Competitive Rivalry in Audit Markets

Simon Dekeyser a*

Simon.dekeyser@kuleuven.be

Ann Gaeremynck a

Ann.gaeremynck@kuleuven.be

W. Robert Knechel a,b

W.knechel@warrington.ufl.edu

Marleen Willekens a,c

Marleen.willekens@kuleuven.be

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

2

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).

3

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

4

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

5

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.

6

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.

7

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 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

8

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

9

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

10

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.

11

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-

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

12

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

13

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).

14

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. Research design

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. 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).

15

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

16

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.

17

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:

𝑀𝑀𝑀𝑀_𝑀𝑀𝑀𝑀𝑀𝑀𝑡𝑡 = ∑ |𝑀𝑀𝑀𝑀𝑖𝑖𝑡𝑡−1 − 𝑀𝑀𝑀𝑀𝑖𝑖𝑡𝑡|𝑛𝑛𝑖𝑖=1 /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:

𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀_𝑀𝑀𝐼𝐼𝐼𝐼_𝐴𝐴𝑀𝑀𝑀𝑀𝑡𝑡 = ln ((� � (𝑎𝑎𝑖𝑖𝑖𝑖𝑡𝑡−1 )) / (𝑛𝑛 ∗ (𝑛𝑛 − 1)/2)𝑛𝑛

𝑖𝑖=𝑖𝑖+1

𝑛𝑛

𝑖𝑖=1

( 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

18

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%.

19

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., 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

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

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

21

(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 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

22

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.

23

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 >>>>>

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.

24

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.

25

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%.

26

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.

<<<<< 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.

27

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 >>>>>

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

28

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

29

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.

<<<<< Insert Table 7 about here >>>>>

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-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.

30

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 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 >>>>>

31

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

32

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).

33

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

34

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.

35

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 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.

36

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.

37

REFERENCES

Armstrong, J.S., and Collopy, F. 1996. Competitor orientation: Effects of objectives and information on managerial decisions and profitability. Journal of Marketing Research, 33: 188-199.

Balsam, S., Krishnan, J., and Yang, J. S. 2003. Auditor industry specialization and earnings quality. Auditing: A Journal of Practice and Theory, 22(2), 71-97.

Bandyopadhyay, S. P., and Kao, J. L., 2004. Market Structure and Audit Fees: A Local Analysis*. Contemporary Accounting Research, 21(3), 529-562.

Banker, R. D., Chang, H., and Natarajan, R. 2005. Productivity change, technical progress, and relative efficiency change in the public accounting industry. Management Science, 51(2), 291-304

Barros, P. 1999. Multimarket competition in banking, with an example from the Portuguese market. International Journal of Industrial Organization, 17(3), 335-352.

Baumol, W. J., 1982. Contestable markets: an uprising in the theory of industry structure. American economic review, 72(1), 1-15.

Bernheim, B. D., and Whinston, M. D. 1990. Multimarket contact and collusive behavior. Rand journal of economics, 21(1), 1-26.

Bills, K. L., and Stephens, N. M. 2015. Spatial competition at the intersection of the large and small audit firm markets. Auditing: A Journal of Practice & Theory, 35(1), 23-45.

Brown, S. and Knechel, W.R. 2015. Auditor Client Compatibility and Audit Firm Selection. Working paper, University of Florida.

Bujink, W. F., Maijoor, S. J., and Meuwissen, R. H. 1998. Competition in Auditing: Evidence from Entry, Exit, and Market-share mobility in Germany versus The Netherlands*. Contemporary Accounting Research, 15(3), 385-404.

Carcello, J. V., and Nagy, A. L. 2004. Client size, auditor specialization and fraudulent financial reporting. Managerial Auditing Journal, 19(5), 651-668.

Casterella, J. R., Francis, J. R., Lewis, B. L., and Walker, P. L. 2004. Auditor industry specialization, client bargaining power, and audit pricing. Auditing: A Journal of Practice and Theory, 23(1), 123-140.

Caves, R. E., and Porter, M. E. 1978. Market structure, oligopoly, and stability of market shares. The Journal of Industrial Economics, 289-313.

Chan, D. K. 1999. “Low‐Balling” and Efficiency in a Two‐Period Specialization Model of Auditing Competition. Contemporary Accounting Research, 16(4), 609-642.

38

Chang, W. J., Chen, Y. S., and Chan, M. P. 2009. Impact of Audit Fee Deregulation on Audit-market Competition. Asia-Pacific Journal of Accounting and Economics, 16(1), 69-94.

Chi, H. Y., and Chin, C. L. 2011. Firm versus partner measures of auditor industry expertise and effects on auditor quality. Auditing: A Journal of Practice and Theory, 30(2), 201-229.

Ciconte, W., Knechel, W. R., and Schelleman, C. 2015. An examination of the relation between market structure and the profitability of audit engagements. Accounting & Finance, 55(3), 749-781.

Craswell, A. T., Francis, J. R., and Taylor, S. L. 1995. Auditor brand name reputations and industry specializations. Journal of accounting and economics, 20(3), 297-322.

Danos, P., and Eichenseher, J. W. 1982. Audit industry instability: Factors affecting changes in client-industry market shares. Journal of Accounting Research, 604-616.

Davies, S. W., and Geroski, P. A. 1997. Changes in concentration, turbulence, and the instability of market shares. Review of Economics and Statistics, 79(3), 383-391.

De Bonis, R., and Ferrando, A. 2000. The Italian banking structure in the 1990s: testing the multimarket contact hypothesis. Economic Notes, 29(2), 215-241.

Dedman, E., and Lennox, C. (2009). Perceived competition, profitability and the withholding of information about sales and the cost of sales. Journal of Accounting and Economics, 48(2), 210-230.

DeFond, M. L., Francis, J. R., and Wong, T. J. 2000. Auditor industry specialization and market segmentation: Evidence from Hong Kong. Auditing: A Journal of Practice and Theory, 19(1), 49-66.

Dunn, K. A., and Mayhew, B. W. 2004. Audit firm industry specialization and client disclosure quality. Review of Accounting Studies, 9(1), 35-58.

Edwards, C. D. 1955. Conglomerate bigness as a source of power. In Business concentration and price policy (pp. 331-359). Princeton University Press.

European Commission, 2010. Green Paper. Audit Policy: Lessons from the Crisis, October (Brussels: European Commission).

European Commission, 2011. Regulation to increase the quality of audits of financial statements of public-interest entities, November (Brussels: European Commission).

Evans, W. N., and Kessides, I. N. 1994. Living by the “golden rule”: Multimarket contact in the US airline industry. The Quarterly Journal of Economics, 109(2), 341-366.

Feldman, E. R. 2006. A basic quantification of the competitive implications of the demise of Arthur Andersen. Review of Industrial Organization, 29(3), 193-212.

39

Ferguson, A., Francis, J. R., and Stokes, D. J. 2003. The effects of firm-wide and office-level industry expertise on audit pricing. The Accounting Review, 78(2), 429-448.

Ferrier, W. J., Smith, K. G., and Grimm, C. M. 1999. The role of competitive action in market share erosion and industry dethronement: A study of industry leaders and challengers. Academy of Management Journal, 42(4), 372-388.

Francis, J. R. 2004. What do we know about audit quality?. The British accounting review, 36(4), 345-368.

Francis, J. R., Reichelt, K., and Wang, D. 2005. The pricing of national and city-specific reputations for industry expertise in the US audit market. The Accounting Review, 80(1), 113-136.

Fusillo, M. 2013. The Stability of Market Shares in Liner Shipping. Review of Industrial Organization, 42(1), 85-106.

Gimeno, J. 2002. The performance effects of unintended and purposive multimarket contact. Managerial and Decision Economics, 23(4‐5), 209-224.

Greve, H. R. 2008. Multimarket contact and sales growth: Evidence from insurance. Strategic Management Journal, 29(3), 229-249.

Hogan, C. E., and Jeter, D. C., 1999. Industry specialization by auditors. Auditing: A Journal of Practice and Theory, 18(1), 1-17.

Huang, H. W., Liu, L. L., Raghunandan, K., and Rama, D. V. 2007. Auditor industry specialization, client bargaining power, and audit fees: Further evidence. Auditing: A Journal of Practice and Theory, 26(1), 147-158

Huang, Y., and Scholz, S. 2012. Evidence on the association between financial restatements and auditor resignations. Accounting Horizons, 26(3), 439-464.

Johnson, W.B. and T. Lys. 1990. The Market for Audit Services : Evidence from Voluntary Auditor Changes. Journal of Accounting and Economics 12 (1990): 281–308.

Jones, J. 1991. Earnings management during import relief investigations. Journal of accounting research, 29(2), 193-228.

Kato, M., and Honjo, Y. 2006. Market share instability and the dynamics of competition: a panel data analysis of Japanese manufacturing industries. Review of Industrial Organization, 28(2), 165-182.

Klemperer, P. 1987. Markets with consumer switching costs. The Quarterly Journal of Economics, 102(2), 375-394.

40

Knechel, W.R., L. Niemi and M. Zerni. 2013. Empirical Evidence on the Implicit Determinants of Compensation in Big four Audit Partnerships. Journal of Accounting Research (May 2013), 349-388.

Kwon, S. Y. 1996. The impact of competition within the client's industry on the auditor selection decision. Auditing: a Journal of practice and Theory, 15(1), 53-70.

Landsman, W. R., Nelson, K. K., and Rountree, B. R. 2009. Auditor switches in the pre-and post-Enron eras: Risk or realignment?. The Accounting Review, 84(2), 531-558.

Li, S. X., and Greenwood, R. 2004. The effect of within‐industry diversification on firm performance: synergy creation, multi‐market contact and market structuration. Strategic Management Journal, 25(12), 1131-1153.

Matsushima, H. 2001. Multimarket contact, imperfect monitoring and implicit collusion. Journal of Economic Theory, 98, 158-178.

Mayhew, B. W., and Wilkins, M. S. 2003. Audit firm industry specialization as a differentiation strategy: Evidence from fees charged to firms going public. Auditing: A Journal of Practice and Theory, 22(2), 33-52.

Melvin and Boyes, Microeconomics 5th ed.. Houghton Mifflin 2002

Motta, M., 2004. Competition policy: theory and practice. Cambridge University Press.

Numan, W., and Willekens, M., 2012. An empirical test of spatial competition in the audit market. Journal of Accounting and Economics, 53(1), 450-465.

Palmrose, Z. V., Richardson, V. J., and Scholz, S. 2004. Determinants of market reactions to restatement announcements. Journal of accounting and economics, 37(1), 59-89.

Papke, L. E., & Wooldridge, J. (1993). Econometric methods for fractional response variables with an application to 401 (k) plan participation rates.

Parker, P. M., and Roller, L. H. 1994. Collusive conduct in duopolies: multimarket contact and cross-ownership in the mobile telephone industry.

Pearson, T., and Trompeter, G., 1994. Competition in the Market for Audit Services: The Effect of Supplier Concentration on Audit Fees*. Contemporary Accounting Research, 11(1), 115-135.

Reichelt, K. J., and Wang, D. 2010. National and office‐specific measures of auditor industry expertise and effects on audit quality. Journal of Accounting Research, 48(3), 647-686.

Sakakibara, M., and Porter, M. E. 2001. Competing at home to win abroad: evidence from Japanese industry. Review of Economics and Statistics, 83(2), 310-322.

Scherer, F. M., and Ross, D., 1990. Industrial market structure and market performance.

41

Schmalensee, R. 1989 Intra-industry profitability differences in US manufacturing 1953-1983. The journal of industrial economics, 337-357.

Skinner, D. J., and Srinivasan, S. 2012. Audit quality and auditor reputation: Evidence from Japan. The Accounting Review, 87(5), 1737-1765.

Staiger, R. W., and Wolak, F. A. 1992. Collusive pricing with capacity constraints in the presence of demand uncertainty. RAND Journal of Economics, 23(2), 203-220.

Stigler, G. J. 1964. A theory of oligopoly. The Journal of Political Economy, 44-61.

U.S. Government Accountability Office (GAO). 2003. Public Accounting Firms: Mandated Study on Consolidation and Competition. Available at: http://www.gao.gov/new.items/d03864.pdf

U.S. Government Accountability Office (GAO). 2008. Audits of Public Companies: Continued Concentration in Audit Market for Large Public Companies Does Not Call for Immediate Action. Available at: http://www.gao.gov/new.items/d08163.pdf

Weber, J., Willenborg, M., Zhang, J. 2008. Does Auditor Reputation Matter? The Case of KPMG Germany and ComROAD AG, 46 (4), 941-972.

Willekens, M., and Achmadi, C. 2003. Pricing and supplier concentration in the private client segment of the audit market: Market power or competition?. The International Journal of Accounting, 38(4), 431-455.

Wolk, C. M., Michelson, S. E., and Wootton, C. W. 2001. Auditor concentration and market shares in the US: 1988–1999 a descriptive note. The British Accounting Review, 33(2), 157-174.

42

Table 1: Variable Definitions Variable definition Dependent variables

L_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 otherwise

MS_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 variables RESTAT_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 variables BIG4_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.

43

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_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.

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_OTH

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.

44

Table 2: Sample Selection Number of MSA-industry-year observations 20,154 Less 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

45

Table 3: Descriptive statistics

N Mean StdDev Min P25 Median P75 Max Dependent variables L_DETHR 3,279 0.193 0.395 0.000 0.000 0.000 0.000 1.000 MS_MOB 3,279 0.163 0.169 0.001 0.050 0.104 0.210 0.988 Test variables Exp(MULTI_IND_LF) 3,279 8.480 7.076 1.000 2.000 7.000 13.000 32.000 MULTI_IND_LF 3,279 1.664 1.084 0.000 0.693 1.946 2.565 3.466 Exp(MULTI_IND_ALL) 3,279 3.402 3.091 1.000 1.361 2.333 4.250 30.333 MULTI_IND_ALL 3,279 1.319 0.530 0.693 0.859 1.204 1.658 3.445 HHI_CLIENT 3,279 0.306 0.187 0.019 0.174 0.269 0.398 0.986 RESTAT_L 3,279 0.184 0.387 0.000 0.000 0.000 0.000 1.000 RESTAT_ALL 3,279 0.504 0.500 0.000 0.000 1.000 1.000 1.000 Control variables BIG4_L 3,279 0.807 0.395 0.000 1.000 1.000 1.000 1.000 AVG_CLIENTSIZE 3,279 18.918 2.362 7.178 17.628 19.032 20.623 25.964 MAR_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 0.996 MS_L_MSA 3,279 0.253 0.144 0.000 0.170 0.234 0.333 0.866 CL_ENTRY 3,279 0.038 0.129 0.000 0.000 0.000 0.012 0.990 CL_EXIT 3,279 0.075 0.144 0.000 0.000 0.012 0.079 0.989 CL_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.

46

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*

47

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.

48

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 0.779 0.417 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 0.000 1.341 *** 9.38 0.000 CL_EXIT 3.051 *** 10.70 0.000 3.045 *** 26.67 0.000 CL_GROWTH 0.157 1.31 0.190 0.163 *** 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 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, 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

.

49

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

Dependent variable L_DETHR L_DETHR Coef. z-stat p-value Coef. z-stat p-value Intercept -4.048 *** -4.25 0.000 -4.427 *** -5.07 0.000 MULTI_IND_LF (H1) -0.086 ** -2.18 0.015 HHI_CLIENT (H2) 1.378 *** 4.86 0.000 RESTAT_L (H3) 0.167 ** 2.00 0.023 BIG4_L -0.438 *** -3.68 0.000 -0.312 ** -2.40 0.016 AVG_CLIENTSIZE -0.006 -0.30 0.767 -0.005 -0.23 0.820 MAR_SIZE -43.514 *** -3.32 0.001 -27.465 ** -2.18 0.029 |DISTANCE| -2.405 *** -13.73 0.000 -3.065 *** -15.86 0.000 MS_L_MSA -0.332 -1.11 0.266 -0.413 -1.42 0.156 CL_ENTRY 1.455 *** 6.43 0.000 1.545 *** 6.62 0.000 CL_EXIT 2.823 *** 15.21 0.000 2.841 *** 15.24 0.000 CL_GROWTH 0.068 0.77 0.441 0.086 1.01 0.311 N 3,255 3,255 Pseudo R² 0.268 0.280 LR Chi² 862.01 899.08 P-value (LR-Chi²) 0.000 0.000 Year fixed effects Included Included Industry 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.

50

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 0.124 MULTI_IND_ALL (H1) -0.370 *** -6.25 0.000 MULTI_IND_LF (H1) -0.196 ** -2.41 0.008 HHI_CLIENT (H2) 0.793 *** 4.47 0.000 -0.284 -0.53 0.703§ RESTAT_ALL (H3) 0.075 1.48 0.069 RESTAT_L (H3) 0.091 *** 2.84 0.003 BIG4_L 0.083 0.78 0.438 -1.016 *** -3.18 0.001 AVG_CLIENTSIZE -0.108 *** -9.29 0.000 -0.024 -0.72 0.472 MAR_SIZE 0.460 0.10 0.920 7.194 0.60 0.549 |DISTANCE| -0.326 ** -2.45 0.014 -0.171 -0.50 0.619 MS_L_MSA -0.426 * -1.84 0.066 -0.362 -0.46 0.648 N 3,279 3,279 LR Chi² 378.65 100.979 P-value (LR-Chi²) 0.000 0.000

Included Not Included

Year fixed effects Included Industry 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

51

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 of market segment leaders

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

value Intercept 0.328 0.94 0.348 0.271 0.66 0.506 MULTI_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 0.002 RESTAT_ALL (H3) -0.026 ** -2.03 0.022 RESTAT_L (H3) -0.035 ** -2.00 0.023 BIG4_L -0.041 -1.45 0.146 0.036 1.08 0.279 AVG_CLIENTSIZE -0.005 -1.15 0.251 -0.005 -1.01 0.314 MAR_SIZE -5.311 *** -4.88 0.000 -4.934 *** -3.79 0.000 |DISTANCE| -0.008 -0.29 0.770 -0.027 -0.84 0.404 MS_L_MSA 0.059 1.08 0.282 0.147 ** 2.38 0.018 N 3,279 3,106 Adjusted R² 0.310 0.260 Year fixed effects Included Included Industry 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

52

Table 9: Sensitivity of Model 2 – Industry Specialization Dethronement (IS_DETHR)

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

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