB = market-to-book ratio at fiscal year end;
SIZE = natural log of market value at fiscal year end;
LOSS = 1 if a firm reports a loss, and 0 otherwise;
FIN = 1 if the firm issued securities or acquired another company, and 0 otherwise;
LAG_ACA = absolute current accruals in the previous year, deflated by total assets;
ZS = Zmijewski’s (1984) bankruptcy scores, coded one if the score is positive and zero
otherwise6;
SPEC = 1 if the auditor has the largest market share in the industry, and 0 otherwise;
INST = percent of the company’s aggregate common stock held by institutions;
Y00 = year dummy.
We include the FEE*INST interaction term in the regression model to test the associ-
ation of non-audit fees on absolute level of performance-adjusted discretionary current
accruals, conditional on institutional ownership. Based on our hypothesis, we expect the
coefficient for FEE*INST (x16) to be negative. A negative x16 indicates that non-audit fees
are associated with lower APA_DCA when level of institutional ownership increases.
Consistent with Lim and Tan (2008), we also include an interaction term FEE*SPEC to
assess the moderating role of auditor specialization. More importantly, we assess whether
institutional ownership plays an important moderating role after controlling for the mod-
erating role of auditor specialization.
4.2 Empirical results
We report the descriptive statistics and the correlation coefficients for the fee metrics and
variables used in the discretionary current accruals model in Table 2. The mean (median)
absolute value for APA_DCA is 0.267 (0.096).7 On average, 40 % of the firms’ shares are
held by institutions and 24 % of the firms are audited by audit specialists. Other variables
used in discretionary accruals regression, and the correlation coefficients among them, are
also reported in Table 2.
We report the regression results for the absolute discretionary current accruals for the
full sample in Table 3, Panel A. The adjusted R2 is around 19 %, compared to the adjusted
R2 of 18–21 % reported in Ashbaugh et al. (2003). In the models with FEE, INST alone
(without the interaction term FEE*INST), the association between non-audit fees and
discretionary current accruals varies depending on the different proxies used. We find no
association between LNAU and discretionary current accruals (Ashbaugh et al. 2003) while
PRNAU is positively and significantly associated with discretionary current accruals
6 According to Zmijewski (1984), a positive score is an indicator of greater than 50 % likelihood ofbankruptcy.7 The apparent high mean and median values of APA_DCA is due to the absolute transformation. Thesigned mean and median discretionary current accruals are -0.062 and -0.018 respectively.
Non-audit fees, institutional monitoring, and audit quality 351
123
Tab
le2
Des
crip
tive
stat
isti
csan
dco
rrel
atio
nbet
wee
nvar
iable
suse
din
the
dis
cret
ionar
ycu
rren
tac
crual
sm
odel
Pan
elA
:D
escr
ipti
ve
stat
isti
cs
Mea
nM
edia
n1
stQ
uar
tile
3rd
Qu
arti
leS
D
AP
A_
DC
A0
.267
0.0
96
0.0
35
0.2
74
0.4
85
LN
AU
12
.00
91
2.3
33
11
.15
61
3.4
81
2.8
66
PR
NA
U5
0.4
17
51
.00
02
6.0
00
75
.00
02
8.5
20
LT
OT
13
.24
81
3.0
68
12
.28
01
4.0
01
1.3
31
TE
NU
10
.32
87
.000
4.0
00
15
.00
08
.179
OC
F0
.007
0.0
60
-0
.02
90
.12
10
.242
LE
V0
.527
0.2
80
0.0
06
0.9
37
2.8
13
LIT
IG0
.382
0.0
00
0.0
00
1.0
00
0.4
86
MB
2.7
35
1.7
51
0.8
93
3.2
49
3.8
30
SIZ
E5
.471
5.4
73
3.9
10
6.9
06
2.1
96
LO
SS
0.4
44
0.0
00
0.0
00
1.0
00
0.4
97
FIN
0.2
47
0.0
00
0.0
00
0.0
00
0.4
32
LA
G_
AC
A0
.080
0.0
34
0.0
14
0.0
79
0.1
70
ZS
0.0
50
0.0
00
0.0
00
0.0
00
0.2
17
SP
EC
0.2
43
0.0
00
0.0
00
0.0
00
0.4
29
INS
T0
.402
0.3
85
0.1
54
0.6
32
0.2
72
Pan
elB
:P
ears
on
corr
elat
ion
mat
rix
AP
A_
DC
AL
NA
UP
RN
AU
LT
OT
TE
NU
CF
OL
EV
LIT
IGM
BS
IZE
LO
SS
FIN
LA
G_
AC
AZ
SS
PE
CIN
ST
AP
A_
DC
A1
.00
LN
AU
-0
.11
1.0
0
PR
NA
U-
0.1
20
.99
1.0
0
LT
OT
-0
.15
0.9
50
.94
1.0
0
TE
NU
-0
.21
0.2
00
.20
0.2
21
.00
352 C. Y. Lim et al.
123
Tab
le2
con
tin
ued
Pan
elB
:P
ears
on
corr
elat
ion
mat
rix
AP
A_
DC
AL
NA
UP
RN
AU
LT
OT
TE
NU
CF
OL
EV
LIT
IGM
BS
IZE
LO
SS
FIN
LA
G_
AC
AZ
SS
PE
CIN
ST
CF
O-
0.2
40
.17
0.1
70
.19
0.2
71
.00
LE
V-
0.2
80
.24
0.2
40
.29
0.1
90
.14
1.0
0
LIT
IG0
.24
-0
.07
-0
.07
-0
.11
-0
.16
-0
.20
-0
.32
1.0
0
MB
0.1
30
.18
0.1
80
.14
0.0
10
.13
-0
.01
0.1
61
.00
SIZ
E-
0.1
30
.68
0.6
80
.70
0.2
30
.31
0.1
4-
0.0
30
.54
1.0
0
LO
SS
0.2
6-
0.1
8-
0.1
8-
0.2
0-
0.2
9-
0.5
9-
0.1
80
.23
-0
.15
-0
.35
1.0
0
FIN
0.1
2-
0.0
3-
0.0
3-
0.0
6-
0.0
9-
0.1
50
.00
0.0
70
.14
0.0
30
.08
1.0
0
LA
G_
AC
A0
.19
-0
.22
-0
.22
-0
.21
-0
.13
-0
.19
-0
.21
0.1
4-
0.1
0-
0.3
20
.23
0.0
11
.00
ZS
0.1
7-
0.0
8-
0.0
7-
0.0
6-
0.1
3-
0.2
2-
0.1
70
.08
-0
.16
-0
.19
0.2
30
.04
0.1
91
.00
SP
EC
-0
.05
0.0
80
.03
0.0
80
.03
0.0
30
.00
0.0
00
.01
0.0
7-
0.0
10
.00
0.0
0-
0.0
21
.00
INS
T-
0.1
80
.47
0.4
70
.49
0.2
30
.30
0.1
3-
0.0
60
.30
0.6
7-
0.3
0-
0.0
2-
0.2
4-
0.1
80
.05
1.0
0
Th
eta
ble
rep
ort
sth
ed
escr
ipti
ve
stat
isti
csfo
rth
ev
aria
ble
san
dth
eco
rrel
atio
nb
etw
een
var
iab
les
use
din
the
dis
cret
ion
ary
curr
ent
accr
ual
sm
odel
.A
PA
_D
CA
isth
eab
solu
teval
ue
of
per
form
ance
-adju
sted
dis
cret
ionar
ycu
rren
tac
crual
ses
tim
ated
wit
hla
gged
RO
Ain
the
cross
-sec
tional
Jones
model
.L
NA
Uis
the
nat
ura
llo
go
fn
on
-aud
itfe
es.
PR
NA
Uis
the
per
cen
tile
rank
of
ap
arti
cula
rcl
ien
t’s
no
n-a
ud
itfe
esg
iven
all
no
n-a
ud
itfe
esre
ceiv
edb
yth
eau
dit
firm
.L
TO
Tis
the
nat
ura
llo
go
fto
tal
fees
.T
EN
Uis
the
nu
mb
ero
fy
ears
that
the
aud
ito
rh
asau
dit
edth
efi
rm’s
fin
anci
alst
atem
ents
.C
FO
isca
shfl
ow
fro
mo
per
atio
ns
scal
edb
yto
tal
asse
tsat
the
beg
inn
ing
of
the
fisc
aly
ear.
LE
Vis
deb
t-to
-eq
uit
yra
tio.
LIT
IGeq
ual
s1
ifth
efi
rmo
per
ates
ina
hig
h-l
itig
atio
nin
du
stry
,an
d0
oth
erw
ise.
Hig
h-l
itig
atio
nin
du
stri
esar
ein
du
stri
esw
ith
SIC
cod
es2
83
3–
28
36
,3
57
0–
35
77
,3
60
0–
36
74
,5
20
0–
59
61
,an
d7
37
0–
737
4.
MB
ism
ark
et-t
o-b
oo
kra
tio
.S
IZE
isth
en
atura
llo
go
fm
ark
etv
alue.
LO
SS
equ
als
1if
afi
rmre
po
rts
alo
ss,
and
0o
ther
wis
e.F
INeq
ual
s1
ifth
efi
rmis
sued
secu
riti
eso
rac
qu
ired
ano
ther
com
pan
y,
and
0o
ther
wis
e.L
AG
_A
CA
isth
eab
solu
teval
ue
of
curr
ent
accr
ual
sin
the
pri
or
yea
r,defl
ated
by
tota
las
sets
.Z
Sis
Zm
ijew
ski’
s(1
98
4)
ban
kru
ptc
ysc
ore
s,co
ded
on
eif
the
sco
reis
po
siti
ve
and
zero
oth
erw
ise;
SP
EC
isco
ded
1if
the
aud
ito
rh
asth
ela
rges
tm
ark
etsh
are
inth
ein
dust
ry,
and
0oth
erw
ise;
INS
Tis
the
agg
reg
ate
shar
esh
eld
by
inst
itu
tio
nal
inves
tors
Non-audit fees, institutional monitoring, and audit quality 353
123
Tab
le3
Dis
cret
ionar
ycu
rren
tac
crual
sm
odel
:F
eem
etri
csan
din
stit
uti
onal
ow
ner
ship
Pan
elA
:A
bso
lute
dis
cret
ionar
ycu
rren
tac
crual
s
LN
AU
PR
NA
UL
TO
T
Inte
rcep
tx
00
.061
(2.1
6)*
*0
.001
(0.0
2)
0.0
90
(4.1
3)*
**
0.0
59
(2.2
7)*
*0
.221
(3.0
9)*
**
0.0
22
7(1
.82
)*
FE
Ex
10
.004
(1.5
1)
0.0
08
(2.4
5)*
*0
.00
1(1
.94
)*0
.001
(2.9
3)*
**
-0
.01
3(-
1.9
5)*
*-
0.0
13
(-1
.28)
TE
NU
x2
-0
.00
4(-
5.6
1)*
**
-0
.004
(-5
.17)*
**
-0
.004
(-5
.63)*
**
-0
.004
(-5
.24
)**
*-
0.0
04
(-5
.13
)**
*-
0.0
04
(-5
.09)*
**
CF
Ox
3-
0.2
58
(-8
.68)*
**
-0
.259
(-8
.70)*
**
-0
.260
(-8
.76)*
**
-0
.264
(-8
.85
)**
*-
0.2
57
(-8
.64
)**
*-
0.2
57
(-8
.60)*
**
LE
Vx
4-
0.0
05
(-2
.39)*
*-
0.0
05
(-2
.33)*
*-
0.0
05
(-2
.47)*
*-
0.0
05
(-2
.48
)**
-0
.00
4(-
2.0
9)*
*-
0.0
04
(-2
.09)*
*
LIT
IGx
50
.133
(10
.77)*
**
0.1
32
(10
.72
)**
*0
.13
4(1
0.8
4)*
**
0.1
33
(10
.79)*
**
0.1
30
(10
.47)*
**
0.1
30
(10
.46)*
**
MB
x6
0.0
13
(7.8
7)*
**
0.0
13
(7.8
7)*
**
0.0
13
(7.9
7)*
**
0.0
13
(8.0
7)*
**
0.0
12
(7.2
5)*
**
0.0
12
(7.2
2)*
**
SIZ
Ex
70
.000
(0.0
0)
0.0
01
(0.3
0)
-0
.002
(-0
.54)
-0
.003
(-0
.67
)0
.009
(1.7
8)*
0.0
09
(1.7
8)*
LO
SS
x8
0.1
10
(7.9
5)*
**
0.1
11
(8.0
2)*
**
0.1
08
(7.8
1)*
**
0.1
08
(7.7
8)*
**
0.1
15
(8.3
0)*
**
0.1
15
(8.3
0)*
**
FIN
x9
0.0
75
(5.6
1)*
**
0.0
74
(5.5
0)*
**
0.0
76
(5.6
8)*
**
0.0
76
(5.6
2)*
**
0.0
73
(5.4
2)*
**
0.0
73
(5.4
2)*
**
LA
G_
AC
Ax
10
0.1
77
(4.8
2)*
**
0.1
82
(4.9
5)*
**
0.1
75
(4.7
7)*
**
0.1
79
(4.8
8)*
**
0.1
80
(4.9
0)*
**
0.1
80
(4.8
9)*
**
ZS
x11
0.0
64
(2.2
0)*
*0
.066
(2.2
5)*
*0
.06
1(2
.08
)**
0.0
60
(2.0
5)*
*0
.074
(2.5
2)*
**
0.0
74
(2.5
2)*
**
SP
EC
x12
-0
.10
6(2
.45
)**
-0
.039
(-1
.74)*
-0
.027
(-1
.75)*
-0
.022
(-0
.83
)-
0.1
08
(2.2
6)*
*-
0.0
27
(-0
.22)
354 C. Y. Lim et al.
123
Tab
le3
con
tin
ued
Pan
elA
:A
bso
lute
dis
cret
ionar
ycu
rren
tac
crual
s
LN
AU
PR
NA
UL
TO
T
INS
Tx
13
-0
.114
(-4
.18)*
**
0.1
43
(1.4
2)
-0
.115
(-4
.22
)**
*-
0.0
29
(-0
.59)
-0
.112
(-4
.11)*
**
-0
.139
(-0
.59
)
Y0
0x
14
0.1
97
(16
.93)*
**
0.1
97
(16
.99)*
**
0.1
96
(16
.82)*
**
0.1
96
(16
.82)*
**
0.1
98
(17
.07)*
**
0.1
98
(17
.07)*
**
FE
E*
SP
EC
x15
-0
.004
(0.8
4)
-0
.00
1(-
0.6
2)
-0
.001
(-0
.15
)
FE
E*
INS
Tx
16
-0
.021
(-2
.66
)**
*-
0.0
02
(-2
.09)*
*0
.00
2(0
.12
)
N5
,951
5,9
51
5,9
51
5,9
51
5,9
51
5,9
51
F-s
tati
stic
10
0.2
8*
**
88
.31
**
*1
00
.38
**
*8
8.9
9*
**
10
0.4
2*
**
87
.84
**
*
Ad
jR
21
8.9
4%
19
.01
%1
8.9
6%
18
.99
%1
8.9
6%
18
.93
%
Pan
elB
:A
bso
lute
dis
cret
ion
ary
curr
ent
accr
ual
sb
ased
on
hig
han
dlo
win
stit
uti
on
alo
wn
ersh
ip
Hig
hIn
stit
uti
on
alO
wn
ersh
ipL
ow
Inst
ituti
on
alO
wn
ersh
ip
LN
AU
PR
NA
UL
TO
TL
NA
UP
RN
AU
LT
OT
Inte
rcep
tx
00
.147
(3.8
6)*
**
0.0
90
(3.4
3)*
**
0.2
70
(3.5
1)*
**
-0
.05
1(-
1.0
1)
-0
.01
7(-
0.4
4)
0.0
96
(0.6
4)
FE
Ex
1-
0.0
06
(-1
.90)*
-0
.00
1(-
0.5
8)
-0
.01
7(-
2.5
8)*
**
0.0
05
(1.6
6)*
0.0
01
(2.3
4)*
*-
0.0
10
(-0
.80)
TE
NU
x2
-0
.003
(-5
.00)*
**
-0
.00
3(-
5.1
1)*
**
-0
.00
3(-
4.6
0)*
**
-0
.00
5(-
3.3
3)*
**
-0
.00
5(-
3.2
7)*
**
-0
.00
5(-
3.2
1)*
**
CF
Ox
3-
0.3
07
(-6
.32)*
**
-0
.30
6(-
6.3
0)*
**
-0
.31
3(-
6.4
2)*
**
-0
.20
4(-
4.9
4)*
**
-0
.21
0(-
5.0
7)*
**
-0
.20
1(-
4.8
3)*
**
LE
Vx
4-
0.0
03
(-1
.53)
-0
.00
3(-
1.5
2)
-0
.00
3(-
1.3
5)
-0
.00
7(-
1.9
7)*
*-
0.0
07
(-2
.15)*
*-
0.0
06
(-1
.75)*
Non-audit fees, institutional monitoring, and audit quality 355
123
Tab
le3
con
tin
ued
Pan
elB
:A
bso
lute
dis
cret
ion
ary
curr
ent
accr
ual
sb
ased
on
hig
han
dlo
win
stit
uti
on
alo
wn
ersh
ip
Hig
hIn
stit
uti
on
alO
wn
ersh
ipL
ow
Inst
itu
tio
nal
Ow
ner
ship
LN
AU
PR
NA
UL
TO
TL
NA
UP
RN
AU
LT
OT
LIT
IGx
50
.07
8(6
.55
)**
*0
.07
8(6
.50
)**
*0
.07
3(6
.07
)**
*0
.18
2(8
.64
)**
*0
.18
3(8
.69
)**
*0
.18
1(8
.57
)**
*
MB
x6
0.0
09
(6.9
4)*
**
0.0
09
(6.9
5)*
**
0.0
09
(6.5
5)*
**
0.0
21
(6.3
2)*
**
0.0
22
(6.5
1)*
**
0.0
20
(5.7
9)*
**
SIZ
Ex
70
.00
5(1
.21
)0
.00
4(0
.87
)0
.01
1(2
.25
)**
-0
.005
(-0
.74)
-0
.010
(-1
.33
)0
.00
5(0
.64
)
LO
SS
x8
0.0
50
(3.6
6)*
**
0.0
49
(3.6
0)*
**
0.0
52
(3.8
0)*
**
0.1
55
(6.5
9)*
**
0.1
51
(6.4
0)*
**
0.1
62
(6.8
4)*
**
FIN
x9
0.0
34
(2.6
6)*
**
0.0
35
(2.6
8)*
**
0.0
31
(2.3
8)*
*0
.10
1(4
.37
)**
*0
.10
3(4
.45
)**
*0
.10
1(4
.35
)**
*
LA
G_
AC
Ax
10
0.3
12
(4.5
8)*
**
0.3
09
(4.5
2)*
**
0.3
17
(4.6
5)*
**
0.1
78
(3.6
4)*
**
0.1
76
(3.6
0)*
**
0.1
79
(3.6
6)*
**
ZS
x11
0.2
28
(5.3
4)*
**
0.2
29
(5.3
7)*
**
0.2
31
(5.4
2)*
**
0.0
31
(0.7
7)
0.0
24
(0.5
8)
0.0
44
(1.0
7)
SP
EC
x12
-0
.103
(-1
.35)
-0
.006
(-0
.18)
-0
.066
(-0
.52)
-0
.014
(-0
.17)
0.0
52
(1.2
5)
0.1
37
(0.5
2)
Y0
0x
14
0.1
12
(10
.22)*
**
0.1
11
(10
.17)*
**
0.1
13
(10
.28)*
**
0.2
80
(13
.84)*
**
0.2
77
(13
.70)*
**
0.2
82
(13
.92)*
**
FE
E*
SP
EC
x15
-0
.008
(-1
.38)
-0
.000
(-0
.20)
0.0
05
(0.5
5)
0.0
03
(0.4
2)
-0
.001
(-0
.83
)-
0.0
09
(-0
.44
)
N2
,976
2,9
76
2,9
76
2,9
75
2,9
75
2,9
75
356 C. Y. Lim et al.
123
Tab
le3
con
tin
ued
Pan
elB
:A
bso
lute
dis
cret
ion
ary
curr
ent
accr
ual
sb
ased
on
hig
han
dlo
win
stit
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e
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Non-audit fees, institutional monitoring, and audit quality 357
123
(Srinidhi and Gul 2007). However, we find that total fees are negatively and significantly
associated with discretionary current accruals.8 The coefficient estimate for INST is neg-
ative and significant at the 1 % level, consistent with the argument that institution investors
are effective monitors in constraining firms’ earnings management.
We then include the interaction terms (FEE*INST and FEE*SPEC) in the models.
Consistent with our prediction in H1, the coefficient estimate for the interaction term
(FEE*INST) is negative and statistically significant when FEE is measured by LNAU and
PRNAU, which suggests that increased non-audit fees are associated with higher audit
quality when institutional ownership increases. However, we do not find significant
interaction when FEE is measured by LTOT. Hence, our results for the interaction effect
are sensitive to the proxies used to measure fee dependence. Consistent with Lim and
Tan (2008), the coefficient estimate for the interaction term (FEE*SPEC) is insignificant
in all three models.
We next partition the sample into two groups based on median institutional ownership.
The results are reported in Panel B of Table 3. For the firms with high institutional
ownership, PRNAU is not associated with APA_DCA, while LNAU and LTOT are nega-
tively and significantly associated with APA_DCA. For the firms with low institutional
ownership, LNAU and PRNAU are positively and significantly associated with APA_DCA,
while LTOT is not associated with APA_DCA. These results are consistent with the con-
tention that audit quality is reduced only for firms with low institutional ownership, but not
for firms with high institutional ownership. Again, we find that FEE*SPEC is not asso-
ciated with APA_DCA for both subsamples. Taken together, our results suggest that
institutional investors, rather than audit specialists, moderate the relation between non-
audit fees and audit quality measured by discretionary current accruals.
Overall, our results suggest a moderating effect of institutional ownership on the
association between non-audit fees (measured by LNAU and PRNAU) and performance-
adjusted discretionary current accruals.
5 The earnings return regression
5.1 Empirical model
We use the earnings response coefficients (ERC) from the returns-earnings regressions as a
proxy for investor perceptions of earnings quality (Francis and Ke 2006; Ghosh and Moon
2005, 2010). The fee information is only made available to investors when firms file their
10 K reports with the SEC. Hence, we use short-window returns to infer the effect of non-
audit fees (together with institutional ownership) on audit quality. Specifically, we measure
three-day cumulative abnormal returns for days -1, 0, and 1, where day 0 is the day of
quarterly earnings announcement immediately after fee disclosure in the proxy statements,
where abnormal returns is defined as the difference between a firm’s returns and the CRSP
8 Total fees are the aggregate value and are meaningful only if both the audit and non-audit fees havesimilar effects on economic bonding. Srinidhi and Gul (2007) find that audit (non-audit) fees are positively(negatively) associated with accrual quality. Hence, our results suggest that the negative relation betweentotal fees and discretionary current accruals is driven by the beneficial effect of audit fees on audit quality.Our untabulated results indicate that audit fees are negatively and significantly associated with discretionarycurrent accruals (t = -3.11, p \ 0.01).
358 C. Y. Lim et al.
123
value-weighted market returns (Francis and Ke 2006).9 Based on prior studies (e.g.,
Francis and Ke 2006; Ghosh and Moon 2005), we run the following regression:
CAR ¼ a0 þ a1ðUEÞ þ a2ðFEEÞ þ a3ðUE � FEEÞ þ a4ðMBÞ þ a5ðUE �MBÞþ a6ðLEVÞ þ a7ðUE � LEVÞ þ a8ðPERSÞ þ a9ðUE � PERSÞþ a10ðBETAÞ þ a11ðUE � BETAÞ þ a12ðVOLÞ þ a13ðUE � VOLÞþ a14ðAGEÞ þ a15ðUE � AGEÞ þ a16ðREGÞ þ a17ðUE � REGÞþ a18ðSIZEÞ þ a19ðUE � SIZEÞ þ a20ðLOSSÞ þ a21ðUE � LOSSÞþ a22ðRESTRÞ þ a23ðUE � RESTRÞ þ a24ðSPECÞ þ a25ðUE � SPECÞþ a26ðINSTÞ þ a27ðUE � INSTÞ þ a28ðFEE � SPECÞ þ a29ðFEE � INSTÞþ a30ðUE � FEE � SPECÞ þ a31ðUE � FEE � INSTÞþ a32ðY00Þ þ a33ðUE � Y00Þ þ e ð4Þ
where
CAR = three-day cumulative abnormal returns around the first quarterly earnings
announcement after the fee discourse in the proxy statements;
UE = earnings surprise, measured by the difference between actual earnings per share
and most recent median earnings forecast for the quarter immediately after the
disclosure of fee information in the proxy statement, scaled by stock price at the
beginning of the quarter. Both actual and forecasted earnings per share are from I/B/E/S
detailed files;
FEE = fee metrics, LNAU, PRNAU, and LTOT, as defined earlier;
MB = market-to-book ratio at end of quarter;
LEV = debt-to-equity ratio at end of quarter;
PERS = earnings persistence measured by the first-order autocorrelation of earnings
before extraordinary items per share for the past 16 quarters;
BETA = beta estimated from daily stock returns over a 90-day window ending 7 days
prior to the earnings announcement date;
VOL = the variance of the residual from the market model over a 90-day window
ending 7 days prior to the earnings announcement date;
AGE = number of years that the firm is publicly traded as of the fiscal year end;
REG = an indicator equals one for firms in a regulated industry (two-digit SIC 40–49
and SIC 60–63), and zero otherwise;
SIZE = natural log of market capitalization at end of quarter;
LOSS = 1 if the current quarter’s earnings is negative, and 0 otherwise;
RESTR = 1 if the special item as a percentage of total assets in the quarter is less than or
equal to -5%, and 0 otherwise;
SPEC = 1 if the auditor has the largest market share in the industry, and 0 otherwise;
INST = percent of the company’s aggregate common stock held by institutions;
Y00 = year dummy.
The coefficient for UE*FEE*INST (a31) shows the incremental effect of INST on ERC
when non-audit fees increase. To the extent that the audit quality is higher for firms with
9 The Audit Analytics database record the date when proxy statements on fee information are filed with theSEC. Most filings occur three to 4 months after the fiscal year end. We tracked the dates recorded in AuditAnalytics to ensure that the fee information is available to investors when firms make the first quarterlyearnings announcements after the fiscal year end.
Non-audit fees, institutional monitoring, and audit quality 359
123
high institutional ownership when non-audit fees increase, we expect the coefficient for
UE*FEE*INST to be positive. Following Lim and Tan (2008), we control for the mod-
erating effect of auditor specialization by including SPEC, UE*SPEC, FEE*SPEC and
UE*FEE*SPEC in the model. We also run Eq. (4) separately (without the variables–INST,
FEE*INST and UE*FEE*INST) for firms with high/low level of institutional ownership
based on median institutional ownership. To the extent that institutional investors provide
an effective monitoring role and that auditors are therefore likely to provide high audit
quality, we expect non-audit fees to be associated with lower ERC only for firms with low
institutional ownership but not firms with high institutional ownership.
5.2 Empirical results
We report the descriptive statistics and the correlation coefficients for the variables used in
the earnings return regression model in Table 4. On average, 52 % of company’s shares are
held by institutions and 24 % of firms are audited by audit specialists. The mean (median)
three-day cumulative abnormal returns (CAR) are 1.2 % (0.50 %). The mean (median)
earnings surprise is 0.2 % (0 %) of the stock price at the beginning of the quarter. Other
variables used in the earnings return regression, and the correlation coefficients among
them, are also reported in Table 4.
We report the results for the earnings return regression in Table 5. Panel A reports the
results for the ERC test for the full sample. In the models without the interaction terms
FEE*INST and UE*FEE*INST, the three proxies for fees are not associated significantly
with ERC. This result is inconsistent with Francis and Ke (2006) and Krishnan et al.
(2005) but is consistent with Higgs and Skantz (2006). Audit quality is not perceived to
be lower with high fees. In line with prior studies that document the information role of
institutional investors (e.g., Jiambalvo et al. 2002), INST is associated significantly with
higher ERC. For the set of control variables, firms audited by audit specialists, high
growth firms and firms in regulated industry are associated with higher ERC. On the
other hand, older firms, firms making losses, firms that restructure their operations and
risky firms (measured by VOL) are associated significantly with lower ERC. Other
control variables are not statistically significant.
We then include the interaction terms, FEE*INST, UE*FEE*INST, FEE*SPEC and
UE*FEE*SPEC in the models. Consistent with our prediction in H1, the coefficient
estimate for the interaction term (UE*FEE*INST) is positive and statistically significant
at the 1 % level for all three fee measures. The results indicate that, as non-audit fees
increase, audit quality is higher for firms with high level of institutional ownership.
Consistent with Lim and Tan (2008), we also find that the coefficient estimate for the
interaction term (UE*FEE*SPEC) is positive and statistically significant at the 5 %
level.
Next, we split the sample into two groups based on median institutional ownership.
The results are reported in Panel B of Table 5. Consistent with our prediction, for firms
with high institutional ownership, all three fee measures are not associated with lower
ERC. In contrast, for firms with low institutional ownership, all three fee proxies are
associated significantly with lower ERC. Interestingly, the coefficient estimate for the
interaction term (UE*FEE*SPEC) is positive and statistically significant only for firms
with low institutional ownership but not for firms with high institutional ownership. The
results suggest that the moderating role of audit specialists is present only in firms with
low institutional ownership.
360 C. Y. Lim et al.
123
Ta
ble
4D
escr
ipti
ve
stat
isti
csan
dco
rrel
atio
nbet
wee
nvar
iable
suse
din
the
earn
ings
retu
rnre
gre
ssio
n
Pan
elA
:D
escr
ipti
ve
stat
isti
cs
Mea
nM
edia
n1
stQ
uar
tile
3rd
Qu
arti
leS
D
CA
R0
.012
0.0
05
-0
.03
50
.05
10
.103
UE
0.0
02
0.0
00
-0
.00
10
.00
20
.031
LN
AU
5.9
00
5.9
29
4.7
96
7.0
60
1.8
11
PR
NA
U5
8.3
60
62
.00
03
7.0
00
82
.00
02
7.1
33
LT
OT
6.6
96
6.5
75
5.7
30
7.5
12
1.3
38
MB
3.1
26
2.1
73
1.3
67
3.6
18
3.4
86
LE
V0
.627
0.3
38
0.0
16
0.9
54
1.9
06
BE
TA
1.0
16
0.8
20
0.4
62
1.3
88
0.8
08
PE
RS
0.2
72
0.2
21
-0
.00
90
.52
90
.771
VO
L0
.002
0.0
01
0.0
00
0.0
02
0.0
02
AG
E1
4.2
95
9.0
55
4.5
86
22
.05
51
2.5
62
RE
G0
.112
0.0
00
0.0
00
0.0
00
0.3
16
SIZ
E6
.464
6.3
50
5.2
49
7.5
47
1.7
34
LO
SS
0.3
41
0.0
00
0.0
00
1.0
00
0.4
74
RE
ST
R0
.013
0.0
00
0.0
00
0.0
00
0.1
15
SP
EC
0.2
40
0.0
00
0.0
00
0.0
00
0.4
27
INS
T0
.517
0.5
34
0.3
27
0.7
18
0.2
50
Pan
elB
:P
ears
on
corr
elat
ion
mat
rix
CA
RU
EL
NA
UP
RN
AU
LT
OT
MB
LE
VP
ER
SB
ET
AV
OL
AG
ER
EG
SIZ
EL
OS
SR
ES
TR
SP
EC
INS
T
CA
R1
.00
UE
0.0
01
.00
LN
AU
-0
.01
-0
.02
1.0
0
PR
NA
U0
.00
-0
.03
0.9
71
.00
Non-audit fees, institutional monitoring, and audit quality 361
123
Tab
le4
con
tin
ued
Pan
elB
:P
ears
on
corr
elat
ion
mat
rix
CA
RU
EL
NA
UP
RN
AU
LT
OT
MB
LE
VP
ER
SB
ET
AV
OL
AG
ER
EG
SIZ
EL
OS
SR
ES
TR
SP
EC
INS
T
LT
OT
-0
.01
-0
.02
0.9
60
.94
1.0
0
MB
-0
.02
0.0
10
.05
0.0
40
.02
1.0
0
LE
V0
.01
-0
.05
0.3
20
.33
0.3
8-
0.0
51
.00
PE
RS
0.0
0-
0.0
2-
0.0
6-
0.0
5-
0.0
80
.02
-0
.12
1.0
0
BE
TA
0.0
10
.01
0.0
60
.07
0.0
30
.13
-0
.29
0.1
71
.00
VO
L0
.03
0.0
3-
0.3
3-
0.3
2-
0.3
9-
0.0
9-
0.3
70
.13
0.4
41
.00
AG
E-
0.0
1-
0.0
50
.27
0.2
70
.33
-0
.02
0.3
4-
0.0
3-
0.2
3-
0.5
31
.00
RE
G-
0.0
1-
0.0
10
.07
0.0
80
.09
-0
.13
0.3
1-
0.1
0-
0.1
3-
0.1
80
.13
1.0
0
SIZ
E0
.00
0.0
10
.66
0.6
60
.70
0.3
80
.24
-0
.05
0.0
9-
0.5
10
.36
0.1
31
.00
LO
SS
-0
.10
-0
.06
-0
.17
-0
.16
-0
.21
-0
.12
-0
.19
0.0
20
.28
0.4
4-
0.3
0-
0.0
4-
0.3
01
.00
RE
ST
R-
0.0
30
.01
0.0
20
.02
0.0
1-
0.0
2-
0.0
40
.01
0.1
20
.12
-0
.09
-0
.03
-0
.04
0.1
61
.00
SP
EC
0.0
20
.00
0.1
00
.06
0.1
00
.00
-0
.01
0.0
0-
0.0
1-
0.0
40
.03
-0
.02
0.0
70
.01
0.0
11
.00
INS
T0
.04
-0
.02
0.3
30
.33
0.3
60
.15
0.1
30
.02
0.0
9-
0.3
10
.24
-0
.05
0.5
0-
0.2
3-
0.0
40
.03
1.0
0
Th
esa
mple
for
the
earn
ings
retu
rnre
gre
ssio
n(E
RC
)m
od
elis
3,4
25
firm
-yea
ro
bse
rvat
ion
s,w
ith
all
info
rmat
ion
avai
lab
lefr
om
Com
pu
stat
,I/
B/E
/Sd
etai
led
file
s,an
dC
RS
P.
LN
AU
isth
en
atura
llo
go
fn
on
-aud
itfe
es.C
AR
isth
ree-
day
cum
ula
tiv
eab
no
rmal
retu
rns
arou
nd
the
firs
tq
uar
terl
yea
rnin
gs
ann
oun
cem
ent
afte
rth
efe
ed
isco
urs
ein
the
pro
xy
stat
emen
ts.
UE
isea
rnin
gs
surp
rise
,m
easu
red
by
the
dif
fere
nce
bet
wee
nac
tual
earn
ings
per
shar
ean
dm
ost
rece
nt
med
ian
earn
ings
fore
cast
for
the
quar
ter
imm
edia
tely
afte
rth
edis
closu
reof
fee
info
rmat
ion
inth
epro
xy
stat
emen
t,sc
aled
by
stock
pri
ceat
the
beg
innin
gof
the
quar
ter.
Both
actu
alan
dfo
reca
sted
earn
ings
per
shar
ear
efr
om
I/B
/E/S
det
aile
dfi
les.
LN
AU
isth
en
atu
ral
log
of
no
n-a
ud
itfe
es.P
RN
AU
isth
ep
erce
nti
lera
nk
of
ap
arti
cula
rcl
ien
t’s
no
n-a
ud
itfe
esg
iven
all
no
n-a
ud
itfe
esre
ceiv
edb
yth
eau
dit
firm
.L
TO
Tis
the
nat
ura
llo
go
fto
tal
fees
.M
Bis
mar
ket
-to
-bo
ok
rati
oat
end
of
qu
arte
r.L
EV
isd
ebt-
to-e
quit
yra
tio
aten
do
fq
uar
ter.
PE
RS
isea
rnin
gs
per
sist
ence
mea
sure
dby
the
firs
t-ord
erau
toco
rrel
atio
nof
earn
ings
bef
ore
extr
aord
inar
yit
ems
per
shar
efo
rth
epas
t16
quar
ters
;B
ET
Ais
esti
mat
edfr
om
dai
lyst
ock
retu
rns
ov
era
90
-day
win
do
wen
din
g7
day
spri
or
toth
eea
rnin
gs
announce
men
tdat
e;V
OL
isth
ev
aria
nce
of
the
resi
du
alfr
om
the
mar
ket
mo
del
ov
era
90
-day
win
do
wen
din
g7
day
sp
rio
rto
the
earn
ings
ann
oun
cem
ent
dat
e;A
GE
isn
um
ber
of
yea
rsth
atth
efi
rmis
pu
bli
cly
trad
edas
of
the
fisc
aly
ear
end
;R
EG
isan
indic
ato
rv
aria
ble
that
equ
als
on
efo
rfi
rms
ina
reg
ula
ted
indu
stry
(tw
o-d
igit
SIC
40
–4
9),
and
zero
oth
erw
ise;
SIZ
Eis
nat
ura
llo
gof
mar
ket
capit
aliz
atio
nat
end
of
quar
ter.
LO
SS
equ
als
1if
the
curr
ent
qu
arte
r’s
earn
ings
isn
egat
ive,
and
0oth
erw
ise.
RE
ST
Req
ual
s1
ifth
esp
ecia
lit
emas
aper
centa
ge
of
tota
las
sets
inth
equar
ter
isle
ssth
anor
equal
to-
5%
,an
d0
oth
erw
ise;
SP
EC
isco
ded
1if
the
aud
ito
rh
asth
ela
rges
tm
ark
etsh
are
inth
ein
du
stry
,an
d0
oth
erw
ise;
INS
Tis
the
per
cen
to
fth
eco
mp
any
’sag
gre
gat
eco
mm
on
stock
hel
db
yin
stit
uti
on
s
362 C. Y. Lim et al.
123
Ta
ble
5F
eem
etri
cs,
inst
itu
tio
nal
ow
ner
ship
,an
dth
eea
rnin
gs
retu
rnre
lati
on
Pan
elA
:F
ull
sam
ple
LN
AU
PR
NA
UL
TO
T
Inte
rcep
ta 0
0.0
08
(0.8
8)
0.0
15
(1.0
6)
0.0
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(0.4
0)
1.3
56
(0.3
3)
0.8
45
(2.1
3)*
*0
.278
(1.0
7)
0.9
04
(1.2
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FE
E*
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EC
a 28
0.0
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(0.8
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0.0
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0.0
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(1.3
9)
0.0
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(0.3
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(0.1
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00
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0.1
72
(2.1
6)*
*0
.005
(2.8
7)*
**
0.1
57
(2.2
3)*
*
Non-audit fees, institutional monitoring, and audit quality 367
123
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368 C. Y. Lim et al.
123
Overall, our results are consistent with the prediction in H1, and indicate that, as non-
audit fees increase, earnings response coefficients (and hence audit quality) correspond-
ingly reduces only for firms with low institutional ownership, but not for firms with high
institutional ownership.
6 Additional analyses
6.1 Control for operating volatility
Hribar and Nichols (2007) show that the error variance in the discretionary accrual esti-
mation models is associated with operating volatility. Hence, following their recommen-
dation, we include standard deviation of operating cash flow (rCFO) and standard deviation
of sales (rREV) in Eq. (3) as additional independent variables. Both variables are deflated
by total assets, over the current and prior 4 years. The results are reported in Table 6.
Consistent with the evidence reported in Hribar and Nichols (2007), both rCFO and rREV
are positive and significantly associated with APA_DCA at the 1 % level. The coefficient
estimates for the interaction term (LNAU*INST) and (PRNAU*INST) are still negative and
significant at the conventional levels. Our results are robust after controlling for firms’
operating volatility and that our main results are not driven by omitted correlated variables.
6.2 Alternative proxy for performance-adjusted discretionary current accruals
Prior research documents that discretionary accrual estimates are correlated with firm
performance (e.g., Kothari et al. 2005). In the main test, we adjust performance by
including a lagged ROA in the modified Jones (1991) model. As a robustness check, we
compute an alternative proxy for performance-adjusted discretionary current accruals. As
suggested by Kothari et al. (2005) and consistent with Ashbaugh et al. (2003), we array
firms in each industry (by two-digit SIC code) into deciles based on the prior year’s return
on assets (ROA), and obtain the performance-adjusted abnormal accruals by subtracting
from each firm’s abnormal accrual the median abnormal accrual from the corresponding
ROA industry decile to which the firm belongs. We then take the absolute value of the
accruals measure. Our untabulated results indicate that our main results still hold with this
alternative proxy for discretionary current accruals. Specifically, the coefficient for
LNAU*INST and PRNAU*INST are both negative and significant at the 1 % level for the
full sample. Splitting the sample into high or low institutional ownership, the coefficient
for LNAU and PRNAU are positive and significant at the 5 % level for firms with low
institutional ownership, but insignificant for firms with high institutional ownership.
6.3 Fee ratio as a proxy for fee dependence
The ratio of non-audit fees to total fees as a proxy for fee dependence has been criticized in
the prior literature (e.g., Kinney and Libby 2002). Nevertheless, we investigate the effect of
the fee ratio on audit quality on the full sample since the original regulatory concern about
auditor independence is that non-audit fees exceeds audit fee.
In the accruals test, consistent with Frankel et al. (2002), our untabulated results indicate
that fee ratio (FEERATIO) is positively associated with APA_DCA at the 1 % level.
Further, we find that the coefficient for the interaction term (FEERATIO*INST) is negative
and significant at the 1 % level. We obtain similar results for the ERC test. Specifically, the
Non-audit fees, institutional monitoring, and audit quality 369
123
Table 6 Discretionary current accruals model: controlling for operating volatility
LNAU PRNAU LTOT
Intercept x0 -0.064(-1.52)
-0.003(-0.12)
0.149(1.19)
FEE x1 0.008(2.56)***
0.001(3.05)***
-0.012(-1.14)
TENU x2 -0.003(-4.12)***
-0.003(-4.20)***
-0.003(-4.08)***
CFO x3 -0.206(-6.61)***
-0.211(-6.77)***
-0.207(-6.65)***
LEV x4 -0.003(-1.33)
-0.003(-1.49)
-0.002(-1.09)
LITIG x5 0.119(9.59)***
0.120(9.66)***
0.116(9.35)***
MB x6 0.012(7.41)***
0.012(7.62)***
0.011(6.79)***
SIZE x7 0.003(0.75)
-0.001(-0.26)
0.010(2.15)**
LOSS x8 0.107(7.74)***
0.104(7.51)***
0.111(8.03)***
FIN x9 0.070(5.25)***
0.072(5.36)***
0.069(5.17)***
LAG_ACA x10 0.149(4.01)***
0.147(3.96)***
0.147(3.95)***
ZS x11 0.052(1.77)*
0.046(1.57)
0.060(2.02)**
SPEC x12 -0.062(-1.69)*
-0.018(-0.67)
-0.021(-0.17)
INST x13 0.190(1.89)*
0.001(0.00)
-0.074(-0.31)
Y00 x14 0.196(16.90)***
0.194(16.74)***
0.197(16.98)***
FEE*SPEC x15 -0.006(-1.23)
-0.001(-0.47)
-0.001(-0.11)
FEE*INST x16 -0.024(-2.96)***
-0.002(-2.36)**
-0.002(-0.09)
rCFO x17 0.089(5.45)***
0.087(5.33)***
0.092(5.57)***
rREV x18 0.220(7.55)***
0.219(7.52)***
0.211(7.21)***
N 5,817 5,817 5,817
F-statistic 83.91*** 83.66*** 83.29***
Adj R2 20.29 % 20.24 % 20.17 %
The sample for the accruals test consists of 5,817 firm-year observations for the period 2000–2001 that have
complete financial information in the COMPUSTAT. The regression model is: APA DCA ¼x0 þ x1FEEþ x2TENUþx3CFOþ u4LEVþ x5LITIGþ x6MBþx7SIZE þ x8LOSS þ x9FINþ x10
LAG ACAþþx11ZSþ x12SPECþ x13INSTþ x14Y00þ x15FEE � SPECþ x16FEE � INSTþx17rCFO
þx18 rREV þ eSee Table 2 for the definitions of the variables used in the regression. rCFO is standard deviationof operating cash flow deflated by total assets, over the current and prior 4 years. rREV is standard deviationof sales deflated by total assets, over the current and prior 4 years. Y00 is year dummy. ‘*’, ‘**’, and ‘***’ denotesignificance at 10, 5, and 1 % levels (two-tailed), respectively
370 C. Y. Lim et al.
123
coefficient for UE*FEERATIO is negative and significant at the 1 % level. In addition, the
coefficient for UE*FEERATIO*INST is positive and significant at the 1 % level. Overall
our results suggest that audit quality is indeed lower when the proportion of non-audit fees
is high relative to total fees. However, the negative effect of fee ratio on audit quality is
moderated by the presence of high institutional ownership.
6.4 Size effect and multi-collinearity
Since many variables used in the regression analyses are correlated with SIZE, we perform
a sensitivity analysis to test whether the study’s main results are confounded by size
effects. There is high correlation between firm size and fee proxies (ranges from 0.66 to
0.70, see Panel B of Tables 2 and 4). The high correlations also raise concerns about multi-
collinearity in the analysis. To ensure that our results are not spurious due to the high
correlations between fees and firm size, following Simunic (1984) and Abbott et al. (2003),
we deflate non-audit and total fees by the square root of total assets and regress the deflated
non-audit fees (NAU/TA) and deflated total fees (TOT/TA) on APA_DCA and ERC in the
presence of other controls except SIZE. The results are reported in Table 7. For the
accruals test (reported in Panel A), the coefficient estimates for the interaction term
between fees and institutional ownership are negative and significant for NAU/TA and
TOT/AT at 5 % level or lower. In the ERC test (reported in Panel B), the coefficient
estimates for the interaction term, UE*FEE*INST, are positive and significant at the 1 %
level, when fees are measured by both NAU/AT and TOT/AT. Overall, these findings are
consistent with the main results reported in Tables 3 and 5.
The correlations between size and institution ownership are also high (0.67 and 0.5, see
Panel B of Tables 2, 4), which raises the possibility that fees and institutions are substitutes
and not distinct constructs. If this were the case, it would imply that our findings of an
interaction between INST by FEE should be replicated when we replace INST with firm
size. On the other hand, if they are distinct constructs, the results may be distinct. We
report the results for the accruals and ERC tests in Panel A and B of Table 8, respectively.
For the accruals test, we exclude INST but include FEE, SIZE, and their interaction in
the models. Our results indicate that the coefficients of the interaction term FEE*SIZE are
all insignificant across the three fee measures whereas, in our main analysis, we find the
coefficient of the interaction term FEE*INST to be negative and significant when fee is
measured by LNAU and PRNAU.
For the ERC test, we exclude INST but include FEE, SIZE, and their interactions with
UE in the models. If firm size and institutional ownership are indeed the same construct,
then we will expect the coefficient for UE*FEE*SIZE to be positive and significant.
However, we find that the coefficients for UE*FEE*SIZE are insignificant in all models,
whereas, in our main analysis, we find that the coefficients for UE*FEE*INST are positive
and significant in all models. Taken together, our findings for the accruals and ERC tests
suggest that institutions and firm size are different constructs.
Lastly, to further address any concerns about multi-collinearity, we check the variance-
inflation factors (VIF) of the variables in the regression model. In the accruals test, we find
that the VIF for all the variables (including the interaction terms) are less than ten. In the
ERC test, the threat of multi-collinearity is higher because of the many interaction terms
included in the model. We examine the VIF for the subsample of firms with high and low
institutional ownership. All the variables (including the interaction terms) have VIF less
than ten, with the exception for UE*SIZE (with VIF ranging from 24 to 78). These VIF
values appear large and may pose a threat to the reliability of the inferences made.
Non-audit fees, institutional monitoring, and audit quality 371
123
Table 7 Additional analysis: non-audit fees and firm size
Panel A: Discretionary current accruals
NAU/AT LTOT/AT
Intercept x0 0.067(3.64)***
0.072(3.77)***
FEE x1 0.001(5.47)***
0.001(2.52)***
TENU x2 -0.004(-5.00)***
-0.004(-4.89)***
CFO x3 -0.206(-6.82)***
-0.201(-6.44)***
LEV x4 -0.004(-2.08)**
-0.004(-2.01)**
LITIG x5 0.127(10.36)***
0.126(10.31)***
MB x6 0.012(7.57)***
0.012(7.63)***
LOSS x8 0.106(7.86)***
0.104(7.66)***
FIN x9 0.075(5.62)***
0.075(5.67)***
LAG_ACA x10 0.151(4.17)***
0.137(3.74)***
ZS x11 0.057(1.98)**
0.059(2.05)**
SPEC x12 -0.039(-2.47)**
-0.033(-2.02)**
INST x13 -0.077(-3.03)***
-0.114(-4.33)***
Y00 x14 0.194(16.86)***
0.198(17.15)***
FEE*SPEC x15 -0.001(-1.16)
0.001(-1.11)
FEE*INST x16 -0.003(-2.04)**
-0.002(-4.41)***
N 5,951 5,951
F-statistic 103.70*** 100.55***
Adj R2 20.57 % 20.06 %
Panel B: The earnings return relation
NAU/AT LTOT/AT
Intercept a0 0.005(0.84)
0.006(0.91)
UE a1 0.799(3.37)***
1.035(4.11)***
FEE a2 -0.005(-2.26)**
-0.002(-1.72)*
UE*FEE a3 -0.237(-3.74)***
-0.151(-3.99)***
372 C. Y. Lim et al.
123
Table 7 continued
Panel B: The earnings return relation
NAU/AT LTOT/AT
MB a4 -0.001(-1.30)
-0.001(-1.13)
UE*MB a5 0.036(1.66)*
0.059(2.31)**
LEV a6 0.002(1.61)
0.002(1.53)
UE*LEV a7 -0.012(-0.69)
-0.005(-0.27)
PERS a8 0.003(1.16)
0.002(1.02)
UE*PERS a9 0.358(2.57)***
0.278(2.04)**
BETA a10 0.005(2.19)**
0.0053(2.04)**
UE*BETA a11 0.181(1.08)
0.181(1.98)**
VOL a12 7.323(6.36)***
7.232(6.21)***
UE*VOL a13 -45.317(-2.41)**
-33.027(-1.76)*
AGE a14 -0.001(-1.30)
-0.001(-1.33)
UE*AGE a15 -0.001(-4.95)***
-0.041(-4.47)***
REG a16 -0.002(-0.36)
-0.002(-0.41)
UE*REG a17 0.545(2.59)***
0.444(2.01)**
LOSS a20 -0.029(-7.01)***
-0.029(-6.92)***
UE*LOSS a21 -0.209(-1.12)
-0.233(-1.24)
RESTR a22 -0.030(-1.90)*
-0.031(-1.95)**
UE*RESTR a23 -0.631(-1.26)
-0.791(-1.65)*
SPEC a24 -0.002(-0.38)
0.002(0.14)
UE*SPEC a25 0.434(2.28)**
0.580(2.34)**
INST a26 0.003(0.32)
0.002(0.19)
UE*INST a27 -0.319(-0.72)
-0.609(-1.12)
FEE*SPEC a28 -0.001(-0.52)
-0.002(-1.24)
Non-audit fees, institutional monitoring, and audit quality 373
123
However, the concern is somewhat mitigated because, from the results of our sensitivity
tests, our main findings do not appear to be driven by the firm size effect. Notwithstanding
this, our results should be interpreted with caution as we cannot resolve completely the
multi-collinearity problem in the ERC test.
6.5 Endogeneity between institutional ownership and audit quality
The relation between audit quality (measured by discretionary current accruals and ERC)
and institutions may be endogenous. Institutional investors may choose to invest in firms
with low discretionary accruals or high ERC. To address this potential endogeneity issue, we
employ a two-stage least-squares analysis by using the following first-stage regression to
cross-sectionally estimate the intercept and coefficients of the determinant variables of INST:
INST ¼ b0 þ b1APA DCAþ b2SIZE þ b3SQSIZE þ b4LITIGþ b5MBþ b6LOSS
þ b7DIVIDENDþ b8LIQUIDITY þ b9SNPþ b10LAG RET þ b11Y00þ e ð5Þ
where
SQSIZE = squared term of SIZE;
DIVIDEND = dividend pay-out ratio;
LIQUIDITY = log(trading volume/shares outstanding);
SNP = 1 if the company is part of the S&P 500, and 0 otherwise;
LAG_RET = stock returns for the previous fiscal year;
Other variables are as defined in Eq. (3).
The instrumental variables for Eq. (5) are drawn from Jiambalvo et al. (2002) and
Ajinkya et al. (2005). Institutions prefer to invest in large firms (measured by SIZE),
Table 7 continued
Panel B: The earnings return relation
NAU/AT LTOT/AT
FEE*INST a29 0.006(1.35)
0.003(1.03)
UE*FEE*SPEC a30 0.077(1.14)
0.048(1.09)
UE*FEE*INST a31 0.703(3.58)***
0.327(2.86)***
Y00 a32 0.008(2.17)**
0.007(1.95)**
UE*Y00 a33 1.013(0.66)
0.047(0.31)
N 3,425 3,425
F-statistic 7.03*** 7.03***
Adj R2 5.17 % 5.17 %
We deflate non-audit and total fees by the square root of total assets and regress the deflated non-audit fees(NAU/TA) and deflated total fees (TOT/TA) on APA_DCA and ERC in the presence of other controls exceptSIZE. The results for the accruals test are reported in Panel A. The results for the ERC test are reported in PanelB. The models used in accruals and ERC tests, are similar to those reported in Tables 3 and 5, respectively. Forease of reporting, in Panel A, the coefficient estimate for NAU_AT, TOT_AT, and their interactions with INSTare multiply by 104. In Panel B, the coefficient estimate for NAU_AT and TOT_AT are multiplied by 104. ‘*’,‘**’, and ‘***’ denote significance at 10, 5, and 1 % levels (two-tailed), respectively
374 C. Y. Lim et al.
123
Table 8 Additional analysis: institutional ownership and firm size
Panel A: Discretionary current accruals
LNAU PRNAU LTOT
Intercept x0 0.063(1.89)*
0.066(3.15)***
0.252(2.60)***
FEE x1 0.004(1.34)
0.001(2.71)**
-0.015(-1.71)*
TENU x2 -0.004(-5.45)***
-0.004(-5.23)***
-0.004(-5.17)***
CFO x3 -0.257(-8.64)***
-0.260(-8.76)***
-0.256(-8.62)***
LEV x4 -0.005(-2.43)**
-0.005(-2.57)***
-0.004(-2.12)**
LITIG x5 0.134(10.85)***
0.135(10.91)***
0.130(10.49)***
MB x6 0.013(8.14)***
0.014(8.50)***
0.012(7.32)***
SIZE x7 0.010(0.84)
-0.002(-0.37)
-0.003(-0.12)
LOSS x8 0.108(7.85)***
0.105(7.68)***
0.115(8.25)***
FIN x9 0.076(5.63)***
0.077(5.71)***
0.073(5.45)***
LAG_ACA x10 0.174(4.75)***
0.175(4.79)***
0.178(4.86)***
ZS x11 0.063(2.17)**
0.060(2.05)**
0.074(2.50)***
SPEC x12 -0.041(-1.72)*
-0.022(-1.81)*
-0.032(-2.25)**
Y00 x14 0.196(16.92)***
0.195(16.81)***
0.198(17.06)***
FEE*SPEC x15 0.004(0.85)
-0.001(-0.56)
-0.002(-0.19)
FEE*SIZE x17 -0.001(-0.79)
-0.001(-1.11)
0.001(1.54)
N 5,851 5,851 5,851
F-statistic 93.69*** 93.99*** 93.65***
Adj R2 18.94 % 18.99 % 18.93 %
Panel B: ERC test
LNAU PRNAU LTOT
Intercept a0 0.008(0.40)
-0.001(-0.05)
-0.016(-0.49)
UE a1 0.760(0.99)
0.324(0.60)
1.038(0.94)
FEE a2 -0.001(-0.08)
0.001(0.69)
0.004(0.77)
UE*FEE a3 0.299(2.21)**
0.026(2.85)***
0.291(1.61)
Non-audit fees, institutional monitoring, and audit quality 375
123
Table 8 continued
Panel B: ERC test
LNAU PRNAU LTOT
MB a4 -0.001(-1.25)
-0.001(-1.15)
-0.001(-1.00)
UE*MB a5 0.033(1.35)
0.027(1.10)
0.034(1.39)
LEV a6 0.002(1.80)*
0.002(1.69)*
0.002(1.62)
UE*LEV a7 -0.005(-0.30)
0.001(0.05)
-0.006(-0.35)
PERS a8 0.002(1.04)
0.004(1.50)
0.002(1.05)
UE*PERS a9 0.410(2.95)***
0.445(3.23)***
0.408(2.99)***
BETA a10 0.008(2.86)***
0.007(2.72)***
0.007(2.67)***
UE*BETA a11 0.164(1.76)*
0.169(1.73)*
0.158(1.66)*
VOL a12 5.978(5.06)***
6.171(5.20)***
6.048(5.10)***
UE*VOL a13 -57.231(-2.63)***
-58.458(-2.71)***
-57.350(-2.70)***
AGE a14 -0.001(-0.69)
-0.001(-0.58)
-0.001(-0.66)
UE*AGE a15 -0.038(-4.55)***
-0.036(-4.14)***
-0.039(-4.69)***
REG a16 0.001(0.05)
0.001(0.04)
0.001(0.10)
UE*REG a17 0.784(3.54)***
0.777(3.43)***
0.732(3.24)***
SIZE a18 0.000(0.00)
0.001(0.33)
0.003(0.63)
UE*SIZE a19 0.372(2.22)**
0.230(1.85)*
0.515(2.23)**
LOSS a20 -0.031(-7.46)***
-0.031(-7.35)***
-0.031(-7.43)***
UE*LOSS a21 -0.186(-0.95)
-0.137(-0.68)
-0.202(-1.03)
RESTR a22 -0.031(-1.99)**
-0.031(-1.99)**
-0.031(-2.00)**
UE*RESTR a23 -0.985(-2.08)**
-1.001(-2.11)**
-1.027(-2.16)**
SPEC a24 -0.007(-0.50)
-0.009(-0.91)
-0.011(-0.55)
UE*SPEC a25 0.029(0.08)
0.157(0.68)
0.018(0.03)
FEE*SPEC a28 0.001(0.30)
0.001(0.62)
0.001(0.40)
UE*FEE*SPEC a30 0.080(2.04)**
0.008(1.69)*
0.074(1.65)*
376 C. Y. Lim et al.
123
although their demand for stocks is a concave function of firm’s market capitalization
(measured by SQSIZE). Furthermore, institutions prefer to invest in liquid stocks (LIQU-
DIITY), firms that are listed in S&P500 (SNP) and firms that have lower returns in the
previous year (LAG_RET). Institutions also tend to avoid investing in loss-making firms
(LOSS), firms with high dividend yield (DIVIDEND) and market-to-book ratios (MB). We
compute the inverse Mills ratio (IMR) from stage one regression. In the second stage, we
estimate Eqs. (3) and (4) after including IMR as an additional independent variable.
The results for the first-stage regression are reported in Panel A of Table 9. Most
variables are significant and behave as conjectured. Institutional investors tend to invest in
large firms, firms with high accrual quality, and more liquid stocks. They also tend to
invest less in loss-making firms, dividend-paying firms, firms with high market-to-book
ratio, and firms with high past stock returns. The adjusted R-square for the model is 50 %.
We report the second-stage regression results for the accruals and ERC tests in Panel B
and C of Table 9 respectively, with IMR as an additional independent variable. For the
accruals test, IMR is positive and significantly associated with APA_DCA in all three
models. After controlling for the self-selection problem, the interaction term (FEE*INST)
is negative and significant at conventional level for all three fee proxies, consistent with
our main results. For the ERC test, IMR is not associated with ERC, and the coefficients for
UE*FEE*INST continue to be significant at 1 % level in all three models.
Overall, the results reported in Table 9 are similar to those reported in Tables 3 and 5;
our main results are not likely driven by the potential endogeneity between institutional
ownership and audit quality.
6.6 Inclusion of firms audited by non-Big 5 auditors
In this study, we only include firms audited by Big 5 auditors in the sample to control for
the effect of brand name (e.g., Lim and Tan 2008). We test the robustness of our results
Table 8 continued
Panel B: ERC test
LNAU PRNAU LTOT
Y00 a32 0.008(2.07)**
0.007(2.03)**
0.007(2.05)**
UE*Y00 a33 0.142(0.80)
0.104(0.56)
0.140(0.78)
FEE*SIZE a34 -0.001(-0.16)
-0.001(-0.78)
-0.001(-0.89)
UE*FEE*SIZE a35 -0.079(1.02)
-0.007(-1.28)
-0.074(-0.65)
N 3,425 3,425 3,425
F-statistic 6.35*** 6.50*** 6.35***
Adj R2 4.62 % 4.80 % 4.62 %
In Panel A, we report the results for the accruals test. We exclude INST but include SIZE, FEE, andFEE*SIZE in the models. We report the results for the ERC test in Panel B. For the ERC test, we excludeINST but include FEE, SIZE, and their interactions with UE in the models. The models used in accruals andERC tests, are same as those reported in Tables 3 and 5, respectively. ‘*’, ‘**’, and ‘***’ denote signifi-cance at 10, 5, and 1 % levels (two-tailed), respectively
Non-audit fees, institutional monitoring, and audit quality 377
123
Table 9 Two-stage least squares–controlling for endogeneity between institutional ownership and auditquality
Panel A: Determinants of institutional ownership
Variable Predicted sign Coefficient estimate (t statistic)
Intercept -0.584(-26.19)***
APA_DCA – -0.047(-6.49)***
SIZE ? 0.153(25.87)***
SQSIZE – -0.008(-15.32)***
LITIG ? -0.042(-6.74)***
MB – -0.002(-2.57)***
LOSS – -0.043(-6.75)***
DIVIDEND – -0.074(-2.29)**
LIQUIDITY ? 0.070(22.63)***
SNP ? 0.019(1.57)
LAG_RET – -0.008(-3.18)***
Y00 ? 0.016(2.77)***
N 4,856
Adjusted R2 (%) 50.34
F statistic 448.39
Panel B: Discretionary current accruals
LNAU PRNAU LTOT
Intercept x0 -0.127(-2.46)***
-0.079(-1.99)**
0.217(1.70)*
FEE x1 0.004(1.28)
-0.001(-0.24)
-0.029(-2.91)***
TENU x2 -0.002(-4.28)***
-0.003(-4.35)***
-0.003(-3.85)***
CFO x3 -0.271(-8.52)***
-0.270(-8.48)***
-0.267(-8.38)***
LEV x4 -0.005(-2.63)***
-0.005(-2.57)***
-0.004(-2.18)**
LITIG x5 0.083(7.12)***
0.082(7.02)***
0.075(6.48)***
MB x6 0.012(7.73)***
0.012(7.46)***
0.010(6.60)***
378 C. Y. Lim et al.
123
Table 9 continued
Panel B: Discretionary current accruals
LNAU PRNAU LTOT
SIZE x7 0.023(4.56)***
0.025(4.56)***
0.038(6.29)***
LOSS x8 0.068(5.23)***
0.069(5.30)***
0.076(5.83)***
FIN x9 0.049(3.83)***
0.048(3.76)***
0.044(3.45)***
LAG_ACA x10 0.306(7.92)***
0.305(7.89)***
0.311(8.05)***
ZS x11 0.026(0.88)
0.029(0.97)
0.039(1.29)
SPEC x12 -0.046(-1.89)*
-0.016(-1.64)*
-0.053(-2.27)**
INST x13 0.333(1.45)
0.051(1.01)
0.147(0.65)
Y00 x14 0.135(12.53)***
0.135(12.54)***
0.137(12.71)***
FEE*SPEC x15 0.004(0.94)
-0.001(-0.84)
-0.004(-0.45)
FEE*INST x16 -0.029(-3.83)***
-0.001(-1.65)*
-0.013(-2.75)***
IMR x17 0.048(6.57)***
0.045(6.08)***
0.050(6.60)***
N 4.856 4.856 4.856
F-statistic 59.48*** 58.99*** 60.59***
Adj R2 17.00 % 16.88 % 17.26 %
Panel C: The earnings return relation
LNAU PRNAU LTOT
Intercept a0 -0.007(-0.39)
-0.010(-0.61)
-0.033(-1.20)
UE a1 2.423(4.92)***
1.834(4.33)***
3.267(4.57)***
FEE a2 0.001(0.01)
0.001(0.70)
0.005(1.16)
UE*FEE a3 -0.282(-2.88)***
-0.019(-3.30)***
-0.407(-3.11)***
MB a4 -0.001(-1.28)
-0.001(-1.07)
-0.001(-0.97)
UE*MB a5 0.057(2.29)**
0.061(2.45)**
0.058(2.34)**
LEV a6 0.001(0.82)
0.001(0.68)
0.001(0.71)
UE*LEV a7 -0.020(-1.07)
-0.025(-1.29)
-0.019(-1.03)
PERS a8 0.002(0.60)
0.002(0.62)
0.002(0.57)
Non-audit fees, institutional monitoring, and audit quality 379
123
Table 9 continued
Panel C: The earnings return relation
LNAU PRNAU LTOT
UE*PERS a9 0.341(1.96)**
0.449(2.57)***
0.294(1.71)*
BETA a10 0.007(2.33)**
0.007(2.32)**
0.007(2.24)**
UE*BETA a11 0.304(2.92)***
0.333(3.17)***
0.283(2.69)***
VOL a12 8.382(6.27)***
8.367(6.29)***
8.293(6.19)***
UE*VOL a13 -80.585(-3.12)***
-78.928(-3.10)***
-78.892(-3.10)***
AGE a14 -0.001(-0.49)
-0.001(-0.51)
-0.001(-0.49)
UE*AGE a15 -0.061(-6.47)***
-0.061(-6.46)***
-0.060(-6.30)***
REG a16 0.002(0.36)
0.002(0.33)
0.002(0.34)
UE*REG a17 0.627(2.59)***
0.545(2.26)**
0.626(2.57)***
SIZE a18 -0.001(-0.23)
-0.001(-0.56)
-0.001(-0.61)
UE*SIZE a19 -0.147(-1.48)
-0.154(-1.58)
-0.098(-0.96)
LOSS a20 -0.031(-6.71)***
-0.031(-6.80)***
-0.031(-6.71)***
UE*LOSS a21 -0.357(-1.70)*
-0.422(-2.02)**
-0.345(-1.64)*
RESTR a22 -0.020(-1.18)
-0.021(-1.23)
-0.021(-1.23)
UE*RESTR a23 -1.576(-2.59)***
-1.782(-2.46)***
-1.921(-2.66)***
SPEC a24 -0.005(-0.37)
-0.007(-0.73)
-0.008(-0.39)
UE*SPEC a25 0.250(1.65)*
0.179(1.68)*
0.381(2.44)**
INST a26 0.021(0.82)
0.028(1.51)
0.066(1.56)
UE*INST a27 -0.282(-2.20)**
-1.858(-2.67)***
-4.283(-2.06)**
FEE*SPEC a28 0.001(0.03)
0.001(-0.23)
0.001(0.15)
FEE*INST a29 -0.001(-0.12)
-0.001(-0.60)
-0.007(-1.15)
UE*FEE*SPEC a30 0.028(1.68)*
0.007(1.73)*
0.005(0.03)
UE*FEE*INST a81 0.735(3.27)***
0.059(4.75)***
0.858(2.60)***
380 C. Y. Lim et al.
123
when we also include firms audited by non-big 5 auditors.10 Specifically, in the accruals
test, we include an additional dummy for Big5 (1 if the firm is audited by a Big 5 auditor
and 0 otherwise) while in the ERC test, we include Big5 and UE*Big5 in the regression
model. Our main results are robust with the inclusion of these variables. Specifically, in the
accruals test, the coefficient for FEE*INST is negative and significant at the 1 % level
when fee is measured by LNAU and PRNAU. In the ERC test, we continue to find all three
proxies for fee to be negatively associated with ERC at the 1 % level for firms with a low
level of institutional ownership but not for those whose institutional ownership is high.
Overall, our inferences remain unchanged when we also include firms audited by non-Big
5 auditors in the sample.
6.7 Removing firms from dominant industries
Firms from two industries (computers and durable manufacturers) comprise more than
40 % of the total sample, see Panel A of Table 1). We conduct additional sensitivity
analysis by excluding firms in these two industries. The results (untabulated) are robust for
both the accruals- and ERC- tests. Specifically, in the accruals test, the coefficient for
Table 9 continued
Panel C: The earnings return relation
LNAU PRNAU LTOT
Y00 a32 0.007(1.79)*
0.007(1.75)*
0.007(1.78)*
UE*Y00 a33 0.281(1.38)
0.331(1.62)
0.279(1.37)
IMR a34 0.003(0.98)
0.003(1.03)
0.004(1.05)
N 3,067 3,067 3,067
F-statistic 6.13*** 6.47*** 6.09***
Adj R2 5.39 % 5.72 % 5.35 %
To address the potential endogeneity between institutional ownership and audit quality, we employ a two-stage least-squares analysis. We report the results for the first stage in this table. Panel A reports the resultsfor running the following first-stage regression: INST ¼ b0 þ b1APA DCAþ b2SIZE þ b3SQSIZE þb4LITIGþ b5MBþ b6LOSSþ b7DIVIDENDþ b8LIQUIDITY þ b9SNPþ b10LAG RET þ b11Y00þ ewhere SQSIZE is the squared term of SIZE; DIVIDEND is dividend pay-out ratio; LIQUIDITY is log oftrading volume divided by number of shares outstanding; SNP is an indicator variable, coded 1 if thecompany is part of the S&P 500, and 0 otherwise. LAG_RET is the stock returns for the previous fiscal year.Other variables are as defined in footnotes of Table 2. The inverse-Mill-ratio is computed from stage oneregression and is included as an additional independent variable in stage-two regression. We report theresults for the second-stage regression for the accruals and ERC tests in Panel B and C. The models used inaccruals and ERC tests, are same as those reported in Tables 3 and 5, respectively. ‘*’, ‘**’, and ‘***’denote significance at 10, 5, and 1 % levels (two-tailed), respectively
10 The number of firms audited by non-Big 5 auditors and the extent of institutional ownership of thesefirms are very small in comparison to firms audited by Big 5 auditors. For example, in the discretionarycurrent accruals test, 772 firms are audited by non-big 5, compared to 5,951 firms audited by Big 5. Themean (median) institutional ownership of firms audited by non-big 5 auditors is only 13 % (4 %) whereasthe mean (median) institutional ownership of firms audited by Big 5 auditors is 40 % (39 %). Hence, we donot include those firms audited by non-Big 5 auditors in our main analysis because of the small number andlow institutional ownership.
Non-audit fees, institutional monitoring, and audit quality 381
123
FEE*INST is negative and significant when fee is measured by LNAU and PRNAU while in
the ERC test, the coefficient for UE*FEE*INST is positive and significant for all three fee
measures.
7 Conclusion
Recent studies on whether the provision of non-audit services impairs auditor indepen-
dence and audit quality have found mixed results, depending on the proxy for audit quality
used. We posit and provide consistent evidence that external monitoring by institutional
investors affects the association between non-audit fees and audit quality. We argue that
sophisticated institutional investors play an active monitoring role in influencing firms’
financial reporting. Auditors are likely to be more independent and preserve audit quality
due to their concerns about reputations and litigation exposure when they also provide non-
audit services to clients.
Our empirical results indicate that the relation between non-audit fees and audit quality
is contingent on the extent of institutional monitoring. We document that, as non-audit fees
increase, audit quality is reduced only for firms with low institutional ownership, but audit
quality is not impaired for firms with high institutional ownership. Our results lend support
for the beneficial monitoring effect of financial intermediaries in mitigating the potential
impairment of auditor independence arising from high fee dependence between clients and
auditors. Our results remain robust even after controlling for auditor industry specializa-
tion, firms’ operating volatility, the size effect, and the potential endogeneity between
institutional ownership and audit quality.
Acknowledgments The authors gratefully acknowledge the helpful comments on various versions of thepaper by Hun-Tong Tan, Tiong-Yang Thong, Terence Ng, Divesh Shankar Sharma, El’fred Boo, andseminar participants at the Nanyang Business School’s Faculty Workshop.
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