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.
3
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
6
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.
7
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.
8
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
9
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
10
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
11
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
12
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
13
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.
14
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
15
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
17
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.
18
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.
20
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
References
Aobdia, D. 2015. Proprietary information spillovers and supplier choice: Evidence from
auditors. Review of Accounting Studies 20 (4): 1504-1539.
Aobdia, D., S. Siddiqui, and A. Vinelli. 2019. Does engagement partner perceived
expertise matter? Evidence from the US operations of the Big 4 audit firms. Working
paper.
Bae, G.S., Choi, S.U., Rho, J.H., 2016. Audit hours and unit audit price of industry
specialist auditors: Evidence from Korea. Contemporary Accounting Research 33,
314-340.
Bae, G.S., Choi, S.U., Dhaliwal, D.S., Lamoreaux, P.T., 2017. Auditors and client
investment efficiency. The Accounting Review 92, 19-40.
Balsam, S., J. Krishnan, and J. Wang. 2003. Auditor industry specialization and earnings
quality. Auditing: A Journal of Practice & Theory 22: 71-97.
Becker, G. 1973. A theory of marriage: Part 1. Journal of Political Economy 81 (4): 813-846.
Bills, K.L., Jeter, D.C., Stein, S.E., 2015. Auditor industry specialization and evidence of
cost efficiencies in homogenous industries. The Accounting Review 90, 1721-1754.
Bills, K. L., M. Cobabe, J. Pittman, and S. E. Stein. 2019. To share or not to share: The
importance of peer firm similarity to auditor choice. Working paper.
Bonner, S.E., Lewis, B.L., 1990. Determinants of auditor expertise. Journal of Accounting
Research, 1-20.
Brown, S. V., and W. R. Knechel. 2016. Auditor–client compatibility and audit firm
selection. Journal of Accounting Research 54 (3): 725-775.
Cairney, T.D., Young, G.R., 2006. Homogenous industries and auditor specialization: An
indication of production economies. Auditing: A Journal of Practice & Theory 25,
49-67.
Cahan, S.F., Godfrey, J.M., Hamilton, J., Jeter, D.C., 2008. Auditor specialization, auditor
dominance, and audit fees: The role of investment opportunities. The Accounting
Review 83, 1393-1423.
Carson, E., and N. Fargher. 2007. Note on audit fee premiums to client size and industry
specialization. Accounting and Finance 47 (3): 423-446.
33
Chade, H., J. Eeckhout, and L. Smith. 2017. Sorting through search and matching models
in economics. Journal of Economic Literature 55 (2): 493-544.
Craswell, A., J. R. Francis, and S. L. Taylor. 1995. Auditor brand name reputations and
industry specializations. Journal of Accounting and Economics 20: 297-322.
Dekeyser, S., Gaeremynck, A., Willekens, M., 2019. Evidence of Industry Scale Effects on
Audit Hours, Billing Rates, and Pricing. Contemporary Accounting Research 36 (2):
666-693.
Demski, J. S., T. R. Lewis, D. Yao, and H. Yildirim. 1999. Practices for managing
information flows within organizations. Journal of Law, Economics, and
Organization 15 (1): 107-131.
De Simone, L., Ege, M.S., Stomberg, B., 2014. Internal control quality: The role of auditor-
provided tax services. The Accounting Review 90, 1469-1496.
Dhaliwal, D., P. Lamoreaux, L. Litov, and J. Neyland. 2016. Shared auditors in mergers
and acquisitions. Journal of Accounting and Economics 61: 49-76.
Dunn, K.A., Mayhew, B.W., 2004. Audit firm industry specialization and client disclosure
quality. Review of accounting studies 9, 35-58.
Eichenseher, J.W., Danos, P., 1981. The analysis of industry-specific auditor
concentration: Towards an explanatory model. The Accounting Review 56, 479.
Ellis, J. A., C. E. Fee, and S. E. Thomas. 2012. Proprietary costs and the disclosure of
information about customers. Journal of Accounting Research 50 (3): 685-727.
Ferguson, A., and D. Stokes. 2002. Brand name audit pricing, industry specialization, and
leadership premiums post‐Big 8 and Big 6 mergers. Contemporary Accounting
Research 19 (1): 77-110.
Ferguson, A., J. R. Francis, and D. Stokes. 2003. The effects of firm-wide and office-level
industry expertise on audit pricing. The Accounting Review 78 (2): 429-448.
Francis, J. R., K. Reichelt, and D. Wang. 2005. The pricing of national and city-specific
reputations for industry expertise in the US audit market. The Accounting Review
80 (1): 113-136.
Fung, S.Y.K., Gul, F.A., Krishnan, J., 2012. City-level auditor industry specialization,
economies of scale, and audit pricing. The Accounting Review 87, 1281-1307.
Gale, D. and L. Shapley. 1962. College admissions and the stability of marriage. The
American Mathematical Monthly 69 (1): 9-15.
34
Gilson, R.J., Mnookin, R.H., 1984. Sharing among the human capitalists: An economic
inquiry into the corporate law firm and how partners split profits. Stanford Law
Review 37: 313-392.
Glaeser, S. 2018. The effects of proprietary information on corporate disclosure and
transparency: Evidence from trade secrets. Journal of Accounting and Economics 66
(1): 163-193.
Goodwin, J., and D. Wu. 2014. Is the effect of industry expertise on audit pricing an office-
level or a partner-level phenomenon? Review of Accounting Studies 19 (4): 1532-
1578.
Hoberg, G., and G. Phillips. 2010. Product market synergies and competition in mergers
and acquisitions: A text-based analysis. Review of Financial Studies 23 (10): 3773-
3811.
Hoberg, G., and G. Phillips. 2016. Text-based network industries and endogenous
product differentiation. Journal of Political Economy 124 (5): 1423-1465.
Johnson, W.B., Lys, T., 1990. The market for audit services: Evidence from voluntary
auditor changes. Journal of Accounting and Economics 12, 281-308.
Johnstone, K.M., Li, C., Luo, S., 2014. Client-auditor supply chain relationships, audit
quality, and audit pricing. Auditing: A Journal of Practice & Theory 33, 119-166.
Kogan, L., Papanikolaou, D., Seru, A., Stoffman, N., 2017. Technological innovation,
resource allocation, and growth. The Quarterly Journal of Economics 132, 665–712.
Koh, K., Rajgopal, S., Srinivasan, S., 2013. Non-audit services and financial reporting
quality: evidence from 1978 to 1980. Review of Accounting Studies 18, 1-33.
Kwon, S. 1996. The impact of competition within the client's industry on the auditor
selection decision. Auditing: A Journal of Practice & Theory 15 (1): 53.
Lennox, C. and X. Wu. 2018. A review of the archival literature on audit partners.
Accounting Horizons 32 (2): 1-35.
Lennox, C., Park, C.W., 2007. Audit firm appointments, audit firm alumni, and audit
committee independence. Contemporary Accounting Research 24, 235-258.
McAllister, B. and B. Cripe. 2008. Improper release of proprietary information: Firm
specialization increases the risks. The CPA Journal 78 (3): 52-55.
Mayhew, B. W., and M. S. Wilkins. 2003. Audit firm industry specialization as a
differentiation strategy: Evidence from fees charged to firms going public. Auditing:
A Journal of Practice & Theory 22 (2): 33-52.
35
Minutti-Meza, M. 2013. Does auditor industry specialization improve audit quality?
Journal of Accounting Research 51 (4): 779-817.
Reichelt, K. and D. Wang. 2010. National and office-specific measures of auditor industry
expertise and effects on audit quality. Journal of Accounting Research 48: 647-686.
Verrecchia, R. E. 1983. Discretionary disclosure. Journal of Accounting and Economics
5:179-194.
Verrecchia, R.E., Weber, J., 2006. Redacted disclosure. Journal of Accounting Research 44,
791-814.
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