The Role of Network Resources in Market Entry: Commercial Banks' Entry into Investment Banking,...

The Role of Network Resources in Market Entry: Commercial Banks' Entry into InvestmentBanking, 1991-1997Author(s): Michael JensenSource: Administrative Science Quarterly, Vol. 48, No. 3 (Sep., 2003), pp. 466-497Published by: Sage Publications, Inc. on behalf of the Johnson Graduate School of Management,Cornell UniversityStable URL: http://www.jstor.org/stable/3556681 .

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The Role of Network Resources in Market Entry: Commercial Banks' Entry into Investment Banking, 1991-1997

Michael Jensen University of Michigan

@ 2003 by Johnson Graduate School, Cornell University. 0001-8392/03/4803-0466/$3.00.

An earlier version of this paper received the Academy of Management's William H. Newman Award for Best Paper Based on a Dissertation and appeared in the 2002 Academy of Management Confer- ence Proceedings. I am particularly grate- ful to Ed Zajac for his guidance and support of my dissertation research. Ron Burt, Jerry Davis, Mark Kennedy, Deepak Malhotra, Mark Mizruchi, and Willie Oca- sio offered valuable comments and suggestions. I also thank Linda Johanson, Don Palmer, and the anonymous ASO reviewers for their careful and construc- tive feedback and suggestions.

This study focuses on the roles of interfirm ties and network status in firms gaining access to customers in newly entered markets, examining whether these network resources are transferable and therefore can be deployed outside the market in which they originated. The role of market ties and network status is examined in a comprehensive longitudinal sample of commercial banks' entry into investment banking from 1991 to 1997. Results show that though market ties and network status facilitate market entry, the importance of network status decreases in the presence of market ties, and the value of network status, unlike market ties, decreases over time after market entry and is less important to customers with more market experience.0

It is well established that network resources are important in market competition (Podolny and Page, 1998); however, the transferability of network resources from one market to another and the relationships among different kinds of network resources remains largely unexplored. A network resource is transferable to the extent that it is valuable outside the network in which it originated and therefore can provide firms with advantages in multiple markets. Social networks are important because firms accrue resources from the individual dyadic relationships in which they participate and the aggregate positions they occupy within a network. For example, close dyadic ties increase the value of exchanges by facilitating the development of trust between exchange partners (Larson, 1992; Uzzi, 1997), and prominent network positions reduce market uncertainty by providing access to information and defining status hierarchies (Burt, 1992; Podolny, 2001). Network resources are unique because they reside in specific networks outside the control of individual firms (Gulati, 1999), which naturally raises questions about the transferability of network resources from one network to another (Coleman, 1988). This study focuses on the transferability of two network resources: market ties, or the dyadic business relationships firms maintain with their customers (Baker, Faulkner, and Fisher, 1998), and network status, or the relative prominence of the positions firms occupy in the networks of firms competing in a given market (Podol- ny, 1993).

The transferability of network resources is important because firms often participate in several networks at the same time and enter markets dominated by other networks than the markets they entered from. Most market-entry research has neglected networks, instead focusing on how the economic structure of industries or the economic resources of firms facilitate or impede market entry (Caves, 1998; Helfat and Lieberman, 2002). Although these streams of research con- tribute significantly to understanding market entry, the neglect of networks is unfortunate because networks often influence the structure and level of competition within a given market (Dyer and Singh, 1998; Gulati, Nohria, and Zaheer, 2000). Since market-entry research has shown that the structure and level of competition are important factors in market entry (Siegfried and Evans, 1994), networks also may have the potential to influence the rate of entry into a given market and the performance of individual firms after market

466/Administrative Science Quarterly, 48 (2003): 466-497



Network Resources in Market Entry

entry. As Powell (1990: 305) noted, "By establishing enduring patterns of repeat trading, networks restrict access. Opportu- nities are thus foreclosed to newcomers, either intentionally or more subtly through such barriers as unwritten rules or informal codes of conduct." To the extent that network resources are transferable, however, firms may be able to use their existing network resources to overcome network- based barriers to entry. This study seeks to bridge network and market-entry research by investigating the transferability of network resources in the context of firms' access to customers in newly entered markets.

The question is how the market ties and network status entering firms bring from their old market or develop in their new market might affect their access to customers in their new market. Market ties and network status could facilitate market entry by either reducing the market uncertainty surrounding entering firms or increasing the value of exchanges with entering firms, and their effects could vary over time and be interdependent. Though market ties and network status facilitate market entry, the importance of network status decreases in the presence of market ties. Market ties represent a more direct mechanism than network status to reduce market uncertainty and increase exchange value than network status, suggesting that network resources are not necessarily additive resources. The value of network status, unlike market ties, also decreases over time after market entry and is less important to customers with more market experience. By exploring the conditions under which network status and market ties are most valuable, it is possible to identify the mechanisms that account for the effects of market ties and network status and thereby provide a more detailed account of the transferability of market ties and network status and their differences and similarities.

This study tests the transferability of market ties and network status and the relationships between them in the empirical context of commercial banks' entry into the investment-banking industry from 1991 to 1997. The investment-banking industry is an excellent empirical setting in which to test hypotheses about the transferability of network resources for several reasons. First, qualitative and quantitative research suggests that network resources, such as market ties and network status, constitute important resources that help dif- ferentiate investment banks, thus providing a strong back- ground for this study (e.g., Hayes, Spence, and Marks, 1983; Eccles and Crane, 1988; Podolny, 1993, 1994). Second, the deregulation of the industry in the late 1980s and the gradual removal of the Banking (Glass-Steagall) Act of 1933, which had barred commercial banks from entering the investment- banking industry, resulted in a sizable influx of commercial banks. The influx of several banks from the same industry allows distinctions to be made between internally comparable network resources based in either the investment-banking industry or the commercial-banking industry. The deregulation of the investment-banking industry and the influx of commercial banks thus represent a unique natural experi- ment in which to study the transferability of network resources.

467/ASQ, September 2003



THE TRANSFERABILITY OF NETWORK RESOURCES

Market Ties Market ties based in one market are transferable to the extent that the advantages they provide in one market can be leveraged into another market and provide value in the new market. The degree to which a particular tie is renewed over time represents a potentially important indicator of how much the tie is valued. Firms often renew the dyadic ties they had with other firms when they seek new ties in the same product markets in which they had formed the first ties. Carlton (1986) reported that the average duration of buy- ers' and suppliers' ties in the manufacturing industry typically exceeded five years and often approached 10 years. Similar- ly, Levinthal and Fichman (1988) found that ties between auditors and clients were more likely to be renewed as the duration of these ties increased, after an initial short period of decreased likelihood of renewal. Finally, Martin, Mitchell, and Swaminathan (1995) reported that automobile assemblers and component manufacturers with long-term market ties in their domestic market tended to recreate these ties in new geographic markets. Although the tendency to renew past ties is well established empirically, some ambiguity exists about why firms tend to renew past ties or how past ties provide value.

Trust often has been emphasized as an important source of value creation in market ties and has been therefore used to explain the tendency to renew ties (Baker, 1990; Gulati, 1995; Uzzi, 1997). Trust refers here to the belief based on past ties that a potential partner is trustworthy and therefore would adhere to norms of equity in future ties and avoid acts of self-interest that could damage the tie (Ring and Van de Ven, 1992; Uzzi, 1997). Whether firms are trustworthy as a result of the personalities of their managers, the institutional environment, or the social and economic consequences of being untrustworthy (e.g., Zucker, 1986; Greif, 1989), the ties themselves facilitate trust because they provide direct experience of the other firms. When exchange partners trust each other, they are more willing to share private resources and information, which promotes value creation in exchanges by making it easier for exchange partners to match their resources and competencies (Uzzi, 1999). Eccles and Crane (1988) found that firms often prefer to repeat past ties with investment banks because it enhances the flow of information between firms and banks and makes it easier for banks to understand and meet the particular investment banking needs of each individual firm. Although the development of trust helps explain the tendency to renew ties, firms also may renew their past ties because it reduces the search costs of identifying and selecting partners for new exchanges (March, 1994). As firms become familiar with their exchange partners and develop unique routines to interact with them, they may avoid full searches for new partners and focus their attention on the subset of potential partners that they know from past experiences can satisfy their demands (Podolny, 1994). Finally, since managers are concerned not only about future decisions but also about appearing consistent and competent in their previous decisions, they may simply





repeat past ties and escalate their commitment to past partners because it makes them appear more consistent and competent (Staw, 1981).

There are limits to the benefits of renewing ties, however, and the overall value of doing so may eventually decline. By relying on old ties too much, firms risk losing access to potentially important resources and information held by other firms and isolating themselves and their suppliers from market pressures that could increase their long-term efficiency (Baker, 1990; Uzzi, 1997). Ironically, as trust develops in repeated exchanges, it becomes more attractive to continue to exploit these ties for new transactions, which discourages firms from exploring new ties and developing new relational resources (Levitt and March, 1988; March, 1991). The negative consequences of repeating exchanges too much in closed networks have been shown to include a decreased ability to adapt to environmental changes and survive (Portes and Sensenbrenner, 1993; Uzzi, 1996).

The observation that firms tend to repeat the past ties they have in a particular product market does not necessarily imply that firms also are more likely to extend these ties into other product markets. When firms repeat dyadic exchanges in a given product market, they become familiar with their partners' resources and capabilities and become better able to assess whether their past partners can meet their current needs. Familiarity with resources and capabilities based in one market cannot always be extrapolated to another market, however, because the resources and capabilities required for success differ between markets. Eccles and Crane (1988: 219) reported that commercial banks had to "transform themselves from large, hierarchical, and slow-moving organizations into organizations that are flat and flexible with self- designing capabilities" to be successful in investment banking. It is even more difficult to extrapolate familiarity with resources and capabilities from one market to another market when they reside in different parts of the organization. The Federal Reserve Board mandated physical, operational, and financial firewalls between the commercial and investment bank subsidiaries of bank holding companies (Johnson, 1996; Macey, 2000). The firewalls made it difficult for investment banks and commercial banks within the same holding company to share their resources and capabilities directly. The firewalls also prevented them from closely integrating their operations through client-focused, cross-functional tie managers, which made escalation of commitment less likely because they forced firms to interact with more different bankers (Staw, 1981).

Although familiarity with the specific resources and capabilities a firm possesses in one market is less valuable for decisions about partners in another market, firms still may tend to extend past ties into new markets to leverage trust. Since trust is rooted in the belief that firms adhere to norms of equity and refrain from damaging self-interest, it depends less on the specific markets in which firms operate or the specific resources and capabilities they possess and more on the actual business policies and practices firms adhere to in their market ties. If managers have come to trust the busi-




ness policies and practices of a particular part of a larger firm, they may be more willing to adopt an open and cooperative approach to forming ties with other parts of the firm, even if they are unfamiliar with the specific resources and capabilities of the other part of the firm. The difference between familiarity and trust is that familiarity refers to knowing a firm has the necessary resources and capabilities to perform a particular activity well, whereas trust refers to believing a firm has the moral character to do its best to ensure that any activity is performed well.

These arguments suggest that ties based in one market (commercial banking) may be valuable in another market (investment banking) because they help firms work more effectively together by increasing mutual trust or, to the extent that specific resources and capabilities are transferable between markets, because they reduce search costs by increasing familiarity with the resources and capabilities other firms possess. In investment banking, trust from a prior relationship is likely to be manifested in a nonfinancial firm's choice of lead manager, the bank chosen to organize and manage the underwriting of a security.

Hypothesis 1: A nonfinancial firm is more likely to use a given commercial bank as lead manager in investment banking if a commercial banking tie already exists between them.

Network Status

Network status based in one market is transferable to the extent that the advantages provided by occupying a particular status position in one market can be leveraged into another market and provide value in the new market. Status here refers to the relative position a bank occupies in a given market based on its direct and indirect ties with other firms as compared with the positions that other firms occupy based on their direct and indirect ties (Linton, 1936; Burt, 1982). Status is a potentially valuable resource in market entry because it functions as a market signal that firms can use to make inferences about the value of forming exchange relationships with entering commercial banks when other more direct market information is unavailable, uncertain, or ambiguous (Podolny, 2001). Status, like reputation, can function as a market signal firms can use to make inferences about future unobservable quality, but status is based on past ties, whereas reputation is based on past performance (Wil- son, 1985). Status and reputation are often difficult to distinguish from each other, however, because the past performance of firms often affects the formation of ties and because status affects future performance (Gulati, 1995; Benjamin and Podolny, 1999).

While relationships give actors status, which in turn functions as a signal of quality, they do not give them a reputation for quality; they simply provide actors with the opportunity to build their own reputation by improving the quality of their products. For example, being affiliated with a prestigious university provides young scholars with status, which may be taken as a signal of their future academic performance and help them be productive scholars, but the affiliations do not





in themselves give the scholars a reputation for academic performance. In the absence of information on performance, status may function as a signal of quality, especially because firms are more likely to affiliate with banks whose reputations for quality are similar to their own than they are to affiliate with banks whose reputation for quality is different from their own. When most firms follow this simple affiliation pat- tern, they can switch their attention from evaluating the unobservable quality of products and base their exchange- partner decisions on the observable status positions firms occupy (Podolny, 1994). The emergence of homophilous affiliation patterns is most likely when market uncertainty is high, quality is important, and firms differ in the quality of their investments. In these situations, firms that have invested aggressively in quality and developed a reputation for high quality may avoid affiliating with banks that have invested less in quality because it can reduce the quality of their future products and thereby tarnish their reputation for quality. Status also may influence the formation of market ties because it represents an important aspect of social identity. The social identity of individuals refers to those aspects of individuals' identity that derive from the social categories or groups, such as race and gender, to which they perceive themselves as belonging (Tajfel and Turner, 1979). The social identity of firms refers similarly to those aspects of firms' identity that derive from the formal and informal groups in which firms participate together with other firms and economic entities. Rao, Davis, and Ward (2000) argued that firms view their choice of stock exchange as an important aspect of their social identity and that some firms strove for a positive social identity by leaving NASDAQ and joining the New York Stock Exchange. The status hierarchy that emerges when firms follow homophilous affiliation patterns represents an important structural framework that firms may use to define their social identity (Podolny, 1993; White, 2002). When firms enter into ties with other firms, they also enter into specific status positions surrounded by other firms of similar status, which provides them with social identities of varying prestige, depending on where in the hierarchy they are positioned. Status may therefore influence the formation of market ties when firms are concerned about their social identity and seek partners of a particular status to maintain or enhance their social identity, regardless of the pure economic value of the ties.

The status positions investment banks occupy within a particular market have been observed to influence the formation of cooperative ties between investment banks in that market (Podolny, 1994; Chung, Singh, and Lee, 2000). The status position that investment banks occupy may also influence which banks firms choose to underwrite their securities. Mar- ket signals are important when firms make decisions about who should be their service providers because the pre-pur- chase evaluation of service quality is typically vague and partial (Weigelt and Camerer, 1988). The investment banking industry is no exception. When firms seek investment banks to underwrite their securities, their main quality concerns are the abilities of banks to price and place their securities at




terms that favor the issuing firms. The investment banking industry, however, is characterized by a loose linkage between the amount and quality of services provided to clients and the fees clients pay to investment banks, which makes it difficult to evaluate the relative quality of banks a priori (Eccles and Crane, 1988). For example, investment banks continuously incur relationship costs, such as the costs of providing firms with industry information and investment advice, without charging directly for these services or requir- ing contractual guarantees for access to new underwriting business, which makes price less relevant as a signal of quality.

Although market signals in general are important in investment banking, the role of status is more complex for commercial banks that recently have entered the industry. When firms enter a new market from an old market, they initially have to rely on their status position in their old market because they have yet to form the ties that grant status in the new market. But status based in the old market, when compared with status based in the new market, may be a weaker market signal because it builds on less relevant ties and less relevant activities. The signaling value of status based in investment banking is thus likely to be stronger in investment banking than is the signaling value of status based in commercial banking because it is based on ties formed around relevant investment banking activities. Because incumbent investment banks have the expertise and experience to evaluate if entering commercial banks have the requisite abilities to participate in the underwriting business, cooperative ties between incumbent investment banks and entering commercial banks represent a strong signal that the commercial banks have the necessary resources and capabilities to participate in the industry. The value of establishing cooperative ties with incumbent investment banks is particularly high if the incumbent banks occupy high-status positions because these banks have the strongest incentives (and the most resources) to screen potential partners and so protect their reputation for quality and adhere to the expectations of quality inherent in their status positions (Stuart, Hoang, and Hybels, 1999).

Although the status that commercial banks bring from their old market may be less effective in reducing market uncertainty than status based in the new market, it still may be important in market entry because firms can use the status of their exchange partners to augment their own status- based social identity. Since status is implicitly transferred through the ties firms use to exchange products and services, firms can use ties to change their social identity: affiliations with firms of higher status increase the status of a firm, whereas affiliations with firms of lower status decrease it (Podolny and Phillips, 1996). Given this, low-status nonfinancial firms may prefer ties with entering high-status commercial banks, which may initially be willing to work with low-status nonfinancial firms to increase their market exposure and market share, to ties with entering low-status commercial banks because they can improve their own status position and therefore their social identity. Similarly, high-status nonfi-





nancial firms may prefer ties with entering high-status commercial banks because they reduce their dependence on incumbent high-status investment banks and create competition among the incumbent high-status investment banks (Baker, 1990; Gande, Puri, and Saunders, 1999). Also, ties with high-status entering commercial banks do not threaten the social identity of the high-status nonfinancial firms, as would forming ties with low-status investment or commercial banks.

These arguments suggest that status based in the old market (commercial banking) may be valuable in the new market (investment banking) because it functions as a market signal that reduces market uncertainty surrounding new entrants or because the transferability of status through ties makes status an important source of social identity, regardless of its value as a market signal. This suggests the following hypothesis:

Hypothesis 2: A nonfinancial firm is more likely to use a given commercial bank as lead manager in investment banking if that commercial bank occupies a higher status position in commercial banking than if it occupies a lower status position.

Market Ties Versus Network Status

Although old market ties and network status both affect the formation of new market ties by reducing market uncertainty and increasing the exchange value, they reduce market uncertainty and increase exchange value in different ways. Ties reduce market uncertainty by increasing familiarity, and they increase exchange value by facilitating the development of trust. Status reduces market uncertainty by signaling quality and increases exchange value by contributing to social identity. Because ties and status facilitate market entry in different ways, they represent two different network strategies for market entry, which makes it important to examine the interaction between them. For example, entering commercial banks may attempt to leverage the commercial-banking ties they already have with potential investment-banking clients, or they may attempt to form cooperative ties with incumbent investment banks in high-status positions to build their own status. While network status reduces market uncertainty by replacing product-specific information with information about firms' positions in the overall market system, market ties reduce uncertainty by increasing tie-specific familiarity and trust. Status may therefore affect the formation of ties between two firms independent of the ties that already exist between them. The potential interaction between market ties and network status described above has been neglected in prior research, which has implicitly assumed they are additive resources (e.g., Podolny, 1994; Chung, Singh, and Lee, 2000).

An important consequence of the different effects of market ties and network status is that the importance of network status may depend on whether there are market ties between firms. Network status may have a smaller effect in the presence of existing market ties, thus suggesting that while both these network resources are transferable




between markets, they are also partial substitutes. Market ties and network status are likely to be substitutes because market ties provide direct information about potential exchange partners, while status is only a signal of quality. While firms on average can be expected to deliver products and services of a quality that corresponds to their status, it is more difficult to translate this information into what to expect from a specific exchange. For example, though investment banks' network status suggests that their products and services will be of a certain quality, most banks do not deliver the same quality all the time, nor do all their clients necessarily interpret the status signal the same way. Network status may therefore mainly be useful as a signal of average product quality and business practices in the absence of tie-specific information.

Firms seeking exchange partners, however, care less about the average product quality of their potential partners and more about whether they will deliver quality and refrain from opportunism, and this type of tie-specific assurance is mainly a function of prior ties (Granovetter, 1985). Market ties typically imply firm-level investments in tie-specific assets, such as the development of shared norms, mutual understanding, and communication patterns, and it becomes increasingly costly over time to substitute one exchange partner for another (Williamson, 1981; Granovetter, 1985). Firms are therefore more likely to repeat market ties over time as these relationship-specific assets develop and their value increases. There is some empirical support for this argument. For example, firms have been shown to be less likely to ter- minate ties with their accounting firms and advertising agencies as tie-specific assets develop over time (Levinthal and Fichman, 1988; Baker, Faulkner, and Fisher, 1998). Given that the switching costs increase as firms develop stronger dyadic ties, it may become increasingly less attractive to switch partners for status reasons. The potential advantages of seeking out higher-status partners to augment one's own social identity are unlikely to outweigh the switching costs. As familiarity and trust based on market ties develop between firms in the new market, the importance of network status, regardless of its base in the new market or the old market, will decrease because market ties are a more direct mechanism for reducing market uncertainty and because investments in tie-specific assets make it costly to switch for social identity reasons only. This suggests the following hypothesis:

Hypothesis 3: The effect of status based in (a) commercial banking or (b) investment banking on becoming lead manager in investment banking decreases in the presence of investment banking ties between the nonfinancial firm and the commercial bank.

Effects of Market Ties and Network Status over Time

Market ties can reduce market uncertainty through familiarity and increase exchange value through trust, and network status can reduce market uncertainty through signaling and increase exchange value through social identity. It is possible to theorize about the conditions under which familiarity or trust account for the effect of market ties and signaling or





identity account for the effect of network status. On the one hand, if status mainly works because it reduces market uncertainty by functioning as a quality signal, the effect of status should decrease if more information becomes available about commercial banks, and it should be less important to nonfinancial firms that are particularly skilled at assessing quality. If, on the other hand, status mainly works because it increases exchange value by contributing positively to the social identity of firms, the effect of status should not decrease as more information becomes available or be less important to firms that are particularly skilled at assessing quality. Similarly, if ties mainly work because they reduce market uncertainty by increasing familiarity, then the effect of ties should decrease as more information becomes available, and it should be less important to nonfinancial firms that are particularly skilled at assessing quality. If ties, however, mainly work because they increase exchange value through the development of trust, the effect of ties should not decrease as more information becomes available and should be no less important to firms that are particularly skilled at assessing quality. Some evidence suggests that the effect of network status may decrease over time as more information about firms becomes available. Stuart, Hoang, and Hybels (1999) reported that younger biotech firms benefited more from ties with high-status equity investors and investment banks in terms of time to their initial public offering (IPO) and IPO evalua- tions than did older biotech firms. Younger firms benefited more than older firms from their high-status affiliations because there was less alternative information available about younger firms on which to base investment decisions. Podolny and Scott Morton (1999) reported similarly that older firms entering into the British shipping industry from 1879 to 1929 benefited less than younger firms from their founders' membership in higher social classes in terms of avoiding predatory pricing. They argued that class membership was less important for older firms than younger firms because more information was available about their willingness to adhere to the business norms that dominated the shipping industry. Although the importance of status as a market signal therefore may decrease over time as more information about the entering firm emerges, it is less likely that the importance of status for social identity decreases over time. Although research suggests that high-status individuals and firms are more willing to deviate from role expectations than middle-status individuals and firms (Phillips and Zuckerman, 2001), their deviance does not imply that status over time has become less important to them; it simply implies that these actors feel secure enough in their status to deviate occasionally from their role expectations (Hollander, 1958).

Although the effects of status may decrease over time as more information becomes available, suggesting that the quality signal function dominates the social identity function in market entry, the effects of ties are less likely to change over time. In contrast to status, the importance of market ties depends less on the amount of time firms have been in a given market, because the added value they provide cannot




be obtained from general sources of information and indicators of legitimacy. The information exchanged between partners that trust each other is more comprehensive, tacit, and holistic than the information exchanged outside trusting relationships and allows partners to realize the full potential advantages of these relationships, such as complex adapta- tion, economies of time, and integrative agreements (Larson, 1992; Uzzi, 1997). If market ties mainly work by increasing familiarity with entering firms' resources and capabilities, however, the effect of market ties is likely to decrease as more information becomes available about these firms and generalized performance reputations emerge. For example, if market ties mainly convey non-proprietary information about the ability of a particular entering commercial bank to underwrite a particular type of security, this type of information would also become available over time through different public sources, such as rankings of firms by market share.

The arguments suggesting that information decreases the effect of status, but not the effect of market ties, also suggest that market experience may decrease the effect of status, but not the effect of market ties. While increasing information about entering commercial banks reduces the market uncertainty surrounding them, nonfinancial firms more skilled at using the existing information may be less concerned with market uncertainty. Nonfinancial firms that often use the capital markets to raise capital are likely to have considerable expertise in evaluating the resources and capabilities of individual investment banks, even if they have never worked with them. Moreover, experienced nonfinancial firms typically have internalized some investment-banking expertise in their own organization and are aware of the different ways in which the investment banks can best service them, which makes them able to monitor the quality of service their investment banks actually provide. An experienced firm, in other words, is like a wine connoisseur who does not have to rely on the status of a winery to determine the quality of its wines. But there is no particular reason why status should be less important for the social identity of experienced nonfinancial firms than it is for inexperienced firms. Similarly, experienced nonfinancial firms place less value than inexperienced firms on familiarity with the specific resources and capabilities possessed by banks because they are better equipped to base their partner decisions on publicly available information and generalized reputations. There is no particular reason to believe, however, that experienced firms would not benefit as much as inexperienced firms from developing trust and sharing sensitive proprietary information.

Network status and market ties may therefore ultimately affect market entry differently: network status may function more like a market signal, whereas market ties may function more like a conduit of trust. Since the effect of network status is more likely to depend on its ability to reduce market uncertainty than increase exchange value, the effect of network status is likely to decrease over time and be less important for nonfinancial firms with more market experience. In contrast, since the effect of market ties is more likely to depend on their ability to increase exchange value than





reduce market uncertainty, the effect of market ties is unlikely to decrease over time and will be equally important to nonfinancial firms with more market experience. This suggests the following hypotheses:

Hypothesis 4: The effect of status based in (a) commercial banking or (b) investment banking decreases as commercial banks spend more time in investment banking, whereas the effect of ties based in commercial or investment banking does not.

Hypothesis 5: The effect of status based in (a) commercial banking or (b) investment banking decreases as firms' investment banking experience increases, whereas the effect of ties based in commercial or investment banking does not.

METHODS

The investment-banking industry from 1991 to 1997 is an excellent empirical setting in which to examine the importance of network resources in market entry. Legal entry barriers have protected the investment-banking industry since the Banking (Glass-Steagall) Act of 1933 barred commercial banks from underwriting corporate securities. The Federal Reserve Board nevertheless began allowing commercial banks to enter the industry in the late 1980s, and commercial banks started gaining market share in the early 1990s (Gande, Purl, and Saunders, 1999). The opportunity to enter investment banking was attractive to many commercial banks because firms were increasingly using the securities markets to raise capital instead of relying solely on commercial loans (Berger, Kashyap, and Scalise, 1995). For example, from 1983 to 1993, the volume of new commercial loans increased less than 1 percent a year, from $404 billion to $443 billion, whereas new security issues grew almost 12 percent a year, from $184 billion to $554 billion (Johnson, 1996: 8-9). Although the process of restructuring the investment-banking industry continues, the period from 1991 to 1997 represents a period in which commercial banks began entering the market by setting up their own investment-banking subsidiaries (often called "section 20 subsidiaries"). The end of this period was marked by the merger of Salomon Smith Barney (Travelers Group) and Citibank (Citicorp) in 1998 and the Financial Services Modernization (Gramm-Leach- Bliley) Act of 1999, repealing the Banking (Glass-Steagall) Act of 1933, both of which inspired several commercial banks to acquire investment banks. Because the Federal Reserve Board first allowed commercial banks to underwrite corporate bonds in 1989 (Gande, Purl, and Saunders, 1999), 1990 was the first full year in which commercial banks could participate in the bond market, which, given the one-year lag between dependent and independent variables, makes 1991 the first year in the sample.

This study focuses specifically on entry into the market for corporate debt (bond underwriting) because commercial banks initially focused on this market and have gained a larger market share here (more than 20 percent) than in the equity market (less than 5 percent) (Gande, Purl, and Saunders, 1999). The corporate-debt market is also a more conservative setting in which to study the importance of network




resources because strong rating agencies, such as Standard & Poors and Moodys, provide independent information about the quality of bonds, thus reducing market uncertainty (Podol- ny, 1993). Figure 1 shows the yearly market shares of commercial banks from 1990 to 1997 and the cumulative number of commercial banks having entered the market.

Figure 1. Number of commercial banks and percent market shares in bond market. 30 45

28 $ Volume

S26 #Bonds 40 -I - - -- # Banks

24 m ,

35 22

-Z 20 30 E 18 E

E 16 25 E o16 S14 20

o 20) 0 12 E

10 15 Z

110 8- 6

5

.

10 099 192 919 I 1 I 1 10

1990 1991 1992 1993 1994 1995 1996 1997

Sources: Gande, Puri, and Saunders (1999), Board of Govenors of Federal Reserve System, Securities Data Corporation.

This study specifically examines the likelihood that a given commercial bank becomes lead manager on the 5,106 non- convertible corporate bonds issued by U.S. publicly traded non-financial firms in the period 1991 to 1997, drawn from the Securities Data Corporation's new issue database. The unit of analysis is the bond-issue/commercial-bank dyad (cf. Podolny, 1994), which implies that each bond issue con- tributes an observation for each bank in the risk set (each firm can choose its lead manager among all the banks in the risk set). The risk set of banks that potentially could be chosen as lead manager is composed of all the commercial banks that obtained approval from the Federal Reserve Board (from the date they obtained the approval) to enter the industry and that underwrote at least one bond as lead or co-lead manager in the period from 1991 to 1997 (42 by 1997).

Statistical Analyses The dyadic research design creates a potential problem of non-independence among the observations because each bank (one time for each deal) and each bond issue (one time for each bank) enter the analysis several times, which can result in systematic underestimation of the standard errors for bank attributes that do not change from dyad to dyad (Krackhardt, 1988; Mizruchi, 1989). Following Sorenson and Stuart (2001), I used combined samples and rare-events logistic regression analysis to ensure that the large numbers




1 King and Zeng (2000) suggested collecting two to five times more zeros than ones because the marginal informational contribution of adding zeros starts to decrease as the number of zeros passes the number of ones. Alternative analyses based on other combined samples varying in size from 700 to 3,500 provided results that were similar to the results presented here.


of repeat occurrences of each bank and each bond issue did not result in underestimated standard errors. Since the realized dyads provide most of the information for the estimation of the factors that affect dyad formation (Lancaster and Imbens, 1996; King and Zeng, 2001), all the dyads in which a commercial bank was lead manager (ones) were included in the final sample (a total of 698 dyads). The sample of realized dyads then was combined with a random sample of potential dyads that were not realized (zeros), i.e., a sample of dyads in which the sampled commercial bank was not lead manager because another commercial bank or an investment bank was lead manager. Specifically, a random sample of 2,800 dyads that were not realized was added to the sample of realized dyads, thus creating an overall sample of approxi- mately 3,500 dyads, which resulted in a final sample of 3,190 dyads when dyads with missing data were eliminated.'

The combined-sample approach substantially reduces the non-independence problem by reducing the number of times the average bank (from 2,873 times to 94 times) and bond issue (from 22 times to 1.78 times) enter the overall sample. Since each bank still enters the overall sample more than once, I followed Mizruchi and Stearns (2001) and report robust standard errors adjusted for clustered observations, which do not assume independence across dyads in which the same banks participate (White, 1980; Rogers, 1993). The combined-sample approach, however, introduces a new problem when it is used in logistic regression. The problem occurs because logistic regression tends to underestimate the factors that predict a positive outcome when used on a combined sample in which the proportion of positive outcomes is different from the proportion of positive outcomes in the population (King and Zeng, 2001). Specifically, the outcome variable in simple logistic regression follows a Bernoulli probability function that takes the value of one with probability 7ti:

1 + e-x1

where X is a vector of explanatory variables, and P is a vector with parameters corresponding to the explanatory variables. King and Zeng (2001) showed that the following weighted least-squares expression can be used to correct the bias in p13 generated by oversampling rare events:

bias(13) = (X'WX)-1 X'W ,

where

( = 0.5Qi[(1

+ w)?i, - w,] ,

the Q are the diagonal elements of Q = X(X'WX)-1,




W = diag{ft(1 - ft)w},

and w1 represents the proportion of ones in the combined sample relative to the fraction in the population. Tomz's (2001) Stata procedure was used to estimate the rare-events logit models.

Dependent and Independent Variables

The dependent variable is firms' choice of lead manager. When firms issue securities, they typically choose a bank (lead manager) to organize and manage the underwriter syndicate, who then chooses (alone or together with the client) the rest of the management group (co-lead managers) and underwriter syndicate members. The lead management position is the most attractive position in the underwriter syndicate because the lead manager gets the largest part of the total underwriting compensation, determines (alone or together with the client) which other banks can participate in the syndicate, and maintains close contacts with clients that may result in other business opportunities (Hayes, 1971). Since lead managers also get full credit for underwriting the bond in various important and attended-to rankings, such as the market share rankings published in the Wall Street Jour- nal and Institutional Investors, lead managerships are an important measure of market-entry performance. The dependent variable (Lead manager) is therefore a dichotomous variable, coded one if a given commercial bank is lead manager on a particular bond issue (a firm/bank dyad is realized) and zero otherwise.

The main independent variables are banks' market ties with the issuing firms in investment and commercial banking and banks' network status in these two different markets. The investment- and commercial-banking networks include all banks, i.e., the investment-banking network includes both investment banks and commercial banks, and the commercial-banking network also includes the commercial banks that did not enter investment banking. I used two different measures of market ties. First, a binary variable indicates the presence (coded one) or absence (coded zero) of market ties based on whether or not a firm had used a given bank as a lead manager or co-lead manager on bond or stock issues or as a merger and acquisition advisor in the last five years (Investment banking ties). This measure is appropriate given that the decision to use an entering commercial bank at all is likely to be a more important decision than subsequent decisions to strengthen the tie. The relative infrequency with which firms issue bonds and the fact that most banks during the period of market entry had not yet been used by specific firms before also suggest that a binary measure is appropriate. For consistency, a binary variable was also used to indicate the presence (coded one) or absence (coded zero) of market ties in commercial banking based on whether or not a given bank helped arrange syndicated loans for a firm in the previous five years (Commercial banking ties). Commercial loans often are provided by syndicates through which banks, called arrangers or agents, organize and manage (for fees) syndicates of banks that together provide the loan to the




2 Other researchers, such as Hayes (1971), Carter and Manaster (1990), and Podolny (1993), based their network status measures on the distribution network (as defined by banks' position in "tomb- stone" advertisements) instead of the management network. I used the management network because large underwriting syndicates with elaborate status hierarchies became increasingly rare in the 1990s as banks grew in size and therefore needed other banks less to share risk and distribute securities. More- over, since most of the small banks that participate in the distribution of securities typically do not aspire to become lead managers, the management network is an appropriate alternative to the distribution network in research that focuses explicitly on becoming lead manager.

3 The eigenvector measure may produce misleading status scores if it is used on an asymmetric network in which some actors are not the object of deferential relations from other actors (Bonacich and Lloyd, 2001). Since some banks never acted as lead managers and therefore never were the object of deferential (co- lead manager) relations from other banks, I also used Hubbell's (1965) status measure, which does not create the same problem (Bonacich and Lloyd, 2001), and found that the results reported here did not change.


firm. Syndicated loans are different from corporate bonds because corporate bonds are placed with independent investors, whereas syndicated loans are maintained by banks for their duration (some banks keep only part of the syndicated loan and securitize the rest).

Second, while the simple binary measure may be preferable in the context of market entry, it also may be important to consider differences in the strength of market ties. I therefore used the combined allocated proportion of deals in the bond, stock, and mergers and acquisition markets in which a firm used a bank as lead manager or co-lead manager (advisor in mergers and acquisition) as a measure of the strength of the tie. Following Baker (1990), a bank's share of a bond or stock deal was calculated as:

where n is the number of banks in lead- or co-lead-manager roles, m is the number of banks in lead manager roles, and where the lead manager gets two shares and each co-lead manager gets one share. For mergers and acquisitions, I calculated a bank's share of the deal by dividing one by the number of banks that a particular firm used as advisors. The strength of a market tie based in investment banking was then defined as the sum of the shares allocated to a bank for each deal a firm did in the prior five years divided by the total number of deals a firm did in the prior five years. To calculate the strength of market ties based in commercial banking, I simply calculated a bank's share of a loan by dividing one by the number of banks in management positions on the loan before adding the shares allocated to a bank for each loan a firm obtained in the prior five years and dividing by the total number of loans a firm obtained in the prior five years.

Network status in investment banking was defined in terms of commercial banks' status position in the asymmetrical networks of lead-manager/co-lead-manager ties based in the bond market (Eccles and Crane, 1988).2 Following Podolny (1993), I used Bonacich's (1987) eigenvector measure, which suggests that a firm has network status to the extent that it maintains strong ties with other firms that have network status because they maintain strong ties with other firms that have network status and so on, to calculate banks' network status.3 This measure is formally defined as:

c(a,p) = CP kRk+11, k=0

where a is a scaling factor that normalizes the measure, P is a weighting factor, R is a relational matrix in which

cellij sum-

marizes the number of times investment banki (lead manager) works together with investment bank. (co-lead manager), and 1 is a column vector of ones. The p-parameter indicates how much emphasis should be put on the status of the actors a given actor is tied to: larger positive values increase




the weight given to being connected to high-status actors, whereas larger negative values increase the weight given to being connected to low-status actors. Bonacich (1987) suggested selecting P so that its absolute value is less than the absolute value of the reciprocal of the largest eigenvalue of R. To make sure that the relative network-status measures are insensitive to the choice of p (which was set to three quarters of the largest eigenvalue), I tried different p-values, and they produced comparable rankings of the investment banks.

I also used Bonacich's (1987) eigenvector measure to calculate the status of banks in commercial banking. Although the distinction between lead and co-lead managers is well established and uniform in investment banking, the distinction between different management positions in bank loan syndi- cations is more ambiguous. Although it is possible to distinguish between at least four different management levels, the management levels are not used consistently on different loans, which makes comparisons of management positions across loans highly ambiguous. To avoid a problem in compa- rability, I combined the four management levels and created a symmetric network of agent ties in which

cellij equals cellji and denotes how many times commercial banki and commer-

cial bankj worked together as managers of a loan syndicate. Both measures of network status were based on yearly updated networks, lagged one year relative to the dependent variable, standardized to account for yearly differences in network size, and calculated using Ucinet 5.8 (Borgatti, Everett, and Freeman, 1999).

Since both the investment- and commercial-banking networks are based on banks' participation in specific deals, banks that participate in a large number of deals will typically come across as prominent regardless of whether they participate in these deals with other prominent banks. Though several methods exist to remove the size component from the eigenvector measure (see Wasserman and Faust, 1994: 323), I followed Mizruchi (1989) and standardized the networks by dividing the number of two banks' common deals by the geo- metric mean of the number of deals each bank participated in before calculating their network status:

nij

Moderator Variables

I used two moderator variables to test hypotheses 4 and 5. To test whether the effects of network resources changed as entering commercial banks spent more time in investment banking, I multiplied market ties and network status with the number of years banks had been present in investment banking, measured as the number of years passed since they first obtained investment-banking approval from the Federal Reserve Board (Bank years in market). To test whether the effects of network resources depended on the market experience of issuing firms, I multiplied market ties and network





status with their investment banking experience, measured as the number (natural logarithm) of times firms had used the markets for bonds, stocks, and mergers and acquisitions in the prior five years (Firm market experience). While the main purpose of these variables was to test the time and experience dependence of market ties and network status, I entered them in all the models to control for the effects of legitimacy (banks may gain constitutive legitimacy through simple endurance in the new market) and more diffuse information (more information becomes available as banks stay longer in the market).

Control Variables

Several control variables were used to rule out alternative explanations. The first group of control variables focuses on characteristics of the firms that issued bonds that may affect the propensity of firms to use a bank as lead manager. Issu- ing firms differ in the extent to which they tend to use many different investment banks to handle their business or concentrate their business among a few investment banks (Baker, 1990). If a firm concentrates its business with a single or a few investment banks, it may be less willing to use a different bank, regardless of its network resources. Following Baker (1990), I used the Herfindahl index of concentration, which sums the squared share of the bond, stock, and mergers and acquisition deals a firm allocated in the prior five years to each investment or commercial bank, to measure the degree to which firms concentrate, or embed (Uzzi, 1996), their investment-banking business (Firm market interface). The Herfindahl index reaches its maximum of one when a firm concentrates all its business with one bank, and it approaches zero as firms use more and more banks. When firms had not done any investment banking business in the previous five years, I set the Herfindahl index to be zero (the index is not mathematically defined in this situation) and coded a separate binary variable as one (Firm inactive in investment banking). The second group of control variables focuses on the characteristics of the entering commercial banks. Regardless of their network resources, banks that have considerable investment-banking experience are attractive lead managers in their own right. Issuing firms may prefer to use banks with extensive experience in their own industry because these banks have more extensive ties with investors seeking investment opportunities in their industry. Banks with extensive industry experience also may be more knowledgeable about the specific circumstances that make bond offerings in particular industries more or less successful (Hayes, Spence, and Marks, 1983; Eccles and Crane, 1988). To control for industry-specific lead-management experiences, I entered a variable measuring the proportion of bond offerings on which a given bank was lead manager within each (two-digit SIC) industry (Bank industry market share). Similarly, general lead- management experience also may be an important lead-manager quality. Banks with larger market shares in the bond market have more experience and resources to ensure that their bond offerings are successful, and they have extensive ties with potential investors, which I controlled for with Bank

483/ASO, September 2003



total market share. I also controlled for bank size by including variables measuring total worldwide assets (Bank total assets) and total syndicated loans provided in the U.S. (Bank total syndicated lending), because larger banks may in themselves be more attractive exchange partners. Given that the U.S. capital market is centralized in New York, investment banks that are headquartered outside New York may be per- ceived as more peripheral (socially and economically) and therefore less attractive exchange partners than banks headquartered in New York. To control for geographical location, I used two binary variables (Bank headquartered outside U.S. and Bank headquartered outside New York) that identify investment banks headquartered outside New York (coded one if outside New York, zero otherwise) and foreign-based investment banks (coded one if foreign, zero otherwise).

The third group of control variables focuses on other characteristics of the firm-bank dyad. I controlled for geographical colocation, because firms may be more likely to use banks that are headquartered in their own geographical area (cf. Sorenson and Stuart, 2001). I used a binary variable (Firm and bank geographic colocation), coded one if firm and bank were headquartered in the same economic region (as defined by the U.S. Department of Commerce Bureau of Economic Analysis) and zero otherwise, to control for colocation. Some research has suggested that the presence of interlocking directorates between firms and banks may influence the way in which firms raise capital. For example, firms may use interlocks with commercial banks to access capital, and commercial banks may use the interlocks to put formal or informal pressure on firms to be the provider of capital (e.g., Pfeffer and Salancik, 1978; Burt, 1983). Mizruchi and Stearns (1994) found more specifically that the representation of executives from financial institutions, such as commercial banks, investment banks, insurance companies, or diversified financial cor- porations, increased the amount of external capital used by manufacturing firms. Since executive directors have the strongest incentives and the most power to promote the interests of their firm or their bank, I used a binary variable to control for the presence of executive interlocks (coded one if one or more of the top executives of the bank was on the board of the firm or vice versa) between a firm and a bank (Firm and bank board interlock). Because interlocks tend to be relatively durable (Mizruchi, 1996), I restricted collecting interlock data to 1994 (the midpoint of the time period of this study).

The fourth group of control variables relates to characteristics of the bond issue itself. It was necessary to control for bond size because the market might be segmented, and some new entrants might be more likely to attract smaller and therefore less profitable and prestigious bond offerings than more established banks (Johnson, 1996; Gande, Purl, and Saunders, 1999). The total dollar amount of each bond issue was used to measure bond size (Bond size). A measure of bond quality was included for the same reasons: new entrants might be more likely to underwrite lower-quality bonds shunned by more established banks. Standard & Poor's bond ratings were used as a control for bond quality.




4 The commercial-banking variables were not defined for investment banks, which were not allowed to accept federally insured deposits and therefore did not have access to as much capital as commercial banks did (Macey, 2000). It was therefore impossible to include investment banks in the risk set.


These ratings are based on various considerations, such as the likelihood (both in terms of capacity and willingness) of bond repayment and the protection and relative position of investors in the event of bankruptcy. Four binary variables were used to capture the quality of a particular issue (AAA, AA, A, BBB), with the left-out category being bonds that were not rated and bonds that were rated lower than BBB (non-investment-grade bonds). Although the quality of bonds is highly (negatively) correlated with the price (underwriting fee) banks charge for underwriting the bond, a control for price was also included because entrants may try to capture business by lowering their underwriting fee. Because price data (gross spread as a percentage of bond size) were available for only about half the bonds and including the price control in the reduced sample did not change the results, the price variable is not included in the results reported here.

Finally, a set of variables controlled for temporal variations in which banks become lead managers and sample selection bias. A continuous time variable (Year) that captures linear temporal influences on the probabilities of becoming lead and co-lead managers and binary year variables (1991 is the base year) that capture qualitative differences between years also were included (the binary year variables are not reported here). Given that more banks are included in the risk set in later years, the probability of each individual bank becoming lead manager should, by definition, decrease over time. It was also necessary to control for sample selection bias, because only dyads including commercial banks were included in the analyses, whereas bonds may be underwritten by either incumbent investment banks or entering commercial banks.4 The sample-selection problem emerges because the average probabilities that bonds are underwritten by an investment bank or by a commercial bank may not be identi- cal. To address the sample-selection problem, I used Lee's (1983) generalization of Heckman's (1979) two-stage estima- tor to estimate a selectivity model. Specifically, all 5,106 bond issues underwritten by either an investment bank or a commercial bank were used to estimate a logit model in which the type of lead manager (investment bank versus commercial bank) was the dependent variable. The bond size, bond quality, firms' experience with commercial banks as lead managers, firms' return on assets and financial leverage, and continuous and binary year variables were used as independent variables. The logit model suggested that commercial banks on average were less likely to underwrite large, high- quality bond issues, but they were more likely to underwrite bonds in later years and to underwrite bonds issued by firms that in the previous year had used commercial banks to underwrite bonds. The logit model was used to calculate the probability a given bond issue was underwritten by a commercial bank, which then was inserted as a control variable in the regression models presented here.

The main sources of data were the Securities Data Corpora- tion for data on investment banking, the Loan Pricing Corpo- ration for data on commercial banking, and COMPUSTAT for data on the firms offering the bonds. Table 1 contains summary statistics and bivariate correlations.




Table 1

Summary Statistics and Bivariate Correlations*

Variable Mean S.D. 1 2 3 4 5 6

1. Lead manager (0,1) .19 .40 2. Investment-banking ties (strength) .04 .15 .45 3. Investment-banking ties (presence) .15 .36 .52 .63 4. Investment-banking status .11 1.04 .53 .39 .52 5. Commercial-banking ties (presence) .25 .43 .21 .17 .14 .25 6. Commercial-banking ties (strength) .08 .23 .29 .27 .24 .31 .62 7. Commercial-banking status 1.13 1.30 .29 .19 .19 .53 .21 .23 8. Firm market experience (Ln Deal count) 2.50 1.40 -.05 -.12 .19 -.04 .00 -.05 9. Firm market interface (Herfindahl index) .28 .22 .03 .11 -.08 .03 -.04 .01

10. Firm inactive in investment banking (0,1) .05 .22 .05 -.06 -.10 .03 -.03 .01 11. Bank industry market share (bonds) .01 .05 .31 .36 .29 .33 .03 .08 12. Bank total market share (bonds) .01 .02 .50 .42 .53 .77 .19 .26 13. Bank headquartered outside U.S. (0,1) .41 .49 -.23 -.17 -.17 -.44 -.01 -.14 14. Bank headquartered outside New York (0,1) .32 .47 -.23 -.16 -.22 -.27 -.15 -.10 15. Bank years in market 4.39 3.11 .44 .29 .42 .72 .23 .24 16. Firm and bank board interlock (0,1) .05 .22 .08 .01 .13 .19 .09 .12 17. Bank total assets (Ln $M) 11.88 .97 .14 .08 .13 .21 .18 .11 18. Bank total syndicated lending (Ln $M) 20.60 7.36 .07 .08 .02 .27 .09 .13 19. Firm and bank geographic colocation (0,1) .21 .41 .02 .05 .02 .03 .06 .06 20. Bond size (Ln) 4.34 1.26 -.08 .00 .01 -.01 .06 .03 21. Bond quality: AAA (0,1) .06 .24 .02 -.03 .10 -.01 -.14 -.09 22. Bond quality: AA (0,1) .09 .29 -.02 -.02 .02 -.01 -.07 -.06 23. Bond quality: A (0,1) .48 .50 -.02 -.05 .00 .00 .08 .04 24. Bond quality: BBB (0,1) .29 .46 .02 .09 -.02 .02 .03 .02 25. Year 1995 1.81 -.05 .00 .00 .00 .02 .01 26. Sample selection .16 .08 .12 .07 .13 .02 -.01 -.01

Variable 7 8 9 10 11 12 13 14 15 16

8. Firm market experience (Ln Deal count) -.07 9. Firm market interface (Herfindahl index) .07 -.43

10. Firm inactive in investment banking (0,1) .01 -.42 -.30 11. Bank industry market share (bonds) .11 -.02 .00 .00 12. Bank total market share (bonds) .27 -.01 .00 .02 .44 13. Bank headquartered outside U.S. (0,1) -.51 .08 -.06 -.04 -.12 -.29 14. Bank headquartered outside New York (0,1) -.06 -.01 -.02 .01 -.13 -.29 -.40 15. Bank years in market .35 -.01 .01 .00 .25 .55 -.21 -.33 16. Firm and bank board interlock (0,1) .17 .17 -.05 -.04 .08 .17 -.19 -.06 .13 17. Bank total assets (Ln $M) .29 .02 -.01 -.03 .06 .14 .47 -.55 .28 .01 18. Bank total syndicated lending (Ln $M) .57 -.06 .04 .01 .05 .16 -.37 .03 .10 .12 19. Firm and bank geographic colocation (0,1) .04 .01 .05 -.02 -.03 .02 .04 -.10 .01 .05 20. Bond size (Ln) -.04 .03 -.08 .03 .05 .02 -.01 -.04 -.03 .04 21. Bond quality: AAA (0,1) -.05 .31 -.12 -.06 .03 .01 .01 -.01 -.01 .21 22. Bond quality: AA (0,1) -.01 .10 -.07 -.04 -.02 -.02 .03 -.03 .02 .04 23. Bond quality: A (0,1) .03 .23 -.04 -.10 -.07 .01 .01 .02 -.03 .01 24. Bond quality: BBB (0,1) .00 -.32 .10 .05 .05 .01 -.03 .01 .03 -.11 25. Year -.01 .01 -.09 -.06 .06 .11 .12 .18 .00 -.08 26. Sample selection .00 .23 -.12 -.10 .06 .03 .07 .03 .06 -.04

Variable 18 19 20 21 22 23 24 25 26

18. Bank total syndicated lending (Ln $M) .16 19. Firm and bank geographic colocation (0,1) .08 .03 20. Bond size (Ln) -.06 .00 .00 21. Bond quality: AAA (0,1) -.02 -.03 -.09 .03 22. Bond quality: AA (0,1) .03 -.03 -.01 .01 -.08 23. Bond quality: A (0,1) .03 .03 .15 -.11 -.24 -.30 24. Bond quality: BBB (0,1) -.01 -.01 -.10 .03 -.16 -.20 -.62 25. Year .12 .02 -.02 -.18 -.09 -.08 .13 -.01 26. Sample selection .09 -.04 -.05 -.69 .04 -.08 -.09 .16 .34 * All models include year dummies not reported here.





The correlation table shows that multicollinearity may pose a problem because of the high correlations between some of the variables. I took different steps to test if multicollinearity in fact posed a problem and to reduce potential problems. First, following Fox (1991), the variance inflation factor was calculated to examine the extent to which multicollinearity has an impact on the precision of the coefficient estimates. The variance inflation factors for all variables in all models were well below the recommended value of 10 (Kennedy, 1998). Second, following Cronbach (1987), all the main variables were centered before calculating the interaction variables, which reduced the correlation between the main effects and the interaction effects. The results were the same whether the raw or the centered variables were used. Finally, if multicollinearity was a serious problem, even small changes in the sample could result in dramatic changes in the coefficient estimates (Fox, 1991). The models were therefore reestimated after randomly eliminating 10 percent to 25 percent of the sample, with no significant changes compared with the results reported here.

Heckman and Borjas (1980) showed that unobserved heterogeneity could result in a positive effect of prior outcomes on the occurrence of future outcomes. Specifically, a firm may have chosen a bank in one year because of its superior quality on a particular unmeasured dimension and then, regardless of the development of trust, chose to use the same bank the following year for the same unmeasured reasons. As a partial control for occurrence dependence, a binary variable indicating the first time a firm used a given bank as lead manager was entered into the models. The reestimated models (not reported here) confirmed that prior market ties based in investment banking increased the likelihood of new market ties, thus reducing the likelihood that the reported results simply were driven by positive occurrence dependence. A partial solution to the problem of unobserved heterogeneity among banks is to enter a fixed effect for each firm to account for within-firm variation (Sayrs, 1989). Since some of the banks in the sample did not actually become lead managers during the time frame of this study, the firm-specific fixed effects predict failure completely, and the observations for these banks were therefore dropped. Although omitting these observations biases the sample toward successful entrants, the models were reestimated with firm-specific fixed effects on the reduced, biased sample, and the results were comparable to the results reported here.

RESULTS

The results presented in Table 2 provide broad support for the hypotheses presented in this paper. The results are the same whether market ties are measured in terms of the presence of ties or in terms of the strength of ties. Because the presence of ties is more significant in the context of market entry, when an absence of ties dominates (86 percent of the dyads), I focus on the results for the presence of ties. The models with binary tie measures also fit the data better than the models with continuous tie measures (see the Appendix for the models with strength of ties).

487/ASO, September 2003



Table 2

Rare-events Logistic Regression with Robust Standard Errors: Choice of Lead Manager (N = 3190)*

Presence of Market Ties

Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Investment-banking -0.09" status x Firm (0.04) market experience

Investment-banking -0.09 ties x Firm market (0.12) experience

Commercial-banking -0.180"0 status x Firm (0.03) market experience

Commercial-banking -0.02 ties x Firm market (0.12) experience

Investment-banking -0.30"" status x Bank (0.07) years in market

Investment-banking -0.09 ties x Bank years (0.11) in market

Commercial-banking -0.09" status x Bank (0.05) years in market

Commercial-banking 0.00 ties x Bank years (0.04) in market

Investment-banking -0.32" status x (0.18) Investment- banking ties

Commercial-banking -0.440" status x (0.14) Investment- banking ties

Commercial-banking 0.83"" 0.81 ". 0.82"" 0.84" 0.81" 0.80"" ties (0.21) (0.20) (0.21) (0.21) (0.21) (0.20)

Commercial-banking 0.51" 0.55" 0.57" 0.23 0.51" 0.52" status (0.26) (0.24) (0.26) (0.22) (0.24) (0.25)

Investment-banking 1.82"" 1.78" 2.43"" 1.78"" 2.02"" 1.70"" 1.86"" ties (0.16) (0.15) (0.25) (0.15) (0.40) (0.14) (0.16)

Investment-banking 0.45 0.440 0.440 0.470 0.940"m 0.49" 0.44? status (0.32) (0.26) (0.24) (0.28) (0.28) (0.24) (0.25)

Bank total assets (Ln 0.58" -0.04 -0.08 0.04 0.37 -0.05 -0.03 $M) (0.30) (0.37) (0.35) (0.41) (0.44) (0.36) (0.37)

Bank total -0.04" -0.04 -0.03 -0.03 -0.04" -0.04 -0.04 syndicated lending (0.02) (0.02) (0.02) (0.03) (0.02) (0.02) (0.02) (Ln $M)

Firm market -0.41" -0.38" -0.40" -0.39"" -0.35 " -0.26" -0.28"" experience (Ln (0.08) (0.07) (0.07) (0.07) (0.07) (0.09) (0.08) Deal count)

Firm market -0.31 -0.47 -0.72 -0.55 -0.34 -0.44 -0.48 interface (1.44) (1.51) (1.54) (1.55) (1.53) (1.50) (1.55) (Herfindahl index)

Firm market 0.49 0.82 0.99 0.87 0.80 0.69 0.87 interface (1.03) (1.13) (1.15) (1.15) (1.14) (1.09) (1.15) (Herfindahl index)2

Firm inactive in 0.28 0.47 0.37 0.43 0.58 0.45 0.57 investment (0.49) (0.46) (0.50) (0.48) (0.49) (0.51) (0.49) banking (0,1)

Bank industry 6.10C" 6.64i" 6.33" 6.51" 6.16i" 6.16" 6.81" market share (2.25) (2.36) (2.28) (2.34) (2.28) (2.29) (2.34) (bonds)

Bank total market 3.28 5.86 8.95 5.65 26.85" 6.08 6.11 share (bonds) (13.03) (9.34) (9.99) (9.16) (3.40) (9.56) (9.61)

Bank headquartered -1.39" 0.00 0.01 -0.05 -0.83 0.04 -0.00 outside U.S. (0,1) (0.48) (0.77) (0.77) (0.85) (0.85) (0.74) (0.77)





Table 2 (Continued)

Presence of Market Ties


Bank headquartered -0.54 -0.64 -0.65 -0.49 -0.47 -0.63 -0.62 outside New York (0.58) (0.56) (0.52) (0.50) (0.37) (0.52) (0.54) (0,1)

Bank years in 0.20" 0.25"' 0.23m"0 0.29" 0.250"? 0.250N0 0.24i" market (0.07) (0.07) (0.08) (0.06) (0.06) (0.07) (0.07)

Firm and bank 0.10 0.09 0.12 0.10 0.06 0.11 0.09 geographic (0.09) (0.09) (0.08) (0.09) (0.08) (0.09) (0.09) colocation (0,1)

Firm and bank board -0.30 -0.46 -0.39 -0.48 -0.37 -0.30 -0.38 interlock (0,1) (0.40) (0.35) (0.31) (0.35) (0.29) (0.31) (0.34)

Bond size (Ln) -0.05 -0.08 -0.09 -0.08 -0.12 -0.07 -0.08 (0.14) (0.15) (0.16) (0.15) (0.16) (0.16) (0.15)

Bond quality: AAA 0.28 0.65 0.55 0.68 0.56 0.44 0.56 (0,1) (0.55) (0.49) (0.52) (0.50) (0.52) (0.50) (0.47)

Bond quality: AA -0.12 0.04 -0.01 0.00 -0.10 -0.07 -0.01 (0,1) (0.44) (0.42) (0.42) (0.44) (0.47) (0.42) (0.42)

Bond quality: A (0,1) 0.07 0.07 0.07 0.08 0.02 0.00 0.01 (0.40) (0.38) (0.40) (0.39) (0.41) (0.41) (0.39)

Bond quality: BBB -0.47 -0.45 -0.41 -0.46 -0.46 -0.52 -0.50 (0,1) (0.39) (0.38) (0.40) (0.39) (0.41) (0.41) (0.39)

Year -0.32" -0.3300* -0.31 oo -0.33"• -0.26o 0

-0.34•• -0.33"" (0.10) (0.10) (0.10) (0.09) (0.09) (0.09) (0.09)

Sample selection 5.88" 5.92?" 5.42*?? 5.96"0 5.13*" 6.13"m 5.90"Cm (1.85) (1.87) (2.01) (1.84) (1.91) (2.08) (1.82)

Intercept 621.50C"m 653.45?0 609.72"* 658.20"" 504.22* 676.53"" 653.76?" (197.77) (190.89) (195.88) (187.38) (181.97) (181.99) (187.51)

LR 1388.45"" 1451.15"" 1475.72*"" 1463.17m" 1506.52 " 1475.07"" 1456.23" ALR 62.70"" 24.57*"' 12.02" 55.37"" 23.92" 5.08? Degrees of freedom 26 28 30 30 30 30 30

p < .10; " p < .05; '

p < .01; " p< .001.

* Robust standard errors are in parentheses; one-tailed tests for main variables; two-tailed tests for control variables; all models include year dummies.

5 I also estimated full models including all the interaction effects not reported here. Even though the results in general were similar to the results reported here, multicollinearity from entering the key variables up to five times each made the estimated coefficients and standard errors unreliable.

Model 1 is a baseline model that only contains control variables. As a final precaution against multicollinearity, all the main effects and all the interaction effects were entered one at a time to ensure they all increased model fit significantly (to preserve space, only the full main effects model and models with two compared interaction effects are reported) (Jac- card, 2001). Model 2 shows that hypotheses 1 and 2, which suggested that market ties and network status based in the old market increase access to customers in the new market, are supported: ties and status based in commercial banking both significantly increased the likelihood of becoming lead manager in the bond market. The positive effects of market ties and network status based in commercial banking suggest that these network resources were at least partly transferable. The interaction models (models 3-7) provide further support for the conclusion that market ties and network status based in the old market facilitate market entry. The interaction models also suggest that caution is needed when interpreting the main effects because they show that the strength of these network resources is contingent on the level of the interaction effects.

The interaction effects are introduced in models 3-7.5 Model 3 shows that the interactions between both investment- and commercial-banking status and investment-banking ties are negative and significant. Hypothesis 3, which suggested that




the effects of network status based in either the new or the old market decrease in the presence of ties based in the new market, is therefore supported. These results suggest that the status of an entering commercial bank, regardless of whether it is based in commercial banking or in investment banking, matters less to firms once they have established ties in investment banking. Models 4 and 5 show that the interactions between status based in investment or commercial banking and years since market entry are negative and significant, whereas the interactions between ties based in investment and commercial banking and years since market entry are insignificant. Models 6 and 7 show, similarly, that the interactions between status based in investment or commercial banking and firms' market experience are negative and significant, whereas the interactions between ties based in investment and commercial banking and market experience are insignificant. Together, these results suggest that network status residing in either the new or the old market, unlike market ties residing in these markets, becomes less important as more information about the entering banks becomes available over time and that network status is less important for firms with more market experience. Hypothe- ses 4 and 5 are therefore supported.

DISCUSSION AND CONCLUSION

This study examined the transferability of market ties and network status between two different markets and the ties between these two different network resources in the context of commercial banks' entry into investment banking. The first set of arguments suggested that market ties and network status are transferable resources and therefore can be leveraged from one market to another. The results showed that commercial banks that had market ties or network status based in commercial banking were more likely to become lead managers on bond issues, thus indicating that these resources were at least partly transferable. Though network resources are always grounded in specific social systems (Granovetter, 1985), the results showing the transferability of network resources suggest that their value extends beyond the social systems in which they originated, thus adding to the overall importance of network resources. The results also suggest that firms must evaluate their current network resources to determine how they can be used in other markets before they decide to enter into markets in which competitive success is partly determined by network resources. Though Powell (1990) argued that networks restrict market entry by establishing enduring patterns of repeat trading, the results presented here demonstrate that networks also facilitate market entry by allowing firms to leverage network resources based in old markets into new markets. The transferability of market ties and network status may therefore represent an important base for market entry that has been overlooked in prior research focusing on economic industry structure or firm-specific resources (Caves, 1998; Helfat and Lieberman, 2002).

The second set of arguments focused on the interaction between market ties and network status and suggested that market ties and network status are substitutable resources





rather than additive resources. The results showed that the effect of network status based in either market decreased in the presence of market ties based in investment banking, which suggests that market ties and network status represent primary and secondary approaches to resolving market uncertainty (Podolny, 1994). It also indicates that it is necessary to consider interaction effects in network research to specify the ties between different types of network resources appropriately and not simply assume, as most research does, that network resources are additive. The partial substitutability of network status and market ties suggests more broadly that firms may overcome network-status disadvantages by building strong dyadic ties with potential clients. For example, though incumbent investment banks may have avoided partnering with entering commercial banks to protect their own network status, commercial banks could leverage their commercial-banking ties to gain access to investment-banking clients and use the new investment- banking ties to reduce the status advantages of the incumbent investment banks. The ability of commercial banks to leverage their commercial-banking ties into investment banking was an important reason behind the investment-banking community's failed appeal to the Supreme Court of the Fed- eral Reserve Board's decision to allow commercial banks into investment banking (Johnson, 1996).

The final set of arguments focused on mechanisms accounting for the effects of market ties and network status and suggested that network status mainly works through reducing market uncertainty, whereas market ties mainly work through increasing exchange value. The results provided support for these arguments by showing that the effects of network status decrease over time as more information becomes available about banks and were less important for issuing firms with considerable investment banking experience, whereas the effects of market ties did not decrease. These results indicate that network status and market ties are different network resources. Given that network status is most important initially because it primarily reduces market uncertainty, it is particularly useful for commercial banks early on, to get a foot in the door and gain market presence. Network status may therefore be viewed as a mechanism for gaining the market ties that allow trust to develop between firms and banks and that reduce the importance of status in subsequent exchanges. These results also suggest that it is necessary to incorporate temporal dynamics more explicitly into network research, since both the value of specific network resources and the relative value of different network resources may change over time (Haveman, 2000). In particular, while firms gain network status over time, the return on their network status also deteriorates relative to that of market ties, which suggests that the relative appropriateness of different network strategies may change over time. Firms could rely on network status early on to build market ties that will compensate later on for the decreasing return to status.

Although this study makes important contributions to network research and market-entry research, it also has some limitations. One limitation relates to the generalizability of the




results. First, although the results suggest that network resources are transferable, it is also important to ask about the limits of their transferability. Specifically, while the culture and structure of investment banking and commercial banking were very different (Eccles and Crane, 1988), for which reason it was never obvious that commercial banks would eventually be successful in investment banking, the two industries still serve largely the same functions in providing capital to corporate clients. Because the results presented here are based only on the extent to which network resources could be leveraged from commercial banking to investment banking, further research is necessary to answer questions about the transferability of network resources between markets that are more different. Such research may distinguish between transferability between related markets, such as commercial banking and investment banking, and unrelated markets, such as commercial banking and management con- sulting, to identify the boundaries of the transferability of network resources and to document the extent to which network resources can provide a base for both related and unrelated diversification (Rumelt, 1974). A more general issue about the generalizability of the results relates to the extent to which network resources are at all important in other industries. I argued that the investment- banking industry is an excellent research site in which to study network resources because prior research has shown that these resources are important in this industry. The investment-banking industry is also somewhat unique in terms of the strict legal barriers that once separated it from commercial banking, and the whole deregulation process may therefore in many ways be a unique historical event that makes generalizability even more questionable. The investment-banking industry is undoubtedly unique in terms of its traditional focus on market ties, status stratification, and legal protection. Nevertheless, some of the characteristics of the investment-banking industry may have become more preva- lent in recent decades. For example, the proliferation of strategic alliances suggests that interfirm ties and networks have become increasingly important aspects of industrial organization (Gulati, Nohria, and Zaheer, 2000). The results reported here may therefore be generalizable not only to other deregulated industries but also to industries in which interfirm ties proliferate. A final limitation relates to data availability. It would be ideal to have detailed information about the economic and human resources that were available to commercial banks and not simply rely on market shares as indicators of these resources. For example, prior research has suggested that human resources-investment bankers-are particularly important determinants of competitive outcomes in investment banking (Hayes, Spence, and Marks, 1983; Eccles and Crane, 1988). Because commercial banks organized their investment-banking businesses as fully owned subsidiaries, they were not required to make information about their investment-banking subsidiaries publicly available. Although prior research has relied on other indicators, such as the amount of capital and the number of offices and employees





that investment banks voluntarily report to the Securities Industry Association (Podolny, 1993), this information was typically only available for traditional investment banks based in the U.S., and most commercial bank subsidiaries were not members of the association in this period.

Despite these limitations, this study sheds new light on network resources by developing and testing new theoretical arguments focused on the transferability of market ties and network status and their different effects over time in market entry. By showing that these network resources are at least partly transferable and therefore can be leveraged from one market to another, this study not only illustrates that network resources are important in market entry, it also shows that even though network resources are always grounded in specific networks, their value extends beyond these networks. Furthermore, by showing that market ties and network status are substitutes and that the value of network status is more likely to deteriorate over time and in the presence of market experience than is the value of market ties, this study provides a comprehensive understanding of the different nature of these network resources and points to the importance of determining the effects of network resources both individual- ly and as they may interact over time.

REFERENCES

Baker, W. E. 1990 "Market networks and corpo-

rate behavior." American Journal of Sociology, 96: 589-625.

Baker, W. E., R. R. Faulkner, and G. A. Fisher 1998 "Hazards of the market: Con-

tinuity and dissolution of interorganizational market ties." American Sociological Review, 63: 147-177.

Benjamin, B. A., and J. M. Podolny 1999 "Status, quality, and social

order in the California wine industry." Administrative Sci- ence Quarterly, 44: 563-589.

Berger, A. N., A. K. Kashyap, and J. M. Scalise 1995 "The transformation of the

U.S. banking industry: What a long strange trip it's been." In W. C. Brainard and G. L. Perry (eds.), Brookings Papers on Economic Activity: 55-218. Washington, DC: Brookings Institution.

Bonacich, P. 1987 "Power and centrality: A fam-

ily of measures." American Journal of Sociology, 92: 1170-1182.

Bonacich, P., and P. Lloyd 2001 "Eigenvector-like measures of

centrality for asymmetric relations." Social Networks, 23: 191-201.

Borgatti, S. P., M. G. Everett, and L. C. Freeman 1999 Ucinet 5 for Windows: Soft-

ware for Social Network Analysis. Natick, MA: Analytic Technologies.

Burt, R. S. 1982 Toward a Structural Theory of

Action. New York: Academic Press.

1983 Corporate Profits and Coopta- tion. New York: Academic Press.

1992 Structural Holes. Cambridge, MA: Harvard University Press.

Carlton, D. W. 1986 "The rigidity of prices."

American Economic Review, 76: 637-658.

Carter, R. B., and S. Manaster 1990 "Initial public offerings and

underwriter reputation." Jour- nal of Finance, 45: 1045-1067.

Caves, R. E. 1998 "Industrial organization and

new findings on the turnover and mobility of firms." Jour- nal of Economic Literature, 36: 1947-1982.

Chung, S., H. Singh, and K. Lee 2000 "Complementarity, status

similarity and social capital as drivers of alliance formation." Strategic Management Jour- nal, 21: 1-22.

Coleman, J. S. 1988 "Social capital in the creation

of human capital." American Journal of Sociology, 94 (Sup- plement): S95-S120.

Cronbach, L. J. 1987 "Statistical tests for modera-

tor variables: Flaws in analyses recently proposed." Psy- chological Bulletin, 102: 414-417.

Dyer, J. H., and H. Singh 1998 "The relational view: Cooper-

ative strategy and sources of interorganizational competitive advantage." Academy of Management Review, 23: 660-679.

Eccles, R. G., and D. B. Crane 1988 Doing Deals: Investment

Banks at Work. Boston: Har- vard Business School Press.

Fox, J. 1991 Regression Diagnostics. New-

bury Park, CA: Sage.




Gande, A., M. Puri, and A. Saunders 1999 "Bank entry, competition, and

the market for corporate securities underwriting." Journal of Financial Econom- ics, 54: 165-195.

Granovetter, M. S. 1985 "Economic action and social

structure: The problem of embeddedness." American Journal of Sociology, 91: 53-81.

Greif, A. 1989 "Reputation and coalitions in

medieval trade: Evidence on the Maghribi traders." Journal of Economic History, 49: 857-882.

Gulati, R. 1995 "Social structure and alliance

formation patterns: A longitudinal analysis." Administrative Science Quarterly, 40: 619-652.

1999 "Network location and learning: The influence of network resources and firm capabilities on alliance formation." Strategic Management Jour- nal, 20: 397-420.

Gulati, R., N. Nohria, and A. Zaheer 2000 "Strategic networks." Strate-

gic Management Journal, 21: 203-215.

Haveman, H. A. 2000 "The future of organizational

sociology: Forging ties among paradigms." Contemporary Sociology, 29: 476-486.

Hayes, S. L., ill 1971 "Investment banking: Power

structure in flux." Harvard Business Review, 49 (2): 136-152.

Hayes, S. L., III, A. M. Spence, and D. V. P. Marks 1983 Competition in the Invest-

ment Banking Industry. Cam- bridge, MA: Harvard Universi- ty Press.

Heckman, J. J. 1979 "Sample selection bias as a

specification error." Econo- metrica, 47: 153-161.

Heckman, J. J., and G. J. Borjas 1980 "Does unemployment cause

future unemployment? Defini- tions, questions and answers from a continuous time model of heterogeneity and state dependence." Economica, 47: 247-283.

Helfat, C. E., and M. Lieberman 2002 "The birth of capabilities:

Market entry and the importance of pre-history." Industri- al and Corporate Change, 14: 725-760.

Hollander, E. P. 1958 "Conformity, status, and idio-

syncrasy credit." Psychologi- cal Review, 65: 117-127.

Hubbell, C. H. 1965 "An input-output approach to

clique identification." Sociom- etry, 28: 377-399.

Jaccard, J. 2001 Interaction Effects in Logistic

Regression. Thousand Oaks, CA: Sage.

Johnson, H. J. 1996 The Banker's Guide to Invest-

ment Banking: Securities and Underwriting Activities in Commercial Banking. Chica- go: Irwin Professional Publish- ing.

Kennedy, P. 1998 A Guide to Econometrics.

Oxford: Blackwell.

King, G., and L. Zeng 2000 "Explaining rare events in

international relations." Work- ing paper, Department of Government, Harvard Univer- sity.

2001 "Logistic regression in rare events data." Political Analy- sis, 9: 1-27.

Krackhardt, D. 1988 "Predicting with networks:

Nonparametric multiple regression analysis of dyadic data." Social Networks, 10: 359-381.

Lancaster, T., and G. Imbens 1996 "Efficient estimation and

stratified sampling." Journal of Econometrics, 74: 289-318.

Larson, A. 1992 "Network dyads in entrepre-

neurial settings: A study of the governance of external relationships." Administrative Science Quarterly, 37: 76-104.

Lee, L.-F. 1983 "Generalized econometric

models with selectivity." Econometrica, 51: 507-512.

Levinthal, D. A., and M. Fichman 1988 "Dynamics of interorganiza-

tional attachments: Auditor- client relationships." Adminis- trative Science Quarterly, 33: 345-369.

Levitt, B., and J. G. March 1988 "Organizational learning."

Annual Review of Sociology, 14: 319-340.

Linton, R. 1936 The Study of Man. New York:

D. Appleton-Century.

Macey, J. R. 2000 "The business of banking:

Before and after Gramm- Leach-Bliley." Journal of Cor- porate Law, 25: 691-722.

March, J. G. 1991 "Exploration and exploitation

in organizational learning." Organization Science, 2: 71-87.

1994 A Primer on Decision Making. New York: Free Press.

Martin, X., W. Mitchell, and A. Swaminathan 1995 "Recreating and extending

Japanese automobile buyer- supplier links in North Ameri- ca." Strategic Management Journal, 16: 589-619.

Mizruchi, M. S. 1989 "Similarity of political behav-

ior among large American cor- porations." American Journal of Sociology, 95: 401-424.

1996 "What do interlocks do? An analysis, critique, and assess- ment of research on interlocking directorates." Annual Review of Sociology, 22: 271-298.

Mizruchi, M. S., and L. B. Stearns 1994 "A longitudinal study of bor-

rowing by large American cor- porations." Administrative Science Quarterly, 39: 118-140.

2001 "Getting deals done: The use of social networks in bank decision-making." American Sociological Review, 66: 647-671.

Pfeffer, J., and G. R. Salancik 1978 The External Control of Orga-

nizations. New York: Harper & Row.

Phillips, D. J., and E. W. Zuckerman 2001 "Middle-status conformity:

Theoretical restatement and empirical demonstration in two markets." American Journal of Sociology, 107: 379-429.

Podolny, J. M. 1993 "A status-based model of

market competition." Ameri- can Journal of Sociology, 98: 829-872.





1994 "Market uncertainty and the social character of economic exchange." Administrative Science Quarterly, 39: 458-483.

2001 "Networks as the pipes and prisms of the market." Ameri- can Journal of Sociology, 107: 33-60.

Podolny, J. M., and K. L. Page 1998 "Networks forms of organiza-

tion." Annual Review of Soci- ology, 24: 57-76.

Podolny, J. M., and D. J. Phillips 1996 "The dynamics of organiza-

tional status." Industrial and Corporate Change, 5: 453-471.

Podolny, J. M., and F. M. Scott Morton 1999 "Social status, entry and pre-

dation: The case of British shipping cartels 1879-1929." Journal of Industrial Econom- ics, 47: 41-67.

Portes, A., and J. Sensenbrenner 1993 "Embeddedness and immi-

gration: Notes on the social determinants of economic action." American Journal of Sociology, 98: 1320-1350.

Powell, W. W. 1990 "Neither market nor hierar-

chy: Network forms of organization." In B. M. Staw and L. L. Cummings (eds.), Research in Organizational Behavior, 12: 295-336. Greenwich, CT: JAI Press.

Rao, H., G. F. Davis, and A. Ward 2000 "Embeddedness, social iden-

tity, and mobility: Why firms leave the NASDAQ and join the New York Stock Exchange." Administrative Science Quarterly, 45: 268-292.

Ring, P. S., and A. H. Van de Ven 1992 "Structuring cooperative rela-

tionships between organizations." Strategic Managment Journal, 13: 483-498.

Rogers, W. H. 1993 "Regression standard errors

in clustered samples." Stata Technical Bulletin, 13: 19-23.

Rumelt, R. P. 1974 Strategy, Structure and Eco-

nomic Performance. Cam- bridge, MA: Harvard Universi- ty Press.

Sayrs, L. W. 1989 Pooled Time Series Analysis.

Newbury Park, CA: Sage.

Siegfried, J. J., and L. B. Evans 1994 "Empirical studies of entry

and exit: A survey of the evidence." Review of Industrial Organization, 9: 121-155.

Sorenson, 0., and T. E. Stuart 2001 "Syndication networks and

the spatial distribution of ven- ture capital investments." American Journal of Sociolo- gy, 106: 1546-1588.

Staw, B. M. 1981 "The escalation of commit-

ment to a course of action." Academy of Management Review, 6: 577-587.

Stuart, T. E., H. Hoang, and R. C. Hybels 1999 "Interorganizational endorse-

ments and the performance of entrepreneurial ventures." Administrative Science Quar- terly, 44: 315-349.

Tajfel, H., and J. Turner 1979 "An integrative theory of

intergroup conflict." In W. G. Austin and S. Worchel (eds.), The Social Psychology of Intergroup Relations: 33-47. Monterey, CA: Brooks/Cole.

Tomz, M. 2001 "Relogit: Rare events logistic

regression." http://gking.harvard.edu/stats.html.

Uzzi, B. 1996 "The sources and conse-

quences of embeddedness for the economic performance of organizations: The network effect." American Sociological Review, 61: 674-698.

1997 "Social structure and competition in interfirm networks: The paradox of embeddedness." Administrative Science Quarterly, 42: 35-67.

1999 "Social relations and networks in the making of financial capital." American Socio- logical Review, 64: 481-505.

Wasserman, S., and K. Faust 1994 Social Network Analysis:

Methods and Applications. Cambridge: Cambridge Uni- versity Press.

Weigelt, K., and C. Camerer 1988 "Reputation and corporate

strategy: A review of recent theory and applications." Strategic Management Jour- nal, 9: 443-454.

White, H. 1980 "A heteroskedasticity-consis-

tent covariance matrix estima- tor and a direct test for heteroskedasticity." Econometrica, 48: 817-838.

White, H. C. 2002 Markets from Networks:

Socioeconomic Models of Production. Princeton, NJ: Princeton University Press.

Williamson, O. E. 1981 "The economics of organiza-

tion: The transaction cost approach." American Journal of Sociology, 87: 548-577.

Wilson, R. 1985 "Reputations in games and

markets." In A. E. Roth (ed.), Game-theoretic Models of Bargaining: 27-62. Cam- bridge: Cambridge University Press.

Zucker, L. G. 1986 "Production of trust: Institu-

tional sources of economic structure, 1840-1920." In B. M. Staw and L. L. Cum- mings (eds.), Research in Organizational Behavior, 8: 53-111. Greenwich, CT: JAI Press.




APPENDIX


Strength of Market Ties


Investment-banking -0.05 status x Firm (0.04) market experience

Investment-banking 2.35w" ties x Firm market (0.61) experience

Commercial-banking -0.18"" status x Firm (0.03) market experience

Commercial-banking -0.17 ties x Firm market (0.21) experience

Investment-banking -0.31 status x Bank (0.08) years in market

Investment-banking -0.38 ties x Bank years (0.88) in market

Commercial-banking -0.09" status x Bank (0.05) years in market

Commercial-banking -0.13 ties x Bank years (0.12) in market

Investment-banking -0.94 status x (1.31) Investment- banking ties

Commercial-banking -0.97" status x (0.54) Investment- banking ties

Commercial-banking 1.29*" 1.27'" 1.60*" 1.22"* 1.24*" 1.25" ties (0.42) (0.40) (0.58) (0.41) (0.40) (0.39)

Commercial-banking 0.51 " 0.48? 0.55?? 0.20 0.55" 0.54"

status (0.26) (0.26) (0.26) (0.24) (0.24) (0.26) Investment-banking 4.65w" 4.17w*"? 6.24" 4.19."* 5.37 4.02" 6.07""

ties (0.57) (0.62) (2.32) (0.62) (3.27) (0.66) (0.79) Investment-banking 0.590 0.58" 0.55" 0.610 1.12" 0.630" 0.58"

status (0.31) (0.25) (0.26) (0.24) (0.32) (0.24) (0.25) Bank total assets (Ln 0.640 0.04 0.03 0.16 0.51 0.04 0.01

$M) (0.33) (0.40) (0.40) (0.46) (0.50) (0.39) (0.40) Bank total -0.05" -0.05? -0.040 -0.03 -0.05"? -0.050 -0.04?

syndicated lending (0.02) (0.03) (0.03) (0.02) (0.02) (0.02) (0.03) (Ln $M)

Firm market -0.15 -0.16' -0.160 -0.16? -0.13 -0.06 -0.08 experience (Ln (0.09) (0.09) (0.09) (0.09) (0.09) (0.10) (0.08) Deal count)

Firm market -1.75 -2.03 -2.09 -1.99 -1.94 -1.98 -1.55 interface (1.41) (1.45) (1.45) (1.42) (1.38) (1.42) (1.62) (Herfindahl index)

Firm market 1.800 2.030 2.11* 1.97? 2.04" 1.880 1.68 interface (1.04) (1.08) (1.10) (1.06) (1.02) (1.02) (1.20) (Herfindahl index)2

Firm inactive in 0.39 0.33 0.34 0.32 0.43 0.27 0.39 investment (0.48) (0.49) (0.48) (0.49) (0.47) (0.53) (0.52) banking (0,1)

Bank industry 6.88" 7.36" 7.23" 7.220" 6.900" 6.830" 6.86" market share (2.32) (2.43) (2.53) (2.39) (2.41) (2.41) (2.34) (bonds)

Bank total market 1.75 4.95 6.12 5.26 26.01 5.39 1.33 share (bonds) (12.17) (8.82) (9.07) (8.42) (3.17) (8.85) (9.33)

Bank headquartered -1.35" 0.05 0.06 -0.06 -0.80 0.11 0.09 outside U.S. (0,1) (0.54) (0.75) (0.74) (0.81) (0.90) (0.70) (0.75)





APPENDIX (Continued)


Strength of Market Ties


Bank headquartered -0.55 -0.66 -0.66 -0.48 -0.43 -0.65 -0.66 outside New York (0.61) (0.56) (0.55) (0.49) (0.38) (0.53) (0.54) (0,1)

Bank years in 0.210" 0.26"" 0.25o0" 0.30"" 0.26o" 0.26"" 0.25"o market (0.07) (0.07) (0.07) (0.06) (0.08) (0.07) (0.07)

Firm and bank -0.42 -0.58 -0.57 -0.63? -0.49 -0.37 -0.60* geographic (0.41) (0.37) (0.36) (0.37) (0.31) (0.33) (0.34) colocation (0,1)

Firm and bank board 0.05 0.05 0.04 0.04 -0.00 0.07 0.11 interlock (0,1) (0.10) (0.12) (0.11) (0.11) (0.11) (0.10) (0.12)

Bond size (Ln) -0.03 -0.05 -0.06 -0.04 -0.09 -0.05 -0.08 (0.14) (0.15) (0.15) (0.15) (0.16) (0.16) (0.15)

Bond quality: AAA 0.49 0.79 0.79* 0.85? 0.70 0.55 0.75 (0, 1) (0.51) (0.48) (0.48) (0.49) (0.51) (0.47) (0.48)

Bond quality: AA -0.05 0.08 0.08 0.05 -0.06 -0.04 0.02 (0,1) (0.39) (0.39) (0.39) (0.42) (0.45) (0.40) (0.40)

Bond quality: A (0,1) 0.13 0.16 0.18 0.20 0.14 0.10 0.11 (0.38) (0.39) (0.40) (0.40) (0.41) (0.42) (0.39)

Bond quality: BBB -0.43 -0.37 -0.35 -0.37 -0.36 -0.45 -0.43 (0,1) (0.35) (0.37) (0.38) (0.38) (0.40) (0.40) (0.38)

Year -0.32" -0.35"" -0.330 -0.35 " -0.28?" -0.370" -0.310"

(0.11) (0.10) (0.10) (0.10) (0.10) (0.09) (0.09) Sample selection 6.16" 6.320" 5.950 6.45"? 5.54" 6.42? 5.81"

(1.84) (2.05) (2.01) (2.03) (2.11) (2.17) (1.92) Intercept 636.620" 692.11 656.96" 699.20" 554.640 729.59" 624.930"

(210.75) (191.35) (203.42) (198.82) (204.97) (187.92) (187.46) LR 1371.09""1430.62" 5.28? 1446.30" 1487.42"" 1457.27" 1444.95"" ALR 59.53" 8.03" 15.68" 56.80" 26.65"? 14.33?" Degrees of freedom 26 28 30 30 30 30 30

p < .10; "= p < .05; ,

p < .01; " p< .001.

* Robust standard errors are in parentheses; one-tailed tests for main variables; two-tailed tests for control variables; all models include year dummies.




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