Electronic copy available at: http://ssrn.com/abstract=1032282

BETTER WITH AGE? TIE LONGEVITY AND THE PERFORMANCE IMPLICATIONS OF

BRIDGING AND CLOSURE••••

Joel A.C. Baum, Bill McEvily and Tim J. Rowley

Rotman School of Management

University of Toronto

Date: November 24, 2007

• The order of authorship is alphabetical. This research is funded by a grant from the Social Sciences and Humanities Research Council of Canada. We are grateful to Waverly Ding, David Krackhardt, Ray Reagans, and seminar participants at the Haas School, University of California – Berkeley, the Tepper School, Carnegie Mellon University, BETA, Universite Louis Pasteur, and INSEAD for their comments, and to Stan Li, Barak Aharonson, and Xuesong Geng for their help with data collection and coding. Direct all correspondence to the first author: Rotman School of Management, University of Toronto, 105 St. George Street, Toronto, ON, M5S 3E6, Canada. Voice: 416.978.4914. Fax: 416.978.4629. Email: baum@rotman.utoronto.ca.

Electronic copy available at: http://ssrn.com/abstract=1032282

2

BETTER WITH AGE?

TIE LONGEVITY AND THE PERFORMANCE IMPLICATIONS OF BRIDGING AND CLOSURE

Abstract

We examine the dependence of the performance effects of firms’ network positions on the ages of the ties

comprising them. Our analysis of Canadian investment banks’ underwriting syndicate ties indicates that the

performance benefits of closure increase with tie age, while benefits of bridging decrease with tie age. We

also find that benefits yielded by hybrid network positions combining elements of both closure and bridging

are greatest when old closure ties are combined with either very young or very old bridging ties. Our findings

support the idea that the advantages firms gain (or do not) from their network positions depend on the

character of the ties (e.g., age) comprising them, highlighting the risk of theorizing structural network effects

without also considering the qualities of the ties through which particular structural benefits accrue.

Electronic copy available at: http://ssrn.com/abstract=1032282

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INTRODUCTION

At the intersection of network and strategy literatures, researchers are concerned with how patterns of

interfirm connections – structural embeddedness – create network-based advantages for well-positioned

firms. Initially, researchers engaged in a debate over whether firms should occupy positions in highly

interconnected structures (closure positions), which create coordination and integration advantages (Coleman,

1988, 1990), or in sparse structures (bridging positions), which provide timely information and resource

access and brokerage opportunities (Burt, 1992).

In the face of equivocal empirical evidence, however, this debate gave way to contingency arguments

specifying conditions (e.g., firm life-cycle stage, environmental uncertainty, tie function) under which each

type of position was more advantageous (Ahuja, 2000; Walker, Kogut and Shan, 1997; Rowley, Behrens and

Krackhardt, 2000). More recently, researchers have begun to examine the benefits of hybrid network

positions that contain elements of both bridging and closure (Baldassarri and Diani, 2007; Baum, van Liere

and Rowley, 2007; Reagans and McEvily, 2006; Reagans and Zuckerman, 2001; Reagans, Zuckerman and

McEvily, 2004; Schilling and Phelps, 2007).

Despite steady progress toward understanding the relationships between firms’ network positions

and performance, structural embeddedness research remains disconnected from research on relational

embeddedness. Relational embeddedness highlights the character of dyadic ties between firms (Uzzi, 1996).

Ties, for example, can be more or less preferential and stable, more or less trustworthy, and entail richer or

more limited information exchange. Researchers concerned with relational embeddedness thus study the

qualities of a firm’s ties (Uzzi, 1996), rather than the topology or configuration (i.e., network structure) of ties

around firms. While the distinction between structural and relational embeddedness provides conceptual

clarity, it also overlooks their basic connection. The advantages firms gain (or do not) from their structural

positions depends on the character of the ties comprising those structures. Indeed, it is difficult to theorize

structural network effects without referring to the quality of the ties through which structural benefits accrue.

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To begin to connect structural and relational embeddedness research streams, we examine the extent

to which performance effects of firms’ structural positions depend on the character of the ties comprising the

positions. Specifically, we examine how the age of closure and bridging ties affects the benefits they produce.

Longevity seems a particularly germane characteristic of closure and bridging ties given that the benefits

typically associated with closure take time – often years – to develop (Burt, 2005; Gulati, 1995; Larson, 1992;

Powell, 1990; Uzzi, 1996), while those associated with bridging are typically characterized as short-term and

short-lived (Burt, 2000, 2002, 2005; Soda, Usai and Zaheer, 2004). In addition, we examine how tie age

affects the benefits of hybrid network positions combining bridging and closure.

We investigate the influence of tie age on the relationship between network position and

performance using comprehensive data covering all underwriting syndicates formed by investment banks in

Canada from 1952 to 1990. In contrast to earlier analyses, which typically assess firms’ network positions

without consideration of the character of the ties comprising them, we compute measures of closure and

bridging that incorporate time-varying information on the ages of a firm’s ties. Representing network

positions in this way permits us to contrast the performance implications of firms’ network structures as a

function of the ages of ties comprising them.

TEMPORAL DYNAMICS OF STRUCTURAL EMBEDDEDNESS

Although several properties of network structure have been the focus of research, primary attention has been

on closure versus bridging positions. In closure positions, a firm is embedded in a network of partners,

which are themselves highly interconnected. The benefits of such a close-knit group of firms include the

development of reciprocity norms, amplification of trust, and emergence of a shared identity, all of which

lead to a high level of cooperation (e.g., Burt and Knez, 1995; Coleman, 1988; Van de Ven, 1976).

Researchers suggest the advantages of dense interfirm networks include greater volume and depth of

information sharing, and monitoring and detection of misbehavior that lowers the frequency of opportunistic

behavior (e.g., Dyer and Nobeoka, 2000; Dyer and Singh, 1998; Walker et al., 1997). These benefits are semi-

public goods available to all firms within the same dense sub-network (Coleman, 1988; Rowley, 1997).

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While closure is about fostering cooperation and integration within close-knit groups, bridging is

about seeing variation in ideas and practices across groups. Firms occupying bridging positions span

structural holes – positions between other firms that are not themselves linked directly. Bridging positions

afford timely access to diverse information and resources from non-redundant contacts, and opportunities to

broker this novel information and resources between unconnected partners (Burt, 1992). Because there is a

limit to the ideas and opportunities that can be created using a given knowledge base, bridging ties also

increase a firm’s potential for finding new combinations by exposing it to novel variations (Fleming and

Sorenson, 2000; Nelson and Winter, 1982). Indeed, research indicates that firms occupying bridging

positions exhibit higher innovation rates, as well as secure more favorable partnership terms (Ahuja, 2000;

Hargadon, 1998; 2001; McEvily and Zaheer, 1999; Pollock, Porac and Wade, 2004).

Although both closure and bridging have been linked to performance benefits under a range of

conditions, each displays a distinct temporal dynamic. Closure tie advantages are collective in nature –

building trust, information sharing and collaboration routines – all of which are the culmination of repeated

exchanges over time. Bridging tie advantages, in contrast, are more individual and immediate in nature, as firms

attempt to realize benefits associated with differential access to information that tend to dissipate rapidly as

ideas diffuse. Consequently, we expect the benefits of closure and bridging will vary distinctively with the

longevity of the ties constituting them. Building on this insight, below we develop the idea that the effect of a

firm’s network position on its performance is contingent on the age of the ties constituting the position, and,

more specifically, that the influence of tie age differs for closure and bridging.

Closure and Bridging Tie Longevity

Most interfirm relationships begin as weak ties and develop strength over time as partners learn to cooperate

and trust one another (Gulati, 1995; Levinthal and Fichman, 1988; Van de Ven and Walker, 1984). In the

early stages, firms face uncertainty about each other’s capabilities and likelihood of cooperating or engaging in

an opportunistic learning race (Khanna, Gulati and Nohria, 1998; Li and Rowley, 2002). Together or

separately, misalignment of partner capabilities, interests or intentions can undermine and potentially

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overwhelm any gains derived from a partnership. Initially firms thus tend to be reluctant to commit fully to a

partnership. Exchange relations evolve in a slow process, starting with minor exchanges in which little risk is

involved, little trust required, and the trustworthiness can be proven, enabling the partners to develop the

trust require to engage in more significant collaborations (Ring and Van de Ven, 1994). Larson’s (1992) study

of interfirm relationships suggests that only after a period of incremental back-and-forth small exchanges do

partners fully commit to a relationship.

Persistent interaction over time is thus a condition for establishing mutually beneficial partnerships.

Time permits partners to develop routines for joint planning, information exchange and conflict resolution

that help them coordinate their efforts for mutual gain (Zollo, Reuer and Singh, 2002). Time also allows

partners to establish a history of exchange from which to learn about (and reduce the uncertainty associated

with) each other’s competencies, conduct and reliability (Gulati and Gargiulo, 1999). Familiarity and mutual

understanding in turn facilitate trust building and development of cooperative norms of exchange based on

the expectation of future interaction (Gulati, 1995; Powell, 1990; Uzzi, 1996). Finally, time permits reputation

and third-party referrals to emerge as a source of information about potential partners’ capabilities and

motives that shapes patterns of new relationships (Gulati and Gargiulo, 1999). The more a firm resorts to

third-party referrals as cues for new partners, the greater the closure of its local network (Baum and Ingram,

2002), and the more stable and durable its ties tend to be (Krackhardt, 1998, 1999).

Although closure ties embedded within dense structures tend to endure and be less vulnerable to

opportunism, their benefits are not immediate (Soda et al., 2004). As already noted, firms are reluctant to

fully commit to open exchange with a new partner immediately (Larson, 1992). Closure’s role as a

governance mechanism that conditions partnering behavior also take time to materialize, emerging only after

cooperative norms, collective sanctioning mechanisms, and a concern for reputation have developed (Rowley,

1997; Walker et al., 1997).

In contrast, bridging is typically conceived in terms of concrete, short-term goals and its advantages

relatively short-lived (Burt, 2005: 93). Bridging ties are valuable because they provide timely access to novel

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information and resources that can then be brokered to others. As information diffuses through a network

over time, however, its uniqueness and associated arbitrage opportunities diminish quickly (Burt, 1992). The

ephemeral nature of bridging’s benefits is particularly relevant in environments characterized by rapid change

and a short-term orientation, where novelty and uniqueness are of little value if outdated (Soda et al., 2004).

The benefits of bridging ties also decline over time as others establish bridging ties that span the same

structural hole as the initial broker, rendering an initially unique tie increasingly redundant.

In addition to the short half-life of brokering benefits, the difficulty and cost of maintaining bridging

ties also influences the duration of their performance effects. By definition, bridging ties connect firms

operating in different spheres. As such, norms of cooperation and routines for managing partnerships for

mutual gain are likely to differ, resulting in conflicts between partners as well as a greater likelihood of

corruption by opportunism (Krackhardt, 1998). Firms that operate in different spheres may also find it more

difficult to share, absorb and integrate knowledge from each sphere because they lack sufficient overlap in

their knowledge bases (Cohen and Levinthal, 1990), and because, the language, heuristics, and standards that

develop among exchange partners tend to be situation specific (Uzzi, 1997). As a result, we expect interfirm

bridging ties to decay faster than closure ties between firms sharing a common third-party (Burt, 2002;

Krackhardt, 1999), limiting the duration of their potential benefits.

Taken together, the foregoing observations suggest that 1) since the integration, information

processing, and governance benefits associated with closure ties take time to develop, their rate of return

increases with their duration, and 2) since the window of opportunity for leveraging bridging ties can be quite

narrow and limited to the near term, their rate of return decreases with their duration. Therefore, we predict:

H1: The performance benefits of closure ties increase with their age.

H2: The performance benefits of bridging ties decrease with their age.

Hybrid Positions and Tie Longevity

Research indicates that few firms participate only in either closure or bridging ties, but instead, are more

typically situated in hybrid network position comprised of both types of ties (Baum et al., 2003, 2007; Walker

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et al., 1997). Like small world network structures (e.g., Watts, 1999), hybrid positions enhance performance

because the coexistence of clustered closure ties with distant bridging ties enable trust and close collaboration

within groups to be combined with fresh ideas and non-redundant information from other groups

(Baldassarri and Diani, 2007; Baum et al., 2007; Burt, 2005; Reagans and McEvily, 2006; Reagans et al. 2004;

Reagans and Zuckerman, 2001; Schilling and Phelps, 2007). Hybrid positions are precisely those described by

Burt’s (1980, 1982) concept of structural autonomy: A structurally autonomous actor belongs to a densely

interconnected group of partners, but also has bridges beyond them. Indeed, Burt (2005: 139) defines

structural autonomy explicitly as the interaction of closure and bridging.

For firms emphasizing closure, bridging ties create opportunities to realize advantages through access

to unique information and resources available from firms outside its local network. For firms emphasizing

bridging, closure ties create a collaborative context within which to engage in the kind of coordinated action

necessary to transform new ideas into concrete and valuable applications (Obstfeld, 2005). Thus by mixing

bridging and closure ties, firms combine the cooperative properties of closure with the information and

resource access and brokering properties of bridging. Or, as Burt (2005:7) explains it: “Facilitating the trust

and collaborative alignment needed to deliver the value of brokerage, closure is a complement to brokerage

such that the two together define social capital in a general way in terms of closure within a group and

brokerage beyond the group.” Put more strongly, a reputation for trustworthiness within a close-knit group

may be vital to building intergroup bridges that would otherwise be too risky (Burt, 2005: 107).

We expect the benefits of combining closure and bridging ties to depend on their ages. Taken

together, hypotheses H1 and H2, which predict that the benefits of closure ties increase with their age while

those of bridging ties decline, suggest that firms can maximize the benefits of hybrid positions by combining

the trust and integration benefits associated with old closure ties with the timely information access and

brokering benefits associated with young bridging ties.

Such interplay of old closure ties and new bridging ties is not risk-free, however. Information

imported through new bridging ties can benefit both the brokering firm and members of its cohesive, local

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group. In contrast, shared information exported from the group through new bridges, while also benefiting

those brokering it, may be perceived as leaking (intentionally or unintentionally) information and resources

that erode the group’s competitive advantage, undermining the trust on which closure ties’ successful joint

action is contingent. New bridging ties thus risk undermining a long-standing group’s collective benefits, and

so may result in sanctions from local group members, or their withdrawal from further collaboration.

Although bridging tie advantages may typically be short-lived, as brokerage opportunities disappear

and previously disconnected actors are linked to each other, in some cases disintermediation of brokers does

not occur. Indeed, the survival rate of bridging ties that survive past a threshold age has been shown to

increase over time (Burt, 2002). When the structural hole a bridging tie spans is not absorbed into the

surrounding local network, the brokering advantage it generates can persist. In this case, the tie is more

focused on a partner “with whom good things could happen” (Burt, 2005: 95) rather than a particular short-

term goal. The tie could present a brokering opportunity, or not, but it is viewed as a productive relationship

to establish and maintain, in part because the private information critical to brokers tends to be transmitted

through long-term, embedded relationships (Uzzi, 1996).1

Given that some bridging ties may be enduring, rather than one-time, sources of timely access to

unique information, resources and associated opportunities, long-lived rather than new bridging ties may

prove more valuable to hybrid positions. The benefits of such relationships and their potential stream of

time-sensitive information and resource advantages would be difficult to realize, however, unless combined

with a well-established closure position that provides ready access to a collaborative context within which to

rapidly deploy the information and that ensures the trust “needed to realize the value of bridging a structural

hole” (Burt, 2005: 97). Information imported through such enduring bridging ties not only provides a stream

of private benefits to firms possessing them, but also collective benefits to partners with whom they share

1 Structural holes may resist absorption for several reasons. In some cases, the costs of initiating and maintaining bridging ties are prohibitive relative to the benefit of being the second, third, or fourth bridge across the hole (Burt, 2005: 232). In others, while costs are not prohibitive relative to benefits, first movers strategically protect the structural hole, increasing barriers to bridging for later entrants. For example, executive recruiters and temporary employment agencies actively defend the structural holes they bridge between employers and employees (Bidwell and Fernandez-Mateo, 2007). In still others cases, while the costs are not prohibitive, the benefits are unclear, deterring formation of additional bridges. In the context of syndicate underwriting, empirical evidence suggests that investment banks strategically protect the structural holes they span, increasing barriers to bridging for later entrants (Baum et al., 2003).

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long-lived closure ties, reinforcing the trust and integration benefits of their cohesive subgroups. And,

compared to new bridging ties, shared information exported through enduring bridges is less likely to be seen

by partners of long-lived closure ties as a threat to the group or to undermine group cohesion as a result of

their history or indirect interaction with a trustworthy common partner.

The foregoing arguments suggest that two hybrid position configurations will be most beneficial.

First, following from hypotheses H1 and H2, firms can maximize the benefits of hybrid positions by

combining the trust and integration associated with long-lived closure ties with the timely information access

and brokering associated with new bridging ties. Second, firms can maximize hybrid positions benefits by

combining long-lived closure ties with access to an ongoing stream of potential brokering benefits from

enduring, and indirectly trusted, bridging ties. Accordingly, we predict:

H3: Hybrid network positions that combine old closure ties with young bridging ties enhance performance.

H4: Hybrid network positions that combine old closure ties with old bridging ties enhance performance.

METHODS

Research Setting and Network Definition

We study the relationship between network position age and performance using data on all underwriting

syndicates formed by Canadian investment banks between 1952 and 1990. Data were compiled from the

Record of New Issues published annually since 1952 by the Financial Data Group. Additional data were

collected from annual reports of the Toronto Stock Exchange as well as from the Canadian Sociometric

Information and Management Database. In 1952, 19 investment banks participated in 27 underwriting

syndicates in Canada. Over the observation period, the number of syndicates grew rapidly, and by 1989, 83

banks participated in 422 syndicates. Thus, although life-histories for a small number of banks are left-

censored (i.e., in operation prior to 1952), our data covers the period during which raising equity in the capital

markets superseded bank debt as the dominant mode of corporate financing (Davis and Mizruchi, 1999). Our

comprehensive network data and longitudinal research design permit us to avoid the problem of network

boundary setting (Doreian and Woodard, 1992), and to model partner selection over a period of time

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sufficient to yield meaningful variation in network structure and composition.

Underwriting Syndicates

Underwriting syndicates provide an excellent setting for our analysis. Investment banks act as financial

intermediaries linking issuers (firms) wishing to raise funds on capital markets to investors. They add value to

corporations raising capital in primary markets by effectively pricing and placing their issues. The industry is

characterized as “relationship-oriented” because banks commonly collaborate in underwriting deals.

Relationships are not only a common practice but also the primary assets banks employ, acting as vital

conduits to underwriting opportunities, investors, and fundamental to their reputation (Podolny, 1993). In

this setting, bridging ties can be beneficial because they provide access to deals and banks that can distribute

the offering to diverse investors, while closure ties can be beneficial because they smooth cooperation and

enhance trust, facilitating the transfer of information required for complex financial transactions and

syndicate efficiency.

The syndication process begins with an issuer choosing a lead bank to oversee underwriting

responsibilities. Then, in most cases, the lead bank invites additional investment banks to participate as co-

leads in an underwriting syndicate as a means of spreading risk, acquiring industry-specific skills, and investor

contacts and distribution capabilities that help the syndicate to reach a wider range of investors (Pollock,

Porac, and Wade, 2004). Co-lead banks have more limited discretion, either accepting or rejecting an

invitation to participate in a syndicate. In aggregate, these two types of partnering decisions – lead banks

forming and colead banks joining underwriting syndicates – generate the syndicate network. The network

thus comprised all dyadic lead-colead syndicate ties.2

Network Definition

We constructed networks for each observation year based on banks’ underwriting syndicate memberships.

Operationally, we constructed networks from adjacency matrices comprised of lead-colead syndicate dyads

2 Because coleads participating in the same syndicate typically have minimal contact – their interactions are primarily with the lead bank – following Baum et al. (2005), Chung, Singh and Lee (2000), and Jensen (2003) we did not consider the coleads participating in a given syndicate to be partners.

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for four-year moving windows (i.e., 1952-55, 1953-56, 1954-57, etc…). We measured the network for a given

year using the four-year window that ended in that year. Thus, a lead-colead dyad that existed at any point

between 1952 and 1955 was coded 1 for 1955; at any point between 1953 and 1956, 1 for 1956, and so on.

We used four-year windows for three reasons. First, syndicate ties represent the visible manifestation

of relationships; lead and colead banks participating in syndicates together in any given year are also likely to

interact with each other in years proximate to the syndicate (e.g., bidding to the issuer prior to the syndicate

and soliciting post-syndicate services to the issuer). Second, because syndicates can remain intact up to six

months or more prior to the date of the offering, syndicates that conclude in any given year may have been

formed in prior years. And third, the four-year window permits us to gauge more accurately and reliably the

strength of network ties by incorporating information on repeated ties over a number of years.

Dependent Variable and Estimation

We conduct our analysis at the organization level to examine how a bank’s performance varies with the ages

and mix of its closure and bridging ties. Our dependent variable is an investment bank’s annual market share.

High market share places underwriters at the top of the league table rankings by Institutional Investor, Investment

Dealers’ Digest and other publications used to compare different banks in an industry (Eccles and Crane, 1988;

Podolny, 1993). Since underwriting fees and margins banks earned are relatively constant across the industry

(a standard underwriting gross spread of seven percent is the norm) (Chen and Ritter, 2000), banks’ earnings

increase with increases in their volume of underwriting transactions (Eccles and Crane, 1988; Ellis, Michaely,

and O'Hara, 2000).

To measure banks’ market share at time t, we allocated the dollar value (inflation adjusted) of each

offering made during the previous year among the members of the syndicate that underwrote the deal. For

deals involving a lead manager only (i.e., with no syndicate), the bank was assigned 100% of the offering’s

dollar value. For deals involving multiple syndicate members, we assigned the lead bank 50% of the

underwriting value, and split the remaining value among other members equally. To compute banks’ market

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share, we then divided each bank’s deal values by the total value of public offerings at time t.3

We estimated banks’ market share using the following logarithmic specification:

ln(MSit) = α ln(MSit-1) + β Xit-1 +ε (1)

where MSit (Market Share) is bank i’s performance at time t, α is an adjustment parameter that indicates the

dependence of current market share on prior market share, and β is a vector of parameters for the effects of

independent and control variables. Inclusion of last year’s performance (MSit-1) to predict the current year’s

performance (MSit) helps account for the possibility that our empirical models suffer from specification bias

due to unobserved heterogeneity, and permits us greater confidence when inferring causal relationships

between independent and dependent variables (Jacobsen, 1990). In particular, if banks’ network positions are a

product of unobserved factors affecting performance, controlling for the lagged dependent variable will help

eliminate spurious effects resulting from such endogeneity.

The model was estimated on a pooled time-series dataset, with each bank contributing a panel based

on the number of years it was active in the underwriting market. For example, if a bank had four years of

data, it would contribute four observations. Altogether, we had 2,561 bank-years after taking into account the

lagged dependent variable.

Pooling repeated observations on the same banks is likely to violate the assumption of independence

from observation to observation, resulting in autocorrelation of the model’s residuals. This renders OLS

estimates inefficient, so we relied upon random effects GLS estimation. Furthermore, in the model with

lagged dependent variable, autocorrelation could generate biased estimates (Judge, Griffiths, Hill, and Lee,

1985). To check whether our estimates were indeed unbiased, in an additional supplementary analysis, we

estimated separate GLS models allowing for panel-specific autocorrelation of disturbances, which did not

substantively alter our results.

3 We examined several alternative specifications of banks’ market share (e.g., equally splitting the value of the deal among all syndicate members; assigning 25% or 75% of the syndicate’s value to lead managers). Because the average correlation among these specifications was 0.98, we used a simple 50-50 split.

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Independent Variables

Closure and Bridging Tie Age. The age of a syndicate tie was set to 1 in the first year two banks appeared

as syndicate partners in the four-year adjacency matrices described above. Subsequently, the age of their tie

was set equal to the number of years since that first syndicate as long as the banks partnered in at least one

syndicate again within the next four years. If the two banks did not partner again within this window, their tie

was removed from the adjacency matrix defining the network for the next year. If the two banks

subsequently partnered in a syndicate, the age of their tie was restarted and computed in the same way.4

Closure and bridging ties were identified by inventorying the triads in each bank’s ego network for

each four-year network. Triads are foundational network structures (Wasserman and Faust, 1994), and this

procedure permitted us to identify bridging and closure ties individually so that their ages could be assigned,

which standard closure and bridging measures (e.g., ego density, effective size) would not permit. As

illustrated in Figure 1, we defined closure ties as those comprising ‘closure triads’ in which the focal bank, A,

was syndicate partners with banks B and C, and B and C were also partners. Bridging ties, in contrast, were

defined as those comprising ‘bridging triads’ in which the focal bank, A, was syndicate partners with banks B

and C, but B and C were disconnected.5

Insert Figure 1 about here.

We operationalized our hypothesis tests three alternative ways. First, the minimum, mean, and

maximum ages of a bank’s closure and bridging ties were used to test hypotheses H1 and H2 predicting the

temporal effects of closure and bridging ties, respectively. To test hypothesis H3 predicting the hybrid effect

of old closure ties and young bridging ties, we divided a bank’s maximum closure tie age by its minimum

bridging tie age. For hypothesis H4 predicting the hybrid effect of old closure ties and old bridging ties, we

multiplied a bank’s maximum closure tie age by its maximum bridging tie age. The ratio variable for

4 Ties were rarely interrupted in this manner, with only 84 occurring during the observation period.

5 The number of closure triads, divided by (n × n-1)/2, where n is ego’s number of partners, equals ego density, a standard metric of closure (Rowley et al., 2000). The number of closure triads corresponds closely to effective size (r = .91), the number of partners a bank has minus the average number of ties its partners have, a standard metric of bridging (Burt, 1992). We use the triad-based measures because of the ease and transparency of computing age-based specifications.

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hypothesis H3 is largest when maximum closure tie age is high and minimum bridging tie age low, while the

interaction variable for hypothesis H4 is largest when both maximum closure tie age and maximum bridging

tie age high.

Second, to avoid reliance on single, extreme values to test hypotheses H1 and H2, we replaced

minimum, mean and maximum ages of a bank’s closure and bridging ties with the mean ages of ties in the

first, second and third (combined), and fourth quartiles of the tie age distribution. Again, we test hypotheses

H3 and H4 (respectively) by dividing a bank’s fourth quartile mean closure tie age by its first quartile mean

bridging tie age, and multiplying a bank’s fourth quartile mean closure tie age by its fourth quartile mean

bridging tie age.

Third, because the within quartile age distributions (particularly the fourth) tended to be skewed, we

substituted the mean ages of ties in the first, second and third (combined), and fourth quartiles of the tie age

distribution with their respective medians, and computed analogous variables to those for the quartile means

to test hypotheses H3 and H4.

The panels in Figure 2 plot the distributions of closure and bridging tie minimum, mean, and

maximum age. Panels a and b show the greater mean and maximum longevity of banks’ closure ties relative

to bridging ties. It is rare for banks’ bridging ties to average more than four years of age, or for their oldest

bridging tie to exceed 10 years of age. In contrast, the mean age of banks’ closure ties often exceeds four

years, and their oldest closure ties frequently exceed 10 years of age. As Panel c shows, however, the

minimum age distributions of banks’ closure and bridging ties are quite similar.

Insert Figure 2 about here.

Control Variables

Many other factors may influence a bank’s performance. Accordingly, we control for a baseline model that

includes a range of bank attributes as well as industry-level network and environmental characteristics. Unless

noted otherwise, the control variables are time varying and lagged one year to avoid simultaneity.

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Bank Controls. As noted above, we include lagged values of the dependent variable (bank’s market

share, logged) in all models. Additionally, because banks’ ego networks vary in size, we controlled separately

for each bank’s number of closure and bridging ties. Their interaction (logged to reduce skewness) was also

included to account for any age-independent benefits of adopting hybrid network positions that combine

bridging and closure ties.

To establish that our theoretical estimates do not spuriously capture the influence of a bank’s

network centrality on its access to other banks, we also controlled for betweenness centrality, calculated as Σi

sjk(i)/sjk, where where sjk is the number of shortest paths from banks j to k, and sjk(i) is the number of shortest

paths from j to k that pass through the focal bank, i, is also controlled. We divided the measure by (n − 1)(n

− 2), the number of pairs of other banks, for normalization.

We also controlled for the characteristics of a bank’s ‘pendant ties,’ a third type of triadic relationship in

which the focal firm is subject to brokering. As shown in Figure 1, pendant ties comprise triads in which the

focal bank, A, is syndicate partner with bank B, and B and C are also partners. This places bank B in a

bridging position between disconnected banks A and C. As with closure and bridging ties, pendant ties were

identified by inventorying the triads in each bank’s ego network for each four-year network, and the number

and ages of a bank’s pendant ties were controlled in the same manner as closure and bridging ties. To

account for effects of change in the character of a bank’s ego network, we controlled for the number of a bank’s

closure triads that became bridging triads in each four-year network as a result of banks B and C no longer

being tied (see Figure 1). And, conversely, the number of a bank’s bridging triads that became closure triads in

each four-year network as a result of banks B and C becoming tied (see Figure 1), which captures the extent to

which a bank’s bridging ties decay as a result of its disconnected partners establishing ties with one another.

In addition, we controlled for each bank’s degree of lead-role specialization, measured as the proportion

of a bank’s syndicates in each four-year window for which it acted as lead bank. We also controlled for the

length of time each bank participated in the syndication network using a count variable set to the number of

years that the bank had been underwriting public offerings in Canada up to and including the present year.

17

As well, we controlled for the extent to which each bank participated in syndicates for issuers in seven

economic sectors: manufacturing, non-manufacturing, natural resources, utilities, financial, technology and

government. We defined Sector Specialization using a variant of the Herfindahl index as Σj (Sjt / nit)2, where nit

is the total number of syndicates bank i participated in four-year window t, and Sjt is the number of syndicates

bank i participated in four-year window t that were in sector j. If the value of this variable is one (its

maximum), a bank’s syndicates were all in a single sector; lower values indicate that a bank participated in

syndicates more broadly distributed across the various sectors. Banks participating in syndicates created to

place equity instruments face greater uncertainty and risk than banks participating in debt placement

syndicates (Podolny, 1993, 1994). As a result, the types of issues a bank underwrites may affect its

performance. Therefore, we created a control variable measuring the proportion of nondebt syndicates in

which each bank participated. Finally, we included a censoring dummy variable to control for the possibility

that the performance of banks that began operations prior to the start of our observation period (1952) differ,

for example, because they are survivors of industry competition prior to the observation period.

Industry Controls. At the industry level, we controlled for environmental munificence using the

logged Value of All Syndicate Deals completed in each four-year network and for Environmental Uncertainty using

the variance in the yearly number of underwriting syndicates in each four-year network. We also controlled

for the number of banks active in the underwriting syndicate network, as well as for the changing

composition of the syndicate network by including counts of the numbers of banks entering and exiting the

network in the prior four year window. The number and turnover of banks in the network may affect bank

performance, for example, by affecting the level of competition for deals or partners. Finally, to control for

other sources of performance variation over time, we created decade dummy variables for deals taking place

in 1950s, 60s, 70s and 80s, with the latter decade excluded as the comparison.

Descriptive Statistics

Means, standard deviations and correlations for all variables are given in Table 1. The correlations are

generally small in magnitude (i.e., <.3, or 9% shared variance), although larger among the tie age variables for

18

each specification within each type of tie, as well as among a several control variables.6 Such levels of

multicollinearity among explanatory variables can result in less precise parameter estimates (i.e., larger

standard errors) for correlated variables but will not bias parameter estimates (Greene, 2000; Kennedy, 1992).

So, while this does not pose a serious estimation problem, it can make it more difficult to draw inferences

about the effects of adding particular variables to the models. Therefore, when estimating our models, we

estimated a set of hierarchically nested models to check that multicollinearity was not causing less precise

parameter estimates and suppressing some variables’ significance (Kmenta, 1971). Although we observed

some minor inflation of standard errors across nested models, we found little to suggest that multicollinearity

materially affected our estimation.

Insert Table 1 about here.

RESULTS

Tables 2-4 reports the Random Effects GLS estimates for the effects of theoretical and control variables on

banks’ market share performance. In Table 2, estimates are based on mean, minimum and maximum tie ages,

in Table 3, quartile mean ages, and in Table 4, quartile median ages.

In each table, Model 1 is a baseline that includes all the control variables. Models 2-5 introduce the

variables for the number and ages of bridging and closure ties, as well as pendant ties. Given the moderate

correlations noted among the tie age variables, we introduce the effects for the number and ages of closure

and bridging ties incrementally in Models 2-4, and then estimate them together in Model 5. Although we

observe some inflation of coefficient standard errors in Model 5, the significance levels and signs of

theoretical variable estimates are generally consistent across nested models, indicating that multicollinearity is

not materially affecting our estimation. Finally, we estimate interactive effects of the number and age of

closure and bridging ties in Models 6 and 7.

Insert Tables 2-4 about here.

6 The correlations are also generally large among the different specifications of the age variables across the three specifications (e.g., minimum age, first quartile mean age, and first quartile modal age). This is to be expected, given that they are intended as alternative specifications of the same variable, but not problematic for the empirical analysis since variables based on the different specifications are estimated separately.

19

Estimates for Model 5 in Table 2 indicate that a bank’s market share is negatively related to its

number of closure ties, but positively related to their mean and maximum age. These estimates support H1,

which predicted that closure tie benefits would increase with their age. In this case, however, increasing

closure tie age serves, initially, to moderate the negative effect of the number of closure ties. Estimates for

Model 5 presented in Tables 3 and 4 are largely consistent with those in Table 2, however the coefficients for

mean closure tie age are not significant.

A bank’s market share is, in contrast, positively related to its number of bridging ties, and negatively

related to both its maximum and minimum bridging tie age. Thus, bank’s performance was lower when their

oldest and youngest bridging ties were older, supporting H2. Again, estimates for Model 5 presented in

Tables 3 and 4 reinforce those in Table 2; the maximum tie age effect is not, however, replicated by the

fourth quartile mean tie age variable (Table 3). Notably, while mean bridging tie age is not significant in

Table 2, providing further evidence for H2 the coefficient for the second and third quartile (combined) mean

tie age variable is significant and negative in Tables 3.

Also apparent in Tables 2-4 is evidence that a bank’s market share is negatively related to its number

of pendant ties, although unrelated to their age.

Models 6 and 7 in Table 2 add the interactive effects of the number and minimum and maximum age

of closure and bridging ties, respectively. The significant, positive coefficient for the interaction of the

numbers of closure and bridging ties in Model 6 demonstrates a basic, age-independent advantage of hybrid

network positions. Both tie age interactions are significant, with coefficients positive for maximum closure tie

age ÷ minimum bridging tie age, supporting H3, and maximum closure tie age × maximum bridging tie age, supporting

H4. Interactions based on quartile mean and median ages yield analogous results in Tables 3 and 4.7 Thus, as

predicted, banks occupying hybrid network positions that combine established closure ties with either young

7 We also estimated models including maximum closure tie age ÷ mean bridging tie age and maximum closure tie age × mean bridging tie age (and their age quartile mean and median specification equivalents) to confirm that hybrid benefits derived from combining old closure ties with the oldest and youngest bridging ties. Estimates for both multiplicative and ratio interactions were negative in sign, and significant in three of six cases. Combining old closure ties with ‘middle-aged’ bridging ties thus had either no effect or lowered bank performance. Estimates for the theoretical variables of interest were unaltered in significance or sign in these models, which are available on request.

20

or old bridging ties enhanced their performance.8

In the presence of the hybrid tie age effect terms, estimates for the main effects of tie are generally

robust, but there are some changes in the sizes and significance of several coefficients. These changes reflect

the conversion of the main effect coefficients from estimates of unconditional to conditional marginal effects

in the presence of the hybrid effects (Greene, 2000; Jaccard, Turrisi and Wan 1990).9 Interpretation of the

hybrid tie age effects is thus complicated by the need to make sense of them jointly with main effects of tie

age. To aid in their interpretation we therefore plot the joint effects of closure and bridging tie age based on

estimates from Model 7 in Figures 3 (Table 2), 4 (Table 3), and 5 (Table 4). In the figures, the y-axis indicates

the multiplier of market share relative to the case where closure and bridging tie age are both zero, the x-axis

indicates the value of closure tie age and the z-axis indicates the value of bridging tie age. We plot the

interactions from age one to roughly two standard deviations above the means of the tie age variables.

Insert Figures 3, 4, and 5 about here.

Panel a in Figure 3 graphs the effects of maximum closure tie age ÷ minimum bridging tie age, which

support H3. As this figure shows, as the age of a bank’s oldest closure tie increases and its youngest bridging

ties decreases, its performance increases. However, as either its oldest closure tie declines in age or its

youngest bridging tie increases in age the bank’s performance declines. Panel a in Figures 4 and 5 plot the

same effect based on the mean and median quartile tie age variables, respectively.

Panel b in Figure 3 plots the effects of maximum closure tie age × maximum bridging tie age, which support

H4. As this figure shows, performance increases with the age of a bank’s oldest closure and bridging ties. As

the age of either type of tie declines, however, the bank’s performance also declines. Panel b in Figures 4 and

5 illustrate the same effect based on the mean and median quartile tie age variables, respectively.

A comparison of the multiplier magnitudes indicates that hybrid positions comprised of old closure

8 As a robustness check, we reran the analysis excluding observations for the 1950s (N = 301) to ensure that the underestimation of the ages of ties established prior to 1952 was not affecting our results. Estimates for the reduced sample (available on request) are similar in sign and significance for all theoretical variables.

9 Inspection of the standard errors indicates some inflation for variables constituting the interactions, but since none loses significance this does not materially affect our estimation.

21

and bridging ties yield a 35-65% larger performance enhancement than hybrid positions comprised of old

closure and young bridging ties. The smaller benefit of hybrid positions involving new bridging ties is

consistent with greater risk, noted earlier, of such ties being perceived as undermining (intentionally or

unintentionally) a long-standing group’s collective benefits. Additionally, effect sizes are smaller for the

quartile age variables that moderate the extreme tie age values.

DISCUSSION

This study was motivated by the observation that the benefits firms derive from their network positions are

typically theorized and modeled without reference to the character of the ties comprising the position. In the

face of equivocal empirical evidence, and propagation of contingency arguments specifying conditions under

which particular structural positions are more advantageous, we suggested linking research streams on

structural and relational embeddedness to make more systematic, and integrative progress. This connection

focuses attention on the qualities of ties underlying network positions, encouraging theorizing of structural

network effects that takes into account the character of the ties through which structural benefits accrue.

Our focus was on the dependence of the performance effects of firms’ network positions on the age

of the ties comprising them. Tie longevity seemed particularly germane given that the benefits typically

associated with closure take time to develop, while those associated with bridging are typically characterized

as short-lived. In contrast to earlier analyses, which assess the effects of firms’ network positions without

regard to the characteristics of the ties that underlie the them, this focus led us to consider the influence of tie

age on the value of network positions among Canadian investment banks. Our findings support the idea that

the advantages firms gain (or do not) from their network positions depend on the character of the ties

comprising them, highlighting the risk of theorizing structural network effects without referring to the

qualities of the ties through which particular structural benefits accrue.

At a most basic level, our results support the view that the benefits associated with closure take time

to develop, while those associated with bridging are often short-lived. Our findings also show closure and

bridging operating together, and their combination enhancing performance, particularly when long-lived

22

closure ties are mixed with either nascent or well-established bridging ties. Such hybrid positions contribute

to firm performance by providing ready access to diverse information sources through bridging and

facilitating efficient exchange and integration of information through closure.

Although we anticipated the benefits of hybrid positions comprised of old bridging and old closure

ties, and recognized the greater risk of combining new bridging ties with old closure ties, we were somewhat

surprised that combining old closure ties with old rather than young bridging ties raised the performance gain

by a third or more. For firms with well-established closure ties, initiating and defending bridging ties with

partners “with whom good things could happen” (Burt, 2005: 95), may prove a particularly fruitful network

strategy. Bridging ties resistant to disintermediation may be particularly valuable in combination with long-

lived closure ties because the private information critical to brokering tends to be transmitted through them

(Bidwell and Fernandez-Mateo, 2007; Uzzi, 1996). Investment banks are likely to strategically protect the

structural holes they span to maintain their superior access to information and resources (Baum et al., 2003).

Central banks in the network, however, have more flexibility than peripheral banks in choosing partners to

meet their current needs, but they are also likely to collaborate with banks of similar centrality because of the

signalling role of their hierarchical positions (Chung et al., 2000; Podolny, 1993, 2001). As a result, where

central banks bridged cohesive subgroups, barriers to bridging were likely to be high for later entrants as the

central banks endeavored to maintain and reinforce their positions of power and control (Baum et al., 2003).10

Our study also informs the debate over whether firm performance is enhanced by closure or

bridging. In addition to showing closure and bridging operating together to enhance performance, our

findings also show how the advantages of firms’ network positions depend on the ages of the ties comprising

them. This highlights the possibility that equivocal findings in prior empirical studies of structural network

effects may stem from specification error. Indeed, had we relied on estimates for the number or closure and

bridging ties alone, we would have concluded, erroneously, that banks benefited only from bridging, and not

from closure (unless combined with bridging).

10 Consistent with this idea, the maximum bridging tie age averaged 7.8 years (s.d. = 2.7) among banks in the top 10% of centrality distribution, compared with 1.4 years (s.d. = 2.0) for the remaining 90% of the banks.

23

Of course, it is possible that our specific pattern of results will not generalize to other empirical

settings where, for example, the risks and costs of forming bridging ties differ, where the benefits sought

from ties or network positions differ (e.g., market performance vs. innovative performance), or where the

nature of information and resources being accessed, transferred and recombined in the network differ (e.g.,

tacit versus explicit, complex versus simple, and so on). Generalizing our results may thus require a search

for contingencies affecting the benefits of different mixes of closure or bridging ties. At the same time,

however, our analysis suggests a reframing of the search for conditions under which closure and bridging

positions are beneficial; in particular, one focused on the character of the ties comprising the positions.

Finally, our findings have implications for how firms might deliberately structure their network

positions to enhance performance. For example, while firms typically enter networks at the periphery of a

network, as they develop their capabilities and visibility over time, they may become more sought-after as

partners and develop bridging ties to other local clusters, leveraging their success toward hybrid positions.

Firms that perform poorly, or remain peripheral to their local cluster, may attempt to improve their network

positions by developing bridging ties (particularly ones that are defensible or difficult to disintermediate) to

firms that are otherwise unconnected to its cluster to leverage informational and resource asymmetries.

While few firms are able to control their industry network, many are likely to be able to influence the

structure and longevity of their ties sufficiently to establish more advantageous positions. Thus, firms can

benefit from analyzing the qualities of their ties and structure of their network position in order to identify

opportunities to improve their position and enhance their performance.

Interfirm networks are a prevalent and fast expanding phenomenon, and we are rapidly gaining an

understanding of their properties and consequences. We further this understanding by incorporating a

concern with the qualities of closure and bridging ties within the structural embeddedness perspective, and

demonstrating how the aging of closure and bridging ties influences firm performance. We hope our findings

spark further research linking the qualities of ties to their structural benefits.

24

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Figure 1. Triad Types

A

B C

Closure Triad

A

B C

Bridging Triad

A

B C

Pendant Triad

Closure Ties Bridging Ties Pendant Ties

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