audit partner performance: a network perspective

Audit Partner Performance: A Network Perspective

Joanne Horton University of Exeter Business School

[email protected]

İrem Tuna* London Business School

[email protected]

Anthony Wood University of Exeter Business School

[email protected]

First draft: 9 June 2014 This draft: 9 October 2014

*İrem Tuna gratefully acknowledges the financial support of the European Research Council (Grant ERC-2010-263525). We thank Emmanuel De George, Stuart Turley, and the seminar participants at the 5th EIASM Workshop on Audit Quality and at Queen Mary University of London for helpful comments.

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Audit Partner Performance: A Network Perspective

ABSTRACT

We provide a partner level analysis on the association between the social capital of an audit partner and their audit quality and fees. We use social capital theory and techniques developed in social network analysis to measure the audit partner’s level of connectedness and investigate whether these connections provide information advantages and enhance their social influence. We argue that both of these potential network benefits support the audit partner attributes, namely personal capabilities and level of independence, necessary to provide high quality audits. Using a sample of French listed firms, we construct a network consisting of 4,159 board directors and 734 audit partners, mapping the connections between audit partners and directors, between directors and between audit partners. We find that better-connected audit partners provide higher quality audits and earn higher audit fees. The level of an audit partner’s social capital decreases if they have longer tenure and greater economic bonding with their clients.

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INTRODUCTION

Auditors play an essential role in the effective functioning of corporate governance and

are among the most important gatekeepers in the capital markets (Coffee, 2005). The

audit process provides the market with ‘independent’ verification of the financial

statements, signaling that the management information is reliable, and thereby

reducing the cost of the information exchange between managers and investors

(Dopuch and Simunic, 1980, 1982). However, it is the management that controls the

process of hiring and firing auditors and there are quasi-rents associated with such

auditing contracts (DeAngelo 1981a, 1981b). Consequently auditors have incentives to

yield to pressure from management, implying that the quality of the information

contained in audited financial statement depends upon the ability of the audit partner

to resist such pressures (Becker et al. 1998). Audit quality is therefore determined by

the probability that the auditor will uncover a breach in the client’s accounting system

and will report the breach (DeAngelo 1981b). The ability to uncover the breach will

depend on the audit partners’ capabilities while the reporting of the breach will depend

on the auditor’s level of independence from the client (DeAngelo 1981a). If the auditor

does not remain independent, the auditor will be less likely to report the irregularities

and audit quality will be impaired. Prior studies have identified various factors that

explain differences in the level of audit quality provided by auditors (Larcker and

Richardson, 2004, DeFond and Zhang 2014), and the effect of their implicit or explicit

incentives (Ruddock, Taylor, and Taylor 2006).

In this paper we investigate whether an audit partner’s network position is a

factor in determining audit quality and audit fees. Why would we expect the network

position of an audit partner to affect their audit quality and audit fees? Social capital

theory would suggest that centrally located individuals within a network have greater

access to a broader source of information, resources, people and instrumentalities, such

that their network position represents a structural source of influence or power. These

network benefits are ideally suited to enhance and reinforce partner attributes

necessary to produce high quality audits – auditor capabilities and independence. This

in turn should be rewarded with fee premiums based on an assessment of the quality of

the partner’s audits. For instance a better-connected audit partner will have greater

opportunities to be privy to more and better quality information, which ultimately

provides them with opportunities to enhance their personal capabilities and thereby

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increase the likelihood of uncovering a breach in the clients’ accounting systems. At the

basic level the more connections (or better connections) an audit partner has, the more

peers with whom to consult, the wider the range of expertise to which she will have

access. For example, a better-connected audit partner will have greater opportunities to

achieve a comprehensive ‘business scan’ of the latest auditing and business practices

and of the overall business environment in which their client is situated (Useem, 1984).

Moreover, a better-connected audit partner will also have greater influence (or

power) over her connections potentially enhancing her level of independence – the

second partner attribute necessary for high quality audit. Many sociologists would

agree that an individual’s influence over others is a fundamental property of social

structures. Social influence in this context refers to the capacity of an audit partner to

influence others and the ability to resist others’ influences. Both of these elements

should increase the audit partner’s level of independence. For instance, the more

influence an audit partner gains from their connections the more likely they are to be

independent and the less likely they are to yield to management pressure.

Although economic agents may gain information advantages as well as enhanced

influence though their network connections and network position (Coleman 1988; Burt

1992; Bonacich 1972; Cohen et al. 2011; Horton and Serafeim 2012), it is not clear that

such advantages would exist among audit partners leading to better performance. First,

studies have shown that audit partners who audit more firms and cover more industries

provide relatively lower audit quality (Goodwin, 2011; Gul et al. 2013; Ittonen et al.,

2010). This result is counter-intuitive from a network perspective, because if

connections do provide information advantages and enhanced influence to an audit

partner then audit partners who audit relatively more clients over more industries are

more likely to be better-connected. Second, studies have also found that ties between

audit partners and directors do not always results in higher audit quality – indeed the

opposite is true – alumni ties for instance have been associated with poor quality audit

(Lennox, 2005; Menon and Williams, 2004). Third, information flows from one audit

partner to another competing audit partner only if complementarities exist in the

information structure (Stein 2008). Whether such complementaries, which allow

information exchange through networks, exist is an empirical question.

To investigate the possible impact of an audit partner’s network on audit quality

and fees, we construct a network of French listed firms, their directors (executive and

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non-executive), and their external audit partners. There are two key advantages of

focusing on the French audit market. First, France has a long history of requiring the

engagement partner’s signature on the audit report thereby enabling us to identify each

individual audit partner over a longer period of time compared to the UK who only

recently required such a signature. Second, France has one of the lowest levels of audit

firm concentration due to its joint audit requirement – i.e. that two independent audit

firms must audit every listed firm1. Since each audit partner has the potential to be

connected to a greater number of firms and to other audit partners outside of their

immediate audit firm, the French audit market provides a richer and denser network

over what could be observed in alternate audit markets.

The network we construct comprises of 4,159 board directors from 326 client

firms and 734 audit partners from 192 audit firms. We measure the quality of the audit

partner’s network position and use this measure to proxy for the likelihood that the

audit partner is able to obtain valuable information and generate greater social

influence. An audit partner’s position within the network will determine the quality,

type, timing and amount of the information she receives, as well as the amount of

influence she could potentially exert over others, or inversely prevent her from being

influenced by others, within the network. The proxy does not rely on the presumption

that an audit partner will have explicit one-to-one conversations with all her

connections. The proxy merely captures the increased likelihood that an audit partner

with a relatively better network position will have more opportunities to be privy to

more or better information and to be able to create higher level of social influence. 2

We find that the more central an audit partner is within the network the better

the quality of the audit. We find that both abnormal working capital accruals, and the

likelihood of firms reporting a small earnings increase, are negatively related to quality

of the audit partner’s network position. An alternative explanation for this

performance-enhancing role of audit partner’s network position could simply be audit

partner experience. It is plausible that the better-networked audit partner is also the

most senior and more experienced partner. To rule out the possibility that our network

1Mandatory joint audit has been required in France since 1966 (Huber, 2011). 2 We acknowledge that this network is incomplete. Both directors and audit partners have many other possible avenues through which they can obtain information or influence, for example golf club membership, political affiliations, professional bodies etc. Nevertheless, these social or ‘grey ties’ will add noise to our network estimates potentially biasing downwards the effects we can identify.

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measure merely proxies for experience, our models explicitly control for a variety of

dimensions of audit partner experience, both time variant and time invariant. For

example, in addition to controlling for the generic ability of the audit partner using

partner fixed effects we also include the partner’s industry expertise, tenure with the

client, the number of clients the partner audits during the year and the audit firm the

partner works for. These controls are important since the concept of social capital and

human capital have clear parallels, although they are not identical (Field, 2003)3. Under

all specifications the audit partner’s network measure continues to be negative and

significantly associated with all the audit quality measures.

Our findings also hold when we limit our sample to those clients whose audit

team comprised of one Big4 audit firm and one non-Big4 audit firm, thereby reducing

the possibility of self-selection (Ferguson et al 2003; Francis et al 2005) and other

potential confounding effects (Chan and Wu, 2011). The results continue to hold.

Since an audit partner’s position in the network can explain variation in audit

quality, we also investigate whether it has implications for the level of audit fees

charged to the client. We find evidence of a significantly positive association between

the audit fees and the network position of the audit partner. This result is consistent

with the findings of Zerni (2012) and Taylor (2011), that different audit partners – even

from the same audit firm – can earn different levels of audit fees.

Finally we provide evidence on the determinants of our audit partner’s network

position. We find that an audit partner’s network position improves if the partner

works for an audit firm that is an industry specialist, is employed by a Big4 audit firm,

audits large clients and numerous clients, and declines if the audit partner has a long

tenure with a client, is associated with high levels of non-audit fees and has higher

levels of economic dependence on the client.

We make numerous contributions to the literature. First, our paper contributes

to the growing literature on social network effects in economics (Hong, Kubik and Stein

2005; Hochberg, Ljungqvist and Lu 2007; Cohen et al. 2011; Cohen et al. 2008; Stein

2008). Given the importance of the auditors within the capital markets, understanding

the factors that affect their performance and implicit incentives is important. Our

analysis provides a deeper understanding of the possible drivers of cross-sectional 3 As Gould (2002) notes; “even if the network position is initially caused by exogenous personal qualities, central actors likely receive a disproportionate share of new nominations, not because of personal qualities, but by virtue of having already received many nominations”.

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variation in performance of audit partners, and to the best of our knowledge is the first

to show that audit partners’ position in their professional network is a significant

determinant of audit quality and audit fees.

Second, we answer the call for more research at audit partner level (Reynolds

and Francis 2000; DeFond and Francis 2005; Chen et al. 2009; Chen et al. 2010) in order

to gain a deeper understanding of audit partner behavior and its implications for audit

quality, providing further evidence that audit quality is not uniform across audit

partners (O’Keefe and Westort, 1992; Carcello et al. 1996; Carcello et al 2000; Carey and

Simnett 2006; Gul et al. 2013). Third, we also add to the audit fee literature by

providing further evidence that audit partners earn individual audit fees that are not

fully explained by the audit firm at which they work (Taylor 2011; Burrows and Black

1998). Fourth, we provide evidence regarding the determinants of an audit partner’s

network position and contribute to the audit independence literature. Assuming our

proxy captures, inter alia, the level of audit partner independence, we find, consistent

with the prior literature, that certain partner attributes, such as long tenure and high

levels of economic dependency reduce the advantageous network position of the audit

partner and thereby reduce the opportunities to be more independent.

Last but not least, our findings have potential implications for audit partners,

audit firms, as well as regulators. Our evidence suggests that enhancing one’s network

position should be an important strategic consideration for an incumbent audit partner

and their audit firm. Moreover, the policymakers should also be interested in our

findings as they highlight that an environment in which audit partners have

opportunities to enhance their network position could contribute to increased audit

quality. We are however very cautious to imply that these opportunities only exist in a

joint-audit setting, such as the one investigated here. Our findings do however suggest

that policymakers should examine audit quality and audit market structure

enhancement together as a complex whole.

The paper is organized as follows. In Section I, we review the related literature

and form the hypotheses of our study. Section II describes the sample. Section III

constructs the necessary measures and presents the research design. Section IV

discusses the results on the association between an audit partner’s network position,

therefore social influence, and audit quality and audit fees. Section V presents the

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results on the determinants of the audit partner’s social influence. We conclude in

section VI.

I. Literature Review and Hypothesis Development

Audit Quality

DeAngelo (1981) defines audit quality as ‘the market assessed joint probability that a

given auditor will both discover a breach in a client’s accounting system, and report the

breach’. This definition is often interpreted to break down audit quality into two

components: (1) the likelihood that an auditor discovers existing misstatements, and

(2) appropriately acts on the discovery. The first component is related to an auditor’s

capabilities and level of effort while the latter relates to an auditor’s objectivity,

professional skepticism, and independence.4 These two components also suggest that

different aspects of the audit can influence overall audit quality.

Prior literature confirms the there is a direct link between the quality of the

audit and individual audit partner capabilities such as knowledge and expertise.

Domain-specific knowledge (e.g. knowledge accumulated through client, task and

industry experience) is associated with higher quality auditor judgment (Knechel et al.

2013; Bonner and Lewis 1990). A number of experimental studies have found industry-

experienced auditors are better able to detect errors among clients within their

industry specialization than those outside their specialization (Owhoso et al. 2002;

Bedard and Biggs 1991; Wright and Wright 1997). Hamilton and Wright (1982) find a

negative association between years of experience and consensus on internal control

evaluation. While Ashton (1991) finds that months of general audit experience are not

related to how accurately auditors judge the frequency of specific accounting errors.

Moreover, differences in audit partner behaviors and attitudes (Ponemon and Gabhart

1990; Trompeter 1994; Tan 1999; Emby et al 2002; Ayers and Kaplan 2003) have also

been found to influence the behavior and attitude of their audit staff (DeZoort and Lord

1994; Tan et al 1997; Kaplan and Lord 2001; Lord and DeZoort 2011; Wilks 2002).

Recent empirical studies also document a positive association between audit

quality and individual audit partner specialism (Chin and Chi, 2009; Chi and Chin 2011; 4 Francis (2011) notes that audit quality is a complex concept and cannot be reduced to a simple definition. He argues that there are gradations of audit quality across a continuum from low- to high- quality audits and that quality is affected by a number of elements. However, even under this more rigorous approach to defining and understanding audit quality, audit partner capabilities and the need for independence are still identified as important elements.

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Kallunki et al. 2009; Duh et al. 2009; Ittonen et al., 2010). Extending previous studies

Ittonen et al. (2010) find that clients of audit partners with both higher public

specialization and longer auditing experience report lower discretionary accruals.

While Chi and Chin (2011) find differential discretionary accruals is driven by a

combination of firm and partner industry expertise, although the differential likelihood

of a modified audit opinion is primarily attributable to the signing audit partner’s

industry specialism.

The link between independence and audit quality has also been empirically

tested but given audit partner independence ‘in fact’ is generally unobservable, studies

tend to investigate the relationship between audit quality and certain audit partner

features that could potentially reduce an audit partner’s level of independence5 (either

in fact or in appearance). For instance alumni affiliations (Menon and Williams 2004;

Lennox 2005; Lennox and Park 2007; JeanJean et al. 2013), long tenure with client firm

(Carey and Simnett 2006; Bamber and Iyer 2007; Chen et al 2008; Chi et al. 2009; Gul et

al. 2013), and economic dependency/bonding (Chen et al. 2010; Chi et al. 2012) in some

instances have been found to reduce audit quality, although there is no evidence to

suggest client importance impacts audit partner independence.

Social Capital Theory

Given audit partners need to have both the personal capabilities and be

independent, the auditing industry is an ideal testing ground to investigate whether an

audit partner’s network position enhances these two antecedents by increasing the

level of information available to them and by providing them with the opportunity to

increase their social influence. Both of these network advantages potentially could

increase the quality of their audits and determine their audit fee premiums. Social

capital is based on the notion that the actions of individuals are facilitated by their

membership in social networks. Therefore social capital can explain the differential

success of individuals and firms in their competitive environments (Alder and Kwon,

2002). As Burt (2000) notes social capital is a metaphor for advantage: people who do

better in business are better-connected in one way or another. For example, research

shows that social ties help individuals (firms) gain access to information about job

opportunities (potential clients) (Burt, 1992; Meyerson, 1994; Fernandez et al., 2000), 5 Bartlett (1993) notes that independence is a matter of degree, rather than an absolute concept.

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new innovations (Burt, 1987) and provide a finer grained information set (Uzzi, 1997).

Prior research documents that agents at the core of the social network have more

valuable information (Freeman 1980; Cohen et al. 2011). Many network studies

repeatedly demonstrate a link between social capital and performance in a variety of

settings. For example, Mizruchi and Stearns (2001) show that loan officers in a large

commercial bank with more social capital are more likely to close a successful deal.

Hochberg, Ljungqvist and Lu (2007) find that better-network venture capitalists have

better performance. Horton et al. (2009) find director’s connectedness is positively

associated with compensation and with their firm’s future performance, while Horton

and Serafeim (2012) find better-connected analysts make more accurate, timely and

bold forecasts.

In addition to providing informational advantages, being better-connected has

also been found to be associated with an individual’s level of power/influence. Power is

defined here as the ability to influence others or not be influenced by others.

Power/influence is inherently a structural phenomenon where one actor’s influence

over another needs to be considered within a wider network of relationships (Pfeffer,

1981; McClurg and Young 2011), and that it is distinct from the influence gained from

individual attributes (Brass and Krackhardt 2012). As Brass and Krackhardt (2012)

state “the structure of social networks strongly affects the extent to which personal

attributes, cognition and behavior result in power and influence within an

organization”. Network analysis emphasizes that power and influence are inherently

relational, and individual does not have power in abstract; they have power because

they can dominate others (Hammerman and Riddle, 2005).

Prior research demonstrates that being better-connected not only increases

access to different resources but also increases legitimacy and social influence and

power (Brass, 1984, 1985, 1992; Mizruchi and Potts 1998; Brass and Burkhardt 1992,

1993; Burkhardt and Brass 1990; Krackardt, 1990; Friedkin 1991; Sparrowe and Liden

1997; Mehra, Kilduff and Brass 2001; Ibarra and Andrews 1993; Setton and Mossholder

2002; Reagans and Zuckermann, 2001; Grosser, Kidwell-Lopez and Labianca 2010).

Consistent with the power-dependency theory, networks create tangible imbalances by

regulating the availability of alternative exchange partners (Cook et al 1983; Emerson

1972; Molm 1990). As Galaskiewicz (1979) noted, it is not the level of resources per se

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that determines influence but rather the ‘set of resources that actors [could] mobilize

through their existing set of social relationships’ (p.151).

The study of inter-corporate power relations, via interlocking directorships, was

one of the earliest streams of research to employ social network techniques (Bearden et

al 1975). Recent studies continue to find that the better-connected the board member

the greater their influence on the decision-making process of the board (Stevenson and

Radin 2008). Specifically Stevenson and Radin (2008) find that social capital of board

members in the form of ties to others is a much stronger factor in gaining influence on

the board as compared to the human capital attributes of board members such as

management experience or committee membership. El-Khatib et al. (2012) find that a

CEOs personal network provides them with enough power to pursue any acquisition,

regardless of the impact on shareholder wealth. Renneboog and Zhao (2011) examine

the relation between directors’ networks, CEO compensation and pay-for-performance

sensitivities. They find that a CEO’ s social influence and power is positively associated

to their compensation and negatively related to their pay-for-performance, suggesting

potential rent extraction by these powerful CEOs.

Connections to others may also create the perception of social influence and

power. For instance, from a prism perspective, these central actors are more likely to be

viewed by others as more powerful due to their connections to other prominent

individuals (Kilduff and Krackhardt 1994)6. Whether or not the perception is accurate,

these actors are more likely to receive deferential treatment based on that perception.

For example, as reported by Kilduff and Krackhardt (1994) when approached for a loan

the wealthy Baron de Rothschild replied, ‘I won’t give you a loan myself but I will walk

arm-in-arm with you across the floor of the stock exchange and you will soon have

willing lenders to spare’ (Cialdini 1989, p.45).

Based on the social network research noted above, if audit partners are able to

obtain an informational advantage as well as some level of social influence through their

network position, this could enhance the quality of their audits. Better-connected audit

partners will have access to a wider range of expertise, contacts, and pools of social

capital, thereby providing them with greater opportunities to achieve a comprehensive

‘business scan’ of the latest auditing and business practices and of the overall business

6 Deephouse and Suchman (2008) describe this form of power as an individual having greater visibility and legitimacy.

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environment in which their client is situated, all of which would provide the audit

partner with greater capabilities to perform higher quality audits. Moreover, given the

ability to influence others or not be influenced by others is a major condition for

independence, an audit partner with relatively more (and more powerful) connections

is more likely to resist management pressure. In a recent paper, Francis and Yu (2009)

elude to the network benefits of audit partners when they suggest that their findings -

larger offices of Big 4 auditors have higher audit quality - are consistent with the

argument that auditors working in large offices have more peers with whom to consult,

which improves audit quality.

One could also equate the idea of being better-connected to the concept of

‘reputational capital’ introduced by DeAngelo (1981a). DeAngelo (1981a) argues that

audit partners with a higher ‘reputational capital’ have greater incentives to be

independent since they earn client-specific rents, which they stand to lose in the event

of an audit failure. One could use this same argument for audit partners who have

greater levels of social capital since in the event of an audit failure they would

potentially risk the loss of these connections and any benefits (potential or actual)

associated with them, which in turn also means that these audit partners have greater

incentives to be independent. Therefore our first hypothesis is:

Hypothesis 1: An audit partner with more social capital will provide a higher

quality audit.

Past research has also examined audit partner fees (Zerni, 2012; Taylor, 2011). Taylor

(2011) finds that individual audit partners earn individual audit fee premiums (or

discounts) that are not explainable by the audit firms of which they are members. Zerni

(2012) finds that engagement partners who are both industry and public firm

specialists earn higher fees. Therefore if audit partners gain information and influence

benefits from their network position and consequently are able to provide better

quality audits, then the audit services market should reward individual audit partners

with fee premiums based on an assessment of the quality of their audits (Taylor 2011).

Similar to the demand for partners with industry specialism, if demand exists for audit

partners with higher social capital, then that demand gives the audit partner greater

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‘power’ in pricing – relative to audit partners with less social capital. Therefore our

second hypothesis is:

Hypothesis 2: An audit partner with more social capital will earn higher audit fees.

II. Sample

Network sample and construction

To construct the network, we obtained an initial sample from the Boardex

database for the years 2004 to 2010. In total we obtained data for 326 unique listed

French firms (1,859 firm-year observations), with a total 19,957 directorships and

4,159 unique directors (See Table 1, Panel A). Following a review of the firms yearly

audit reports we were able to identify 734 unique audit partners working for 192

unique audit firms. Of the firms within the Boardex database we were unable to identify

the audit partners for 61 of the firm-year observations.7

For each year we construct the network by connecting audit partners to audit

partners, audit partners to directors, and directors to directors. A director is directly

linked to another director if they sit on the same board, and is directly linked to an audit

partner if the audit partner audits their company. An audit partner is also linked to

another audit partner if they work for the same audit firm or if they audit the same

client during the year (see Appendix). We assume that it is more probable that audit

partners and directors interact with each other under these conditions. For example,

audit partner A is more likely to communicate with audit partner B if they are part of

the same audit team compared to audit partner C that neither audits the same clients

nor works in the same auditing firm as audit partner A. Similarly, if audit partner A

audits company E then she is more likely to be associated with director D who sits on

the board of company E than director F who sits on the board of company G, which

audit partner A does not audit. One could argue that audit partners should only be

connected to directors if the director is a member of the audit committee. However, one

would have to assume a) that the audit partner would never have the opportunity to

communicate with a director who is not on the audit committee, and b) that there is no

potential benefit to an audit partner of being seen to be associated with a particular

7 Despite not being able to identify the audit partners for some firm-year observations, we continue to include the directors in the network, so the network is as complete as possible.

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director. We believe both of these assumptions are unlikely to hold.8 First, certainly in

France there is a strong ‘collegiality’ principle in the sense that board sub-committees

have only a consultative role, they are not a substitute for board responsibilities –

rendering the board of directors collectively responsible for financial reporting quality –

thereby providing an incentive for directors to communicate with the audit partner(s)

(Bouton Report, 2002, p.6). Second, audit partners are required to attend board

meetings when the board approves the accounts; otherwise audit partners attendance

is optional. Third, Kilduff and Krackhardt (1994) highlight that even being perceived to

be connected to an individual benefits a person’s influence, reputation, status, etc.,

which is consistent with the old proverb ‘you are known by the company you keep’.

Analysis Sample

To test Hypothesis 1 and 2, we collected all the relevant client-specific

information from Datastream (except the number of subsidiaries which we hand

collected). Audit partner data such as tenure, audit office, audit fees and non-audit fees

was also hand collected directly from the annual reports. 9

In practice, joint-auditorships in France are often characterized by a lead auditor

who imposes its quality standards and a ‘second’ auditor of a lower caliber (Piot and

Janin 2007; Andrè et al. 2011). Following an investigation of the audit fees paid to each

independent auditor, and consistent with Le Maux (2004), we find that the distribution

of fees (both audit and non-audit) between the joint-auditors – and probably the

budgeted audit hours – is far from being equal, with the lead auditor charging on

average nearly 60% more audit fees than the non-lead auditor. We therefore contend

that the quality of the external audit can be proxied by the characteristics of the audit

partners from the dominant (lead) audit firm. Therefore, although both lead and non-

lead audit partners are included when we construct the network to capture the network

position of audit partners, we exclude the non-lead auditor partners from the analysis

(Piot and Janin 2007). This results in a total 2,096 observations remaining for analysis.

8 To note, if in constructing our network we have not identified the ‘right’ connections then this will only bias against us finding any association between our network measure and audit quality and fees 9 The disclosure of the audit fees for French listed companies became compulsory in 2003. This disclosure requires companies to distinguish (a) between fees paid for statutory audits and those paid for other services (non-audit fees), and (b) between the specific fees paid to each of the two independent auditors.

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The observations from the financial industry are also excluded (590) as their total asset

base and financial information is not comparable to those of the other companies.

To obtain the data for the audit quality analysis, we delete 67 observations with

missing data. We also exclude the first year the firms introduced IFRS due to

comparability issues (given the use of lagged accounting data) resulting in the exclusion

of a further 188 observations. These procedures result in a total of 1,251 observations

for the audit quality analysis. Table 1 Panel A summarizes the sample selection process.

Shown in Table 1 Panel C is the available data for the fee analysis. Again we were

unable to identify data for some of the control variables but we did not need to exclude

the year IFRS was implemented so the final audit fee sample comprises of 1,585

observations.

[Insert Table 1]

III. Measurement of Variables and Research Design

Network measure

‘It does not only matter that actors have a lot of contacts, but they have highly

connected, prominent contacts.’ (Hannerman & Riddle, 2005)

On the basis of its theoretical relevance we use the eigenvector centrality

measure (EVC) to proxy for an audit partner’s social capital. Friedkin (1991) describes

this index as ‘total effects centrality’ in the context of network influence models and for

many researchers centrality is EVC (Faust, 1997). The EVC measure has been widely

used in a variety of settings to capture the opportunities available to individuals in

obtaining information and for creating social influence and power (Borgatti, 2006;

Mizruchi and Bunting, 1981; Frenandez and McAdam 1988; Friedkin, 1991; Scott and

Davis, 2007), from interlocking corporate boards of directors (Mintz and Scwartz,

1981a, b; Mizruchi and Bunting, 1981; Mizruchi 1982; Roy 1983; Rosenthal et al., 1985;

Liu, 2009; Renneboog and Zhao, 2011; El-Khatib et al. 2012; Foge et al. 2013), to

venture capitalists (Hochberg et al., 2007), and inter-bank networks (Bech et al. 2010;

Cohen-Cole et al. 2010; Akram and Christophersen 2010).

Eigenvector centrality is defined as the principle eigenvector of the adjacency

matrix defining the network (See Appendix). 10 It is a recursive measure of degree (the

10 There are several variations of this simple eigenvector centrality, for example, PageRank the network centrality originally used in Google search engines.

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number of direct connections one has), whereby the actor’s centrality is defined as the

sum of her ties to others weighted by their respective centralities. So while degree

centrality simply counts the number of direct connections an individual has, EVC takes

into account their ‘quality’. Thus connections to neighbors that are in turn well

connected themselves are rated higher than connections to neighbors that are weakly

connected i.e. this measure does not assume all ties are equal. For example, suppose

audit partner A and audit partner B each has the same number of direct connections.

However, audit partner A’s connections happen to be fairly isolated individuals (e.g.

their client firms have very few board-interlocks, and their joint audit partners are from

a small one person audit firm, who does not audit many clients). In contrast audit

partner B connections each have lots of connections, who also have lots of connections,

and so on (e.g. their client firms have many board-interlocks, and their joint audit

partners are from large audit firms and audit many clients). Under this scenario despite

having the same number of direct connections audit partner B is clearly better-

connected e.g. she has the ‘right connections’. Audit partner B’s ties to individuals with

many connections creates greater opportunities for indirect flows of information and

social influence whether this influence is actual or perceived (Kilduff and Krachardt,

1994).

As indicated in the prior literature this measure has several advantages over

other centrality measures such as closeness and betweenness (Bonacich 2007) when

trying to capture both informational benefits and the opportunity to create social

influence and power.11 Unlike these other centrality measures, which weights every

connection equally, EVC is based on the idea that a connection to other powerful actors

contributes more to a given actor’s power and informational opportunities than equal

connections to less powerful actors. Moreover the EVC measure assumes that

information, influence etc. is able to move via unrestricted walks rather than being

constrained by triads, paths or geodesics – as is the case with closeness for example.

The measure does not in any way assume that things flow by transferring or by

replicating to one’s connections at a time. Rather it is consistent with a mechanism in

11 Although betweenness centrality is argued to be associated with power and influence, this is more related to the control of information flowing through the connections rather than the power and influence that these connections generate.

16

which each actor can affect all its neighbors simultaneously (Borgatti, 2005). Hence the

EVC measure is ideally suited for influence type processes.12

Empirical Models: Audit Quality

Higher-quality audits should mitigate errors or earnings management in financial

statements, which is often introduced through the use or abuse of the accruals process

(Richardson et al. 2006; Ettredge et al. 2010). Therefore to test Hypothesis 1 we follow

prior literature and capture audit quality using two proxies a) abnormal working capital

accruals (Ashbaugh et al. 2003; Carey and Simnett 2006; Frankel et al. 2002; Carcello

and Li 2013) and, b) the likelihood of firms reporting a small earnings increase (Frankel

et al. 2002; Ashbaugh et al. 2003; Carey and Simnett 2006; Carcello and Li 2013). Ideally

we would have liked to use other proxies such as going concern opinions and earnings

restatements (Carey and Simnett 2006; Geiger and Raghunanden 2002; Myers et al.

2003) but the number of such cases is very small for our sample.13

Abnormal working capital accruals

We measure abnormal working capital accruals (AWCA) utilizing the DeFond and Park

(2001) model, which has also been used in subsequent audit partner research (Carey

and Simnett 2006). AWCA is the difference between reported working capital and an

expected level of working capital needed to support current sales level, where a historic

relation of working capital to sales captures expected working capital. We focus on

working capital accruals for two reasons. First, DeFond and Park (2001) find this

measure to be a more powerful test in comparison to a test using total accruals. Second,

it avoids the estimation problems, associated with the more sophisticated models

(Jones 1991) especially for studies with European data (Francis et al. 2009; Cameran et

al. 2012; Maijoor and Vanstraelen 2006; Andrè et al. 2011). 14

12 Costenbader and Valente (2003) evaluated the stability of centrality measures when networks are sampled in the face of inaccurate or incomplete network data. It turns out that the most robust centrality measure under these circumstances is the EVC measure. Consequently, not being able to identify a number of actors, as is the case for our sample, should not cause significant problems. 13 An investigation of audit reports and financial statements revealed that less than 0.5% of our sample firms had either a going concern opinion or a restatement during 2004 - 2010. 14 A cross-sectional Jones (1991) model is not used to estimate abnormal accruals because it requires more data than the approach in DeFond and Park (2001) and would cause a large reduction in the number of observations. In our French sample, if two-digit SIC codes are used to define industry categories, as is typically the case, then only a small fraction of our industries would have the rule-of-thumb of 10 minimum observations required to estimate the Jones model. In addition, a recent study suggests that the Jones model does not perform well on non-U.S. data, and this may in part be caused by small industry samples in international settings (Peek et al. 2013).

17

If an audit partner’s social capital – their eigenvector centrality score (EVC) - is

associated with the quality of their audit, then we expect lower absolute AWCA for audit

partners with higher EVC scores. We therefore estimate the following model:

|𝐴𝑊𝐶𝐴| = ∝ +𝛽1𝐸𝑉𝐶𝑝𝑡 + �𝛾𝑖 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒+ �𝜌𝑖 𝐴𝑢𝑑𝑖𝑡 𝐹𝑖𝑟𝑚 𝐹.𝐸.

+�𝜑𝑖𝐴𝑢𝑑𝑖𝑡 𝑂𝑓𝑓𝑖𝑐𝑒 𝐹.𝐸. + �𝜇𝑖 𝐴𝑢𝑑𝑖𝑡 𝑃𝑎𝑟𝑡𝑛𝑒𝑟 𝐹.𝐸. +

�𝛿𝑖 𝑌𝑒𝑎𝑟 𝐹.𝐸. +�𝛾𝑖 𝐶𝑙𝑖𝑒𝑛𝑡 𝐹𝑖𝑟𝑚 𝐹.𝐸. + 𝜖

(1)

where AWCA is the DeFond and Park (2001) measure of abnormal working capital

accruals: AWCAt = WCt –[(WCt-1/St-1) * St], with WC = non-cash working capital in the

current year which calculated as (current assets – cash and short-term investments) –

(current liabilities – short-term debt) and S = sales.

Control Variables

Audit Partner Specific

We include a number of audit partner controls that have been found in the prior

literature to be associated with audit quality: whether the audit partner is an industry

specialist (AP_INDEXP), the number of clients the partner audits during the year (BUSY),

and the partner’s firm-specific experience (TENURE). Studies document a positive

association between audit quality and individual audit partner industry specialization

(Kallunki et al. 2009; Duh et al. 2009; Ittonen et al., 2010); a negative association with

audit quality and the number of clients the audit partner audits (Goodwin 2011 and Gul

et al. 2013), whilst mix results have been found in relation to auditor tenure and audit

quality (Chi and Huang 2005; Chen et al. 2008; Carey and Simnett 2006; Manry et al.

2008). We also control for the audit firm and audit office the partner works for, and

whether the audit firm is an industry expert (AF_INDEXP).

In addition, an audit partner’s time-invariant generic ability, which cannot be

directly observed and measured, could also be an important variable. In particular,

generic ability could be positively correlated with an audit partner’s network position

e.g. high ability audit partners are just better-connected. Therefore, in addition to the

audit firm and audit office fixed effects we also include audit partner fixed effects. If the

audit partner’s EVC score captures the partner’s generic ability rather than her social

18

capital, then the EVC coefficient should be insignificant given partner fixed-effects are

also included.

Firm Specific

Following prior research (Becker et al. 1998; Myers et al. 2003; Johnson et al.

2002; Frankel et al. 2002; Ashbaugh et al. 2003; DeFond et al. 2002; Andrè et al. 2011)

we control for firm size (SIZE), profitability (ROA, and LOSS), probability of bankruptcy

(PBANK) based on Zmijewski (1984), leverage (LEV), book-to-market ratio (BM), last

year’s total current accruals (LAGCAR), current assets (CA), cash flow from operations

(CFFO), sales growth (SALESG), age of the company (AGE), busy audit period

(DECEMBER), proportion of non-audit fees to audit fees (NAAF) and how many audit

partners signed the audit report (NUM_PARTNERS). In addition, given the French joint

audit regime, we also control for the possible combinations of audit firms: whether the

client firm is jointly audited by two Big 4 audit firms (TWO_BIG4) or whether the client

firm is audited by one Big4 audit firm and one non-Big4 audit firm (ONE_BIG4). Notes to

Table 2 contain each variable’s definition.

The resulting model may also be biased due to the omission of unobservable

client characteristics, such as the extent of the client’s preference for a particular audit

firm, combination of audit firms, or a specific partner, and these client characteristics

may also be correlated with audit quality and with the audit partner’s social capital

(Minutti-Meza 2013). One approach to this potential problem is to use the method of

instrumental variables (IVs) and two-stage least squares (2SLS) as in Zerni (2012).

Unfortunately, we are unable to find a suitable IV. We therefore include client fixed

effects in our regressions. Assuming any client-related omitted variables are time

invariant and captured by the client-specific fixed-effects, this approach mitigates this

endogeneity concern.

Small Earnings Increase Analysis

Our second proxy of audit quality measures the extent of earnings management by

investigating the likelihood of reporting a small earnings increase (Carey & Simnett

2006; Carecello and Li 2013). Prior studies document that firms avoid reporting losses,

tend to report small increases in earnings more frequently than small decreases, and

manage earnings towards a target (Bannister and Newman 1996; Degeorge et. al 1999;

Matsumoto 2002; Abarbanell and Lehavy 2003; Hutton 2005). We therefore estimate

19

the following logistic regression model to test the association between the audit

partner’s social capital and a small earnings increase:

𝐼𝑁𝐶𝑅𝐸𝐴𝑆𝐸 = ∝ +𝛽1𝐸𝑉𝐶𝑝𝑡 + �𝛾𝑖 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒+ �𝜌𝑖 𝐴𝑢𝑑𝑖𝑡 𝐹𝑖𝑟𝑚 𝐹.𝐸.



(2)

INCREASE is an indicator variable coded one if the difference between a firm’s income

before extraordinary items in years t and t-1 (scaled by the market value at the end of

the year t-1) falls in the interval [0.00, 0.02] and zero otherwise. If an audit partner’s

social capital is associated with the quality of their audit then we expect EVC to be

negatively associated with INCREASE. Control variables are the same as in Eq (1) and

are defined in the notes to Table 2.

Empirical Model: Audit Fee To test our second hypothesis we examine whether audit fees are associated with the

social capital of the audit partners. Following a stream of audit fee studies (Ferguson et

al. 2003; Francis et al. 2005; Hay et al. 2006) we control for various determinants of

audit fees in the following regression model:

𝐿𝑁𝐴𝐹𝐸𝐸 = ∝ +𝛽1𝐸𝑉𝐶𝑝𝑡 + �𝛾𝑖 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒 + �𝜌𝑖 𝐴𝑢𝑑𝑖𝑡 𝐹𝑖𝑟𝑚 𝐹.𝐸.



(3) We expect EVC to be positively associated with LNAFEE if demand exists for audit

partners with social capital. Control variables are the same as in Eq. (1) and Eq (2)

except we control for the number of subsidiaries the client firm has (SUBSID) but not for

last year’s total current accruals (LCACCR). Moreover, since we do not need to exclude

the first year the firms implemented IFRS, we include an additional control variable

20

(IFRS) to capture this period. We adjust for firm and year clustering effects in each of

the regression models.

IV. Descriptive Statistics and Results

Audit Quality Analysis

Table 2 Panel A provides descriptive statistics for the audit quality sample. The average

absolute AWCA is 0.078 with an approximate standard deviation of 0.115 and 39.7% of

the sample firms report a small earnings increase (INCREASE). The average eigenvector

centrality score (EVC) for audit partners is 0.267 and 9% of the audit partners are

identified as industry experts (AP_INDEXP). Audit partners are on average BUSY with

2.2 clients per year and have an average TENURE of approximately 4 years. The average

log of firm size is 7.631 and approximately 15% of the firms are loss making. The

average return on assets (ROA) is 0.031 with an average leverage (LEV) of 0.623 and a

book-to-market (BM) of 0.794. 48% of firms are audited by one Big4 and one non-Big4

auditor (ONE_BIG4), while 30% of firms are audited by two Big4 auditors (TWO_BIG4).

Proportion of non-audit fees to audit fees on average is approximately 6%. This is

consistent with the long standing practice in France that auditors are not allowed to

provide both auditing and consulting services to a company. This principle of separation

has been much stricter in France than in English-speaking countries (Andre 2011).

[Insert Table 2]

To obtain a better feel for our network measure, Table 3 presents the

correlations between the EVC and other audit partner and firm characteristics. Above

the diagonal we present correlations for the audit quality sample and below the

diagonal for the audit fee sample. Ex ante we expect the EVC score of an audit partner to

have a positive association with their level of busyness (BUSY) since covering more

firms and possibly more industries will provide, relatively, more opportunities for an

audit partner to gain more social capital. In addition, working for a Big 4 audit firm,

auditing larger clients, and being part of a two Big4 audit team should also increase

their social capital. Table 3 confirms these predictions. Interestingly the audit firm’s

industry expertise (AF_INDEXP) is more highly correlated with an audit partner’s social

capital than the size of the client firm or being part of a two Big4 audit team, while audit

partner tenure (TENURE) is negatively associated with their level of social capital.

[Insert Table 3]

21

Abnormal working capital accruals

Table 4 Panel A presents the estimates of Eq. (1). We find that audit partners

with higher social capital are associated with significantly lower levels of absolute

working capital accruals, indicated by the negative coefficient on EVC significant at the

one percent level. Column (1) only includes industry and year fixed effects, while

column (2) includes all fixed effects. In column (3) we substitute the audit partner’s

industry expertise (AP_INDEXP) with an alternative measure (AP_INDEXP_2) similar to

the one used by Zerni (2012), whereby an audit partner is identified as an industry

expert if they a) audit at least one of the top two largest firms (based on total assets)

within the industry in the fiscal year, and b) audit at least two additional clients

belonging to that industry.15 The results are not sensitive to this new measure. Under all

specifications, the coefficients for the client-specific control variables all have signs

consistent with expectations, except for LEV, CFFO and Ln(AGE), which are insignificant.

ROA and LCACCR are negative and significant at the five percent level, while SALESG and

PBANK are positively and significant at the one and ten percent level respectively.

Coefficients on both TWO_BIG4 and ONE_BIG4 are positive (consistent with Andrè et al.

2011) and significant at the ten percent level.

[Insert Table 4 Panel A]

In untabulated analysis, we split the accrual sample into two groups, firms with

positive abnormal working capital accruals and firms with negative abnormal working

capital accruals, to examine signed accruals. We find that audit partners with high levels

of social capital are associated with higher accruals quality by significantly reducing

both positive and negative accruals.

To assess the sensitivity of our findings to possible adverse effects of self-

selection bias (Ferguson et al. 2003; Francis et al. 2005) we re-run the model on a

subsample – those firms whose audit team comprised of only one Big4 audit firm. Table

4 Panel B presents these results. Under both specifications, EVC is negative and

significant at either the five or ten percent level.

[Insert Table 4 Panel B]

15 We use a less strict identification procedure than that of Zerni (2012) since imposing Zerni’s (2012) procedure yields an extremely small number of audit partners identified as industry specialists. Zerni (2012) identifies an audit partner as an industrial specialist if the individual auditor is (i) ranked among the top two auditors in industry k in fiscal year t based on the amount of audited total assets and (ii) audited at least five clients belonging to industry k in fiscal year t.

22

Overall these results provide support for hypothesis 1. Audit partners with

higher EVC scores, therefore social capital, are associated with higher levels of audit

quality.

Small earnings increase

Table 5 Panels A and B present our logistic regression of the likelihood of firms

reporting a small earnings increase. Consistent with hypothesis 1, EVC is negative and

significant at the one percent level under both model specifications for the full sample -

and negative and significant at five percent level under both specifications for the

ONE_BIG4 sample. For instance, Panel A column (1) reports the coefficient on EVC is

negative and significant (-0.065 with a p-value of <0.001) for our full sample, indicating

that firms whose audit partners have higher EVC scores are less likely to report small

earning increases. This finding is robust to substituting our alternative audit partner

industry specialism measure (AP_INDEXP_2).

In Panel B where we focus on the ONE_BIG4 sample, we report similar results. In

column (1), we find the coefficient on EVC is negative and significant (-0.113 with a p-

value of <0.05). As for our control variables, for the full sample, the results suggest that

larger firms and firms that are older are more likely to report small earnings increases.

On the other hand, loss firms, firms with high levels of current assets to total assets and

firms audited by two Big 4 audit firms are less likely to report a small earnings increase.

In addition for the ONE_BIG4 sample, firms audited by an audit firm with industry

expertise are more likely to report a small earnings increase, while firms with a greater

chance of bankruptcy and higher levels of cash flow from operations and higher levels

of non-audit services are less likely to report a small earnings increase.

[Insert Table 5]

Thus these overall findings tabulated in Table 4 and Table 5 provide support for

our hypothesis (1) that audit partners with higher EVC scores provide higher quality

audits, consistent with both their ability and influence due to social capital obtained via

their network connections benefiting the audit quality.

Audit Fee Analysis

Table 2 Panel B provides descriptive statistics for the audit fee sample. The average

audit fee is €3,019,000 with a standard deviation of €4,383,000. The EVC score and

control variables are not significantly different from the audit quality sample; the EVC

23

score average is 0.24 and has a standard deviation of 0.04. The additional control

variable – the number of subsidiaries (SUBSID) – has an average of 25 with a standard

deviation of approximately 39.

[Insert Table 6 Panel A]

Table 6 Panel A presents the estimates of Eq. (2) using the full sample. We find

that audit partners’ EVC score is positively associated with audit fees and this

association is significant at the ten percent level. Column (1) only includes industry and

year fixed effects, while column (2) includes all but industry fixed effects. In column (3)

we substitute the audit partner’s industry expertise (AP_INDEXP) with an alternative

measure (AP_INDEXP_2) similar to the one used by Zerni (2012). The results are not

sensitive to these new specifications. Under all specifications the coefficients for the

client-specific control variables all have signs consistent with expectations, except for

LOSS, LEV, CA and SALESG, which are insignificant in most specifications. SIZE and

LNSUBSID are positive and significant at the one percent and PBANK is positive and

significant at the five percent level while ROA and BM are negative and significant at the

ten and five percent level respectively. Coefficients on both TWO_BIG4 and ONE_BIG4

are positive and significant at the one percent level consistent with the findings of the

prior literature.

Again to assess the sensitivity of our findings to possible adverse effects of self-

selection bias (Ferguson et al. 2003; Francis et al. 2005) we re-run the model on a

subsample of firms whose audit team consists of only one Big4 audit firm. Table 6 Panel

B presents these results. Under both specifications, EVC is positive and significant at

either the five or 10 percent level. Unlike the results presented in panel A, the

coefficient on the alternative measure of audit partner industry expertise, AP_INDEXP_2,

is positive and significant at the one percent level in column (2). This is consistent with

the findings of Goodwin and Wu (2013) that auditor industry expertise fee premium is

mainly a partner level phenomenon. In unreported tests, we also assess the sensitivity

of the results to an alternative measure of audit firm industry expertise constructed in

the same way as AP_INDEXP_2. The results are robust: the coefficients on audit partner

social capital (EVC) and expertise (AP_INDEXP_2) continue to be positive and significant,

whilst that on the alternative measure of audit firm industry expertise is positive but

not significant.

[Insert Table 6 Panel B]

24

Overall these results provide support for hypothesis 2. Audit partners with

higher levels of social capital earn higher levels of audit fees.

V. Determinants of an audit partner’s social influence.

The results so far suggest that better-networked audit partners provide better

quality audits and command higher audit fees. In Table 7, we present our analysis of the

determinants of audit partners’ EVC score, i.e. social capital. Consistent with the

univariate results in Table 3, the results show that an audit partner’s EVC score is

higher if she audits many clients, works for a Big4 audit firm, is part of a two Big4 audit

team, audits larger clients, works in larger audit-office, and works for an audit firm who

is identified as an industry specialist. An audit partner’s EVC score is lower as the length

of the client relationship increases (TENURE) and if the client is economically significant

for the audit partner (ECON_SIG) with respect to audit fees paid. The regression model

in Table 7 has high explanatory power with an adjusted R-squared of approximately 71

percent.

[Insert Table 7]

One could claim that these results, with respect to an audit partners’ busyness,

tenure, and economic significance of the client are consistent with the prior audit

independence literature. If, as we argue, an audit partners EVC score captures inter alia

the partner’s level of social influence and thereby their level of independence, our

findings also reveal a negative association with auditor independence and the length of

the partner’s client relationship as well as the economic significance of their client.

(Carey and Simnett 2006; Bamber and Iyer 2007; Chi et al. 2009; Ye et al. 2011) The

results are also consistent with the DeAngelo’s (1981a) argument that being busy

auditing many clients, and thereby creating ‘reputational capital’ increases an auditor’s

independence.

Is this a joint-audit phenomenon?

To investigate whether these results hold in a non-joint audit regime we

constructed the network of German audit partners from 2006 to 2010. We chose

Germany since it is a country with both a single audit regime and a requirement for

audit partner signatures. We hand collected from the annual reports the audit partner

data such as, partner name, tenure, audit office, audit fees and non-audit fees. The

25

network comprises of 4,316 directors, 239 client firms, with 870 audit partners from 35

audit firms.

We are unable to replicate our earlier results using the sample of German firms.

One possible explanation is that under a joint-audit regime there are greater

opportunities for an audit partner to create social capital, which they are able to

leverage and provide higher quality audits. In the French setting, there is lower audit

firm concentration, greater chance to audit many clients, along with the opportunity to

work with audit partners from competing audit firms, compared to the German setting.

Further investigation of our German sample reveals that unlike French audit partners,

we observe German audit partners on average conducting only one audit per year. The

mean for BUSY is 1.4 and the median is 1, compared to France where the mean of BUSY

was 2.2 and the median is 2. Consequently, in any given year the German audit partner

is on average only connected to one client (via their directors) and connected to audit

partners from their own audit firm. This results in a network with very low density

providing very little opportunity for the audit partners to create informational

advantages and social influence beyond their audit firm and their one client. Prior

research finds that as density increases (and the number of ties between network

members grows) communication across the network becomes more efficient and power

imbalances are able to flourish (Rowley, 1997).

This would be the first paper to provide some empirical support to suggest that a

joint-audit regime provides opportunities to increase audit quality (Holm and

Thinggaard 2011; Lesage et al 2012; Lesage and Ratzinger-Sakel 2012). To-date prior

research has been unable to find any association between joint-audits and audit quality,

except in the case of voluntary joint-audits (Zerni et al. 2012). However, at this stage we

are very cautious to suggest that the lack of any significant association between a

German audit partner’s social capital and audit quality indicates that audit partners can

only create and leverage their social capital in a joint-audit regime. It might be the case

that we did not construct the ‘right’ network, and that German audit partners are part of

a denser network that we are unable to observe, e.g. connections through consulting

service or connections from being members of advisory boards, etc., through which they

are able to create social capital.

26

VI. Conclusion

This paper is the first to connect an audit partner’s structural position in a network to

her performance. We find evidence in support of the hypothesis that audit partners

appear to gain benefits through their position in the network, which enhance their audit

quality and enable them to command an audit fee premium. We also document that

audit partners’ social capital is higher if they audit more clients, and is lower if the

partners have longer tenure with their clients and the more economically significant the

clients are to the audit partner.

This research suggests that regulators should consider providing an

environment in which audit partners have opportunities to enhance their networks, and

in turn to improve audit quality. This however may or may not manifest itself in a joint

audit system. Our findings do suggest that policymakers should examine audit quality

and audit market structure enhancement together as a complex whole.

27

Appendix Construction of the Network

The diagram below shows a network of 10 people (actors). Each line represents a

connection between the individuals. In the context of this study we can consider, for

example, actors 1 and 2 as directors of two different firms, firm A and firm B. These

directors do not sit on any other boards and therefore there is no possibility that they

are connected to each other. Actor 3 is an audit partner who works for audit firm C and

audits firms A and B. Therefore she is connected both to Director 1 and Director 2. Actor

6 is a director in firm F that audit partner 3 also audits, hence audit partner 3 is also

connected to Director 6. Director 6 does not sit on the same board with either Director 1

or Director 2 and therefore is not connected to either of them. Actors 4, 5 and 7 are

audit partners working for different audit firms and jointly audit with actor 3. Therefore

actor 3 is also connected to: actor 4 since they jointly audit firm A, actor 5 since the

jointly audit firm B, and actor 7 since they jointly audit firm F. Actors 4, 5 and 7 do not

audit the same firms and therefore are not directly connected to each other. Actor 8 is a

director in Firm H and also sits on the same board of directors as Director 6 in Firm F.

Therefore, Director 6 and Director 8 are also connected to each other through sitting on

the same board Firm Z. Actors 9 and 10 are audit partners working for different audit

firms who jointly audit firm H. Thereby they are both connected to Director 8 but not

Director 6. They are also directly connected to each other since they audit the same

firm, Firm H.

Director 1 Auditor 5 Auditor 7 Auditor 9 (Firm A) (Audit Firm D) (Audit Firm G) (Audit Firm I) Auditor 3, Director 6, Director 8, (Audit Firm C) (Firm F) (Firm H) Auditor 10 (Audit Firm J) Director 2 (Firm B) Auditor 4 (Audit Firm E)

28

Eigenvector centrality measure

The eigenvector centrality (EVC) is a relative score recursively defined as a function of

the number of strengths of connections to an actor’s neighbors and as well as those

neighbors’ centralities. Let x(i) be the EVC score of node vi. Then,

𝑥(𝑖) =1λ

� 𝑥(𝑗)𝑗∈Γ(υ𝑖)

=1λ�𝐴𝑖,𝑗𝑥(𝑗)𝑁

𝑗=1

Here λ is a constant. Equation (1) can be rewritten in vector form, where x ={x(1), x(2),

x(3),…..,x(N)}′ is the vector of EVC scores of all nodes.

𝐱 =1λ𝐀𝐱

λx = Ax

This is the well-known eigenvector equation where this centrality takes its name from λ

is an eigenvalue and x is the corresponding eigenvector of matrix A. Obviously several

eigenvalue/eigenvector pairs exist for an adjacency matrix A. The EVC of nodes are

defined on the basis of the Perron eigenvalue λA (the Perron eigenvalue is the largest of

all eigenvalues of A and is also called the principal eigenvalue). If λ is any other

eigenvalue of A then λA> |λ|. The eigenvector x = {x(1), x(2),….,x(N)}′ corresponding to

the Perron eigenvalue or principal eigenvector. Thus the EVC of a node vi is the

corresponding element x(i) of the Perron eigenvector x. Note that when the adjacency

matrix A is symmetric all elements of the principal eigenvector x are positive.

Computing a node’s EVC it takes into consideration its neighbor’s EVC scores.

Because of its recursive definition, EVC is suited to measure nodes’ power to influence

other nodes in the network both directly and indirectly through its neighbors.

Connections to neighbors that are in turn well connected themselves are rated higher

than connections to neighbors that are weakly connected.

29

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Table 1 Sample Selection

Panel A: Network Sample

Year # of Directors # of Companies with

Directors # of Audit Partners # of Audit Firms

2004 2375 207 395 109 2005 2757 253 460 115 2006 2807 261 500 113 2007 3020 284 556 118 2008 3049 293 572 112 2009 3012 288 590 117 2010 2937 273 577 110 Total 19,957 1,859 3,650 794

Unique 4,159 326 734 192 Panel B: Sample for audit quality analysis

Number of Observations

Audit partner observations sample from 2004-2010 3,650 Delete:

Financial firms (590) Missing data to construct control variables ( 67) Partner’s from non-lead audit firms (1,554)

1,439 Delete: First year implemented IFRS (188)

Sample for audit quality analysis 1,251

Panel C: Sample for audit fee analysis

Number of Observations

Audit partner observations sample from 2004-2010 3,650 Delete:

Financial firms (590) Missing data to construct control variables ( 59) Partner’s from non-lead audit firms (1,554)

Sample for audit fee analysis 1,485

39

Table 2 Descriptive Statistics Panel A: Accounting Quality Analysis (n=1,251) Variable Mean Std. Deviation Quartile 1 Quartile 3 ABS_AWCA 0.078 0.115 0.020 0.089 INCREASE 0.397 0.489 0.000 1.000 EVC 0.267 0.044 0.001 0.016 AP_INDEXP 0.094 0.450 0.000 0.000 BUSY 2.209 1.511 1.000 3.000 TENURE 4.080 2.854 2.000 6.000 AF_INDEXP 0.282 0.450 0.000 1.000 SIZE 12338.070 27885.330 424.452 10734.000 Ln(SIZE) 7.631 2.029 6.051 9.281 ROA 0.031 0.082 0.015 0.059 LOSS 0.146 0.354 0.000 0.000 PBANK 0.114 0.169 0.009 0.160 LEV 0.623 0.265 0.504 0.737 BM 0.794 0.817 0.355 0.932 LCACCR -0.003 0.133 -0.001 -0.049 CA 0.481 0.187 0.348 0.607 CFFO 0.077 0.074 0.047 0.108 SALESG 0.084 0.321 0.011 0.127 AGE 30.909 19.558 15.000 50.000 Ln(AGE) 3.209 0.703 2.708 3.912 DECEMBER 0.846 0.361 1.000 1.000 NAAF 0.058 0.114 0.000 0.069 NUM_PART 1.296 0.457 1.000 2.000 TWO_BIG4 0.297 0.457 0.000 1.000 ONE_BIG4 0.478 0.500 0.000 1.000 Panel B: Audit Fee Analysis (n=1,485) Variable Mean Std. Deviation Quartile 1 Quartile 3 AUDIT_FEE (th.) 3019.220 4383.590 323.000 4104.610 Ln(AUDIT_FEE) 13.889 1.549 12.685 15.228 EVC 0.241 0.043 0.001 0.013 AP_INDEXP 0.096 0.294 0.000 0.000 BUSY 2.247 1.615 1.000 3.000 TENURE 4.063 2.874 2.000 6.000 AF_INDEXP 0.285 0.452 0.000 1.000 SUBSID 24.796 38.799 6.000 23.000 Ln(SUBSID) 2.620 1.091 1.946 3.178 SIZE (mil.) 11777.170 26978.970 388.500 9426.000 Ln(SIZE) 7.545 2.051 5.962 9.151 ROA 0.032 0.083 0.016 0.060 LOSS 0.141 0.349 0.000 0.000 PBANK 0.114 0.168 0.009 0.156 LEV 0.622 0.252 0.504 0.735 BM 0.850 0.889 0.369 1.010 CA 0.487 0.192 0.348 0.616 CFFO 0.076 0.079 0.047 0.107 SALESG 0.099 0.333 0.008 0.140 AGE 30.119 19.488 14.000 49.000 Ln(AGE) 3.167 0.739 2.639 3.892 DECEMBER 0.844 0.363 1.000 1.000 NAAF 0.059 0.113 0.000 0.074 NUM_PART 1.295 0.456 1.000 2.000 TWO_BIG4 0.286 0.452 0.000 1.000 ONE_BIG4 0.483 0.499 0.000 1.000

40

Variables Definition ABS_AWCA = Absolute abnormal working capital accruals divided by average total assets at the

end of year t. Abnormal working capital accruals is measured as follows: Non-cash working capitalt – ((Non-cash working capitalt-1/Salest-1) * Salest), where: Non-cash working capital = (Current assets - Cash and short term investments) – (Current liabilities - Short-term debt).

INCREASE = an indicator variable coded one if the difference between a firm’s income before extraordinary items in years t and t-1 (scaled by the market value at the end of the year t-1) falls in the interval [0.00, 0.02] and zero otherwise.

AUDIT_FEE = The audit fees (in thousands of euros) in time t. Ln(A_FEE) = The natural logarithm of audit fee in time t. EVC = The audit partner’s eigenvector centrality score at time t. AP_INDEXP = An indicator variable with the value of one if the audit partner is identified as an

industry expert for industry i in time t and zero otherwise. In line with prior studies (Balsam et al. 2003; Chin and Chi 2009) we measure market share using the number of clients audited by an audit partner within an industry. We then rank audit partners in each industry by their market share and identify an audit partner as an industry expert if they are the largest supplier in the industry. Chin and Chi (2009) argues that such a base avoids a bias toward large clients that is implied by using sales or firm size as the base.

BUSY = The number of audits signed off by the audit partner in year t. TENURE = The number of consecutive years that the incumbent audit partner has audited the

client by year t. AF_INDEXP = An indicator variable with the value of one if the audit firm is identified as an

industry expert for industry i in time t, ad zero otherwise. In line with prior studies (Balsam et al. 2003; Chin and Chi 2009) we measure market share using the number of clients audited by an audit firm within an industry. We then rank audit firms in each industry by their market share and identify an audit firm as an industry expert if they are the largest supplier in the industry.

SIZE = Total assets (in millions of euros) at the end of year t. Ln(SIZE) = The natural logarithm of total assets at the end of year t. ROA = Earnings before extraordinary items in year t divided by total assets at the end of

year t. LOSS = An indicator variable with a value of one if net income in year t is less than zero,

and zero otherwise. PBANK = Probability of bankruptcy as measured by adjusted Zmijewski (1984) score. LEV = Total liabilities divided by total assets. BM = The book value at the end of year t divided by market value at the end of year t. LCACCR = The prior year’s total current accruals (net income before extraordinary items +

depreciation and amortization – operating cash flows) divided by lagged total assets.

SUBSID = The number of subsidiaries in time t. Ln(SUBSID) = The natural logarithm of the number of subsidiaries in time t. CA = The current assets at the end of year t divided by total assets at the end of year t. CFFO = The cash flow from operations divided by total assets at the end of year t. SALESG = The percentage increase in sales over year t. AGE = The number of years since the firm was listed. Ln(AGE) = The natural logarithm of AGE DECEMBER = An indicator variable with a value of one if the fiscal year end is December 31st,

and zero otherwise. NAAF = The level of non-audit fees to audit fees in year t. NUM_PART = The number of audit partners who signed the audit report on behalf of their audit

firm in year t. TWO_BIG4 = An indicator variable with a value of one if the firm was audited by two Big 4 audit

firms in year t, and zero otherwise. ONE_BIG4 = An indicator variable with a value of one if the firm was audited by one Big 4 audit

firm and one non-Big 4 audit firm in year t, and zero otherwise.

41

Table 3 Correlation coefficients for audit quality sample (above diagonal) and fee sample (below diagonal).

Audit Quality Sample (n=1,251) Fe

e Sa

mpl

e (n

=1,4

85)

Variables

EV

C AP

_IN

DEXP

BU

SY

TE

NUR

E

AF

_IN

DEXP

LN

(SIZ

E)

RO

A LO

SS

PB

ANK

LE

V BM

LC

ACCR

CA

CFFO

EVC 0.068* 0.064* -0.079* 0.352* 0.108* -0.031 0.029 -0.016 0.009 -0.042 -0.011 -0.068* -0.014 AP_INDEXP 0.079* 0.403 0.085* 0.132* 0.138* 0.055 -0.025 -0.004 0.008 0.004 -0.023 -0.041 0.060 BUSY 0.093* 0.421* 0.128* 0.092* 0.083* 0.049 -0.026 0.027 0.050 0.027 -0.017 -0.082* 0.042 TENURE -0.072* 0.078* 0.119* -0.023 0.002 0.025 -0.008 0.013 -0.007 -0.025 0.012 0.028 0.025 AF_INDEXP 0.305* 0.135* 0.071* -0.031 -0.003 -0.028 0.037 -0.057 -0.083* 0.010 -0.028 -0.114* -0.003 Ln(SIZE) 0.111* 0.167* 0.094* 0.019 -0.008 0.064* -0.154* 0.117* 0.185* -0.245* -0.035 -0.451* 0.038 ROA -0.061 0.045* 0.059 0.020 -0.022 0.068* -0.596* -0.267* 0.031 0.278* 0.046 0.054 0.601* LOSS 0.046 -0.007 -0.025 -0.008 0.031 -0.154* -0.596* 0.262* 0.029 -0.082* -0.063 0.007 -0.310* PBANK -0.000 -0.012 0.014 0.009 -0.043 0.120* -0.283* 0.270* 0.738* -0.169* -0.122* 0.075* -0.130* LEV -0.011 0.002 0.041 0.001 -0.082* 0.202* 0.026* 0.029 0.729* -0.118* -0.047 0.050 0.068* BM -0.042 -0.002 0.041 -0.056 -0.013 -0.291* 0.225* -0.072* -0.173* -0.143* 0.055 0.203* 0.233* LCACCR -0.011 -0.012 -0.008 0.016 -0.018 -0.032 0.046 -0.053 -0.103* -0.481 0.076* -0.021 0.033 CA -0.068* -0.020 -0.070 0.019 -0.975 -0.045 0.035 0.169 0.081* 0.039 0.228* -0.016 -0.068* CFFO -0.041 0.057 0.058 0.018 -0.002 0.062 0.649* -0.322* -0.159* 0.064 0.159* 0.020 -0.102* SALESG 0.035 -0.016 0.014 -0.006 -0.007 -0.087* 0.060 -0.045 -0.070* -0.065 0.201* 0.001 0.059 -0.011 Ln(AGE) -0.006 0.024 0.018 0.082* 0.045 0.303* 0.032 -0.099* -0.089* -0.044 -0.181* -0.032 -0.153* 0.014 DECEMBER -0.027 0.020 -0.003 0.016 -0.083* 0.122* 0.103* -0.108* -0.059 -0.008 0.076* -0.027 -0.032 0.090 NAAF -0.016 -0.023 0.039 0.006 0.034 0.159* 0.046 -0.074* -0.057 -0.021 -0.058 0.001 -0.003 0.010 NUM_PART 0.013 0.096* 0.061* -0.040 -0.068* 0.449* 0.042 -0.051 0.058 0.113* -0.108* -0.037 -0.096* -0.012 TWO_BIG 0.073* 0.073* 0.051 -0.050 -0.046 0.361* 0.054 -0.026 -0.020 0.050 -0.005 -0.003 -0.101* 0.071* ONE_BIG 0.156* 0.020 0.072 -0.035 0.307* -0.117* -0.077* 0.072* 0.077* -0.013 0.007 0.014 0.007 -0.026 Ln(SUBSID) -0.049 -0.046 0.019 0.003 -0.048 0.145* 0.038 0.007 0.004 0.022 0.015 -0.047 -0.127* 0.106*

CONT’D

42

Audit Quality Sample (n=1,251)

Fee

Sam

ple

(n=1

,485

)

Variables

SA

LESG

Ln

(AGE

)

DE

CEM

BER

N

AAF

N

UM_P

ART

TW

O_BI

G

ON

E_BI

G

EVC 0.065 0.004 -0.038 -0.015 -0.006 0.069* 0.165* AP_INDEXP -0.001 0.034 0.039 -0.017 0.085 0.083* 0.009 BUSY 0.004 0.038 -0.003 0.038 0.033 0.051 0.067 TENURE 0.011 0.053 0.010 0.001 -0.045 -0.044 -0.027 AF_INDEXP -0.005 0.051 -0.072 0.036 -0.064 -0.035 0.289* Ln(SIZE) -0.061 0.313* 0.135* 0.156* 0.442* 0.358* -0.116* ROA 0.077* 0.040 0.113* 0.039 0.025 0.060 -0.076* LOSS -0.061 -0.109* -0.118* -0.067 -0.030 -0.037 0.070 PBANK -0.062 -0.087* -0.045 -0.046 0.072 -0.003 0.062 LEV -0.052 -0.046 0.004 -0.014 0.107* 0.064 -0.027 BM 0.165* -0.145* 0.069 -0.047 -0.122* 0.009 0.013 LCACCR -0.006 -0.025 -0.033 0.003 -0.038 0.005 0.006 CA 0.060 -0.152* -0.022 0.015 -0.105* -0.093* -0.003 CFFO -0.015 0.007 0.096* -0.001 -0.035 0.069 -0.020 SALESG -0.082 0.042 -0.046 -0.018 -0.039 0.042 Ln(AGE) -0.107* 0.019 0.163* 0.151* 0.147* -0.108* DECEMBER 0.041 0.027 -0.034 0.112* 0.094* -0.118* NAAF -0.054 0.155* -0.046 0.159* 0.063 0.055 NUM_PART -0.027 0.150* 0.123* 0.135* 0.199* -0.052 TWO_BIG -0.039 0.155* 0.095* 0.058 0.208* -0.623* ONE_BIG 0.042 -0.111* -0.113* 0.052 -0.055 -0.612* Ln(SUBSID) -0.040 -0.007 -0.032 0.040 0.038 0.068* -0.023

Note: The table presents Pearson correlation coefficients for both audit quality and fee samples. Variables are defined in Table 2. * denotes two-tailed significance at the 1% level.

43

Table 4: Regression results for audit quality: Abnormal working capital accruals

Panel A: Full Sample

(1) (2) (3) +/- Coeff. t-stat Coeff. t-stat. Coeff. t-stat. Intercept 0.169 3.93*** -0.076 -0.26 -0.075 -0.26 Log EVC - -0.003 -2.28*** -0.007 -2.54*** -0.007 -2.71*** AP_INDEXP ? 0.009 0.94 0.033 1.44 AP_INDEXP_2 ? 0.020 1.30 BUSY - 0.001 0.54 0.003 0.64 0.005 1.21 TENURE - -0.001 -0.13 0.003 1.35 0.003 1.49 AF_INDEXP ? -0.010 -1.42 -0.010 -0.76 -0.012 -0.96 Ln(SIZE) - -0.006 -3.02*** -0.007 -0.28 -0.006 -0.22 ROA - -0.183 -1.31* -0.345 -1.99** -0.336 -1.94** LOSS + 0.022 1.48* 0.010 0.53 0.011 0.58 PBANK + 0.119 2.06** 0.178 1.78* 0.182 1.77* LEV + -0.022 -0.97 -0.086 -0.95 -0.093 -1.02 BM - -0.010 -2.29*** -0.004 -0.46 -0.002 -0.25 LCACCR - -0.155 -4.27*** -0.134 -2.36** -0.135 -2.35** CA + -0.034 -1.66* 0.074 0.85 0.075 0.85 CFFO - 0.012 0.17 0.075 0.85 0.068 0.78 SALESG + 0.089 6.19*** 0.087 4.63*** 0.087 4.57*** Ln(AGE) - 0.001 0.12 0.020 0.30 0.026 0.39 DECEMBER + 0.003 0.36 0.007 0.19 0.004 0.11 NAAF - -0.012 -0.53 -0.009 -0.17 -0.009 -0.16 NUM_PART ? 0.004 0.55 0.006 0.45 0.008 0.67 TWO_BIG4 ? 0.011 1.22 0.387 1.85* 0.372 1.77* ONE_BIG4 ? 0.180 2.28** 0.369 1.74* 0.353 1.66* Year F.E. Yes Yes Yes Industry F.E. Yes No No Client Firm F.E No Yes Yes Audit Office F.E. No Yes Yes Audit Firm F.E. No Yes Yes Audit Partner F.E No Yes Yes N 1,246 1,246 1,246 Adjusted R2 25.06% 38.45% 38.05%

44

Panel B: One_Big4 Sample Note: The dependent variable is absolute abnormal working capital accruals divided by total assets (ABS_AWCA). AP_INDEXP_2 is an indicator variable with the value of one if the audit partner is an industry expert and zero otherwise. An audit partner is identified as an industry expert if they a) audit at least one of the top two largest firms (based on total assets) within the industry in the fiscal year, and b) audit at least two additional clients belonging to that industry. The t-statistics are adjusted for firm and year clustering effects ***, **, *: Significant at 1%, 5%, and 10% level. P-values are one-tailed for signed expectations and two-tailed for unsigned expectations. All other variables are defined in Table 2. After calculating DFBETAS, we exclude any outliers.

(1) (2) +/- Coeff. t-stat. Coeff. t-stat. Intercept 0.877 0.68 0.830 0.65 Log EVC - -0.009 -1.83* -0.010 -2.11** AP_INDEXP ? 0.047 1.17 AP_INDEXP_2 ? 0.013 0.43 BUSY - 0.012 1.31 0.016 1.85* TENURE - 0.010 1.49 0.010 1.46 AF_INDEXP ? -0.045 -1.84* -0.045 -1.89* Ln(SIZE) - -0.093 -0.87 -0.088 -0.80 ROA - -0.141 -0.34 -0.130 -0.31 LOSS + 0.025 0.43 0.025 0.42 PBANK + 0.116 0.75 0.121 0.77 LEV + 0.008 0.05 0.006 0.04 BM - -0.022 -0.70 -0.017 -0.54 LCACCR - -0.136 -1.23 -0.141 -1.25 CA + 0.056 0.32 0.058 0.33 CFFO - 0.032 0.20 0.023 0.14 SALESG + 0.100 3.99*** 0.100 3.91*** Ln(AGE) - -0.072 -0.56 -0.062 -0.51 DECEMBER + -0.011 -0.21 -0.010 -0.21 NAAF - -0.157 -0.71 -0.152 -0.67 NUM_PART ? -0.003 -0.12 0.001 0.05 Year F.E. Yes Yes Client Firm F.E Yes Yes Audit Office F.E. Yes Yes Audit Firm F.E. Yes Yes Audit Partner F.E Yes Yes N 595 595 Adjusted R2 23.89% 23.38%

45

Table 5: Regression results for audit quality: Likelihood of a firm reporting a small

earnings increase. Panel A: Full sample.

(1) (2) +/- Coeff. Chi-Sqr. Coeff. Chi-Sqr. Intercept -1.487 6.31*** -1.434 6.04** Log EVC - -0.065 6.78*** -0.064 6.46*** AP_INDEXP ? -0.124 0.42 AP_INDEXP_2 ? -0.353 0.77 BUSY - 0.033 0.69 0.028 0.59 TENURE - 0.010 0.28 0.008 0.20 AF_INDEXP ? 0.118 0.68 0.112 0.64 Ln(SIZE) - 0.104 6.48*** 0.105 6.64*** ROA - 1.730 0.98 1.679 0.6793 LOSS + -2.323 44.68*** -2.330 44.70*** PBANK + -0.273 0.20 -0.289 0.23 LEV + -0.037 0.01 -0.038 0.01 BM - -0.138 2.14 -0.133 2.01 LCACCR - -0.242 1.17 -0.245 1.20 CA + -0.700 3.36* -0.715 3.56* CFFO - -1.304 0.96 -1.313 0.98 SALESG + 0.017 0.01 0.036 0.03 Ln(AGE) - 0.189 5.00** 0.186 4.74** DECEMBER + -0.087 0.26 -0.097 0.31 NAAF - -0.789 1.92 -0.763 1.83 NUM_PART ? 0.220 2.45 0.217 2.41 TWO_BIG4 ? -0.596 10.46*** -0.602 10.75*** ONE_BIG4 ? -0.092 0.32 -0.100 0.34 Year F.E. Yes Yes Client Firm F.E Yes Yes Audit Office F.E. Yes Yes Audit Firm F.E. Yes Yes Audit Partner F.E Yes Yes N 1,242 1,242 Pseudo R2 20.76% 20.80%

46

Panel B: One_Big4 Sample. (1) (2) +/- Coeff. Chi-Sqr. Coeff. Chi-Sqr. Intercept -2.710 8.04*** -2.673 8.15*** Log EVC - -0.113 3.94** -0.114 4.01** AP_INDEXP ? -0.080 0.09 AP_INDEXP_2 ? 0.060 0.01 BUSY - 0.026 0.25 0.019 0.15 TENURE - 0.054 2.79* 0.054 2.86* AF_INDEXP ? 0.509 6.26** 0.502 6.26** Ln(SIZE) - 0.091 2.43 0.089 2.33 ROA - 2.719 1.77 2.710 1.75 LOSS + -2.229 27.52*** -2.230 27.55*** PBANK + -2.256 6.09** -2.254 6.10** LEV + 1.784 3.29* 1.786 3.32* BM - 0.069 0.20 0.070 0.20 LCACCR - -0.361 2.52 -0.360 2.52 CA + -1.014 2.56 -1.021 2.61 CFFO - -2.789 2.78* -2.806 2.81* SALESG + -0.263 0.63 -0.274 0.66 Ln(AGE) - 0.255 3.77* 0.254 3.70** DECEMBER + -0.056 0.05 -0.056 0.05 NAAF - -2.648 7.54*** -2.620 7.59*** NUM_PART ? -0.049 0.05 -0.049 0.05 Year F.E. Yes Yes Client Firm F.E Yes Yes Audit Office F.E. Yes Yes Audit Firm F.E. Yes Yes Audit Partner F.E

Yes Yes

N 595 595 Pseudo R2 24.39% 24.38% Note: The dependent variable is INCREASE. AP_INDEXP_2 is an indicator variable with the value of one if the audit partner is an industry expert and zero otherwise. An audit partner is identified as an industry expert if they a) audit at least one of the top two largest firms (based on total assets) within the industry in the fiscal year, and b) audit at least two additional clients belonging to that industry. The t-statistics are adjusted for firm and year clustering effects ***, **, *: Significant at 1%, 5%, and 10% level. P-values are one-tailed for signed expectations and two-tailed for unsigned expectations. All other variables are defined in Table 2. After calculating DFBETAS, we exclude any outliers.

47

Table 6: Regression results for audit fees Panel A: Full sample. (1) (2) (3) +/- Coeff. t-stat. Coeff. t-stat. Coeff. t-stat. Intercept 7.118 39.33*** 10.415 14.28*** 10.438 14.28*** Log EVC + 0.012 1.83** 0.011 1.85** 0.011 1.72* * AP_INDEXP + -0.043 -0.85 0.035 0.85 AP_INDEXP_2 + 0.097 0.85 BUSY ? 0.0264 2.79*** -0.012 -1.02 -0.011 -0.93 TENURE ? 0.0063 1.16 -0.001 -0.04 -0.001 -0.04 AF_INDEXP + 0.0273 0.71 0.012 0.25 0.012 0.25 Ln(SIZE) + 0.6522 48.25*** 0.434 6.47*** 0.434 6.46*** ROA - -0.5363 -2.08** -0.353 -1.67* -0.348 -1.67* LOSS + -0.0022 -0.04 -0.024 -0.59 -0.023 -0.57 PBANK + 0.7711 5.72*** 0.316 2.14** 0.317 2.15** LEV + -0.1709 -2.39*** -0.079 -0.80 -0.075 -0.76 BM - -0.0637 -2.95*** -0.050 -2.43** -0.050 -2.42** CA + 0.0271 2.45*** -0.093 -0.40 -0.088 -0.38 CFFO + -0.0586 -0.23 0.090 0.47 0.092 0.49 SALESG + -0.0446 -0.87 -0.026 -0.78 -0.027 -0.82 Ln(AGE) - 0.0008 0.03 -0.123 -1.02 -0.132 -1.09 Ln(SUBSID) + 0.1000 5.44*** 0.145 2.92*** 0.148 2.99*** DECEMBER + -0.1217 -2.07*** 0.024 0.35 0.019 0.28 NAAF - 0.2195 1.62 -0.222 -1.54 -0.224 -1.55 NUM_PART + 0.2659 6.46*** 0.022 0.52 0.024 0.56 TWO_BIG4 + 0.0930 1.36 2.250 4.66*** 2.270 4.71*** ONE_BIG4 + 0.2886 5.08*** 2.513 5.12*** 2.535 5.16*** IFRS + 0.0191 0.21 0.005 0.13 0.002 0.04 Year F.E. Yes Yes Yes Industry F.E. Yes No No Client Firm F.E No Yes Yes Audit Office F.E. No Yes Yes Audit Firm F.E. No Yes Yes Audit Partner F.E No Yes Yes N 1,478 1,478 1,478 Adjusted R2 88.01% 97.63% 97.63%

48

Panel B: One_Big4 Sample.

(1) (2) +/- Coeff. t-stat. Coeff. t-stat. Intercept 11.249 10.67*** 11.714 11.18*** Log EVC + 0.015 1.96** 0.013 1.76* AP_INDEXP + 0.028 0.48 AP_INDEXP_2 + 0.267 2.58*** BUSY ? 0.002 0.12 -0.001 -0.05 TENURE ? -0.010 -1.06 -0.011 -1.19 AF_INDEXP + -0.006 -0.10 -0.004 -0.08 Ln(SIZE) + 0.504 6.01*** 0.508 6.11*** ROA - -0.450 -1.81* -0.488 -1.98** LOSS + -0.053 -0.90 -0.049 -0.83 PBANK + 0.220 1.24 0.227 1.29 LEV + 0.375 1.25 0.348 1.17 BM - 0.026 0.64 0.026 0.66 CA + -0.313 -1.14 -0.288 -1.08 CFFO + 0.398 1.62 0.429 1.87* SALESG + -0.026 -0.71 -0.032 -0.89 Ln(AGE) - -0.231 -1.27 -0.345 -1.91* Ln(SUBSID) + 0.147 2.22** 0.159 2.45** DECEMBER + 0.100 1.25 0.094 1.17 NAAF - -0.174 -0.91 -0.179 -0.95 NUM_PART + 0.001 0.01 0.010 0.15 IFRS + 0.001 0.02 -0.011 -0.24 Year F.E. Yes Yes Client Firm F.E Yes Yes Audit Office F.E. Yes Yes Audit Firm F.E. Yes Yes Audit Partner F.E Yes Yes N 705 705 Adjusted R2 94.86% 97.55%

Note: The dependent variable is Ln(A_FEE).. AP_INDEXP_2 is an indicator variable with the value of one if the audit partner is an industry expert and zero otherwise. An audit partner is identified as an industry expert if they a) audit at least one of the top two largest firms (based on total assets) within the industry in the fiscal year, and b) audit at least two additional clients belonging to that industry. The t-statistics are adjusted for firm and year clustering effects ***, **, *: Significant at 1%, 5%, and 10% level. P-values are one-tailed for signed expectations and two-tailed for unsigned expectations. All other variables are defined in Table 2. After calculating DFBETAS, we exclude any outliers.

49

Table 7: Determinants of an audit partner’s social capital: EVC score

+/- Coeff. t-stat. Intercept -9.388 -12.96*** AP_INDEXP + -0.049 -0.23 BUSY + 0.252 2.28** TENURE - -0.068 -3.01*** AF_INDEXP + 1.069 7.39*** NAAF - -0.409 -0.96 ECON_SIG - -0.658 -2.20** TWO_BIG4 + 3.485 15.39*** ONE_BIG4 + 3.176 15.26*** Ln(SIZE) + 0.262 5.56*** ROA ? -1.269 -1.55 PBANK ? 0.281 0.57 LEV ? 0.027 0.09 BM ? -0.087 -1.05 Ln(AGE) ? -0.007 -0.06 NUM_PART ? -0.097 -0.61 Paris Office + 0.404 1.80* Year F.E. Yes Industry F.E Yes N 1,493 Adjusted R2 70.54%

Note: The dependent variable is the natural logarithm of EVC. ECON_SIG is the level of audit fees from the client firm divided by total audit fees the partner receives from all other audits conducted in the fiscal year t. The t-statistics are adjusted for audit partner and year clustering effects ***, **, *: Significant at 1%, 5%, and 10% level. P-values are one-tailed for signed expectations and two-tailed for unsigned expectations. All other variables are defined in Table 2. After calculating DFBETAS, we exclude any outliers.