A Signaling Perspective on Partner Selection in Venture Capital Syndicates

Christian Hopp Christian Lukas

This paper analyzes the factors impacting partnering decisions in venture capital syndicates using a unique data set of 2,373 venture capitalist (VC) transactions in Germany. We employ

a signaling perspective to partner-selection strategies within VC syndicates. By including time-varying information about industry experience and cooperation patterns, we explicitly take into account not only the changing social context for partner selection, but also the dynamic nature of signals sent and received. Our analysis documents that the informative- ness of investment experience as a signal depends on the existence and frequency of previous joint deals with the lead VC. Experience becomes a much stronger signal if previous invitations to syndicates are bilateral rather than unilateral. The willingness to invite others to deals signals the ability to reciprocate through one’s own deal flow. More- over, we show how the value of signals erodes over time, that is, information from the previous year carries more informational value than signals from more distant years. In sum, the data reveal that different signals carry weight for lead VCs, and that the frequency of signals sent and the stage of development of the portfolio firm positively moderate the value and relevance of signaling behavior. While early stage investments are mainly characterized by need to diversify and to spread risks, value-added advice is necessary in later rounds, and hence, the strength of the signals sent and received gain in relevance and in value.

Introduction

The syndication of venture capital involves cooperation between partner venture capitalists (VCs) who jointly finance promising growth companies. Their relationship is characterized by uncertainty over the prospects of the ventures they fund, and the con- siderable time and effort they apply to guiding and advising the entrepreneurs (Ferrary, 2010; Gompers & Lerner, 2002; Manigart et al., 2005). There are various ways in which VCs can benefit from each other, in terms of sharing financial and managerial resources (Brander, Antweiler, & Amit, 2002; Lerner, 1994) or sharing risk (Manigart et al.). As VCs are mutually dependent on each other, it is important to choose the best possible

Please send correspondence to: Christian Hopp, tel.: +43-1-4277-38166; e-mail: christian.hopp@univie.ac.at.

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Generally, information asymmetry is also present in the entrepreneur’s decision of whom to invite to join the young firm’s team, or whom to choose as financier; and for the decision by a financier—be it a VC, bank, or business angel—of which entrepreneur to fund. The common feature of these examples is that the information available about the quality of a potential partner or project differs between contracting partners, and in general, the party making the selection decision possesses less information. However, the party making the selection may use additional observable information to infer the part- ner’s or the project’s quality. Such a situation conforms to the typical setup analyzed in signaling theory: There is information asymmetry between contracting parties, and it can

be reduced by signals that are observable and known in advance (Certo, 2003; Certo, Daily, & Dalton, 2001; Spence, 1974). For example, Kaplan and Strömberg (2004) show that ex-ante (i.e., within-round) staging of deals may allow very professional VC firms to signal their type to entrepreneurs. Another signal is the presence of a VC, which helps to verify that a young firm is not holding back initial public offering (IPO)-relevant information (Megginson & Weiss, 1991). In their meta-analysis, Daily, Certo, Dalton, and Roengpitya (2003) review a number of studies evaluating possible signals of IPO quality, like firm size, auditor reputation, and venture capital equity. Certo proposes board struc- ture as an important signal in that context. Following Filatotchev and Bishop (2002), share ownership of board members represents a signal of the young firm’s quality. However, depending on institutional factors, the value of otherwise-identical signals in different IPOs may vary (Moore, Bell, & Filatotchev, 2010). When judging the prospects of young firms, financiers may also consider the investment behavior of entrepreneurs (Prasad, Bruton, & Vozikis, 2000) or new venture teams (Busenitz, Fiet, & Moesel, 2005), VC funding events (Davila, Foster, & Gupta, 2003), and/or the status and reputation of VCs (Dimov & Milanov, 2009). Hence, signals can in general mitigate information asymmetry involved in entrepreneurial financing.

More particularly, in VC partnering decisions, the lead VC thinking about whether to select a partner can also turn to observable signals such as records of deals, status and reputation, or board composition to cope with and reduce information asymmetry. 1 Valliere (2011) demonstrates that success in early stage financing constitutes a signal of the VC’s ability to screen investment proposals; Manigart et al. (2005) argue that an invitation from a reputable lead investor could be a valuable signal of the quality of a nonlead investor.

The theoretical literature on VC syndication places great emphasis on experience as a signal for the selection of potential partner VCs (Booth, Orkunt, & Young, 2004; Casamatta & Haritchabalet, 2007; Cestone, White, & Lerner, 2006; Dorobantu, 2006). In practice, however, experience is likely to be an imperfect proxy for the quality of a VC; other signals are available, embedded in the wider investment context. The question arises of when and how the signal “experience” can be strengthened so that it better helps to infer the quality of a VC as a potential partner before a syndication decision is made.

In this paper, we are particularly interested in one part of the asymmetric information that VCs face, namely adverse selection in partnering decisions when one party lacks skills in selecting or managing deals, yet claims to possess these abilities. One way that

1. See also the review by Connelly, Certo, Ireland, and Reutzel (2010) and the references therein.

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We analyze the partnering decision in VC syndicates using a unique sample of 2,373 venture capital transactions in Germany. The underlying units of analysis are the syndi- cates of VCs formed during the period of 1995–2005. We address the questions of which partner VC(s) the lead investor chooses and which characteristics of the potential partner influence the likelihood of collaboration.

Our results suggest that signaling in venture capital investing comprises continuous investments to develop one’s own competence, subsequently engaging in relationships with other VC partners in syndicate transactions, being able and willing to reciprocate and act as lead investor, and lastly being able to continuously make this effort of ambidex- trously managing networking resources and individual investments. Based on our find- ings, different combinations of signals can spur cooperation between VCs. Especially a combination of signals of quality and intent—industry experience and reciprocation— strongly increases the probability of cooperation.

The remainder of the paper is structured as follows. We first introduce the theoretical background and develop the hypotheses. Next, we present the data set, the variables used, and the methodology. A presentation of the regression results follows, along with a discussion of and our findings, potential limitations and avenues for future research, and

a conclusion.

Theoretical Background

Analyzing the decisive factors in partner-selection processes certainly represents

a promising way to get an understanding of why syndication adds value to a VC-backed firm. As current and desired expertise forms the basis of value creation in the VC market, the VCs’ strategic actions are characterized by new opportunities and the corresponding competencies to master them. Related work argues that VCs are likely to lack (at least to some extent) potential resources, such as technological or invest- ment expertise, that are needed to achieve long-term competitive advantages; they refer to the ability to earn above-normal economic rents through the exploitation of capa- bilities or financial resources to provide adequate financing (Ferrary, 2010; Manigart et al., 2005). Accordingly, interorganizational relationships (syndicates) can create value by allowing VCs to combine resources and to share knowledge (Brander et al., 2002; Manigart et al.). While a VC’s internal resources are key to acquiring and sustain- ing competitive advantages, when those resources are lacking, alternative routes of generating and accessing knowledge are needed to prosper (Barney, 1991; Pfeffer & Salancik, 1978).

The resource motive may lead VCs to form syndicates, thereby gaining access to valuable resources of partners that aid in the management of financed transactions (Harrison, Hitt, Hoskisson, & Ireland, 2001; Larson, 1992; McEvily & Marcus, 2005; Rumelt, 1984). Syndication, however, can come at a cost, namely improper partner selection (mainly due to poor quality of partner VCs and only a negligible part due to

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hazard concerns; Meuleman, Wright, Manigart, and Lockett (2009) analyze empirically when the agency costs of syndication exceed its benefits. Syndication unavoidably entails giving up some of the expected profits to partner VCs (Brander et al., 2002). Casamatta and Haritchabalet (2007) and Cestone et al. (2006) found that more experi- enced VCs are less likely to syndicate, while less experienced VCs would want to reap the benefits of the formers’ more reliable evaluation of a venture’s success prospects. Yet there may be situations where inexperienced VCs never syndicate (Dorobantu, 2006). Hence, despite the possible lack of resources, many firms do not enter into interfirm relationships because the costs associated with partner selection do not out- weigh the benefits of getting access to new knowledge (Barringer & Harrison, 2000; Dimov & Milanov, 2009).

Given the earlier arguments, a major problem associated with the decision of whom to select in a syndicate is to find out exactly what resources a VC can contribute and what the level of the quality of those resources is. It is easy to calculate financial resources, but much harder to calculate nonfinancial resources such as knowledge, familiarity with sector-specific factors, and the ability to analyze, guide, and decide. These latter hard-to- measure and hard-to-communicate characteristics naturally give rise to an information asymmetry between partners in a syndicate. Even if a VC wishes to communicate truth- fully, it may not be possible (Busenitz et al., 2005; Williamson, 1991a) because “the limits of language are real” (Williamson, 1991b, p. 168).

Consequently, the lead VC selecting a partner has to cope with information asymme- try (Ferrary, 2010; Keil, Maula, & Wilson, 2010). The signaling theory offers a solution to the problem. It suggests that the better-informed party provides additional information (“signals”) to better communicate its own quality to less-informed parties (Certo et al., 2001; Spence, 1973, 1974). The receiver is referred to as the external, less-informed party, and the sender is referred to as the internal, better-informed party emitting the signal. In order to be valuable, signals must be freely accessible (i.e., observable), understood in advance, and costly to imitate (Certo et al.; Connelly et al., 2010). In the VC context, observability may be accomplished by communicating deal flows into the VC network (Ferrary; Hochberg, Ljungqvist, & Lu, 2007). Signals meet the costly-to-imitate criterion if they entail positive costs, and these costs or the benefits derived from the signals are type dependent (Spence, 2002). A VC’s deal record certainly cannot be replicated at zero costs because generating a deal flow requires effort; different VCs may incur different costs due to differences in abilities. But even if they have identical costs, in terms of amount invested, or labor costs for offering advice, VCs are likely to receive different returns (Cochrane, 2005; Hochberg et al.). Here the quality of advice as a resource matters, and it largely determines the possible benefits.

In our study we adopt a broad notion of VC quality and understand it as an “under- lying, unobservable ability . . . to fulfill the needs or demands of an outsider observing the signal” (Connelly et al., 2010, p. 43), where potential partner VCs form the group of

2. Besides unobservable quality, the intent of the VC may be unobservable as well. This is usually referred to as a hidden intention in the agency framework. Opportunistic behavior by a partner VC would certainly lead to inefficiencies, and in such a case, precautionary measures like comprehensive contractual arrangements or monitoring would seem appropriate. One could, however, argue that the entrepreneur is most likely to earn

a profit when each VC provides service and guidance as expected by the other VCs; hence, the threat of opportunistic behavior may not be substantial. We do not deny that threat, but we focus on quality uncertainty in this study. We thank an anonymous reviewer for clarifications on these issues.

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The basic idea behind our partner selection model is that a lack of resources triggers the quest for partner VCs. Since characteristics of VCs cannot easily be observed or communicated, information about them remains private, giving rise to information asym- metry. Signaling offers a way to deal with this problem. In light of the previous con- siderations, a lead VC considers the following properties of potential partners

• experience levels in different industries, • history of syndicating activity, including existence of joint deals, • willingness to provide access to deal flow, • the frequency of signal occurrence, and • the stage of development of the portfolio firm and corresponding signal relevance

to assess their quality. The more precise that assessment, the higher the likelihood of a good partnering decision, which is vital for the success of a VC syndicate. Furthermore, most of these signals result from actions and hence may be worth more than words (Busenitz et al., 2005). Figure 1 presents a stylized depiction of our theoretical model linking various competing signals to the probability of cooperation between a given lead VC and a potential selection from a universe of partners.

We link three main underlying signals to the probability of cooperation: first, the existence of additional industry-relevant investment experience (measured relative to the lead VC) to proxy for value-adding advice and capabilities (Brander et al., 2002; Manigart et al., 2005); second, the existence of joint experiences on which the lead can draw (Higgins & Gulati, 2003); and third, reciprocated ties to account for deal flow (Hochberg et al., 2007). The two signals measuring previous interactions of lead investor and potential partners should positively moderate the impact of the base signal experience. Additionally, the corresponding effects are reinforced by the frequency of occurrence

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Figure 1 Graphical Illustration of Theoretical Model and Hypotheses

(Janney & Folta, 2003). Moreover, the development stage of the portfolio firm positively moderates the value of signals. In later stages that involve lower levels of uncertainty about the portfolio firm signals about partner ability to provide value-adding advice become more relevant (Connelly et al., 2010; Lester, Certo, Dalton, Dalton, & Cannella, 2006). To account for potential partners conveying signals relative to each other (higher quality firms signaling at the expense of lower quality firms), we employ a case control design and account for relative strength of signals vis-à-vis competitors and the receiver (Connelly et al.; Sorenson & Stuart, 2008; Stiglitz, 2002).

Hypotheses

Experience Level

The inherent resources and investment experience of VCs form the basis for strategic value creation and address corresponding demands of entrepreneurs. In fact, better resources allow VCs to provide better advice and/or to better screen business proposals to generate superior long-term returns for fund shareholders (Brander et al., 2002; Lerner, 1994). Investment experience within a particular industry yields valuable insights into structuring deals and advising the funded entrepreneur, so it is crucial for understanding how resources shape competitive advantages in entrepreneurial financing. Therefore, investment experience likely signals the ability of a potential partner VC and helps the lead VC to distinguish between high-quality and low-quality VCs in the market. Having high-quality VCs in the syndicate would enhance the expected profitability of a deal by reducing the probability that partners do not live up to expectations (Lerner), and thereby increasing the chances for cooperation among a lead VC and potential partners (Dimov & Milanov, 2009). In a similar vein, Casamatta and Haritchabalet (2007) show analytically that a lead VC who lacks a needed competence is more likely to choose a partner who possesses that competence.

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Hence, we argue that the experience potential partners have gained within a transaction-relevant industry (possibly in excess of the lead investor’s existing knowl- edge) should be positively related to the likelihood of being chosen as a syndicate member. This leads to the following hypothesis:

Hypothesis 1: More investment experience of a potential partner within the given transaction-relevant industry increases the likelihood of collaboration with a given lead

VC.

History of Syndicating Activity

Given the opaqueness of the market, some VCs use their investment strategy to obfuscate information. Less able VCs might actively engage in a large number of trans- actions to look attractive to potentially superior partners. The search for investment transactions not only documents what firms prefer, but also affects the belief of others about them. Due to the long time horizon and the difficulty of disentangling bad luck from

a lack of managerial skills in VC investing, a large number of deals do not necessarily yield full information to separate less able from more able VCs. One of the key elements of signaling when firms have incentives to mislead others about their true ability is to create a set of choices through which firms with different character- istics self-select and reveal their ability. Following this underlying logic, VC firms that are less able in sourcing, evaluating, and managing transactions will act differently from VCs that possess the necessary skills to strive. While VC firms do reveal their track record, the market is fairly opaque and returns are difficult to verify (Cochrane, 2005). Given that talk is literally cheap, less able VC firms do not necessarily have incentives to fully reveal their

abilities. 3 Consequently, market actions are the necessary mechanisms through which better able VCs can reveal their true abilities. 4 One way to overcome these deficient signals is to

engage in ongoing syndication activities with other VCs to signal quality to potential partners (Granovetter, 1994; Hanneman & Riddle, 2005; Podolny, 1994). Recent work by Hochberg et al. (2007) reports higher returns for well-connected VCs. This finding under- scores the value of signaling in venture capital markets: More able VCs will receive higher returns for their investments if they can establish that they are more productive.

Previous exchanges between partners mirror the joint history of deals. As argued earlier, direct interactions allow for a more precise assessment of a partner’s quality than the mere observation that the partner participated in or initiated a certain financing event. The interactions may even help to get to know a VC’s founding team characteristics, which are predictors of a VC’s future success (Walske & Zacharakis, 2009). Hence, a joint deal history should matter for upcoming partnering decisions and increase the strength of experience as a base signal.

Accordingly, we formulate the following hypothesis: Hypothesis 2: A joint history of syndicating activity between the lead VC and a

potential partner positively moderates the value of experience as a base signal and increases the likelihood of collaboration with the given lead VC.

3. Incentives to misrepresent ability exist for less able VC firms when the expected gains from falsely claiming high quality outweigh the losses suffered in the event of detection, leading to a pooling equilibrium (Kirmani & Rao, 2000; Spence, 2002; Stiglitz, 2002).

4. Busenitz et al. (2005) point out that signals based on actions could be worth more than those based on verbal pronouncements.

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Deal flow and the willingness to let others participate also convey valuable information to the market. It then seems natural to ask whether inviting or both inviting and being invited to a deal makes a difference. In other words, does the relative strength of the quality signal increase through reciprocation of an invitation? In the event of recipro- cation, the current lead VC has observed the potential partner in different roles, as lead VC and as nonlead VC. One could argue that seeing a partner in different roles helps to assess the quality better. Moreover, the number of deals that VCs are willing to jointly invest in reveals information about the quality of the deals sourced and, hence, the ability of the corresponding VCs. In fact, the willingness to invite others to deals signals the ability to reciprocate through one’s own generated deal flow and is therefore a stronger signal than the pure ability to invest in as many firms as possible. Thus, better infor- mation about the ability of a VC is disclosed not only when investments were made in the past, but also when a willingness to let other VCs participate in deal flow was shown. In essence, less able VCs will not be willing to let other firms participate in their transactions, as they will reveal to partners the characteristics of the transactions chosen. Signaling through actions of VCs is costly and more importantly, more costly for some than others.

Hence, actions of superior VCs need to be more costly or more profitable to signal ability and achieve a separating equilibrium (Spence, 2002). Work by Basdeo et al. (2006) reveals that the complexity of one’s actions affects how they are perceived by the market, as they are more difficult for competitors to imitate. In general, the costs of signaling are borne by high-quality firms to separate themselves from low-quality firms. Given the time lag between deals entered into and the point at which success/failure is discovered, signaling deals undertaken do not suffice as a strong enough signal to actually achieve a separating equilibrium. The discrepancy between the initial signal and subsequent actions may lead to decoupling. While VCs signal quality through previous investments, the reluctance to provide deal flow renders the signal sent erroneous. In other words, the pure presence of experience without reciprocated ties is only a weak signal due to decoupling concerning the abilities present and the sought-after quality of experience or deal flow within the VC market (Connelly et al., 2010; Hochberg, Ljungqvist, & Lu, 2010). This leads to the following hypothesis:

Hypothesis 3: A reciprocated history of syndicating activity between a lead VC and a potential partner positively moderates the value of experience as a signal, and increases the likelihood of collaboration with the lead VC.

Returning to hypothesis 1, one may ask whether the effect of experience accumulates over time or whether experience in the VC business is subject to rather fast obsolescence, meaning that it has to be renewed by recurring activities in the VC market. Since the VC market deals with innovative business proposals, one could argue that experience accu- mulated years ago may not be worth so much.

Assuming that signals that were emitted previously become obsolescent, senders have the opportunity to further the strength of the signals through repetitively signaling quality.

A higher signaling frequency by making more transactions or providing more access to deal flow would provide a much stronger signal than a once-off engagement in these signaling activities. Hence, being able to not only engage in the provision of various signals at the same time, but being able to also do this continuously increases the strength of the underlying base signal emitted to potential partners. This way, senders can move from simple snapshots to a continuum of activities that provide signals, indicating the underlying yet unobservable quality they possess. Being able to signal repetitively fosters

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Hypothesis 4: The frequency of signaling activities positively moderates the effect of signaling strength and increases the likelihood of collaboration with a given lead

VC. Another difficulty VCs face when signaling in uncertain and ambiguous environments is

that the decision to syndicate is contingent on the underlying venture. VC financing takes place in emerging and knowledge-intensive industries where the value of the funded projects is highly uncertain and future payoffs are distant. The difficulty of disentangling the contribution of individual activities gives substantial leeway to the entrepreneur. Hence, the characteristics of the financed firm and the corresponding uncertainty could erode the value of a signal by creating noise so that the underlying message of VC ability becomes less evident in earlier rounds. Among other things, a coherent view of signaling therefore needs to take into account the value of signals conditional on venture-level characteristics. VCs usually fund ventures in several financing rounds or stages. The need for additional partner skills is anticipated to be greater in later stages of an investment than in earlier stages. This is mainly due to the fact that more mature firms funded already have an established management structure and market position (Brander et al., 2002; Bygrave, 1987; Bygrave & Timmons, 1992; Lockett & Wright, 1999). Consequently, the advice becomes more specific and context dependent in later rounds, while it is rather general (i.e., it addresses basic management topics) in earlier rounds. Research suggests that VC financing goes hand in hand with institutionalizing human resource management (Hell- mann & Puri, 2002), development of the accounting system allowing for more frequent monitoring (Mitchell, Reid, & Terry, 1997), or internationalization strategies (Mäkelä & Maula, 2005). Arguably, all of these activities become more important in later stages. With every round, the ambiguity and uncertainty of the project decreases. This allows for improved judgment about the managerial advice needed to support the funded firm (Lerner, 1994).

Of course, partners contribute financial resources in every round. Yet as argued earlier the need to add specific knowledge and expertise grows over time. Consistent with this argument, during initial rounds of funding, empirical evidence highlights the role of risk sharing among the VCs involved in a syndicate (Manigart et al., 2005). The value of signals provided could be lower in early rounds, as lead investors will simply be looking for potential partners to share the financial burden, but not necessarily for characteristics proxying quality and ability. Although information is provided and available in the VC network, lead VCs possibly do not look for these characteristics. Signals sent, alone and in combination, only have explanatory power when receivers are actively looking for these signals (Lester et al., 2006). Therefore, the value of the signals is context specific and varies across the different financing stages of VC investing.

Accordingly, the various financing stages involved in VC investments positively moderate the strength of signals sent and received; signals are less sought after in early stages due to pronounced risk and uncertainty and limits to add value, while VCs in later stages place a stronger emphasis on signals and combinations of signals due to an increased need for value-added advice. With respect to partnering decisions, we would expect signals sent by potential partner VCs to become more relevant or decisive in later rounds of financing; here, the lead VC does have a clear conception about the contribu- tions of partner VCs.

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Hypothesis 5: As the stage of development of the portfolio firms advances, the strength of the signals emitted rises and increases the likelihood of collaboration with

a given lead VC.

Data and Methods

Dataset and Summary Statistics

The sample consists of 2,373 venture capital transactions in Germany within the period of 1995–2005. The number of total financing events (2,373) comprises capital injections from 447 VCs that are made over different stages (start-up, early stage, and late stage) into 964 firms. The focus on the underlying German data has the advantage that we can study VC decision making with respect to partner selection from the inception of Germany’s Neuer Markt, the growth stock segment at the Frankfurt Stock Exchange in 1997, and up to 300 firms that were taken public until 2000, and a paralleling increase in VC investments, until the closure of the Neuer Markt in 2003 and a corresponding decline in VC transactions (von Kalckreuth & Silbermann, 2010). Given the growing focus of investment into these high-risk ventures and a lack of comparable investment histories (unlike in more established markets in other countries), the task of disentangling the role of signals (alone and in combination) is not obscured by longer lasting networks of VC investments, as evidenced by the work of Hochberg et al. (2007, 2010). Hence, being able to signal ability and making use of multiple signals might be more important in environments prone to uncertainty about market participants. And notwithstanding differences between the U.S. VC market and the ones in Germany and Europe as a whole, it is interesting to note that the study by Bottazzi, Da Rin, and Hellmann (2004) covering the years 1998–2001 (which are included in our sample) found that European VC firms were “increasingly emulating U.S. investment practices” and had established links to the United States; furthermore, over a third of European VC had worked in the United States before. Therefore, the results of our study are likely to be relevant also for VC markets beyond Germany.

The transactions were compiled by using public sources and the Thomson Venture Economics (TVE) Database. On average, a funded firm goes through 2.2 rounds of financing. We identify the involved parties in each transaction and the corresponding information on the VCs along with the funded firms. The result is a deal survey exhib- iting who funded a new company and who was joined by which partner. Moreover, we collect information about each financing round to infer which VC made an investment into a target firm at which point in time. In addition we supplement the database with information regarding the VCs and the funded firms, along with information specific to each deal. The analysis is carried out on the basis of investment rounds as indicated by TVE. 5

To calculate measures of investment experience acquired by the VCs, we include information on the industries that the funded firms are active in. This also makes it

5. A distinction between milestone and round financing cannot be observed. Gompers and Lerner (2002) study the completeness of the TVE database, and argue that most VC investments are contained in it and that those missing are among the less significant ones. The sample resembles the aggregate statistics published by the German Venture Capital and Private Equity Association and comparable representative studies in terms of industries and stages studied (see among others Bascha & Walz [2007], Bundesverband deutscher Kapitalan- lagegesellschaften [BVK] [2005], Mayer, Schoors, & Yafeh [2005]). The sample we studied is therefore unlikely to suffer from sample selection bias by focusing on TVE data.

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Partner Selection Into the Syndicate as the Unit of Analysis

A syndicate is usually defined as a group of VCs that jointly invest in a certain firm. We look at VCs that invested in the same company simultaneously (within the same round) and in different rounds, thus employing a wider definition of a syndicate. We are less concerned whether VCs invested in the same round, as VC relationships are built by formal interaction (such as board meetings) as well as by informal interaction (Brander et al., 2002; Gompers & Lerner, 2002; Manigart et al., 2005). Thus, previous interactions can yield insights into the decision patterns. Accordingly, a VC who invested in the first round interacts with an investor who joins the syndicate in a subsequent round. The unit of analysis is each accepted invitation of a partner to form a new syndicate or expand an existing syndicate further. Rather than focusing on the dyad level alone, we analyze which

partner VC was chosen by the lead investor at which point in time. 6 Hence, we employ

a methodology that shares many features with a case–control setup. This design is beneficial, as selection among potential VC partners is relatively rare. Units of analysis are selected based on outcomes and not hypothesized relationships (Pennings & Harianto, 1992). By studying the signaling behavior of potential partners relative to each other, we place our analysis into the wider context of signaling models, in which the costs of the signal are borne by higher quality firms at the expense of lower quality firms. Hence, setting oneself apart from competitors signals ability. Accordingly, we need to study signaling behavior vis-à-vis the universe of potential partners to draw inferences (Connelly et al., 2010; Stiglitz, 2002).

In order to cover the dynamics of partner selection in the most comprehensive way, we place no restrictions on the size of the VCs in the sample, thus including both large and small VCs. However, for the list of VCs from which a potential partner is chosen, we restrict the analysis to the most active VCs to ensure variation in the explanatory variables over time (and to avoid problems with autocorrelation). Here we choose a cutoff point of at least 10 deals over the time period of 1995–2005. This reduces the list of partners to 35 among whom the lead investor can choose. In explaining the dynamics of partner selection, we therefore make inferences about the major players within the market, rather than analyzing marginal ones. We do, however, include syndicates formed between major and peripheral VCs. In this case, the dependent variable for the peripheral VC is equal to one, and the entries for the other 35 (more active) VCs are

6. A transaction example is included in the Appendix.

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The Role of the Lead Investor

The role in managing and monitoring the underlying investment differs substantially between lead and nonlead investors. Gorman and Sahlman (1989) find that the lead investor spends about 10 times more time on monitoring and managing the investment. In

a network where one VC has invited partners to assist in the transaction, it is possible to account for the direction of the relationship. We include a measure of a “leading role” of VCs for each transaction. Hochberg et al. (2007) define the lead investor as the investor who acquires the largest stake in a portfolio company. Megginson and Weiss (1991) and Sorensen (2007) support this definition. As TVE reports only the total amount invested per round and does not distinguish between the sum invested by each VC involved on a stand-alone basis, we proxy for the lead investor(s) using two criteria that have to be fulfilled simultaneously: The maximum number of rounds and the involvement in the initial financing round. The lead investor is defined as the VC that has participated in the maximum number of rounds among all investing VCs and was also involved in the first round of financing. The argument underlying this assumption is the same as in Megginson and Weiss and Sorensen, that the lead investor usually has the largest amount of money at stake and therefore an incentive to take a more active role in managing the syndicate and advising the portfolio company. Thus, we can account for the direction of the ties that are established. A VC that has a leading role within a syndicate thus invites one or more new investors to participate in the deal; those do not have a leading role. Correspondingly, the partnering decision by the lead investor forms the basis of analysis. As the lead investors appear as often as they invited new partners in the data set, we adjust the standard errors for clustering on the lead investor level. We provide a transaction example in the Appendix to illustrate our approach.

Methodology

Each invitation record for a specific transaction includes various VC attributes for the lead investor, as well as for the VCs from which the partner is chosen. The VC attributes are based on the cumulative cooperation behavior until the end of the year prior to the given year. The resulting structure is a cross-section of transactions over time, with varying covariates (such as network status, number of deals, and funds managed) over the years. In order to account for the fact that the sample includes a larger number of nonevents for the dependent variable (indicating all the VCs that have not been chosen to participate in the syndicate), we estimate the coefficients using the rare events logistic adjustments suggested by King and Zeng (2001a).

Our sample reflects the total number of transactions that have been subject to part- nership behavior and are to some extent driven by the behavior of a distinct number of VCs that were involved in multiple transactions over the years. Those serial VCs require

a control for clustering in the error terms and an adjustment of the standard errors for the

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is dropped (to avoid perfect collinearity) from all regressions. All measures are either calculated as the cumulative number until the end of the year prior to the year in which the deal takes place, or by just using the relevant information from events happening in the year prior to the year in which the deal takes place (the variable description clearly indicates which time horizon was used). This way, issues of causality between the dependent and independent variables are circumvented. For example, the total number of transactions that a VC has made in (or until the end of the year) 2004 is used to explain his partnering decisions in 2005. Hence, a partnering decision in 2005 cannot influence the independent variables in 2004.

Explanatory Variables: Experience

In order to test our hypotheses, we calculate various VC characteristics that act as signals to increase the chances of a potential partner VC to be invited by a given lead VC.

Industry Experience. Hypothesis 1 states that the investment experience of the potential partner VC positively affects the likelihood of collaboration. We compute the total number of transactions within the industry in which the funded firms operates that the lead investor(s) as well as the potential partners have invested in during the year prior to the year in which the deal takes place. To be able to better track the signaling behavior and the quality of the signal, we investigate relative performance, which reveals more infor- mation than absolute performance (Stiglitz, 1975). Accordingly, we calculate the differ- ence between the number of transactions the lead investor and the potential partner engaged in to proxy for additional experience that could be expected from a potential partner. A negative number would therefore indicate that the partner possesses more experience within the given industry than the lead investor.

Explanatory Variables: Direct Relationships

Lead-Invited VC. With respect to hypothesis 2 and the impact of direct previous rela- tionships between the lead investor and the potential partner, we include a measure indicating how often the lead investor previously invited the potential partners (lead- invited VC). Based on the past transactions in which the current lead investor also acted as a lead investor, we count the number of previous collaborations with each potential partner. We hypothesize this measure to positively moderate the effect of investment experience.

7. We also estimate two additional variants of all models shown. First, we estimate a model without any clustering. Generally, if we were to neglect the underlying covariance structure due to repeated observations of VCs that invest numerous times, we would underestimate our standard errors, and hence, we would likely make wrong inferences concerning our hypothesis tests. Indeed, in the model without clustering, standard errors are much lower, and significance levels are much higher. Second, we also estimate a set of models where we cluster the standard errors across the VCs chosen as potential partners. Given that we have an exclusion restriction concerning which partner the lead VCs can choose, this might bias our results. The results suggest that all of the effects reported in the paper remain significant at least at the 5% level. Results are available upon request from the authors.

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Reciprocated Tie (Dummy). We include a measure for reciprocated ties that originate from both sides. Asymmetries in relationship building are detrimental for dyadic stability, and thus we account for a reciprocated tie if the current lead investor has invited the potential partner in the past and if the corresponding partner has also previously invited the current lead VC. That is, the direction of the tie should be bilaterally shared among the partners (Hanneman & Riddle, 2005; Lockett & Wright, 1999; Shane & Cable, 2002). The dummy takes on the value of one if bilaterally shared ties are present and zero otherwise. Again, these measures are calculated cumulatively over the past years and solely over the previous year.

Interaction Terms

Experience ¥ Lead-Invited VC. To measure the interplay between the signal experience and the directed tie as a potential amplifier in hypothesis 2, we interact the variables for the direct relationships between VCs and the excess industry experience of potential partners. We use the interaction terms with two separate variables: one based on the contacts during the previous year alone, and a second one using the cumulative informa- tion on partnering behavior, taking into consideration the entire past investment horizon. This way, long-term and more immediate effects (informed by hypothesis 4) can be disentangled.

Experience ¥ Reciprocated Tie. To proxy for the effect of reciprocated ties in combina- tion with transaction-relevant industry experience on the propensity to work collabora- tively on a given deal (presented in hypothesis 3), we interact the reciprocated tie dummy with the excess industry experience of the potential partner. Here we use the presence of

a reciprocated tie during the previous year for one variable and over the previous invest- ment horizon for a second variable, and insert them in separate regression specifications.

Type of Financing Round (Dummy). In hypothesis 5, we argue that the relevance of signals depends on which stage the investment is made and when potential partners are to

be selected. Hence, while in early rounds VCs aim at spreading risk and therefore are reluctant to search for meaningful signals, the signals become more important in later rounds. TVE gives information about five different stage categories: start-up/seed, early stage, expansion, later stage, and other. Similar to Gompers (1995) who labels the categories for bridge, second, and third stage financing as “late stage” financing, we combine the TVE categories expansion, later stage, and other to form a new category, which we also label “late stage.” As there is no clear distinction between expansion financing, which almost always occurs in later phases, and other financing activities from the “later stage” category, namely bridge financing and special purpose financing, this combination appears to be the most reasonable classification scheme. In the following we split the regressions into separate stages and compare coefficients across the different models, following Hoetker (2007).

Control Variables

Funds and Capital Managed. To account for experience acquired through the manage- ment of funds and capital, we include two variables indicating the difference in capital and

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Total Deals Previous Year. We control for the activity (and possible investment over- load) of VCs by summing over all transactions a VC was involved in during the pre-

vious year. The control variable measures the number of total transactions financed alone and/or as a member of a syndicate. VCs that were involved in a larger number of transactions previously are less inclined to join in on an upcoming transaction due to limitations in management capacity. We include the number of total transactions in all regressions.

Analysis and Results

Table 1 reports the summary statistics and the correlation matrix for the independent variables. With respect to the experience within the given industry, one can infer that on average, the lead investors financed about the same number of deals during the previous year as the potential partners did. The measures of previous direct relationships show that over the years, various relationships were formed at differing levels of intensity. Among the 35 potential partners, some .07 ties are present, which indicates that on average, the given lead investor has roughly two ties established over the previous investment horizon and about 1.5 during the previous year alone. Among the relation- ships, there is less than one tie for the previous year where the direction of establishment has been reciprocated with the potential partner and roughly one for the cumulative number of reciprocated ties. The focus of investments is concentrated into late stage investments with around 55%; the early and start-up stage have some 30% and 15%,

Table 1 Descriptive Statistics and Correlation Matrix

Variable

Mean SD

2 Industry experience (previous

2.16 -.01

year) 3 Lead-invited VC

4 Lead-invited VC (previous year)

5 Reciprocated tie (dummy)

6 Reciprocated tie previous year

( dummy) 7 Total deals (Previous year)

.16 8 Start-up (dummy)

.00 .01 9 Early stage (dummy)

-.02 -.01 -.28 10 Late stage (dummy)

.02 .00 -.47 -.71 11 Capital managed

.00 -.04 -.01 .01 .00 12 Funds managed

.00 -.18 .00 -.05 .04 .37

Notes: n = 23,968. All correlations above .01 are significant at least at the 5% level. SD, standard deviation.

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1.4, thus showing no sign of problems with multicollinearity. Moreover, all condition numbers calculated have values of around 5, and are well below the critical thresholds. It is noteworthy that only when we introduce squared terms into the analysis do the variance inflation factors rise to 3.6, but then they are still well below critical values (Hair, Anderson, Tatham, & Black, 2005).

Table 2a presents the regression output using the industry experience measure; it includes only transactions undertaken within the year prior to the year in which a deal takes place. To contrast these results, Table 2b uses cumulative industry experience as a measure of all transactions undertaken prior to that year. Using these two measures, we can disentangle current effects from long-term signaling behavior. The results support

Table 2 Rare Events Logit Regression Using Robust Standard Errors

(a)

Industry experience (previous year)

(.048) (.073) Lead-invited VC

Experience ¥ lead-invited VC

Lead-invited VC (previous year)

Experience ¥ lead-invited VC (previous year)

Reciprocated tie (dummy) .442** (.004) Experience ¥ reciprocated tie

Reciprocated tie (previous year) (dummy) 1.012** (.002)

Experience ¥ reciprocated tie (previous year) -.145* (.019)

Total deals (previous year)

(.065) (.024) Start-up stage (dummy)

(.011) (.011) Early stage (dummy)

(.426) (.412) Capital managed

(.008) (.008) Funds managed

198.40 195.26 p > chi-square

650 ENTREPRENEURSHIP THEORY and PRACTICE

Table 2 Continued

(b)

Industry experience

(.086) (.296) Lead-invited VC

Experience ¥ lead-invited VC

Lead-invited VC (previous year)

Experience ¥ lead-invited VC (previous year)

Reciprocated tie .392* (.017) Experience ¥ reciprocated tie

Reciprocated tie (previous year) .845* (.018) Experience ¥ reciprocated tie (previous year)

-.069* (.027) Total deals (previous year)

(.321) (.165) Start-up stage

(.019) (.018) Early stage

(.365) (.350) Capital managed

(.010) (.009) Funds managed

164.28 176.98 p > chi-square