auditors and principal-principal agency conflicts in
TRANSCRIPT
1
Auditors and Principal-Principal Agency Conflicts in
Family-controlled Firms
Chiraz Ben Ali1
IPAG Lab, France
Sabri Boubaker
IRG, Université Paris Est, France
Champagne School of Management, Groupe ESC Troyes, France
Michel Magnan
Concordia university
Abstract
This study examines whether control competition between the largest controlling shareholders
(LCS) and multiple large shareholders (MLS) has an impact on audit fees. Auditors’ effort
(measured by audit fees) decrease in firms where auditors perceived lower audit risk. MLS are
likely to play a corporate governance role to mitigate LCS control power reducing agency
conflicts and auditors’ effort. Using a hand-collected sample of 1,483 firm-year observations
representing French family controlled firms over the period 2003-2010, our results show (1) a
positive relationship between excess control of LCS and audit fees suggesting that auditors
ask for a fee premium for higher principal-principal conflict (2) audit fees decrease with the
presence, number and voting power of MLS evidencing that multiple large shareholders play
an important monitoring role.
Keywords: agency problems, audit fees, corporate governance
1 Chiraz Ben Ali, Associate Professor of Accounting IPAG Business School 184 Boulevard Saint-Germain 75006 Paris, France Tel (Office) : +33 (1) 55 04 11 45 Tél : +33 (0)1 55041145 [email protected]
Preliminary draft. No citation permitted. Comments welcome. This draft –27th, February, 2014
2
Auditors and Principal-Principal Agency Conflicts in
Family-controlled Firms
1. Introduction
Previous corporate governance literature evidenced that ownership concentration is
predominant in listed firms suggesting the prevalence of the principal-principal conflict
(Holderness, 2009). However, these studies focuses on the largest controlling shareholder
(LCS) or consider all controlling shareholders together to study his/their opportunity to
extract private benefits at the expense of minority shareholders. Conversely, the presence of
other blockholders, called multiple large shareholders (MLS) besides the largest controlling
shareholder (LCS) (Laeven and Levine, 2008) could bring new insight in this corporate
governance framework. Indeed, MLS are likely to monitor the largest LCS (Bolton and Von
Thadden, 1998; Laeven and Levine, 2008; Mishra, 2011) and compete for control (Attig et
al., 2008) which alleviate information asymmetry and agency conflicts. As auditors consider
agency conflicts in their audit risk assessment (Khalil et al., 2008) to determine audit effort,
firms facing opportunistic behavior from managers or largest controlling shareholder present
higher audit fees. At the opposte firms with low agency conflicts face lower audit fees.
We believe the distinction between MLS and LCS is important in explaining why previous
research has provided mixed evidence of the relation between blockholders ownership and
audit fees (Hay et al., 2006). Our paper examine whether control contestability between LCS
and MLS has an impact on audit pricing in a concentrated ownership context. We focus on
family controlled firms because families have stronger incentives to expropriate wealth from
minority shareholders compared to widely-held corporations because private benefits of
control are not diluted among several independent owners (Villalonga and Amit, 2006)2.
We conduct our research in the context of French firms because France presents an interesting
setting. First, it is a country which has been used as a typical representative of a weak investor
protection country family (La Porta et al., 2000). Second, listed French firms present high
ownership concentration (Faccio and Lang, 2002; Ben Ali, 2013) and are mostly controlled by
families that participate to the management (Boubaker et al., 2014).
2 For information, we also have data for non-family controlled firms see Table 9: Robustness tests Set 2:
Alternative sample compositions
3
We use regression analyses on non-financial French listed companies and focus on family
controlled firms. Our final sample comprises a panel of 1,483 firm-year observations over the
period 2003-2010. Our results are consistent with our hypotheses evidencing a negative
relationship between audit fees and multiple large shareholders (MLS) power which supports
that MLS play an important role in monitoring LCS. First, we find that audit fees are
positively associated to LCS excess of control, measured by the difference between his voting
rights and his cash flow rights. Consistent to prior studies, we evidence that auditors are more
likely to supply greater effort to prevent misstatement associated with moral hazard and
adverse selection problems resulting from principal-principal conflict (Khalil et al., 2008;
Hope et al., 2012). Second, we show a negative relationship between audit fees and the
presence, number and voting power of MLS consistent with MLS corporate governance role.
These results are robust to alternative measures of contestability (Shapley value, PCA index),
endogeneity propensity score matching consideration and alternatives sample compositions
(adding non-controlled family firms, excluding cross listed firms or pyramid firms). Because
auditing is an agency costs that should increase when agency conflicts are high, when auditors
perceive high risk of minority expropriation by LCS, they incorporate such risk in their audit
pricing. MLS play an important role in a firm’s governance by monitoring the LCS. The
presence, number and voting power of MLS alleviate the power of the LCS and weaken his
opportunity to extract private benefits reducing therefore information asymmetry and agency
conflict which result in lower audit fees
Our results highlight that auditors adjust their level of effort depending on expected agency
conflicts level when auditing financial accounting information. Our theoretical developments
and empirical results present new explanations for previous scant and ambiguous results (Hay
et al., 2006) regarding the relationship between audit fees and blockholders ownership. Prior
research (1) do not isolate MLS and LCS (Fan and Wong, 2005; Niemi, 2005) or focus only
on LCS ownership and control (Khalil et al., 2008).
Our examinations draw on very detailed data on ultimate ownership that has been hand
collected on all listed french firms on 2003-2010 period while most of prior research consider
ownership stable in a short period (less than four year) or examine direct ownership. Our data
consider the ultimate 1st, 2
nd, 3
rd and 4
th shareholder in seven years and to the best of our
knowledge, no prior study has been able to test the effects of ownership on audit fees using
such detailed data. We also contribute to the existing audit literature in several ways. First,
very scarce studies examine the relation between principal-principal conflict and audit fees
4
(Fan and Wong, 2005; Khalil et al., 2008; Hope et al., 2012) and to the best of our
knowledge, this is the first study that examines a sample of family controlled firms while
family have many particularities affecting many accounting dimensions including information
asymmetry, earnings quality, corporate governance (Lennox, 2005; Dechun, 2006; Ali et al.,
2007; Chau and Gray, 2010; Anderson et al., 2012). Finally, there is a debate on the role of
MLS in corporate governance. While some of the research evidences that MLS play a
monitoring role to enable minority expropriation by the LCS (Mishra, 2011), other research
finds that MLS may conspire with LCS to extract divisible private benefits of control
(Zwiebel, 1995; Bennedsen and Wolfenzon, 2000).We contribute to this debate by
empirically examining the effect of MLS on audit fees and answer previous call for research
to examine the special corporate governance features of publicly traded firms with multiple
large owners (Laeven and Levine, 2008).
The paper is organized as follows. The next section (section 2) provides the theoretical
framework and develops our hypotheses. Section 3 presents the research design, and section 4
provides the sample selection procedures and descriptive statistics. Regression results are
disclosed in section 5, with robustness analyses reported in section 6. We summarize the main
findings and limitations of our study in section 8.
2. Audit fees in ownership concentrated firms
The ownership concentration is considered as an institutional arrangement that facilitates
transactions in a weak investor protection environment, for instance France (La Porta et al.,
1999). Indeed, shareholders need to hold large percentage of capital to monitor the manager
and avoid expropriation since the legal system could not efficiently protect then. However, in
a context of high ownership concentration, shareholders who obtain dominant control of a
firm are able to determine the profit distribution and are likely to deprive minority
shareholders of their rights which raise the prinicpal-principal agency conflict (Fan and
Wong, 2005). Controlling shareholders have the incentive and/or opportunity to divert firm
resources at the expense of outside shareholders (Gul et al., 2010). This expropriation can take
various forms, for instance: higher compensation, misappropriation of assets, and dilution of
minority shareholders’ interests through the issuance of stocks or dividends (Hope et al.,
2012). One stream of research considers that controlling shareholders collude together to
perceive substantial private benefits at the expenses of minority shareholders (Zwiebel, 1995;
Bennedsen and Wolfenzon, 2000) while recent studies evidence that MLS play an important
role in a firm’s governance by monitoring the LCS (Maury and Pajuste, 2005; Laeven and
5
Levine, 2008; Mishra, 2011). ISAs (International Standars on Auditing §5) “require the
auditor to obtain reasonable assurance about whether the financial statements as a whole are
free from material misstatement, whether due to fraud or error”. Agency conflicts increase
audit risk, consequently auditors effort is high in firms suffering from more severe agency
conflicts (Khalil et al., 2008; Hope et al., 2012).
2.1. Audit fees and private benefits by the LCS
Delgado-Garcia et al. (2010) show that ownership concentration in the hands of the LCS
harms corporate reputation. Most of LCSs participate to the top management as managers,
president or vice-president of the board (La Porta et al., 1999), they can therefore manipulate
accounting information and dismiss information to continue to expropriate minority
shareholders. Also, the non-respect of the “one share one vote” rule in France confirms the
idea of weak investor protection suggested by La Porta, et al. (2000). Prior studies suggest
that firms with deviations between ultimate control and ownership have lower firm value,
poorer information disclosure and more severe information asymmetry and illiquidity (Chau
and Gray, 2002; Claessens et al., 2002; Fan and Wong, 2002; Attig et al., 2006). Attig, et al.
(2006) explain that a larger deviation between voting rights and cash flow rights is associated
to more selfish behavior by the ultimate shareholder. We suggest that when the legal
environment is not able to protect efficiently investors, investors have more incentives to pay
for monitoring costs, for instance external audit, to protect their wealth.
The role of auditing is to provide assurance of the quality of accounting information via the
enforcement of the application of proper accounting policies (Francis and Wang, 2008).
Auditing is considered therefore as one of the key component of the corporate governance
system (Francis et al., 2003) and assumed to be a monitoring cost (Jensen and Meckling,
1976) that will limit the LCS ability to manipulate accounting numbers and hence his ability
to extract private benefits of control.
Since the largest controlling shareholder (LCS) is likely to hold voting rights in excess of his
cash flow rights (La Porta et al., 1999; Faccio and Lang, 2002), he can easily expropriate
minority interests and benefit financially from related party transactions in which profits are
transferred to other companies he controls (Doidge et al., 2009). That excess control built
through dual-class shares, pyramidal structures or cross-holding among firms exacerbate the
agency conflict between the LCS and minority shareholders because it enables him to accrue
private benefits (Claessens et al., 2000; Fan and Wong, 2005). Indeed, the higher the
deviation between voting rights and cash flow rights of the LCS, the more entrenched is his
6
position, and therefore the more able he is to expropriate wealth from minority shareholders
(Fan and Wong, 2005). Fan and Wong (2002) and Chau and Gray (2002) posit that the
ultimate owner could minimize and delay the disclosure of information to increase the chance
of executing his plans and avoid the intervention of other shareholders or to base their
decisions on inadequate information. Outside investors are likely to perceive the possible
manipulation of earnings for outright expropriation by LCS (Fan and Wong, 2002) and will
ask for high audit quality. To form an opinion on the truth and the fairness of financial
statements of these firms, auditors charge higher audit fee to bear the additional effort
engaged because of additional firm risk (Choi et al., 2008). For instance, when examining the
canadian market, Khalil et al. (2008) find a positive relation between audit fees and the wedge
between cash flow rights and control rights. They assume that higher audit fees result from an
increase in audit cost as auditors conduct wider scope audits as dual-class shares increase
audit risk and/or auditor business risk.
We therefore state the following hypothesis consistently with previous development.
Hypothesis 1: Greater divergence between the control and cash flow rights of the ultimate
LCS is associated with higher audit fees.
2.2. Audit fees in firms with multiple large shareholders
Although many studies examine the impact of excess control by the LCS scant attention has
been given to the governance role of MLS, despite their pervasiveness around the world
(Attig et al., 2009). Indeed, recent studies show the prevalence of firms with multiple large
shareholders (MLS) besides the large controlling shareholder (LCS) (Mishra, 2011). For
instance, Laeven and Levine (2008) use a sample of 1,657 European firms and find that one-
third of publicly listed firms in Europe have MLS. Boubaker (2012) finds that MLS are
present in 38% of French publicly listed firms. our study aims to respond to this call for
research on this governance issue (Laeven and Levine, 2008).
The MLS shareholding structure differs significantly from the one with only one dominant
shareholder in term of corporate governance. Consistent with recent research about MLS
(Attig et al., 2008; Mishra, 2011; Boubaker et al., 2012), the presence of MLS can shape
agency problems (Laeven and Levine, 2008; Mishra, 2011). Pagano and Röell (1998) model
that firms with multiple large shareholders enjoy stronger internal monitoring that prevents
opportunistic behavior by the controlling shareholder
7
Attig et al. (2008) argue that MLS can achieve valuable internal monitoring, either by forming
coalitions with large equity stakes (Bennedsen and Wolfenzon, 2000) or by competing for
corporate control (Bloch and Hege, 2003). The authors examine the impact of MLS on cost
of equity and find a negative relation between the cost of equity and both the size of the
second largest shareholders and the voting power of the four controlling shareholders beyond
the largest one. They conclude that investors associate the presence of MLS to an efficient
monitoring of the firm. These results are also consistent with Maury and Pajuste (2005)’s
finding who find that a more equal distribution of votes among large blockholder improves
corporate governance.
Moreover, in a sample of 1,686 firms drawn from nine Eastern Asian countries, Mishra
(2011) finds that the power and presence of MLS improve internal governance by mitigating
agency problems between the dominant shareholder and minority shareholders and help
promote a more optimal non-conservative investment policy while in the absence of MLS, the
LCS have the power and incentives to extract private benefits of control by undertaking a
conservative investment policy. Mishra (2011)’s results support that in firms with a dominant
shareholder, the presence of MLS reduces agency problems and thus aligns the managerial
interests with those of other minority shareholders.
Consistent with the substitution hypothesis between internal and external corporate
governance mechanisms (ownership as internal governance and auditing as external
governance), auditor will engage less effort in firms with MLS because of corporate
governance improvement since the MLS play an efficient monitoring role of the LCS. For
instance, El Ghoul, et al. (2013) find that firms with MLS are less likely to hire Big Four
auditors because MLS presence strengthens internal monitoring. We suggest that MLS power
decrease accounting manipulation risk, thus, auditors will charge less audit fees.
We therefore state the following hypothesis:
Hypothesis 2: The presence, number and voting power of MLS are associated with lower
audit fees.
3. Empirical design
We use the following regression model to test our hypotheses:
8
Dependent variable: Following prior research (Hay et al., 2006),we use LNFEE: the natural
logarithm of audit fees to proxy for audit effort.
Corporate governance variables: VARMLS is composed of all MLS and LCS variables.
VARMLS equal: EXCESS CONTROL, MLSD, MLSN, VR234, VRRATIO and HERF. We
Consider La Porta’s (1999) definition to identify large shareholders. Hence, a large
shareholder is a legal entity that controls, directly or indirectly, at least 10% of the firm’s
voting rights. To capture the entrenchment effect of the LCS, we use EXCESS CONTROL,
which equals the largest ultimate shareholder’s control rights (UCO) in excess of his of
cashflow rights (UCF) all divided by his control rights (UCO). For each firm in our sample,
we compute UCF and UCO of the LCSs as follows: First, we determine the shareholder that
controls the largest block of direct voting rights. Second, we identify the latter’s direct largest
shareholder, and we repeat this procedure until reaching the ultimate LCS of each sampled
firm. Following Claessens et al.(2002), we calculate UCO by summing the weakest links
along the different control chains and using a 10% threshold. UCF are obtained by summing
the products of direct cash flow rights along the different ownership chains.
Consistent with Attig et al. (2009), we employ variables reflecting various attributes of MLS.
First, we identify whether the firm has more than one large shareholder by constructing a
dummy variable (MLSD) equal 1 if at least one large shareholder other than LCS controls
more than 10% of the firm, and 0 otherwise. Second, we use MLSN to measure the number of
MLS other than the LCS, up to the fourth. Third, we refine our analysis by including VR234
and VRRATIO to study the impact of control contestability on audit fees. VR234 is the sum
of voting rights of the second, third and fourth largest blockholders; and VRRATIO calculate
the ratio of this sum to the voting rights of the LCS measuring contestability of the LCS’s
voting power by MLS. Greater contestability of LCS power by MLS is associated with higher
dilution of minority shareholders’ interests, lower agency costs and lower audit fees therefore.
Hence, we expect a negative effect of variables measuring contestability (i.e., VR234,
VRRATIO) on audit fees.
Finally, we investigate the importance of control dispersion in shaping the governance role of
MLS by examining HERF, the Herfindahl index of the differences between the voting rights
9
(VR) of the four largest shareholders : HERF = (VR1 − VR2) 2
+ (VR2 − VR3) 2
+ (VR3 −
VR4)2. All else equal, higher HERF implies lower contestability of the control of the LCS by
the coalition of multiple large shareholders. Consistent with the MLS efficient-monitoring
hypothesis, we expect a positive effect of the variable HERF measuring the dispersion of
voting rights on audit fees.
Control Variables: Our audit fees model includes two types of firm specific control variables,
which control for: (1) audit costs (size and complexity); (2) the risk of loss that an audit could
face in the future (Simunic, 1980; Francis, 1984; Hay et al., 2006). Size is coded by
LNASSET (natural logarithm of total assets expressed in k€). We then add two variables
which proxy for client complexity: INVREC (sum of the inventories and receivable, scaled by
total sales), INT (percentage of international sales) and NBS (natural log of business segments
+ 1). Similar to Simunic (1980) and Choi et al. (2009), we include LOSS (dummy that equal 1
if net income is negative) and LEVERAGE (total debt divided by total assets) to measure the
client-specific litigation risk potentially borne by the auditors, and we add ROA (Return on
Assets), LQD (the ratio of current assets to current liabilities), and CROSSLIST to code for
being cross-listed in a higher litigation risk market (US or UK) (Choi et al., 2009). We also
include the audit firm size (BIG_4) to capture the Big 4 premium (Francis, 1984). We also
add the year-end peak (FISYEND) to capture the busy season effect (Hay et al., 2006) related
to the auditor’s peak of activity of the 31st of December. Finally we include an audit demand
effect with a Book-to-market ratio (BTM). Finally, model (2) includes also fixed year and
industry effects and an error term (). Expected signs for the variables are reported in the
regression tables.
4. Sample and descriptive statistics
The initial sample consists of all French listed firms appearing in the Worldscope database
over the 2003-2010 period. We exclude from the sample: (1) financial firm having a two-digit
SIC code between 6000 and 6999 (2) firms with less than two usable observations during the
sample period, (3) widely held firms where there is no ultimate shareholder who owns more
than 10% of the voting rights, (4) firm with missing or incomplete ownership, return or
financial data. We focus only on family controlled firms. Our final sample comprises a panel
of 1,483 firm-year observations. Ownership and voting data are hand-collected from the
firm’s annual reports. Financial data are from Worldscope database.
Table 1 summarizes the definitions and data sources for all variables and Table 2 describes
the distribution of sample firms across industries and years. On one hand, the services and
10
consumer durables industries dominate our sample, firms in these two sectors represent
respectively 24.34% and 18.00% of the total number of firm-year observations. On the other
hand, petroleum and transportation sectors make up the smallest share of the sample with only
0.20% et 2.43%. Table 2 shows also that the firms are evenly distributed across the studied
period.
Table 3 presents descriptive values concerning the dependent and independent variables. To
limit the influence of outliers, all continuous variables are winsorized at 1%. Panel A
(Governance variables) shows a high separation between cash flow rights and voting rights of
the LCS. The ratio of difference between control and ownership rights of the LCS divided by
his control rights has an average value of 24.5%. This finding indicates that our sampled firms
are, in general, subject to agency costs between LCSs and minority shareholders. We also find
that MLS are present in almost 35.9% of the sampled firms. This finding is consistent with
Faccio and Lang (2002) who find that 39% of Western European firms have more than one
large shareholder (at the 10% threshold). This percentage is also similar to Attig et al. (2009)
who document that one-third of the firms in East Asian listed firms have MLS. Our
subsample of firms with MLS (533 firm-year observations) shows that the average (median)
total voting rights held by the three largest shareholders, beyond the LCS, is 26.8% (25.3%).
When considering the whole sample, we find that the average power of the second, third and
fourth largest shareholders is 39.8% higher to the LCS voting rights. Panel B presents
summary statistics concerning firm characteristics representing audit fees and control
variables. Table 3 shows an important variance of control variables, which illustrates the
diversity of the firms included in our sample.
5. Empirical evidence
5.1. Univariate Tests
We present initial insights on the relation between audit fees and MLS from univariate tests.
Table 4 reports mean and median difference tests of the governance variables used in our
analysis. We find that the mean (median) value of audit fees are significantly higher for firms
with higher excess control of LCS. This result suggest apriori that auditors ask for a fee
premimum for firms with higher risk of corporate wealth extraction by the LCS which is
consistent with hypothesis 1. Table 4 shows also that firms with MLS have significantly
lower audit fees compared to firms with firms without MLS. We further evidence that the
mean audit fees is significantly lower at 1% for firms with higher MLSN (number of MLS),
VR234 (MLS voting rights) and VRRATIO (MLS voting power compared to LCS). These
11
last results suggest that MLS play a corporate governance role reducing engagement risk. This
is appreciated by auditors who consequently charge lower audit fees.
Table 4 presents a significant negative difference in the mean (median) audit fees between
firms with low HERF values and firms with high HERF values. When control dispersion
(HERF) is high, the LCS is more entrenched and control contestability is low. Consequently,
to cover the increased audit risk, auditors expend more effort and ask therefore for higher
audit fees.
5.2. Regression results for audit fees
Table 5 reports the ordinary least squares (OLS) estimates for the model discussed above. The
p-values are computed using robust standard errors, adjusted for heteroscedasticity and
clustered at the firm level. We include year and, industry effects in all regressions.
**** Insert Table 5 about here****
All model specifications control for the determinants of the audit fess reported in the previous
studies and discussed above. In interpreting the results, we primarily focus on the effects of
MLS and LCS related variables. In Model 1, our basic regression indicates a positive and
statistically significant coefficient for Excess control (at the 1% level), suggesting that the
separation between ownership and control rights of the ultimate controlling shareholder
increases the audit fees. We therefore validate H1: Greater divergence between the control
and cash flow rights of the ultimate LCS is associated with higher audit fees. This also lends
support to recent evidence that firms with cash flow and voting rights divergence incur higher
audit fees to compensate for higher audit risk and/or higher auditor business risk (Khalil et al.,
2008). In Models 1 through 5, we separately include MLS variables to avoid multicolinearity.
In Model 1, we start by examining the impact on audit fees of a binary variable: the presence
of MLS beyond the ultimate owner. Column 1 reports a negative and statistically significant
at the 1% level coefficient of MLSD ( coeff. = -0.170, p < 0.01). In otherwords, this relation
implies lower audit fees for firms with MLS compared to firms with a unique controlling
shareholder. This result is consistent with MLS efficient-monitoring hypothesis. Additionally,
we evidence in Model 2 a negative and statistically significant coefficient for the number of
MLS (coeff. =-0.121, p < 0.01), suggesting that increasing number of controlling shareholders
is statistically significant to reduce auditor’s efforts. This result, in line with our predictions,
is also consistent to prior studies evidincing a corporate governance role of MLS (Bennedsen
and Wolfenzon, 2000; Attig et al., 2008; Boubaker and Sami, 2011; Mishra, 2011; Attig et
al., 2013). Conversly to prior studies that consider all controlling shareholders as a block
12
harm shareholder rights (Fan and Wong, 2005; Ben Ali and Lesage, 2013), the presence of
MLS is likely to mitigate firm's agency costs and therefore benefit minority interests.
In the rest of Table 5, we report the results for more stringent MLS variables measuring the
contestability of the controlling shareholder's power. For instance, model 3 reports a
significant negative relation between audit fees and the total voting rights of the three
controlling shareholders beyond the largest (VR234: -0.438; p < 0.05). This finding is further
confirmed in Model 4, which includes the voting rights power of the these three controlling
shareholders compared to the largest one (VRRATIO: -0.133; p < 0.01). These findings
indicate that not only the presence of MLS or number of MLS, but also the size of the voting
rights and its weight to LCS’s voting rights, is economically significant in reducing firm's
agency costs.
We further test the influence of control dispersion between blockholders on firms' audit fees
by examining the Herfindahl index of the differences between the voting rights of the four
largest shareholders. Results are reported in column 5 and show that the coefficient of
HERFINDAHL is negative and statistically significant at the 1% level. This evidence is
consistent with Boubaker et al. (2012) and Attig et al.(2008) results evidencing that greater
parity in voting rights among the blockholders improves corporate governance, priorly
suggested by Bloch and Hege (2003). These results indicate that a high risk of control
contestability of the LCS is associated with a firm's enhanced information quality and thus
lower misreproting, lower audit risk and consequently lower auditor’s effrots (as measured by
audit fees). Finally, similarly to Attig et al. (2008), we argue that MLS play a valuable
information role by lowering the cost of audit engagement for firms with severe information
problems embedded in their ownership structures.
When considering the control variables,we observe several significant variable coefficients
consistent with previous audit fees litterature. With respect to our predicted signs on control
variables, we find a positive and significant relation at the 1% level between audit fees and (1)
size, (2) the proportion of international sales and (3) the number of business segments. The
results evidence also a negative and significant at the 5% level of performance measured with
ROA. Finally, table 5 reports that the coefficients of the BTM is negative and significant at
the 1% level variable consistent to the audit demand effect.
13
6. Robustness analyses
In this section we check the reliability of our results by performing several sensitivity tests.
The relationship between audit fees and MLS is robust with respect to a number of alternative
tests that we report in Tables 6 to 10.
***Insert table 6***
***Insert table 7***
First, we run our regression using a PSM ( propensity score matching) sample to control for
endogeneity issue. Following Chaney et al. (2004), we believe that client firms are not
randomly assigned to auditors. Ho and Fei (2013) argue that “it is likely that firms self-select
audit firms based on firm characteristics, private information, or other unobservable
characteristics” affecting audit fees. Therefore, we adopt the propensity scoring matching
(PSM) to address potential endogeneity in the standard ordinary least squares (OLS)
regressions. The results remain qualitatively similar (see table 6). Table 7 presents a
comparison of variables considered to determine the presence of MLS in original and matched
samples.
***Insert table 8***
Second, we replicate in table 8 our tests after changing the proxies for certain independent
variables. For instance, we change the measure of LCS excess control ( EXCESS CONTROL)
by 1) UCO-UCF measured by the difference between voting rights and cash flow rights and
2) EXCESS DUMMY which is dummy variable that takes one if the voting rights of LCS is
higher than his cashflow rights. Results in table 7 are similar to our predictions on a positive
relation between audit fees and the excess of voting rights to cash flow rights of the LCS (see
table 8).
When considering hypothesis 2, our results hold if we change the measure of the MLS
variables. Indeed, we introduce various control contestability variables that have been used in
previous literature. For instance, we introduce SHAPLEY1 measured by the Shapley value
solution for the largest controlling shareholder in a four shareholder voting game. Our model
include a contestability index defined as the common factor extracted from a principal
component analysis to resume our previous MLS variable: MLSD, MLSN, VR234,
VRRATIO and HERFINDAHL (see table 8). In table 8, column 5 replicates our tests after
changing the proxies for certain control variables (e.g., size proxied by log sales and number
14
of employees instead of log assets). Results are similar to those presented in the main
analysis.
***Insert table 9 ***
Third, we perform alternative sample compositions to assuage the concern that our results are
unduly influenced by sample selection bias. Our main analysis focus only on family
controlled firms; when, enlarging our sample to all controlled firms (Table 9, column 1) and
to closely and widely held firms (Table 9, column 2), the conclusions remains unaffected and
our two hypothesis are validated. We also replicates our regression after eliminating firms
controlled through a pyramid. Column 3 reports these last results. Holding a firm through
sucessive tiers dramatically exacerbates the seperation of control and cashflow rights of the
ultimate owner, giving him higher incentives to extrac private benefits (Boubaker et al.,
2013). Also, as pyramids suffer from more information asymetry, it should increase audit
efforts and therefore audit fees since auditors garantie that financial reporting is free from
mistakes. For instance, Jensen and Meckling (1976) suggest auditing is one of the main
external monitoring costs to reduce information asymmetry. In table 9, model 3 shows that the
inferences remain unchanged showing that our results are not driving by pyramid-affliation
effect. Choi et al. (2009) argue that legal environments play a crucial role in determining the
auditor's legal liability and that firm cross listed in law enforced countries are subject to
higher audit fees. To acertain that our results are not influenced by this cross-listed fees
premium, we exclude cross-listed firms from our intial sample and run our regressions. Our
results remain unchaged evidencing that they are not driven by cross-listed effect. Finally,
Table 9, Eq . (5) reports results using balanced panel data and confirm our prior results.
***Insert table 10 ***
Finally, to check the robustness of our main results, we include additional control variables:
discretionnary accruals, new stock issuance and IFRS adoption. Large controlling
shareholders participate often in the management or serve as directors on the board (Chen et
al., 2008) and could therefore manipulate financial reporting and induce higher earnings
management (Mei-Ling, 2010). However, auditors are likely to anticipate this effect and ask
for higher fees to compensate increased audit effort (Bedard and Johnstone, 2004). Also,
when performing their monitoring role, multiple large shareholders increase auditor’s efforts
because they have enough power to accuse auditors for audit default, consequently audit fees
are higher. These prior development suggests to consider earnings management in our model.
Hence, we include 3 measures of discretionnary accruals to control for earnings management
15
in table 10, Eq . (1), Eq . (2) and Eq . (3). Our results remain qualitatively similar. Our results
are also robust after controlling firm complexity, specifically after IFRS adoption and new
stock issuance. Table 10 also presents others control variables that could increase audit
engagement complexity for instance: number of analysts following the firm, asset tangibility,
sales growth and free cash flow.
***Insert table 10 ***
7. Conclusion
Although the literature on audit fees is well developed, the relations between principal-
principal conflicts and audit fees are less established. Also previous studies focus either on
widely held firms or firms with a single controlling owner. However this conventional
dichotomy of corporate governance is controversial since recent studies evidence a strong
presence of multiple controlling shareholders in most firms (Attig et al., 2008; Laeven and
Levine, 2008; Mishra, 2011). However, these growing body of research do not provide
unequivocal evidence of the governance role of MLS (Attig et al., 2008).
Our research use very detailed data on ultimate ownership in French listed firms and spans
2003–2010. Consistent with agency theory framework, we suggest that shows that audit fees,
as an agency cost, depend on the level of agency conflicts (Jensen and Meckling, 1976). Our
results evidence that auditors adjust their efforts (i.e. audit fees) when auditing firms with
high agency conflicts. Similarly to prior research, we find that audit fees is higher in firms
where the largest controlling shareholders hods voting rights in excess of his cashflow rights
(Khalil et al., 2008) because of increased risk due to high minority expropriation threat.
Conversly, the presence, number and voting power of MLS decrease audit fees. This result
evidences that that multiple large shareholders play an important monitoring role. Our results
are robust to alternative measures of MLS-LCS contestability (Shapley value, PCA index),
endogeneity propensity score matching consideration and alternatives sample compositions
(adding non-controlled family firms, excluding cross listed firms or pyramid firms).
Similarly to (Ho and Fei, 2013), we believe our findings on the empirical link between auditor
choice and audit fees enhance our understanding of the effect of ownership structure in
financial reporting and the audit planning process. The paper contributes to the audit pricing
literature in the following ways. First, it fills a gap in the literature by investigating how
multiple large shareholdings affect audit fees. It suplements the prior empirical evidence in
prior studies (Fan and Wong, 2005; Khalil et al., 2008; Hope et al., 2012) by investigating
16
ultimate ownership of the second, third and fourth controlling shareholders and the power
contest between blockholders and examine a different institutional context: France that has
been pointed as typical of the least investor protection family (La Porta et al., 1999). Finally,
it also control for a wide array of audit fees determinant variables that has not been examined
priorly.
17
8. Tables
Table 1: Variables definitions and sources
Variable Definition Source
Dependent variable
LNFEE Natural logarithm of audit fees in thousands of euros. Worldscope
Test variables
EXCESS CONTROL
The difference between the ultimate owner’s control rights
and cash-flow rights (at the 10% threshold) all divided by her
control rights.
Annual reports and authors’
calculations
MLSD Dummy variable that equals one if the firm has at least two
large shareholders (at the 10% threshold) and zero otherwise.
Annual reports and authors’
calculations
MLSN The number of large shareholders other than the largest
shareholder up to the fourth
Annual reports and authors’
calculations
VR234 The sum of voting rights of the second third and fourth largest
shareholders.
Annual reports and authors’
calculations
VRRATIO
The sum of voting rights of the second third and fourth
largest blockholders divided by the voting rights of the largest
shareholder.
Annual reports and authors’
calculations
HERFINDAHL
The sum of squared differences between the voting rights of
the four largest shareholders that is (VR1 − VR2)² +
(VR2−VR3)² + (VR3−VR4)²; where VR1 VR2 VR3 and
VR4 equal the voting rights of the first second third and
fourth largest shareholders.
Annual reports and authors’
calculations
Other variables
SIZE Natural logarithm of total assets in thousands of euros.
Worldscope Worldscope
INTSALES International sales divided by total sales. Worldscope
INVREC The sum of inventories and accounts receivables scaled by
total sales. Worldscope
LEVERAGE LEVERAGE Total debt divided by total assets, Worldscope
BIG_4 Dummy variable that equals one if the firm uses one of the
Big 4 auditing firms or their forerunners and zero otherwise. Worldscope
ROA Earnings before interest and taxes divided by the book value
of assets at the beginning of the fiscal year. Worldscope
BTM The book-to-market ratio. Worldscope
NBS The natural logarithm of the firm’s number of business
segments + 1. Worldscope
LOSS
Dummy variable that equals one if the firm reports a net loss
in year t (that is its net income after taxes before
extraordinary item and taxes on extraordinary item < 0) AND
zero otherwise.
Worldscope
CURRATIO Current ratio which equals to the ratio of current assets to
current liabilities at the end of the fiscal year. Worldscope
FISYREND Dummy variable that equals one if the fiscal year-end is
December 31 and zero otherwise. Worldscope
CROSSLIST Dummy variable that equals one if the firm is cross-listed in
foreign capital markets in year t and zero otherwise Bank of New York
18
Table 2: Distribution of sample firms across industries and years
Total
Industry
(SIC codes) 2003 2004 2005 2006 2007 2008 2009 2010
Number per
industry
Percentage
of total
Petroleum
(13, 29) 0 0 0 0 0 1 1 1 3 0.20
Consumer durables
(25, 30, 36, 37, 50, 55, 57) 16 22 34 41 42 39 38 35 267 18.00
Basic industry
(10, 12, 14, 24, 26, 28, 33) 14 17 24 27 24 22 21 17 166 11.19
Food and tobacco
(1, 2, 9, 20, 21, 54) 5 7 12 14 14 12 13 12 89 6.00
Construction
(15, 16, 17, 32, 52) 6 7 10 11 9 8 10 9 70 4.72
Capital goods
(34, 35, 38) 13 15 20 23 24 25 26 26 172 11.60
Transportation
(40, 41, 42, 44, 45, 47) 3 4 4 4 4 5 6 6 36 2.43
Utilities
(46, 48, 49) 2 4 9 12 10 10 11 11 69 4.65
Textiles and trade
(22, 23, 31, 51, 53, 56, 59) 14 16 22 24 22 21 22 19 160 10.79
Services
(72, 73, 75, 76, 80, 82, 87,
89)
41 47 54 52 49 41 39 38 361 24.34
Leisure
(27, 58, 70, 78 , 79) 9 14 13 10 12 11 12 9 90 6.07
Total number per year 123 153 202 218 210 195 199 183 1,483 100.00
Percentage of total 8.29 10.32 13.62 14.70 14.10 13.15 13.42 12.34 100
This table shows the distribution of the sample firm-year observations across industries and years. Our sample comprises a
panel of 1,483 firm-year observations over the period 2003-2010. We use Campbell (1996) to classify firms into 11
industries. The industries are petroleum (SIC 13, 29), consumer durables (SIC 25, 30, 36, 37, 50, 55, 57), basic industry (SIC
10, 12, 14, 24, 26, 28, 33), food and tobacco (SIC 1, 2, 9, 20, 21, 54), construction (SIC 15, 16, 17, 32, 52), capital goods
(SIC 34, 35, 38), transportation (SIC 40, 41, 42, 44, 45, 47), utilities (SIC 46, 48, 49), textiles and trade (SIC 22, 23, 31, 51,
53, 56, 59), services (SIC 72, 73, 75, 76, 80, 82, 87, 89) and leisure (SIC 27, 58, 70, 78, 79).
19
Table 3: Summary statistics for ownership structure variables and firm
characteristics
Variable N Mean S.D. Min. 25th percentile Median 75th percentile Max.
Panel A: Governance variables
EXCESS CONTROL 1,483 0.245 0.208 -0.024 0.077 0.219 0.360 0.858
MLSD (N(MLSD=1) = 533) 1,483 0.359 0.480 0.000 0.000 0.000 1.000 1.000
MLSN 1,483 0.440 0.650 0.000 0.000 0.000 1.000 3.000
VR234 533 0.268 0.097 0.101 0.200 0.253 0.340 0.478
VRRATIO 1,483 0.398 0.497 0.000 0.000 0.194 0.597 2.012
HERFINDAHL 1,483 0.264 0.241 0.000 0.051 0.195 0.431 0.886
Panel B: Firm characteristics
LNFEE 1,483 5.755 1.524 1.977 4.787 5.631 6.668 9.717
SIZE 1,483 12.560 2.041 8.454 11.114 12.307 13.801 17.516
INTSALES 1,483 0.379 0.266 0.000 0.167 0.386 0.583 0.999
INVREC 1,483 0.455 0.280 0.103 0.315 0.402 0.506 2.031
LEVERAGE 1,483 0.230 0.167 0.000 0.092 0.214 0.338 0.752
BIG_4 1,483 0.488 0.500 0.000 0.000 0.000 1.000 1.000
ROA 1,483 3.689 7.868 -26.351 1.607 4.462 7.423 23.933
BTM 1,483 0.816 0.269 0.222 0.620 0.816 0.994 1.551
NBS 1,483 1.569 0.384 0.693 1.386 1.609 1.946 2.197
LOSS 1,483 0.196 0.397 0.000 0.000 0.000 0.000 1.000
CURRATIO 1,483 1.540 0.795 0.394 1.034 1.347 1.803 5.749
FISYREND 1,483 0.786 0.410 0.000 1.000 1.000 1.000 1.000
CROSSLIST 1,483 0.038 0.191 0.000 0.000 0.000 0.000 1.000
This table shows descriptive statistics for the ownership structure variables (Panel A) and firm characteristics (Panel B).
EXCESS CONTROL is the difference between the ultimate owner’s control rights and cash-flow rights (at the 10%
threshold), all divided by her control rights. MLSD is a dummy variable that equals one if the firm has at least two large
shareholders (at the 10% threshold), and zero otherwise. MLSN is the number of large shareholders, other than the largest
shareholder, up to the fourth. VR234 is the sum of voting rights of the second, third and fourth largest shareholders.
VRRATIO is the sum of voting rights of the second, third and fourth largest blockholders divided by the voting rights of the
largest shareholder. HERFINDAHL is the sum of squared differences between the voting rights of the four largest
shareholders, that is, (VR1 − VR2)² + (VR2−VR3)² + (VR3−VR4)²; where VR1, VR2, VR3 and VR4 equal the voting rights of
the first, second, third and fourth largest shareholders. LNFEE is the natural logarithm of audit fees in thousands of euros.
SIZE is the natural logarithm of total assets in thousands of euros. INTSALES equals to the international sales divided by total
sales. INVREC is computed as the sum of inventories and accounts receivables scaled by total sales. LEVERAGE equals to
total debt divided by total assets. BIG_4 is a dummy variable that equals one if the firm uses one of the Big 4 auditing firms
or their forerunners, and zero otherwise. ROA is the firm’s return on assets defined as earnings before interest and taxes
divided by the book value of assets at the beginning of the fiscal year. BTM is the book-to-market ratio. NBS equals to the
natural logarithm of the firm’s number of business segments + 1. LOSS is a dummy variable that equals one if the firm
reports a net loss in year t (that is, its net income after taxes before extraordinary item and taxes on extraordinary item < 0),
and zero otherwise. CURRATIO is the firms’ current ratio, which equals to the ratio of current assets to current liabilities at
the end of the fiscal year. FISYREND is a dummy variable that equals one if the fiscal year-end is December 31, and zero
otherwise. CROSSLIST is a dummy variable that equals one if the firm is cross-listed in foreign capital markets in year t, and
zero otherwise.
20
Table 4: Univariate Tests: governance variables
Mean Median
Low High t-statistic Low High z-statistic
EXCESS CONTROL 5.514 6.069 -7.055*** 5.594 5.733 -2.954***
MLSD 5.936 5.431 6.200*** 5.795 5.241 6.413***
MLSN 5.935 5.436 6.133*** 5.795 5.241 6.342***
VR234 5.956 5.488 5.931*** 5.844 5.393 5.386***
VRRATIO 5.858 5.567 3.530*** 5.783 5.497 3.158***
HERFINDAHL 5.696 5.840 -1.791* 5.669 5.838 -2.146**
UCF 5.876 5.622 3.211*** 5.615 5.645 2.286**
This table presents mean and median difference tests of the governance variables used in our analysis for 1,483 nonfinancial
French firms over the period 2003-2010. LNFEE is the natural logarithm of audit fees in thousands of euros. EXCESS
CONTROL is the difference between the ultimate owner’s control rights and cash-flow rights (at the 10% threshold), all
divided by her control rights. MLSD is a dummy variable that equals one if the firm has at least two large shareholders (at the
10% threshold), and zero otherwise. MLSN is the number of large shareholders, other than the largest shareholder, up to the
fourth. VR234 is the sum of voting rights of the second, third and fourth largest shareholders. VRRATIO is the sum of voting
rights of the second, third and fourth largest blockholders divided by the voting rights of the largest shareholder.
HERFINDAHL is the sum of squared differences between the voting rights of the four largest shareholders, that is, (VR1 −
VR2)² + (VR2−VR3)² + (VR3−VR4)²; where VR1, VR2, VR3 and VR4 equal the voting rights of the first, second, third and
fourth largest shareholders, UCF equals to the cash-flow rights of the ultimate owner (at the 10% threshold) measured by the
sum of the products of ownership stakes along the different control chains. The superscript asterisks ***, **, and * denote
statistical significance at the 1%, 5%, and 10% levels, respectively
21
Table 5: Influence of control contestability on audit fees
Variable (1) (2) (3) (4) (5)
EXCESS CONTROL 0.40989*** (2.64)
0.41397*** (2.63)
0.38551** (2.44)
0.42034*** (2.60)
0.35401** (2.25)
MLSD -0.17056*** (-2.87)
MLSN -0.12142*** (-2.94)
VR234
-0.43856** (-2.11)
VRRATIO -0.13348*** (-2.60)
HERFINDAHL 0.04798** (2.45)
SIZE 0.49269*** (24.88)
0.49119*** (24.65)
0.49513*** (25.12)
0.49674*** (25.33)
0.49837*** (25.52)
INTSALES 0.61776*** (5.73)
0.61707*** (5.71)
0.61649*** (5.70)
0.61790*** (5.72)
0.61720*** (5.71)
INVREC -0.02544 (-1.45)
-0.02538 (-1.45)
-0.02435 (-1.39)
-0.02406 (-1.38)
-0.02419 (-1.41)
LEVERAGE -0.22317 (-1.12)
-0.19364 (-0.98)
-0.20046 (-1.01)
-0.19075 (-0.96)
-0.19436 (-0.98)
BIG_4 0.06698 (1.31)
0.06361 (1.24)
0.06702 (1.30)
0.07009 (1.36)
0.06343 (1.23)
ROA -0.00843** (-2.03)
-0.00819** (-1.97)
-0.00892** (-2.14)
-0.00923** (-2.22)
-0.00925** (-2.22)
BTM -0.47568*** (-4.51)
-0.46450*** (-4.41)
-0.46908*** (-4.46)
-0.46202*** (-4.40)
-0.44973*** (-4.30)
NBS 0.18883*** (2.68)
0.19406*** (2.78)
0.19031*** (2.69)
0.18776*** (2.68)
0.20680*** (2.98)
LOSS -0.10049 (-1.18)
-0.10460 (-1.24)
-0.11630 (-1.38)
-0.11304 (-1.34)
-0.12929 (-1.54)
CURRATIO -0.03729 (-0.92)
-0.03775 (-0.93)
-0.04085 (-0.99)
-0.03666 (-0.90)
-0.03575 (-0.87)
FISYREND -0.05536 (-0.78)
-0.05774 (-0.82)
-0.05585 (-0.79)
-0.06084 (-0.86)
-0.05612 (-0.80)
CROSSLIST -0.30789 (-1.43)
-0.30612 (-1.42)
-0.30615 (-1.42)
-0.30080 (-1.39)
-0.28978
(-1.34)
Intercept
-1.88113*** (-5.43)
-1.94899*** (-5.79)
-1.98238*** (-5.75)
-2.00970*** (-6.03)
-2.18620*** (-6.87)
Year dummies Yes Yes Yes Yes Yes
Industry dummies Yes Yes Yes Yes Yes
Sample size 1,483 1,483 1,483 1,483 1,483
Adjusted R² 0.582 0.582 0.581 0.581 0.580
This table presents regression results for the impact of multiple large shareholders on audit fees. The dependent variable
is LNFEE, defined as the natural logarithm of audit fees in thousands of euros. Eight specifications are considered.
Specification 1 is our basic regression specification. In specifications 2 and 3, we include the variables EXCESS
CONTROL and MLSD, respectively. EXCESS CONTROL is the difference between the ultimate owner’s control rights
and cash-flow rights (at the 10% threshold), all divided by her control rights. MLSD is a dummy variable that equals one
if the firm has at least two large shareholders (at the 10% threshold), and zero otherwise. In specifications 4, 5, 6, 7 and 8
we include the variables MLSD, MLSN, VR234, VRRATIO and HERFINDHAL, respectively, to proxy for the presence,
number and voting power of MLS. MLSN is the number of large shareholders, other than the largest shareholder, up to
the fourth. VR234 is the sum of voting rights of the second, third and fourth largest shareholders. VRRATIO is the sum of
voting rights of the second, third and fourth largest blockholders divided by the voting rights of the largest shareholder.
22
HERFINDAHL is the sum of squared differences between the voting rights of the four largest shareholders, that is, (VR1
− VR2)² + (VR2−VR3)² + (VR3−VR4)²; where VR1, VR2, VR3 and VR4 equal the voting rights of the first, second, third
and fourth largest shareholders. SIZE is the natural logarithm of total assets in thousands of euros. INTSALES equals to
the international sales divided by total sales. INVREC is computed as the sum of inventories and accounts receivables
scaled by total sales. LEVERAGE equals to total debt divided by total assets. BIG_4 is a dummy variable that equals one
if the firm uses one of the Big 4 auditing firms or their forerunners, and zero otherwise. ROA is the firm’s return on assets
defined as earnings before interest and taxes divided by the book value of assets at the beginning of the fiscal year. BTM
is the book-to-market ratio. NBS equals to the natural logarithm of the firm’s number of business segments + 1. LOSS is a
dummy variable that equals one if the firm reports a net loss in year t (that is, its net income after taxes before
extraordinary item and taxes on extraordinary item < 0), and zero otherwise. CURRATIO is the firms’ current ratio, which
equals to the ratio of current assets to current liabilities at the end of the fiscal year. FISYREND is a dummy variable that
equals one if the fiscal year-end is December 31, and zero otherwise. CROSSLIST is a dummy variable that equals one if
the firm is cross-listed in foreign capital markets in year t, and zero otherwise. All continuous variables are winsorized at
the 1st and 99th percentiles. t-values based on White (1980) heteroscedasticity-consistent standard errors are in
parentheses beneath coefficient estimates. The superscript asterisks ***, **, and * denote statistical significance at the
1%, 5%, and 10% levels, respectively.
23
Table 6: Influence of control contestability on audit fees: PSM sample
Variable (1) (2) (3) (4) (5)
EXCESS CONTROL 0.44972** (2.32)
0.46073** (2.35)
0.42321** (2.15)
0.44128** (2.19)
0.37654* (1.93)
MLSD -0.17099*** (-2.75)
MLSN -0.12929*** (-3.08)
VR234
-0.50878** (-2.24)
VRRATIO -0.11016** (-1.99)
HERFINDAHL 0.03286** (2.16)
SIZE 0.47765*** (19.45)
0.47435*** (19.23)
0.47817*** (19.50)
0.48037*** (19.63)
0.47907*** (19.53)
INTSALES 0.56613*** (4.47)
0.56255*** (4.44)
0.56383*** (4.44)
0.56260*** (4.44)
0.55914*** (4.41)
INVREC -0.02122 (-1.29)
-0.02137 (-1.30)
-0.02014 (-1.21)
-0.01990 (-1.22)
-0.02006 (-1.24)
LEVERAGE -0.32509 (-1.45)
-0.28848 (-1.30)
-0.31062 (-1.38)
-0.29308 (-1.32)
-0.29809 (-1.34)
BIG_4 0.01678 (0.28)
0.01330 (0.22)
0.01913 (0.32)
0.02070 (0.34)
0.01584 (0.26)
ROA -0.00852* (-1.80)
-0.00817* (-1.73)
-0.00917* (-1.92)
-0.00956** (-2.00)
-0.00960** (-2.00)
BTM -0.36972*** (-3.01)
-0.35764*** (-2.92)
-0.37563*** (-3.07)
-0.36048*** (-2.95)
-0.35835*** (-2.94)
NBS 0.18515** (2.24)
0.19139** (2.34)
0.18346** (2.20)
0.18638** (2.27)
0.20999*** (2.60)
LOSS -0.09690 (-1.01)
-0.09933 (-1.04)
-0.11580 (-1.20)
-0.11874 (-1.22)
-0.13543 (-1.40)
CURRATIO -0.03074 (-0.65)
-0.03067 (-0.64)
-0.03652 (-0.75)
-0.02963 (-0.62)
-0.02761 (-0.58)
FISYREND -0.07775 (-0.88)
-0.08020 (-0.91)
-0.08154 (-0.93)
-0.08447 (-0.96)
-0.08353 (-0.95)
CROSSLIST -0.34707 (-1.07)
-0.34352 (-1.06)
-0.35782 (-1.11)
-0.34364 (-1.07)
-0.33013
(-1.02)
Intercept
-1.35923*** (-3.35)
-1.41001*** (-3.57)
-1.39541*** (-3.44)
-1.48696*** (-3.76)
-1.59858*** (-4.17)
Year dummies Yes Yes Yes Yes Yes
Industry dummies Yes Yes Yes Yes Yes
Sample size 1,066 1,066 1,066 1,066 1,066
Adjusted R² 0.536 0.537 0.535 0.534 0.533
This table presents regression results for the impact of multiple large shareholders on audit fees using a propensity score
matched sample that consists of 1,066 firm–year observations. The dependent variable is LNFEE, defined as the natural
logarithm of audit fees in thousands of euros. Eight specifications are considered. Specification 1 is our basic regression
specification. In specifications 2 and 3, we include the variables EXCESS CONTROL and MLSD, respectively. EXCESS
CONTROL is the difference between the ultimate owner’s control rights and cash-flow rights (at the 10% threshold), all
divided by her control rights. MLSD is a dummy variable that equals one if the firm has at least two large shareholders (at the
10% threshold), and zero otherwise. In specifications 4, 5, 6, 7 and 8 we include the variables MLSD, MLSN, VR234,
VRRATIO and HERFINDHAL, respectively, to proxy for the presence, number and voting power of MLS. MLSN is the
number of large shareholders, other than the largest shareholder, up to the fourth. VR234 is the sum of voting rights of the
second, third and fourth largest shareholders. VRRATIO is the sum of voting rights of the second, third and fourth largest
24
blockholders divided by the voting rights of the largest shareholder. HERFINDAHL is the sum of squared differences
between the voting rights of the four largest shareholders, that is, (VR1 − VR2)² + (VR2−VR3)² + (VR3−VR4)²; where VR1,
VR2, VR3 and VR4 equal the voting rights of the first, second, third and fourth largest shareholders. SIZE is the natural
logarithm of total assets in thousands of euros. INTSALES equals to the international sales divided by total sales. INVREC is
computed as the sum of inventories and accounts receivables scaled by total sales. LEVERAGE equals to total debt divided by
total assets. BIG_4 is a dummy variable that equals one if the firm uses one of the Big 4 auditing firms or their forerunners,
and zero otherwise. ROA is the firm’s return on assets defined as earnings before interest and taxes divided by the book value
of assets at the beginning of the fiscal year. BTM is the book-to-market ratio. NBS equals to the natural logarithm of the
firm’s number of business segments + 1. LOSS is a dummy variable that equals one if the firm reports a net loss in year t (that
is, its net income after taxes before extraordinary item and taxes on extraordinary item < 0), and zero otherwise.
CURRATIO is the firms’ current ratio, which equals to the ratio of current assets to current liabilities at the end of the fiscal
year. FISYREND is a dummy variable that equals one if the fiscal year-end is December 31, and zero otherwise. CROSSLIST
is a dummy variable that equals one if the firm is cross-listed in foreign capital markets in year t, and zero otherwise. All
continuous variables are winsorized at the 1st and 99th percentiles. t-values based on White (1980) heteroscedasticity-
consistent standard errors are in parentheses beneath coefficient estimates. The superscript asterisks ***, **, and * denote
statistical significance at the 1%, 5%, and 10% levels, respectively.
25
Table 7: Comparison of variables considered to determine the presence of
MLS in original and matched samples
This table shows the mean difference tests of the variables used to determine the presence of MLS using the original and the
propensity score matching samples. It also provides the standardized percentage biases for each variable and the percentage
reduction in standardized bias. SIZE is the natural logarithm of total assets in thousands of euros. AGE is the firm age.
LEVERAGE equals to total debt divided by total assets. FCF is computed as earnings before interest, taxes, depreciation, and
amortization less interest, taxes, and common dividends, all divided by total assets. Tangibility equals to the firm’s property,
plants and equipment, divided by total assets. All continuous variables are winsorized at the 1st and 99th percentiles. The
superscript asterisks ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Variable
Original sample Propensity score matching sample
Reduction
in bias
(%)
Firms
with
MLS
Firms
without
MLS
Diff. in
means
(t-stat)
Standa
rdized
bias
(%)
Firms
with
MLS
Firms
without
MLS
Diff. in
means
(t-stat)
Standar
dized
bias (%)
SIZE 12.095 12.824 -0.729***
(-6.68)
-36.4 12.095 12.141 -0.051
(-0.39) -2.3 93.7
AGE 39.484 46.414 -6.930***
(-4.19)
-22.5 39.484 40.931 -1.447
(-0.77) -4.7 79.1
LEVERAGE 0.216 0.236 -0.020**
(-2.23)
-11.9 0.216 0.198 0.018*
(1.71) 10.5 12.1
FCF 0.016 0.006 0.010
(1.50)
7.8 0.016 0.000 0.016*
(1.85) 11.9 -52.7
TANGIBLIT
Y
0.162 0.180 -0.018**
(-2.28)
-12.1 0.162 0.172 -0.010**
(-1.08)
-6.9 43.3
26
Table 8: Robustness tests Set 1: Alternative variables
Variable (1) (2) (3) (4) (5) (6)
UCO − UCF 0.47017*** (3.15)
EXCESS DUMMY 0.13057** (-2.40)
EXCESS CONTROL 0.33738** (2.13)
0.46151*** (2.96)
0.51053*** (3.33)
0.48312*** (3.09)
MLSD -0.14354** (-2.36)
-0.15528*** (-2.58)
-0.16287*** (-2.82)
-0.20067*** (-3.26)
SHAPLEY1 -0.19715** (-2.35)
PCA_INDEX -0.04440*** (-3.02)
SIZE 0.49582*** (25.26)
0.49318*** (24.93)
0.49809*** (25.66)
0.49266*** (24.88)
LN(SALES) 0.51445*** (26.09)
LN(EMPLOYEES) 0.43522*** (22.12)
INTSALES 0.61410*** (5.73)
0.61227*** (5.70)
0.59085*** (5.38)
0.62071*** (5.75)
0.75845*** (7.16)
0.91872*** (7.99)
INVREC -0.02596 (-1.48)
-0.02614 (-1.50)
-0.02577 (-1.55)
-0.02453 (-1.40)
0.00390 (0.29)
-0.00071 (-0.05)
LEVERAGE -0.17560 (-0.88)
-0.19731 (-1.00)
-0.18734 (-0.95)
-0.20883 (-1.05)
0.29766 (1.50)
0.47766** (2.28)
BIG_4 0.07209 (1.41)
0.07700 (1.50)
0.04742 (0.91)
0.06921 (1.35)
0.08312* (1.65)
0.12468** (2.29)
ROA -0.00856** (-2.07)
-0.00843** (-2.04)
-0.00834** (-2.00)
-0.00869** (-2.09)
-0.00680* (-1.67)
-0.00155 (-0.34)
BTM -0.47343*** (-4.49)
-0.48144*** (-4.57)
-0.42309*** (-4.02)
-0.47279*** (-4.48)
-0.44430*** (-4.18)
-0.65313*** (-5.39)
NBS 0.18913*** (2.67)
0.18726*** (2.66)
0.20945** (3.03)
0.18827*** (2.67)
0.15284** (2.14)
0.14496* (1.89)
LOSS -0.09637 (-1.14)
-0.10841 (-1.27)
-0.14538* (-1.72)
-0.10403 (-1.23)
-0.08903 (-1.07)
-0.16100* (-1.77)
CURRATIO -0.03369 (-0.82)
-0.03595 (-0.89)
-0.03551 (-0.87)
-0.03876 (-0.95)
0.03474 (0.85)
0.00146 (0.03)
FISYREND -0.05107 (-0.73)
-0.05990 (-0.84)
-0.06022 (-0.86)
-0.05864 (-0.83)
-0.02997 (-0.43)
-0.08165 (-1.11)
CROSSLIST -0.30146 (-1.39)
-0.29510 (-1.36)
-0.33145 (-1.54)
-0.30836 (-1.43)
-0.32084 (-1.43)
-0.04638 (-0.22)
Intercept
-1.99030*** (-5.81)
-1.91798*** (-5.59)
-2.11616*** (-6.61)
-1.96821*** (-5.88)
-1.13168*** (-3.49)
0.27147 (0.85)
Year dummies Yes Yes Yes Yes Yes Yes
Industry dummies Yes Yes Yes Yes Yes Yes
Sample size 1,483 1,483 1,483 1,483 1,483 1,483
Adjusted R² 0.581 0.581 0.581 0.582 0.590 0.540
This table presents a set of sensitivity tests as a robustness check of our results. The dependent variable is LNFEE, defined as
the natural logarithm of audit fees in thousands of euros. UCO equals to the control rights of the ultimate owner (at the 10%
threshold) measured by the sum of weakest links along each control chain. UCF equals to the cash-flow rights of the ultimate
owner (at the 10% threshold) measured by the sum of the products of ownership stakes along the different control chains.
27
EXCESS DUMMY is a dummy variable that equals one if the control rights of the ultimate owner are higher than her cash-
flow rights; and zero otherwise. EXCESS CONTROL is the difference between the ultimate owner’s control rights and cash-
flow rights (at the 10%threshold), all divided by her control rights. MLSD is a dummy variable that equals one if the firm has
at least two large shareholders (at the 10% threshold), and zero otherwise. SHAPLEY1 is the Shapley value solution for the
largest controlling shareholder in a four shareholder voting game. PCA_INDEX is a contestability index defined as the
common factor extracted from the variables MLSD, MLSN, VR234, VRRATIO and HERFINDAHL using principal component
analysis. MLSN is the number of large shareholders, other than the largest shareholder, up to the fourth. VR234 is the sum of
voting rights of the second, third and fourth largest shareholders. VRRATIO is the sum of voting rights of the second, third
and fourth largest blockholders divided by the voting rights of the largest shareholder. HERFINDAHL is the sum of squared
differences between the voting rights of the four largest shareholders, that is, (VR1 − VR2)² + (VR2−VR3)² + (VR3−VR4)²;
where VR1, VR2, VR3 and VR4 equal the voting rights of the first, second, third and fourth largest shareholders. SIZE is the
natural logarithm of total assets in thousands of euros. LN (SALES) is the natural logarithm of sales. LN(EMPLOYEES) is the
natural logarithm of the number of employees. INTSALES equals to the international sales divided by total sales. INVREC is
computed as the sum of inventories and accounts receivables scaled by total sales. LEVERAGE equals to total debt divided by
total assets. BIG_4 is a dummy variable that equals one if the firm uses one of the Big 4 auditing firms or their forerunners,
and zero otherwise. ROA is the firm’s return on assets defined as earnings before interest and taxes divided by the book value
of assets at the beginning of the fiscal year. BTM is the book-to-market ratio. NBS equals to the natural logarithm of the
firm’s number of business segments + 1. LOSS is a dummy variable that equals one if the firm reports a net loss in year t (that
is, its net income after taxes before extraordinary item and taxes on extraordinary item < 0), and zero otherwise. CURRATIO
is the firms’ current ratio, which equals to the ratio of current assets to current liabilities at the end of the fiscal year.
FISYREND is a dummy variable that equals one if the fiscal year-end is December 31, and zero otherwise. CROSSLIST is a
dummy variable that equals one if the firm is cross-listed in foreign capital markets in year t, and zero otherwise. All
continuous variables are winsorized at the 1st and 99th percentiles. t-values based on White (1980) heteroscedasticity-
consistent standard errors are in parentheses beneath coefficient estimates. The superscript asterisks ***, **, and * denote
statistical significance at the 1%, 5%, and 10% levels, respectively.
28
Table 9: Robustness tests Set 2: Alternative sample compositions
Variable
CLOSELY HELD FIRMS
CLOSELY HELD AND WIDELY HELD FIRMS
EXCLUDING PYRAMID
FIRMS
EXCLUDING CROSS-LISTED
FIRMS
USING ONLY BALANCED
PANEL DATA
(1) (2) (3) (4) (5)
EXCESS CONTROL 0.45082*** (2.77)
0.36709** (2.30)
0.46580** (2.14)
0.44387*** (2.87)
0.49900** (2.09)
MLSD -0.14968*** (-2.68)
-0.18135*** (-3.18)
-0.22435*** (-2.82)
-0.16850*** (-2.91)
-0.15765** (-2.14)
SIZE 0.54490*** (28.56)
0.53845*** (28.09)
0.48482*** (17.96)
0.51006*** (25.55)
0.45864*** (17.64)
INTSALES 0.78334*** (7.84)
0.81927*** (7.45)
0.51610*** (3.32)
0.45740*** (4.48)
0.52375*** (3.59)
INVREC -0.02378 (-1.43)
-0.02411 (-1.45)
-0.00828 (-1.29)
-0.02386 (-1.42)
-0.12313 (-1.03)
LEVERAGE -0.27624 (-1.63)
-0.23688 (-1.46)
0.19758 (0.74)
-0.24003 (-1.22)
0.24509 (0.92)
BIG_4 -0.07486 (-1.47)
-0.00711 (-0.13)
-0.03652 (-0.58)
0.05513 (1.08)
0.02632 (0.41)
ROA -0.00979*** (-2.68)
-0.01162*** (-3.18)
-0.01065** (-2.23)
-0.00989** (-2.39)
-0.00788* (-1.68)
BTM -0.33245*** (-3.68)
-0.38375*** (-4.30)
-0.64195*** (-5.11)
-0.44144*** (-4.22)
-0.51055*** (-3.65)
NBS 0.28810*** (4.84)
0.33060*** (5.22)
0.24463*** (2.59)
0.24301*** (3.53)
0.36677*** (3.62)
LOSS 0.04981 (0.56)
0.05351 (0.61)
-0.16776 (-1.58)
-0.10277 (-1.22)
-0.19818* (-1.94)
CURRATIO -0.06347** (-2.10)
-0.05884* (-1.91)
0.00277 (0.06)
-0.04219 (-1.04)
0.04882 (0.94)
FISYREND -0.00086 (-0.01)
-0.00034 (-0.01)
0.14230 (1.87)
-0.05636 (-0.79)
-0.11526 (-1.24)
CROSSLIST 0.09355 (0.49)
0.43648*** (2.74)
-0.12053 (-0.41)
--- -0.30106 (-1.37)
Intercept
-1.47461*** (-5.37)
-1.49942*** (-5.20)
-2.13817*** (-4.80)
-1.91386*** (-5.83)
-1.14261** (-2.07)
Year dummies Yes Yes Yes Yes Yes
Industry dummies Yes Yes Yes Yes Yes
Sample size 1,943 2,035 976 1,427 808
Adjusted R² 0.610 0.620 0.560 0.592 0.563
This table presents a set of sensitivity tests as a robustness check of our results. The dependent variable is LNFEE, defined as
the natural logarithm of audit fees in thousands of euros. Column 1 presents regression results for the impact of multiple large
shareholders on audit fees using 1,943 family and non-family closely held firms. Column 2 shows results after using 2,035
closely held and widely held French listed firms. In Columns 3 and 4, we repeat our analysis after excluding all firms which
are controlled through pyramiding and all cross-listed firms, respectively. EXCESS CONTROL is the difference between the
ultimate owner’s control rights and cash-flow rights (at the 10% threshold), all divided by her control rights. MLSD is a
dummy variable that equals one if the firm has at least two large shareholders (at the 10% threshold), and zero otherwise.
SIZE is the natural logarithm of total assets in thousands of euros. INTSALES equals to the international sales divided by total
sales. INVREC is computed as the sum of inventories and accounts receivables scaled by total sales. LEVERAGE equals to
total debt divided by total assets. BIG_4 is a dummy variable that equals one if the firm uses one of the Big 4 auditing firms
or their forerunners, and zero otherwise. ROA is the firm’s return on assets defined as earnings before interest and taxes
divided by the book value of assets at the beginning of the fiscal year. BTM is the book-to-market ratio. NBS equals to the
natural logarithm of the firm’s number of business segments + 1. LOSS is a dummy variable that equals one if the firm
reports a net loss in year t (that is, its net income after taxes before extraordinary item and taxes on extraordinary item < 0),
and zero otherwise. CURRATIO is the firms’ current ratio, which equals to the ratio of current assets to current liabilities at
the end of the fiscal year. FISYREND is a dummy variable that equals one if the fiscal year-end is December 31, and zero
29
otherwise. CROSSLIST is a dummy variable that equals one if the firm is cross-listed in foreign capital markets in year t, and
zero otherwise. All continuous variables are winsorized at the 1st and 99th percentiles. t-values based on White (1980)
heteroscedasticity-consistent standard errors are in parentheses beneath coefficient estimates. The superscript asterisks ***,
**, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
30
Table 10: Robustness tests Set 2: Alternative control variables
Variable ANALYST UCF TANGIBILITY SALES_GR FCF PYRAMID AQ1 AQ2 EM IFRS ISSUE
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (10)
EXCESS CONTROL 0.47176*** (3.03)
0.30241* (1.65)
0.43446*** (2.82)
0.44500*** (2.90)
0.43839*** (2.84)
0.32606** (1.93)
0.44926*** (2.93)
0.44758*** (2.92)
0.44600*** (2.90)
0.44788*** (2.91)
0.43502*** (2.82)
MLSD -0.18202*** (-3.05)
-0.19705*** (-3.29)
-0.17155*** (-2.90)
-0.17100*** (-2.87)
-0.17227*** (-2.91)
-0.16901*** (-2.86)
-0.17701*** (-2.98)
-0.17512*** (-2.95)
-0.17355*** (-2.93)
-0.17366*** (-2.93)
-0.17163*** (-2.91)
ADDITIONAL CONTROL VARIABLE
-0.00579 (-0.81)
-0.24932* (-1.67)
-0.56323*** (-2.61)
-0.09728 (-0.72)
0.22432** (1.97)
0.10950* (1.76)
0.34840 (1.55)
0.31729 (1.35)
0.00037* (1.77)
0.69363*** (6.32)
0.26014** (2.29)
SIZE 0.50799*** (20.88)
0.48974*** (24.76)
0.49463*** (24.92)
0.49190*** (24.83)
0.49231*** (24.87)
0.49251*** (25.05)
0.49435*** (24.96)
0.49341*** (24.92)
0.49327*** (24.85)
0.49237*** (24.88)
0.49020*** (24.60)
INTSALES 0.62550*** (5.83)
0.60537*** (5.59)
0.61595*** (5.74)
0.61980*** (5.75)
0.62130*** (5.78)
0.61479*** (5.65)
0.62415*** (5.78)
0.62570*** (5.79)
0.61876*** (5.75)
0.62023*** (5.76)
0.61319*** (5.72)
INVREC -0.02486 (-1.43)
-0.02705 (-1.55)
-0.02718 (-1.54)
-0.02557 (-1.47)
-0.02484 (-1.42)
-0.02634 (-1.44)
-0.02315 (-1.39)
-0.02322 (-1.39)
-0.02529 (-1.45)
-0.02524 (-1.44)
-0.02502 (-1.44)
LEVERAGE -0.24687 (-1.25)
-0.23714 (-1.20)
-0.11389 (-0.56)
-0.21836 (-1.10)
-0.21857 (-1.10)
-0.23781 (-1.20)
-0.25369 (-1.27)
-0.25131 (-1.26)
-0.23250 (-1.17)
-0.22816 (-1.15)
-0.24370 (-1.23)
BIG_4 0.06694 (1.31)
0.05913 (1.15)
0.07104 (1.39)
0.06619 (1.29)
0.06893 (1.34)
0.07875 (1.54)
0.06694 (1.31)
0.06699 (1.31)
0.06518 (1.27)
0.06644 (1.30)
0.06368 (1.24)
ROA -0.00853** (-2.05)
-0.00819** (-1.97)
-0.00714* (-1.68)
-0.00787* (-1.92)
-0.00916** (-2.16)
-0.00879** (-2.11)
-0.00967** (-2.28)
-0.00921** (-2.20)
-0.00849** (-2.04)
-0.00847** (-2.04)
-0.00823** (-1.98)
BTM -0.50914*** (-4.46)
-0.46711*** (-4.42)
-0.41610*** (-3.76)
-0.48706*** (-4.43)
-0.47443*** (-4.49)
-0.47007*** (-4.44)
-0.46845*** (-4.44)
-0.47296*** (-4.48)
-0.47389*** (-4.48)
-0.47394*** (-4.48)
-0.46772*** (-4.43)
NBS 0.18556*** (2.62)
0.19247*** (2.73)
0.20204*** (2.89)
0.18977*** (2.69)
0.19118*** (2.72)
0.19799*** (2.79)
0.18243*** (2.59)
0.18421*** (2.61)
0.18795*** (2.67)
0.18896*** (2.68)
0.18568*** (2.64)
LOSS -0.09742 (-1.15)
-0.10798 (-1.26)
-0.08128 (-0.94)
-0.10799 (-1.25)
-0.10205 (-1.20)
-0.10970 (-1.30)
-0.10230 (-1.20)
-0.10292 (-1.21)
-0.10083 (-1.18)
-0.10089 (-1.18)
-0.09267 (-1.08)
CURRATIO -0.03538(-0.87)
-0.03860 (-0.96)
-0.03631 (-0.89)
-0.03794 (-0.94)
-0.04173 (-1.03)
-0.03162 (-0.77)
-0.04738 (-1.17)
-0.04747 (-1.17)
-0.03671 (-0.91)
-0.03710 (-0.92)
-0.03805 (-0.94)
FISYREND -0.05818 (-0.82)
-0.06752 (-0.96)
-0.04973 (-0.70)
-0.05325 (-0.76)
-0.05746 (-0.81)
-0.06275 (-0.89)
-0.06070 (-0.86)
-0.05930 (-0.84)
-0.05790 (-0.82)
-0.05683 (-0.81)
-0.05899 (-0.84)
31
CROSSLIST -0.25176 (-1.13)
-0.33276 (-1.55)
-0.30103 (-1.39)
-0.31331 (-1.45)
-0.30807 (-1.43)
-0.30815 (-1.42)
-0.31570 (-1.46)
-0.31253 (-1.45)
-0.30923 (-1.43)
-0.30932 (-1.43)
-0.30315 (-1.41)
Intercept
-1.96914*** (-5.57)
-1.69933*** (-4.81)
-1.75350*** (-5.09)
-1.78316*** (-4.85)
-1.88415*** (-5.44)
-1.89554*** (-5.50)
-1.84025*** (-5.30)
-1.84052*** (-5.31)
-1.93634*** (-5.58)
-1.61321*** (-4.80)
-1.83070*** (-5.26)
Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Industry dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Sample size 1,483 1,483 1,483 1,483 1,483 1,483 1,483 1,483 1,483 1,483 1,483
Adjusted R² 0.582 0.583 0.584 0.582 0.583 0.583 0.583 0.582 0.582 0.582 0.583
This table presents a set of sensitivity tests as a robustness check of our results. The dependent variable is LNFEE, defined as the natural logarithm of audit fees in thousands of euros. EXCESS CONTROL is the difference between the ultimate owner’s control rights and cash-flow rights (at the 10% threshold), all divided by her control rights. MLSD is a dummy variable that equals one if the firm has at least two large shareholders (at the 10% threshold), and zero otherwise. ANALYST equals to the number of analysts reported if in I/B/E/S. UCF equals to the cash-flow rights of the ultimate owner (at the 10% threshold) measured by the sum of the products of ownership stakes along the different control chains. TANGIBILITY equals to the firm’s property, plants and equipment, divided by total assets. SALES_GR proxies for the firm’s sales growth, and computed as (SALESt−SALESt−1)/ SALESt−1. FCF = (Earnings before interest, taxes, depreciation, and amortization − interest, taxes, and common dividends) / total assets. PYRAMID is a dummy variable that equals one if the firm is controlled through pyramiding, and 0 otherwise. AQ1 and AQ2 are measures of accruals quality based on Dechow and Dichev’s (2002) model and on McNichols’s (2002) model, respectively. EM is Leus et al.’s (2003) earnings management measure, defined as the ratio of the absolute value of accruals and the absolute value of the cash flow from operations, where Accruals = (Δtotal current assets − Δcash) − ( Δtotal current liabilities − Δshort-term debt − Δtaxes payable) − depreciation expense. SIZE is the natural logarithm of total assets in thousands of euros. INTSALES equals to the international sales divided by total sales. INVREC is computed as the sum of inventories and accounts receivables scaled by total sales. LEVERAGE equals to total debt divided by total assets. BIG_4 is a dummy variable that equals one if the firm uses one of the Big 4 auditing firms or their forerunners, and zero otherwise. ROA is the firm’s return on assets defined as earnings before interest and taxes divided by the book value of assets at the beginning of the fiscal year. BTM is the book-to-market ratio. NBS equals to the natural logarithm of the firm’s number of business segments + 1. LOSS is a dummy variable that equals one if the firm reports a net loss in year t (that is, its net income after taxes before extraordinary item and taxes on extraordinary item < 0), and zero otherwise. CURRATIO is the firms’ current ratio, which equals to the ratio of current assets to current liabilities at the end of the fiscal year. FISYREND is a dummy variable that equals one if the fiscal year-end is December 31, and zero otherwise. CROSSLIST is a dummy variable that equals one if the firm is cross-listed in foreign capital markets in year t, and zero otherwise. All continuous variables are winsorized at the 1st and 99th percentiles. t-values based on White (1980) heteroscedasticity-consistent standard errors are in parentheses beneath coefficient estimates. The superscript asterisks ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
32
References
Ali, A., Chen, T.-Y. and Radhakrishnan, S. (2007) Corporate disclosures by family firms, Journal of Accounting and Economics, 44(1-2), pp. 238-286.
Anderson, R. C., Reeb, D. M. and Zhao, W. (2012) Family-controlled firms and informed trading: Evidence from short sales, Journal of Finance, 67(1), pp. 351-386.
Attig, N., El Ghoul, S. and Guedhami, O. (2009) Do multiple large shareholders play a corporate governance role? Evidence from East Asia, Journal of Financial Research, 32(4), pp. 395-422.
Attig, N., El Ghoul, S., Guedhami, O. and Rizeanu, S. (2013) The governance role of multiple large shareholders: Evidence from the valuation of cash holdings, Journal of Management & Governance, 17(2), pp. 419-451.
Attig, N., Fong, W. F., Gadhoum, Y. and Lang, L. H. P. (2006) Effects of large shareholding on information asymmetry and stock liquidity, Journal of Banking and Finance, 30, pp. 2875-2892.
Attig, N., Guedhami, O. and Mishra, D. (2008) Multiple large shareholders, control contests, and implied cost of equity, Journal of Corporate Finance, 14(5), pp. 721-737.
Bedard, J. C. and Johnstone, K. M. (2004) Earnings manipulation risk, corporate governance risk, and auditors' planning and pricing decisions, Accounting Review, 79(2), pp. 277-304.
Ben Ali, C. (2013) Qualité de la publication financière et mécanismes de gouvernance en France, Management & Avenir, 61(3), pp. 109-128.
Ben Ali, C. and Lesage, C. (2013) Les auditeurs financiers face aux conflits d'agence : Une étude des déterminants des honoraires d'audit en France, Comptabilité - Contrôle - Audit, 19(1), pp. 59-89.
Bennedsen, M. and Wolfenzon, D. (2000) The balance of power in closely held corporations, Journal of Financial Economics, 58(1/2), pp. 113-139.
Bloch, F. and Hege, U. (2003) Multiple shareholders and control contests, working paper HEC, pp.
Bolton, P. and Von Thadden, E.-L. (1998) Blocks, liquidity, and corporate control, Journal of Finance, 53(1), pp. 1-25.
Boubaker, S., Mansali, H. and Rjiba, H. (2013) Large controlling shareholders and stock price synchronicity, journal of banking and finance, forthcoming pp.
Boubaker, S., Mansali, H. and Rjiba, H. (2014) Large controlling shareholders and stock price synchronicity, Journal of Banking & Finance, 40, pp. 80-96.
Boubaker, S., Rouatbi, W. and Nguyen, P. (2012) Multiple large shareholders and corporate risk-taking: Evidence from France, SSRN eLibrary, pp.
Boubaker, S. and Sami, H. (2011) Multiple large shareholders and earnings informativeness, Review of Accounting and Finance, 10(3), pp. 246-266.
Chaney, P. K., Jeter, D. and Shivakumar, L. (2004) Self-selection of auditors and audit pricing in private firms., Accounting Review, 79(1), pp. 51-72.
Chau, G. and Gray, S. J. (2010) Family ownership, board independence and voluntary disclosure: Evidence from Hong Kong, Journal of International Accounting, Auditing and Taxation, 19(2), pp. 93-109.
Chau, G. K. and Gray, S. J. (2002) Ownership structure and corporate voluntary disclosure in Hong Kong and Singapore, The International Journal of Accounting, 37(2), pp. 247-265.
33
Chen, S., Chen, X. I. A. and Cheng, Q. (2008) Do family firms provide more or less voluntary disclosure?, Journal of Accounting Research, 46(3), pp. 499-536.
Choi, J.-H., Kim, J.-B., Liu, X. and Simunic, D. A. (2008) Audit pricing, legal liability regimes, and big 4 premiums: Theory and cross-country evidence, Contemporary Accounting Research, 25(1), pp. 55-99.
Choi, J.-H., Kim, J.-B., Liu, X. and Simunic, D. A. (2009) Cross-listing audit fee premiums: Theory and evidence, Accounting Review, 84(5), pp. 1429-1463.
Claessens, S., Djankov, S., Fan, J. P. H. and Lang, L. H. P. (2002) Disentangling the incentive and entrenchment effects of large shareholdings, Journal of Finance, 57(6), pp. 2741-2771.
Claessens, S., Djankov, S. and Lang, L. H. P. (2000) The separation of ownership and control in East Asian corporations, Journal of Financial Economics, 58(1-2 ), pp. 81-112.
Dechun, W. (2006) Founding family ownership and earnings quality, Journal of Accounting Research, 44(3), pp. 619-656.
Delgado-Garcia, J. B., De Quevedo-Puente, E. and De La Fuente-Sabaté, J. M. (2010) The impact of ownership structure on corporate reputation: Evidence from Spain, Corporate Governance: An International Review, 18(6), pp. 540-556.
Doidge, C., Karolyi, G. A., Lins, K. V., Miller, D. P. and Stulz, R. M. (2009) Private benefits of control, ownership, and the cross-listing decision, Journal of Finance, 64(1), pp. 425-466.
El Ghoul, S., Guedhami, O., Lennox, C. S. and Pittman, J. (2013) External versus internal monitoring: The importance of multiple large shareholders and families to auditor choice in Western European firms, SSRN eLibrary, December 20(Available at http://ssrn.com/abstract=1373808 or http://dx.doi.org/10.2139/ssrn.1373808), pp.
Faccio, M. and Lang, L. H. P. (2002) The ultimate ownership of Western European corporations, Journal of Financial Economics, 65(3), pp. 365-395.
Fan, J. P. H. and Wong, T. J. (2002) Corporate ownership structure and the informativeness of accounting earnings in East Asia, Journal of Accounting and Economics, 33(3), pp. 401-425.
Fan, J. P. H. and Wong, T. J. (2005) Do external auditors perform a corporate governance role in emerging markets? Evidence from East Asia, Journal of Accounting Research, 43(1), pp. 35-72.
Francis, J. R. (1984) The effect of audit firm size on audit prices : A study of the Australian market, Journal of Accounting and Economics, 6(2), pp. 133-151.
Francis, J. R., Khurana, I. K. and Pereira, R. (2003) The role of accounting and auditing in corporate governance and the development of financial markets around the world, Asia-Pacific Journal of Accounting & Economics, 10, pp. 1-30.
Francis, J. R. and Wang, D. (2008) The joint effect of investor protection and big 4 audits on earnings quality around the world, Contemporary Accounting Research, 25(1), pp. 157-191.
Gul, F. A., Kim, J.-B. and Qiu, A. A. (2010) Ownership concentration, foreign shareholding, audit quality, and stock price synchronicity: Evidence from china, Journal of Financial Economics, 95(3), pp. 425-442.
Hay, D. C., Knechel, W. R. and Wong, N. (2006) Audit fees: A meta-analysis of the effect of supply and demand attributes, Contemporary Accounting Research, 23(1), pp. 141-191.
Ho, J. L. and Fei, K. (2013) Auditor choice and audit fees in family firms: Evidence from the S&P 1500, Auditing, 32(4), pp. 71-93.
34
Holderness, C. G. (2009) The myth of diffuse ownership in the United States, Review of Financial Studies, 22(4), pp. 1377-1408.
Hope, O.-K., Langli, J. C. and Thomas, W. B. (2012) Agency conflicts and auditing in private firms, Accounting, Organizations & Society, 37(7), pp. 500-517.
Jensen, M. and Meckling, W. (1976) Theory of the firm: Managerial behavior, agency costs and ownership structure, Journal of Financial Economics, 3(4), pp. 305-360.
Khalil, S., Magnan, M. L. and Cohen, J. R. (2008) Dual-class shares and audit pricing: Evidence from the Canadian markets, Auditing, 27(2), pp. 199-216.
La Porta, R., Lopez-de-Silanes, F. and Shleifer, A. (1999) Corporate ownership around the world, Journal of Finance, 54(2), pp. 471-517.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A. and Vishny, R. (2000) Investor protection and corporate governance, Journal of Financial Economics, 58(1-2), pp. 3-27.
Laeven, L. and Levine, R. (2008) Complex ownership structures and corporate valuations, Review of Financial Studies, 21(2), pp. 579-604.
Lennox, C. (2005) Management ownership and audit firm size, Contemporary Accounting Research, 22(1), pp. 205-227.
Maury, B. and Pajuste, A. (2005) Multiple large shareholders and firm value, Journal of Banking & Finance, 29(7), pp. 1813-1834.
Mei-Ling, Y. (2010) The impact of controlling families and family ceos on earnings management, Family Business Review, 23(3), pp. 266-279.
Mishra, D. R. (2011) Multiple large shareholders and corporate risk taking: Evidence from East Asia, Corporate Governance: An International Review, 19(6), pp. 507-528.
Niemi, L. (2005) Audit effort and fees under concentrated client ownership: Evidence from four international audit firms, The International Journal of Accounting, 40(4), pp. 303-323.
Pagano, M. and Roëll, A. (1998) The choice of stock ownership structure: Agency costs, monitoring, and the decision to go public., Quarterly Journal of Economics, 113(1), pp. 187.
Simunic, D. A. (1980) The pricing of audit services: Theory and evidence, Journal of Accounting Research, 18(1), pp. 161-190.
Villalonga, B. and Amit, R. (2006) How do family ownership, control and management affect firm value?, Journal of Financial Economics, 80(2), pp. 385-417.
Zwiebel, J. (1995) Block investment and partial benefits of corporate control, Review of Economic Studies, 62(211), pp. 161.