Sharing Knowledge or Proprietary Information?

An Examination of Audit Clients Who Share the Same Audit Partner *

Jung Koo Kang

University of Southern California

Clive Lennox

University of Southern California

Vivek Pandey

University of Southern California

July 2019

* We appreciate helpful comments from workshop participants at the South Western University of Finance and Economics conference, Regina Wittenberg-Moerman, … The authors acknowledge research support

from the University of Southern California.

Sharing Knowledge or Proprietary Information?

An Examination of Audit Clients Who Share the Same Audit Partner

ABSTRACT

We argue that knowledge transfers are more valuable when partners audit

clients from the same product market. However, there are potential costs

when rival companies share the same partner due to concerns about the

leakage of proprietary information. Consistent with beneficial effects of

knowledge transfers, we find that audit fees are lower and accounting

misstatements are less frequent when rival companies share the same

partner. On the other hand, we find that rival companies are less likely to

share the same partner when they are more concerned about confidential

proprietary information, as measured by redactions of proprietary

information from SEC filings, the existence of trade secrets or proprietary

information, and high levels of R&D and patents.

1

1. Introduction

Audit partners are responsible for collecting sufficient audit evidence and rendering

appropriate audit opinions on financial statements. Recent years have seen a tremendous

increase in research on audit partners, with many studies reporting that partner

characteristics are associated with audit outcomes.2 Yet we know practically nothing

about the factors that determine partner-client assignments. In the first attempt to address

this issue, our study examines the costs and benefits of audit clients sharing the same

audit partner.3

Audit partners generally serve multiple clients. Therefore, an audit firm needs to

consider a partner’s entire client portfolio when deciding how to best match the partner

with a client and a potential client needs to take into account a partner’s other clients

when selecting an audit partner. We examine the benefits and costs that arise when a

partner audits a company that operates in the same product market as the partner’s other

clients. When a partner’s clients operate in the same product market, we expect the

partner is better able to transfer knowledge from one audit engagement to another,

allowing greater gains in efficiency and effectiveness. This would suggest that product

market competitors may benefit from lower audit fees and higher quality auditing when

they share the same audit partner.

2 See Lennox and Wu (2018) for a review of the audit partner literature. 3 Matching problems have been extensively studied in economics (Gale and Shapley 1962; Becker 1973;

Chade et al. 2017). For example, colleges look to offer admission to the ‘right’ applicants; people look for the ‘right’ partner in the marriage market; and home-buyers hire real estate agents to find the property that

best matches their tastes and budget.

2

On the other hand, there are potential costs when rival companies share the same

partner because the companies may be concerned about commercially sensitive

information being leaked to their competitors. During an audit, a partner can learn

valuable inside information about a company’s innovative technologies, its trade secrets,

and major customers. A spillover of such information to rival companies could harm the

client’s competitive position (Verrecchia 1983; Kwon 1996; Aobdia 2015). Audit offices

and audit firms minimize this risk by building Chinese walls and implementing other

controls that prevent proprietary client information being passed from one partner to

another (Demski et al. 1999; McAllister and Cripe 2008). However, such controls only

prevent information being shared between partners. They are less effective when

companies share the same partner because a partner cannot ‘unlearn’ the knowledge that

she already gained from auditing other clients in the same product market. Therefore,

concerns about information leakage are more salient when competitors share the same

partner, compared to the situation where competitors share the same audit office (or same

audit firm) but do not share the same partner.4

To provide evidence on the benefits and costs of partner sharing, we use PCAOB

data on U.S. engagement partners in 2016-2017. 5 We are particularly interested in

whether a company’s audit partner has another audit client in the same product market

4 Consistent with information being leaked when bidder and target companies share the same audit office, Dhaliwal et al. (2016) find that in M&A transactions, shared auditor deals are associated with lower deal

premiums, lower target event returns, higher bidder event returns, and higher deal completion rates. Their study did not examine the issue of partner sharing because partners’ identities were not publicly observable

during their sample period. 5 Our sample starts in 2016 when the PCAOB started to provide audit partner identities and it ends in 2017

when the product similarity scores of Hoberg and Phillips (2016) cease to be available.

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as the focal company. We identify product markets using the product similarity scores of

Hoberg and Phillips (2016), which capture the cosine similarity between the text-based

product descriptions in 10-K filings. Relative to industry classifications, the product

similarity scores are advantageous because they capture how product markets change

over time and they allow the researcher to measure product similarity between

companies within the same industry as well as companies that operate across industries.6

Our first finding is that audit fees are significantly lower when product market

rivals share the same partner. This result is economically large as well as statistically

significant. In fact, the audit fees paid by rivals who share the same partner are

approximately 15% less than the fees paid by rivals who do not share the same partner.

This suggests the existence of large efficiency gains when partners audit companies that

compete with each other. Our second finding is that accounting misstatements are

significantly less likely to occur when rival companies share the same partner. Again, this

result is economically large. We find the probability of an accounting misstatement is just

1.76% for product market rivals who share the same partner compared to 4.53% when

they do not share the same partner. In additional analyses, we demonstrate that the

results for audit fees and accounting misstatements become insignificant when rival

companies share the same audit firm (or office) but do not share the same partner. Thus,

6 See Hoberg and Phillips (2010, 2016) for a comprehensive discussion of the merits of using product similarity scores rather than traditional industry classifications. In our sample, 22% of observations from

the same SIC 3-digit industry do not belong to the same product market. In addition, 30% of observations

from the same product market do not belong to the same SIC 3-digit industry. Therefore, the differences between product markets and industry classifications are empirically meaningful.

4

the knowledge transfers pertain to rival companies sharing the same audit partner and

do not extend to rival companies who share the same audit firm or audit office but do not

share the same partner.7

These findings suggest there are gains in audit efficiency and audit quality when

companies share partners with their competitors. These benefits could result in partner-

client assignments being clustered in product markets. On the other hand, rival

companies may not want to share the same partner if they have significant concerns about

the leakage of proprietary information.8 To determine whether the benefits exceed the

costs, we begin by testing whether partners are more likely to be matched to companies

in the same product market or different product markets. To test this, we create every

possible pair of companies within a given audit office. For example, an audit office with

four clients (W, X, Y, and Z) has a total of six possible client pairings: W+X, W+Y, W+Z,

X+Y, X+Z, and Y+Z.9 For each pair, we then measure whether the two companies operate

in the same product market and whether they share the same partner. If the benefits of

partner sharing exceed the costs, we would expect the pair is more likely to share the

same partner when the pair operates within the same product market.

7 In a concurrent working paper, Bills et al. (2019) find that audit fees are lower and accounting misstatements are less frequent when competitors share the same audit firm. We demonstrate that these

results are driven by rival companies sharing the same partner rather than the same audit firm. In particular,

we show that audit fees are not significantly lower and accounting misstatements do not occur less often when competitors share the same audit firm but do not share the same partner. 8 We expect that in many cases the leakage would be accidental rather than intentional. However, there is anecdotal evidence that leakage sometimes occurs intentionally. For example, a partner at KPMG is alleged

to have repeatedly leaked confidential information about his clients to someone who traded on that information (www.latimes.com/business/la-fi-mo-kpmg-scott-london-sentencing-story.html).

9 More generally, the number of unique pairs in an office with N clients is �×(���)

�.

5

Consistent with the benefits exceeding the costs, we find that partner-sharing is

more common among pairs of companies that operate in the same product market.

Among company-pairs that operate in the same product market, we find that 21% share

the same partner whereas only 11% share the same partner in company-pairs that belong

to different product markets. This result remains economically and statistically

significant in regressions that control for company fixed effects, audit office fixed effects,

and time-varying controls. We conclude that, on average, it is more advantageous for rival

companies to share the same partner than to have different partners.

Although this result holds on average, we also find that a lot of companies do not

share the same partner with their rivals. We therefore test whether concerns about

confidential proprietary information explain why they have different partners. Following

prior studies in the voluntary disclosure literature, we measure proprietary information

concerns by identifying whether companies: 1) redact information from their 10-K filings

(Weber and Verrecchia 2006; Ellis et al. 2012), 2) disclose that they have valuable trade

secrets or proprietary information (Glaeser 2018), and 3) have high R&D expenditures or

a large number of patents (Ellis et al. 2012). When these measures of proprietary

information costs are greater, we find that product market rivals are much less likely to

share the same partner.

Our study contributes to several areas of the literature. We contribute to the audit

partner literature by examining the partner-client matching process. Although there has

been research on auditor-client compatibility at the audit firm level (Brown and Knechel

2016), our study is the first to examine this at the audit partner level. We show significant

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benefits and costs when partners are assigned to companies that compete in the same

product market. The benefits include more efficient auditing (lower fees) and higher

quality auditing (fewer misstatements), while the costs are the companies’ increased

concerns about the possible leakage of proprietary information.

Second, we contribute to evidence on the impact of knowledge spillovers on audit

fees. We show that audit fees are significantly lower (suggesting efficiency gains) when

competing companies share the same partner. This contrasts with the prior industry

specialization literature, which usually finds that audit fees are higher when companies

are audited by industry experts (Craswell et al. 1995; Ferguson and Stokes 2002; Ferguson

et al. 2003; Francis et al. 2005; Goodwin and Wu 2014). It is important to note, however,

that these prior studies regress the company’s audit fee on an industry specialization

variable that is also constructed using the company’s audit fee and which therefore

generates a mechanical relation between the dependent variable (audit fees) and the

variable of interest (industry specialization).10 We avoid this problem by not including an

audit fee variable on the right-hand side of our audit fee regressions.11

Third, we contribute to the literature on audit quality and knowledge spillovers

by showing that accounting misstatements occur less often when competing companies

share the same partner. In contrast, prior studies obtain inconsistent findings when they

10 Some studies identify industry specialists using assets rather than audit fees. Carson and Fargher (2007) find that the asset-based measures of industry expertise can inadvertently capture the effects of outliers or

nonlinearities in client size. 11 Our results for audit fees are different from prior research because of this research design choice rather

than because we use the product similarity scores of Hoberg and Phillips (2016). Specifically, we continue to find significantly lower audit fees when companies in the same 3-digit SIC code share the same audit

partner.

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examine the effects of industry specialization on audit quality (Balsam et al. 2003; Reichelt

and Wang 2010; Minutti-Meza 2013). In a study conducted at the audit partner level,

Aobdia et al. (2019) find an insignificant association between partner expertise and audit

quality. Our study is different because we examine the benefits of knowledge sharing

when a partner’s clients operate in the same product market.

Fourth, ours is the first audit study to use redacted filings and the presence of trade

secrets to measure companies’ concerns about proprietary information (Verrecchia 1983;

Weber and Verrecchia 2006; Ellis et al. 2012; Glaeser 2018). Prior auditing studies point

out that proprietary information concerns might affect a company’s choice of audit firm

or audit office (Kwon 1996; Aobdia 2015; Bills et al. 2019). Our study is different because

our analysis is conducted at the partner level rather than the audit firm level or office

level. This is important because information leakages between partners are less likely to

be a concern given the controls that audit firms have in place to prevent sensitive client

data being passed from one partner to another (Demski et al. 1999; McAllister and Cripe

2008). Proprietary information concerns are much more salient when companies share

the same partner because a partner cannot unlearn what she already knows from her

other audit engagements.

Section 2 discusses the related literature and develops the hypotheses. Section 3

describes the research design and introduces the sample. Section 4 reports our findings

and Section 5 concludes.

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2. Related Literature and Hypothesis Development

2.1 The impact of partner sharing on audit fees

The prior literature on auditor industry specialization argues that audit fees may be

affected by both demand and supply-side factors. On the demand side, companies prefer

auditors with industry expertise because companies benefit from having auditors with

specialized knowledge (Craswell et al. 1995; Ferguson et al. 2003; Francis et al. 2005;

Goodwin and Wu 2014; Aobdia et al. 2019). A higher demand for industry specialists

would be expected to increase audit fees. Consistent with this demand-side perspective,

the existing literature mostly provides evidence of audit fee premiums for auditor

industry specialists (Craswell et al. 1995; Ferguson et al. 2003; Francis et al. 2005; Goodwin

and Wu 2014; Aobdia et al. 2019).

On the supply side, industry experts can achieve economies of scale by applying

similar audit processes and spreading the costs of acquiring industry-specific knowledge

over a larger number of clients (Eichenseher and Danos 1981; Johnson et al. 1990; Cairney

and Young 2006). There is some evidence that the efficiency gains provided by industry

specialists might result in lower audit fees. Clients can bargain for lower audit fees when

industry specialists are not highly differentiated from their competitors (Mayhew and

Wilkins 2003). Fung et al. (2012) document audit fee discounts when office (city) level

industry specialists achieve scale economies. In addition, industry specialists charge

lower fees for companies with homogeneous operations (Bills et al. 2015). Using data on

audit hours, Bae et al. (2016) suggest that industry specialist auditors can achieve cost

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efficiencies by reducing the marginal costs of audit effort (i.e., lower costs per audit hour).

Similarly, Dekeyser et al. (2019) show that industry expertise is associated with efficiency

gains and a reduction in variable costs (i.e., fewer total audit hours).

However, there are no studies that examine whether there are audit efficiencies

when companies in the same product market share the same audit partner. By auditing

companies and their competitors, we argue that partners can gain deep knowledge and

expertise about their clients’ product markets. On one hand, such partners may be able

to charge a fee premium due to the higher demand for their specialized knowledge. On

the other hand, the partner may achieve cost savings by auditing companies in the same

product market and this may allow the partner to retain more clients by offering them

lower audit fees. Given these competing views, we state our first hypothesis in the null

form:

H1: Audit fees are not significantly different when rival companies share the same partner.

2.2 The impact of partner sharing on audit quality

We next investigate whether audit quality improves when rival companies share the

same partner. By auditing multiple clients, partners can accumulate greater knowledge

which can significantly improve their performance on audit tasks (Bonner and Lewis

1990). Prior literature provides evidence consistent with knowledge transfers in auditing.

For example, knowledge spillovers from non-audit services can enhance an auditor’s

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understanding of risks and thus improve audit quality (Koh et al. 2013). Companies can

also improve internal control quality when their auditors provide tax services due to the

auditors’ increased awareness of material transactions (De Simone et al. 2014).

Furthermore, auditors can transfer knowledge about their clients’ supply chains and this

can enhance audit quality (Johnstone et al. 2014). The knowledge provided by specialist

auditors can also help to improve investment efficiency (Bae et al. 2017).

We expect that knowledge transfers are particularly important when product

market rivals share the same partner. Knowledge transfers are less likely between

different partners in a shared audit firm (or audit office) because audit firms have controls

in place to prevent confidential client information being passed from one partner to

another (Demski et al. 1999; McAllister and Cripe 2008). Such controls do not prevent an

audit partner employing the knowledge that she gained from auditing other companies

in the same product market. Thus, when a partner’s has multiple clients in the same

product market, we expect that the partner is better able to transfer knowledge from one

audit engagement to another, which is likely to make audit procedures more effective.

We therefore predict that audit quality is higher when product market rivals share the

same audit partner.

H2: Audit quality is higher when rival companies share the same partner.

In contrast to the existing literature which examines knowledge transfers between

partners, we examine the benefits of knowledge sharing when rival companies share the

same partner. Moreover, prior studies obtain mixed findings for the relationship between

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audit quality and measures of industry expertise at the audit firm level. On one hand,

some studies argue that industry specialist audit firms develop greater expertise leading

to higher quality auditing (Balsam, Krishnan, and Yang 2003; Dunn and Mayhew 2004;

Reichelt and Wang 2010). On the other hand, Minutti-Meza (2013) find that the audit

quality differential between industry specialist and non-specialist audit firms disappears

when the clients of specialist audit firms are matched to those of non-specialist audit

firms. Furthermore, in a recent study of industry expertise at the audit partner level,

Aobdia et al. (2019) find an insignificant association between a partner’s industry

expertise and audit quality.

2.3 The partner-client matching process

The prior literature examines how clients choose audit firms and how audit firms select

clients, but there is no prior evidence on the matching process between audit partners

and audit clients. Johnson and Lys (1990) argue that realignments between audit firms

and clients represent efficient responses to changes in client operations and activities.

Lennox and Park (2007) show that clients are more likely to select audit firms that

previously employed their executives. Moreover, there is evidence that companies switch

(retain) audit firms if they are less (more) similar to their audit firm’s existing portfolio of

clients (Brown and Knechel 2016; Bills et al. 2019). In contrast to these studies, we examine

client-auditor matching at the partner level. This is important because the lead

engagement partner has a much bigger influence over a client’s audit outcomes than

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partners who do not work on the engagement but who work in the same audit office or

audit firm.

Product market rivals may prefer to share the same partner because this allows

them to benefit from the partner’s specialized knowledge. In addition, an existing

relationship between a partner and a peer company can serve as an implicit ‘referral’ to

other companies in the same product market (e.g., Gilson and Mnookin 1985). Because a

client can be expected to have knowledge of the partner’s expertise and ability, the very

existence of this relationship can serve as an important quality assurance signal to

prospective clients. If a potential client knows that one of her rivals has knowledge of the

partner’s quality, then the existence of the relationship between the partner and the rival

can be an important source of quality assurance and ‘referral’ to potential clients.

On the other hand, companies may not want to have the same partner as their

rivals if they are concerned about commercially sensitive information being leaked.

During an audit, a partner can learn valuable inside information about a company’s

innovative technologies, its trade secrets, and major customers. A spillover of such

information to rival companies could harm the company’s competitive position

(Verrecchia 1983; Verrecchia and Weber 2006; Ellis et al. 2012). Consistent with this

argument, prior literature suggests that higher proprietary information costs might make

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rival companies more reluctant to share the same audit firm (Kwon 1996; Cahan et al.

2008; Aobdia 2015). 12

Concerns about the leakage of proprietary information are likely to be greater

when companies share the same partner compared to the situation where companies

share the same audit firm (or audit office) but do not share the same partner. To minimize

the risk of confidential client information being leaked, audit firms develop Chinese walls

and implement other controls that prevent proprietary information being passed from

one partner to another (Demski et al. 1999; McAllister and Cripe 2008). Yet such controls

can only prevent information being shared between partners. They are less effective when

companies share the same partner because a partner cannot ‘unlearn’ the knowledge she

gained from auditing her other clients. Therefore, rival companies may be reluctant to

share the same partner due to concerns about the possible leakage of their proprietary

information.

Overall, there are potential benefits and costs from sharing the same partner with

product market rivals. Therefore, we state our next hypothesis in the null form:

H3: The probability of sharing the same partner is not different for companies that operate in the same product market.

12 Aobdia (2015) explains that the results reported in Kwon (1996) and Cahan et al. (2008) are likely to be

mechanical because their dependent variables (the average number of clients per auditor for each industry and a measure of dispersion of this number) are mechanically correlated with their variable of interest by

construction (industry concentration). To more directly examine concerns about proprietary information spillovers, Aobdia (2015) uses several shocks, including audit firm mergers, quasi-exogenous changes in

product market competition, and changes in enforcement of noncompete agreements.

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Concerns about the leakage of proprietary information are likely to differ across

companies even when they operate in the same product market. For example, a company

is likely to be more concerned about the confidentiality of its proprietary information if

it chooses to redact sensitive information from its SEC filings. Moreover, a company is

likely to have greater proprietary information concerns if it has trade secrets or it has

previously invested a lot of money on R&D activities. We therefore examine whether the

tendency of rival companies to avoid sharing the same partner is attributable to their

concerns about proprietary information. We expect rival companies are less likely to

share the same partner when they are more concerned about the leakage of proprietary

information:

H4: The probability of rival companies sharing the same partner is lower when their concerns about proprietary information are greater.

3. Research Design

3.1. Audit fee model (H1)

We test the association between audit fees and partner sharing (H1) by estimating the

following model:

Ln(AF) = 0 + 1 Partner Sharing Rival + 2 Partner Sharing Non-Rival + CONTROLS +

Audit office fixed effects + Year fixed effects + u (1)

In eq. (1), the dependent variable (Ln(AF)) is the natural logarithm of the company’s audit

fee. The variable of interest (Partner Sharing Rival) equals one if the company’s audit

partner has another client in the same product market as the focal company (zero

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otherwise). The Partner Sharing Non-Rival variable equals one if the company’s audit

partner has another client but the other client does not operate in the same product

market as the focal company (zero otherwise).

If sharing the same partner results in a more efficient audit and cost savings, we

would expect audit fees to be lower when rival companies share the same partner. In this

case, the Partner Sharing Rival coefficient would be significantly less than the Partner

Sharing Non-Rival coefficient (i.e., 1 < 2). We do not expect efficiency gains when a

company shares its partner with companies that are not in the same product market.

Therefore, we do not expect a statistically significant coefficient for the Partner Sharing

Non-Rival variable (i.e., 2 = 0). Putting these two predictions together would mean that

fees are significantly lower for rivals who share the same partner compared to all other

companies; i.e., 1 < 0.

The CONTROLS in the audit fee model are based on prior literature (Francis et al.

2005; DeFond and Lennox 2017). We control for client size (Size), profitability (Loss Firm

and ROA), complexity (Bus_Segment, Foreign Op, M&A, Restructure, Inventory, Ext Fin),

and non-audit fees (Ln(NAF)). We also control for the characteristics of audit firms,

offices, and partners. Specifically, we control for the number of partners in the office

(Ln(#Partners)), the number of clients audited by the partner (#Clients), and whether the

auditor is a Big 4 firm (Big 4). We include audit office fixed effects to control for

unobservable time-invariant characteristics of audit offices and we include year fixed

effects to control for time-varying economy-wide factors that affect audit fees. Standard

16

errors are clustered at the company level. Appendix 1 provides definitions for all the

variables.

3.2. Accounting misstatements model (H2)

We test the association between audit quality and partner sharing (H2) by estimating the

following model of accounting misstatements:

Prob (Misstate = 1) = F [0 + 1 Partner Sharing Rival + 2 Partner Sharing Non-Rival +

CONTROLS + Audit office fixed effects + Year fixed effects + u] (2)

In eq. (2), the dependent variable (Misstate) equals one if the company’s annual financial

statements are subsequently restated (zero otherwise). We expect fewer misstatements

when a company shares its partner with a rival company because the knowledge

spillover can help to increase audit quality (i.e., 1 < 2). We do not expect higher quality

auditing when a company shares the same partner with companies not in the same

product market. Therefore, we do not expect a statistically significant coefficient for the

Partner Sharing Non-Rival variable (i.e., 2 = 0). Under H2, we therefore predict that 1 < 0

(i.e., misstatements are less likely when rival companies share the same partner).

The independent variables in eq. (2) are similar to those in eq. (1) except that we

add a control for firm age (Firm Age) because mature companies have less incentives to

manage earnings given that they have more stable operations. We continue to control for

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audit office fixed effects and year fixed effects with standard errors that are clustered at

the company level.

3.3. Company-Year Sample

We estimate eqs. (1) and (2) using a sample in which the unit of analysis is at the

company-year level. We obtain the identities of audit partners and their clients from the

PCAOB, which started disclosing partner identities in 2016.13 We use the text-based

product similarity scores of Hoberg and Phillips (2016), whose data ends in 2017.14

Therefore, our sample period is 2016-2017. We collect company characteristics from

Compustat while the audit and restatement data are from Audit Analytics.

Table 1 describes the sample selection process. We start with all audit partners of

U.S. companies between 2016 and 2017. This yields 20,903 company-year observations

for 12,334 companies. We then merge this sample with Compustat which causes the

number of company-year observations to drop to 10,323. 15 We also require data

availability for companies’ product similarity scores and other variables. As a result, the

final sample comprises 6,433 company-year observations, 3,750 companies, 2,592

partners, and 83 audit firms.

13 Engagement partner identities are available for audit reports issued on or after Jan 31, 2017. The data are available at: https://pcaobus.org/Pages/AuditorSearch.aspx14 The data are available at: http://hobergphillips.tuck.dartmouth.edu/industryclass.htm15 The decrease in the sample size occurs because the Audit Analytics database covers all SEC registrants

whereas Compustat only covers SEC registrants who are publicly traded on a stock exchange.

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Table 2 provides descriptive statistics for the sample. In Panel A, we summarize

the number of observations in which companies share partners. There are 1,153

observations (18%) where partners audit two or more companies in the same product

market. There are another 1,861 observations (29%) where partners audit two or more

companies that are not in the same product market. The remaining 3,419 observations

(53%) are the partners that have just one audit client. Panel B shows that mean audit fees

are $1.216 million and the average rate of annual misstatements (Misstate) is 4%.

Appendix 2 reports the degree of overlap between classifying companies as

product market rivals based on their 3-digit SIC codes versus their product descriptions.

Among companies that are classified as product market rivals based on their product

descriptions (Partner Sharing Rival = 1), we find that 349 (30%) do not belong to the same 3-

digit SIC code. Among companies that are classified as product market rivals based on

their 3-digit SIC codes (Partner Sharing SIC3 Rival = 1), we find that 226 (22%) do not have

the same product descriptions. This indicates that there are meaningful differences

between SIC codes and companies’ own product market descriptions.

3.4. Partner-client matching (H3)

We test the tendency of rival companies to share the same audit partner (H3) by

estimating the following model:

Prob (Same Partner = 1) = F [0 + 1 Same Product Market + CONTROLS +

Audit office fixed effects + Company fixed effects + Year fixed effects + u] (3)

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In eq. (3), the dependent variable (Same Partner) equals one if a pair of companies shares

the same audit partner (zero otherwise). We construct the company-pairs using the

clients within a given audit office. For example, if the audit office has four clients (W, X,

Y, and Z), there are six possible pairs of audit clients: W+X, W+Y, W+Z, X+Y, X+Z, and

Y+Z. The Same Partner variable then takes the value one if both companies in the pair

share the same audit partner (zero otherwise). For example, if companies W and X are

audited by the same audit partner, the Same Partner variable takes the value one for that

paired observation. Similarly, if companies W and X belong to the same product market,

the Same Product Market variable takes the value one for that paired observation.

We test H3 by employing two measures of product market rivalry. First, the Same

Product Market variable equals one if both companies in the pair operate in the same

product market (zero otherwise). Second, the Product Similarity Score is a continuous

measure of the degree of product similarity between both companies in the pair.16 If the

benefits of partner sharing exceed the costs, we would expect a higher probability of

partner sharing for companies that operate in the same product market (i.e., 1 > 0).

16 The Product Similarity Score and Same Product Market variables are measured using the data of Hoberg

and Phillips (2016). They use the product descriptions in companies’ 10-K filings to create product market classifications. For each pair of companies, they provide a continuous product similarity score varying

between zero and one (Product Similarity Score), and a binary classification of whether the two companies operate in the same product market (Same Product Market). Compared to SIC industry classifications, their

data offer two distinct advantages. First, they capture how product markets change over time. Second, they provide measures of product similarity between any given pair of companies even if they operate within

the same industry.

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Eq. (3) includes CONTROLS for both companies in the pair. We control for each

company’s size (Size) and profitability (Loss Firm and ROA). We also control for the

characteristics of audit firms, offices, and partners. Specifically, we control for the number

of partners in the office (Ln(#Partners)), the number of clients audited by the partner

(#Clients), and we control for audits by Big 4 firms (Big 4). We include audit office fixed

effects to control for the unobservable time-invariant characteristics of audit offices. We

also include fixed effects for each company in the pair to control for unobservable time-

invariant company characteristics. Finally, we include year fixed effects to control for

time-varying economy-wide factors that affect partner-client matching. Standard errors

are clustered on each company in the pair.

3.5. Proprietary information concerns (H4)

We test whether proprietary information concerns affect the tendency of rival companies

to share the same partner (H4) by estimating the following model:

Prob (Same Partner = 1) = F [λ0 + λ1 Same Product Market + λ2 Prop Info Concern +

λ3 (Same Product Market × Prop Info Concern) + CONTROLS +

Audit office fixed effects + Company fixed effects + Year fixed effects + u] (4)

Eq. (4) is the same as eq. (3) except that we add a variable for proprietary information

concerns (Prop Info Concern) and its interaction with Same Product Market. Under H4, the

probability of rival companies sharing the same partner is lower when their concerns

about proprietary information leakage are greater (i.e., λ3 < 0).

21

We test H4 using several measures of proprietary information taken from the prior

literature on voluntary disclosure. Companies are allowed to redact information from

their 10-K fillings if public disclosure would cause them competitive harm. Therefore, we

follow prior research by using redactions as a measure of companies’ concerns about

proprietary information (Weber and Verrecchia 2006; Ellis et al. 2012). Companies are

more concerned about the leakage of proprietary information if they have valuable trade

secrets (Glaeser 2018). Finally, companies are likely to be more concerned about

information leakage if they have greater R&D expenditures or more patents (Ellis et al.

2012). Thus, we define a company-pair as facing high proprietary information concerns

if at least one of the companies in the pair: i) redacts information from their 10-K filings

(Redacted), ii) discloses the existence of trade secrets in their 10-K filings (Trade Secret), iii)

discloses the existence of proprietary information in their 10-K filings (Prop Info), iv) has

an above-median level of R&D spending (R&D High), or v) has an above-median number

of patents (Patents High).17

4. Results

4.1. The Effect of Partner Sharing on Audit Fees (H1) and Misstatements (H2)

17 We define these variables on the basis of “at least one of the companies in the pair” rather than “both

companies in the pair” because we expect that it takes just one company to be unhappy with the partner sharing arrangement in order for partner sharing not to occur. For example, suppose that W and X are

product market rivals, and suppose that W is happy to share the same partner as X but X does not want to share the same partner as W. In this situation, we would not expect partner sharing to occur because X can

ask the audit firm to assign a different partner and W cannot force X to share the same partner.

22

Panel A of Table 3 reports univariate tests for H1 (audit fees) and H2 (accounting

misstatements). The mean log of audit fees (Ln(AF)) is 13.453 when rival companies share

the same audit partner (i.e., Partner Sharing Rival = 1), whereas it is 14.133 when partners

audit multiple companies but those companies are not product market rivals (i.e., Partner

Sharing Non-Rival = 0). The difference is highly significant (t-stat. = −17.17), which is

consistent with audit partners achieving efficiency gains when their clients operate in the

same product market (H1).

In addition, Panel A shows that the frequency of annual accounting misstatements

(Misstate) is 1.9% when a company shares its audit partner with one of its product market

rivals, whereas it is 4.4% when a company shares its partner with none of its product

market rivals. Consistent with H2, the difference in these frequencies is highly significant

(t-stat. = −4.00). This result suggests that partner sharing by rival companies is associated

with higher audit quality. However, these univariate tests do not control for other

determinants of audit fees and accounting misstatements, so we next perform

multivariate tests which are reported in Panel B.

Cols. (1) and (2) of Panel B report the regression results for audit fees. The

coefficients on Partner Sharing Rival are negative and highly significant (t-stats. = −5.63,

−5.72), while the coefficients on Partner Sharing Non-Rival are insignificant (t-stats. = 0.74,

−0.59). Furthermore, the coefficients on Partner Sharing Rival are significantly more

negative than the coefficients on Partner Sharing Non-Rival (F-stats. = 42.51, 31.44).

Consistent with Panel A, we therefore conclude that audit fees are significantly lower

23

when rival companies share the same audit partner. Economically, the coefficient on

Partner Sharing Rival in Col. (1) indicates that audit fees are approximately 15% lower

when rival companies share the same partner. These results indicate that there are

substantial efficiency gains from partner sharing.

Col. (3) to (6) of Panel B report regression results for the models of accounting

misstatements. Consistent with H2, the coefficients on Partner Sharing Rival are

consistently negative and significant (t-stats. = −3.53, −3.56, −3.50, and −2.77). Therefore,

accounting misstatements occur less often when product market rivals share the same

audit partner. In contrast, the coefficients on Partner Sharing Non-Rival are insignificant,

implying that accounting misstatements are not significantly different when companies

share their partners with none of their product market rivals. Furthermore, the

coefficients on Partner Sharing Rival are significantly more negative than the coefficients

on Partner Sharing Non-Rival (Chi-sq. = 11.62, 12.39, 12.77, 12.00). To assess the economic

significance of these results, we estimate the likelihood of a misstatement occurring when

rival companies share (do not share) the same audit partner. Using the model in Col. (3),

the estimated probability of a misstatement is only 1.76% when the partner audits rival

companies, whereas it is 4.53% when the partner does not audit rival companies. These

results are consistent with knowledge transfers leading to higher quality auditing when

rival companies share the same audit partner.

24

4.2. Sharing the Same Partner Versus Sharing the Same Audit Firm

A concurrent working paper by Bills et al. (2019) finds that audit fees are lower and

accounting misstatements occur less often when product market rivals share the same

audit firm. Our findings are different because we demonstrate that audit fees are lower

and accounting misstatements are less frequent when competitors share the same audit

partner. The two sets of results raise a question as to whether the findings are driven by

rival companies sharing the same audit partner or they are driven by rival companies

sharing the same audit firm. We expect limited knowledge sharing between different

partners of the same audit firm because audit firms have controls in place to prevent

confidential client-specific information being passed from one partner to another

(Demski et al. 1999; McAllister and Cripe 2008). Therefore, we distinguish between rival

companies who share the same partner and rival companies who share the same audit

firm but do not share the same partner.

For these analyses, we re-estimate eqs. (1) and (2) after replacing Partner Sharing

Rival with Audit Firm Sharing Rival which equals one when a company shares the same

audit firm with its rivals but does not share the same partner with its rivals (zero

otherwise). The results are reported in Appendix 3. As shown in Col. (1) of Panel A, we

find an insignificant coefficient on Audit Firm Sharing Rival which indicates that audit fees

are not significantly different when rival companies share the audit firm but not the same

partner. In Cols. (2) and (3), we find that the probability of an accounting misstatement is

not significantly different when rival companies share the audit firm but do not share the

25

partner. In Panel B, we examine the results for Audit Office Sharing Rival, which equals

one when a company shares the same audit office with its rivals but does not share the

same partner (zero otherwise). We find that audit office sharing is not significantly

related to audit fees or accounting misstatements. Overall, our evidence suggests that

there are gains in audit efficiency and audit quality when rival companies share the same

audit partner, but these gains do not extend to rival companies sharing the same audit

firm or the same audit office.

4.3. Robustness of results to using SIC codes to denote product markets

As previously discussed in Appendix 2, there is some overlap between 3-digit SIC codes

and companies’ product market descriptions but there are also lots of observations that

do not overlap. We therefore examine whether our results for audit fees and accounting

misstatements are robust to using SIC codes rather than product market descriptions to

denote product market rivals. In Appendix 4, we report results when product markets

are defined using companies’ 3-digit SIC codes. We continue to find lower audit fees and

fewer accounting misstatements when companies in the same 3-digit SIC code share the

same partner. Therefore, our results are not sensitive to this research design choice.

4.4. Partner-Client Matching (H3)

Table 4, Panel A, describes the procedure that we use for deriving the sample of

company-pairs in order to conduct our test of partner-client matching. Starting with our

26

company-year sample of 6,433 observations, the pair-wise design requires each partner

to audit at least two companies. This restriction reduces the number of company-year

observations to 3,096. We also require that each audit office has at least two partners

because, when an office has only one partner, the partner assignment decision becomes

a moot point. This further reduces the number of company-year observations to 2,966.

Using this sample, we then create every possible pair of companies in each audit office.

For example, an office with four clients (W, X, Y, and Z) has six possible client pairings:

W+X, W+Y, W+Z, X+Y, X+Z, and Y+Z. We then merge this sample with product

similarity scores, proprietary information proxies, and other variables. This leaves us

with 17,562 company-pairs, 2,061 companies, 856 partners, and 50 audit firms. Our

proxies for redactions, trade secrets, and proprietary information are taken from

companies’ 10-K filings, R&D spending is from Compustat, while the patents data are

from Kogan et al (2017).18

Table 4, Panel B reports descriptive statistics for the pairwise sample. The mean

value of Same Partner is 0.134, implying that 13.4% of company-pairs share the same

partner. The mean value of Same Product Market indicates that 22.6% of company-pairs

operate in the same product market. For 63.6% of company-pairs, we find that at least

one company in the pair redacts information from their 10-K filings (Redacted). For 81.8%,

at least one company in the pair discloses the existence of trade secrets in their 10-K filings

(Trade Secret). For 79.8%, at least one company in the pair discloses the existence of

18 A limitation of the patents data is that this information is only available till year 2010.

27

proprietary information in their 10-K filings (Prop Info). For 63.1%, at least one company

has R&D expenditures above the median (R&D High). Finally, 55.7% of observations have

at least one company with an above median number of patents (Patents High).

As a preliminary univariate test of H3, we begin by plotting the percentage of

company-pairs that share the same partner against the quintiles of product similarity

scores. Fig. 1 shows that companies are much more likely to share the same partner when

they operate in more similar product markets. Panel A of Table 5 reports a formal

univariate test for H3. The mean value of Same Partner is 0.207 for company-pairs that

operate in the same product market whereas it is just 0.112 for company-pairs that

operate in different product markets. This result indicates that 11.2% of company-pairs

share the same partner when they operate in different product markets, whereas the

frequency is 20.7% among company-pairs that operate in the same product market. The

univariate difference is highly significant (t-stat. = 15.60).

Panel B reports the regression results for H3. Consistent with the univariate results

in Fig. 1 and Panel A, we find the coefficients on the Product Similarity Score are positive

and highly significant (t-stats. = 10.56, 9.21, 13.98). Similarly, the coefficients on Same

Product Market are positive and highly significant (t-stats. = 9.42, 8.74, 11.65). These results

confirm that companies are more likely to share the same partner when they operate in

the same product market. This suggests that, on average, the benefits of product market

rivals sharing the same partner exceed the costs. This makes sense given our findings for

28

H1 and H2 that companies pay significantly lower audit fees and receive higher quality

audits when they share the same audit partner as their product market rivals.

4.5. Proprietary Information Concerns (H4)

Next, we examine whether concerns about proprietary information mitigate the tendency

for rival companies to share the same audit partner (H4). We start by plotting the

relationships between partner sharing and product similarity scores after partitioning the

sample between companies with high (vs. low) proprietary information concerns. Fig. 2

plots these relationships using redactions from 10-K filings as the proxy for proprietary

information concerns (i.e., Redacted = 0 vs. Redacted = 1). We find that when proprietary

information concerns are low (Redacted = 0), the probability of sharing the same partner

increases with the degree of product similarity between the pair of companies. However,

when proprietary information concerns are high (Redacted = 1), the probability of partner

increases with product similarity at a much lower rate. In Fig. 3 to 6, we find very similar

evidence using the other proxies for proprietary information concerns: Trade Secret, Prop

Info, R&D High, and Patents High. These results suggest that rival companies are less likely

to share the same partner when they are more concerned about proprietary information.

Table 6 reports the univariate tests for H4. Panel A reports results using Redacted

as the proxy for proprietary information concerns. We find that when proprietary

information concerns are low (Redacted = 0), 41.6% of company-pairs share the same

partner when they operate in the same product market compared to 12.5% for company-

29

pairs that operate in different product markets. In contrast, when proprietary information

concerns are high (Redacted = 1), only 15.0% of company-pairs share the same partner

when they operate in the same product market compared to 10.3% for company-pairs

that operate in different product markets. The difference-in-differences test ((41.6% -

12.5%) - (15.0% - 10.3%)) is highly significant, which means that the tendency for rival

companies to share the same partner is significantly attenuated when there are greater

concerns about proprietary information. This is consistent with the evidence in Fig 2 and

is consistent with H4. In Panels B to E, we find that the results are very similar when we

use the other proxies for proprietary information concerns (Trade Secret, Prop Info, R&D

High, Patents High).

Table 7 reports the regression results for H4. Panel A uses Redacted as the proxy

for proprietary information concerns, while Panels B to E use Trade Secret, Prop Info, R&D

High, and Patents High, respectively. In Panel A, the variables of interest are the interaction

terms, Same Product Market × Redacted and Product Similarity Score × Redacted. Under H4,

we expect negative coefficients on these interaction variables because we expect the

positive association between partner sharing and product market rivalry to be

significantly attenuated when companies are more concerned about proprietary

information. Consistent with H4, Panel A shows that the coefficients are negative and

highly significant for Same Product Market × Redacted (t-stats. = −5.53, −7.00, −7.19) and

Product Similarity Score × Redacted (t-stats. = −4.82, −7.21, −9.82). These results confirm our

univariate evidence and Fig. 2. In Panels B to E, we report similar results using the other

30

four proxies for proprietary information concerns. Overall, our results suggest that rival

companies are less likely to share the same partner when they are more concerned about

proprietary information.

5. Conclusion

We argue that the partner-client matching process reflects both the potential for beneficial

knowledge transfers and clients’ concerns about the potential leakage of proprietary

information to their competitors. Knowledge transfers arise when a partner uses her

knowledge from auditing one client to enhance the efficiency and effectiveness of

auditing a similar client. Consistent with these knowledge transfers being economically

important, we demonstrate that there are significant cost savings (i.e., lower audit fees)

and significant improvements in audit quality (i.e., fewer accounting misstatements)

when product market rivals share the same audit partner. Interestingly, we demonstrate

that these benefits disappear when product market rivals share the same audit office or

the same audit firm but do not share the same partner.

Despite the potential concerns that companies are likely to have about proprietary

information being leaked to their product market rivals, we find that, on average,

companies are more likely to share the same audit partner as their rivals rather than a

different audit partner. Nonetheless, we also find that the tendency for competitors to

share the same partner is significantly attenuated when there are greater concerns about

proprietary information. We therefore conclude that the audit partner matching process

31

reflects both the benefits of positive knowledge transfers and the potential costs of

proprietary information leakage.

32

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36

APPENDIX 1 Variable Definitions

Panel A: Variables used in the company-year sample

Dependent variables

Ln(AF) = The natural logarithm of the company’s audit fee.

Misstate = An indicator equal to 1 if the company’s financial statements are subsequently restated, 0 otherwise.

Variables of interest

Partner Sharing Rival = An indicator equal to 1 if the partner audits another company thatoperates in the same product market as the focal company, 0 otherwise.

Partner Sharing Non-Rival = An indicator equal to 1 if the partner audits another company but that company does not operate in the same product market as the focal company.

Partner Sharing SIC3 Rival

= An indicator equal to 1 if the partner audits another company in the same audit office and the other company operates in the same SIC-3 industry as the focal company, 0 otherwise.

Partner Sharing SIC3 Non-Rival

= An indicator equal to 1 if the audit office has another company in the same SIC-3 industry product market as the focal company and the two companies are not audited by the same audit partner, 0 otherwise.

Audit Firm Sharing Rival = An indicator equal to 1 if the audit firm has another company in the same product market as the focal company and the two companies are not audited by the same audit partner, 0 otherwise.

Audit Office Sharing Rival = An indicator equal to 1 if the audit office has another company in the same product market as the focal company and the two companies are not audited by the same audit partner, 0 otherwise.

Control variables

Big 4 = An indicator equal to 1 if the company is audited by a Big 4 firm, 0 otherwise.

Bus_Segment = The natural logarithm of the number of reported business segments of the company.

Ext Fin = Proceeds from equity or debt issuance divided by total assets.

Firm Age = The natural logarithm of the number of years since the company first appeared in Compustat.

Foreign Op = An indicator equal to 1 if the company has a non-zero foreign currency translation adjustment, 0 otherwise.

Inventory = Inventory divided by total assets.

Ln(NAF) = The natural logarithm of the company’s non-audit fee (source: Audit Analytics).

Ln(#Partners) = The natural logarithm of the number of partners who work in the audit office (source: PCAOB).

Loss Firm = An indicator equal to 1 if ROA < 0, 0 otherwise.

37

APPENDIX 1 (continued)

Panel A: Variables used in the company-year sample (continued)

M&A = An indicator equal to 1 if the company is involved in a merger or acquisition, 0 otherwise.

Restructure = An indicator equal to 1 if the company restructures its operations (non-missing values for RCA, RCEPS, RCP, or RC in Compustat), 0 otherwise.

ROA = Net income divided by total assets.

Size = The natural logarithm of total assets. #Clients = The natural logarithm of the number of companies audited by the

partner.

Panel B: Variables used in the pair-wise sample

Dependent variable

Same Partner = An indicator equal to 1 if the pair of companies is audited by the same partner, 0 otherwise.

Variables of interest

Product Similarity Score = Product similarity score for the company pair (from Hoberg and Phillips 2016).

Same Product Market = An indicator equal to 1 if both companies operate in the same product market, 0 otherwise (from Hoberg and Phillips 2016).

Patents High = An indicator equal to 1 if the number of patents generated between 2006-2010 is greater than the median for at least one company in the pair, 0 otherwise (from Kogan et al. 2017).

Prop Info = An indicator equal to 1 if at least one company in the pair mentions "proprietary information" in its 10-K filing.

R&D High = An indicator equal to 1 if at least one company in the pair has R&D divided by total assets greater than the median, 0 otherwise.

Redacted = An indicator equal to 1 if at least one company in the pair mentions "confidential treatment", "redacted", "ct order", "foia", "rule 406", or "rule 24b-2" in its 10-K filing. These words are commonly used in SEC fillings when companies omit parts of their SEC filings by applying for “confidential treatment” per Rule 406 under the Securities Act of 1933 or Rule 24b-2 under the Securities Exchange Act of 1934.

Trade Secret = An indicator equal to 1 if at least one company in the pair mentions "trade secret" or "trade secrecy" in its 10-K filing.

Control variables

Big 4 = An indicator equal to 1 if the company pair is audited by a Big 4 audit firm, 0 otherwise.

Log (#Partners) = The natural logarithm of the number of partners who work in the audit office.

38

APPENDIX 1 (continued)

Panel B: Variables used in the pair-wise sample (continued)

Loss Firm 1 = An indicator equal to 1 if ROA < 0 for the first company in the pair, 0 otherwise.

Loss Firm 2 = An indicator equal to 1 if ROA < 0 for the second company in the pair, 0 otherwise.

ROA 1 = Net income divided by total assets for the first company in the pair.

ROA 2 = Net income divided by total assets for the second company in the pair.

Size 1 = The natural logarithm of total assets for the first company in the pair.

Size 2 = The natural logarithm of total assets for the second company in the pair.

#Clients 1 = The natural logarithm of number of companies audited by the partner who audits the first company in the pair.

#Clients 2 = The natural logarithm of number of companies audited by the partner who audits the second company in the pair.

39

APPENDIX 2 Degree of Overlap Between Classifying Companies as Product Market Rivals Based on Their 3-digit SIC Codes or Their Product Market Descriptions

Appendix 2 reports the degree of overlap between classifying companies as product market rivals based on their 3-digit SIC codes versus their product descriptions. Panel A shows the distribution of company-year observations across the same product descriptions (Partner Sharing Rival) and the same 3-digit SIC code (Partner Sharing SIC3 Rival). Panels B and C report the same information as percentages.

Panel A: Number of Company-Year Observations

Partner Sharing SIC3 Rival = 0 Partner Sharing SIC3 Rival = 1 Total

Partner Sharing Rival = 0 5,054 226 5,280

Partner Sharing Rival = 1 349 804 1,153

Total 5,403 1,030 6,433

Panel B: Different 3-digit SIC Codes Within the Same Product Market Description

Partner Sharing SIC3 Rival = 0 Partner Sharing SIC3 Rival = 1 Total

Partner Sharing Rival = 1 30% 70% 100%

Panel C: Different Product Market Descriptions within the Same 3-digit SIC Code

Partner Sharing SIC3 Rival = 1

Partner Sharing Rival = 0 22%

Partner Sharing Rival = 1 78%

Total 100%

40

APPENDIX 3 Results for Audit Fees and Misstatements when Product Market Rivals Share the Same Audit Firm or Share the Same Audit Office

This table examines audit fees and misstatements when product market rivals share the same audit firm (Panel A), or share the same audit office (Panel B), but do not share the same partner. t-statistics in parentheses are based on standard errors clustered at the company level. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, respectively. All variables are defined in Appendix 1.

Panel A: Sharing the same audit firm but not the same partner

Dependent Variables (1) (2) (3)

Ln(AF) Misstate Misstate

Audit Firm Sharing Rival 0.018 0.035 0.002

(0.98) (0.22) (0.30)

#Clients −0.113*** 0.036 0.008

(−7.10) (0.27) (1.52)

Size 0.367*** 0.100** 0.002

(56.89) (2.00) (1.03)

ROA −0.232*** 0.166 0.008

(−7.12) (0.45) (0.81)

Loss Firm 0.122*** 0.052 −0.003

(5.94) (0.25) (−0.41)

Ln(#Partners) −0.008 −0.147* 0.012

(−0.25) (−1.69) (0.61)

Big 4 −0.344*

(−1.67)

Bus_Segment 0.176*** 0.095 0.001

(14.33) (0.91) (0.29)

Foreign Op 0.278*** 0.313* 0.008

(13.50) (1.74) (1.12)

Inventory 0.374*** −0.194 −0.014

(4.62) (−0.24) (−0.49)

Ext Fin 0.162*** −0.206 −0.013

(5.97) (−0.81) (−1.56)

M&A 0.129*** 0.137 0.006

(7.14) (0.83) (0.88)

Restructure 0.217*** 0.129 0.005

(11.59) (0.74) (0.69)

Misstate 0.107***

(2.81)

Ln(NAF) 0.018***

(8.15)

41

APPENDIX 3 Results for Audit Fees and Misstatements when Product Market Rivals Share the Same Audit Firm or Share the Same Audit Office

Panel A: Sharing the same audit firm but not the same partner (continued)

Dependent Variables (1) (2) (3)

Ln(AF) Misstate Misstate

Firm Age −0.156 −0.007

(−1.22) (−1.36)

Observations 6,433 6,433 6,433

Model OLS Logit OLS

Year FE Yes Yes Yes

Audit Office FE Yes No Yes

Adjusted R2 0.845 0.020 0.046

Panel B: Sharing the same audit office but not the same partner

Dependent Variables (1) (2) (3)

Ln(AF) Misstate Misstate

Audit Office Sharing Rival −0.024 −0.203 0.001

(−1.20) (−1.25) (0.09)

#Clients −0.122*** 0.000 0.008

(−7.71) (0.00) (1.52)

Size 0.368*** 0.108** 0.002

(56.92) (2.14) (1.05)

ROA −0.234*** 0.148 0.008

(−7.15) (0.40) (0.80)

Loss Firm 0.123*** 0.060 −0.003

(6.00) (0.28) (−0.42)

Ln(#Partners) −0.007 −0.129 0.012

(−0.21) (−1.47) (0.61)

Big 4 −0.354*

(−1.74)

Bus_Segment 0.176*** 0.096 0.001

(14.34) (0.92) (0.30)

Foreign Op 0.275*** 0.297* 0.008

(13.33) (1.65) (1.12)

Inventory 0.363*** −0.268 −0.014

(4.48) (−0.34) (−0.50)

Ext Fin 0.161*** −0.219 −0.013

(5.91) (−0.86) (−1.56)

42

APPENDIX 3 Results for Audit Fees and Misstatements when Product Market Rivals Share the Same Audit Firm or Share the Same Audit Office

Panel B: Sharing the same audit office but not the same partner (continued)

Dependent Variables (1) (2) (3)

Ln(AF) Misstate Misstate

M&A 0.129*** 0.135 0.006

(7.14) (0.82) (0.88)

Restructure 0.216*** 0.129 0.005

(11.56) (0.74) (0.69)

Misstate 0.107***

(2.82)

Ln(NAF) 0.018***

(8.21)

Firm Age −0.168 −0.007

(−1.31) (−1.37)

Observations 6,433 6,433 6,433

Model OLS Logit OLS

Year FE Yes Yes Yes

Audit Office FE Yes No Yes

Adj. (Pseudo) R2 0.845 0.0212 0.046

43

APPENDIX 4 Using 3-digit SIC Codes to Define Product Markets

This table examines the associations between audit fees (misstatements) and partners who audit another company in the same 3-digit SIC code as the focal company. t-statistics in parentheses are based on standard errors clustered at the company level. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, respectively. All variables are defined in Appendix 1.

Dependent Variables (1) (2) (3)

Ln(AF) Misstate Misstate

Partner Sharing SIC3 Rival −0.172*** −0.832*** −0.026**

(−5.56) (−2.77) (−2.36)

Partner Sharing SIC3 Non-Rival −0.021 −0.199 −0.005

(−0.97) (−1.04) (−0.53)

#Clients −0.070*** 0.253 0.015**

(−3.58) (1.58) (2.01)

Size 0.368*** 0.100** 0.002

(57.60) (2.03) (1.08)

ROA −0.245*** 0.110 0.007

(−7.57) (0.31) (0.64)

Loss Firm 0.123*** 0.052 −0.003

(6.03) (0.25) (−0.42)

Ln(#Partners) −0.014 −0.124 0.011

(−0.44) (−1.44) (0.56)

Big 4 −0.331

(−1.63)

Bus_Segment 0.169*** 0.071 0.001

(13.79) (0.68) (0.12)

Foreign Op 0.273*** 0.297* 0.008

(13.31) (1.67) (1.04)

Inventory 0.336*** −0.326 −0.019

(4.13) (−0.41) (−0.65)

Ext Fin 0.163*** −0.221 −0.014

(6.09) (−0.87) (−1.59)

M&A 0.123*** 0.118 0.005

(6.85) (0.72) (0.75)

Restructure 0.213*** 0.110 0.004

(11.43) (0.63) (0.62)

Misstate 0.099***

(2.63)

44

APPENDIX 4 (continued) Using 3-digit SIC Codes to Define Product Markets

Dependent Variables (1) (2) (3)

Ln(AF) Misstate Misstate

Ln(NAF) 0.017***

(7.98)

Firm Age −0.183 −0.008

(−1.43) (−1.52)

Observations 6,433 6,433 6,433

Model OLS Logit OLS

Year FE Y Y Y

Audit Office FE Y N Y

Adj. (Pseudo) R2 0.846 0.0253 0.047

Partner Sharing SIC3 Rival vs Partner Sharing SIC3 Non-Rival

F-(Chi-sq) stat 28.97*** 5.443** 6.392**

45

Figure 1 Product Similarity and Partner Sharing

Figure 1 plots the percentage of shared partners (Y-axis) against the quintiles of product similarity scores (X-axis) for pairs of companies. Company pairs with product similarity scores equal to zero are in bin zero. The vertical axis indicates the percentage of company-pairs that share the same audit partner. The variables are defined in Appendix 1.

46

Figure 2 Redaction, Product Similarity, and Partner Sharing

Figure 2 plots the percentage of shared partners (Y-axis) against the quintiles of product similarity scores (X-axis) for pairs of companies. Company pairs with product similarity scores equal to zero are in bin zero. The vertical axis indicates the percentage of company-pairs that share the same audit partner. Figure 2 shows the relationships separately for company-pairs with Redacted = 0vs. Redacted = 1. The variables are defined in Appendix 1.

47

Figure 3 Trade Secrets, Product Similarity, and Partner Sharing

Figure 3 plots the percentage of shared partners (Y-axis) against the quintiles of product similarity scores (X-axis) for pairs of companies. Company pairs with product similarity scores equal to zero are in bin zero. The vertical axis indicates the percentage of company-pairs that share the same audit partner. Figure 3 shows the relationships separately for company-pairs with Trade Secret = 0 vs. Trade Secret = 1. The variables are defined in Appendix 1.

48

Figure 4 Proprietary Information, Product Similarity, and Partner Sharing

Figure 4 plots the percentage of shared partners (Y-axis) against the quintiles of product similarity scores (X-axis) for pairs of companies. Company pairs with product similarity scores equal to zero are in bin zero. The vertical axis indicates the percentage of company-pairs i that share the same audit partner. Figure 4 shows the relationships separately for company-pairs with Prop. Info. = 0vs. Prop. Info. = 1. The variables are defined in Appendix 1.

49

Figure 5 R&D Expenditures, Product Similarity, and Partner Sharing

Figure 5 plots the percentage of shared partners (Y-axis) against the quintiles of product similarity scores (X-axis) for pairs of companies. Company pairs with product similarity scores equal to zero are in bin zero. The vertical axis indicates the percentage of company-pairs that share the same audit partner. Figure 5 shows the relationships separately for company-pairs with R&D High = 0vs. R&D High = 1. The variables are defined in Appendix 1.

50

Figure 6 Patents, Product Similarity, and Partner Sharing

Figure 6 plots the percentage of shared partners (Y-axis) against the quintiles of product similarity scores (X-axis) for pairs of companies. Company pairs with product similarity scores equal to zero are in bin zero. The vertical axis indicates the percentage of company-pairs that share the same audit partner. Figure 6 shows the relationships separately for company-pairs with Patents High = 0 vs. Patents High = 1. The variables are defined in Appendix 1.

51

TABLE 1 Sample Selection

Table 1 describes the sample selection process for the company-year sample (N = 6,433).

Filters # Obs # Companies # Partners # Audit Firms

(1) US companies and their audit partners in 2016-2017 (source: PCAOB).

20,093 12,334 3,623 289

(2) Non-missing gvkey (source: Compustat). 10,323 5,688 3,288 235

(3) Require data availability for product similarity scores and other variables.

6,433 3,750 2,592 83

52

TABLE 2 Descriptive Statistics

Table 2 reports the descriptive statistics for the company-year sample. Panel A presents the number of observations by partner sharing category whereas Panel B summarizes statistics for the variables. The continuous variables are winsorized at the 1% and 99% percentiles. All variables are defined in Appendix 1.

Panel A: Number of Observations by Partner Sharing Category

N %

# Total number of observations 6,433 100% # Observations with partners who audit two or more clients in the same product market (Partner Sharing Rival)

1,153 18%

# Observations with partners who audit two or more clients not in the same product market (Partner Sharing Non-Rival)

1,861 29%

# Observations with partners who audit only one client 3,419 53%

Panel B: Company-Year Sample (N = 6,433)

Variable Mean SD P25 Median P75

Partner Sharing Rival 0.179 0.384 0.000 0.000 0.000

Partner Sharing Non-Rival 0.289 0.453 0.000 0.000 1.000

Ln(AF) 14.011 1.246 13.150 14.027 14.834

Misstate 0.040 0.196 0.000 0.000 0.000

#Clients 0.659 0.562 0.000 0.693 1.099

Size 7.021 2.173 5.578 7.167 8.512

ROA −0.076 0.311 −0.046 0.012 0.052

Loss Firm 0.346 0.476 0.000 0.000 1.000

Ln(#Partners) 2.270 0.957 1.609 2.303 3.135

Big 4 0.712 0.453 0.000 1.000 1.000

Bus_Segment 0.850 0.832 0.000 0.693 1.609

Foreign Op 0.424 0.494 0.000 0.000 1.000

Inventory 0.063 0.107 0.000 0.005 0.091

Ext Fin 0.186 0.304 0.000 0.036 0.251

M&A 0.289 0.453 0.000 0.000 1.000

Restructure 0.286 0.452 0.000 0.000 1.000

Firm Age 2.936 0.731 2.398 3.045 3.434

Ln(NAF) 10.154 4.652 9.680 11.641 13.053

53

TABLE 3 Partner Sharing, Audit Fees, and Misstatements

Table 3 examines the associations between partner sharing and audit fees (misstatements). t-statistics in parentheses are based on standard errors clustered at the company level. F (Chi-sq) statistics compare the statistical differences between the coefficients on Partner Sharing Rival and Partner Sharing Non-Rival. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, respectively. All variables are defined in Appendix 1.

Panel A: Partner Sharing, Audit Fees, and Misstatements – Univariate Analysis

Partner Sharing Rival = 0(a)

Partner Sharing Rival = 1 (b)

Difference (t-stat) (b) − (a)

Ln(AF) 14.133 13.453 −0.680 (−17.17***)

Misstate 0.044 0.019 −0.025 (−4.00***)

Panel B: Partner Sharing, Audit Fees, and Misstatements – Multivariate Analysis

Dependent Variables (1) (2) (3) (4) (5) (6)

Ln(AF) Ln(AF) Misstate Misstate Misstate Misstate

Partner Sharing Rival −0.160*** −0.167*** −0.979*** −0.987*** −0.030*** −0.029***

(−5.63) (−5.72) (−3.53) (−3.56) (−3.50) (−2.77)

Partner Sharing Non-Rival 0.016 −0.013 −0.138 −0.120 −0.006 −0.002

(0.74) (−0.59) (−0.72) (−0.63) (−0.79) (−0.22)

#Clients −0.096*** −0.071*** 0.230 0.261 0.010 0.016**

(−5.09) (−3.64) (1.44) (1.64) (1.41) (2.05)

Size 0.371*** 0.369*** 0.111** 0.112** 0.004** 0.002

(59.90) (56.85) (2.24) (2.25) (2.13) (1.17)

ROA −0.265*** −0.247*** 0.062 0.097 0.000 0.006

(−7.44) (−7.55) (0.18) (0.27) (0.05) (0.56)

Loss Firm 0.194*** 0.124*** 0.054 0.051 0.002 −0.003

(9.42) (6.07) (0.26) (0.24) (0.28) (−0.41)

Ln(#Partners) 0.124*** −0.011 −0.138 −0.120 −0.005 0.012

(10.95) (−0.33) (−1.63) (−1.39) (−1.51) (0.60)

Big 4 0.579*** −0.325 −0.357* −0.011

(22.02) (−1.59) (−1.75) (−1.40)

Bus_Segment 0.198*** 0.169*** 0.066 0.058 0.003 0.000

(15.77) (13.86) (0.63) (0.56) (0.64) (0.06)

Foreign Op 0.305*** 0.272*** 0.260 0.275 0.010 0.007

(14.18) (13.23) (1.48) (1.55) (1.41) (0.97)

Inventory 0.368*** 0.341*** −0.462 −0.345 −0.018 −0.019

(4.57) (4.22) (−0.59) (−0.44) (−0.65) (−0.64)

54

TABLE 3 (continued) Partner Sharing, Audit Fees and Misstatements

Panel B: Partner Sharing, Audit Fees, and Misstatements – Multivariate Analysis (continued)

Dependent Variables (1) (2) (3) (4) (5) (6)

Ln(AF) Ln(AF) Misstate Misstate Misstate Misstate

Ext Fin 0.214*** 0.166*** −0.246 −0.216 −0.008 −0.013

(7.74) (6.20) (−0.96) (−0.85) (−1.02) (−1.52)

M&A 0.151*** 0.124*** 0.120 0.117 0.005 0.005

(7.87) (6.92) (0.73) (0.72) (0.73) (0.75)

Restructure 0.219*** 0.212*** 0.105 0.104 0.004 0.004

(11.33) (11.40) (0.61) (0.60) (0.63) (0.59)

Restate 0.110*** 0.097**

(2.84) (2.57)

Ln(NAF) 0.011*** 0.017***

(5.08) (7.87)

Firm Age −0.216* −0.197 −0.008* −0.008

(−1.69) (−1.53) (−1.67) (−1.60)

Observations 6,433 6,433 6,433 6,433 6,433 6,433

Model OLS OLS Logit Logit OLS OLS

Year FE N Y N Y N Y Audit Office FE N Y N N N Y Adj. (Pseudo) R2 0.804 0.846 0.021 0.028 0.004 0.048

Partner Sharing Rival vs Partner Sharing Non-Rival F-(Chi-sq) stat 42.51*** 31.44*** 11.62*** 12.39*** 12.77*** 12.00***

55

TABLE 4 Descriptive Statistics – Pairwise Sample

Table 4 presents the sample selection process and summary statistics for the pairwise sample. The values of continuous variables are winsorized at 1% and 99%. All variables are defined in Appendix 1.

Panel A: Sample Selection - Pairwise Sample

Filters # Obs # Companies # Partners # Audit firms

(1) Require data availability for product similarity scores and other variables.

6,433 3,750 2,592 83

(2) Require each partner to audit at least two companies.

3,096 2,132 886 61

(3) Require each audit office to have at least two partners.

2,966 2,061 856 50

(4) Create all possible pairs of companies within the same audit office.

17,562 2,061 856 50

Panel B: Descriptive Statistics - Pairwise Sample

Variable N Mean SD P25 Median P75

Same Partner 17,562 0.134 0.340 0.000 0.000 0.000

Product Similarity Score 17,562 0.071 0.098 0.000 0.025 0.102

Same Product Market 17,562 0.226 0.418 0.000 0.000 0.000

Redacted 1 17,562 0.426 0.495 0.000 0.000 1.000

Redacted 2 17,562 0.466 0.499 0.000 0.000 1.000

Redacted 17,562 0.636 0.481 0.000 1.000 1.000

Trade Secret 1 17,562 0.646 0.478 0.000 1.000 1.000

Trade Secret 2 17,562 0.659 0.474 0.000 1.000 1.000

Trade Secret 17,562 0.818 0.386 1.000 1.000 1.000

Prop Info 1 17,562 0.595 0.491 0.000 1.000 1.000

Prop Info 2 17,562 0.618 0.486 0.000 1.000 1.000

Prop Info 17,562 0.798 0.402 1.000 1.000 1.000

R&D 1 17,562 0.126 0.265 0.000 0.010 0.145

R&D 2 17,562 0.155 0.295 0.000 0.015 0.229

R&D High 17,562 0.631 0.482 0.000 1.000 1.000

Patents 1 13,696 48.655 269.261 0.000 0.000 10.000

Patents 2 7,010 19.764 142.778 0.000 0.000 3.000

Patents High 6,426 0.557 0.497 0.000 1.000 1.000

56

TABLE 5 Product Similarity and Partner Sharing

Table 5 examines whether companies are more likely to share the same audit partner when they operate in the same product market. t-statistics in parentheses are based on standard errors clustered on each company in the pair. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, respectively. All variables are defined in Appendix 1.

Panel A: Product Similarity and Partner Sharing – Univariate Analysis

Same Product Market = 0(a)

Same Product Market = 1(b)

Difference (t-stat)(b) − (a)

Same Partner 0.112 0.207 0.095 (15.60***)

Panel B: Product Similarity and Partner Sharing – Multivariate Analysis

Dependent Var = Same Partner (1) (2) (3) (4) (5) (6)

Product Similarity Score 4.100*** 0.521*** 1.306***

(10.56) (9.21) (13.98)

Same Product Market 0.785*** 0.098*** 0.206***

(9.42) (8.74) (11.65)

#Clients 1 0.432*** 0.441*** 0.041*** 0.110*** 0.044*** 0.115***

(3.72) (3.84) (3.41) (5.73) (3.70) (6.06)

#Clients 2 0.070 0.096 0.008 0.088*** 0.012 0.091***

(0.57) (0.79) (0.62) (4.35) (0.86) (4.52)

Size 1 −0.040** −0.036** −0.002 −0.032*** −0.001 −0.029**

(−2.18) (−1.96) (−0.78) (−2.65) (−0.70) (−2.46)

Size 2 0.015 0.022 0.003 −0.023** 0.004* −0.022**

(0.71) (1.07) (1.50) (−2.08) (1.75) (−1.97)

ROA 1 0.360*** 0.329*** 0.048*** 0.012 0.042*** 0.011

(4.01) (3.70) (4.15) (0.98) (3.61) (0.99)

ROA 2 0.039 0.032 0.007 0.006 0.005 0.007

(0.45) (0.37) (0.66) (0.40) (0.45) (0.43)

Loss Firm 1 −0.159** −0.138** −0.020*** −0.004 −0.018** −0.004

(−2.26) (−1.97) (−2.84) (−0.47) (−2.46) (−0.46)

Loss Firm 2 −0.285*** −0.269*** −0.029*** 0.007 −0.027*** 0.002

(−3.89) (−3.77) (−4.01) (0.87) (−3.80) (0.26)

Ln(#Partners) −1.119*** −1.112*** −0.138*** −0.117*** −0.139*** −0.117***

(−27.02) (−28.61) (−20.93) (−3.97) (−21.68) (−4.11)

Big 4 0.200** 0.220** −0.010 −0.003

(2.07) (2.40) (−0.66) (−0.25)

57

TABLE 5 (continued) Product Similarity and Partner Sharing

Panel B: Product Similarity and Partner Sharing – Multivariate Analysis (continued)

(1) (2) (3) (4) (5) (6)

Observations 17,562 17,562 17,562 17,562 17,562 17,562Model Logit Logit OLS OLS OLS OLS

Audit Office FE No No No Yes No YesCompany1 FE No No No Yes No YesCompany2 FE No No No Yes No Yes

Adj. (Pseudo) R2 0.143 0.136 0.125 0.243 0.118 0.224

58

TABLE 6 Partner Sharing and Proprietary Information Concerns – Univariate Analyses

Table 6 examines whether companies in the same product market are less likely to share the same partner when they have greater concerns about proprietary information. Proprietary information concerns are measured in Panels A to E based on: i) redaction of information from the 10-K filing, ii) mentioning in the 10-K filing the existence of trade secrets, iii) mentioning in the 10-K filing the existence of proprietary information, iv) R&D expenditures, and v) number of patents. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, respectively. All variables are defined in Appendix 1.

Panel A: Mean values of Same Partner (where Redacted is the proxy for proprietary information)

Same Product Market = 0 (a)

Same Product Market = 1(b)

Difference (t-stat) (b) − (a)

Redacted = 0 (c)

0.125 0.416 0.291 (22.21***)

Redacted = 1 (d)

0.103 0.150 0.047 (6.90***)

Difference (t-stat) (d) − (c)

−0.245 (−17.30***)

Panel B: Mean values of Same Partner (where Trade Secret is the proxy for proprietary information)

Same Product Market = 0 (a)

Same Product Market = 1(b)

Difference (t-stat) (b) − (a)

Trade Secret = 0 (c)

0.112 0.391 0.279 (18.54***)

Trade Secret = 1 (d)

0.112 0.163 0.051 (7.68***)

Difference (t-stat) (d) − (c)

−0.229 (−14.86***)

Panel C: Mean values of Same Partner (where Prop Info is the proxy for proprietary information)

Same Product Market = 0 (a)

Same Product Market = 1(b)

Difference (t-stat) (b) − (a)

Prop Info = 0 (c)

0.131 0.365 0.233 (14.59***)

Prop Info = 1 (d)

0.107 0.176 0.069 (10.67***)

Difference (t-stat) (d) − (c)

−0.164 (−10.21***)

59

TABLE 6 (continued) Partner Sharing and Proprietary Information Concerns – Univariate Analyses

Panel D: Mean values of Same Partner (where R&D High is the proxy for proprietary information)

Same Product Market = 0(a)

Same Product Market = 1 (b)

Difference (t-stat) (b) − (a)

R&D High = 0 (c)

0.107 0.377 0.271 (24.01***)

R&D High = 1 (d)

0.116 0.138 0.022 (3.15***)

Difference (t-stat) (d) − (c)

−0.248 (−18.97***)

Panel E: Mean values of Same Partner (where Patents High is the proxy for proprietary information)

Same Product Market = 0(a)

Same Product Market = 1 (b)

Difference (t-stat) (b) − (a)

Patents High = 0 (c)

0.125 0.433 0.308 (16.47***)

Patents High = 1 (d)

0.117 0.157 0.040 (2.37***)

Difference (t-stat) (d) − (c)

−0.268 (−10.72***)

60

TABLE 7 Partner Sharing and Proprietary Information Concerns – Regression Analyses

Table 7 examines whether companies in the same or similar product markets are less likely to share the same partner when they have greater proprietary cost concerns. Proprietary information concerns are measured in Panels A to E based on: i) redaction of information from the 10-K filing, ii) mentioning in the 10-K filing the existence of trade secrets, iii) mentioning in the 10-K filing the existence of proprietary information, iv) R&D expenditures, and v) number of patents. t-statistics in parentheses are based on standard errors clustered on each company in the pair. ***, **, and * indicate significance at the 0.01, 0.05, and 0.10 levels, respectively. All variables are defined in Appendix 1.

Panel A: Redacted is used as the proxy for proprietary information concerns

Dependent Var = Same Partner (1) (2) (3) (4) (5) (6)

Product Similarity Score 6.616*** 1.146*** 2.242***

(9.69) (10.53) (19.51)

Redacted −0.057 −0.116* 0.009 0.021 −0.008 −0.033**

(−0.80) (−1.72) (1.34) (1.45) (−1.24) (−2.20)

Product Similarity Score × Redacted −3.824*** −0.862*** −1.381***

(−4.82) (−7.21) (−9.82)

Same Product Market 1.396*** 0.233*** 0.378***

(10.22) (9.73) (11.30)

Same Product Market × Redacted −0.926*** −0.185*** −0.251***

(−5.53) (−7.00) (−7.19)

Control variables? Yes Yes Yes Yes Yes Yes

Audit Office FE No No No Yes No Yes

Company1 FE No No No Yes No Yes

Company2 FE No No No Yes No Yes

Observations 17,562 17,562 17,562 17,562 17,562 17,562

Model Logit Logit OLS OLS OLS OLS

Adj. (Pseudo) R2 0.149 0.142 0.136 0.256 0.128 0.234

61

TABLE 7 (continued) Partner Sharing and Proprietary Information Concerns – Regression Analyses

Panel B: Trade Secret is used as the proxy for proprietary information concerns

Dependent Var = Same Partner (1) (2) (3) (4) (5) (6)

Product Similarity Score 6.115*** 1.071*** 2.190***

(7.90) (9.07) (17.97)

Trade Secret 0.064 −0.068 0.019** −0.008 −0.007 −0.071***

(0.63) (−0.69) (2.01) (−0.47) (−0.81) (−3.69)

Product Similarity Score × Trade Secret −2.946*** −0.726*** −1.213***

(−3.47) (−5.74) (−8.11)

Same Product Market 1.206*** 0.198*** 0.342***

(7.61) (8.09) (9.87)

Same Product Market × Trade Secret −0.626*** −0.135*** −0.185***

(−3.47) (−5.06) (−5.05)

Control variables? Yes Yes Yes Yes Yes Yes

Audit Office FE No No No Yes No Yes

Company1 FE No No No Yes No Yes

Company2 FE No No No Yes No Yes

Observations 17,562 17,562 17,562 17,562 17,562 17,562

Model Logit Logit OLS OLS OLS OLS

Adj. (Pseudo) R2 0.146 0.138 0.131 0.253 0.123 0.231

Panel C: Proprietary Information is used as the proxy for proprietary information concerns

Dependent Var = Same Partner (1) (2) (3) (4) (5) (6)

Product Similarity Score 6.393*** 1.106*** 2.073***

(8.78) (9.62) (16.71)

Prop Info 0.007 −0.111 0.016* 0.028** −0.008 −0.016

(0.08) (−1.37) (1.77) (1.97) (−0.94) (−1.09)

Product Similarity Score × Prop Info −3.072*** −0.731*** −0.977***

(−3.92) (−6.08) (−6.85)

Same Product Market 1.139*** 0.183*** 0.292***

(7.99) (7.41) (8.92)

Same Product Market × Prop Info −0.476*** −0.107*** −0.109***

(−2.91) (−4.05) (−3.29)

Control variables? Yes Yes Yes Yes Yes Yes

Audit Office FE No No No Yes No Yes

Company1 FE No No No Yes No Yes

Company2 FE No No No Yes No Yes

Observations 17,562 17,562 17,562 17,562 17,562 17,562

Model Logit Logit OLS OLS OLS OLS

Adj. (Pseudo) R2 0.146 0.138 0.131 0.249 0.121 0.225

62

TABLE 7 (continued) Partner Sharing and Proprietary Information Concerns – Regression Analyses

Panel D: R&D High is used as the proxy for proprietary information concerns

Dependent Var = Same Partner (1) (2) (3) (4) (5) (6)

Product Similarity Score 6.800*** 1.132*** 2.357***

(11.55) (12.32) (22.88) R&D High 0.009 −0.126* 0.012 −0.075*** −0.013* −0.147***

(0.12) (−1.67) (1.52) (−2.87) (−1.76) (−4.72) Product Similarity Score × R&D High −4.908*** −0.942*** −1.797***

(−6.82) (−9.33) (−13.28)Same Product Market 1.321*** 0.205*** 0.357***

(11.15) (9.97) (11.60) Same Product Market × R&D High −0.982*** −0.170*** −0.263***

(−6.31) (−7.22) (−7.65)

Control variables? Yes Yes Yes Yes Yes Yes Audit Office FE No No No Yes No Yes Company1 FE No No No Yes No Yes

Company2 FE No No No Yes No Yes Observations 17,562 17,562 17,562 17,562 17,562 17,562 Model Logit Logit OLS OLS OLS OLS

Adj. (Pseudo) R2 0.151 0.143 0.139 0.264 0.128 0.236

Panel E: Patents High is used as the proxy for proprietary information concerns

Dependent Var = Same Partner (1) (2) (3) (4) (5) (6)

Product Similarity Score 6.440*** 1.135*** 2.259***

(7.83) (8.27) (14.58)

Patents High 0.074 −0.002 0.017 −0.033 0.000 −0.071**

(0.70) (−0.02) (1.61) (−0.99) (0.05) (−2.10)

Product Similarity Score × Patents High −3.996*** −0.857*** −1.292***

(−3.06) (−4.58) (−5.98)

Same Product Market 1.353*** 0.230*** 0.386***

(7.50) (7.06) (7.63)

Same Product Market × Patents High −1.149*** −0.210*** −0.274***

(−3.96) (−4.98) (−4.89)

Control variables? Yes Yes Yes Yes Yes Yes

Audit Office FE No No No Yes No Yes

Company1 FE No No No Yes No Yes

Company2 FE No No No Yes No Yes

Observations 6,426 6,426 6,426 6,426 6,426 6,426

Model Logit Logit OLS OLS OLS OLS

Adj. (Pseudo) R2 0.151 0.144 0.146 0.236 0.137 0.211