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Competitive Differentiation Effects of Common Management*

Yang Fan†

Mu-Jeung Yang‡ This Version: May 30, 2020

Abstract

We show that common management, defined as connections of firms through

board directors, implies more competitive differentiation. To estimate causal

effects, we exploit exogenous variation of director deaths to show that firms

that are more commonly managed are more differentiated in terms of product

segment shares, product descriptions, and even patenting and patent citations.

To strengthen identification, we show that our results continue to hold for

third-party firms in the board director network, which are not directly affected

by a director death. We provide evidence that these results are driven by

information sharing, under which commonly managed firms access more

credible information on the competition positioning of potential competitors.

Keywords: Common management, Board Directors, Product Differentiation, Innovation JEL Codes: G34, G31

†Assistant Professor, Department of Economics, Colby College, Email: yffan@colby.edu ‡Visiting Assistant Professor, Department of Finance, Eccles School of Business, University of Utah, Email: mjyang@eccles.utah.edu

*For helpful comments, we would like to thank Nick Bloom, Johnathan Brogaard, Jeff Coles, Jarrad Harford, Davidson Heath, Chris Foreman, Yihui Pan, Matt Ringgenberg, Nathan Seegert, Feng Zhang, and the seminar participants at the Eccles School of Business, and the 2017 Academy of Management Annual Meetings.

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1. Introduction

According to longstanding antitrust doctrine, common management through overlapping board

directors can potentially undermine competition: shared directors can provide links between firms

to share not just competitive information but also facilitate tacit collusion. As a consequence, the

practice of common management through shared directors among direct competitors has been

explicitly outlawed by Section 8 of the Clayton Act of 19141. However, both the current empirical

literature as well as antitrust authorities largely assume in this context, that indirect firm

connections through board members as well as cross-industry competitive effects do not matter.

How problematic these assumptions can be is illustrated by a recent example of cross-industry

director sharing: Google CEO Eric Schmidt and Genentech Chairman Arthur Levinson sat on the

boards of Google and Apple simultaneously since 2006. Shareholder activism stemming from

Google’s acquisition of Android in 2005 led to an investigation by the FTC, resulting in the

resignation of Schmidt from Apple’s board and Levinson from Google’s board in late 2009. This

example shows how indirect common management connections and newly emerging cross-

industry competition can have potentially important consequences for finance, IO, innovation

economics and macroeconomics.

In this study, we provide novel empirical evidence that common management, including

inconspicuous indirect connections of public firms through the network of directors, matters for

competitive positioning, defined as the differentiation of firms along the two dimensions of

product market space and technology space, see Bloom, Schankerman and Van Reenen, 2013. We

focus on competitive positioning, since the multi-market contact of corporations such as Amazon,

Apple, Google and others is increasingly muddying old industry classifications, market definitions,

and identities of competitors. In this context, competitive positioning enables us to analyze all

possible market segments and technology areas where corporations might overlap and therefore

1 In practice, not many public companies are in violation of the Clayton Act, partly due to the vague definition of “direct competition” in the case law surrounding the Clayton Act, which typically is narrower than the 2 digit SIC industries we consider here. Additionally, there are rules exempting lines of businesses that fall below 4% of corporate sales, see https://www.ftc.gov/news-events/blogs/competition-matters/2017/01/have-plan-comply-bar-horizontal-interlocks.

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offers a broader measurement of competition than has traditionally been used.2 In this context, we

build on conceptual arguments by Shaked and Sutton, 1987; Sutton, 1991, who showed that

endogenous product differentiation through advertising and R&D can allow large firms to raise

entry barriers and sustain concentrated markets, while Hoberg and Phillips have shown that

corporate product differentiation across sectors matters for our understanding of M&A

performance (Hoberg and Phillips, 2010), corporate financial policies (Hoberg, Phillips and

Prabhala, 2012), R&D and advertising investments (Hoberg and Phillips, 2016a) and industry

selection choices of conglomerates (Hoberg and Phillips, 2016b).

To our knowledge, this paper is also the first to quantify the degree of common management by

measuring distance in the board network, which is the sum of all direct or indirect links among

public firms through their directors. This measurement strategy enables us to uncover the impact

of more indirect and therefore often inconspicuous common management links on competitive

positioning. Specifically, we ask two related questions. First, what is the impact of common

management on competitive positioning? Second, what is the mechanism driving how common

management impacts competitive positioning?

The first main question - regarding the impact of common management on competitive positioning

- presents a challenging identification problem. The reason is that public firms typically

endogenously select directors for their boards. To address this endogeneity issue, we build on an

approach by Fracassi, 2017, who used exogenous variations of unexpected director deaths as an

instrument for changes in direct board connections of two firms sharing a common director. Since

we are interested in the causal impact of an increase in board network distance as a measure of the

degree of common management, we extend Fracassi’s approach and calculate implied exogenous

increases in board network distance (decreasing common management), resulting from unexpected

director deaths. We then analyze the impact of decreasing common management on competitive

2 Additionally, Bloom, Schankerman and Van Reenen (2013) have shown that competitive positioning influences the type and degree of R&D spillovers across firms. On the one hand, public firms that are more similar in product market space are more likely to exhibit stronger business stealing effects, which implies a stronger negative impact of one firm’s R&D on the second firm’s profit. On the other hand, firms that are more similar in technology space are more likely to be subject to stronger knowledge spillovers, implying a stronger positive impact one firm’s R&D on the second firm’s profit.

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positioning along its two dimensions of product market and technology space. For this purpose,

we bring together measures for competitive positioning from different sources. First, we provide

two measures of endogenous product market differentiation. Our first measure is based on

differences in revenue distribution across industries in Compustat, following a similar approach

by Bloom, Schankerman and Van Reenen, 2013. The second measure is based on product

description differences in regulatory filings, constructed by Hoberg and Phillips, 2016. Second,

we measure technology space positioning by the differences in technology class distribution of

patent applications by firms, using patenting data from the USPTO, as well as a measure of patent

citation flows from Kogan et al., 2017.

Prior work, mostly focusing on direct board connections or social ties, has established that directly

connected firms are more similar along corporate financial policies, Fracassi, 2017; Shue, 2013;

Chen, Dyball and Wright, 2009 and Haunschild, 1993; and corporate governance choices, Nguyen,

2012 and Bouwman, 20113. In contrast, we find that companies with more common management,

tend to be more differentiated instead of more similar with respect to their competitive positioning.

In particular, we estimate that an exogenous increase in common management causes product market

differentiation and patenting differentiation to increase, not to decrease. Companies with more

common management are more differentiated, while companies with less common management

are more similar to each other.

Since our main measure of the degree of common management is the distance in the board

network, we can at the same time strengthen our identification arguments and offer a deeper

exploration of the competitive differentiation effects through the board network. In particular, a

plausible concern with using director deaths as exogenous variation, is that these deaths have a

direct effect on the boards, which then leads to a potential failure of exclusion restrictions for our

instrument4. However, our second set of main results shows that the competitive differentiation

results continue to be significant if we exclude companies that share a common director. In other

3 Specifically, Fracassi 2017 shows that the more professional and social ties two companies share, the more similar their capital investments and R&D decisions are while Bouwman 2011 shows that governance practices like board size, % of outside directors, and CEO compensation, tend to propagate across board networks, again making corporate boards more similar across board networks. 4 For example, Nguyen and Nielsen 2010 find that sudden and unexpected director death may trigger board chaos which result in negative stock price reactions.

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words, even two companies that do not share a director, but whose implied distance in the board

network increases as result of an unexpected director death by a third-party company, still become

more similar in terms product market and technological differentiation.

In the context of our third main question, we provide evidence that sheds light on two mechanisms

that could plausibly drive our main results. The first potential mechanism is that, firms with

common management might collude in order to soften direct price competition, see Shaked and

Sutton, 1982; Bernheim and Whinston, 1990. Bernheim and Whinston, 1990 summarize this

collusion mechanism as follows: “when firms compete simultaneously in several different

markets, each may come to specialize in some subset of these markets, and such specialization

may help firms maintain high prices.” Under this collusion hypothesis, less common management

makes it harder to tacitly collude with firms that are more distant on the board network and

therefore results in less competitive differentiation between the firms. We offer three different

approaches to test the collusion hypothesis, based on implications for markups, average industry

prices, and the interaction of common management with corporate concentration. None of these

three approaches provides evidence in favor of the collusion hypothesis.

A second potential mechanism might be that firms with higher degrees of common management

receive more credible information on the competitive positioning of their potential competitors,

which they might unilaterally use to competitively differentiate, in the spirit of the theoretical

literature on information sharing in oligopoly models such as Gal-Or, 1985; Vives, 1990; Amir,

Jin, Troege, 2010 among others. Under this information sharing hypothesis, firms with less

common management, obtain less credible information on each other’s competitive positioning

and therefore end up more similar. Using a stylized model, we show why sharing information

might be pareto-improving for potential competitors. This analysis highlights that directors

involved in such information sharing are not necessarily in breach of their fiduciary duties. But

this raises the question: If information sharing is beneficial to disclosing firms, why wouldn’t all

firms simply disclose such information publicly instead of using director networks?

We provide two different empirical approaches to test the information sharing hypothesis and shed

light on the question of why director networks are used.

First, we use the impact of patent news shocks from Kogan et al, 2017 on R&D investments of

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potential competitors. Since patent applications become public information after 18 months and

the average time to issuance is about 32 months, these patent news shocks mostly contain

information about the credibility of pursued technologies. We find that non-patenting firms exhibit

stronger R&D reductions prior to patent grants to competitors and weaker R&D reductions after

patents are issued. These patterns are consistent with the idea that more commonly managed firms

are informed ahead of time about the credibility of technologies of their closely connected

competitors. Importantly, this evidence supports the idea that patent information travelling through

the “director rumor mill” is more credible than public information on patent applications alone.

This credibility gap might explain why it is challenging for firms to simply publicly announce their

competitive positioning.

Second, credible public communication of competitive positioning might require effort. If this is

the case, then firms that succeed to be publicly transparent should make less use of common

management as information sharing tool, so that competitive differentiation effects of common

management are weaker among transparent firms. We proxy firm transparency with greater stock

analyst agreement and again find patterns that are supportive of information sharing.

Related Literature

To our knowledge, this study is the first to quantify the impact of common management on

competitive positioning. This study contributes to at least four distinct literatures.

First, our work is related to empirical work on endogenous product market differentiation and its

implications for corporate decisions, as in Hoberg and Phillips, 2010, 2012, 2016a, 2016b and

Bloom, Schankerman and Van Reenen, 2013. Much of this literature takes product market

differentiation as given and explores the implications of differentiation on M&A performance

(Hoberg and Phillips, 2010), corporate financial policies (Hoberg, Phillips and Prabhala, 2012),

industry selection choices of conglomerates (Hoberg and Phillips, 2016b) and R&D spillovers

(Bloom, Schankerman and Van Reenen, 2013). An exception is Hoberg and Phillips, 2016a, who

analyze the relationship between text-based product differentiation measures and R&D and

advertising investments, but none of these studies provides causal evidence on the determinants of

endogenous product differentiation, as our study does.

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Second, our work also complements existing work on the influence of social networks on corporate

finance, as in Fracassi, 2017; Shue, 2013; Nguyen, 2012; Bouwman, 2011; Chen, Dyball and

Wright, 2009, David and Greve, 1997 and Haunschild, 1993. However, while much of this

literature focuses on corporate policies of directly connected firms, we generalize this notion and

emphasize the role of indirect network effects while providing additional evidence on whether

results are driven by collusion or information sharing through board networks.

Third, there is a large literature on information sharing among oligopolistic competitors, see Vives,

1984; 1990; Gal-Or, 1985; 1986; Li, 1985; Sakai, 1985; Sakai and Yamato 1989; Raith, 1996;

Amir, Jin, Troege, 2010, to name just a few references. Much of this work has focused on

information sharing within trade and industry associations and work on the empirical implications

of information sharing has been rare. We contribute to this literature by providing evidence in

support of the importance of information sharing through common management.

Fourth, our findings broadly relate to empirical work on the impact of common ownership on

industry competition, such as Azar, 2012; He and Huang, 2017; Azar, Schmalz and Tecu, 2018.

While this literature analyzes the influence of inconspicuous minority ownership positions on

competitive conduct of public companies within industries, we instead focus on the impact of

direct and indirect common management through shared board members on cross-industry and

cross-technology class competition.

Fifth, our work extends insights from the literature on innovation and management or ownership,

including CEO overconfidence (Galasso and Simcoe, 2011), board independence (Balsmeier,

Fleming and Manso, 2017), and common ownership (Anton, Ederer, Gine and Schmalz, 2018).

While this literature analyzes the impact of common ownership or board independence on the

firm’s overall level of innovative activity, our key dependent variable of competitive positioning

instead focuses on the degree of firm differentiation in terms of product differentiation or

innovation similarity.

2. Data and Measurement

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2.1 Common management 2.1.1 Measurement of Common management

Two firms are defined to have a direct connection in a given year through their boards if these two

firms share a director on their board. In other words, when a director simultaneously sits on the board

of both firms, the two firms are said to be linked, connected or interlocked and the distance between

these two firms is defined to be one.

[Figure 1]

Figure 1. provides an example of how a director can connect two firms. In 2009, Wal- Mart and General

Electric Co. were connected by Doctor Jim Cash Jr. Mr. Cash was a long tenured Wal-Mart

independent director, joining Wal-Mart in 1996 before joining General Electric’s board in 2006.

By joining General Electric’s board while simultaneously sitting on Wal-Marts board, Mr. Cash

interlocks the two firms. Similarly, a different director simultaneously sits on the boards of General

Electric and Proctor Gamble while another director sits on the board of Proctor & Gamble and

American Express.

Related to the definition of connection is the definition of network distance. While connections

describe whether a link between two firms exist or not, the network distance describes the

minimum path between two firms, as measured by how many directors need to be involved in

connecting the two firms. Previously for Wal-Mart and General Electric, a single director connects

both firms directly, therefore the network distance is one. The same is true when we consider the

firms General Electric and Proctor & Gamble as well as Proctor & Gamble and American Express.

For all these companies, we define the degree of common management to be high, as network

distance in the board network is low. However, for firms not directly connected such as Apple and

American Express, network distance is two and therefore the degree of common management is

lower.

Our measurement of common management using the shortest path connecting two companies in

the network of board directors, has the distinct advantage of being generalizable to companies that

are only indirectly connected. To understand this, consider Nordstrom and Apple in Figure 1,

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which do not share a common director. Instead, Apple and Nordstrom can only be indirectly

connected through two paths in this figure: first, Nordstrom shares a director with American

Express, which shares a director with Walmart, which in turn shares a director with Apple. Or

more concisely, one needs three director links to connect Nordstrom to Apple, which therefore

implies a network distance of 3. Note also, that there exists an alternative path, that connects Apple

and Nordstrom in figure 1, which links Apple to Walmart, to General Electric, to Proctor &

Gamble, to American Express and then to Nordstrom. This path has a length of 5, since it requires

5 director links to connect the two companies. As long as Walmart and American Express are

directly connected, the shortest distance between Apple and Nordstrom is 3. However, our

empirical analysis will place special emphasis on cases, where the connection between American

Express and Walmart is dissolved, and this increases the network distance between Nordstrom and

Apple from 3 to 5. The consequences of such indirect network effects are only possible when

including indirectly connected companies in the set of commonly managed firms.

2.1.2 Data on Common Management To measure common management, we need to characterize the network of board of directors

among public firms. Board network connections as well as director characteristics are obtained

from BoardEx. The BoardEx data contains corporate board data as well as director characteristics

data obtained from public firms’ annual proxy statements to shareholders. Matching BoardEx data

to the Compustat list of S&P 1500 firms requires first matching based on security CUSIP. Most of

the firm-years are matched using this method. For firms not matched by CUSIP, we then match by

ticker symbol if the firms are still active, manually verifying a correct match was made. For firms

that are inactive, we match by firm name if available. Using this procedure, we match about 93%

of S&P 1500 firms from 2003 to 20135.

5 Since BoardEx data is obtained from annual proxy reports to shareholders, report dates can and generally do occur mid-year. We apply the standard practice of converting the dates into calendar years. For report dates that occur in July or later, we classify information regarding the firms board to that same year. For report dates that occur in June or before, we classify that as the firms board data for the previous year. This is also our standard procedure for converting Compustat fiscal year data into calendar year data.

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For each firm-year observation that we match to BoardEx board data, we observe information on

the board, including board size, director composition, the name of each director, the role of the

director, and length of time the director has been in that role. Each BoardEx firm has a unique

identifier for the firm as well as the director. Using overlaps of the same director across multiple

firms in a given year, we can map out the board network that connects the S&P 1500 firms. The median

firm in our sample has 10 directors and these directors are connected to 7 other firms each year. The

median director is also quite experienced at 7.3 years on the firm.

[Table 1], [Figure 2]

While direct common management connections between firms are rare, firms can still be partly

commonly managed though indirect connections in the board network. We characterize this notion

of common management based on the minimum number of directors needed to connect two boards.

To get a better sense of this notion of common management, it is informative to provide some

summary statistics on the average shortest path between two boards. Table 1. summarizes the

shortest network distance between two firms in the sample. We also provide a histogram of

network distances across firm-pairs in our data in figure 2. For any two firms in any year, an

average of 4.5 boards connects two firms, while the median number of boards that connect the two

firms is 5. This is slightly larger than the Small-World Phenomenon as described by Davis, Yoo

and Baker, 2003.6

2.2 Measurement of Competitive positioning 2.2.1 Product Segment Differentiation

Each year, firms report product line sales figures in their annual report to the SEC. Suppose

two firms 𝑖𝑖 and 𝑗𝑗, each sell into 𝑛𝑛 product market segments. Each product market segment that

each firm participates in generates revenue for the firms. Let these revenues for the firms be 𝑅𝑅𝑖𝑖𝑖𝑖,𝑛𝑛

and 𝑅𝑅𝑗𝑗𝑖𝑖,𝑛𝑛 at time 𝑡𝑡, for segment 𝑛𝑛, for firms 𝑖𝑖 and 𝑗𝑗, respectively. For two firms that produce

primarily in the same market segment, a majority of sales agents will overlap and thus competition

will be relatively more intense as opposed to a pair of firms that do not overlap in segment sales.

6 Not all firm-pairs can be reached through board networks though and consequently, common management is not a universal phenomenon. About a third of firm boards cannot be reached by other firm boards even though board data exists for both firms. These represent firms that are not commonly managed. In our sample, the vast majority of firm-pairs (80%) that are not connectible through the S&P 1500 board network includes one of the firms from the firm-pair as a member of the S&P 600 index.

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More generally though, firms will distribute their sales force across multiple market segments that

they participate in. The more similar these distributions are for any two firms, the more intense the

competition is likely to be between them.

Formally, let firm 𝑖𝑖’s sales share at time 𝑡𝑡 in 𝑛𝑛 segments be a 1𝑥𝑥𝑥𝑥 vector, 𝐹𝐹𝑖𝑖,𝑖𝑖 =

{𝐹𝐹1,𝑖𝑖,𝐹𝐹2,𝑖𝑖,𝐹𝐹3,𝑖𝑖, …𝐹𝐹𝑛𝑛,𝑖𝑖}. Similarly, let firm 𝑗𝑗’s sales share at time 𝑡𝑡 in 𝑛𝑛 segments also be an

1𝑥𝑥𝑥𝑥 vector, 𝐹𝐹𝑗𝑗,𝑖𝑖 = {𝐹𝐹1,𝑖𝑖,𝐹𝐹2,𝑖𝑖,𝐹𝐹3,𝑖𝑖, …𝐹𝐹𝑛𝑛,𝑖𝑖}. Then, the product segment differentiation score

between firms 𝑖𝑖 and 𝑗𝑗 at time t is:

𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑗𝑗,𝑖𝑖 = −𝐹𝐹𝑖𝑖 𝐹𝐹𝑗𝑗′

(𝐹𝐹𝑖𝑖𝐹𝐹𝑖𝑖′)12�𝐹𝐹𝑗𝑗𝐹𝐹𝑗𝑗′�

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In the extreme case in which both firms sell exactly the same amount into the same

segments, then the vectors, 𝐹𝐹𝑖𝑖,𝑖𝑖 and 𝐹𝐹𝑗𝑗,𝑖𝑖 will be identical, thereby reducing the product market

competition score to -1. If both firms do not sell into any overlapping segments, the product market

competition score between them is exactly 0. More generally, the product market competition

score is between -1 and 0. More negative scores closer to -1 indicate greater similarity and

therefore more competitive intensity between the two firms while a score closer to 0 indicates more

differentiation and therefore less competition. The score also has the attractive properties of being

invariant to the number of segments.

2.2.2 Product Description Differentiation One possible concern with our sales segment differentiation measure may be that abated

competition may not be observed if product market segments are too broadly defined. Therefore,

as a complement to the product segment differentiation score, we also utilize a measure that

captures an alternative measurement of product differentiation. The Hoberg-Philips Text-Based

product description similarity score provides this measurement. Annual 10-K reports to the SEC

contain managerial descriptions of the products that are produced by the firm. Similar to the annual

reports that contain segment sales data, these managerial descriptions are also by law required to

be accurate. The basic premise of the Hoberg-Philips score is that if two managerial descriptions

contain similar wording, the products are more likely to be similar as well. Practically, the Hoberg-

Philips score is created by constructing a word matrix of the product description using a web-

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crawling algorithm to read in the product descriptions for each firm. Common words are excluded.

The remaining words for each firm are used to create a similarity score for each firm pair. We

construct a product differentiation score by making a minor modification to the Hoberg-Philips

score by multiplying it by -1. Therefore, product description differentiation scores for each firm

pair that are close to -1 indicate similar product descriptions, while firm-pairs that report scores

that are closer to zero indicate products that very differently described.

The main attractive feature of the product description differentiation score is that it complements

the sales-based product segment differentiation score with data from a different source. Moreover,

since the product description differentiation score is calculated each year, panel analysis allows us

to compare the average change in product differentiation for any two firms over time.

2.2.3 Patenting Differentiation To measure change in the technology space, we utilize an adapted patenting differentiation score

by Jaffe, 1986. We measure technological differences between two firms based on the types of

patents that the firms produce. The USPTO has 408 technology classes for patents. The firm’s

distribution of patents into these technology classes describes the average technological

positioning of firms in a technology space. Changes in the distribution of patents in technology

classes over time, can be mapped into changes in technological differentiation between two firms.

The construction of technological differentiation of patenting is similar to our measure of product

market competition. Suppose two firms 𝑖𝑖 and 𝑗𝑗, patent into 𝑘𝑘 technology classes. Each technology

class that each firm patents into, requires innovation inputs7. Let these innovation inputs for the

firms be 𝑍𝑍𝑖𝑖𝑖𝑖,𝑘𝑘 and 𝑍𝑍𝑗𝑗𝑖𝑖,𝑘𝑘 at time 𝑡𝑡, for tech class 𝑘𝑘, and for firms 𝑖𝑖 and 𝑗𝑗, respectively. Firms that

patent in the same technology class, will overlap more in innovation inputs, thus the two firms will

be close in some technology space. More generally though, firms will distribute their patents across

multiple technology classes, the more similar these distributions are for any two firms, the more

similar their technology are between them, and the closer they are in some technology space.

Formally, let firms 𝑖𝑖’s patent share (in a technology class) at time 𝑡𝑡 in 𝑛𝑛 segments be a

1𝑥𝑥𝑥𝑥 vector, 𝑇𝑇𝑖𝑖,𝑖𝑖 = {𝑇𝑇1,𝑖𝑖,𝑇𝑇2,𝑖𝑖,𝑇𝑇3,𝑖𝑖, …𝑇𝑇𝑘𝑘,𝑖𝑖}. Similarly, firm 𝑗𝑗’s patent share at time 𝑡𝑡 in 𝑘𝑘 segments

7 Innovation inputs can be R&D expenditures or in the case of Jaffe, 1986, scientist for each technology class.

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also be an 1𝑥𝑥𝑥𝑥 vector, 𝑇𝑇𝑗𝑗,𝑖𝑖 = {𝑇𝑇1,𝑖𝑖,𝑇𝑇2,𝑖𝑖,𝑇𝑇3,𝑖𝑖, …𝑇𝑇𝑘𝑘,𝑖𝑖}. Then, the technology differentiation score

between firms 𝑖𝑖 and 𝑗𝑗 is:

𝑇𝑇𝑃𝑃𝑃𝑃𝑖𝑖𝑗𝑗,𝑖𝑖 = −𝑇𝑇𝑖𝑖 𝑇𝑇𝑗𝑗′

(𝑇𝑇𝑖𝑖𝑇𝑇𝑖𝑖′)12�𝑇𝑇𝑗𝑗𝑇𝑇𝑗𝑗′�

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Similar to the product market competition score for two firms, this technology score is also

invariant to the number of classes. The score value is between -1 and 0. A technology

differentiation score of -1 means that the two firms patent exactly the same number of patents into

the same technology classes, and as a result, are not likely to be technologically differentiated nor

far apart in some technology space. More generally, a technology score that is closer to 0, means

that the two firms are more technologically differentiated. Patenting data and patenting distribution

across time allow us to track how technological positioning may alter following interlocking

events. The data section provides a more detailed treatment on how we go about calculating this

statistic empirically.

2.2.4 Patent Citations The technological differentiation score describes the average positioning of innovations by firms,

but it is silent on the direction of information flows. However, future innovations often build on

past innovations. The implied information flow from knowledge about past innovations to new

innovations is captured in patent citations. (Trajtenberg, 1990).

To measure information flows on innovation, we consider how patent citation numbers change

over time. If firm A and B operate in a similar technological space, they improve on each other’s

existing patents, by applying for new patents. However, to demonstrate that their new

improvements sufficiently warrant a new patent, a patent examiner will consider other patents to

look closely at. These other patents are frequently disclosed ahead of time or cited by the inventor

to ensure the patent examiner closely considers these patents or other patents may be found by the

examiner herself as part of the examination process. Additionally, it is also generally in the best

interest of the inventor to reveal all similar patents to the examiner to avoid patent infringement

litigation.

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2.2.5 Data on Competitive Positioning

Two different sources of data are used to calculate and determine the relative product market space

positioning of each firm-pair. First, we use Compustat Historical Product Segment data to

determine the Product Segment Differentiation score between two firms. Firms in our sample self-

report selling into an average of about 3 product segments. When categorized by SICs, the effective

number of segments is smaller due to overlapping segments. Smaller firms sell in to fewer number of

product segments as compared to larger S&P 500 and S&P 400 firms, on average.

The Product Description Differentiation data is based on Hoberg and Phillips, 2010; 2016a. The data

is based on web crawling and text parsing algorithms that process the text in the business

descriptions of 10-K annual filings on the SEC Edgar website from 1996 to 2015. Since the product

descriptions are legally required to be accurate, they should sufficiently represent the managers

insight to their own firm’s product lines. One of the key benefits of a statistic like the Hoberg-Philips

score is that it can be calculated regardless of whether each firm pair produces in the same primary

SIC and how segments are allocated across SIC classifications.

Our measurements of technological space differentiation come from two main sources. Patent class

and patent-citation data is obtained from USPTO (NBER U.S. Patent Citations Data File) and Kogan

et al. 2017. These datasets contain the patent number, patent application date, CRSP, permno,

technology class that is associated with the patent, and the subsequent citing patents from 2000-

2010. However, a few changes are made in constructing the technology space differentiation

variable that are noteworthy. First, we consider patent applications as opposed to patent grants as patent

granting procedures may include timing lags that firms are not able to control for. Second, since firms

may submit patent applications for many patents one year but no patent for the next few years, we

consider a patent-application window of three years. Therefore, for each firm-pair-year, we use the

past 3-years patent activity as the basis for constructing our score measure8. Summary statistics for

all measures of competitive positioning are provided in table 2.

[Table 2]

8 See Lerner and Seru 2017 for an in-depth discussion on these patent adjustment methodologies.

15

This table shows that the average degree of competitive positioning across firm-pairs is relatively

low, which is unsurprising, as many public firms are active in very different markets and industries.

3. Empirical Approach and Econometric Specifications 3.1 Basic mechanisms and Control Variables Our empirical analysis of the impact of common management on competitive positioning is driven

by two contrasting sets of perspectives. On the one hand, there are at least two mechanisms through

which common management decreases differentiation of product offerings and technologies. First,

firm size might imply less differentiation between a pair of firms by random chance (henceforth

“size effects”). For example, two very diversified firms by definition operate in a variety of sectors

and the chance that they might therefore overlap in a higher number of segments is higher as

opposed to very focused firms. Similar arguments can also be made about firm-pairs in which both

firms have a high number of directors. Second, firms might select to appoint directors to learn

from and imitate other firms, see Davis and Greve, 1997; Shue, 2013; Fracassi, 2017. Under this

“peer imitation” mechanism, firms with higher degrees of common management should be

expected to be less differentiated and more similar in terms of their competitive positioning.

On the other hand, there are also at least two mechanisms that facilitate more differentiation in

response to higher degrees of common management. First, more common management might

increase the opportunity to tacitly collude among firm-pairs that are more closely connected. From

this perspective, more competitive differentiation is indicative of the apportionment of the product

market or technology space among colluding firms to soften price competition and increase

markups in the spirit of Shaked and Sutton, 1982 and Bernheim and Whinston, 1990.

Second, more competitive differentiation among commonly managed firms might reflect flows of

credible information about competitive positioning. From this perspective, credible information

on product offerings, technological investments or potential competitors can be used to avoid

investing in very similar products, or technologies. We call this mechanism “information sharing”.

In this context, it should be noted that the disclosure of credible information on competitive

positioning is plausibly pareto-optimal for all involved firms, as it reduces the likelihood of

16

wasteful duplication of effort and is therefore unlikely to reflect a breach of director’s fiduciary

duties.

[Table 3]

Table 3 documents simple OLS regressions of our measures of competitive positioning on the

degree of common management. While the top panel displays OLS coefficients for the pooled

data, the bottom panel uses first difference specifications to control for firm-pair fixed effects.

Throughout the rest of our analysis, we will control for these firm-pair fixed effects to difference

out any permanent firm-pair level characteristics. The results of table 3 are broadly consistent with

the view that mechanisms that imply more competitive differentiation as well as mechanisms that

imply less competitive differentiation due to common management might potentially apply to the

data. While much of our empirical analysis will be devoted to separating the mechanisms of peer

imitation, collusion, and information sharing, we will use a number of variables to control for

potential size effects directly. This set of control variables includes: (1) a dummy for prior

connections of a firm-pair to control for historical common management effects, rather than

current effects, (2) pair-level total number of connections, following Davis and Greve, 1997 to

control for the centrality of a firm-pair, (3) total number of directors across a company pair to

control for board size effects, (4) total number of industry segments across a firm-pair to control

for the effect that more diversified pairs are more likely to be similar to each other and (5) relative

firm size measured by total assets to control for the fact that firms of different sizes might be

expected to be very different in their competitive positioning.

3.2 Endogeneity Problem and Identification Even after using first differencing to remove time-invariant pair effects and using a number of

proxy variables to control for potential size effects, there are still a number of important

endogeneity problems that need to be addressed. We follow the theoretical literature on

identification in social networks, such as Bramoulle, Djebbari, Fortin, 2009 and Goldsmith-

Pinkham and Imbens, 2013 to understand the associated issues that are also broadly related to the

estimation of peer effects, discussed by Manski, 1993. Social network analysis faces a version of

Manski’s “Reflection Problem”, as board directors and therefore peer firms with common

management are endogenously chosen. As a result, any unobserved common shock within a set of

17

commonly managed firms will create a correlation between policies, that are unlikely to be solely

effects of common management. The ideal solution to address this selection problem is

randomized assignment of firms into groups as argued by Angrist, 2013. In our context this would

be equivalent to random assignment of the degree of common management. To address the

reflection problem, we exploit quasi-random variation in board network distance as induced by

unexpected director deaths.

Nearly all larger public firms have corporate bylaws in place that stipulate how directors are

replaced. In general, for planned vacancies, the nomination committee puts forth nominees to be

voted upon at the company’s next annual shareholder meeting. The outgoing director stays on

during this transition process to ensure a smooth transition. However, for unplanned vacancies such

as a director death, the board seat generally remains vacant until the next shareholder meeting. Data

on director deaths comes from BoardEx and is combined with hand-collected data on whether or

not the director death was unexpected. For this purpose, we use public announcements, news

articles and regulatory statements to determine whether the director death was unexpected or not.

Overall, there are over 300 instances of unexpected director deaths in our 10-year sample. Details

on our data construction of unexpected director deaths can be found in Appendix A.1.

Formally, we use director deaths in the first stage to generate exogenous changes in common

management as measured by board network distance:

Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑛𝑛𝑀𝑀𝑀𝑀𝑛𝑛𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝑀𝑀𝑛𝑛𝑡𝑡𝑖𝑖𝑗𝑗,𝑖𝑖= 𝛼𝛼1 ⋅ 𝑃𝑃𝑖𝑖𝐷𝐷𝑃𝑃𝑀𝑀𝑀𝑀𝑡𝑡ℎ𝑖𝑖𝑗𝑗,𝑖𝑖 + 𝛼𝛼2 ⋅ 𝐶𝐶𝐶𝐶𝑛𝑛𝑛𝑛𝑀𝑀𝐶𝐶𝑡𝑡𝑀𝑀𝑑𝑑𝑖𝑖𝑗𝑗,𝑖𝑖−1 + Δ𝐶𝐶𝐶𝐶𝑛𝑛𝑡𝑡𝐷𝐷𝐶𝐶𝐶𝐶𝑠𝑠𝑖𝑖𝑗𝑗,𝑖𝑖−1+ 𝐺𝐺𝑀𝑀𝐶𝐶𝑖𝑖𝑗𝑗,𝑖𝑖 + 𝑌𝑌𝑀𝑀𝑀𝑀𝐷𝐷𝑖𝑖 + 𝑀𝑀𝐷𝐷𝐷𝐷𝐶𝐶𝐷𝐷

(2)

where ∆ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑛𝑛𝑀𝑀𝑀𝑀𝑛𝑛𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝑀𝑀𝑛𝑛𝑡𝑡𝑖𝑖𝑗𝑗,𝑖𝑖 is the change board network distance between firms 𝑖𝑖 and 𝑗𝑗

at time 𝑡𝑡, 𝑃𝑃𝑖𝑖𝐷𝐷𝑃𝑃𝑀𝑀𝑀𝑀𝑡𝑡ℎ𝑖𝑖𝑗𝑗,𝑖𝑖 is an indicator for whether or not the director death was unexpected,

𝐶𝐶𝐶𝐶𝑛𝑛𝑛𝑛𝑀𝑀𝐶𝐶𝑡𝑡𝑀𝑀𝑑𝑑𝑖𝑖𝑗𝑗,𝑖𝑖−1 captures past connections, Δ𝐶𝐶𝐶𝐶𝑛𝑛𝑡𝑡𝐷𝐷𝐶𝐶𝐶𝐶𝑠𝑠𝑖𝑖𝑗𝑗,𝑖𝑖−1 are controls for size effects, and

𝐺𝐺𝑀𝑀𝐶𝐶𝑖𝑖𝑗𝑗,𝑖𝑖,𝑌𝑌𝑀𝑀𝑀𝑀𝐷𝐷𝑖𝑖 are a set of geography and year fixed effects respectively to control for geographical

spillovers as well as aggregate shocks.

The second stage then uses the exogenous variation in common management to estimate the impact

on competitive positioning:

18

Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑀𝑀𝑡𝑡𝑖𝑖𝑡𝑡𝑖𝑖𝐶𝐶𝑀𝑀𝑃𝑃𝐶𝐶𝑠𝑠𝑖𝑖𝑡𝑡𝑖𝑖𝐶𝐶𝑛𝑛𝑖𝑖𝑗𝑗,𝑖𝑖= 𝛽𝛽1 ⋅ Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑛𝑛𝑀𝑀𝑀𝑀𝑛𝑛𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝑀𝑀𝑛𝑛𝑡𝑡𝑖𝑖𝑗𝑗,𝑖𝑖 + 𝛽𝛽2 ⋅ 𝐶𝐶𝐶𝐶𝑛𝑛𝑛𝑛𝑀𝑀𝐶𝐶𝑡𝑡𝑀𝑀𝑑𝑑𝑖𝑖𝑗𝑗,𝑖𝑖−1+ Δ𝐶𝐶𝐶𝐶𝑛𝑛𝑡𝑡𝐷𝐷𝐶𝐶𝐶𝐶𝑠𝑠𝑖𝑖𝑗𝑗,𝑖𝑖−1 + 𝐺𝐺𝑀𝑀𝐶𝐶𝑖𝑖𝑗𝑗,𝑖𝑖 + 𝑌𝑌𝑀𝑀𝑀𝑀𝐷𝐷𝑖𝑖 + 𝑀𝑀𝐷𝐷𝐷𝐷𝐶𝐶𝐷𝐷

(3)

Throughout our analysis, we estimate all specifications in first differences to control for firm-pair

fixed effects and cluster standard errors on the firm-pair level.

Although the use of unexpected director deaths is well-established in the literature, e.g. Fracassi,

2017, there is good reason to be skeptical that the use of unexpected director deaths by themselves

is potentially problematic (Nguyen and Nielson 2010). To address this potential violation of the

exclusion restriction, we analyze indirect network effects in the spirit of Bramoulle, Djebbari,

Fortin, 2009 and Goldsmith-Pinkham and Imbens, 2013. To understand this strategy, consider the

companies in figure 1. While Wal-Mart and American Express are directly connected through a

director, Apple and Nordstrom are only indirectly connected. If the link between Wal-Mart and

American Express is disconnected, this will reduce the degree of common management between

Apple and Nordstrom by increasing the board network distance from 3 to 5. Since the boards of

the indirectly connected firms Apple and Nordstrom are not directly affected by the unexpected

death of the director that Wal-Mart and American Express shared, there will be no direct impact

of the director death on these boards and therefore no violation of our exclusion restriction.

Formally, this indirect network effect identification strategy leads to the following first stage

Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑛𝑛𝑀𝑀𝑀𝑀𝑛𝑛𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝑀𝑀𝑛𝑛𝑡𝑡𝑘𝑘𝑘𝑘≠𝑖𝑖𝑗𝑗,𝑖𝑖= 𝛼𝛼1 ⋅ 𝑃𝑃𝑖𝑖𝐷𝐷𝑃𝑃𝑀𝑀𝑀𝑀𝑡𝑡ℎ𝑖𝑖𝑗𝑗,𝑖𝑖 + 𝛼𝛼2 ⋅ 𝐶𝐶𝐶𝐶𝑛𝑛𝑛𝑛𝑀𝑀𝐶𝐶𝑡𝑡𝑀𝑀𝑑𝑑𝑘𝑘𝑘𝑘≠𝑖𝑖𝑗𝑗,𝑖𝑖−1+ Δ𝐶𝐶𝐶𝐶𝑛𝑛𝑡𝑡𝐷𝐷𝐶𝐶𝐶𝐶𝑠𝑠𝑘𝑘𝑘𝑘≠𝑖𝑖𝑗𝑗,𝑖𝑖−1 + 𝐺𝐺𝑀𝑀𝐶𝐶𝑘𝑘𝑘𝑘≠𝑖𝑖𝑗𝑗,𝑖𝑖 + 𝑌𝑌𝑀𝑀𝑀𝑀𝐷𝐷𝑖𝑖 + 𝑀𝑀𝐷𝐷𝐷𝐷𝐶𝐶𝐷𝐷

(4)

where the index 𝑘𝑘𝐶𝐶 ≠ 𝑖𝑖𝑗𝑗 captures the fact that we analyze firm-pairs (𝑘𝑘, 𝐶𝐶) which are not the firm-

pair (𝑖𝑖, 𝑗𝑗) directly affected by the death of a shared director. Correspondingly, we will then analyze

the impact of this change in the shortest path on firm behavior of the indirectly affect firm-pair:

Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑀𝑀𝑡𝑡𝑖𝑖𝑡𝑡𝑖𝑖𝐶𝐶𝑀𝑀𝑃𝑃𝐶𝐶𝑠𝑠𝑖𝑖𝑡𝑡𝑖𝑖𝐶𝐶𝑛𝑛𝑘𝑘𝑘𝑘≠𝑖𝑖𝑗𝑗,𝑖𝑖= 𝛽𝛽1 ⋅ Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑛𝑛𝑀𝑀𝑀𝑀𝑛𝑛𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝑀𝑀𝑛𝑛𝑡𝑡𝑘𝑘𝑘𝑘≠𝑖𝑖𝑗𝑗,𝑖𝑖 + 𝛽𝛽2 ⋅ 𝐶𝐶𝐶𝐶𝑛𝑛𝑛𝑛𝑀𝑀𝐶𝐶𝑡𝑡𝑀𝑀𝑑𝑑𝑘𝑘𝑘𝑘≠𝑖𝑖𝑗𝑗,𝑖𝑖−1+ Δ𝐶𝐶𝐶𝐶𝑛𝑛𝑡𝑡𝐷𝐷𝐶𝐶𝐶𝐶𝑠𝑠𝑘𝑘𝑘𝑘≠𝑖𝑖𝑗𝑗,𝑖𝑖−1 + 𝐺𝐺𝑀𝑀𝐶𝐶𝑘𝑘𝑘𝑘≠𝑖𝑖𝑗𝑗,𝑖𝑖 + 𝑌𝑌𝑀𝑀𝑀𝑀𝐷𝐷𝑖𝑖 + 𝑀𝑀𝐷𝐷𝐷𝐷𝐶𝐶𝐷𝐷

(5)

4. Baseline Results

19

4.1 Event Studies We begin by presenting the empirical implications of board director deaths on competitive

positioning in event time. As in the subsequent regression analysis, we conduct our event analysis

on the firm-pair level. The events we analyze are director deaths and we center our analysis around

𝑡𝑡 = 0, which is defined as year of director death. The panels in figure 3 measure event time on the

horizontal axis, which is defined as the years before and after the director deaths in our sample.

Treatment firm-pairs are defined as all pairs for which a director death at 𝑡𝑡 = 0 increases board

network distance. The death event increases the distance in the board network between the firms,

therefore reducing the degree of common management between them. Control firm-pairs are not

affected by director deaths, in the 3 years prior and following the events. We deliberately do not

use any additional control variables or matching procedures at this step to showcase how our

baseline results are already foreshadowed even with this simple approach.

We use the 3-year horizon before and after the respective director deaths to analyze how director

deaths influence competitive positioning. As dependent variables, we use the growth rate in

competitive differentiation, either measured by product segment differentiation, product

description differentiation or patenting differentiation. Average treatment effects are calculated by

averaging changes in competitive differentiation across treatment-pairs and control-pairs and then

taking the difference of the averages.

[Figure 3]

As the panels in figure 3 show, along all of the competitive positioning measures, we see a decline

in competitive differentiation in response to less common management in the wake of director

deaths. The impact of director deaths on our measures of technological competition is consistent

with the results from the product market space. As panel 3C shows, firm-pairs with increased board

network distance or reduced common management, as a result of director deaths, experience less

differentiation in terms of patenting technology classes. At the same time, patent citations (figure

3D) increase, as one would expect if treated firm-pairs start to use similar technologies, relative to

control firm-pairs.

Additionally, figure 4 shows the cumulative sums of the growth rate changes in figure 3.

[Figure 4]

20

This figure highlights that competitive differentiation effects are not transient in their levels but

are persistent, since the steep decline in the change of differentiation is not offset by following

positive changes and the levels do not recover back to their previous values. In other words,

decreasing common management as implied by director deaths, reduces the level of competitive

differentiation among treated firm-pairs.

Although suggestive, this event-study type analysis has several potential shortcomings. First, since

we focus on treated firm-pairs and control-pairs only, we do not exploit the full pair-level data

across all years, which substantially reduces statistical power. Second, the event plots deliberately

did not use any control variables or matching procedure to make treatment and control groups

more comparable. But this has the downside that we did not make sure that firm-pairs are

comparable in terms of relative size, industries, geographies etc. Third, we averaged over all

possible treatments, but the strength of effects might depend on how much network distance

changed as a result of director deaths. All of these shortcomings can be more systematically

addressed within a formal regression framework, to which we now turn.

4.2 Common Management Effects

[Table 4]

Table 4 reports our basic OLS specification, including controls for the size effects discussed in

section 3.1. As discussed in that context, these variables are likely to control for the fact that more

diversified or more central firm-pairs are likely to be less differentiated, even without any effects

working through common management. As a result, it is not surprising, that including these control

variables weakens the positive correlation between closely connected firms’ pairs and their

competitive differentiation. However, what is surprising is that the raw correlation between

common management and competitive positioning switches signs: while firm-pairs that become

more commonly managed become less differentiated in the bottom of table 3, they turn positive in

table 4, implying that more commonly managed firms become more differentiated.

[Table 5]

21

These OLS correlations between competitive positioning and common management should not be

interpreted in causal manner, as they are still likely to suffer from Manski’s reflection problem.

Furthermore, even if these correlations could reflect causal estimates, they are still likely to

confound peer imitation, collusion, and information sharing effects.

Our IV strategy results from equations (2) and (3) start with table 5, which shows the impact of

exogenous disconnections in the product market space. Column (2) displays results for the product

segment differentiation measure, which captures the differences of sales distributions across

Compustat segments. As can be seen, exogenous increases in common management increase

differentiation. In other words, more commonly managed firm-pairs will become more

differentiated in terms of their competitive positioning and will therefore compete less directly

with each other. This is a surprising result in the light of the literature on peer imitation effects of

social networks, such as Fracassi, 2017 and Shue, 2013. A number of features reassure us that our

baseline results are robust. First, OLS and IV results both exhibit the same sign and are highly

significant. Furthermore, the magnitude of our IV results are much larger than the OLS results, as

would be expected if our IV strategy is successful in separating out peer imitation effects (more

similarity of commonly managed firms) from collusion or information sharing effects.

Additionally, these results hold across four different measures of competitive positioning as can

be seen in table 5. Hence, the competitive differentiation effects of common management hold not

just for segment sales, but also for business descriptions and even patenting patterns and patent

citations. Despite the robustness of our initial results, there is reason for concern due to the

potential failure of the exclusion restriction discussed in section 3.2: director deaths might exhibit

direct effects on competitive position, for example through board chaos. While such direct effects

are of interest in their own right, we are more interested in the robustness of common management

effects and are therefore moving to our indirect network identification strategy.

4.3 Indirect Network Effects

[Table 6]

Table 6 reports the results from our analysis of firm-pairs that are not directly affected by a director

death but still see a change in their board network distance as an indirect result of a director death.

This indirect network effect approach still qualitatively uncovers the same competitive

22

differentiation effects of common management. Firm-pairs that see their common management

decrease due to director deaths at third-party firms, become less differentiated over time.

Unsurprisingly, the estimated coefficient sizes of the results in table 6 are much smaller than the

overall effects for all firm-pairs in table 5. This difference might be driven either by the fact that

the indirect network specifications avoid problems with exclusion restrictions or by the fact that

indirect network effects are naturally weaker than direct effects. Regardless, a direct effect of a

shared director death triggering “board chaos” would be absent from these indirect firm-pairs.

Reassuringly, the coefficient sizes are still larger in table 6 than in the corresponding OLS

specifications of table 4, indicating that our IV strategy is successful at addressing potential

endogeneity concerns of the OLS approach.

When evaluating the credibility of the causal effects of common management on competitive

positioning we offer here, it is useful to consider potential violations of exclusion restrictions in

these indirect network specifications. Such violations of the exclusion restrictions would occur if

disconnections between directly connected firm-pairs (𝑖𝑖, 𝑗𝑗) would directly affect the competitive

positioning among only indirectly connected firm-pairs (𝑘𝑘, 𝐶𝐶). One possibility of such a failure of

exclusion are common demand shocks that make similar positioning among a pair (𝑘𝑘, 𝐶𝐶) more

attractive are also correlated with disconnections at the firm-pair (𝑖𝑖, 𝑗𝑗). However, such an effect is

very unlikely, especially since director deaths are unexpected and therefore unrelated to market

opportunities at unrelated third-party firms (𝑘𝑘, 𝐶𝐶). The exclusion restriction might also fail due to

unobserved characteristics among indirectly connected firm-pairs (𝑘𝑘, 𝐶𝐶) that tend to select into

indirect links that also have a high likelihood of disconnection. However, several arguments

suggest that this type of selection is very unlikely. First, these unobserved characteristics of the

indirectly connected firm-pair (𝑘𝑘, 𝐶𝐶) have to be time-varying factors such as expectations, as all

our specifications directly control for firm-pair fixed effects. Second, if director deaths are

unexpected for directly affected firms, they are even harder to forecast by firms that are only

indirectly affected. Additionally, even if a director death at one firm is forecastable, it seems

implausibly challenging to predict these deaths for all possible indirectly connected links through

the board director network. Third, if firms rationally choose directors, it seems implausible that

they would aim to select into networks that are more likely to be fragile. As these considerations

illustrate, explanations that render identification in our indirect network specification invalid are

mostly implausible.

23

5. Robustness and Extensions 5.1 Common Ownership A natural question raised by the common ownership literature in the spirit of Azar, Schmalz and

Tecu, 2018 is whether our results are potentially driven by common ownership effects instead of

common management effects. Such common ownership effects are plausible in the context of

evidence that increased common ownership leads to higher prices, possibly reflecting

internalization of business stealing effects across firms with the same ultimate owner. An

information-based variation of this common ownership hypothesis might state that mutual funds

with joint ownership across companies might question CEOs of different companies about their

product portfolios and pressure them to competitively differentiate. Alternatively, activist funds

might install common directors to guide decision-making towards reduction of business stealing

effects through differentiation. To evaluate the plausibility of such an explanation, we use data on

common ownership stakes by Backus, Conlon and Sinkinson, 2019 to control for common

ownership.

[Table 7]

The results are displayed in table 7 and show that changes in common ownership across specific

firm-pairs are not correlated with competitive positioning among this pair. Importantly, table 7

shows that the inclusion of common ownership controls does not change the common management

coefficients much, which indicates that common management and common ownership are not

strongly correlated. Additionally, to the degree to which common ownership increases the

likelihood of tacit collusion as argued in common ownership literature, these estimates by

themselves suggest that tacit collusion is unlikely to drive our main competitive differentiation

results of common management.

5.2 Alternative Construction of Instrumental Variable

24

Our baseline IV strategy quantifies the impact of director deaths on common management by using

the change in board network distance in the aftermath of a director death. This approach might

raise the concern that the choice of whether to reconnect after a director death can be endogenous.

We therefore pursue an alternative construction of our instrumental variable here. Instead of

calculating the observable change in the board network distance in the wake of director deaths, we

quantify the exogenous variation in common management by calculating the implied distance

change at the time of the director death. In other words, for each firm we calculate the minimum

distance to every other firm in the board network with and without the dead director and use the

difference between these two distances as our measure of exogenous change in common

management. As we show in Appendix A.2, this alternative construction of our IV leaves all

quantitative results almost unchanged.

5.3 Board Chaos Although we argue that our indirect network effects IV approach in section 4.3 addresses the issue

that board chaos in Nguyen and Nielsen, 2010 might explain our results, one might still be

concerned that news about the passing of directors even at indirectly connected firms imposes

“emotional distress” that detracts indirectly disconnected focal firm directors from working on

continued competitive differentiation. If this were the case, we should not find evidence for

competitive differentiation for firms, which become more closely connected through new director

appointments at unrelated third-party firms. Table 8 shows that competitive differentiation effects

of common management are similar when focusing on increased common management through

increased indirect connections. Moreover, the quantitative magnitudes of the coefficients in table

8 are similar to the OLS specifications in table 4, suggesting that competitive differentiation effects

cannot be explained by board chaos effects alone.

5.4 Competitive Differentiation as Result of Peer Imitation Another concern related to director replacement could be that we actually measure the effects of

peer imitation, but that these effects make it seem as if we measure competitive differentiation. To

understand this concern, let us return for a moment to figure 1 and suppose that initially Walmart

and Apple are not connected, but Walmart and American Express are connected. Additionally,

25

assume that American Express and Apple are not very differentiated. Now, suppose the director

connecting Walmart and American Express dies and is replaced by a director connecting Walmart

and Apple. Because Walmart and Apple are connected, Walmart might competitively imitate

Apple and become more similar to Apple. As a consequence, if Apple is very similar to American

Express, Walmart’s imitation of Apple implies that Walmart becomes more similar to American

Express after the director death.

There are several reasons that suggest this form of peer imitation is unlikely to drive our results.

First, note that this explanation requires that the disconnected firm (American Express) and the

newly connecting firm (Apple) always have to be very similar to each other. However, it is more

likely that American Express and Apple are very differentiated. But in this case, Walmart imitating

Apple would imply that it becomes more differentiated relative to American Express after the

director death, which is the opposite of the pattern we find in the data. Second, another necessary

condition for this peer imitation mechanism to work is that it requires that newly connected firms

to become more similar to each other. As we show in Appendix A.3, the data suggest the opposite,

i.e. that newly connected firms start to differentiate from each other and then continue to

differentiate more over time.

5.5 Diversification Effects Our main IV estimates of table 5 are much larger than the OLS estimates of table 4. One reason

why this might be the case is a part of these large quantitative effects might be driven by entry and

exit of firms in new market segments or new business areas. We therefore ask to what degree our

competitive differentiation effects of common management reflect diversification, defined as firms

entering new industries or exiting current industries as opposed to industry intensive margin

effects, which reflect a shift in relative priorities among a given set of industries and products

within a firm. This is especially pertinent to the product segment differentiation measures, since

they capture industry segments, which have traditionally been used to measure firm-level

diversification.

Using Compustat segments data, we evaluate the importance of diversification vs industry

intensive margin effects through two approaches. First, we fix the initial set of segments, so that

the competitive differentiation effects are only driven by shifts in revenue shares of this initial set

26

of segments. Second, we focus on firms that do not change their segments throughout the sample.

Our results in Appendix A.4 show that our competitive differentiation results continue to hold

qualitatively. This is reassuring as it suggests that our competitive differentiation results are not

driven by diversification considerations alone. However, diversification clearly matters for the

quantitative importance of competitive differentiation, suggesting that exogenous shifts in

common management trigger diversification and industry turnover.

6. Evidence on the Mechanism, driving Competitive Differentiation Effects of Common Management 6.1 Collusion In this section we investigate whether our main results of more competitive differentiation in

response to increased common management are driven by collusion. Under this hypothesis,

increased common management is more likely to facilitate tacit collusion and therefore actively

apportion product market segments among cartel members to soften price competition and thereby

increase average markups. Based on this logic, we construct three empirical strategies exploring

the implications of collusion.

6.1.1 Markups Our first empirical strategy directly follows from the main objective of tacit collusion, namely to

increase markups among colluding firms. Therefore, if common management facilitates tacit

collusion, then average markups should be higher for firms’ pairs that have more common

management.

To measure markups at public firms we use three different approaches. First, we calculate profit

rates, defined as ratio of operating revenues minus costs of goods sold (COGS), divided by

operating revenues. Second, we follow the recent empirical IO literature and use GMM estimates

of firm-level production functions with timing restrictions to calculate markups, following the

econometric model of Ackerberg, Caves and Frazier, 2015 and De Loecker and Eekhout, 2018.

These markup estimators identify output elasticities of inputs by assuming that variable cost inputs

contemporaneously respond to unobserved firm-level productivity shocks, while dynamic inputs

27

such as capital stock exhibit no current impact of unobserved firm-level productivity shocks. These

timing assumptions therefore allow the estimation of output elasticities of variable inputs,

controlling for unobserved firm-level productivity shocks, which are then used with revenue shares

of variable inputs to calculate markups. We provide details on our estimation methodology in

Appendix A.5. A key question in this methodology is which inputs can be assumed to be fully

variable and therefore to contemporaneously respond to unobserved firm-level productivity

shocks. While De Loecker and Eekhout, 2018 use COGS as measure for variable cost inputs,

Traina, 2018 argues that Operating Expenses, which includes marketing and other non-production

costs (the item “Selling, General and Administrative Expenses”, SGA) are more appropriate. We

follow both these approaches and calculate two different versions of firm-level markups.

We then use these three different proxies for markups to calculate average pairwise markups,

which we then use as a dependent variable in our baseline IV strategy of equations (2) and (3).

[Table 9]

Table 9 reports the results of our markup specifications. It shows that across all three measures of

markups, firm-pairs with more common management tend to exhibit lower average markups.

These results are qualitatively the exact opposite of what one would expect if tacit collusion drives

the common management effects. In that case, firm-pairs with more common management should

exhibit higher pairwise average markups. However, note that the signs of effects in table 8 are

potentially consistent with an alternative explanation to collusion. More closely connected firms

might be more aware of each other as potential competitors and therefore tend to have lower

pairwise markups. In other words, the markup results might be more consistent with information

sharing than with collusion.

6.1.2 Industry Prices A second potential implication is that tacitly colluding firms pursue product differentiation to

soften price competition in the spirit of Shaked and Sutton, 1982; Bernheim and Whinston, 1990.

Hence if common management facilitates tacit collusion, then more common management across

firms in an industry should increase average prices. To test this hypothesis, we use changes in

average distance of board networks in industries as implied by director deaths to create industry-

28

level exogenous changes in the degree of common management. This empirical approach has the

advantage that we do not need firm-level price data but can instead use industry level price indexes

as provided by the Bureau of Labor Statistics (BLS). These price indexes are likely to capture price

setting by public firms if these firms are the largest firms for every sector and therefore dominate

the industry level price indexes.

[Table 10]

Table 10 shows the results of our industry price index specifications. These results indicate that

the director deaths are significantly affecting the implied average common management changes

within industries and are therefore valid instrumental variables. However, the second stage results

fail to provide convincing evidence for the presence of the predicted price effects. Although the

sign on the price effect of industry-level common management changes is consistent with

collusion, the estimates are too noisy to draw any conclusions.

6.1.3 Concentration and Market Power Potential Our third approach to test for collusion as the leading explanation for our competitive

differentiation effects of common management is more indirect and builds on the relationship of

concentration and tacit collusion. The idea is that if common management facilitates tacit

collusion, then opportunities to tacitly collude should be largest in industries in which

concentration is high. The underlying logic is that in highly concentrated markets, coordination

among the most concentrated firms is easier to achieve. Therefore, if competitive differentiation

effects of common management are driven by collusion, then these competitive differentiation

effects should be stronger for firm-pairs that are mainly active in highly concentrated industries.

To test this hypothesis, we construct an index that measures to what degree firms are active in

highly concentrated industries. This Main Market Concentration Index (MMCI), is the firm-level

revenue-weighted Herfindahl index across all industries in which the firm is active9. We then

interact the pairwise MMCI with the instrumented change in common management to test whether

9 Specifically, firm i’s revenue-weighted HHI index sums all of firm i’s share of the industry’s revenue, weighted by the HHI of that industry for all industries that the firm participates in. A high index score indicates the firm is very active in a very concentrated industry and has potentially more to gain by tacit collusion.

29

firm-pairs that are active in highly concentrated industries indeed exhibit stronger competitive

differentiation effects.

[Table 11]

Table 11 displays our results from our concentration specifications. It shows that in contrast to the

collusion hypothesis, firm-pairs that are active in highly concentrated industries do not in fact

exhibit stronger competitive differentiation effects. Importantly, the results in table 10 also show

that the baseline competitive differentiation effects are not much affected by including this

interaction term as well as the baseline MMCI term, suggesting that concentration does not explain

competitive differentiation effects.

6.2 Information sharing Our analysis of the previous section failed to find strong evidence in favor of collusion to explain

our main competitive differentiation effects of common management. While some of this analysis

– for example on the average markup effects of company pairs with decreasing common

management – was suggestive of the presence of information sharing, such indirect evidence is far

from conclusive. We therefore move to directly test implications of the information sharing

hypothesis in this section. Central to the tests of information sharing is the idea that firm-pairs with

more common management, exhibit more flows of credible information on competitive

positioning among each other.

6.2.1 Theoretical considerations

We start out with addressing the question of whether information sharing by directors should

generally be considered to be a breach of fiduciary duties. Such a breach of fiduciary duties would

indeed be present, if any of the firms an information-sharing director is part of would experience

a loss in profits as result of information sharing. In other words, information sharing would not

necessarily be a breach of fiduciary duties if information sharing leads to a pareto improvement

for all involved firms. We provide a stylized example that this can be the case in the context of a

game theoretic model. Suppose there are two potential competitors 𝑖𝑖, 𝑗𝑗 competing in 𝑘𝑘 = 1, . . ,𝑥𝑥

markets. Each competitor can enter 𝑀𝑀 < 𝑥𝑥 markets simultaneously. Markets are not

distinguishable and therefore the unconditional probability that the other firms has entered a

30

particular market 𝑘𝑘 is 𝐶𝐶0 = 𝑀𝑀𝐾𝐾∈ (0,1). We assume 2 ⋅ 𝑀𝑀 ≪ 𝑥𝑥, so firms are able to completely

avoid each other if they know which markets the other firm is active in. Let 𝐹𝐹 be the entry costs

of entering market k. If at least one other firm is in the market, profits are zero, due to Bertrand

competition. If a firm entered a market alone, it will earn monopoly profits 𝜋𝜋𝑀𝑀. The timing of the

game is as follows:

In stage 1, firms decide whether to share information or not. If they do not share info, they need to

rely on prior 𝐶𝐶0 = 𝑀𝑀𝐾𝐾

to calculate the probability that the other firm will also enter a particular

market 𝑘𝑘. If they do share information, they communicate their strategies and therefore know

whether the other firm plans to enter a market 𝑘𝑘 or not. In stage 2, players make and implement

their entry decisions. Without information sharing, firms decide for all markets whether to enter

or not, given their prior 𝐶𝐶0. Note that after their entry decision, which particular markets a firm

enters is randomized. With information sharing, firms decide whether to enter market 𝑘𝑘 or not

based on the information shared by other firms, therefore introducing a new possibility of

completely avoiding each other.

For each individual market, this is a coordination game with the indifference point at 𝐶𝐶∗ =�𝜋𝜋𝑀𝑀−𝐹𝐹

𝜋𝜋𝑀𝑀� . A firm therefore enters all markets where 𝐶𝐶0 < 𝐶𝐶∗. Hence, expected profits under no info

sharing with 𝐶𝐶0 < 𝐶𝐶∗ and both firms entering M markets are

𝐸𝐸0[𝜋𝜋] = 𝑀𝑀 ⋅ �(1 − 𝐶𝐶0) ⋅ (𝜋𝜋𝑀𝑀 − 𝐹𝐹) − 𝐶𝐶0 ⋅ 𝐹𝐹 �

(6)

In contrast, if firms can perfectly coordinate by sharing which markets they enter and completely

avoiding each other, then profit for each firm is

𝐸𝐸𝑐𝑐[𝜋𝜋] = 𝑀𝑀 ⋅ (𝜋𝜋𝑀𝑀 − 𝐹𝐹)

(7)

The difference in expected profits under full information sharing (7) as opposed to no

information sharing (6) is

𝐸𝐸𝑐𝑐[𝜋𝜋] − 𝐸𝐸0[𝜋𝜋] = 𝐶𝐶0(𝜋𝜋𝑀𝑀 − 𝐹𝐹) + 𝐶𝐶0 ⋅ 𝐹𝐹 (8)

Information sharing therefore increases profits for each firm due to two channels. The first term,

captures the profit gain from receiving information as the focal firm can avoid competition by

31

entering markets that competitors do not enter. Additionally, the second term captures the profit

gain from sending information, as competitors avoid entering markets the focal firm has entered.

6.2.2 News about Credible Technologies In this section we examine the response of firms to shocks of public information to infer the degree

of private information flow among firms with different degrees of common management. The key

idea is to use shocks to information sets of stock markets with unambiguous, observable

implications for firm behavior to infer to what degree information travels along commonly

managed firms. We use the patent shock data from Kogan et al., 2017 as such an information

shock. Kogan et al. construct panel data on the contemporaneous stock market response to the

issuance of patents. Patent applications become common knowledge at least 18 months after the

application, while the average patent is issued after 32 months.10 Therefore, patent issuance is

likely to mostly contain information about the credibility of the technology the firm has patented.11

Importantly, the degree of the stock price response to the patent issuance of a specific firm should

capture the degree the market was surprised by the firm obtaining a patent for that particular

technology. Large positive stock price changes in response to patent issuance announcement

therefore capture large surprises of market participants and therefore large shocks to public

information. We will therefore call these positive stock price responses “patent news”. At the same

time, Kogan et al shows that larger patent news shocks imply market share and profitability losses

of other firms in the same industry. This likely captures the loss of value of existing technological

knowledge for these competitors, which is an important additional advantage of these data for our

purposes. Specifically, since the existing knowledge of competitors is of less value for stronger

patent news shocks, one would expect a negative impact of patent news on R&D spending of

competitors.

We use this combination of patent news shocks and their clear predictions for R&D investments

of competitors to shed light on the information flow effect of common management: if private

information on credible and patentable technologies flows across commonly managed firms, then

10 See: https://www.uspto.gov/about-us/performance-and-planning/uspto-annual-reports 11 Note that our basic interpretation about the sharing of information about patentable technologies is even stronger if markets are uninformed about patent applications, so that patent issuance also reveals information about the firm having pursued this patent.

32

more closely connected competitors should respond much more muted in the wake of patent news

shocks. The reason is that patent news are only surprises to the public, while commonly managed

firms might already have received private information on the credibility of patentable technologies

of their commonly managed competitors.

To formally capture these ideas, let 𝑥𝑥𝑖𝑖,𝑖𝑖 denote R&D investments by firm 𝑖𝑖 at time 𝑡𝑡, while Δ𝐶𝐶𝑘𝑘,𝑖𝑖

captures the patent news shock for patenting firm 𝑘𝑘 at time 𝑡𝑡 with 𝑘𝑘 ≠ 𝑖𝑖. Then, R&D investments

as function of common management as measured by board network distance between 𝑖𝑖 and

𝑘𝑘,𝑑𝑑𝑖𝑖𝑠𝑠𝑡𝑡(𝑖𝑖, 𝑘𝑘)𝑖𝑖 can be written as a “forward difference” specification:

𝑥𝑥𝑖𝑖,𝑖𝑖+1 − 𝑥𝑥𝑖𝑖,𝑖𝑖 = 𝛾𝛾1 ⋅ 𝑑𝑑𝑖𝑖𝑠𝑠𝑡𝑡(𝑖𝑖,𝑘𝑘)𝑖𝑖 + 𝛾𝛾2 ⋅ Δ𝐶𝐶𝑘𝑘,𝑖𝑖 + 𝛾𝛾3 ⋅ �𝑑𝑑𝑖𝑖𝑠𝑠𝑡𝑡(𝑖𝑖,𝑘𝑘)𝑖𝑖 × Δ𝐶𝐶𝑘𝑘,𝑖𝑖� + 𝜖𝜖𝑖𝑖,𝑖𝑖+1 (9)

In specification (9), if less commonly managed firms respond more to patent news shocks, we

should see that 𝛾𝛾2 ≥ 0 and 𝛾𝛾3 < 0, so that more distant firms 𝑖𝑖 exhibit a more negative impact of

competitor 𝑘𝑘 patent news shocks on their R&D spending. The intuition is that after the patent news

is public, more commonly managed firms should respond less, as this information is already

known to them. Less commonly managed firms on the other hand, are responding to the news

shock for the first time; they respond by cutting their R&D spending.

At the same time, the same hypothesis also has implications for R&D investments before the patent

news shock arrives. In particular, if more commonly managed firms are already informed about

the credibility of pending patent applications, then we would expect that they reduce R&D

investments already ahead of time, i.e. before the patents of their competitors are actually issued.

To capture these ideas, we can write this “backward difference” specification as

𝑥𝑥𝑖𝑖,𝑖𝑖 − 𝑥𝑥𝑖𝑖,𝑖𝑖−1 = 𝛿𝛿1 ⋅ 𝑑𝑑𝑖𝑖𝑠𝑠𝑡𝑡(𝑖𝑖,𝑘𝑘)𝑖𝑖 + 𝛿𝛿2 ⋅ Δ𝐶𝐶𝑘𝑘,𝑖𝑖 + 𝛿𝛿3 ⋅ �𝑑𝑑𝑖𝑖𝑠𝑠𝑡𝑡(𝑖𝑖,𝑘𝑘)𝑖𝑖 × Δ𝐶𝐶𝑘𝑘,𝑖𝑖� + 𝜖𝜖𝑖𝑖,𝑖𝑖 (10)

where the information sharing hypothesis would predict that 𝛿𝛿2 < 0 and 𝛿𝛿3 > 0, so that more

commonly managed firms decrease their R&D spending during the time preceding patent

issuance, while less commonly managed firms might not respond much at all.

[Table 12]

33

Table 12 shows that the empirical patterns are consistent with information sharing as an

explanation of differentiation effects of common management. In particular, more commonly

managed firms respond more muted upon announcements of patent issuances of closely connected

competitors, while they cut back their R&D investment already before any official patent issuance

announcements of competitors. Importantly, comparing the interaction effects in the year before

to the year after the patent announcement shows that less commonly managed firms on net expand

R&D investments more than more commonly managed firms. This pattern is indicative of R&D

overinvestment by less commonly managed firms, which more commonly managed firms avoid.

Additionally, table 12 also explores in more detail the timing and persistence patterns of patent

news effects. The backward difference specifications highlight that closely connected firms cut

back R&D investment systematically already 3 years ahead of the patent issuance by connected

competitors. On the flipside, less commonly managed firms stop cutting R&D investments in

response to patent news shocks after a year, presumably because they can easily let their

overinvestments in R&D depreciate.

It should also be noted that this analysis suggests that information on patents by competitors

acquired through board networks is more credible than mere public announcements, even of patent

applications. This credibility advantage of board network information might explain why firms

pay more attention to information acquired through board networks than public disclosures of

competitors.

6.2.3 Firm Intransparency An alternative reason for why firms might fail to credibly disclose all information is that it requires

a structured management process to do so effectively. For example, Yang et al. 2020 find that only

a small fraction of firms run by HBS alumni even have systematic processes to communicate

strategic decisions to employees. Establishing processes to communicate credibly to outside

parties is presumably even more challenging.

Our second approach builds on this insight and analyzes competitive differentiation effects for

firms with different levels of public transparency. The basic idea is that the incremental value of

private information through the board network is low, if firms are already very transparent. In other

words, information flowing through the board network should have its biggest impact on

34

competitive differentiation if it flows among relatively intransparent firms, which fail to

communicate effectively with the public.

To test this hypothesis, we construct measures of public firm intransparency, based on analyst data

from I/B/E/S, see Appendix A.6 for details. In particular, we construct measures of earnings

forecast dispersion across analysts to capture the idea that less consensus among analysts is

indicative of higher degrees of firm intransparency12. We then measure the overall intransparency

of a firm pair, by taking the product of the firm-specific intransparency variables. If information

sharing can explain competitive differentiation effects of common management, then competitive

differentiation effects of common management should be stronger for firm-pairs that are relatively

intransparent.

[Table 13]

Table 13 confirms that these predictions of the Information sharing hypothesis indeed hold in the

data. Firm-pairs that are relatively intransparent see stronger competitive differentiation effects of

more common management, across both, product segment differentiation as well as product

description differentiation measures.

7. Conclusion This study is the first to establish causal evidence for the impact of common management on

competitive positioning among US corporations. Our key finding is that more common

management, as measured by closer connections of board directors across firms, leads to more

competitive differentiation. Furthermore, we document evidence supporting the view that these

competitive differentiation effects are driven by information sharing, under which common

management enables firms to obtain credible information on potential competitor’s product and

technology choices, which enables them in turn to avoid wasteful duplication.

Based on these findings, we can now return to the discussion of potential anti-trust implications,

which we alluded to in the opening paragraph of the introduction. The issues around information

sharing are likely to be closely related to general practices of information sharing among

12 Using analysts’ earnings per share forecast dispersion as a proxy for analyst disagreement follows from a large literature including Diether et al. (2002) and Johnson (2004). For example, Johnson (2004) attributes differences in firm EPS forecast dispersion to differences in the firm’s information setting (parameter risk).

35

competitors, see US vs Container Corp, 1969. Specifically, US courts typically follow the “Rule

of Reason” doctrine, under which a practice is only considered an anti-trust violation if the practice

leads to “an unreasonable restraint of trade”. The degree to which we fail to find evidence in

support of collusion, our analysis suggests that none of our empirical findings supports further

investigation by anti-trust authorities. On the contrary, our main competitive differentiation effects

of common management support the view that firms avoid wasteful duplication of investments

and effort based on information exchange through board networks. Furthermore, even our markup

analysis suggests that more common management leads to a reduction in markups, which is

indicative of static gains in consumer surplus from common management.

This paper suggests at least two major avenues for future research. First, building on our analysis

here, the use of data on board networks together with indirect network effect identification

strategies, suggested by Bramoulle, Djebbari and Fortin, 2009, offers a variety of opportunities to

estimate the causal effects of strategy decision-making and information exchange by directors on

corporate policies. Second, a deeper analysis of the determinants of competitive positioning

promises to uncover more insights on what is driving corporate policies and corporate innovation

decisions.

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39

Figure 1: Illustration of measures for the degree of common management across companies. Links connecting companies are shared directors on company boards. Distance measures in the board network are minimum distances between two companies, also called shortest path or geodesic distance.

40

Figure 3A

Figure 3C

Figure 3B

Figure 3D

Notes: Average competitive differentiation effects in event time. Treatment firm-pairs are defined as pairs for which a director death at t=0 increases board network distance. Control firm-pairs do not experience an increase in board network distance during the event window, which covers 3 years before to 3 years after director deaths. Average treatment effects are calculated by averaging competitive differentiation across treatment-pairs and control-pairs and then taking the difference of the averages. All effects are normalized relative to differentiation at t=0. Event time t=0 is defined as the year of director death. Figures display 95% confidence intervals with standard errors clustered at the firm level.

41

Notes: Cumulative competitive differentiation effects in event time. Treatment firm-pairs are defined as pairs for which a director death at t=0 increases board network distance. Control firm-pairs do not experience an increase in board network distance during the event window, which covers 3 years before to 3 years after director deaths. Lines are cumulative sums of the estimates in figure 3. Event time t=0 is defined as the year of director death.

Figure 4

42

Table 1

S&P Classification Frequency Average Network Distance

Std. 10th percentile

90th percentile

SP 1,500 13,225 4.569 0.97 3.744 5.685 SP 500 to SP 500 4,999 3.761 0.601 3.145 4.525 SP 500 to SP 1,500 5,027 4.21 0.629 3.639 4.967 SP 400 to SP 1,500 3,699 4.641 0.923 4.007 5.606 SP 600 to SP 1,500 4,499 4.911 1.166 4.097 6.0737 Table 1: Degree of common management as measured by network distance for different categories of S&P classifications. Network distance refers to the number of different corporate boards that separate two firms, so more closely connected firm pairs exhibit more common management. A network distance of 1 means that two firms are directly connected through a shared director and therefore directly commonly managed.

Table 2

Variable Observations Mean Std.

Product Segment Differentiation Score 20,654,328 -0.00762 0.0812

Product Description Differentiation Score 24,730,502 -0.00168 0.0122

Patent Differentiation Score 2,770,780 -0.0157 0.0752

Patent-Citations 317,915 41.383 669.893

Table 2: This table summarizes the dependent variables, which measure competitive positioning among firm-pairs. Product Segment Differentiation Score refers to the dissimilarity in revenue shares between firms. The Product Description Differentiation Score is based on the Hoberg-Philips Text-based Network Industry Classifications from firm 10-K product descriptions. The Patent Differentiation Score is based on the differences of technology classes of patents by firms. Patent-Citations is based on the number of patents that one firm cites the other firm each year. Both the patent differentiation score and the patent-citation score use a rolling three-year calculation of patents.

43

Table 3

Competitive positioning

Product Segment

Differentiation

Product Description

Differentiation

Patent Differentiation

Patent Citations

Common management

0.00211*** 0.00137*** 0.00418*** 0.05597*** (0.00007) (0.00005) (0.00033) (0.00178)

Fixed effects Year Year Year Year Observations 13,594,165 14,895,217 2,508,246 342,275 Table 3A: Pooled OLS regression of competitive positioning measures on common management as measured by board network distance. Standard errors in parenthesis. Significance levels used: *: 5%, **: 1%, ***:0.1%. Product Segment Differentiation is measured using revenue dissimilarity of firms in Compustat segments. Product Description differentiation is text differences in 10-K filings, constructed by Hoberg and Phillips. Patent differentiation is measured using NBER technology classes. Patent citations captures degree of citations of patents across firms.

Product Segment

Differentiation

Product Description

Differentiation

Patent Differentiation

Patent Citations

Common management

-0.37832*** -0.34309*** -0.32374*** -0.04811*** (0.00434) (0.00375) (0.01600) (0.00686)

Fixed Effects Firm-Pair, Year Firm-Pair, Year Firm-Pair, Year Firm-Pair, Year

Observations 12,436,322 12,406,768 1,581,412 198,100

Table 3B: Pooled OLS regression of competitive positioning measures on common management as measured by board network distance. All specifications use pair fixed effects. Standard errors in parenthesis. Significance levels displayed: *: 5%, **: 1%, ***:0.1%.

44

Table 4

OLS: Common management Effects

Product Market Space Technology Space

∆(Product Segment

Differentiation)

∆(Product Description

Differentiation) ∆(Patent

Differentiation) ∆(Patent Citations)

∆Common management

0.011*** 0.011*** 0.015*** 0.132** (0.0005) (0.000)

(0.003) (0.045)

Connected -0.242*** -0.222*** -0.368*** -1.591*** (0.008) (0.004) (0.035) (0.282)

∆# of Connections

12.627*** 12.270*** 13.007*** 5.953* (0.209) (0.153)

(0.987) (2.532)

∆# of Directors 0.172*** 0.216*** 0.594*** 1.966*** (0.006) (0.003) (0.053) (0.535)

∆# Industry Segments

0.265*** 0.296*** 0.633*** 1.297* (0.009) (0.004) (0.061) (0.543)

Fixed Effects Year, Industry, Geography

Year, Industry, Geography

Year, Industry, Geography

Year, Industry,

Geography Observations 9,845,179 10,768,967 823,514 155,041 Table 4: First difference OLS specifications to control for pair fixed effects and with baseline control variables. Standard errors are clustered at the firm level. Significance levels displayed: *: 5%, **: 1%, ***:0.1%. Product Segment Differentiation is measured using revenue dissimilarity of firms in Compustat segments. Product Description Differentiation is the text differences in 10-K filings, constructed by Hoberg and Phillips. Additional control for product space specifications includes change in relative size as measured by assets. Patent differentiation is measured using NBER technology classes. Patent citations captures degree of citations of patents across firms. Additional controls for technology space specifications include relative assets and relative R&D intensity. Connected is a dummy that is equal to one if the firm pair were previously directly connected by a shared director.

45

Table 5

IV: Common management Effects - all pairs IV: Product Market Space IV: Technology Space 1st Stage 2nd Stage 1st Stage 2nd Stage 1st Stage 2nd Stage 1st Stage 2nd Stage

∆Common

management ∆(Product Segment

Differentiation) ∆Common

management

∆(Product Description

Differentiation) ∆Common

management ∆(Patent

Differentiation) ∆Common management

∆(Patent Citations)

Director Death -1.74*** -1.874*** -2.084*** -2.042*** (0.178) (0.177) (0.226) (0.222)

∆Common management

1.356*** 1.225*** 1.073*** -6.729** (0.186) (0.159) (0.224) (3.1798)

Connected 0.176*** 0.006 0.187*** -0.004 0.189*** 0.168*** 0.145*** -0.638 (0.003) (0.031) (0.003) (0.028) (0.010) (0.039) (0.010) (0.7302)

∆# of Connections

8.582*** -24.215*** 7.867*** -21.853*** 7.412*** -20.860*** 7.702*** 57.022** (0.089) (1.971) (0.075) (1.560) (0.358) (3.044) (0.225) (26.1914)

∆# of Directors

0.663*** -1.063*** 0.640*** -0.993*** 0.647*** -1.278*** 0.455*** 4.968** (0.004) (0.142) (0.004) (0.117) (0.021) (0.245) (0.032) (2.1647)

∆# Industry Segments

-0.060*** -0.184* -0.071*** -0.209** -0.197*** -0.425*** -0.108*** 0.582 (0.005) (0.078) (0.005) (0.069) (0.024)*** (0.150) (0.025) (1.4227)

Fixed Effects Year, Industry, Geography

Year, Industry, Geography

Year, Industry,

Geography

Year, Industry, Geography

Year, Industry,

Geography

Year, Industry, Geography

Year, Industry,

Geography

Year, Industry,

Geography

Observations 9,845,179 9,845,179 10,768,967 10,768,967 823,514 823,514 155,041 155,041 Table 5: First difference IV specifications to control for pair fixed effects. IV first stage uses director deaths for exogenous variation in degree of common management. Standard errors are clustered at the firm level. Significance levels displayed: *: 5%, **: 1%, ***:0.1%. Product Segment Differentiation is measured using revenue dissimilarity of firms in Compustat segments. Product Description Differentiation is text differences in 10-K filings, constructed by Hoberg and Phillips. Additional control for product space specifications includes change in relative size as measured by assets. Patent differentiation is measured using NBER technology classes. Patent citations captures degree of citations of patents across firms. Additional controls for technology space specifications include relative assets and relative R&D intensity. Connected is a dummy that is equal to one if the firm pair were previously directly connected by a shared director.

46

Table 6

IV: Common management Effects - only pairs, that are indirectly affected by Director Deaths IV: Product Market Space IV: Technology Space 1st Stage 2nd Stage 1st Stage 2nd Stage 1st Stage 2nd Stage 1st Stage 2nd Stage

∆Common

management ∆(Product Segment

Differentiation) ∆Common

management

∆(Product Description

Differentiation) ∆Common

management ∆Patent

Differentiation ∆Common management

∆Patent Citations

Indirect Director Death

-0.230*** -0.226*** -0.620*** -0.494***

(0.008) (0.008) (0.028) (0.077)

∆Common management

0.112*** 0.0612*** 0.065*** -1.244*** (0.012) (0.006) (0.009) (0.245)

∆# of Directors

0.042*** -0.266*** 0.032** -0.274*** 0.118* -0.338 -0.088 1.052 (0.011) (0.060) (0.010) (0.049) (0.049) (0.175) (0.115) (3.040)

∆# Industry Segments

-0.002 -0.225*** -0.004 -0.271*** -0.152** -0.047 -0.122 -0.180 (0.013) (0.049) (0.013) (0.042) (0.051) (0.192) (0.090) (2.614)

Fixed Effects Year,

Industry, Geography

Year, Industry, Geography

Year, Industry,

Geography

Year, Industry, Geography

Year, Industry,

Geography

Year, Industry, Geography

Year, Industry,

Geography

Year, Industry,

Geography

Observations 510,902 510,902 562,636 562,636 30,928 30,928 4,136 4,136 Table 6: First difference IV specifications to control for pair fixed effects for firm pairs that are only indirectly affected by Director Deaths. IV first stage uses director deaths for exogenous variation in degree of common management. Standard errors are clustered at the firm level. Significance levels displayed: *: 5%, **: 1%, ***:0.1%. Product Segment Differentiation is measured using revenue dissimilarity of firms in Compustat segments. Product Description Differentiation is text differences in 10-K filings, constructed by Hoberg and Phillips. Additional control for product space specifications include change in relative size as measured by assets. Patent differentiation is measured using NBER technology classes. Patent citations captures degree of citations of patents across firms. Additional controls for technology space specifications include relative assets and relative R&D intensity. Connected is a dummy that is equal to one if the firm pair were previously directly connected by a shared director.

47

Table 7

IV Common management Effects - all pairs, controlling for Common

Ownership IV Product Market Space IV Technology Space 2nd Stage 2nd Stage 2nd Stage 2nd Stage

∆(Product Segment

Differentiation)

∆(Product Description

Differentiation) ∆(Patent

Differentiation) ∆(Patent Citations)

∆Common management

1.292*** 1.163*** 0.534** -6.605 (0.20) (0.172) (0.18) (3.834)

∆Common Ownership

-0.022 0.007 -0.001 -0.275 (0.030)

(0.026)

(0.034)

(0.519)

Connected -0.033 -0.028 0.099** -0.633 (0.033)

(0.030)

(0.033)

(0.767)

∆# of Connections

-22.180*** -19.920*** -10.971*** 66.693 (1.986) (1.586) (2.324) (35.075)

∆# of Directors -0.982*** -0.943*** -0.674*** 7.006* (0.149) (0.125) (0.189) (2.916)

∆# Industry Segments

-0.261** -0.289*** -0.303** 1.792 (0.083) (0.072) (0.110) (1.085)

Fixed Effects Year, Industry, Geography

Year, Industry, Geography

Year, Industry, Geography

Year, Industry, Geography

Observations 4,985,537 5,472,817 89,169 89,169 Table 7: First difference IV specifications to control for pair fixed effects and control for change in common ownership within firm pairs. IV first stage uses director deaths for exogenous variation in degree of common management. Standard errors are clustered at the firm level. Significance levels displayed: *: 5%, **: 1%, ***:0.1%. Product Segment Differentiation is measured using revenue dissimilarity of firms in Compustat segments. Product Description Differentiation is text differences in 10-K filings, constructed by Hoberg and Phillips. Additional control for product space specifications includes change in relative size as measured by assets. Patent differentiation is measured using NBER technology classes. Patent citations captures degree of citations of patents across firms. Additional controls for technology space specifications include relative assets and relative R&D intensity. Connected is a dummy that is equal to one if the firm pair were previously directly connected by a shared director.

48

Table 8

OLS: Indirect Common Management Effect for Connections only

IV Product Market Space IV Technology Space

∆(Product Segment

Differentiation)

∆(Product Description

Differentiation) ∆(Patent

Differentiation) ∆(Patent Citations)

∆Common Management

0.010*** 0.007*** 0.009 0.223* (0.001) (0.0003) (0.007) (0.106)

∆# of Directors 0.144*** 0.128*** 0.312*** 2.536

(0.014) (0.004) (0.090) (1.389)

∆# Ind. Segments 0.153*** 0.168*** 0.469*** 1.997

(0.021) (0.006) (0.096) (1.592)

Fixed Effects Year, Industry, Geography

Year, Industry,

Geography

Year, Industry,

Geography

Year, Industry,

Geography

Observations 1,599,964 1,599,964 136,094 21,872 Table 8: An increase in the indirect common management effect between two boards occurs when the two boards decrease in board network distance as a result of two intermediary firms becoming directly connected. Estimates are based on an OLS specification for connections only. Standard errors are clustered at the firm level. Significance levels displayed: *: 5%, **: 1%, ***:0.1%. Product Segment Differentiation is measured using revenue dissimilarity of firms in Compustat segments. Product Description Differentiation is text differences in 10-K filings, constructed by Hoberg and Phillips. Additional control for product space specifications includes change in relative size as measured by assets. Patent differentiation is measured using NBER technology classes. Patent citations captures degree of citations of patents across firms. Additional controls for technology space specifications include relative assets and relative R&D intensity.

49

Table 9 IV: Markup Effects

2nd Stage 2nd Stage 2nd Stage

∆Average Profit Rates

∆Average Markup (COGS)

∆Average Markup (Operating Expense)

∆Common management

-0.821*** -0.211*** -0.171*** (0.104) (0.031) (0.030)

Connected 0.009 0.006 0.023*** (0.019) (0.006) (0.006)

∆# of Connections

14.728*** 3.645*** 3.081*** (1.041) (0.311) (0.336)

∆# of Directors

0.555*** 0.122*** 0.087* (0.104) (0.033) (0.042)

∆# Industry Segments

0.130** 0.039 0.027 (0.047) (0.035) (0.040)

Fixed Effects Year, Industry, Geography

Year, Industry, Geography

Year, Industry, Geography

Observations 10,670,071 7,163,161 7,163,161 Table 9: First difference IV specifications to control for pair fixed effects. IV first stage uses director deaths for exogenous variation in degree of common management. Standard errors are clustered at the firm level. Significance levels displayed: *: 5%, **: 1%, ***:0.1%. Dependent variables are changes in pairwise average markup measures. Column 1 uses profit rates, column 2 markups from production function estimation of De Loecker and Eekhout (2018) using COGS as proxy for variable costs and column 3 using operating expenses following Trania (2018). Additional control for change in relative size as measured by assets is included. Connected is a dummy that is equal to one if the firm pair were previously directly connected by a shared director.

50

Table 10

IV: Industry Price Effects 1st Stage 2nd Stage

∆Average Common management ∆Price (log)

Director Death (IV) -0.006**

(0.002)

∆Average Common management

1.150 (1.879)

Constant 2.008*** 3.210 (0.275) (3.798)

Fixed Effects Year Year Observations 733 733 Table 10: ∆Average Common management is measured by the inverse of the change in the average geodesic between firms in the same NAICS industry. For changes in network distances between same-industry firm pairs that become disconnected through the S&P1500 network, this is a subjective value for unconnectable firm pairs of 9 (geodesic of 10 is the 99th percentile sample distance). The director death instrumental variable includes all within-industry pairs of firms that are affected by a director death and deaths that cause network paths to increase from a spillover effect. Prices are based on NAICS annual prices and logged. Standard errors are clustered at the industry level.

51

Table 11 IV: Interaction of Common management and Main Market Concentration

1st Stage 2nd Stage 1st Stage 2nd Stage

∆Common management

∆(Product Segment

Differentiation) ∆Common

management

∆(Product Segment

Differentiation)

∆Common management

1.355*** 1.315*** (0.187) (0.184)

MMCI*∆Common management

1.986*** -2.593*** (0.005) (0.408)

MMCI -0.007*** -0.008

(0.002) (0.024)

Same Director Death -1.74*** -1.794*** (0.178) (0.179)

Connected 0.176*** -0.004 0.157*** 0.029 (0.003) (0.031) (0.003) (0.027)

∆Total #Connections 8.582*** -24.189*** 7.682*** -22.667*** (0.089) (1.982) (0.083) (1.893)

∆# of Directors 0.663*** -1.063*** 0.574*** -0.920*** (0.004) (0.143) (0.004) (0.125)

∆# Industry Segments -0.060*** -0.194** -0.041*** -0.220** (0.005) (0.078) (0.005) (0.072)

Same SIC (any 3-digit) 0.001 0.518*** 0.002 0.517*** (0.002) (0.062) (0.002) (0.062)

Fixed Effects Year, Industry, Geography

Year, Industry, Geography

Year, Industry, Geography

Year, Industry, Geography

Observations 9,845,179 9,845,179 9,845,179 9,845,179 Table 11: First difference IV specifications to control for pair fixed effects. IV first stage uses director deaths for exogenous variation in degree of common management. Standard errors are clustered at the firm level. Significance levels displayed: *: 5%, **: 1%, ***:0.1%. Product Segment Differentiation is measured using revenue dissimilarity of firms in Compustat segments. Additional controls for product space specification includes change in relative size as measured by assets. MMCI is the main market concentration index, which is defined as weighted average of the concentration of markets the firm in competing in, where weights are firm-level revenue shares across markets and concentration is measured by 2-digit SIC Herfindahl indexes.

52

Table 12 Patent News Effects

Firm i Backward Difference R&D Firm k Patents, Firm i not Firm i Forward Difference R&D

Firm k Patents, Firm i not

3-

periods prior

2-

periods prior

1-

period prior

1-

period forward

2-

periods forward

3-

periods forward

0.028***

0.016*

0.020*

-0.043*** 0.024*** -0.014*

(0.008)

(0.008)

(0.009)

(0.006)

(0.006)

(0.006)

-0.108*

-0.129**

-0.118*

0.034 0.023 -0.055 (0.045) (0.046) (0.050) (0.033) (0.032) (0.030)

0.033**

0.035**

0.033*

-0.016* -0.003 0.013 (0.012) (0.013) (0.014) (0.008) (0.007) (0.007)

Fixed Effects Year, Industry

Year, Industry

Year, Industry

Year, Industry

Year, Industry

Year, Industry

Observations 12,164 12,164 12,164 13,979 13,979 13,979 Table 12: Impact of patent issuance news from Kogan et al (2017) by competitor k on R&D spending of firm i as function of Common Management as measured by board network distance. Firms are within same 2-digit SIC industry. Patent news shocks capture the log of stock price responses at date of patent issuance. Network distance is shortest path in board network. Standard errors are clustered at the firm level. Significance levels displayed: *: 5%, **: 1%, ***:0.1%.

Δ𝐶𝐶𝑘𝑘

𝑥𝑥𝑀𝑀𝑡𝑡𝑁𝑁𝐶𝐶𝐷𝐷𝑘𝑘 𝑃𝑃𝑖𝑖𝑠𝑠𝑡𝑡𝑀𝑀𝑛𝑛𝐶𝐶𝑀𝑀𝑖𝑖,𝑘𝑘 × Δ𝐶𝐶𝑘𝑘

𝑥𝑥𝑀𝑀𝑡𝑡𝑁𝑁𝐶𝐶𝐷𝐷𝑘𝑘 𝑃𝑃𝑖𝑖𝑠𝑠𝑡𝑡𝑀𝑀𝑛𝑛𝐶𝐶𝑀𝑀𝑖𝑖,𝑘𝑘

53

Table 13

IV: Interaction of Common management and

Intransparency IV 2nd Stage IV 2nd Stage

∆(Product Segment

Differentiation) ∆(Product Description

Differentiation)

(Intransparency: analyst EPS

forecast dispersion) (Intransparency: analyst EPS

forecast dispersion)

∆Common management 1.597*** 1.336*** (0.249) (0.180)

Relative Intransparency 0.008 0.006 (0.007) (0.006)

Intransparency x ∆Common management

0.686*** 0.545*** (0.099) (0.064)

Connected -0.180** -0.142** (0.063) (0.044)

∆Total #Connections -31.719*** -26.226*** (3.361) (2.014)

∆# of Directors -1.495*** -1.272*** (0.214) (0.145)

∆# Industry Segments -0.190 -0.211 (0.138) (0.111)

Fixed Effects Year, Industry, Geography Year, Industry, Geography

Observations 6,693,023 7,375,401 Table 13: First difference IV specifications to control for pair fixed effects. IV first stage uses director deaths for exogenous variation in degree of common management. Standard errors are clustered at the firm level. Significance levels displayed: *: 5%, **: 1%, ***:0.1%. Product Segment Differentiation is measured using revenue dissimilarity of firms in Compustat segments, while Product Description Differentiation is based on the Hoberg-Phillips 10-K similarity score. Intransparency is measured using dispersion in analysts’ forecasts of earnings per share and forecast errors of earnings. Additional control for product space specifications includes change in relative size as measured by assets. Connected is a dummy that is equal to one if the firm pair were previously directly connected by a shared director.