1
STRATEGIC COMPETITION BY AUDIT FIRMS
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Strategic competition by audit firms
Abstract
In this paper we argue that audit firms compete rationally and will take into account the
immediate benefits and future costs when deciding how fierce to compete. Based on prior
literature in the industrial organizations area, we hypothesize that competing with the same audit
firms across different industries within a geographical region (which we label ‘multi-industry
contact’) facilitates mutual forbearance and leads therefore to less fierce competition.
Furthermore, client concentration within an industry increases the immediate benefits from fierce
competition inducing audit firms to compete more aggressively. Finally, we predict that client
restatements adversely affect an audit market leader’s high-quality reputation resulting in rival
audit firms competing more fiercely for the leader’s clients. We measure competition by two
proxies: first, ‘leader dethronement’ which captures whether the leader is dethroned by a rival,
and second, ‘market share mobility’ which captures the changes in market share of all audit
firms. Consistent with prior research (see, Numan and Willekens 2012; Francis et al. 2005), we
define an audit market segment as an industry within a U.S. Metropolitan Statistical Area (MSA)
and find that multi-industry contact between the incumbent audit firm and its rivals is negatively
related with our competition proxies, whereas as client concentration is positively related with
these proxies. Finally, restatements by a leader’s client are positively associated with leader
dethronement but has no significant effect on market share mobility.
Keywords: competition, leader reputation, market instability
JEL- classification : M42
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I.
INTRODUCTION
In this paper we examine factors that might affect how fierce audit firms compete with
each other in an audit market segment. Audit market competition and the presumed lack thereof
has been a major concern of regulators for a long time (European Commission, 2011; U.S.
Government Accountability Office [GOA] 2003, 2008). The US Government Accountability
Office (GOA) expresses the concerns as follows: “Dominant sellers, in this case accounting
firms, may be more likely or more able to engage in coordinated interaction in ways that can
affect auditing practices or prices” (GOA, 2008). The academic literature typically studies audit
market competition in a one period, static, setting by focusing on concentration (Pearson and
Trompeter, 1994; Bandyopadhay and Kao, 2004; Feldman, 2006), market share (Willekens and
Achmadi, 2003), industry specialization (Craswell et al., 1995; Ferguson et al., 2003; Francis et
al., 2005) or market share differentiation (Numan and Willekens, 2012). However, these static
measures conceal much of the dynamic competitive processes in markets (Davies and Geroski,
1997). Substantial variation over time in leading firms’ market shares may exist even in markets
where competition is labeled as low using static competition measures (GOA, 2008; Scherer and
Ross, 1990; Bujink et al., 1998). More worrisome is that the relationship between competition
and some static measures, most notably concentration, is theoretically ambiguous (Numan and
Willekens, 2012; Dedman and Lennox, 2009). Therefore we use two measures capturing market
share instability over time, i.e. the market share transfers between audit firms, as our proxies for
audit market competition (Ferrier et al., 1999; Schmalensee, 1989, Caves and Porter, 1978). Prior
industrial organizational literature argues that market instability is a sign of high competition and
interfirm rivalry (Kato and Hongo, 2007; Staigler and Wolak, 1992, Schmalensee, 1989).
As the audit market is considered oligopolistic, audit firms have some leeway in how
fierce to compete. In oligopoly, firms actions are interdependent (Melvin and Boyes, 2002,
Stigler, 1964) as each firm is large enough to impact market conditions. Firms thus take into
3
account rivals’ responses to their market actions. We argue that argue that audit firms compete
rationally and will take into account the immediate benefits and future costs when deciding how
fierce to compete. The immediate benefits consist of an increase in the number of clients and
revenues by taking away rival audit firms’ clients. The future costs refer to the reaction of the
rivals who may respond by cutting their fees or targeting clients triggering price wars. Audit
firms will compete more fiercely the higher the former effect and the smaller the latter effect. As
a result, firms can either exert their joint market power decreasing competition or compete
heavily for each other’s clients.
We investigate three factors that impact the cost and benefits of competing fiercely. First,
when rivals compete in multiple market segments, i.e. multimarket contact, they may chose not
to compete heavily in each other market segments as this could result in vigorous competition in
all market segments. Thus, the gain from deviating in one market may be inferior to the future
losses in all markets.1 Hence, competing in multiple market segments with the same rivals
decreases the incentives to compete fiercely (Bernheim and Whinston; 1990). Empirical
evidence indeed shows that multimarket contact leads to higher prices, profits and lower sales
growth rates in amongst others aviation, banking and the mobile telephone market (Greve, 2008;
Gimeno, 2002; Barros, 1999; Parker and Röller, 1997; Evans and Kessides, 1994). Consistent
with these arguments we hypothesize that audit firms that compete in multiple audit market
segments, which we define as a client industry, within a Metropolitan Statistical area have lower
incentives to compete aggressively resulting in lower market instability (Hypothesis 1). Second,
concentrated buyers can exert their bargaining power to increase competition, for instance by
threatening to switch suppliers (Motta, 2004) or by negotiating lower fees (Huang et al., 2007;
Casterella et al., 2004, Mayhew and Wilkins, 2003). Furthermore, the benefit of attracting a
concentrated buyer is very large and likely exceeds the future losses caused by rivals’ responses.
1
In contrast, a firm may choose to deviate in all markets increasing the immediate gain of deviation.
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Consistent with this argument, empirical organizational literature finds that concentrated buyers
are associated with more variation in suppliers’ market shares over time (Kato and Honjo, 2007;
Caves and Porter, 1978). Therefore, we predict that client concentration increases competition
amongst audit firms resulting in higher market share instability (Hypothesis 2). Third, we test the
effect of reputation damage of the industry leader caused by client restatements on competition.
Prior studies have shown that market leaders on average provide higher audit quality and have a
high quality reputation (Reichelt and Wang, 2010; Francis et al., 2005; Ferguson et al., 2003;
Craswell et al., 1995). We posit that restatements of financial statements of clients audited by the
market leader negatively affect the leader’s reputation as restatements result in negative capital
market consequences for the client (Palmrose et al., 2004) and have adverse implications for the
auditor-client relationship (Huang and Scholz, 2012). Damage to the leaders’ reputation is likely
to increase the benefit of fierce competition for rival audit firms as the leader’s clients are more
willing to switch audit firms, while the costs remains unaffected or decreases. Hence, we predict
that clients’ restatements of the industry leader will increase competition positively affecting
market instability (Hypothesis 3).
We test our hypotheses on a U.S. sample of 3,279 market-years over the years 20032012. We follow recent studies that define audit market segments based on industries within
Metropolitan Statistical Areas (MSA) (Numan and Willekens, 2012; Reichelt and Wang., 2010;
Francis et al., 2005)2. In previous literature dynamic market competition analysis and market
instability is proxied by changes in market share and relative rankings of incumbents audit firms
and entrants over time (Ferrier et al., 1999; Schmalensee, 1989, Caves and Porter, 1978)3. In line
2
I
-digit SIC industry within a MSA. The
share in a 2“IC
M“A “
with the second highest market share in a 2-digit SIC industry
M“A T
audit firms in a 2-digit SIC industry within an MSA which are neither leader or follower.
3
In this study, market instability thus refers to the changes in market shares or rankings of the suppliers in a
market over a 1-year period. Therefore, the terms market instability and market share are used interchangeably.
Market instability does not imply anything about the evolutions in the total size of the market or refer to stages in
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with this literature we capture market instability, which is our dependent variable, by two
variables measuring different dimensions: leadership dethronement and market share mobility.
The first measure, leader dethronement, is defined as a dummy variable capturing the year-byyear change in market leader identity in the market segment. We include this variable since the
leadership position is particularly valuable as leaders can exploit economies of scale, have strong
reputations and can charge higher prices (Ferrier et al, 1999; Armstrong and Collopy, 1996).
Thus, the leadership position is likely to be contested in competitive markets. The second
measure, market share mobility, is defined as the sum of the year-by-year market share change of
all competitors within a market segment (Kato and Honjo, 2006; Sakakibara and Porter 2001;
Caves and Porter, 1978). While the former measure focuses exclusively on the leadership
position making abstraction of the other suppliers in the market, the latter measure captures
market instability over all rivals.
To test our three hypotheses we define the following test variables. First, we define multiindustry contact as the natural logarithm of the number of industries within an MSA where the
market segment leader and its rivals within the market segment have all clients in. Second, we
define client concentration as measured by the Herfindahl index of the clients. And third, we
proxy quality reputation damage by a dummy indicating whether a restatement of a financial
statement audited by the industry leader occurred during the year.
In general, our results support our hypotheses. When audit firms compete in multiple
industries withing a MSA competition is less fierce as evidenced by a negative associated
between this measure and market share mobility as well as leadership dethronement. This result
suggests that audit firms use mutual forbearance, i.e. refraining from competing aggressively for
each other’s existing clients. In contrast, our results show that client concentration increases
competition as higher client concentration is positively associated with market share mobility
a product/market life
M
unchanged to complete where all incumbent firms lose their clients to new rivals. We do not attempt to classify
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and leadership dethronement. Finally, the restatement of a financial statement signed by the
industry leader increases the likelihood that the leader will lose its leadership position. This
indicates that the leader’s reputation is seriously damaged by the restatement, increasing
competition leading to higher market share instability. Note however that we do not find
significant results for market share mobility.
As changes in market shares can be caused by client switches as well as by changes in
fees, we perform supplemental analyses to investigate which of these is responsible for the
observed market instability. Our results reveal that multi-industry contact is negatively
associated with the amount of client switching but is not significantly associated with fee
changes of non-switching clients consistent with audit firms using mutual forbearance. On the
contrary client concentration is not significantly associated with client switching rates but is
negatively associated with audit fee growth from non-switching clients suggesting that the higher
competition results in lower fees rather than a higher rate of clients actually switching. Finally,
our results suggest that leader reputation damage results in more clients switching from the
industry leader and in non-switching clients receiving a fee discount, supportive of the higher
competition faced by the industry leader.
Our paper offers a contribution to the audit competition literature in the following ways.
First, we empirically study which factors affect the fierceness of audit market competition
thereby presenting evidence that both the costs and benefits are taken into account when
deciding how fierce to compete. Second, unlike previous studies that used static measures of
audit competition (see, for example, Feldman, 2006; Bandyopadhay and Kao, 2004, Pearson and
Trompeter, 1994) we introduce and test dynamics measures of audit market competition. As
static measures conceal much of the dynamic competitive processes especially in highly
concentrated markets (Davies and Geroski, 1997; (Bandyopadhay and Kao, 2004), studying
dynamic competition measures offers an addition to the literature as they are a good indicator of
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rivalry in concentrated markets (Caves and Porter, 1978). Third, we also link competition to a
proxy of leader reputation damage. Although it is well documented that audit market leaders
enjoy a higher quality reputation than their rivals (e.g. Reichelt and Wang, 2010), it is unclear
whether reputation damage affects the competitive position of leaders over time. Finally, we also
contribute to the regulatory debate about whether there is sufficient audit market competition
(European Commission, 2011; U.S. Government Accountability Office [GOA] (2003, 2008). As
our evidence suggests that the fierceness of competition depends on audit market characteristics,
we illustrate that a one-size-fits-all regulation to encourage audit market competition may not be
optimal.
The remainder of the paper is as follows. In section II, we develop our hypotheses.
Section III presents the research design, while section IV describes the sample selection
procedure. Section V presents the results and section VI concludes.
II.
HYPOTHESES
Following Numan and Willekens (2012) we characterize the audit market as a qualitydifferentiated oligopoly, dominated by a few suppliers. A key feature of oligopoly is that each
supplier’s actions affect the market conditions, such as price. Hence, a profit-maximizing firm
will take into account the direct effect of their market decisions jointly with the secondary effect
of the rivals responses to those decision. In summary, suppliers actions are interdependent in
oligopoly. In such settings, rational firms will weigh the immediate benefit of competing fiercely
with the future costs. The immediate benefit results from new clients and increased revenues,
while the future costs stem from rivals reactions which may reduce fees triggering a price war
resulting in lower prices and profits. Hence, in what follows we will investigate factors that
affect the cost and benefits of competing fiercely and consequently the level of competition.
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Multi-industry contact.
Multimarket contact occurs when firms compete with each other in multiple market
segments. Edwards (1955) first argued that multimarket links could negatively affect
competition: “Firms that compete against each other in many markets may hesitate to fight
vigorously because the prospects of local gain are not worth the risk of general warfare”. From
an economic perspective, the gain of competing fiercely in one market segment may be
outweighed by rivals’ responses in the other market segments. A formal analysis by Bernheim
and Whinston (1990) shows that competing in multiple market segments decreases the fierceness
of competition, when asymmetries between rivals are smaller when considering the combined
multiple market segments than when considering the market segments seperatly. With
asymmetries across markets, a firm will have incentives to practice mutual forbearance, i.e.
avoid competitive attacks in markets where competitors are larger to avoid strong competition in
markets where the firm is dominant. Empirical studies show consistently that multimarket
contact negatively affects competition by facilitating mutual forbearance. Multimarket contact is
shown to increase prices, profits and survival rates and decreases sales growth rates (Greve,
2008; Li and Greenwood, 2004; Gimeno, 2002; Barros, 1999; Evans and Kessides, 1994) in
industries such as the airline industry (Evans and Kessides, 1994), banking (De Bonis and
Ferrando, 2000) and the insurance industry (Greve, 2008).
The audit market provides a setting where competing in multiple market segments may
exist and decrease the fierceness of competition. First, prior auditing research segments the audit
market based on industries within Metropolitan Statistical Areas (MSA’s) (Numan and
Willekens, 2012; Reichelt and Wang, 2010; Francis et al., 2005). Consequently, audit firms
compete with each other for clients in the different industries (market segments) within an MSA,
i.e. multi-industry contact. Thus, the same competing audit firms/offices try to attract clients in
multiple industries. Secondly, asymmetries between different suppliers within one market exist
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both in terms of audit quality (Reichelt and Wang, 2010), in terms of production efficiencies
(Banker et al., 2005) as in market size. These assymetries are likely smaller when considering all
the industries within the MSA together instead of each industry separately (Motta, 2004).
Finally, industry leadership is associated with significant fee premiums (Ferguson et al., 2003;
Francis et al., 2005) especially when the distance with the follower increases (Numan and
Willekens, 2012). Hence, mutual forbearance may occur as industry leaders may be unwilling to
compete vigorously in industries where they are not the leader fearing rival responses in those
markets where they are the leader..
Based on these arguments, we predict that multi-industry contact will decrease the
fierceness of audit market competition leading to lower market instability:
Hypothesis 1: Ceteris paribus, multi-industry contact between audit firms is negatively
associated with market instability.
Client Concentration.
Demand-side characteristics, such as client concentration, can also impact audit market
competition. Higher buyer concentration implies few large and many small clients in contrast to
markets with all equally-sized firm. Consequently, relative large clients have bargaining power
vis-a-vis the supplier because the clients constitute a large part of the supplier’s revenue. Thus,
concentrated buyers can use their power to increase competition destabilizing suppliers’ market
shares (Caves and Porter, 1978). For instance, the large client can stimulate competition by
threatening to switch suppliers, either to an existing competitor or to a new entrant (Motta,
2004). Alternatively, concentrated buyers can negotiate prices downwards decreasing their
suppliers market share consistent with audit fee studies reporting that larger clients have stronger
negotiating power over audit fees (Huang et al., 2007; Casterella et al., 2004, Mayhew and
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Wilkins, 2003). Furthermore, the immediate gain from competing aggresively by attracting a
concentrated client from a rival is large. At the same time, the rival has few opportunities to
retaliate since targeting the smaller clients in the market will unlikely significantly affect the
firm’s portfolio. Hence, the future losses will be small compared to the immediate gains of
competing fiercely. Hence, client concentration increases the fierceness of competition and
hence market instability consistent with empirical organizational literature that finds that
concentrated buyers use their bargaining power to destabilize suppliers’ market shares (Kato and
Honjo, 2007. Caves and Porter, 1978). 4
Hypothesis 2: Ceteris paribus, client concentration is positively associated with market
instability.
Quality reputation damage
In a quality-differentiated oligopoly, suppliers who differentiated themselves from
competitors are able to charge higher prices than competitors without losing market share (Chan,
1999). Furthermore, the audit services can be considered as a credence service (Causholli and
Knechel, 2012). Clients are unable to assess the level of service and subsequently the service
quality both ex-ante, before the service is performed, and ex-post. Thus, clients need to rely on
their supplier’s reputation for making their decisions. It follows that quality-reputations are
highly important in credence good markets. In line with this argument, prior studies show that
reputation damage caused by audit failures severely impact the audit firm’s market position
(Skinner et al., 2012; Weber et al., 2008). We argue that the industry leader will have a high
audit-quality reputation as prior research found that clients of industry leaders have higher
earnings response coefficients (Balsam et al., 2003), have a higher likelihood of receiving going
4
Contrary to the above argument, concentrated clients may be reluctant to share the same auditor in order to prevent
the transfer of proprietary business information (Chang et al., 2009; Kwon, 1996). This reduces the amount of
auditors from which the client can select decreasing market instability.
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concern audit opinions when in financial distress (Reichelt and Wang, 2010), have fewer
accounting restatements (Chin and Chi, 2008), higher disclosure quality (Dunn and Mayhew,
2004) and are less subject to SEC enforcement actions (Carcello and Nagy, 2004). Furthermore,
a positive association between audit fees and market leadership is documented in prior research
(Numan and Willekens, 2012; Francis et al., 2005; Ferguson et al., 2003; Balsam et al, 2003;
Mayhew and Wilkins, 2003; DeFond et al., 2000; Craswell et al., 1995). This fee premium is
interpreted as evidence of the client’s willingness to pay for higher audit quality (Craswell et al,
1995; Francis et al., 2005)5.
Damage to the industry leader’s quality reputation will disrupt the market equilibrium. In
line with the credence attributes of the audit service, clients may question the market leader’s
claims of higher audit quality. Hence, the benefits for rivals to compete fiercely increase as the
leader’s clients are more likely to switch audit firms than before (Skinner et al., 2012; Weber et
al., 2008). Furthermore, because of the damaged reputation the likelihood that the industry leader
is able to attract clients from his rivals decreases. The future costs of competing fiercely thus
decreases.
Hence, reputation damage of the industry leader will increase audit market
competition.
We posit that restatements of financial statements signed by the industry leader damages
his reputation. Restatements result in negative capital market consequences for the client
(Palmrose et al., 2004) and has adverse effects on the auditor-client relationship (Huang and
Scholz, 2012). In summary, restatements of financial statements signed by the market leader will
negatively affect his quality reputation, which will disrupt market equilibrium increasing market
instability.
5
Note that fee premiums can also be interpreted as market leaders exerting their market power to
increase fees above competitive levels, effectively decreasing market competition (Numan and Willekens, 2012). If
the market leader has market power rather than high-quality reputation, restatements of financial statements he
signed will not affect his reputation. Subsequently, restatements will not change in market instability. Hence, this
would bias against our hypothesis.
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Given this argument we formulate the following hypothesis:
Hypothesis 3: Ceteris paribus, restatements of financial statements audited by the industry
leader is positively associated with market instability.
III.
RESEARCH DESIGN
Prior research typically employs static, i.e. cross-sectional, measures to capture audit
market competition, such as concentration (Pearson and Trompeter, 1994; Bandyopadhay and
Kao, 2004; Feldman, 2006), market share (Willekens and Achmadi, 2003), industry
specialization (Craswell et al., 1995; Ferguson et al., 2003; Francis et al., 2005) and market share
differentiation (Numan and Willekens, 2012). However, these measures conceal much of the
underlying competitive conduct in the market as high market turnover could exist in markets
where static measures suggest low competition (Scherer and Ross, 1990; Bujink et al., 1998,
Davies and Geroski, 1997). Therefore, we investigate a dynamic measure of competition: market
instability. Few auditing studies researched market instability (Chang et al., 2009; Wolk et al.,
2001; Hogan and Jeter, 1999; Bujink et al., 1998; Danos and Eichenseher, 1982). These studies
indicate increasing concentration levels caused by market share increases of the leading firms in
a market (Wolk et al., 2001; Hogan and Jeter, 1999). Furthermore, cross-country descriptive
evidence shows that high market concentration and high market instability may coincide (Bujink
et al., 1998). Our study differs from these studies in the following aspects. First, these studies use
data prior to the demise of Arthur Andersen and the implementation of the Sarbanes-Oxley act.
Second, markets are defined at national (industry) level, while we follow recent studies
deleniating a market segment as an industry within an MSA (Numan and Willekens, 2012;
Reichelt and wang, 2010, Francis et al., 2005). Finally, the purpose of these studies was to
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document evolutions over time without explicitly taking into account the factors responsible for
these evolutions.
We specify two dependent variables each measuring a distinct aspect of market share
instability. First, we focus exclusively on the identity of the leader by studying leader
dethronement, i.e. when a rival gains a larger market share than the incumbent leader (Ferrier et
al., 1999; Armstrong and Collopy, 1996). Leaders are able to exploit economies of scale, have
stronger reputations and enjoy market power allowing them to charge higher prices (Ferrier et
al., 1999; Armstrong and Collopy, 1996). Consistent with this view, the audit literature has
identified market share leaders as industry specialists earning significant fee premiums (Francis
et al., 2005; Balsam et al, 2003; Mayhew and Wilkins, 2003, Ferguson et al., 2003) and enjoying
stronger quality reputations (Reichelt and Wang, 2010). These examples highlight the
importance of the leadership position to competitors. Hence, rivals will likely contest the leader.
Therefore, the instability of market shares may be tested by leader dethronement. By focusing
only on the leader; leadership dethronement conceals much of the market share instability of
lower-ranked firms. Therefore we construct a second dependent variable taking all suppliers into
account. Specifically, we construct a measure, which we label market share mobility, by
aggregating the changes in market share of all competing firms.
Consistent with prior research, we define audit market segments as two-digit SIC
industries within U.S. Metropolitan Statistical Areas (Numan and Willekens, 2012; Francis et al.,
2005). This delineation reflects that audit engagements require relevant industry knowledge
which is difficult to transfer within the audit firm across MSA’s (Francis et al., 2005). All
variables in our models are constructed based on this market segment definition6.
Model 1: Leader dethronement
6
One exception is MS_L_MSA which measures the market share of the leader at the MSA-level. This measure
reflects the dominance of the market segment leader across all industries within the MSA.
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We construct L_DETHR, a dummy variable equal to one if the market segment leader in
year t-1 loses his leadership position to a competitor in year t, zero otherwise (Ferrier et al.,
1999). Since the dependent variable is a dummy variable, we specify the following probit model:
L_DETHRt =
0+
1*MULTI_IND_LFt+
2*HHI_CLIENTt+
3*RESTAT_Lt+
controls +
industry and year fixed effects+
Our measure of multi-industry contact, MULTI_IND_LF t, is the natural logarithm of the
number of market segments, i.e. 2-digit SIC industries, within the MSA in which both the market
segment leader and follower have clients in7. This measure focuses only on multi-industry
linkages at the local level. This choice is motivated by studies emphasizing the local nature of
audit market competition (Numan and Willekens, 2012; Francis et al.; 2005). We argue that the
follower will be the fiercest competitor for the leader and has the highest likelihood of
successfully dethroning the leader8. Numan and Willekens (2012) show that competition is more
fierce with the closest competitor. Therefore, we only calculate the multi-industry contact
between leader and follower. We predict a negative association between this measure and market
instability.
We capture client bargaining power by constructing HHI_CLIENTt, measured as the
Herfindahl index of the audit fees paid by the clients in year t-1. While some studies use the C4,
the sum of the four largest clients, of the client industry (Chang et al., 2009; Hogan and Jeter,
1999, Kwon et al., 1996), we utilize the Herfindahl index because some markets have fewer than
7
Define the leader in industry 1 within an MSA as A and the follower as B. Then MULTI_IND_LF measures the
number of industries within the same MSA in which A and B each have at least one client. We do not require that A
(B) is also the leader (follower) in the other industries. The lower bound of this measure is 1 since by construction
A and B compete together in industry 1. The upper bound is the number of industries within the MSA.
8
In our sample, the market segment leader has on average a market share of 58.37% (and a mean of 56.29%). The
market follower has on average a market share of 23.09% (and a mean of 23.70%). This implies that for the
average market, the leader and follower have a conjoint market share of 81.46%.
15
four clients9. We use clients’ audit fees rather than total assets because the theoretical arguments
are based on revenues derived from clients, rather than client size as such. This measurement
choice is consistent with industrial organization literature (Caves and Porter, 1978). Following
hypothesis 2, we predict a positive relationship with market instability.
To test the third hypothesis, we employ a measure capturing the restatements of the
market leader. The variable RESTAT_Lt is equal to one when the market leader has at least one
financial statement which he audited restated during year t, zero otherwise. We use the
restatement date to identify the year in which the restatement occurred. Restatements are issued
after financial statements and reputation damage only occurs when the market becomes aware of
potential quality problems, i.e. at the restatement date. We use the restatement period to identify
the audit firm(s) that issued the audit report of the financial statements that were restated. We
construct a binary variable to mitigate the influence of large markets. Ceteris paribus, with a
similar restatement probability across markets, larger markets will experience more restatements.
Following hypothesis 3, we predict a positive association with market instability.
Model 2: Market share mobility
Following prior studies, we use the market share changes from year t-1 to year t for all
auditors in the market segment to calculate market share mobility (MS_MOB) as follows: (Chang
et al., 2009; Kato and Honjo, 2006; Sakakibara and Porter, 2001; Bujink et al., 1998; Caves and
Porter, 1978)
 MS
n
MS _ MOBt 
i 1
it 1
 MSit
2
Where MSit-1 reflects the market share of audit firm i in an industry within an MSA at
time t-1, and MSit audit firm i in an industry within an MSA at time t. The total number of audit
9
As a consequence, the C4 measure is always 1 in those markets irrespective of the distribution of the audit fees
paid. An market where all firms pay 25% of the total fees generated in the market has the same C4 as a market
where one firms pays 85% of all audit fees and the remaining firms each 5%.
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firms in the market segment is n. By construction, MS_MOBt ranges between zero (no change in
market share for all audit firms) and one (all audit firms at t-1 lose their market shares to new
audit firms at time t), where higher values reflect higher market share instability. We estimate the
following model using fractional logit (Papke and Wooldridge, 1996):
MS_MOBt =
0+
1*MULTI_IND_ALLt+
2*HHI_CLIENTt+
3*RESTAT_Lt+
controls +
industry and year fixed effects+
As MS_MOBt measures the market share changes of all audit firms, we need a multiindustry measure capturing multi-industry linkages between all audit firms active in the market
segment. Following prior studies, we construct MULTI_IND_ALLt in the following way (De
Bonis and Ferrando, 2000):
MULTI _ IND _ ALLt  ln( 
ailt 1
)
i 1 l i 1 n( n  1) / 2
n
n
Where a ilt measures the number of industries in which firm i and firm l each have at least one
client in year t-1, while n denotes the total number of firms active in the market segment. The
measure first aggregates the number of multi-industry contacts between each pair of firms and
subsequently calculates the average multi-industry contact across all pairs10. Finally, we take the
natural logarithm of this measure to normalize the variable. HHI_CLIENTt and RESTAT_Lt are
calculated in the same way as in model 1.
Control variables
We control for the audit firm size of the leader: BIG4_Lt is equal to one if the market
segment leader is a BIG 4 firm in year t-1, zero otherwise. Second, we explicitly control for
10
Suppose a market segment with 3 audit firms: A,B,C. Firms A and B both have at least one client in 4 industries.
Firms A and C have contact in 6 markets, while firm B and C meet in 2 markets. Then MULTI_IND_ALLt can be
constructed by aggregating the number of multimarket contacts of each pair (4+6+2=12) and subsequently taking
the average (12 divided by 3 pairs = 4). The total number of pairs (3) can be calculated using the formula n*(n-1)/2.
In this example: 3*2/2=3.
17
market size: smaller markets only support a limited number of audit firms because of economies
of scale and fixed entry costs which will decrease market instability (Fusillo, 2013; Scherer and
Ross, 1990). In contrast, the effect of one client switching in smaller markets will have a stronger
mathematical impact on market instability. We construct MAR_SIZEt which measures the size of
the MSA-industry market in terms of audit fees relative to the total national market in the prior
year. Hence, MAR_SIZEt reflects market segment size as a percentage of the total U.S. audit
market. We further include the distance between leader and follower: a smaller distance reflects
fiercer competition for the leader position due to a lower differentiation of the leader vis-à-vis
the follower (Numan and Willekens, 2012; Mayhew and Wilkins, 2003). Additionaly, high
distance signals leader dominance (Ferrier et al., 1999; Davies and Geroskie, 1997; Danos and
Eichenseher, 1982). |DISTANCE| t measures the market share distance between the market leader
and the follower in an industry in the prior year (t-1).
Next, we control for the size of the average client (Chang et al., 2009; Hogan and Jeter,
1999). Large clients require more sophisticated auditing techniques and firm-specific knowledge
reducing the amount of audit firms with the required expertise to conduct the audit. Market
instability will decrease with average client size. The variable AVG_CLIENTSIZEt is calculated
as the average of the natural logarithm of clients’ total assets in the prior year. In order to test
whether dominance at MSA-level impact market segment instability, we include MS_L_MSAt
which is market share of the market segment leader at the MSA-level in the prior year.
Finally, we include three variables capturing demand-side instability as they will
positively impacting market instability (Kato and Honjo, 2006; Sakakibara and Porter, 2001,
Ferrier et al., 1999; Caves and Porter, 1978). First, the variable ENTRYt measures the amount of
fees paid by clients buying audit services at time t but not at time t-1 divided by the audit fees of
all clients in the market in t-1. Clients without a previous commitment to any audit firm do not
face switching costs unlike pre-existing clients (Klemperer, 1987). These latter clients have a
18
higher likelihood of reappointing the same audit firm because they are locked-in. Hence, the
higher the switching costs the lower market instability. By consequence, new clients (without
switching costs) will increase market instability. Second, the variable EXITt captures the amount
of fees of clients buying audit services at time t-1 but not in time t divided by total audit market
fees in t-1. Clients exiting, either by takeovers or bankruptcy, will increase market instability.
Third, we include the variable GROWTHt, which measures the percentage change of audit fees of
clients buying audit services t as time t-1. This variable controls for higher competition in higher
growing markets (Sakakibara and Porter, 2001, Ferrier et al., 1999; Caves and Porter, 1978).
Moreover, high client growth can create a mismatch between the client and the audit firm
(Landsman et al., 2009)11. To control for industry- and time- specific factors that might affect
market instability, we include industry and year fixed effects. Because of the industry fixed
effects, we do not include variables capturing regulated industries or highly litigious industries
used in prior research (Chang et al., 2009, Hogan and Jeter, 1999).
IV.
SAMPLE SELECTION
We collect all audit clients with positive and non-zero audit fees from Audit Analytics.
We restrict the dataset to 2003 onwards because the demise of Arthur Anderson and the
Sarbanes-Oxley act induced significant audit market changes affecting market instability. The
last year available was 2012. During this time period, no further consolidation occurred amongst
the Big 4. For each client, we retrieve the 2-digit SIC code and client location. We link the client
location to MSA’s as defined by the U.S. Census bureau based on the FIPS codes of client
locations. We calculate the market shares based on audit fees paid by clients. We include both
Big 4 as non-Big 4 auditors in our sample because we cannot exclude ex-ante that these firms
11
The variables ENTRYt, EXITt and GROWTHt are subject to measurement error if some clients are excluded from
the dataset in a particular year. Moreover, ENTRY t will also include newly-listed firms even when they had a prior
commitment with their current auditor. However, the same limitations also apply for the dependent variables.
Therefore, despite this shortcoming we find it appropriate to use these measures as control variables.
19
compete for the (some) same clients. Furthermore, we retrieve restatement information from
Audit Analytics. Tor each restatement we look to the restatement period and identify the audit
firm signing the original financial statements. Subsequently, we retrieve the date of the
restatement announcement.
Since we investigate market instability, the dataset includes one observation for each
MSA-industry each year. Table 2 displays the composition of the sample. We start with 20,154
market-year observations. In line with prior research we remove markets with one client in year t
or year t-1 (Numan and Willekens, 2012; Francis et al., 2005). This excludes 10,530 marketyears. In addition, we require at least two audit firms in year t and year t-1 (Numan and
Willekens, 2012). In monopolist markets, market share mobility and leader dethronement are not
valid concepts. This results in a loss of 811 observations. 1,042 market-years are excluded
because data required to calculate all variables are missing. As markets with few clients increase
the measurement error of the variables, we remover market segments with less than 5 clients,
resulting in a loss of 4,492 markets resulting in a final sample of 3,279 markets.
[Add Table 2 here]
V.
RESULTS
Descriptive statistics
Descriptive statistics for the regression variables are reported in Table 3. Panel A presents
detailed descriptive statistics for observations (market segments) included in the analysis. The
prior year industry leader is dethroned in about 19,3% of all market segments. The number of
multi-industry linkages between the leader and follower, ranges from 1 to 32, with an average
(median) of 8.480 (7.00). This implies that in some MSA’s the leader and follower only compete
each other in one industry. The average (median) multi-industry contact of all audit firms is
20
3.402 (2.333). The client-side Herfindahl index ranges from 0.019 to 0.986 implying a heavily
concentrated client-side in some market segments while in other markets the client-side is
dispersed. In 18.4% of industries the leader had a financial statement of a client restated. Table 3,
panel A also presents descriptive statistics for the control variables. The industry leader in 89.4%
of the market segments is a Big 4 firm. The majority of market segments only constitute a small
portion of the national market, with a maximum of 4.1%. The average distance between the
industry leader and the follower is 35.3%12. On average, an industry leader has a market share of
25,3% at MSA-level13. The variables capturing evolutions in the client-side of the market
indicate overall stable markets. The average (median) entry rate is 3.8% (0 %), while the average
(median) exit rate is 7.5% (1.2%). Table 3, panel B reports the mean and median of our
dependent and test variables for industries at MSA-level with varying number of clients. Market
instability decreases with an increase in the number of clients. Because large MSA’s will ceteris
paribus have more clients per industry, competitors have more multi-industry contact in larger
market segments. By construction, the client Herfindahl index decreases and the probability of at
least one client restating increases with market segment size.
[Insert Table 3 here]
Table 4 presents Pearson and Spearman correlations. The table show a positive
correlation between L_DETHR and MS_MOB (Pearson: 0.595) supporting the notion that both
measures capture the same underlying construct. The correlations between the dependent and test
variables are in line with the hypotheses, the exception being the insignificant correlation
between RESTAT_L and L_DETHR (-0.009). This univariate result is likely driven by market
12
This is higher than reported in Numan and Willekens (2012) because we calculate the distance only between the
leader and his closest competitor (the follower) while Numan and Willekens (2012) calculate the market share
distance for all audit firms.
13
The average (median) market share of the leader in the MSA-industry is 58.4% (56.3%).
21
size positively affecting the occurrence of a client restatement (0.243) and negatively affecting
leadership dethronement (-0.107). The Pearson correlation is insignificant between
HHI_CLIENT and MS_MOB (-0.012), in contrast to the Spearman correlation. With respect to
the control variables, |DISTANCE| is highly positive associated with HHI_CLIENT (0.635)
indicating that the client-side impacts audit market structure. Variance inflation factors (VIF)
show that these correlations do not pose a multicollinearity threat as all VIF’s are smaller than 3.
[Insert Table 4 here]
Results
Table 5 depicts the results for the model with L_DETHR as dependent variable. The
inclusion of industry fixed effects results in a loss of 24 observations because the fixed effects
predict the outcome variable perfectly14. The pseudo R-squared is 0.280. The likelihood ratio
Chi²-test is 899.08 and the p-value (0.000) indicates that the model explains more variation than
a constant-only model. Consistent with our first hypothesis, the coefficient MULTI_IND_LF is
negative (coefficient = -0.086) and significant (p-value < 0.05) associated with leader
dethronement. In economic terms, one standard deviation increase in multi-industry contact
between leader and follower decreases the likelihood of leader dethronement with 1.83%, which
is high compared to the average likelihood of dethronement of 11.71%15. Furthermore, we find a
significant (p-value < 0.01) positive (coefficient = 1.378) association between HHI_CLIENT
and L_DETHR. This supports the second hypothesis that large clients use their bargaining power
to increase audit firm competition (Motta, 2004; Caves and Porter, 1978). In markets with a
highly concentrated client-side, the industry leader faces a higher likelihood of dethronement. In
economic terms, an increase of one standard deviation of HHI_CLIENT increases the likelihood
of losing industry leadership by 5.07%. The coefficient of RESTAT_L is positively (p-value <
14
This implies that the value for L_DETHR
M“A
This is the predicted likelihood of leader dethronement when all independent variables are measured at their
mean.
15
22
0.05) and significant (coefficient = -0.178), in line with the third hypothesis. The restatement of a
financial statement audited by the market leader damages the leader’s reputation and increases
the likelihood of dethronement indicative of higher competition. In economic terms, the marginal
effects show an increase in the likelihood of losing industry leadership of 3.49%.
The regression coefficients on the control variables are all in line with expectations. The
coefficient BIG4_LEADER shows Big 4 leaders have a lower likelihood (of 7.05%) of losing the
leadership position (coefficient = -0.312, p-value < 0.05). This suggest that the Big 4 are more
successful in competing with a non-Big 4 leader, than vice versa. Unsurprisingly, a higher
distance between leader and follower decreases the likelihood of leadership dethronement
(coefficient = -3.065, p-value < 0.01). We are unable to report a statistical significant effect for
MS_L_MSA (coefficient: -0.412, p-value > 0.10). Hence, dominance at the MSA-level of the
industry leader has no direct impact on industry competition itself. Finally, the measures
capturing changes in the client-side of the market by entry and exit of clients are negatively
associated with L_DETHR. This shows that audit market changes should be evaluated jointly
with client-side evolutions. Client entry and exit are factors destabilizing consensus across
oligopolistic markets.
[Insert Table 5 here]
The results of the analyses with MS_MOB as dependent variable are presented in Table 6.
The likelihood ratio Chi-squared is significant (251.58, p-value < 0.01). MULTI_IND_ALL is
significantly (p-value < 0.05) negative (coefficient = -0.116) associated with market share
mobility. When rivals compete in multiple markets, market shares are less volatile suggesting
that audit firms compete less fiercely and use mutual forbearance (Bernheim and Whinston,
1990). In economic terms, an increase of one standard deviation of multimarket contact
23
decreases market share mobility with 0.74%. Client concentration leads to an increase in
mobility (coeff: 0.570, t-stat: 3.51) consistent with hypothesis 2. An increase of one standard
deviation of CLIENT_CONC increases market mobility with 1.29%. RESTAT_LEADER, in
contrast to the leader dethronement model, is not significant (coefficient = 0.022, p-value >
0.10) associated with market share mobility. Restatements of clients of the leader do not impact
market share mobility. A potential explanation is that reputation damage of the industry leader
directly impacts leadership dethronement, it might not affect the behavior and perceptions of
other rivals and thus market share mobility.
The results of the control variables are consistent with predictions and L_DETHR results.
When the leader is a Big 4, market share mobility decreases with 3.51% (p-value < 0.01).
Furthermore, market share mobility decreases when the average client is larger (coefficient = 0.085, p-value < 0.01), the market is larger (coefficient = -49.730, p-value < 0.01), the distance
between leader and follower is larger (coefficient = -0.942, p-value < 0.01) and the leader has a
high MSA-level market share (coefficient = -0.368, p-value < 0.05). In contrast, mobility
increases with evolutions in the client-side of the market as the coefficients of ENTRY
(coefficient = 1.335, p-value < 0.01), EXIT (coefficient =3.044, p-value < 0.01) and GROWTH
(coeff: 0.161, p-value <0.01) are all significantly positive.
[Insert Table 6 here]
Supplementary analyses
Two important drivers of market instability are client switching and audit fee changes of
non-switching clients. In order to understand the mechanisms through which our test variables
affect market instability, we thus specify regression models capturing client switching and
changes in audit fees. For each industry within a MSA we calculate the percentage of clients
switching from i) the industry leader (%N_SWITCH_L), ii) the industry follower
24
(%N_SWITCH_F) and iii) other competing audit firms (%N_SWITCH_OTH) and use these as
dependent variables in an model with the same explanatory variables as in market instability
models. Because these variables are bounded between zero and one, we use a fractional logit
model. Table 7, Panel A presents the results using MULTI_IND_LF as multimarket measure.
The results show that multi-industry contact between industry leader and industry follower
increases mutual forbearance as both the leader (coefficient = -0.206, p-value < 0.05) and the
follower (coefficient = -0.520, p-value < 0.01) have a lower client switching rate. This suggests
that the industry leader and industry follower decrease competition for each other’s clients.
When client concentration increases, the client switching rate of lower ranked auditors increases
(coefficient = 0.508, p-value < 0.05). These auditors thus have difficulties in retaining their
clients, potentially because the leader or/and follower by auditing the larger clients enjoy
reputational advantages. The occurrence of a restatement increases the client switching rate from
the leader (coefficient = 0.307, p-value < 0.10). Hence, restatements of financial statements
audited by the leader damages the leader’s reputation leading clients to reevaluate their
commitment and increasing competition. Table 7, Panel B shows that higher multi-industry
linkages between all competing firms decreases the client switching rates of all audit firms
documenting the use of mutual forbearance.
[Insert Table 7 here]
In addition, we calculate for each market segment the fee growth of non-switching clients
from i) the industry leader (GROWTH_L), ii) the industry follower (GROWTH_F) and iii) other
competing audit firms (GROWTH_OTH). Table 8, panel A depicts the results for
MULTI_IND_LF as multi-industry variable. Multi-industry linkages between industry leader and
industry follower do not impact audit fee growth. Hence, the decrease in market instability
resulting from multi-industry contact is driven by mutual forbearance. In market with
25
concentrated clients, the fee growth is lower for the leader (coefficient = -0.139, p-value < 0.01).
As the leader likely has the biggest clients, this may signal a slower growth rate for smaller
clients. When the leader has a client restating his financial statements he suffers a decrease in the
growth rate of generated audit fees (coefficient = -0.035, p-value <0.05). Thus, a restatement
results in higher client switching and existing clients receiving higher their audit fees indicating a
severe reputation damage for the leader and increased competition. Table 8, panel B depicts the
results for MULTI_IND_ALL as multi-industry variable showing a lower growth rate for the
leader, although we did not predict a significant negative coefficient.
[Insert Table 8 here]
Sensitivity analyses
Sample
In our main results, we imposed a sample selection criterion of minimum five clients in
each market. This results in removing markets with few clients. Therefore, we rerun our
instability models using less strict criteria including market segments consisting of at least 2, 3
and 4 clients respectively. Note that in those small markets the bankruptcy, merger, entry or
auditor switching of one client significantly impacts our instability measures increasing
measurement error. Multi-industry contact is no longer significant associated with any measure
of market instability when market segments consisting of two of more clients are included.
However, when removing markets with two-clients the results remain consistent. In addition, we
rerun the analysis on a sample where the leader was a Big 4 providing similar results.
Dependent Variable
We construct two variables capturing dethronement of industry specialization instead of
the leadership position. An industry specialization is dethroned when the auditor is considered
26
industry specialist in year t-1 but not in year t. When more than one firm is considered a
specialist, we only analyze the largest one to prevent multiple data points for a single market. We
define industry specialists as those firms having a market share of at least 30% (Craswell et al.;
1995) or if they have a market share distance of at least 10% with the follower (Mayhew and
Wilkins, 2003). Both classifications provide quantitative similar results as reported above,
although multi-industrycontact is not significant associated with the latter definition of industry
specialization. However, note that when the distance from the follower drops below 10%, the
industry specialist is considered dethroned although he might have the largest market share.
Therefore, we run an analysis using L_DETHR for those leaders which had differentiated
themselves 10% in year t-1. The results show that multimarket contact is again significant using
this definition. Finally, we include a binary variable capturing whether the follower exceed the
30% market share threshold and is an industry specialist following the former definition. The
results with respect to the test variables remain unaltered. Furthermore, the results show that
market instability is lower in those markets where the follower is also an industry specialist.
Although counterintuitive, this is suggestive of lower competition in those markets which may
be caused by mutual forbearance between leader and follower.
Test Variables
In addition, we test the sensitivity of our test variables. First, we switch the multi-industry
measures between the leader dethronement and market mobility measures. The results show that
multi-industry contact between leader and follower significantly affects market share mobility.
In contrast, the average multi-industry contact between all competitors is not associated with
leader dethronement. Second, we measure multi-industry using a dummy equal to one when the
multi-industry contact was larger or equal to two, zero otherwise and results were similar. In
addition, we did not use the natural logarithm for multi-industry. The multi-industry variable is
no longer significant. Third, cooperation incentives may be stronger when both firms have a
27
leadership position to defend in a market within the MSA. Therefore, we defined a variable equal
to one when the industry follower is an industry leader in another industry within the MSA, zero
otherwise. This variable is highly negative correlated with market instability.
With respect to the second hypothesis, we construct a binary variable equal to one when
client concentration is above the mean client concentration, zero otherwise. This variable is
significantly associated with market mobility but not with leader dethronement. In addition, we
construct variables capturing the incremental effect of each quartile of client concentration. The
highest leader dethronement rate occurs in market segments with the highest client
concentration. In contrast, market share mobility increases with each quartile of client
concentration. Finally, we use the number of clients as an alternative measure of client
concentration.
In addition, we construct variables capturing whether the follower or another firm has a
financial statement which he signed restated. The results show that only restatements of industry
leaders affect leader dethronement, while restatements of other market participants have no
significant effect. This further supports the notion that the market leader has successfully
differentiated itself in terms of audit quality from rivals. Furthermore, we construct variables
relating the number of restatements of the industry leader, follower and other competitors to the
total clients audited by these firms within a market. This measure negatively affect leader
dethronement at the 5 percent level. This measure thus provides support for our third hypothesis.
Restatements do not affect overall market mobility.
VI.
CONCLUSION
Prior audit market research uses static market structure measures to capture competition
(Numan and Willekens, 2012; Feldman,2006; Bandyopadhay and Kao, 2004; Pearson and
Trompeter, 1994). However, these measures conceal much of the underlying instability of the
competitive process (Davies and Gerosky, 1997). In this study we perform a dynamic analysis of
28
competition by researching market instability. More specifically, we investigate the impact of
three factors - multi-industry contact, client concentration and industry leader’s reputation
damage on market competition.
The results suggest that multi-industry competition results in less fierce competition as it
is negatively associated with our measures of instability, i.e. market share mobility and leader
dethronement. A main driver in this context is the decrease in the rate of client switching
suggesting that multi-industry contact facilitates mutual forbearance between audit firms.
Furthermore, our evidence suggests that buyer concentration increases competition, as it is
associated positively with market instability, implying that concentrated buyers use their
bargaining power to destabilize suppliers market shares. This bargaining power manifests itself
primarily in concentrated buyers being able to negotiate about their audit fees. Our results show
a significant lower growth rate of the leader’s total audit fees in markets with concentrated
buyers, which negatively affects the market share of the largest audit firm. In contrast, we find
no evidence of a higher switching rate of concentrated clients. In addition, we present evidence
consistent with a positive effect of leader reputation damage on one measure of market
instability, namely leadership dethronement. In addition, restatements of financial statements
audited by the leader increase the leader’s switching rate and decreases the amount of fees paid
by non-switching clients.
Our results complement prior research in the following ways. First, by using a dynamic
competition measure we circumvent the theoretically ambiguous relationship between static
measures, most notably concentration, and competition. Second, we extend prior auditing studies
using market share mobility by using a theoretical framework to determinants of market share
instability. Therefore, we add to studies who are mainly descriptive (Bujink et al., 1998), focus
on a single regulatory event (Chang et al., 2009) or are interested in evolutions in a specific
period of time (Wolk et al. 2001; Hogan and Jeter, 1999). Third, we employ a more recent
29
dataset after the demise of Arthur Andersen and the implementation of SOX. Our results may be
of interest to regulators concerned about market competition. It seems useful that regulators
should carefully evaluate those markets where mutual forbearance amongst audit firms is more
sustainable, and in particularly those MSA’s where audit firms meet in multiple industries and
markets with low client concentration. Finally, it seems appropriate that regulators and
researchers take into account that (changes in) the demand side of the audit market also affects
competition measures .
Our study is subject to several limitations. First, we only investigate annual changes.
Future research may investigate changes over a longer time period. Second, the sample consists
of U.S. companies and audit firms and it is unclear whether the results generalize to settings with
different institutional and legal frameworks. Future research may test in which way differences
in legal frameworks or institutional settings could affect the results. Third, the results may not
apply in countries where the geographical proximity between offices is larger.
30
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34
Table 1: Variable Definitions
Variable definition
Dependent variables
L_DETHR
MS_MOB
Independent variables
RESTAT_L
MULTI_IND_LF
MULTI_IND_ALL
HHI_CLIENT
Control variables
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
ENTRY
EXIT
GROWTH
%N_SWITCH_L
Dummy variable equal to one if the MSA-industry leader in year t was
not the leader in the prior year(t-1), zero otherwise
The absolute marketshare change between the current year t and the
prior year t-1 of all audit firms in the MSA-industry. Ranges between
zero (no marketshare changes) and one .
Dummy equal to one if the MSA-industry leader in t-1 has a (past)
client restating their financial statements during the year (from t-1 to
t), zero otherwise.
The natural logarithm of the number of industries where the market
segment leader and the market segment follower of t-1 both have at
least one client in t-1 within the Metropolitan Statistical Area (MSA).
The natural logarithm of the average number of multimarket contact
within the Metropolitan Statistical Area (MSA ) between all pairs of
audit firms active in the market segment in t-1.
Beginning period (t-1) demand concentration calculated as the
M“A-industry
Dummy equal to one if the MSA-industry leader in t-1 is a Big 4 firm
(E&Y, Deloitte, PwC, KPMG).
The beginning period average of the natural logarithm of client total
assets within the MSA-industry.
The beginning period relative size of the market segment (MSAindustry) in terms of audit fees relative to the total national market in
each year.
The beginning period absolute difference between the market shares
of the MSA-industry leader and the MSA-industry follower in the
current year.
The beginning period market share of the prior MSA-industry leader at
the Metropolitan Statistical Area (MSA) level.
The amount of audit fees of clients located in the market segment that
were not included in the dataset in year t-1, but included in the year t
divided by the total audit fees of the MSA-industry market in year t-1.
The amount of audit fees of clients located in the market segment that
were included in the dataset in year t-1, but not included in the year t
divided by the total audit fees of the MSA-industry market in year t-1.
The growth rate of audit fees of clients located in the market
segment included both in year t as year t-1 in the dataset.
The percentage of clients audited by the market segment leader in
year t-1 switching to another audit firm in year t.
35
%N_SWITCH_F
%N_SWITCH_OTH
GROWTH_L
GROWTH_F
GROWTH_OTH
The percentage of clients audited by the market segment follower in
year t-1 switching to another audit firm in year t.
The percentage of clients audited by audit firms that were not the
market segment leader or market segment follower in year t switching
to another audit firm in year t-1.
The percentage change in audit fees of clients audited by the market
leader in both year t-1 and year t.
The percentage change in audit fees of clients audited by the market
follower in both year t-1 and year t.
The percentage change in audit fees of clients audited by audit firms
that were not the market segment leader or market segment follower
and appointed the same auditor in year t-1 and year t.
36
Table 2: Sample Selection
Number of MSA-industry-year observations
Less MSA-industry-years with only one client
Less MSA-industry-years with only one audit firm
Less observations with insufficient data for all control variables
Less markets with fewer than 5 clients
Number of MSA-industry-year observations
20,154
(10,530)
(811)
(1,042)
(4,492)
3,279
37
Table 3, Panel A: Descriptive statistics
N
Mean
StdDev
Min
P25
Median
P75
Max
Dependent variables
L_DETHR
MS_MOB
3,279
3,279
0.193
0.163
0.395
0.169
0.000
0.001
0.000
0.050
0.000
0.104
0.000
0.210
1.000
0.988
Test variables
Exp(MULTI_IND_LF)
MULTI_IND_LF
Exp(MULTI_IND_ALL)
MULTI_IND_ALL
HHI_CLIENT
RESTAT_L
3,279
3,279
3,279
3,279
3,279
3,279
8.480
1.664
3.402
1.319
0.306
0.184
7.076
1.084
3.091
0.530
0.187
0.387
1.000
0.000
1.000
0.693
0.019
0.000
2.000
0.693
1.361
0.859
0.174
0.000
7.000
1.946
2.333
1.204
0.269
0.000
13.000
2.565
4.250
1.658
0.398
0.000
32.000
3.466
30.333
3.445
0.986
1.000
Control variables
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
ENTRY
EXIT
GROWTH
3,279
3,279
3,279
3,279
3,279
3,279
3,279
3,279
0.807
18.918
0.002
0.353
0.253
0.038
0.075
0.156
0.395
2.362
0.004
0.264
0.144
0.129
0.144
0.425
0.000
7.178
0.000
0.000
0.000
0.000
0.000
-0.730
1.000
17.628
0.000
0.124
0.170
0.000
0.000
-0.033
1.000
19.032
0.001
0.300
0.234
0.000
0.012
0.053
1.000
20.623
0.002
0.534
0.333
0.012
0.079
0.199
1.000
25.964
0.041
0.996
0.866
0.990
0.989
7.863
Descriptive statistics for the sample consisting 3,279 market segments-years. Because of some outliers, the
variable ENTRY is winsorized at the top and bottom 1%. Column 1 provides variable name, Column 2 shows the
number of observations. The third column reports the mean, while in the fourth column the standard deviation is
reported. Columns 5 to 9 present the minimum, first quartile, mean, third quartile and the maximum, respectively.
Variable definitions can be found in Table 1.
38
Table 3, Panel B: Detailed descriptive statistics of the dependent and test variables
Number of clients
5
N
572
6-10
1,269
11-20
731
>20
707
Mean
(Median)
Mean
(Median)
Mean
(Median)
Mean
(Median)
L_DETHR
MS_MOB
0.209
(0.000)
0.221
(0.000)
0.181
(0.000)
0.143
(0.000)
0.184
(0.103)
0.184
(0.124)
0.160
(0.109)
0.110
(0.083)
MULTI
MAR_LF
1.200
(1.098)
1.435
(1.609)
1.717
(2.079)
2.395
(2.485)
MULTI_IN
D_ALL
HHI_CLIEN
T
RESTAT_
L
1.272
(1.098)
1.304
(1.145)
1.303
(1.263)
1.399
(1.303)
0.438
(0.396)
0.360
(0.312)
0.270
(0.219)
0.139
(0.112)
0.091
(0.000)
0.140
(0.000)
0.178
(0.000)
0.342
(0.000)
Descriptive statistics for the dependent and test variables. Column 1 provides the number of clients in the market
segment, Column 2 shows the number of observations. Columns 3 and 4 present detailed descriptive statistics for
the dependent variables used in this study: leader dethronement and market share mobility. Columns 5 to 8
present detailed descriptive statistics for the test variables. The numbers without brackets represents the mean,
while the number between brackets represent the median. Variable definitions can be found in Table 1.
39
Table 4: Correlations
1
1 L_DETHR
2 MS_MOB
3 MULTI_IND_LF
4 MULTI_IND_ALL
5 HHI_CLIENT
6 RESTAT_L
7 BIG4_L
8 AVG_CLIENTSIZE
9 MAR_SIZE
10 |DISTANCE|
11 MS_L_MSA
12 ENTRY
13 EXIT
14 GROWTH
2
0.4844*
0.5949*
3
4
6
5
7
8
-0.0646*
-0.0423*
-0.0804*
-0.0088
-0.2207*
-0.1387*
-0.0809*
-0.1002*
-0.1012*
-0.0384*
-0.2570*
-0.3655*
0.7712*
-0.2867*
0.1549*
0.3667*
0.0581*
-0.1525*
0.0680*
0.3178*
0.2464*
-0.1892*
-0.0002
0.0358*
0.0530*
-0.0128
-0.0710*
-0.1897*
-0.0312
-0.1469*
0.7118*
-0.1003*
-0.0118
-0.2872*
-0.1517*
-0.0088
-0.0548*
0.1527*
0.0531*
-0.1657*
-0.2207*
-0.3097*
0.3947*
0.2705*
0.0308
0.0530*
-0.1540*
-0.3334*
0.1615*
0.2986*
0.0079
0.0125
0.3572*
-0.1069*
-0.1919*
0.3312*
0.1581*
-0.2157*
0.2433*
0.1545*
0.1613*
-0.2965*
-0.1611*
-0.2047*
-0.1405*
0.6350*
-0.0446*
0.1482*
0.0935*
-0.2014*
-0.2452*
0.0395*
0.0032
0.1471*
0.0575*
0.5261*
0.2864*
0.1742*
0.2500*
0.0144
0.0061
-0.0446*
-0.0169
-0.0683*
-0.1283*
0.2996*
0.5195*
-0.0689*
-0.0527*
-0.0141
-0.0190
-0.1070*
-0.1224*
0.0841*
0.1525*
0.0050
0.0173
-0.0377*
-0.0477*
-0.0438*
-0.1377*
0.2620*
40
Table 4: Correlations
9
1 L_DETHR
2 MS_MOB
3 MULTI_IND_LF
4 MULTI_IND_ALL
5 HHI_CLIENT
6 RESTAT_L
7 BIG4_L
8 AVG_CLIENTSIZE
9 MAR_SIZE
10 |DISTANCE|
11 MS_L_MSA
12 ENTRY
13 EXIT
14 GROWTH
10
11
12
13
-0.2029*
-0.3219*
-0.1975*
0.0909*
0.1515*
-0.3610*
-0.2668*
-0.2596*
0.1609*
0.3276*
0.5765*
-0.1730*
0.0808*
0.1677*
0.0848*
0.4650*
-0.1332*
0.0708*
0.0210
0.0039
-0.2546*
0.5473*
0.0496*
-0.2660*
-0.2269*
0.2327*
-0.0432*
0.0764*
0.1180*
0.0487*
0.4456*
0.1509*
0.4994*
-0.0198
-0.0336
0.3209*
0.0934*
0.2490*
-0.1781*
-0.1191*
0.0018
0.3281*
0.1138*
0.0680*
0.2522*
-0.1189*
-0.1166*
-0.0197
-0.0264
-0.0799*
0.1500*
0.2956*
-0.0469*
-0.0445*
-0.0704*
-0.0703*
-0.0316
-0.0874*
0.0207
1.0000
-0.0613*
-0.0249
-0.0188
0.1686*
-0.0049
0.1372*
The table present correlations based on the 7,771 market -years. Pearson correlations are reported below
the diagonal, while Spearman correlations are reported above the diagonal. Variables significant at the 5%
level are indicated with an asterix. All continuous variables are winsorized at the one percent level. All
variable definition can be found in Table 1.
41
Table 5: Leader Dethronement
Dependent variable
L_DETHR
Coef.
intercept
MULTI_IND_LF
HHI_CLIENT
RESTAT_L
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
ENTRY
EXIT
GROWTH
N
Pseudo R²
LR Chi²
P-value (LR-Chi²)
Year fixed effects
Industry fixed effects
z-stat
-4.048 ***
-4.25
L_DETHR
pvalue
0.000
Coef.
z-stat
pvalue
-4.427 ***
-5.07
0.000
-0.086 **
-2.18
0.029
1.378 ***
4.86
0.000
0.167 **
2.00
0.045
-0.438 ***
-3.68
0.000
-0.312 **
-2.40
0.016
-0.006
-0.30
0.767
-0.005
-0.23
0.820
-43.514 ***
-3.32
0.001
-2.18
0.029
-2.405 ***
-13.73
0.000
-3.065 ***
-15.86
0.000
-1.11
0.266
-0.413
-1.42
0.156
1.455 ***
6.43
0.000
1.545 ***
6.62
0.000
2.823 ***
15.21
0.000
2.841 ***
15.24
0.000
0.77
0.441
0.086
1.01
0.311
-0.332
0.068
3,255
0.268
862.01
0.000
Included
Included
-27.465 **
3,255
0.280
899.08
0.000
Included
Included
This table presents the results of an probit regression with leader dethronement as dependent
variable. The first column presents the variable names. The second colum present the coefficients, zstatistics and p-values of a model without the test variables. The third column shows the coefficients,
z-statistics and p-values of a model with the variables of interest MULTI_IND_LF, RESTAT_L and
HHI_CLIENT. Year and industry fixed effects are included. Significance (based on two-tailed tests) is
indicated as follows: p<0.10 (*), p<0.05 (**), p<0.01(***). Variable definitions can be found in Table
1.
42
Table 6: Market share mobility
Dependent variable
MS_MOB
Coef.
intercept
MULTI_IND _ALL
HHI_CLIENT
RESTAT_L
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
ENTRY
EXIT
GROWTH
N
LR Chi²
P-value (LR-Chi²)
Year fixed effects
Industry fixed effects
t-stat
0.613
0.28
MS_MOB
pvalue
Coef.
0.779
0.419
t-stat
pvalue
1.64
0.100
-2.49
0.013
0.570 ***
3.51
0.000
0.022
0.42
0.673
-0.116 **
-0.311
-1.60
0.109
-0.269 ***
-3.25
0.001
-0.095 ***
-3.08
0.002
-0.085 ***
-6.81
0.000
-57.477 ***
-2.67
0.008
-49.730 ***
-7.66
0.000
-0.673 ***
-3.12
0.002
-0.942 ***
-9.67
0.000
-0.306
-0.66
0.510
-0.368 **
-2.02
0.044
1.309 ***
4.13
0.000
1.335 ***
9.38
0.000
3.051 ***
10.70
0.000
3.044 ***
26.61
0.000
1.31
0.190
0.161 ***
2.86
0.004
0.157
3,279
249.48
0.000
Included
Included
3,279
251.58
0.000
Included
Included
This table presents the results of an fractional logit model with marketshare mobility as dependent
variable. The first column presents the variable names. The second colum present the coefficients, tstatistics and p-values of a model without the variables of interest. The third column shows the
coefficients, t-statistics and p-values of a model with the variables of interest MULTI_IND_ALL
HHI_CLIENT, RESTAT_L. Year and industry fixed effects are included. Significance (based on twotailed tests) is indicated as follows: p<0.10 (*), p<0.05 (**), p<0.01(***). Variable definitions can be
found in Table 1.
43
Table 7: Switching behavior
Panel A: multimarket measure: MULTI_IND_LF
Dependent variable
Coef.
intercept
MULTI_IND _LF
HHI_CLIENT
RESTAT_L
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
N
LR Chi²
P-value (LR-Chi²)
Year fixed effects
Industry fixed effects
%N_SWITCH_F
%N_SWITCH_L
z-stat
-0.935
-0.206 **
-0.256
0.307 *
-1.053 ***
-0.021
12.021
-0.216
-0.385
-1.53
-2.51
-0.48
1.75
-3.31
-0.65
1.03
-0.63
-0.49
3,265
99.53
0.000
Included
Not Included
pvalue
Coef.
0.126 -0.186
0.012 -0.520 ***
0.632 -0.343
0.079 -0.265
0.001 0.053
0.517 -0.086 ***
0.305 28.425 *
0.531 0.424
0.624 -0.328
%N_SWITCH_OTH
z-stat
p-value
-0.35
-6.67
-0.70
-1.33
0.19
-2.97
1.78
1.16
-0.57
0.727
0.000
0.483
0.183
0.847
0.003
0.075
0.247
0.570
3,238
77.09
0.000
Included
Not Included
Coef.
-0.116
-0.034
0.508 **
0.031
0.157
-0.101 ***
-16.183 **
-0.011
-0.225
z-stat
p-value
-0.47
-0.96
2.13
0.40
1.09
-6.99
-2.43
-0.06
-0.73
0.635
0.339
0.033
0.686
0.275
0.000
0.015
0.949
0.465
3,183
165.13
0.000
Included
Not Included
This table presents the results of an fractional logit model with the proportion of switching clients from the market segment leader
(%N_SWITCH_L), market segment follower (%N_SWITCH_F) and other competitors (%N_SWITCH_OTH) as dependent variables. The first
column presents the variable names. Year fixed effects are included. Significance (based on two-tailed tests) is indicated as follows: p<0.10
(*), p<0.05 (**), p<0.01(***). Variable definitions can be found in Table 1.
44
Panel B: multimarket measure: MULTI_IND_ALL
Dependent variable
Coef.
intercept
MULTI_IND _ALL
HHI_CLIENT
RESTAT_L
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
N
LR Chi²
P-value (LR-Chi²)
Year fixed effects
Industry fixed effects
%N_SWITCH_F
%N_SWITCH_L
z-stat
%N_SWITCH_OTH
pvalue
Coef.
z-stat
p-value
Coef.
z-stat
p-value
-0.904
-1.48
0.140
-0.027
-0.05
0.961
-0.042
-0.18
0.861
-0.292 *
-1.82
0.068
-0.925 ***
-4.92
0.000
-3.97
0.000
-0.126
-0.24
0.812
0.004
0.01
0.993
-0.299 ***
0.485 **
2.12
0.034
0.088 ***
2.70
0.007
-0.026
-0.41
0.681
-0.010
-0.46
0.646
-1.229 ***
-4.20
0.000
-0.315
-1.21
0.225
1.73
0.083
-0.013
-0.37
0.712
-0.063 **
-2.02
0.044
0.228 *
-0.086 ***
-5.81
0.000
-2.01
0.044
-2.974
-0.25
0.801
-0.180
-0.52
0.606
-0.293
-0.36
0.715
3,265
99.32
0.000
Included
Not Included
-5.779
-13.005 **
-0.32
0.750
0.462
1.22
0.224
-0.052
-0.29
0.771
-0.181
-0.30
0.764
-0.364
-1.22
0.223
3,238
87.36
0.000
Included
Not Included
3,183
175.55
0.000
Included
Not Included
This table presents the results of an fractional logit model with the proportion of switching clients from the market segment leader
(%N_SWITCH_L), market segment follower (%N_SWITCH_F) and other competitors (%N_SWITCH_OTH) as dependent variables. The first
column presents the variable names. Year fixed effects are included. Significance (based on two-tailed tests) is indicated as follows: p<0.10
(*), p<0.05 (**), p<0.01(***). Variable definitions can be found in Table 1.
45
Table 8: Audit fee changes of non-switching clients.
Panel A: multimarket measure: MULTI_IND_LF
Dependent variable
Coef.
intercept
MULTI_IND _LF
HHI_CLIENT
RESTAT_L
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
N
Adjusted R²
Year fixed effects
Industry fixed effects
GROWTH_F
GROWTH_L
0.271
-0.013
-0.139
-0.035
0.036
-0.005
-4.934
-0.027
0.147
t-stat
***
**
***
**
0.66
-1.55
-3.02
-2.00
1.08
-1.01
-3.79
-0.84
2.38
pvalue
0.506
0.121
0.003
0.046
0.279
0.314
0.000
0.404
0.018
Coef.
0.067
0.011
0.016
-0.024
-0.079
-0.002
-3.770 **
0.151 ***
-0.094
GROWTH_OTH
t-stat
p-value
Coef.
0.33
0.93
0.2
-1.06
-1.55
-0.28
-2.25
3.07
-1.01
0.738
0.354
0.839
0.291
0.122
0.778
0.024
0.002
0.313
0.872
-0.021
0.079
0.002
-0.005
-0.019
-3.645
0.134
-0.099
t-stat
***
***
**
**
6.82
-1.61
1.03
0.06
-0.10
-2.66
-2.34
2.50
-1.01
3,106
0.260
2,982
0.155
3,094
0.109
Included
Included
Included
Included
Included
Included
p-value
0.000
0.107
0.305
0.950
0.922
0.008
0.019
0.012
0.314
This table presents the results of an ordinary least squares regression with the percentage growth in audit fees of cnon-switching clients of
the market segment leader (GROWTH_L), market segment follower (GROWTH_F) and other competitors (GROWTH_OTH). The first column
presents the variable names. Year fixed effects are included. Significance (based on two-tailed tests) is indicated as follows: p<0.10 (*),
p<0.05 (**), p<0.01(***). Variable definitions can be found in Table 1.
46
Panel B: multimarket measure: MULTI_IND_ALL
Dependent variable
Coef.
intercept
MULTI_IND _ALL
HHI_CLIENT
RESTAT_L
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
N
Adjusted R²
Year fixed effects
Industry fixed effects
GROWTH_F
GROWTH_L
0.280
-0.042
-0.136
-0.036
0.032
-0.003
-5.353
-0.030
0.141
t-stat
**
***
**
***
**
0.69
-2.41
-3.01
-2.07
1.00
-0.56
-4.34
-0.92
2.27
pvalue
0.488
0.016
0.003
0.039
0.316
0.573
0.000
0.358
0.023
Coef.
0.100
-0.009
-0.001
-0.023
-0.059
-0.002
-3.016 *
0.148 ***
-0.111
GROWTH_OTH
t-stat
p-value
Coef.
0.50
-0.38
-0.02
-1.01
-1.22
-0.17
-1.87
3.00
-1.18
0.616
0.700
0.987
0.315
0.222
0.864
0.062
0.003
0.237
0.809
0.024
0.117
-0.001
-0.043
-0.021
-5.111
0.137
-0.062
t-stat
***
***
***
**
6.41
0.84
1.54
-0.04
-0.86
-2.94
-3.37
2.56
-0.64
3,106
0.26
2,982
0.155
3,094
0.108
Included
Included
Included
Included
Included
Included
p-value
0.000
0.404
0.123
0.965
0.388
0.003
0.001
0.011
0.524
This table presents the results of an ordinary least squares regression with the percentage growth in audit fees of cnon-switching clients of
the market segment leader (GROWTH_L), market segment follower (GROWTH_F) and other competitors (GROWTH_OTH). The first column
presents the variable names. Year fixed effects are included. Significance (based on two-tailed tests) is indicated as follows: p<0.10 (*),
p<0.05 (**), p<0.01(***).
47