· web viewin this paper we argue that audit firms compete rationally and consider the...

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 LeuvenFaculty of Economics and Business

Leuven, BELGIUMb University of Florida

|Fisher School of Accounting,FL, United States

c BI Norwegian Business School,Oslo, Norway

October 2016

mailto:[email protected]





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).

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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

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taking clients away from competing 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

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benefit of attracting a large client is substantial and could exceed future losses caused by any

retaliatory 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

1 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.

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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 valuable as leaders in many industries have strong reputations, can exploit

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.

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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

market-share 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

vigorously an audit firm may compete in a market segment. Second, unlike previous studies that

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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

turn, alter market conditions again. Competitive rivals will take into account the direct effect of

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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 prices, greater profits, higher survival rates, and decreases in the rate of sales

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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

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 market-

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.

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share 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

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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).

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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 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.

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).

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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 interpreted as evidence of the client’s willingness to pay for higher audit quality

(Craswell et al., 1995; Francis et al., 2005).6

6 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.

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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. Research design

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

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).

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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 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

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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 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:

MSMOBt=∑i=1

n

|MS¿−1−MS¿|/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 8 One exception to be discussed is a measure that reflects the dominance of the market-segment leader across all industries within the MSA.

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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:

MULTI ¿t=ln ¿ ( 3 )

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 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

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.

18

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 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

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%.

19

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., 2-digit 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 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

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%.

20

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 (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 firm-

specific 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 21

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 market-segment 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 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 >>>>>

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

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 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

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

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 >>>>>

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

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%

24

(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%).


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 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.


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).

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.

25

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%. 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 market-share 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.


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-

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.

26

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

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.


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

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.

27

(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 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

28

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

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. 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-29

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

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

market-segment leader (RESTAT_L: - 0.035, p< 0.05). This suggests that market leaders with

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.

30

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).


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

31

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

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.

32


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

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,

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).

33

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

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

21 We also use the number of clients as an alternative measure of client concentration in our regression models. The results remain unaltered22 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

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.

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

35

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

market-share 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.

Our study is subject to several limitations. For example, the sample consists of publicly-

listed 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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http://www.gao.gov/new.items/d08163.pdf

Table 1: Variable DefinitionsVariable definition

Dependent variablesL_DETHR Dummy variable equal to one if the MSA-industry leader in year t was

not the leader in the prior year(t-1), zero otherwiseMS_MOB 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 .

Independent variablesRESTAT_L 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.

RESTAT_ALL 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.

MULTI_IND_LF 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).

MULTI_IND_ALL 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.

HHI_CLIENT Beginning period (t-1) demand concentration calculated as the herfindahl index of the clients’ audit fees within the MSA-industry

Control variablesBIG4_L Dummy equal to one if the MSA-industry leader in t-1 is a Big 4 firm

(E&Y, Deloitte, PwC, KPMG).AVG_CLIENTSIZE The beginning period average of the natural logarithm of clients’ total

assets within the MSA-industry.MAR_SIZE The beginning period relative size of the market segment (MSA-

industry) in terms of audit fees relative to the total national market in each year.

|DISTANCE| The beginning period absolute difference between the market shares of the MSA-industry leader and the MSA-industry follower in the current year.

MS_L_MSA The beginning period market share of the prior MSA-industry leader at the Metropolitan Statistical Area (MSA) level.

CL_ENTRY 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.

CL_EXIT 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.

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CL_GROWTH 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.

SWITCH_SEGMENT 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 t-1 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_OTHThe 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.

GROWTH_L The percentage change in audit fees of clients audited by the market leader in both year t-1 and year t.

GROWTH_F The percentage change in audit fees of clients audited by the market follower in both year t-1 and year t.

GROWTH_OTHThe 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.

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Table 2: Sample Selection

Number of MSA-industry-year observations 20,154Less MSA-industry-years with only one client (10,530)Less MSA-industry-years with only one audit firm (811)Less observations with insufficient data for all control variables (1,042)Less markets with fewer than 5 clients (4,492)Number of MSA-industry-year observations 3,279

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Table 3: Descriptive statistics

N Mean StdDev Min P25 Median P75 MaxDependent variablesL_DETHR 3,279 0.193 0.395 0.000 0.000 0.000 0.000 1.000MS_MOB 3,279 0.163 0.169 0.001 0.050 0.104 0.210 0.988

Test variablesExp(MULTI_IND_LF) 3,279 8.480 7.076 1.000 2.000 7.000 13.000 32.000MULTI_IND_LF 3,279 1.664 1.084 0.000 0.693 1.946 2.565 3.466Exp(MULTI_IND_ALL) 3,279 3.402 3.091 1.000 1.361 2.333 4.250 30.333MULTI_IND_ALL 3,279 1.319 0.530 0.693 0.859 1.204 1.658 3.445HHI_CLIENT 3,279 0.306 0.187 0.019 0.174 0.269 0.398 0.986RESTAT_L 3,279 0.184 0.387 0.000 0.000 0.000 0.000 1.000RESTAT_ALL 3,279 0.504 0.500 0.000 0.000 1.000 1.000 1.000

Control variablesBIG4_L 3,279 0.807 0.395 0.000 1.000 1.000 1.000 1.000AVG_CLIENTSIZE 3,279 18.918 2.362 7.178 17.628 19.032 20.623 25.964MAR_SIZE 3,279 0.002 0.004 0.000 0.000 0.001 0.002 0.041|DISTANCE| 3,279 0.353 0.264 0.000 0.124 0.300 0.534 0.996MS_L_MSA 3,279 0.253 0.144 0.000 0.170 0.234 0.333 0.866CL_ENTRY 3,279 0.038 0.129 0.000 0.000 0.000 0.012 0.990CL_EXIT 3,279 0.075 0.144 0.000 0.000 0.012 0.079 0.989CL_GROWTH 3,279 0.156 0.425 -0.730 -0.033 0.053 0.199 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 2 3 4 5 6 7 8

1 L_DETHR 0.4844* -0.0646* -0.0423* -0.0804* -0.0088 -0.0167 -0.2207*

2 MS_MOB 0.5949* -0.0809* -0.1002* -0.1012* -0.0384* 0.0215 -0.2570*

3 MULTI_IND_LF -0.0710* -0.1897* 0.7712* -0.2867* 0.1549* 0.2294* 0.3667*

4 MULTI_IND_ALL -0.0312 -0.1469* 0.7118* -0.1525* 0.0680* 0.0755* 0.3178*

5 HHI_CLIENT -0.1003* -0.0118 -0.2872* -0.1517* -0.1892* -0.1923* -0.0002

6 RESTAT_L -0.0088 -0.0548* 0.1527* 0.0531* -0.1657* 0.4700* 0.0530*

7 RESTAT_ALL -0.0167 -0.0307 0.2278* 0.0375* -0.1707* 0.4700* 0.0715*

8 BIG4_L -0.2207* -0.3097* 0.3947* 0.2705* 0.0308 0.0530* 0.0715*

9 AVG_CLIENTSIZE -0.1540* -0.3334* 0.1615* 0.2986* 0.0079 0.0125 -0.1056* 0.3572*

10 MAR_SIZE -0.1069* -0.1919* 0.3312* 0.1581* -0.2157* 0.2433* 0.2271* 0.1545*

11 |DISTANCE| -0.2965* -0.1611* -0.2047* -0.1405* 0.6350* -0.0446* -0.0877* 0.1482*

12 MS_L_MSA -0.2013* -0.2450* 0.0394* 0.0031 0.1470* 0.0575* -0.002 0.5260*

13 CL_ENTRY 0.1742* 0.2500* 0.0144 0.0061 -0.0446* -0.0169 -0.0501* -0.0683*

14 CL_EXIT 0.2996* 0.5195* -0.0689* -0.0527* -0.0141 -0.019 0.0115 -0.1070*

15 CL_GROWTH 0.0841* 0.1525* 0.005 0.0173 -0.0377* -0.0477* -0.1041* -0.0438*

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Table 4: Correlations

9 10 11 12 13 14 15

1 L_DETHR -0.1387* -0.2029* -0.3219* -0.1975* 0.0909* 0.1515* 0.0545*

2 MS_MOB -0.3655* -0.3610* -0.2668* -0.2596* 0.1609* 0.3276* 0.1191*

3 MULTI_IND_LF 0.0581* 0.5765* -0.1730* 0.0807* 0.1677* 0.0848* 0.0286

4 MULTI_IND_ALL 0.2464* 0.4650* -0.1332* 0.0708* 0.021 0.0039 0.0069

5 HHI_CLIENT 0.0358* -0.2546* 0.5473* 0.0496* -0.2660* -0.2269* -0.0850*

6 RESTAT_L -0.0128 0.2327* -0.0432* 0.0764* 0.1180* 0.0487* -0.0454*

7 RESTAT_ALL -0.1508* 0.2969* -0.0783* 0.0228 0.1266* 0.1497* -0.1069*

8 BIG4_L 0.2620* 0.4456* 0.1509* 0.4994* -0.0198 -0.0336 -0.0258

9 AVG_CLIENTSIZE 0.3209* 0.0934* 0.2491* -0.1781* -0.1191* -0.1094*

10 MAR_SIZE 0.1613* 0.0018 0.3281* 0.1138* 0.0680* -0.1114*

11 |DISTANCE| 0.0935* -0.0799* 0.2523* -0.1189* -0.1166* -0.0476*

12 MS_L_MSA 0.2863* 0.1500* 0.2956* -0.0197 -0.0264 -0.0047

13 CL_ENTRY -0.1283* -0.0469* -0.0445* -0.0704* 0.1372* 0.1109*

14 CL_EXIT -0.1224* -0.0703* -0.0316 -0.0874* 0.0207 0.0136

15 CL_GROWTH -0.1377* -0.0613* -0.0249 -0.0188 0.1686* -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.

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Table 5: Estimation of Model 1 - Market share mobility (MS_MOB)

Dependent variable: MS_MOB MS_MOB

Coef. t-stat p-value Coef. t-stat p-value

Intercept 0.613 0.28 0.7790.417 1.63 0.104

MULTI_IND_ALL (H1) -0.118 *** -2.52 0.006

HHI_CLIENT (H2) 0.590 *** 3.66 0.000

RESTAT_ALL (H3) 0.061 * 1.49 0.068

BIG4_L -0.311 -1.60 0.109-0.278 *** -3.34 0.001

AVG_CLIENTSIZE -0.095 *** -3.08 0.002-0.084 *** -6.76 0.000

MAR_SIZE -57.477 *** -2.67 0.008-50.948 *** -7.92 0.000

|DISTANCE| -0.673 *** -3.12 0.002-0.946 *** -9.67 0.000

MS_L_MSA -0.306 -0.66 0.510-0.356 * -1.94 0.052

CL_ENTRY 1.309 *** 4.13 0.0001.341 *** 9.38 0.000

CL_EXIT 3.051 *** 10.70 0.0003.045 *** 26.67 0.000

CL_GROWTH 0.157 1.31 0.1900.163 *** 2.91 0.004

N 3,279 3,279

LR Chi² 249.48 268.33

P-value (LR-Chi²) 0.000 0.000

Year fixed effects Included Included

Industry fixed effects Included IncludedThis 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, t-statistics 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

.

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Table 6: Estimation of Model 2 - Leader Dethronement (L_DETHR)

Dependent variable L_DETHR L_DETHRCoef. z-stat p-value Coef. z-stat p-value

Intercept -4.048 *** -4.25 0.000 -4.427 *** -5.07 0.000MULTI_IND_LF (H1) -0.086 ** -2.18 0.015HHI_CLIENT (H2) 1.378 *** 4.86 0.000RESTAT_L (H3) 0.167 ** 2.00 0.023BIG4_L -0.438 *** -3.68 0.000 -0.312 ** -2.40 0.016AVG_CLIENTSIZE -0.006 -0.30 0.767 -0.005 -0.23 0.820MAR_SIZE -43.514 *** -3.32 0.001 -27.465 ** -2.18 0.029|DISTANCE| -2.405 *** -13.73 0.000 -3.065 *** -15.86 0.000MS_L_MSA -0.332 -1.11 0.266 -0.413 -1.42 0.156CL_ENTRY 1.455 *** 6.43 0.000 1.545 *** 6.62 0.000CL_EXIT 2.823 *** 15.21 0.000 2.841 *** 15.24 0.000CL_GROWTH 0.068 0.77 0.441 0.086 1.01 0.311

N 3,255 3,255Pseudo R² 0.268 0.280LR Chi² 862.01 899.08P-value (LR-Chi²) 0.000 0.000Year fixed effects Included IncludedIndustry fixed effects 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.

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Table 7: Analysis of market segment switching rates (quantity effect analysis)

(1)Overall switch rate

(2)Loss rate Leader

LOSS_L

Dependent variable: SWITCH_SEGMENT

Coef. z-stat p-value

p-value z-stat p-value

Intercept 0.182 0.98 0.326 -0.934 -1.54 0.124MULTI_IND_ALL (H1) -0.370 *** -6.25 0.000MULTI_IND_LF (H1) -0.196 ** -2.41 0.008HHI_CLIENT (H2) 0.793 *** 4.47 0.000 -0.284 -0.53 0.703§

RESTAT_ALL (H3) 0.075 1.48 0.069RESTAT_L (H3) 0.091 *** 2.84 0.003BIG4_L 0.083 0.78 0.438 -1.016 *** -3.18 0.001AVG_CLIENTSIZE -0.108 *** -9.29 0.000 -0.024 -0.72 0.472MAR_SIZE 0.460 0.10 0.920 7.194 0.60 0.549|DISTANCE| -0.326 ** -2.45 0.014 -0.171 -0.50 0.619MS_L_MSA -0.426 * -1.84 0.066 -0.362 -0.46 0.648

N 3,279 3,279LR Chi² 378.65 100.979P-value (LR-Chi²) 0.000 0.000

IncludedNot Included

Year fixed effects IncludedIndustry fixed effects 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 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

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Table 8: Analysis of market segment audit fee revenue growth from non-switching clients

(price effect analysis)

(1)Segment level

revenue growth from non-switching clients of

all audit firms

(2)Segment level

revenue growth from non-switching clients ofmarket segment leaders

Dependent variable: GROWTH_SEGMENT GROWTH_LCoef. t-stat p-value Coef. t-stat p-

valueIntercept 0.328 0.94 0.348 0.271 0.66 0.506MULTI_IND_ALL (H1) -0.020 -1.43 0.925§

MULTI_IND_LF (H1) -0.013 -1.55 0.940§

HHI_CLIENT (H2) -0.078 * -1.83 0.034 -0.139 *** -3.02 0.002RESTAT_ALL (H3) -0.026 ** -2.03 0.022RESTAT_L (H3) -0.035 ** -2.00 0.023BIG4_L -0.041 -1.45 0.146 0.036 1.08 0.279AVG_CLIENTSIZE -0.005 -1.15 0.251 -0.005 -1.01 0.314MAR_SIZE -5.311 *** -4.88 0.000 -4.934 *** -3.79 0.000|DISTANCE| -0.008 -0.29 0.770 -0.027 -0.84 0.404MS_L_MSA 0.059 1.08 0.282 0.147 ** 2.38 0.018

N 3,279 3,106Adjusted R² 0.310 0.260

Year fixed effects Included IncludedIndustry fixed effects Included Included

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_DETHRCoef. z-stat p-value

Intercept -3.742 *** -3.88 0.000MULTI_IND_LF (H1) -0.100 *** -2.77 0.003HHI_CLIENT (H2) 0.705 *** 2.81 0.002RESTAT_L (H3) 0.146 ** 1.97 0.024BIG4_L -0.299 *** -2.60 0.009AVG_CLIENTSIZE -0.055 *** -3.11 0.002MAR_SIZE -15.843 -1.61 0.106|DISTANCE| -2.268 *** -14.50 0.000MS_L_MSA 0.370 1.48 0.140CL_ENTRY 1.225 *** 6.26 0.000CL_EXIT 2.545 *** 15.43 0.000CL_GROWTH 0.111 1.36 0.174

N 3,995Pseudo R² 0.280LR Chi² 899.08P-value (LR-Chi²) 0.000Year fixed effects IncludedIndustry fixed effects 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.

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· web viewin this paper we argue that audit firms compete rationally and consider the...

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