Competitive Rivalry in Audit Markets
Simon Dekeyser a*
[email protected]
Ann Gaeremynck a
[email protected]
W. Robert Knechel a,b
[email protected]
Marleen Willekens a,c
[email protected]
a
KU Leuven
Faculty of Economics and Business
Leuven, BELGIUM
b
University of Florida
|Fisher School of Accounting,
FL, United States
c
BI Norwegian Business School,
Oslo, Norway
October 2016
Competitive Rivalry in Audit Markets
Abstract
In this paper we argue that audit firms compete rationally and consider the potential actions of
other firms when deciding how fiercely to compete with market rivals. Based on prior literature in
the field of industrial organization, we hypothesize that competing with the same audit firms across
different industries within a geographical region (which we label “multi-industry contact”) leads
to less competition overall, which suggests mutual forbearance among rivals. However, client
concentration within an industry increases the immediate benefits of vigorous competition
inducing audit firms to compete more aggressively. Further, a drop in quality for an audit firm can
adversely affect the firm’s reputation, making the firm more vulnerable to aggressive competition
from other audit firms. We measure rivalry using two dynamic measures of competition (i.e.,
market-share mobility and leader dethronement) and find that multi-market contact, market
concentration and reputation damage all affect competitive rivalry as predicted.
Keywords: competition, leader reputation, market instability, multimarket contact
JEL- classification : M42
* The authors are indebted to Liesbeth Bruynseels, Joseph Gerakos and John-Christian Langli for
useful comments as well as to participants at the 2016 EIASM Audit Quality workshop in Florence
(Italy), the 2015 Auditing Section Midyear Meeting, the 2015 EARNET conference and
workshops at BI Norwegian Business School, University of Exeter, Tulane University and KU
Leuven. Simon Dekeyser gratefully acknowledges financial support from the Research Foundation
– Flanders (FWO).
2
Competitive Rivalry in Audit Markets
Introduction
The level of competition in audit markets has been a major concern of regulators over the
past decade (European Commission, 2011; U.S. Government Accountability Office [GAO], 2003,
2008). The US Government Accountability Office (GAO) clearly articulated these concerns in
2008: “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” (GAO, 2008). Such
“coordinated interaction” can be explicit (collusion) or implicit (strategic or mutual forbearance).
In this paper, we examine factors that affect how aggressively audit firms compete with each other
based on an analysis of the US market by industry and location (Metropolitan Statistical Area, or
MSA). More specifically, we assume that audit firms consider the potential actions of other firms
when deciding how to compete in a specific market (industry, MSA). We do not assume or require
active collusion among audit firms, although such collusion is not ruled out by our analysis. We
then analyze how this strategic forbearance influences the behavior of participants across audit
market segments.
The audit market can be viewed as oligopolistic because a small number of individual audit
firms (e.g., the Big Four) are large enough to alter market conditions through their own actions.
Consequently, their decisions concerning how fiercely to compete in a market are dependent on
the potential and expected reactions of other large firms in the same market (Melvin and Boyes,
2002). A competitive action by one firm can significantly alter market conditions, leading rivals
to alter their own competitive strategy which, in turn, will further impact market conditions. We
argue that audit firms compete rationally and will consider both the immediate benefits and future
costs when deciding how vigorously to compete in a specific market. Potential benefits include an
increase in the number of clients and revenues obtained by taking clients away from competing
3
audit firms. Future costs arise because rivals may retaliate through increased price competition
(even on retained clients) and targeting of the aggressor’s own clients, possibly resulting in a loss
of industry market share and profits. In general, audit firms will compete more fiercely the higher
the benefits and the lower the costs of their competitive actions (Motta, 2004). As a result, audit
firms can choose either to compete aggressively, and risk retaliation, or act passively to decrease
the effect of potential competition either explicitly or implicitly.
We investigate three factors that can impact the cost and benefits of competition:
multimarket contact between audit suppliers, buyer concentration, and reputation damage incurred
due to a drop in audit quality. First, empirical evidence shows that multimarket contact leads to
higher prices, profits and lower sales growth rates in markets for aviation, banking, and mobile
phone (Barros, 1999; Evans and Kessides, 1994; Gimeno, 2002; Greve, 2008; Parker and Röller,
1997). Audit rivals are likely to compete in multiple industries within an MSA (i.e., firms have
direct contact across multiple industries in a given location). In such situations, they may choose
not to compete heavily in each other’s focal industries because that could result in retaliatory and
vigorous competition in all industries. The net gain from competing aggressively in one industry
may therefore be reduced by losses across other industries in which audit firms compete. This
reduces the incentives for audit firms to compete fiercely in all industries in which other firms also
compete (Bernheim and Whinston, 1990). Consistent with these arguments, we hypothesize that
audit firms that compete in multiple industries within a locale (MSA) have lower incentives to
compete aggressively against other firms in the same markets (Hypothesis 1).
Second, large clients can exert their bargaining power by negotiating lower fees from their
current auditor (Casterella et al., 2004; Huang et al., 2007; Mayhew and Wilkins, 2003) or increase
competition among auditors by threatening to switch suppliers (Motta, 2004). The benefit of
attracting a large client is substantial and could exceed future losses caused by any retaliatory
4
reactions by rivals.1 Consistent with this argument, the industrial organizational literature finds
that markets in which buyer concentration is high exhibit greater variation in suppliers’ market
shares over time (Caves and Porter, 1978; Kato and Honjo, 2006). We therefore predict that audit
client concentration is positively associated with the aggressiveness of competition among audit
firms in a market segment (Hypothesis 2).
Third, we test the effect of damage to an audit firm’s reputation as measured by accounting
restatements experienced by the clients of a firm. Our perspective is based on evidence from prior
studies show that firms with a large market share provide higher audit quality and/or have a
reputation for high quality (Craswell et al., 1995; Ferguson et al., 2003; Francis et al., 2005;
Reichelt and Wang, 2010). As a result, they may become vulnerable if the market believes that
their audit quality has declined. We presume that restatements of financial statements by a firm’s
clients negatively affect the incumbent audit firm’s reputation since 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 an audit firm’s reputation
is likely to increase the willingness of its clients to switch audit firms, make it easier for
competitive rivals to attract the firm’s clients, and make it harder for the incumbent to retaliate due
to this loss of reputation. We therefore predict that restatements by clients will be associated with
an increase in competitive aggressiveness in the industry market segment where the restatement
occurred (Hypothesis 3).
The academic literature typically measures audit market competition in a single-period,
static setting by focusing on supplier concentration (Bandyopadhay and Kao, 2004; Feldman,
2006; Pearson and Trompeter, 1994), industry specialization (Craswell et al., 1995; Ferguson et
Obtaining a particularly high profile, large client, may also enhance the audit firm’s reputation, effectively insulating
it from the worst of retaliatory “poaching” by other audit firms.
5
1
al., 2003; Francis et al., 2005), or market-share distance from the closest competitor (Numan and
Willekens, 2012). These static measures, however, 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 (Bujink et al., 1998; GOA, 2008; Scherer and Ross, 1990). Prior industrial
organizational literature argues that market instability is a sign of high competition and inter-firm
rivalry (Kato and Honjo, 2006; Schmalensee, 1989; Staigler and Wolak, 1992). We therefore use
measures of market-share instability as our proxies for competition in an audit market (Caves and
Porter, 1978; Ferrier et al., 1999; Schmalensee, 1989).
Our analysis uses a U.S. sample of 3,279 market-segment-years at the MSA level over the
period 2003–2012.2 In previous literature, dynamic market competition and market instability is
proxied by changes in market share and relative rankings of incumbents and entrants over time
(Caves and Porter, 1978; Ferrier et al., 1999; Schmalensee, 1989). In line with this literature, we
capture competitive rivalry using two measures: (1) market-share mobility and (2) leadership
dethronement.3 Market-share mobility is the sum of the year-on-year market share changes of all
competitors within a market segment (Caves and Porter, 1978; Kato and Honjo, 2006; Sakakibara
and Porter 2001). Leader dethronement is the year-on-year change in the identity of the market
leader in a market segment. We include this variable since the leadership position is particularly
2
We follow recent studies that define audit market segments based on industries within MSAs (Francis et al., 2005;
Numan and Willekens, 2012; Reichelt and Wang, 2010). In what follows, we will use the label “market” or “market
segment” for a 2-digit Standard Industrial Code (SIC) industry within an MSA. The terms “leader” and “market
leader” are used interchangeably and reflect the audit firm with the higher market share in a 2-digit SIC industry within
an MSA. Similarly, the terms “follower” or “market follower” refer to the audit firm with the second highest market
share in a 2-digit SIC industry within an MSA. The term “other firms” represent audit firms in a 2-digit SIC industry
within an MSA which are neither leader nor follower.
3
In this study, market instability thus refers to the changes in market shares or rankings of the suppliers in a market
segment over a period of one year. Therefore, the terms “market instability” and “market-share instability” are used
interchangeably. Market instability does not imply anything about the evolution in the total size of the market or the
stages in a product/market life cycle.
6
valuable as leaders in many industries have strong reputations, can exploit economies of scale, and
can charge higher prices (Armstrong and Collopy, 1996; Ferrier et al., 1999), which makes the
leadership position highly contested in competitive markets.
In general, our results support our hypotheses. When audit firms compete in multiple
industries within an MSA, competition is less fierce, as evidenced by a negative association
between our measure for multi-industry contact and both market-share mobility and leadership
dethronement. This result suggests that audit firms follow a strategy of “mutual forbearance” when
they are in potential competition in many market segments (industries), that is, they refrain from
competing aggressively for each other’s existing clients. Our results also show that client
concentration is associated with more aggressive competition in an audit market segment (i.e., the
market is made up of larger clients). Finally, we also find a significant change in overall marketshare mobility in industry market segments where reputation damage occurred due to client
accounting restatements. Moreover, we also find that an accounting restatement by a client of the
industry leader increases the likelihood that the leader will lose its leadership position. This
indicates that the leader’s reputation may be seriously damaged by the restatement, opening the
door for more aggressive competition from rivals. As changes in auditor market shares can be
caused by client switches or changes in fees, we perform supplemental analyses to investigate if
these are responsible for the observed market instability. In general, the supplemental analysis
reveals that multi-industry contact between audit suppliers is negatively associated with the
amount of client switching, but is not associated with fee changes of non-switching clients. These
results are also consistent with audit firms engaging in mutual forbearance.
Our paper offers a number of contributions to the literature on audit competition. First, we
empirically study factors that affect the aggressiveness of the competitive rivalry among audit
firms, presenting evidence suggesting that both the costs and benefits can influence how
7
vigorously an audit firm may compete in a market segment. Second, unlike previous studies that
have used static measures of audit competition (Bandyopadhay and Kao, 2004; Feldman, 2006;
Pearson and Trompeter, 1994), we introduce and test two dynamic measures. Because static
measures conceal much of the dynamic competitive processes, especially in highly concentrated
markets (Bandyopadhay and Kao, 2004; Davies and Geroski, 1997), studying dynamic
competition measures adds to the literature because they are a good indicator of rivalry in
concentrated markets (Caves and Porter, 1978). Third, we also link competition to a proxy for
reputation damage, i.e., we demonstrate that market instability is larger in industry market
segments in which clients have accounting restatements, suggesting that such restatements result
in damage to a firm’s reputation which makes them more vulnerable to aggressive competition..
Finally, we contribute to the regulatory debate about whether there is sufficient competition in the
audit market (European Commission, 2011; U.S. Government Accountability Office [GOA],
2003, 2008). Since our evidence suggests that the fierceness of competition depends on audit
market characteristics, we illustrate that one-size-fits-all regulation to encourage audit market
competition may not be optimal.
The remainder of the paper is organized as follows. In section 2, we develop our
hypotheses. Section 3 presents the research design, while section 4 describes the sample selection
procedure. Section 5 presents the results and section 6 concludes.
1.
Hypotheses
We characterize the audit market as a quality-differentiated oligopoly, dominated by a few
suppliers (Numan and Willekens, 2012). A key feature of oligopoly is that each supplier’s
competitive moves affect market conditions, including the market clearing price, and that
suppliers’ actions are interdependent. Competitors will respond to the actions of one firm by
adjusting their own competitive strategies. The adjustments they make in their strategies will, in
8
turn, alter market conditions again. Competitive rivals will take into account the direct effect of
their market decisions as well as secondary effects that follow from the reactions of other firms.
Rational firms competing in such markets will weigh the immediate benefits of competing
aggressively to obtain new clients and increased revenues against potential future costs arising
from the reactions of rivals. In the extreme, the market dynamics may result in a “price war” among
rivals. This paper focuses on how the benefits and costs of aggressive competition can influence a
firm’s actions across the various markets in which it competes with its rivals (i.e., compete for
clients in different industries).
1.1. Multi-industry contact
Prior research divides the audit market into segments based on industries within MSAs
(Francis et al., 2005; Numan and Willekens, 2012; Reichelt and Wang, 2010). Thus, the same
competing audit firms/offices try to attract clients in multiple industries/market segments. As a
result, audit firms may find it more profitable to focus on some key industries rather than
competing aggressively in all industries, especially in a single geographical area. From an
economic perspective, the gain from an aggressive approach in one industry segment may be
outweighed by rivals’ reactions in other industry segments. Competing firms might therefore
practice mutual forbearance, refraining from competing aggressively in their rivals’ focal
industries to avoid aggressive competition in their own focal industries. Edwards (1955) first
argued that multimarket links could 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.” A formal analysis by Bernheim and Whinston (1990) shows that
competing in multiple market segments decreases suppliers’ incentives to compete vigorously.
Empirical studies consistently show that multimarket contact negatively affects competition
because of mutual forbearance. As a result, multimarket contact has been shown to result in higher
9
prices, greater profits, higher survival rates, and decreases in the rate of sales growth (Barros, 1999;
Evans and Kessides, 1994; Gimeno, 2002; Greve, 2008; Li and Greenwood, 2004) in industries
such as air travel (Evans and Kessides, 1994), banking (De Bonis and Ferrando, 2000) and
insurance (Greve, 2008). Note further that it has also been shown that the retaliatory response of
firms is reinforced when differences in market shares across market segments exist (Bernheim and
Whilston, 1990).
The characteristics of the audit market fit this theory very well. First, audit firms compete
with each other for clients in the different industries (market segments) that exist within an MSA.
In other words, there is multi-industry contact. Trying to encourage rivals’ clients to switch audit
firms in one market segment (i.e., industry) could induce a retaliatory response by the rival, not
only in that market, but in all other market segments (i.e., industries). This increases the future
costs of fierce competition. As a result, competition between audit firms may be influenced even
in the absence of any explicit collusion among audit firms. Second, prior audit literature has shown
that asymmetries between different audit suppliers exist in terms of market share, audit quality
(Reichelt and Wang, 2010), and production efficiencies (Banker et al., 2005). These differences
or asymmetries across audit firms in different markets are likely to influence firms’ incentives to
practice mutual forbearance, that is, to avoid competitive attacks in markets where competitors
have larger market shares and to avoid strong competition in other markets. A multi-market
strategic approach encourages an audit firm to assess the joint effects of its competitive decisions
on all industries, decreasing the likelihood of fierce competition in a single industry.4
4
A potential counter-argument is that the decision to compete for specific clients may be made by an audit partner
who specializes in a certain market. That partner may wish to be aggressive because he or she does not directly bear
the cost of rival reactions in other markets and may make decisions that are in their own best interests rather than the
firm’s (Knechel, Niemi, and Zerni, 2013). To the extent that the individual partner incentives are not aligned with the
firm’s, the behavior we discuss in this paper may be muted.
10
Finally, it has been documented that an auditor’s industry leadership is associated with
significant fee premiums (Ferguson et al., 2003; Francis et al., 2005) especially when the marketshare distance from the closest competitor increases (Numan and Willekens, 2012). Hence, mutual
forbearance may occur as audit firms may be unwilling to compete vigorously in industries where
a rival is the industry leader as they may fear fierce competition in their own markets where they
are the leader and earn fee premiums. While this theory has been developed for homogeneous
product/service markets, Matsushima (2001) has extended the theory to markets that are
heterogeneous and where competition might be based on non-price competition, such as the market
for audit services (Francis et al., 2005; Reichelt and Wang, 2010). Based on these arguments, we
predict that multi-industry contact will decrease the fierceness of audit market competition leading
to our first hypothesis:
MUTUAL FORBEARANCE HYPOTHESIS (Hypothesis 1). Multi-industry contact between
audit firms within an MSA is negatively associated with the aggressiveness of competition
in an audit market segment.
As we discuss in more detail below, we proxy for the aggressiveness of competition or audit firm
rivalry by using two measures of market instability, namely: (1) the mobility of market shares
across firms within an MSA/industry (Buijink et al., 1998; Chang et al.,2009; Sakakibara and
Porter, 2001) and (2) changes in the industry-MSA leader over time (Ferrier et al., 1999).
1.2. Client Concentration
11
High buyer concentration implies that there are a few large clients in a market segment.
Large clients have bargaining power vis-a-vis the suppliers because the client’s fee constitutes a
large part of the total market fees that are available (Casterella et al., 2004). Thus, large clients can
use their power to negotiate their fees downwards by threatening to switch suppliers. A switch of
auditor by a large client will obviously have a significant impact on the market share of the
predecessor and successor auditor. Even without a switch, the mere threat of changing auditors
could result in a fee reduction that decreases the auditor’s market share relative to other firms.
Such an effect is consistent with audit fee studies that report that larger clients have stronger
negotiating power over audit fees (Casterella et al., 2004; Huang et al., 2007; Mayhew and
Wilkins, 2003).
In addition to this demand-side effect, there is also a supply-side effect of buyer
concentration. Audit firms will have incentives to compete more aggressively for larger clients
because the immediate gain from snatching a large client from a rival is large. At the same time,
the rival’s competitive responses may only have a modest impact, since competing aggressively
for the smaller clients in the market will have much less effect on the aggressive firm’s market
share. In other words, the predecessor auditor would have to attract multiple clients from the
successor auditor’s client portfolio to make up for the loss of the large client. Further, the prestige
that might accrue to the successor auditor of obtaining a large client may somewhat insulate the
firm from the competitive threats of the losing firm. Therefore, future losses might be relatively
small compared to the immediate gains of competing fiercely. These arguments are consistent with
the empirical industrial organization literature, which finds that concentrated buyers use their
12
bargaining power to destabilize suppliers’ market shares (Caves and Porter, 1978; Kato and Honjo,
2006), leading to our second hypothesis:5
CLIENT BARGAINING POWER HYPOTHESIS (Hypothesis 2). Client concentration is
positively associated with the aggressiveness of competition in an audit market segment.
In this study, we measure client concentration using the Herfindahl index calculated for each
industry-MSA dyad.
1.3. Reputation damage
An audit can be considered a credence service (Causholli and Knechel, 2012), meaning,
clients are unable to fully assess the level of service and service quality without incurring
significant costs both ex ante, before the service is performed, or ex post. As a result, clients rely
on a supplier’s reputation as a signal for audit quality. It follows that a firm’s quality reputation is
highly important in credence good markets. In a quality-differentiated oligopoly, suppliers
perceived to have higher quality than competitors are able to charge higher prices without losing
market share (Chan, 1999). We posit that the most dominant leaders in an industry will have a
reputation for high audit quality since prior research has found that clients of industry leaders have
higher earnings response coefficients (Balsam et al., 2003), have a higher likelihood of receiving
going concern audit opinions when in financial distress (Reichelt and Wang, 2010), have fewer
accounting restatements (Chin and Chi, 2008), exhibit higher disclosure quality (Dunn and
Mayhew, 2004), and are less subject to SEC enforcement actions (Carcello and Nagy, 2004). Prior
research has also documented a positive association between audit fees and market leadership
(Balsam et al., 2003; Craswell et al., 1995; DeFond et al., 2000; Ferguson et al., 2003; Francis et
al., 2005; Mayhew and Wilkins, 2003; Numan and Willekens, 2012). This fee premium is
5
Contrary to this 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).
13
interpreted as evidence of the client’s willingness to pay for higher audit quality (Craswell et al.,
1995; Francis et al., 2005).6
In line with the credence attributes of the audit service, clients may question an audit firm’s
abilities if evidence comes to their attention suggesting the firm has experienced a decline in its
overall quality. Prior research shows that reputation damage caused by audit failures can severely
impact an audit firm’s market position (Skinner et al., 2012; Weber et al., 2008). This makes it
possible for rivals to compete more aggressively when they view another firm as vulnerable due
to a decline in its reputation. Such a decline may make it more likely that the clients of the
damaged firm will switch auditors (Weber et al., 2008; Skinner et al., 2012). Further, the audit
firm’s damaged reputation will impair its ability to attract new clients, thereby decreasing their
ability to retaliate against aggressive rivals. This reputation damage, and the increased rivalry
resulting from it, also increase the likelihood a market leader could lose its leadership position in
an audit market segment. The net effect would increase the level of competition in the market (i.e.,
MSA/industry), which leads us to our third hypothesis:
REPUTATION DAMAGE HYPOTHESIS (Hypothesis 3). A loss of reputation of an audit firm
is positively associated with the aggressiveness of competition in an audit market segment.
In this study we use accounting restatements as a signal of a decline in audit quality that could
undermine an audit firm’s reputation, making it more vulnerable to competitive rivals.7
2.
6
Research design
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 a high-quality reputation, restatements of financial statements audited by the firm will not
affect its reputation and will not, therefore, affect market instability. This possibility works against us finding evidence
to support our hypothesis.
7
Restatements have been shown to be associated with negative capital market consequences (Palmrose et al., 2004)
and to have adverse effects on the auditor-client relationship (Huang and Scholz, 2012).
14
Prior research typically employs static, cross-sectional measures to capture audit market
competition. Such measures include concentration (Bandyopadhay and Kao, 2004; Ciconte et al.,
2015; Feldman, 2006; Pearson and Trompeter, 1994), market share (Willekens and Achmadi,
2003), industry specialization (Craswell et al., 1995; Ferguson et al., 2003; Francis et al., 2005),
and market-share distance from the closest competitor (Numan and Willekens, 2012). However,
these measures conceal much of the underlying competitive conduct in the market, given that high
market turnover could exist in markets where static measures suggest low competition (Bujink et
al., 1998; Davies and Geroski, 1997; Scherer and Ross, 1990). We therefore investigate a dynamic
measure of competition and rivalry: market instability. The few auditing studies that have
researched market instability (Bujink et al., 1998; Chang et al., 2009; Danos and Eichenseher,
1982; Hogan and Jeter, 1999; Wolk et al., 2001) have found that concentration levels have
increased over time because of increases in the market share of market leaders (Hogan and Jeter,
1999; Wolk et al., 2001). 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 a number of ways. First, the purpose of the earlier studies was to document
evolution of audit markets over time rather than explain it. Second, the earlier studies only
considered the audit market at the national level, while we follow recent studies delineating a
market segment as an industry within an MSA (Francis et al., 2005; Numan and Willekens, 2012;
Reichelt and Wang, 2010). Finally, these studies use data prior to the demise of Arthur Andersen
and the implementation of the Sarbanes-Oxley Act of 2002 (SOX).
We specify two alternative dependent variables to measure distinct aspects of market-share
instability based on the industrial organization literature (Caves and Porter, 1978; Ferrier et al.,
1999; Schmalensee, 1989). Our first measure, which we define as market-share mobility takes all
suppliers in a market segment into account and is constructed by aggregating the changes in market
share of all competing firms in a market segment. Our second measure, leader dethronement
15
focuses on the identity of the leader in a market segment and captures when a rival gains a larger
market share than the incumbent market leader (Armstrong and Collopy, 1996; Ferrier et al.,
1999). Leaders are able to exploit economies of scale, have stronger reputations and enjoy market
power allowing them to charge higher prices (Armstrong and Collopy, 1996; Ferrier et al., 1999).
Consistent with this view, the audit literature has identified market share leaders as industry
specialists earning significant fee premiums (Balsam et al., 2003; Ferguson et al., 2003; Francis et
al., 2005; Mayhew and Wilkins, 2003) and enjoying a strong reputation based on perceived
superior audit quality (Reichelt and Wang, 2010). These examples highlight the importance of the
leadership position to competitors, which creates incentives for rivals to contest the leader, thereby
generating greater instability in a market segment. Note that leader dethronement conceals much
of the market-share instability of lower-ranked firms as compared to market-share mobility and
therefore both measures together provide a more complete picture of the market dynamics.
Consistent with prior research, we define an audit market segment as a two-digit SIC
industry within a U.S. MSA (Francis et al., 2005; Numan and Willekens, 2012). This classification
reflects the fact that audit engagements require relevant industry knowledge that is difficult to
transfer within the audit firm across MSAs (Francis et al., 2005). All variables in our models are
constructed based on this market segment definition and the level of analysis is thus the audit
market segment.8
2.1. Model 1: Market-share mobility
As indicated, we first construct a market-segment instability measure that relates to all
suppliers in the market segment. Specifically, we measure market-share mobility (MS_MOB) per
market segment using the market share changes from year t-1 to year t for all auditors in the market
8
One exception to be discussed is a measure that reflects the dominance of the market-segment leader across all
industries within the MSA.
16
segment. Following prior studies (Bujink et al., 1998; Caves and Porter, 1978; Chang et al., 2009;
Kato and Honjo, 2006; Sakakibara and Porter, 2001), market share mobility is calculated as
follows:
𝑀𝑆_𝑀𝑂𝐵𝑡 = ∑𝑛𝑖=1|𝑀𝑆𝑖𝑡−1 − 𝑀𝑆𝑖𝑡 | /2 =
(1)
where MSit-1 reflects the market share of audit firm i in an industry within an MSA at time t-1 and
MSit reflects the market share of audit firm i in an industry within an MSA at time t. The total
number of audit 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 = α0 + α1*MULTI_IND_ALLt + α2*HHI_CLIENTt + α3*RESTAT_ALLt +
{controls} + {industry and year fixed effects} + ε
(2)
As the dependent variable in eq. 2 (MS_MOBt) measures the market share changes of all
audit firms in a market segment, the test variable capturing mutual forbearance (Hypothesis 1)
needs to be a multi-industry measure capturing multi-industry linkages between all audit firms
active in the market segment. Following prior studies (e.g., De Bonis and Ferrando, 2000), we
construct MULTI_IND_ALLt in the following way:
𝑛
𝑛
(3)
𝑀𝑈𝐿𝑇𝐼_𝐼𝑁𝐷_𝐴𝐿𝐿𝑡 = ln((∑ ∑ (𝑎𝑖𝑗𝑡−1 )) / (𝑛 ∗ (𝑛 − 1)/2)
𝑖=1 𝑗=𝑖+1
where aijt measures the number of industries in which firm i and firm j 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
17
calculates the average multi-industry contact across all pairs in the market segment.9 Finally, we
take the natural logarithm of this measure to normalize the variable.
We capture client concentration using HHI_CLIENTt, measured as the Herfindahl index of
the audit fees paid by the clients in a market segment (i.e., industry/MSA) in year t-1. While some
studies use the C4 measure, 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 four clients.10 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 (e.g., Caves and
Porter, 1978). Following our client concentration hypothesis (Hypothesis 2), we predict a positive
relationship with market-share mobility.
To test the reputation damage hypothesis (Hypothesis 3), we employ a measure capturing,
per market segment, whether there are audit firms that experienced clients restating their financial
statements. The variable RESTAT_ALLt is equal to one when there is an audit firm in the market
segment that has at least one client restatement during year t and zero otherwise. We use the year
of the restatement rather than the year in which the underlying error occurred because reputation
damage only occurs when the error is revealed, that is, when the market becomes aware of a
potential quality problem at the restatement date rather than at the error date. We construct a binary
variable to mitigate the influence of large markets. Ceteris paribus, with a similar restatement
9
Presume a market segment with three audit firms: A, B, and C. Firms A and B both have at least one client in four
industries. Firms A and C have contact in six markets, while firms B and C meet in two 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.
10
As a consequence, the C4 measure is always one in those markets, irrespective of the distribution of the audit fees
paid. A market in which all firms pay 25% of the total fees generated in the market has the same C4 as a market in
which one firms pays 85% of all audit fees and the remaining firms each pay 5%.
18
probability across markets, larger markets will experience more audit firms with client
restatements. Following our reputational damage hypothesis, we predict a positive association with
market-share mobility.
3.2. Model 2: Leader dethronement
Based on prior literature in industrial organization, we also test our hypotheses using leader
dethronement as the measure of market instability. Note that market leadership is particularly
relevant to the audit setting given the prior evidence on quality differentiation by market leaders
in the auditing literature. To that end we use the following probit model where L_DETHR is a
dummy variable equal to one if the market-segment leader in year t-1 loses its leadership position
to a competitor in year t and zero otherwise (Ferrier et al., 1999):
L_DETHRt = α0+ α1*MULTI_IND_LFt + α2*HHI_CLIENTt + α3*RESTAT_Lt
+ {controls} + {industry and year fixed effects} + ε
( 4)
To test the mutual forbearance hypothesis (Hypothesis 1), we argue that the closest rival by market
share (i.e., the follower) will be the fiercest potential competitor for the leader and has the highest
likelihood of successfully dethroning the leader (Numan and Willekens 2012).11 Consequently,
we specify MULTI_IND_LFt as the natural logarithm of the number of market segments (i.e., 2digit SIC industries) within an MSA in which both the market leader and the next closest rival by
market share (i.e., the follower) have clients. To illustrate, define the leader in industry X within
an MSA as A and the follower as B. MULTI_IND_LFt measures the number of industries within
the same MSA in which both A and B each have at least one client. We do not require A or B to
be the leader in the other industries. The lower bound of this measure is equal to one and the upper
11
In our sample, the market-segment leader has a market share of 58.37% on average (and a mean of 56.29%). The
market follower has a market share of 23.09% on average (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%.
19
bound is the number of industries within the MSA (i.e., both A and B have a client in every industry
in the MSA). We predict a negative association between MULTI_IND_LFt and leader
dethronement. To test our client concentration hypothesis (Hypothesis 2), we use HHI_CLIENTt,
which is calculated in the same way as in Model 1. We expect HHI_CLIENTt to be positively
associated with L_DETHRt. To test our reputational damage hypothesis (Hypothesis 3), we define
RESTAT_Lt to be equal to one when the leading audit firm in the market segment has at least one
client restatement during year t, zero otherwise. We use a binary variable to mitigate the influence
of large markets. Consistent with Hypothesis 3, we expect RESTAT_Lt and L_DETHRt to be
positively associated.
3.3. Control variables
We include a number of control variables in our market instability models. First, we
explicitly control for market size because smaller markets can only support a limited number of
audit firms due to economies of scale and fixed entry costs typical for the audit industry, which
negatively affect market instability (Fusillo, 2013; Scherer and Ross, 1990). In contrast, the effect
of one client switching will have a stronger mathematical impact on market instability in smaller
markets. Hence, MAR_SIZEt reflects market segment size as a percentage of the total U.S. audit
market and is measured as the size of the MSA-industry market in terms of audit fees relative to
the total national market in the prior year. Second, the Big Four have more resources to compete
or protect themselves from rival competition than non-Big Four firms. In addition, large audit
firms are likely to compete across many more markets than smaller ones are. Hence, we expect
competitive rivalry to be smaller when the leader in the market segment is a Big 4 firm and define
BIG4_L to be equal to one if the market-segment leader is a Big Four firm, zero otherwise. Second,
We also include the market-share distance between leader and follower in the market segment
since a smaller market-share distance implies less differentiation and could lead to more rivalry
20
(Numan and Willekens, 2012; Mayhew and Wilkins, 2003). Additionally, high market-share
distance signals greater leader dominance (Danos and Eichenseher, 1982; Davies and Geroskie,
1997; Ferrier et al., 1999). Thus, |DISTANCE|t measures the market-share distance between the
market leader and the market follower in a given industry in the prior year (t-1).
Next, we control for the average client size in the market segment (Chang et al., 2009;
Hogan and Jeter, 1999). Large clients require more sophisticated auditing techniques and firmspecific knowledge, which reduces the number of audit firms with the expertise required to conduct
the audit. We expect that market instability will be negatively associated 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. Next, competition might be different when the market-segment leader
has a large market share across all market segments (industries) within the MSA than when the
market-segment leader has specialized in one industry and has a low market share at the overall
MSA-level. To account for this, we include MS_L_MSAt, which is the market share of the marketsegment leader at the MSA-level in the prior year.
Finally, we include three variables that capture demand-side instability, which might affect
overall market instability (Caves and Porter, 1978; Ferrier et al., 1999; Kato and Honjo, 2006;
Sakakibara and Porter, 2001). First, CL_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
(Klemperer, 1987). Existing clients in the market have a higher likelihood of reappointing the same
audit firm because they are locked in. We expect that clients that newly enter the market—those
without switching costs—will increase market-segment instability since all firms can compete for
those clients without the implicit threat of retaliation. Second, CL_EXITt captures the amount of
fees of clients buying audit services at time t-1—but not at time t—divided by total audit market
21
fees in t-1. Clients exiting, either by takeovers or bankruptcy, will likely increase market-segment
instability (Caves and Porter, 1978). Third, CL_GROWTHt measures the percentage change of
total audit fees of clients buying audit services in the market segment at time t compared to time
t-1. This variable controls for higher competition induced by the level of growth in markets (Caves
and Porter, 1978; Ferrier et al., 1999; Sakakibara and Porter, 2001).12 We also include industry
and year fixed effects.13
<<<<< Add Table 1 about here
>>>>>
4. Sample Selection
Since we investigate market-segment instability, the unit of analysis is a market segment
and the dataset includes one observation for each MSA-industry each year. To construct the market
segment level data, we collect all audit clients with positive audit fees from Audit Analytics. We
restrict the dataset to 2003 through 2012 because the demise of Arthur Andersen (2001) and the
introduction of SOX (2002) induced significant audit market changes affecting market instability.
During this time period, no further consolidation occurred among the Big Four. For each client,
we retrieve the 2-digit SIC code and client location. We link the client location to an MSA 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 Four and non-Big Four
clients in our sample because we cannot exclude ex ante the possibility that these audit firms
12
High client growth can also create a mismatch between the client and the audit firm (Brown and Knechel 2015;
Johnson and Lys 1990; Landsman et al., 2009).
13
Because of industry fixed effects, we do not include the variables capturing regulated industries or highly litigious
industries that were used in prior research (Chang et al., 2009, Hogan and Jeter, 1999). The variables CL_ENTRYt,
CL_EXITt, and CL_GROWTHt are subject to measurement error if some clients are excluded from the dataset in a
particular year. Moreover, CL_ENTRYt will also include newly-listed firms even when they had a prior commitment
with their current auditor. The same limitations also apply for the dependent variables, however. Despite this
shortcoming, therefore, we find it appropriate to use these measures as control variables.
22
compete for the same clients (Bills and Stephens, 2015). Furthermore, we retrieve restatement
information from Audit Analytics. For each restatement, we identify the restatement period as well
as the audit firm signing the original financial statements. Subsequently, we retrieve the date of
the restatement announcement.14
Table 2 displays the composition of the sample. We start with 20,154 market segment-year
observations. In line with prior research, we remove markets with one client in year t or year t-1
(Francis et al., 2005; Numan and Willekens, 2012). This excludes 10,530 market-years. In
addition, we require at least two audit firms in year t and year t-1 (Numan and Willekens, 2012)
as market-share mobility and leader dethronement are not valid concepts in monopolist market
segments. This results in a loss of 811 observations. Another 1,042 market-years are excluded
because the data required to calculate some variables is missing. Because markets with few clients
increase the likelihood of a measurement error in the variables, we remove market segments with
less than five clients, resulting in a loss of 4,492 market segment years. The final sample contains
3,279 market segment-years.
<<<<<
Insert Table 2 about here
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5. Results
5.1. Descriptive statistics
Descriptive statistics for the variables used in Models 1 and 2 are reported in Table 3. Table
3 also presents detailed descriptive statistics for the 3,279 market segments included in the
analysis. The prior year industry leader is dethroned in 19.3% of all market segments. Across all
14
The auditor might have changed between the error date and the restatement date. We use the error date to identify
which auditor audited the restating firm and the restatement date to identify the period in which the restatement became
public knowledge. The restatement is assigned to the auditor auditing the restated financial statements using the error
date in the year in which the restatement date occurred.
23
market segments the number of multi-industry linkages between segment leader and follower
ranges from one to thirty-two, with an average (median) of 8.480 (7.00). This implies that in some
MSAs leader and follower only compete with each other in one industry, whereas in others they
compete in up to thirty-two industries. The average (median) multi-industry contact of all audit
firms across MSAs is 3.402 (2.333). Client concentration (CLIENT_HHI) ranges from 0.019 to
0.986, which indicates strong variation of client concentration across market segments. Note
further that in 50.4% of the market segments at least one audit firm had the financial statements of
a client restated and in 18.4% of the market segments the leader audit firm had a financial statement
of a client restated.
Table 3 also presents descriptive statistics for the control variables. The industry leader is
a Big Four firm in 80.7% of market segments. Most market segments represent a relatively small
portion of the national market, with the largest segment representing 4.1%. The average (median)
market-share distance between the market-segment leader and its follower is 35.3% (30%).15 On
average, a market-segment leader has a market share of 58.4% (not tabulated) at the market
segment level (i.e., industry within MSA) and a market share of 25.3% at the MSA-level. The
variables capturing the demand side of the audit market show that the average (median) entry rate
is 3.8% (0%), that the average (median) exit rate is 7.5% (1.2%), and that the average (median)
market segment growth rate (in total audit fees) is 15.6% (5.3%).
<<<<<
Insert Table 3 about here
>>>>>
Table 4 presents Pearson and Spearman correlations. The table shows a positive correlation
between L_DETHR and MS_MOB (Pearson: 0.595) supporting the notion that both measures
15
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 in relation to their respective closest competitors.
24
capture the same underlying construct. Inspection of the correlations between the independent
variables reveals little evidence of multicollinearity, which is confirmed by the size of the variance
inflation factors (VIF) which are all less than 3.
<<<<<
Insert Table 4 about here
>>>>>
5.2. Main analyses: Results from Models 1 and 2
Table 5 reports the results from estimating the market-share mobility model, MS_MOB, as
the dependent variable (Model 1). We first present the results of our baseline model in Column 1,
and find that the model is significant (likelihood Chi Squared ratio equals 249.48, p-value < 0.01).
We also find that most control variables are significant and in the expected direction. In particular,
market-share mobility decreases when the average client size in the market segment is larger (0.095, p<0.01), the overall market is larger (-57.477, p<0.01), and the larger the distance between
the market-segment leader and its follower (-0.673, p<0.01). Market-share mobility also increases
with new client entries (1.309, p<0.01) and client exits (3.051, p<0.01).
Next, we present the results of our hypotheses tests in Column 2. We find support for the
mutual forbearance hypothesis (H1) because MULTI_IND_ALL is significantly and negatively (0.118, p<0.01) associated with market-share mobility. This indicates that market shares are less
volatile, and suggesting audit firm rivals compete less fiercely, when they compete in multiple
industry segments of the market. This result is consistent with mutual forbearance by rivals in a
market (Bernheim and Whinston, 1990). In economic terms, an increase of one standard deviation
of multimarket contact decreases market-share mobility by 0.75%. In contrast, we observe that
higher client concentration is associated with more market-share mobility (0.590, p<0.01),
indicating more aggressive competition when there are a few large clients, implying larger fees to
be gained in the market segment. This is consistent with our client concentration hypothesis (H2).
An increase of one standard deviation of HHI_CLIENT increases market-share mobility by 1.33%.
25
Finally, we find support for our reputation damage hypothesis (H3) in that RESTAT_ALL is
positive and significant (0.061; p-value < 0.10). This means that in a market segment where at
least one audit firm had a client firm that had to restate its financial statements, overall marketshare mobility is affected. The economic significance of the restatement variable is 0.74%, which
means if a market segment changes from no restatement to restatement, the mobility increases by
0.74%. Note that the results of all control variables are consistent with predictions.
<<<<<
Insert Table 5 about here
>>>>>
The results of the leader dethronement model (Model 2) where L_DETHR is the dependent
variable are presented in Table 6.16 We first present the results of our base model (Column 1) and
then present the outcome of our hypotheses tests (Column 2). The pseudo R-squared of the latter
model is 0.280. 17 Consistent with our mutual forbearance hypothesis (H1), the coefficient
MULTI_IND_LF is significant and negatively (-0.086, p<0.05) associated with leader
dethronement. In economic terms, one standard deviation increase in multi-industry contact
between a leader and its follower in a market segment decreases the likelihood of leader
dethronement by 1.83%, which is high compared with the average likelihood of dethronement of
11.71%.18 Furthermore, we find a significant and positive (1.378, p<0.01) association between
HHI_CLIENT and L_DETHR. This supports the client concentration hypothesis (H2) that the
implicit bargaining power of large clients is associated with increased audit firm rivalry. In
economic terms, an increase of one standard deviation of HHI_CLIENT increases the likelihood
16
The inclusion of industry fixed effects results in a loss of twenty-four observations because the fixed effects predict
the outcome variable perfectly. This implies that the value for L_DETHR is the same for that industry across all years
and MSAs.
17
The likelihood ratio Chi²-test is 899.08 and the p-value (0.000) indicates that this model explains more variation
than a constant-only model.
18
This is the predicted likelihood of leader dethronement when all independent variables are
measured at their mean.
26
of losing industry leadership by 5.07%. The coefficient of RESTAT_L is positive and significant
(0.167, p<0.05), in line with the reputation damage hypothesis (H3). This suggests that the damage
to the market leader’s reputation following accounting restatements by its clients increases the
aggressiveness of competitive rivals and the likelihood of leader dethronement. In economic terms,
the marginal effects indicate a restatement increases the likelihood of losing market leadership by
3.49%. Again, the regression coefficients related to the control variables are generally in line with
expectations.
<<<<<
Insert Table 6 about here
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5.3. Supplementary analysis: Instability due to client switching versus audit fee changes
In this section, we describe the results of supplemental tests designed to extend and further
refine our results. As both measures of market instability (MS_MOB and L_DETHR) are calculated
using market shares based on audit fees, changes in these measures can be either driven by changes
in number of clients (quantity changes), in the fee charged to these clients (price changes), or in
both. In this section we distinguish between two potentially important drivers of market instability:
instability due to client switching (quantity effect) versus instability triggered by audit fee changes
in continuing auditor-client relationships (audit pricing effect). We investigate how strategic
competition affects each of these. In line with our prior tests, we will study these quantity and price
effects at both the overall market segment level and the market leader level.
5.3.1. Instability due to client switching
For each market segment (i.e., industry within an MSA), we first calculate the total client
switch rate as the percentage of all clients in the market segment that change auditors
(SWITCH_SEGMENT). We also look at the switch rate of clients switching away from the industry
leader to a rival firm (LOSS_L). We then use each of these measures of market-segment instability
27
as dependent variables to estimate regression models similar to those specified in Models (2) and
(4). Because the switch rate variables are bounded between zero and one, we use fractional logit
models.
Table 7 presents the results of estimating each of the client switching models using
SWITCH_SEGMENT (Column 1) and LOSS_L (Column 2) respectively as the dependent
variables. To test the mutual forbearance hypothesis, we use the measure of multi-industry contact
that
is
appropriate
for
the
particular
model,
namely
MULTI_IND_ALL,
in
the
SWITCH_SEGMENT model, and MULTI_IND_LF in the LOSS_L model. The results for
SWITCH_SEGMENT (Column 1) show that relatively less switching occurs in markets where
there is more multimarket contact (MULTI_IND_ALL: -0.370, p<0.01). When we test the
hypothesis for switches away from the market leader, we find similar results as reported in Column
2. The more multi-industry contact, the less switching there is away from the market leader
(MULTI_IND_LF, -0.196, p-value < 0.01). Overall, we find very strong support for our first
hypothesis that mutual forbearance through multi-industry contact negatively affects client
switching in the audit market.
Next, we test our client concentration hypothesis. Client concentration, HHI_CLIENT, is
positively associated with the overall market segment switching rate (0.793, p<0.01). We do not
find evidence, however, that client concentration affects client switching away from the market
leader. This seems to suggest that large clients do not use their bargaining power by switching
away from the market leader, but do so by switching away from lower-ranked audit firms in the
market segment.
Finally, we test our reputation damage hypothesis using RESTAT_ALL in the
SWITCH_SEGMENT model and RESTAT_L in the LOSS_L model. We find a weakly significant
positive association between RESTAT_ALL in a market segment and the proportion of overall
28
client switching in that segment (0.075, p<0.10). We find that this is driven by switches away from
the industry leader firm as evidenced by a positive and strongly significant result in the LOSS_L
model (0.091, p<0.01). That is, accounting restatements by clients of the leader seem to damage
the leader’s reputation, possibly causing clients to reevaluate their commitment and inducing more
aggressive competition from other audit firms in the market segment. Accounting restatements by
clients of other firms do not influence the level of competitive rivalry. In sum, our evidence
suggests that market segment level client switch rates are affected by mutual forbearance and client
concentration, as well as the market leader’s reputation damage following a client restatement.
<<<<<
5.3.2.
Insert Table 7 about here
>>>>>
Instability due to changes in audit fees of non-switching clients
As indicated earlier, competitive pressure and rivalry may also manifest itself through fee
adjustments in order to prevent a client from considering a change in auditors. Thus, market-share
instability may not only be triggered by client switching (quantity effect), but also by audit fee
changes in continuing auditor-client relationships (audit pricing effect). In this section, we perform
an analysis of audit fee growth of non-switching (continuing) clients. The results are reported in
Table 8.19 We calculate the total fee revenue growth per market segment from non-switching
clients for all audit firms, which we define as GROWTH_SEGMENT, as well as audit fee growth
realized by the industry leader, GROWTH_L. The same independent variables are used in Table 8
as are used as in Table 7.
We find no support for a mutual forbearance effect on audit pricing in continuing
engagements, as witnessed by insignificant results on our measures of multi-industry contact in
19
We assume that because there is no change in auditor, there is no change in audit quality. That allows us to attribute
a change in fees to competitive pressure market rivals.
29
both models in Table 8. Taken together with the significant results in Table 7, we conclude that
multi-industry contact between auditor competitors across industry market segments is associated
with less client switching but is not associated with the pricing of continuing audit engagements.
We do find a large client bargaining power effect (HHI_CLIENT), however, as we report
a negative and significant association between client concentration and audit fee growth both in
the overall growth model (- 0.078, p< 0.01) and when the auditor is the market-segment leader (0.139, p< 0.01). Note that the effect is particularly strong in the GROWTH_L (leader) model
suggesting that large clients do have bargaining power to reduce audit fees downwards because
such clients are crucial for the position of market leaders.
Finally, we also find support for our reputation damage hypothesis. We report a negative
and significant association between our relevant proxy for reputation damage and audit fee growth
in the overall growth model (RESTAT_ALL: - 0.026, p< 0.05) and when the auditor is the marketsegment leader (RESTAT_L: - 0.035, p< 0.05). This suggests that market leaders with restating
clients are forced to reduce audit fees for ongoing audit engagements after their reputation is
damaged by restatements in order to maintain the clients in their portfolio.
Overall, the results of our audit fee revenue growth analyses confirm that large client
bargaining power and reputation damage put pressure on audit fees charged to continuing audit
clients, and that these effects are typically even stronger for market-segment leaders. However, we
do not find evidence that mutual forbearance puts pressure on fee growth for continuing client
engagements. This may indicate that incumbent auditors may not feel the need to restrain their
fees, comfortable in the knowledge that competitive rivals will not target their clients absent
atypical market conditions (i.e., a high level of client concentration or audit firms suffering
reputation damage).
<<<<<
Insert Table 8 about here
>>>>>
30
5.4. Sensitivity Analyses
In this section we report the outcome of a number of robustness checks related to our
segment-level tests.
5.4.1. Alternative Market Size Constraints
Recall that in our main analyses we imposed a sample selection criterion of a minimum of
five clients in each market segment, which resulted in removing markets with only a few clients.
We rerun our instability models using less strict criteria, including small market segments
consisting of three and four clients. Note that in such small markets, bankruptcy, merger, entry, or
auditor switching of one client significantly impacts our instability measures, which could increase
measurement error. However, our (untabulated) results are qualitatively similar to our primary
analysis. In addition, we rerun the analysis on a sample including only market segments in which
the leader was a Big Four firm and obtain results consistent with our main analysis.
5.4.2. Big Four Clients Only
We rerun the analysis including only clients of the Big Four, i.e., using industry-MSA-Big
Four market segments. To that end, we recalculate all dependent and independent variables using
only Big Four clients. We require that each market segment has at least three Big Four clients,
which reduces the sample to 2,965 Big Four industry-MSA-years. The (untabulated) results show
that multi-industry contact negatively affects Big Four market-share mobility, but has no
significant effect on leadership dethronement. In line with the main model, client concentration is
positively associated with both market-share mobility and leadership dethronement. Finally, a
restatement of the leading Big Four audit firm in a market does not affect market-share mobility
but it does affect leadership dethronement.
5.4.3.
Industry Specialization Dethronement as the Dependent Variable
31
In addition to testing leader dethronement, we also test industry specialization
dethronement. We define industry specialists as audit firms that have a market share of at least
30% in a market segment (Craswell et al., 1995). An industry specialist is considered dethroned
when the audit firm is an industry specialists in year t-1, but not in year t. Since more than one
firm can exceed the 30% market share threshold, the number of observations increases to 3,995.
Table 9 shows that the results remain unaltered. Multi-industry contact between the leader and
follower decreases the probability of industry specialist dethronement consistent with the mutual
forbearance hypothesis (MULTI_IND_LF: -0.100; p<0.01). Furthermore, client concentration
(HHI_CLIENT: 0.705, p< 0.01) and the occurrence of a restatement by clients of the industry
leader (RESTAT_L: 0.146, p< 0.05) increases the likelihood of industry specialization
dethronement.
<<<<<
Insert Table 9 about here
>>>>>
We also examine the situation where an industry specialist is also a market leader. In this
analysis we only consider the dethronement of a firm that is joint the market leader and a specialist
at the 30% level. We also include a binary additional test variable in this model capturing whether
the second largest audit firm in the market segment exceeds the 30% market share threshold and
is thus also an industry specialist. The results (untabulated) with respect to the test variables remain
unaltered. Furthermore, the results show that market stability is higher in those market segments
where the follower is also an industry specialist, suggesting that the two specialists exercise mutual
forbearance relative to each other and to the smaller, non-specialist firms in the market.20
20
Since not all industry specialists are leaders, we also tested the MULTI_IND_ALL variable in the industry
specialization dethronement model without the dummy variable for whether the follower is a specialist. Multi-industry
contact between all firms in a market segment also decreases the probability of industry specialization dethronement
(-0.130, p< 0.05).
32
5.4.4.
Alternative Test Variables
Finally, we also test the robustness of our results to specifications of our test variables.
First, we construct a number of alternative measures to test the mutual forbearance hypothesis. For
example, we measure multi-industry contact using a dummy equal to one when the multi-industry
contact was larger or equal to two overlapping industry segments and zero otherwise, instead of
using a continuous measure. Our results do not change. Next, we test whether mutual forbearance
is more pronounced between a pair of audit firms that both have a leadership position to defend in
an industry within the MSA. We therefore alternatively define multi-industry contact as equal to
one when the industry follower is an industry leader in another industry within the same MSA and
zero otherwise. This variable is highly negatively correlated with market instability, which
suggests that a follower that has the most to lose—namely, leadership in another market segment—
is even less likely to aggressively compete with a market-segment leader.
With respect to testing the robustness of the client concentration hypothesis, we construct
a binary variable equal to one when client concentration is above the mean client concentration
and 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.21
Finally, we construct variables testing the robustness of our reputational damage
hypothesis. For MS_MOB, we replace RESTAT_ALL with three variables indicating the level of
21
We also use the number of clients as an alternative measure of client concentration in our regression models. The
results remain unaltered
33
restatements for the leader, the level of restatements for the follower (second largest market share),
and the level of restatements for the remainder of the firms in the market. For L_DETHR, we
supplement the variable RESTAT_L with two additional variables, one for the restatements of the
follower and one for the restatements of all other firms in the market. In both cases, we find that
that only restatements by clients of industry leaders affect leader dethronement, while restatements
of other market participants have no significant effect. 22 This further supports the notion that
market leaders successfully differentiate themselves in terms of audit quality from rivals, and that
their reputation is damaged more when one of their clients issues a restatement. In short,
restatements by the clients of the non-leading firms do not affect overall market mobility.
6. Conclusion
Prior audit market research uses static market structure measures to capture competition
(Bandyopadhay and Kao, 2004; Feldman, 2006; Numan and Willekens, 2012; Pearson and
Trompeter, 1994). 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 competitive
rivalry among audit firms by examining audit market-segment instability conditional on audit
firms competing in multiple markets. We argue that audit firms compete rationally and their
decisions concerning how vigorously to compete are dependent on the potential and expected
actions competing audit firms might take in response. More specifically, we investigate the impact
of three factors—multi-industry contact between competing audit firms, market segment client
concentration, and audit-firm reputation damage—on the aggressiveness with which audit firms
compete and the stability of an audit market segment.
22
We also include these additional variables in the industry specialization dethronement model. The results also
suggest that only restatements of the leader are positively associated with industry specialization dethronement.
34
The results suggest that multi-industry contact results in less fierce competition because it
is negatively associated with our measures of market-segment instability: market-share mobility
and leader dethronement. We find that a main driver in this context is the decrease in the rate of
client switching, which suggests that multi-industry contact fosters mutual forbearance between
audit firms. Furthermore, our evidence suggests that buyer concentration increases competition,
as it is positively associated with market instability, implying that concentrated buyers use their
bargaining power to destabilize supplier market shares. This bargaining power manifests itself
primarily in concentrated buyers being able to negotiate a relatively lower growth rate of audit
fees. Our results show a significantly 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. We
also document a higher client switch rate. In addition, we present evidence consistent with a
positive effect of audit firm reputation damage on market segment instability, and show in
particular that reputation damage to the market leader is associated with a higher incidence of
leadership dethronement. In addition, restatements of financial statements audited by the leader
increase the leader’s switching rate and decrease the growth rate in fees by non-switching clients.
Our results complement prior research in the following ways. First, we extend prior
auditing studies using market-share mobility by using a theoretical framework to analyze marketshare instability. Therefore, we add to studies that are mainly descriptive (Bujink et al., 1998), that
focus on a single regulatory event (Chang et al., 2009), or that are interested in market changes in
a specific period of time (Hogan and Jeter, 1999; Wolk et al. 2001). Second, by using a dynamic
competition measure, we circumvent the theoretically ambiguous relationship between static
measures, most notably concentration, and competition. Third, we employ data from the period
after the demise of Arthur Andersen and the implementation of SOX.
35
Our study is subject to several limitations. For example, the sample consists of publiclylisted U.S. companies and their audit firms. It is unclear whether the results generalize to settings
with different institutional and legal frameworks. More specifically, since audit-firm private
clients are not included in the analysis, some forms of competition may not be visible through our
analysis. Future research may test the ways in which differences in legal frameworks or
institutional settings could affect the results. In addition, we are unable to observe the actual
competitive moves of audit firms. For instance, our measures do not capture whether audit firms
tried to, but where unsuccessful in, inducing rivals’ clients to switch. In spite of these and other
limitations, our results generate important new insights into competition in the market for audit
services. 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 among
audit firms is more sustainable, and particularly those MSAs where audit firms meet in multiple
industries and markets with low client concentration.
36
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41
Table 1: Variable Definitions
Variable definition
Dependent variables
L_DETHR
MS_MOB
Independent variables
RESTAT_L
RESTAT_ALL
MULTI_IND_LF
MULTI_IND_ALL
HHI_CLIENT
Control variables
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
CL_ENTRY
CL_EXIT
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.
Dummy equal to one if one of the audit firms active in a MSA-industry
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
herfindahl index of the clients’ audit fees within the MSA-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 clients’ 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.
42
CL_GROWTH
SWITCH_SEGMENT
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 in the MSA-industry in year t-1 switching to
another audit firm in year t.
LOSS_L
The percentage of clients audited the market segment leader in year t1 lost to another audit firm in year t.
LOSS_F
The percentage of clients the market segment follower in year t-1 lost
to another audit firm in year t.
LOSS_OTH
GROWTH_L
GROWTH_F
GROWTH_OTH
The percentage of clients audited by audit firms that were not the
market segment leader or market segment follower in year t lost to
other audit firms 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.
43
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
44
Table 3: Descriptive statistics
N
Dependent variables
L_DETHR
MS_MOB
Test variables
Exp(MULTI_IND_LF)
MULTI_IND_LF
Exp(MULTI_IND_ALL)
MULTI_IND_ALL
HHI_CLIENT
RESTAT_L
RESTAT_ALL
Control variables
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
CL_ENTRY
CL_EXIT
CL_GROWTH
Mean
StdDev
Min
P25
Median
P75
Max
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
3,279
3,279
3,279
3,279
3,279
3,279
3,279
8.480
1.664
3.402
1.319
0.306
0.184
0.504
7.076
1.084
3.091
0.530
0.187
0.387
0.500
1.000
0.000
1.000
0.693
0.019
0.000
0.000
2.000
0.693
1.361
0.859
0.174
0.000
0.000
7.000
1.946
2.333
1.204
0.269
0.000
1.000
13.000
2.565
4.250
1.658
0.398
0.000
1.000
32.000
3.466
30.333
3.445
0.986
1.000
1.000
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.
45
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 RESTAT_ALL
8 BIG4_L
9 AVG_CLIENTSIZE
10 MAR_SIZE
11 |DISTANCE|
12 MS_L_MSA
13 CL_ENTRY
14 CL_EXIT
15 CL_GROWTH
2
0.4844*
0.5949*
3
4
5
6
7
8
-0.0646*
-0.0423*
-0.0804*
-0.0088
-0.0167
-0.2207*
-0.0809*
-0.1002*
-0.1012*
-0.0384*
0.0215
-0.2570*
0.7712*
-0.2867*
0.1549*
0.2294*
0.3667*
-0.1525*
0.0680*
0.0755*
0.3178*
-0.1892*
-0.1923*
-0.0002
0.4700*
0.0530*
-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*
-0.0167
-0.0307
0.2278*
0.0375*
-0.1707*
0.4700*
-0.2207*
-0.3097*
0.3947*
0.2705*
0.0308
0.0530*
0.0715*
-0.1540*
-0.3334*
0.1615*
0.2986*
0.0079
0.0125
-0.1056*
0.3572*
-0.1069*
-0.1919*
0.3312*
0.1581*
-0.2157*
0.2433*
0.2271*
0.1545*
-0.2965*
-0.1611*
-0.2047*
-0.1405*
0.6350*
-0.0446*
-0.0877*
0.1482*
-0.2013*
-0.2450*
0.0394*
0.0031
0.1470*
0.0575*
-0.002
0.5260*
0.1742*
0.2500*
0.0144
0.0061
-0.0446*
-0.0169
-0.0501*
-0.0683*
0.2996*
0.5195*
-0.0689*
-0.0527*
-0.0141
-0.019
0.0115
-0.1070*
0.0841*
0.1525*
0.005
0.0173
-0.0377*
-0.0477*
-0.1041*
-0.0438*
0.0715*
46
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 RESTAT_ALL
8 BIG4_L
9 AVG_CLIENTSIZE
10 MAR_SIZE
11 |DISTANCE|
12 MS_L_MSA
13 CL_ENTRY
14 CL_EXIT
15 CL_GROWTH
10
11
12
13
14
15
-0.1387*
-0.2029*
-0.3219*
-0.1975*
0.0909*
0.1515*
0.0545*
-0.3655*
-0.3610*
-0.2668*
-0.2596*
0.1609*
0.3276*
0.1191*
0.0581*
0.5765*
-0.1730*
0.0807*
0.1677*
0.0848*
0.0286
0.2464*
0.4650*
-0.1332*
0.0708*
0.021
0.0039
0.0069
0.0358*
-0.2546*
0.5473*
0.0496*
-0.2660*
-0.2269*
-0.0850*
-0.0128
0.2327*
-0.0432*
0.0764*
0.1180*
0.0487*
-0.0454*
-0.1508*
0.2969*
-0.0783*
0.0228
0.1266*
0.1497*
-0.1069*
0.2620*
0.4456*
0.1509*
0.4994*
-0.0198
-0.0336
-0.0258
0.3209*
0.0934*
0.2491*
-0.1781*
-0.1191*
-0.1094*
0.0018
0.3281*
0.1138*
0.0680*
-0.1114*
0.2523*
-0.1189*
-0.1166*
-0.0476*
0.1613*
0.0935*
-0.0799*
0.2863*
0.1500*
0.2956*
-0.1283*
-0.0469*
-0.0445*
-0.0704*
-0.0197
-0.1224*
-0.0703*
-0.0316
-0.0874*
0.0207
-0.1377*
-0.0613*
-0.0249
-0.0188
0.1686*
-0.0264
-0.0047
0.1372*
0.1109*
0.0136
-0.0049
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.
47
Table 5: Estimation of Model 1 - Market share mobility (MS_MOB)
Dependent variable:
MS_MOB
Coef.
Intercept
MULTI_IND_ALL (H1)
HHI_CLIENT (H2)
RESTAT_ALL (H3)
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
CL_ENTRY
CL_EXIT
CL_GROWTH
N
LR Chi²
P-value (LR-Chi²)
Year fixed effects
Industry fixed effects
t-stat
0.613
-0.311
-0.095
-57.477
-0.673
-0.306
1.309
3.051
0.157
0.28
***
***
***
***
***
-1.60
-3.08
-2.67
-3.12
-0.66
4.13
10.70
1.31
3,279
249.48
0.000
Included
Included
MS_MOB
p-value
Coef.
0.779
0.417
-0.118
0.590
0.061
-0.278
-0.084
-50.948
-0.946
-0.356
1.341
3.045
0.163
0.109
0.002
0.008
0.002
0.510
0.000
0.000
0.190
***
***
*
***
***
***
***
*
***
***
***
t-stat
p-value
1.63
-2.52
3.66
1.49
-3.34
-6.76
-7.92
-9.67
-1.94
9.38
26.67
2.91
0.104
0.006
0.000
0.068
0.001
0.000
0.000
0.000
0.052
0.000
0.000
0.004
3,279
268.33
0.000
Included
Included
This table presents the results of a fractional logit model with market share mobility as dependent
variable. The first column presents the variable names. The second column presents 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_ALL. Year and industry fixed effects are included. Significance based on
one-tailed tests for the hypotheses and two-tailed tests for the control variables) is indicated as follows:
p<0.10 (*), p<0.05 (**), p<0.01(***). Variable definitions can be found in Table 1
.
48
Table 6: Estimation of Model 2 - Leader Dethronement (L_DETHR)
Dependent variable
L_DETHR
Coef.
Intercept
MULTI_IND_LF (H1)
HHI_CLIENT (H2)
RESTAT_L (H3)
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
CL_ENTRY
CL_EXIT
CL_GROWTH
N
Pseudo R²
LR Chi²
P-value (LR-Chi²)
Year fixed effects
Industry fixed effects
z-stat
-4.048 ***
-0.438
-0.006
-43.514
-2.405
-0.332
1.455
2.823
0.068
***
***
***
***
***
L_DETHR
p-value
Coef.
-4.25
0.000
-3.68
-0.30
-3.32
-13.73
-1.11
6.43
15.21
0.77
0.000
0.767
0.001
0.000
0.266
0.000
0.000
0.441
-4.427
-0.086
1.378
0.167
-0.312
-0.005
-27.465
-3.065
-0.413
1.545
2.841
0.086
3,255
0.268
862.01
0.000
Included
Included
***
**
***
**
**
**
***
***
***
z-stat
p-value
-5.07
-2.18
4.86
2.00
-2.40
-0.23
-2.18
-15.86
-1.42
6.62
15.24
1.01
0.000
0.015
0.000
0.023
0.016
0.820
0.029
0.000
0.156
0.000
0.000
0.311
3,255
0.280
899.08
0.000
Included
Included
This table presents the results of a probit regression with leader dethronement as dependent variable.
The first column presents the variable names. The second column presents the coefficients, z-statistics
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 one-tailed tests for
the hypotheses and two-tailed tests for the control variables) is indicated as follows: p<0.10 (*), p<0.05
(**), p<0.01(***). Variable definitions can be found in Table 1.
49
Table 7: Analysis of market segment switching rates (quantity effect analysis)
Dependent variable:
(1)
Overall switch rate
(2)
Loss rate Leader
SWITCH_SEGMENT
LOSS_L
Coef.
Intercept
MULTI_IND_ALL (H1)
MULTI_IND_LF (H1)
HHI_CLIENT (H2)
RESTAT_ALL (H3)
RESTAT_L (H3)
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
0.182
-0.370
0.793
0.075
0.083
-0.108
0.460
-0.326
-0.426
z-stat
***
***
***
**
*
0.98
-6.25
4.47
1.48
0.78
-9.29
0.10
-2.45
-1.84
pvalue
0.326
0.000
0.000
0.069
0.438
0.000
0.920
0.014
0.066
p-value
z-stat
p-value
-0.934
-1.54
0.124
-0.196
-0.284
**
-2.41
-0.53
0.008
0.703§
0.091
-1.016
-0.024
7.194
-0.171
-0.362
***
***
2.84
-3.18
-0.72
0.60
-0.50
-0.46
0.003
0.001
0.472
0.549
0.619
0.648
N
3,279
3,279
LR Chi²
378.65
100.979
P-value (LR-Chi²)
0.000
0.000
Year fixed effects
Included
Included
Industry fixed effects
Not Included
Not Included
This table presents the results of a fractional logit model with the proportion of switching clients
in the market segment (SWITCH_SEGMENT) and the percentage of clients lost to competing
audit firms for the market segment leader (LOSS_L). The first column presents the variable names.
Year fixed effects are included. Significance (based on one-tailed tests for the hypotheses and twotailed tests for the control variables) is indicated as follows: p<0.10 (*), p<0.05 (**), p<0.01(***).
Variable definitions can be found in Table 1.
§
p-value from one-tailed test P > t
50
Table 8: Analysis of market segment audit fee revenue growth from non-switching clients
(price effect analysis)
Dependent variable:
Intercept
MULTI_IND_ALL (H1)
MULTI_IND_LF (H1)
HHI_CLIENT (H2)
RESTAT_ALL (H3)
RESTAT_L (H3)
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
N
Adjusted R²
(1)
Segment level
revenue growth from
non-switching clients of
all audit firms
GROWTH_SEGMENT
Coef.
t-stat p-value
0.328
-0.020
-0.078
-0.026
-0.041
-0.005
-5.311
-0.008
0.059
0.94
-1.43
0.348
0.925§
*
**
-1.83
-2.03
0.034
0.022
***
-1.45
-1.15
-4.88
-0.29
1.08
0.146
0.251
0.000
0.770
0.282
3,279
0.310
(2)
Segment level
revenue growth from
non-switching clients of
market segment leaders
GROWTH_L
Coef.
t-stat
pvalue
0.271
0.66 0.506
-0.013
-0.139 ***
-1.55 0.940§
-3.02 0.002
-0.035 **
0.036
-0.005
-4.934 ***
-0.027
0.147 **
-2.00
1.08
-1.01
-3.79
-0.84
2.38
0.023
0.279
0.314
0.000
0.404
0.018
3,106
0.260
Included
Included
Year fixed effects
Included
Included
Industry fixed effects
This table presents the results of an ordinary least squares regression with the percentage growth
in audit fees of non-switching clients in the entire market segment (GROWTH_SEGMENT) and
for the market segment leader (GROWTH_L). The first column presents the variable names. Year
fixed effects are included. Significance (based on one-tailed tests for the hypotheses and two-tailed
tests for the control variables) is indicated as follows: p<0.10 (*), p<0.05 (**), p<0.01(***).
Variable definitions can be found in Table 1.
§
p-value from one-tailed test P > t
51
Table 9: Sensitivity of Model 2 – Industry Specialization Dethronement (IS_DETHR)
Dependent variable
IS_DETHR
Coef.
Intercept
MULTI_IND_LF (H1)
HHI_CLIENT (H2)
RESTAT_L (H3)
BIG4_L
AVG_CLIENTSIZE
MAR_SIZE
|DISTANCE|
MS_L_MSA
CL_ENTRY
CL_EXIT
CL_GROWTH
N
Pseudo R²
LR Chi²
P-value (LR-Chi²)
Year fixed effects
Industry fixed effects
-3.742
-0.100
0.705
0.146
-0.299
-0.055
-15.843
-2.268
0.370
1.225
2.545
0.111
***
***
***
**
***
***
***
***
***
z-stat
p-value
-3.88
-2.77
2.81
1.97
-2.60
-3.11
-1.61
-14.50
1.48
6.26
15.43
1.36
0.000
0.003
0.002
0.024
0.009
0.002
0.106
0.000
0.140
0.000
0.000
0.174
3,995
0.280
899.08
0.000
Included
Included
This table presents the results of a probit regression with industry specialization dethronement as
dependent variable. An audit firm is considered an industry specialist when its market share exceeds 30
percent in a msa-industry. The first column presents the variable names. The second column presents
the coefficients, z-statistics 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
one-tailed tests for the hypotheses and two-tailed tests for the control variables) is indicated as follows:
p<0.10 (*), p<0.05 (**), p<0.01(***). Variable definitions can be found in Table 1.
52