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Essays in venture capital, entrepreneurship, and managerial success Du, Qianqian 2009

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ESSAYS IN VENTURE CAPITAL, ENTREPRENEURSHIP, AND MANAGERIAL SUCCESS  by  QIANQIAN DU  M.Sc., The University of Oxford, 2004 B.A., Shandong University, 2003  THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Business Administration)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2009  © Qianqian Du, 2009  ABSTRACT The first chapter of my dissertation examines the preferences of venture capitalists for syndication partners. Heterogeneity among syndication partners may cause efficiency loss and increase transaction costs but offer syndication partners valuable learning opportunities in the long run, suggesting a tradeoff between the short-term costs versus long-term benefits. Using data on U.S. venture capital investments, I find that venture capital firms are less likely to syndicate with partners who are different from them. The preferences for syndication partners, however, have different implications for the portfolio companies and the venture capital firms. Companies funded by heterogeneous syndicates are less likely to go public or get acquired by other companies. However, venture capital firms that co-invest with more heterogeneous partners are more likely to survive. This paper develops a new method for empirically examining the formation of syndication among multiple firms. It also addresses issues of endogeneity.  In the second chapter, we develop an economic framework which articulates the impact of the quality of legal protection offered to investors on the incentives of start-up founders to recruit partners or opt for sole ownership. The theoretical analysis predicts that a positive relationship is likely to exist between the quality of the legal system and ownership concentration of start-ups. This prediction is supported by the data obtained from the Adult Population Survey of the Global Entrepreneurship Monitor project between 2001 and 2004.  The third chapter finds that the number of CEOs born in summer is disproportionately small, and firms with summer born CEOs have higher market valuation. Our evidence is consistent with the “relative-age effect” due to school admissions grouping together children with age differences up to one year, with summer-born children disadvantaged throughout life by being younger than non-summer-born classmates. Those younger children who nevertheless succeed have to be particularly capable.  ii  TABLE OF CONTENTS ABSTRACT .................................................................................................................. ii TABLE OF CONTENTS ............................................................................................. iii LIST OF TABLES ....................................................................................................... iv LIST OF FIGURES .......................................................................................................v ACKNOWLEDGEMENTS ......................................................................................... vi DEDICATION ............................................................................................................ vii CO-AUTHORSHIP STATEMENT .......................................................................... viii 1 INTRODUCTION ......................................................................................................1 2 BIRDS OF A FEATHER OR CELEBRATING DIFFERENCES? THE FORMATION AND IMPACT OF VENTURE CAPITAL SYNDICATION ................6 2.1 Introduction ..........................................................................................................6 2.2 Theoretical Considerations ................................................................................11 2.3 Data and Variables .............................................................................................17 2.4 Empirical Analysis .............................................................................................26 2.5 Conclusions ........................................................................................................52 2.6 References ..........................................................................................................54 3 INTERNATIONAL PATTERNS OF OWNERSHIP STRUCTURE CHOICES OF START-UPS: DOES THE QUALITY OF LAW MATTER? ...............................58 3.1 Introduction ........................................................................................................58 3.2 Does Law Matter? ..............................................................................................61 3.3 Law and Ownership Decisions ..........................................................................62 3.4 Data and Methodology .......................................................................................64 3.5 Econometric Analysis ........................................................................................73 3.6 Discussions and Conclusions .............................................................................80 3.7 References ..........................................................................................................82 4 BORN LEADERS: THE RELATIVE-AGE EFFECT AND MANAGERIAL SUCCESS ..................................................................................................................84 4.1 Introduction ........................................................................................................84 4.2 The Relative-age or Birth-date Effect ................................................................86 4.3 The Relevant Seasons of Birth ...........................................................................89 4.4 Data Employed: CEO Birth-dates and Firm Characteristics .............................90 4.5 The Prevalence and Performance of CEOs by Birth Season .............................94 4.6 Relative Age, CEO Birth-dates and Birthday-related Performance .................112 4.7 Conclusions ......................................................................................................113 4.8 References ........................................................................................................115 5 CONCLUDING CHAPTER ...................................................................................117 5.1 Conclusion and Discussions ............................................................................117 5.2 References ........................................................................................................120  iii  LIST OF TABLES Table 1 The Definition of Variables .............................................................................23 Table 2 Descriptive Statistics of Samples ....................................................................27 Table 3 Correlation Matrix of Key Variables ...............................................................27 Table 4 The Base Model ..............................................................................................29 Table 5 Network Centralities and Geographic Distances ............................................33 Table 6 Prior Relations .................................................................................................34 Table 7 Sample for Portfolio Companies’ Performance ..............................................36 Table 8 Sample for the Performance of Syndication ...................................................38 Table 9 The Performance of Syndication .....................................................................39 Table 10 Selection Issues of the Performance of Syndication .....................................43 Table 11 Sample for VCs’ Survival ..............................................................................44 Table 12 Correlation Matrix for VCs’ Survival ...........................................................45 Table 13 VCs’ Survival ................................................................................................50 Table 14 Selection Issues of VCs’ Survival .................................................................51 Table 15 Country Composition and Legal Origin ........................................................65 Table 16 Description of the Legal Variables ................................................................68 Table 17 Description of Other Variables ......................................................................69 Table 18 Descriptive Statistics of Variables .................................................................71 Table 19 Regressions on Legal Origins .......................................................................74 Table 20 Regressions on Legal Enforcement ...............................................................76 Table 21 Robustness Checks ........................................................................................77 Table 22 Country Level Regressions ...........................................................................80 Table 23 Descriptive Statistics .....................................................................................92 Table 24 Correlation Matrix .........................................................................................93 Table 25 Season of CEO Birth and Firms’ Valuation ..................................................98 Table 26 Season of CEO Birth and Firms’ Stock Performance .................................101 Table 27 U.S. School Cutoff Dates ............................................................................105 Table 28 Relative Age and Firms’ Valuation ..............................................................109 Table 29 The Latest Possible Quarter and Firms’ Valuation ......................................110 Table 30 Relative Quarter and Firms’ Valuation ........................................................111  iv  LIST OF FIGURES Figure 1 Number of CEOs by Birth Season .................................................................94 Figure 2 Season of Birth: CEO Sample versus U.S. Population .................................95 Figure 3 Performance of Portfolios by CEO Birth Season ........................................100 Figure 4 Number of CEOs by Relative Age ..............................................................107  v  ACKNOWLEDGEMENTS  I feel grateful for all of my advisors who have offered invaluable help and advice during my doctoral study at UBC, and all of my co-authors for their great inspirations and support.  I am greatly indebted to Professor Thomas Hellmann, who led me to an exciting research area and trained me to think more deeply and critically. I also want to thank Professor Ilan Vertinsky in particular for his excellent guidance.  I owe special thanks to my parents, who always try their best to support me in any possible way at anytime.  vi  DEDICATION  To my parents  vii  CO-AUTHORSHIP STATEMENT  My second chapter is co-authored with Ilan Vertinsky. I developed the research idea. I obtained the data from Global Entrepreneurship Monitor and performed econometric analysis. I also prepared for the first draft of the paper and made my contribution to the subsequent revisions of the paper.  My third chapter is co-authored with Huasheng Gao and Maurice Levi. I developed the research idea and discussed it with my co-authors. I also collected data from various sources with my co-authors. I performed econometric analysis independently and then discussed and compared the results with my co-authors. I also contributed to the manuscript preparation.  viii  1. INTRODUCTION  1.1 Venture Capital Syndication  Venture capital firms (VCs) have played an important role in providing equity financing to technology intensive start-up companies. A common feature of venture capital investments is syndication among venture capitalists, which means different VCs co-investing in a financing round of their backed portfolio company. Whether or not venture capital syndication can reduce investment risks and achieve better performance than standalone investments crucially depends on what kind of syndication partners a venture capital firm is able to attract. My first chapter focuses on the heterogeneity among syndication partners and studies how heterogeneity among syndication partners affects the formation and performance of syndication.  Heterogeneity entails costs. Based on positive assortative matching on VCs’ experience, which is a key dimension of heterogeneity, VCs with different levels of experience are less likely to form syndication in the first place. After the syndication is formed, heterogeneous syndicates may suffer from higher transaction costs, ex post. There are also benefits of heterogeneity. Heterogeneous syndicates can provide valuable learning opportunities to syndication partners. We also expect that the benefits from learning may not be realized immediately but more likely to be harvested by VCs in the long-term. To empirically examine the impact of investor heterogeneity, I use the venture capital investments made to U.S. companies between 1990 and 2005 from the Thomson Financial’s VentureXpert database. I also developed a new algorism to predict the formation of syndication among a group of VCs while prior research mostly predicts alliance formation between two firms.  After addressing endogeneity problems, I obtain the following findings. First, VCs have strong preferences for syndication partners with similar levels of experience and performance. The  1  findings remain robust after controlling for other characteristics of VCs. Second, companies funded by heterogeneous syndicates, in which VCs have different levels of performance, are less likely to have IPOs and sales to other companies, which proxy for the performance of venture capital investments. Third, VCs, whose partners are more heterogeneous, are more likely to make new investments and diversify their investment portfolios, and eventually survive in the future.  This paper makes multiple contributions to the literature. First, it captures heterogeneity among VCs in a syndicate for the first time, and shows that such investor heterogeneity can partially explain both the formation and impact of syndication. Second, this paper develops a new matching algorism and predicts the syndication formation among a group of investors for the first time. Third, although this paper uses venture capital as research context, it has general implications for alliances among firms, teams, and social networks.  1.2 Legal System and Entrepreneurs’ Ownership Choices  Prior research suggests that high quality legal systems, which offer effective investor protection, can lead to better developed capital markets and more dispersed ownership (La Porta et al., 1998; Glaeser, Johnson, and Shleifer, 2001; Djankov et al., 2003; and Demirguc-Kunt and Levine 2001). Despite the important contributions of small and medium sized enterprises (SMEs) to economic growth (Berger and Udell, 1998), to our knowledge, no study has considered the impact of legal systems on the ownership structures at the founding stage of small and medium firms that are not backed by private equity firms. This segment of new enterprises contains the majority of start-ups, both by value and number.1  In the theoretical framework, we assume that there are two sources of capital to finance start-ups at founding stage: internal capital and external capital. Internal capital mainly refers to the equity 1  For example, 94.5% of U.S. nonfarm, nonfinancial, nonreal-estate small businesses or $1582.4 billion in monetary  value belong to this segment (Berger and Udeall, 1998).  2  capital obtained from start-up founders and their co-owners while external capital mainly refers to the debt capital raised by entrepreneurs from banks. We argue that the quality of a legal system can have different impacts on internal and external investors. If the legal system fails to provide adequate protection, increases in internal investors’ ownership and control rights can work as a substitute for the inadequate legal protection. External investors, however, do not have such substitute and thus become less willing to invest in start-ups. Therefore, in a poor legal system, the relative cost of external capital to internal capital tends to be higher, resulting in less external debt financing and more dispersed ownership structures. Using data from the Adult Population Survey of the Global Entrepreneurship Monitor project between 2001 and 2004, our predications are supported.  1.3 Relative-age and Managerial Success  Who are more likely to become CEOs of S&P 500 companies? What makes a successful CEO? My third chapter suggests one important factor that partially explains the probability of becoming a CEO and the subsequent success of a CEO.  There is mounting empirical evidence that summer born children are at a disadvantage as a result of being up to a year younger than other classmates in their school grade, due to the fact that the cutoff dates for admission into school generally fall at the end of summer. The disadvantage faced by summer born children has been shown to exist throughout school, and even to affect the success at entering university. This well-documented condition has become known as the “relative-age effect” or the “birth-date effect”. My third chapter examines whether a relative-age or birth-date effect extends to the selection and performance of CEOs of S&P 500 companies.  We argue that as non-summer born children are relatively older in the class, they have a better chance of gaining leadership-related experience (e.g. becoming a team captain, a school monitor,  3  etc.). Some summer-born children who are exceptional in ability within their age cohort manage to gain the same experience. To be selected for leadership related activities that will help become CEOs later, a threshold of demonstrated performance is required. The threshold can be met by a combination of development which is related to age, and innate ability which is evenly distributed across birth seasons. Via their greater development, more non-summer born is given such leadership related experience and therefore more of them become CEOs. Since non-summer born achieve the threshold of demonstrated performance more through development than through innate ability while the summer born achieve the threshold more through innate ability than through development, the average ability of summer born CEOs is likely to be higher than that of the non-summer born CEOs. As a result, firms headed by summer born CEOs outperform those headed by non-summer born CEOs.  We construct a birth-date dataset for the CEOs of S&P 500 companies between 1992 and 2006 and supplement it with other characteristics of CEOs and company characteristics from ExecuComp, CRSP, and Compustat. We find that non-summer born individuals have a significantly higher chance of becoming a CEO of an S&P 500 company. Conditional on becoming a CEO, those who were born in summer add higher value to their company whether this is considered via Tobin’s Q involving market and book value of assets, or the market to book value of equity, M/B. We also show the return from a policy of forming a portfolio based on buying companies with summer born CEOs and selling short companies with non-summer born CEOs. This generates an annual abnormal return of 8.32 percent.  4  1.4 References Berger, A., and G. Udell. 1998. The Economics of Small Business Finance: The Roles of Private Equity and Debt Markets in the Financial Growth Cycles. Journal of Banking & Finance, 22: 613-673. Demirguc-Kunt, A. and R. Levine. 2001. Financial Structure and Economic Growth: A Cross-Country Comparison of Banks, Markets, and Development. Cambridge, MA: MIT Press. Djankov, S., R. La Porta, F. Lopez-De-Silanes, and A. Shleifer. 2003. Courts. The Quarterly Journal of Economics, 118 (2): 453-517 Glaeser, E., S. Johnson, and A. Shleifer. 2001. Coase versus the Coasinas. The Quarterly Journal of Economics, 116 (3): 853-899. La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. Vishny. 1998. Law and Finance. Journal of Political Economy, 106 (6): 1113-1155.  5  2 BIRDS OF A FEATHER OR CELEBRATING DIFFERENCES? THE FORMATION AND IMPACT OF VENTURE CAPITAL SYNDICATION2  2.1 Introduction  Venture capital firms (VCs) provide equity financing to technology intensive start-up companies and realize returns if their funded companies go public or get acquired by other companies. To reduce the high risks associated with investing in private companies, venture capital firms may choose to syndicate their investments. Syndication is formed when different VCs co-invest in a financing round of their backed company. Syndication is very common among VCs. For example, around 60% of venture capital backed start-up companies have received at least one syndicated financing rounds in the past two decades in the U.S. It is the syndication that helps to create a national network of VCs (Kogut, Urso, and Walker, 2007).  Compared with standalone investments, venture capital syndication is formed to achieve different goals, for example, to obtain a second opinion from partners and improve the assessment of the investment opportunities (Lerner, 1994), to access complementary management skills of syndication partners (Brander, Amit, and Antweiler, 2002), to invest in geographically distant companies (Sorenson and Stuart, 2001), or to gain future reciprocity (Hochberg et al., 2007). Syndication also matters for performance. For example, Brander et al. (2002) found that syndicated investments tend to outperform standalone investments. Hochberg et al. (2007) showed that venture capital firms that are more connected in a network are associated with better performance.  Whether syndicates can achieve the above goals and meet performance targets crucially depend on what kind of syndication partners a venture capital firm is able to attract. This paper focuses 2  A version of this chapter will be submitted for publication. Du, Q. Birds of a Feather or Celebrating Differences?  The Formation and Impact of Venture Capital Syndication.  6  on the heterogeneity among syndication partners and studies how heterogeneity among syndication partners affects the formation and performance of syndication.3 Prior research has documented a long list of the costs and benefits of heterogeneity. On the one hand, heterogeneity may make communication and coordination less effective among group members (Van den Steen, 2004), resulting in slower actions and responses in the competitive environments (Hambrick, Cho, and Chen, 1996). On the other hand, heterogeneous groups provide valuable learning opportunities for the group members in the long term. Heterogeneity may encourage group members to collect new information (Van den Steen, 2004), improve the group’s problem solving ability (Hoffman and Maier, 1961), and may lead to increased innovation within groups. The tradeoff between the costs and the benefits of heterogeneity, therefore, has important implications for firms’ preferences for alliance partners and the performance of the formed alliances. Specifically, this paper studies the following three research questions: (1) Do VCs prefer partners that are similar to or different from them? (2) Will VCs’ preferences for partners affect the performance of the syndicated investments? (3) Will VCs’ preferences for partners have any impact on the VCs?  One important characteristic that differentiates VCs from each other is their experience. Through deal selection and value added services, VCs’ experience has a positive impact on venture success (Gorman and Sahlman, 1989; Hellmann and Puri, 2002; Sorensen, 2007). As returns to private equity persist (Kaplan and Schoar, 2005), a VC’s prior performance is an appropriate indicator of its future performance. Therefore, experience and performance are two key attributes VCs use to select syndication partners.  Heterogeneity entails costs. First, given the importance of VCs’ experience for venture success, it is reasonable to assume that VCs’ experience is complementary to the returns of the syndicated 3  As this chapter studies how VCs choose syndication partners, it focuses on VCs that prefer to syndicate their  investments to invest alone. Therefore, both the theoretical and empirical analysis is based on VC syndicates instead of standalone investments.  7  investments. Therefore, VCs try to syndicate with partners that are at least as experienced as they are in order to maximize returns. In equilibrium, the levels of VCs’ experience in a syndicate are positively correlated. Second, heterogeneous syndicates may incur higher transaction costs ex post. Communication and coordination costs can be higher, leading to more conflicts, slower decision making, and delayed execution. Different knowledge of one market can increase the information asymmetry among VCs, leading to less effort from the less experienced investors. Third, similarity attraction makes heterogeneous syndicates less attractive. The costs of heterogeneity imply that VCs are less likely to form syndicates with partners who are different from them. After the syndication is formed, companies funded by heterogeneous syndicates may underperform those funded by homogeneous syndicates.  There are also benefits of heterogeneity. First of all, heterogeneous partners can bring in different insights and perspectives which enable them to make better investment decisions in the future. The benefits of heterogeneity may be greater for VCs which operate in highly risky environments, in which decision making can be “subjective”. Through co-investing with partners with diverse experience backgrounds, VCs can observe how their partners make investment decisions and expose themselves to more options to solve problems. Although learning can be beneficial, we would not expect the benefits of learning to realize immediately. Instead, we expect that VCs learn to invest better gradually and harvest the benefits of learning in the long-term. The benefits of heterogeneity suggest that VCs, whose partners are more heterogeneous, may be able to make more and better investments in the long term.  To test the above empirical implications, I use the venture capital investments made to U.S. companies between 1990 and 2005 from the Thomson Financial’s VentureXpert database. To predict the syndication formation, I need to collect realized syndicates and construct hypothetical syndicates that are not realized. I develop the “Single Deviation Method”, in which I construct hypothetical syndicates by replacing each VC in a realized syndicate with each potential investor  8  one at a time. While prior research mostly predicts the alliance formation among two firms, the “Single Deviation Method” allows me to predict syndication formation among a group of investors, and measure heterogeneity among these investors. Results remain the same after I perform robustness checks on this method. When I test the second implication, I base the analysis on the realized syndicates only. To examine the long-term benefits of learning for VCs, I construct a panel data with VC-year pairs as units of analysis and include VCs with at least two syndication partners in any year between 1995 and 2005. To measure the heterogeneity among VCs, I apply the coefficient of variation measure to continuous variables and the entropy measure to categorical variables, both of which are widely used in the sociology literature. I focus on two dimensions of heterogeneity in VCs: VCs’ prior experience, and performance in the portfolio companies’ industries.4  The empirical findings are summarized in three parts: First, VCs have strong preferences for syndication partners that are similar to themselves. Specifically, syndicates are more likely to be formed among VCs with similar levels of experience and performance. The findings remain robust after controlling for VCs’ network centrality scores that capture how connected VCs are in networks, geographic distances among VCs, geographic distances between VCs and their portfolio companies, and prior syndication among VCs.  Second, companies funded by heterogeneous syndicates, in which VCs have different levels of performance, are less likely to have IPOs and sales to other companies, which capture performance of venture capital investments. Two types of selection may bias the results. The first type is the selection among VCs which tend to form less heterogeneous syndicates. To study how heterogeneity among VCs affects the performance of their syndication, we need to base the 4  Due to the limited space, I use VCs’ overall experience and performance, and their experience and performance in  the portfolio companies’ geographic location (states) as robustness checks. I obtain similar results, which are available upon request.  9  analysis on the realized syndicates that are in general less heterogeneous than those not realized. To address the first type of selection, I apply the Heckman two-step procedure which includes a selection equation and a performance equation. The identification of the Heckman two-step model comes from the geographic distances among VCs, and the heterogeneity in distances between each VC and the portfolio company. Such geographic distances have a negative impact on the formation of syndication but should not have a direct impact on the performance of the syndication once it is formed. The second type is the more well-known selection between VCs and start-ups (Sorensen, 2007), in which top tier VCs invest in top tier start-ups. To address this type of selection, I adopt an instrumental variable approach based on Ackerberg and Botticini (2002). The instruments are dummy variables of companies’ local markets, constructed as an interaction of companies’ industries with their geographic location (states). The predictions from the Heckman two-step procedure and the Ackerberg and Botticini Approach are consistent with the original results.  Third, since it takes years for VCs to successfully exit their investments, my sample of relatively recent transactions limits my ability to study the long-term performance of VC funds, which are typically organized as ten-year close end funds. Instead, I focus on another important feature of VCs – their survival. I find that VCs, whose partners are more heterogeneous, are more likely to make new investments and diversify their investment portfolios, and eventually survive in the future. The results of survival remain robust to different definitions of VCs’ survival, and in a sub-sample of U.S. independent VCs. Anticipating heterogeneous partners may help survival in the future, VCs may select into more heterogeneous syndicates. To address this selection, I construct the local availability of heterogeneous partners for each VC as an instrument for the heterogeneity in VCs’ syndication partners. The local availability of heterogeneous partners can affect a VC’s choice of syndication partners but should not affect the survival of that particular VC. The instrument variable approach confirms the original predictions.  10  This paper makes multiple contributions to the literature. First, it captures heterogeneity among VCs in a syndicate, which has not been studied in the literature, and shows that such investor heterogeneity can partially explain both the formation and impact of syndication. Second, this paper develops a new matching algorism and predicts the syndication formation among a group of investors for the first time, to the best of my knowledge. Third, although this paper uses venture capital as research context, it has general implications for alliances among firms, teams, and social networks. For example, firms and people are more likely to be attracted to those that are similar and easy to find. The preference for similarity may generate some immediate benefits. In the long run, however, it may limit learning and future business opportunities.  The remainder of this paper is organized as follows: Section 2.2 presents the theoretical considerations and reviews the related literature. Data and variables are described in Section 2.3 and empirical results are presented in Section 2.4. Section 2.5 concludes the paper. 2. 2 Theoretical Considerations 2.2.1 Dimensions of heterogeneity One important characteristic that differentiates VCs from each other is their experience. VCs’ experience can improve the performance of their portfolio companies through two channels: First, more experienced VCs can add more value to their funded companies. Unlike traditional financial intermediaries, VCs closely monitor and advise their portfolio companies, including sitting on the boards of portfolio companies (Lerner, 1995; Baker and Gompers, 2003), recruiting management teams (Gorman and Sahlman, 1989), advising portfolio companies to adopt stock options and hiring outside CEOs (Hellmann and Puri, 2002), reducing the costs of going public (Megginson and Weiss, 1991), and facilitating strategic alliances among their portfolio companies (Lindsey, 2008). Second, more experienced VCs invest in higher quality deals (Sorensen, 2007). More experienced VCs may have access to some “proprietary deal flow” (Kaplan and Schoar, 2005) and are more able to select promising start-ups (Casamatta and  11  Haritchabalet, 2007). Entrepreneurs also tend to accept financing offers from more reputable and experienced VCs (Hsu, 2004). It is the heterogeneity in VCs’ skills that may cause the heterogeneous performance of private equity funds (Kaplan and Schoar, 2005).  Another important characteristic is VCs’ prior performance. Kaplan and Schoar (2005) find that the returns to private equity investments persist. Therefore, VCs’ prior performance is a good indicator of their future success. Both prior experience and performance are two important attributes VCs use to select syndication partners.  VCs are usually specialized in certain industries and geographic location. VCs’ industry specialization is attributed to their general partners’ (GPs) business experience before entering the VC industry. Such business experience enables them to be more actively involved with their portfolio companies (Bottazzi, Da Rin, and Hellmann, 2007). VCs may prefer closely located portfolio companies because the geographic proximity helps them to monitor the investees more effectively (Sorenson and Stuart, 2001; Tian, 2008). Therefore, when forming syndication, VCs will also consider as important factors partners’ relevant experience and performance in the target companies’ industries and geographic location.  2.2.2 The costs of heterogeneity In a two-sided matching framework (Roth and Sotomayor, 1990), heterogeneous syndicates may incur efficiency loss.5 Given the importance of VCs’ experience for venture success, it is reasonable to assume that VCs’ experience is complementary to the returns of the syndicated investments, which means the return to a VC’s experience will increase with its partners’ experience. Therefore, VCs try to syndicate with more experienced partners to maximize their returns. In equilibrium, the levels of VCs’ experience in a syndicate are positively associated. The following symbolic representation illustrates the idea. 5  I borrowed the idea from two-sided matching but the problem we have here is not a standard two sided matching  problem as we are matching within VCs not matching VCs and companies.  12  The economic problem is how VCs select syndication partners to maximize the returns of the syndicated investments.6 The incumbent VC that already commits to investing in the target company needs to “recruit” another VC to form a syndicate. The incumbent is denoted by i and the entrant by j. The matching is based on the levels of VCs’ experience denoted by E ( EH for high level of experience while EL for low level of experience), to maximize the returns denoted by U ij . I also assume complete information so that VCs’ experience is common knowledge. As the problem focuses on the selection of syndication partners after VCs have chosen to syndicate, every VC will be matched with a partner in equilibrium. The complementarities of VCs’ experience are given by: 2  U ij (iE , jE )  0 or U (iEH , jEH )  U (iEL , jEH )  U (iEH , jEL )  U (iEL , jEL ) iE jE Although inexperienced VCs prefer more experienced partners, they are not chosen by more experienced VCs and have to syndicate with other inexperienced VCs. In equilibrium, only VCs with similar levels of experience form syndication. The total payoff of the positive assortative matching is superior to that of the negative assortative matching:  U (iEH , jEH )  U (iEL , jEL )  U (iEH , jEL )  U (iEL , jEH ) Anticipating that heterogeneous syndicates may incur higher transaction costs ex post, VCs may consider syndication partners with different levels of experience less attractive. In a game-theoretical model, Van den Steen (2004) shows that agents with heterogeneous beliefs and preferences may have more information distortion during their communication, and less alignment of actions. In venture capital syndication, VCs with heterogeneous venture experience may have more disagreements and conflicts when making investment decisions. In addition, concerns with agency costs may prevent experienced VCs from co-investing with inexperienced VCs. The less experienced VCs may exert less effort due to the fact that they may not be able to 6  This example takes the matching between VCs and companies (Sorensen, 2007) as given. It only considers the  matching among VCs to simplify the problem.  13  provide high quality advice to the portfolio companies or they simply free ride the services provided by more experienced partners.  Homophily exists in various relationships, including friendship, co-membership, and marriage relationship (McPherson, Smith-Lovin, and Cook, 2001). The similarity attraction among VCs may be caused by lower costs of searching for similar partners, because the information of investment opportunities tends to be circulated in the markets where VCs with similar investment preferences participate. A VC’s experience may also signal its reputation. More experienced VCs may not be willing to be associated with partners that are less reputable.  The costs of heterogeneity have implications on both the formation and performance of syndication. VCs may be less likely to form syndicates with partners who are different from them. Conditional on syndicates which are already formed, companies, which are funded by heterogeneous syndicates and thus suffer more from transaction costs, may underperform those funded by homogeneous syndicates.  2.2.3 The benefits of heterogeneity Syndicating with heterogeneous partners can create valuable learning opportunities for VCs in the long run. First of all, heterogeneous partners bring in different insights and perspectives that can improve firms’ decision making in the future. The evidence of learning can be drawn from both the lab experiments, in which heterogeneous groups generate higher quality solutions to complex and non-routine problems (Hoffman and Maier, 1961), and the real world firm actions, in which firms can sample various decision making processes and learn how different decisions lead to different outcomes (Beckman and Haunschild, 2002). Although heterogeneity in partners’ experience may cause conflicts, firms may thus have incentives to present evidence and collect new information in support of their own actions. If all disagreeing parties try to do so, there will be more useful information available for good decisions. This type of learning can be an  14  application of a more general economic model developed by Rotemberg and Saloner (1995), in which the overt conflicts between different function areas in a firm can be beneficial for top management’s decision making.  The benefits of heterogeneity may be greater for VCs which face many complex and non-routine problems when investing in highly risky private companies.7 Decision making in venture capital investments, such as opportunity assessment, recruiting the right management team, and the timing of exit, can be “subjective”. Co-investing with partners with diverse experience can be an effective way of learning different management skills and collecting more useful information. Such learning can improve VCs’ abilities of deal selection and enable VCs to capture more investment opportunities in their specialized industries. However, we do not expect the benefits of learning to realize immediately. Instead, we would expect VCs’ learning from the current investments will benefit their investments in the future so that VCs may be able to make more successful investments in the long run. The benefits of heterogeneity suggest that VCs, whose partners are more heterogeneous, may be able to make more and better investments in the long term.  2.2.4. Related literature The first paper studying venture capital syndication is Lerner (1994), based on 271 biotechnology firms that received VC financing between 1978 and 1989. Lerner (1994) shows that VCs have more syndication partners with similar fund sizes in the first financing rounds but the paper does not intend to predict the formation of a particular syndicate, which is one focus of this paper. After analyzing detailed financing contracts between venture capitalists and start-up companies, Kaplan and Stromberg (2004) found that the size of the syndicate, measured by the number of venture capital funds forming the syndicate, is positively associated with monitoring and support provided to the start-up companies. 7  Please see Sorensen (2008) on how VCs mitigate the investment uncertainties through “exploitative” and  “exploratory” learning.  15  Casamatta and Haritchabalet (2007) provided a theoretical model explaining why venture capital firms syndicate. They argued that since inexperienced VCs receive a noisy signal of the true quality of an investment project, they do not fear to disclose their own evaluation of the project to attract syndication partners. However, due to the competition among VCs, more experienced VCs, who receive an accurate signal of the true quality of the project, are reluctant to reveal their own evaluation and hence syndicate less frequently unless they have even more experienced partners. Cestone, Lerner, and White (2006) focused on a contract design to induce VCs to truthfully disclose their signals of the investment opportunities. Depending on whether or not the lead VCs can manipulate their signals, the gains from syndication may or may not be maximized if syndication partners are too experienced. The two theoretical models have similar empirical implication on the syndication pattern for experienced VCs, that is, experienced VCs tend to syndicate with other experienced VCs. This paper lends empirical support to their theoretical models.  Prior work also studied the formation and performance of venture syndication but with very different perspectives from this paper. For example, a recent paper by Sorenson and Stuart (2008) studied the formation of distant dies among VCs when there is “heat” in target companies’ industries and location. This paper differentiates from Sorenson and Stuart (2008) by focusing on the formation and impact of syndication based on the tradeoff between the costs and the benefits of heterogeneity, and treating syndication as a group concept instead of dyadic relations. When studying the performance of venture capital syndicates, Brander et al. (2002) compared the performance of syndicated deals with that of standalone deals. This paper focuses on the syndicated deals and investigates the impact of heterogeneity among syndication partners on the performance of syndicates. Hochberg et al. (2007, 2009) have studied how venture capital firms’ network centralities influenced venture capital funds’ performance and deterred entry. Their key variable of a VC’s network centrality, which is a function of the quantity and the quality of  16  syndication partners, is very different from the heterogeneity measure of syndicating VCs used in this paper.  2. 3 Data and Variables 2.3.1 Sources of data The data are complied from the Thomson Financial’s VentureXpert database, which provides information on the characteristics of VCs, portfolio companies, and the deals. I also merge the VentureXpert database with Thomson Financial’s Global New Issues and Mergers and Acquisitions databases to identify more IPOs and acquisitions. I use the matching software8 to take care of the inconsistent records of company names across these databases (Egan, 2007).  The VentureXpert database tends to record parent VC firms and their affiliates as different firms. It also tends to report an international VC which has overseas offices in the U.S. as a U.S. firm. I visit VCs’ websites whenever available and check with the Hoover’s company records to identify changes in firms’ names, acquisitions, firms’ affiliates, firms’ countries of origin, firms’ types, and their branch offices. I also exclude the financing rounds at the stages of “Buyout/Acquisition”, “Other”, “Unknown”, and focus on the “MoneyTree”9 deals.  Another well recognized problem with VentureXpert is the “round-splitting” problem (Gompers and Lerner, 1999), in which the same financing rounds were recorded more than once. To identify these splitted rounds, I analyzed a sample of financing rounds if the duration between any two of them is less than or equal to 3 months. If two financing rounds had the same VCs and raised the same amount of money within 3 months, I eliminate the later round which is likely to  8  Egan (2007) describes different techniques of “normalizing” companies’ names. The technique used here is to  remove the redundant suffix such as Inc., Corp., Ltd., etc. 9  MoneyTree report is a collaborated report between the PricewaterhouseCoopers and the National Venture Capital  Association in the U.S. It produces quarterly reports on venture capital activities in the U.S. VentureXpert has a variable to indicate if a deal is a MoneyTree deal.  17  be a repetition of an earlier round and was included in VentureXpert by mistake. If two financing rounds had different VCs and raised different amounts of money within three months, I combined these two rounds as a single round and added up VCs and the amount of financing. This exercise is helpful to identify some suspicious splitted rounds and test whether they cause any biases. Econometric analysis performed in a sample without suspicious splitted rounds generated consistent predictions with those obtained in the main sample used in this paper.  Detailed data of VCs’ financial returns is not publicly available because the disclosure of their returns is not required by law. I therefore adopt a widely used proxy for VCs’ returns in the literature – the number of successful exits, which is measured by the number of portfolio companies which have IPOs or acquisitions by other companies (Hochberg et al., 2007). Based on a sample of VC investments with real financial returns, Phalippou and Gottschalg (2007) find a positive and significant correlation between the real financial returns and the number of VCs’ successful exits through IPOs or acquisitions.  To study the formation of syndication, I focus on the first round syndicated VC investments made to U.S. companies between 1995 and 200510. Syndication is defined as different VCs co-investing in the first financing round. I use the first round deals because the syndication in later financing rounds largely depends on who has participated in the first rounds (Admati and Pfeiderer, 1994), and the risks of investments are greatly reduced at later stages. Therefore the demand for risk sharing and the criteria for partners are most salient in the first rounds.11 After excluding syndicates with undisclosed VCs and companies, there are 3,385 U.S. companies that received their first round financing from VC syndicates between 1995 and 2005.  10  The entire sample covers VC investments between 1990 and 2005. As I calculate VCs’ experience in a year as  the number of companies it funded in the past five years, the analysis of the syndication formation therefore starts with 1995. 11  Sorensen (2007) also uses the first rounds to study how VCs’ experience affects the chance of IPOs.  18  Examining the survival of VCs requires a different data structure, in which I can trace the activities of each VC over time. Therefore, I construct a panel data with VC-year pairs as units of analysis and include VCs if they have at least two syndication partners in any year between 1995 and 2005.12 The final sample includes 3,380 VCs with 12,434 VC-year observations.  2.3.2 The “Single Deviation Method” To predict whether the syndicates are formed or not, I need to have realized syndicates that are already recorded in the dataset and hypothetical syndicates that are not realized. The key task of constructing hypothetical syndicates is to find VCs who are potential investors in the realized deals but do not make actual investments. The potential investors then form hypothetical syndicates for each realized deal. The following two steps describe how I construct the hypothetical syndicates:  Step 1: I first match each realized syndicate with a pool of first round syndicated deals that meet the three “inclusion criteria”, that the realized syndicate and its matched syndicates should be in the same industry, state, and funded in the same quarter of the year. I then form a set of “potential investors” by including VCs participating in the matched syndicates and excluding the actual investors from the realized syndicate.  As VCs are specialized in certain industries and geographic areas, the criteria of the same industries and states include VCs who are actually interested in investing in such industries and states. The criterion of having investments in the same quarter of the year makes sure that VCs still have funds available for investments. The final sample has 2,451 portfolio companies that  12  A syndication partner is defined as a co-investor in a financing round. For each VC-year observation, I include  all of the syndication partners a VC had in the year. The requirement of at least two partners is to make sure a meaningful measure of heterogeneity in VCs’ syndication partners.  19  are matched with “potential investors” based on the three “inclusion criteria”.13 Such criteria will be relaxed in the later robustness checks.  Step 2: I then apply the “Single Deviation Method”, which replaces each VC in a realized syndicate by each potential investor one at a time. It looks for a local maximum in a neighborhood as there is only one different investor between a realized syndicate and a hypothetical syndicate. Alternatively, the “Multiple Deviation Method” searches for a global maximum, in which multiple VCs in a realized syndicate are replaced by other potential investors. As the “Multiple Deviation Method” will create a sample with too many observations for a computer to handle efficiently, I only apply the “Multiple Deviation Method” to syndicates formed by three VCs as a robustness check and focus on the “Single Deviation Method”.  The two steps are summarized in the following example: “Star Inc.” is a biotechnology company in California and was funded in the first quarter of 2002 by VCs A, B, and C. Meanwhile, VCs D and E funded another biotechnology company in California in the first quarter of 2002. Therefore, D and E are potential investors that could have funded “Star Inc.” The following table illustrates all of the hypothetical syndicates constructed by the “Single Deviation Method” and the “Multiple Deviation Method”, respectively. Each column represents a syndicate of VCs. VCs who are “potential investors” of “Star Inc.” are in italic.  13  There are about 900 companies which are not matched with other deals because they are the only companies  funded by VCs in a state, or in an industry, or in a quarter of the year. To check whether excluding these “unpopular” companies affects the empirical results, I add them back in an extended dataset. I also relax the three “inclusion criteria” by focusing on any two of them at a time. The number of observations increases dramatically but the empirical results remain the same. To make the later analysis more efficient, I only focus on the sample of 2,451 companies.  20  The Formation of Hypothetical Syndicates Real  Hypothetical Syndicates “Single Deviation Method”  “Multiple Deviation Method”  A  D  E  A  A  A  A  D  A  D  B  B  B  D  E  B  B  E  D  B  C  C  C  C  C  D  E  C  E  E  Total  3 x 2 + 1=7  3 x 1 +1 =4  There are several advantages of using the “Single Deviation Method”. It allows me to study the syndication formed by a group of investors. It also enables me to focus on the time-varying characteristics of VCs. As regards the type of syndicates VCs form, it is determined by their characteristics at the time of syndication formation. Third, constructing hypothetical syndicates based on portfolio companies allows me to study company specific characteristics. For example, I can control for company specific variables and construct investor characteristics that are the most relevant for the portfolio companies.  2.3.3 Variables Dependent variables To predict syndication formation, I construct a dummy variable REALIZED as the dependent variable, which is equal to 1 if the syndication is realized. To predict the performance of syndication, I use EXIT as the dependent variable, which is equal to 1 if the portfolio company funded by the syndication has an IPO or sale to another company, and 0 otherwise.14 To study 14  It is possible that some acquisitions of VC-backed companies do not generate profits for their investors.  Therefore, I constructed a variable as a proxy for the return of acquisition. It is the difference between the amount paid by the acquirers and the total amount of money raised by the startup from VCs, divided by the total amount of money raised by the startup. I replicated the regression analysis in samples where the successful exits include IPOs and acquisitions with returns bigger than 10% or 20%. I obtained similar results. Given the risks and illiquidity associated with VC investments, the returns of acquisition of 10% or 20% only serve as a lower bound of a profitable acquisition.  21  VCs’ survival, I use a dummy variable DEATH as the dependent variable. As VCs are organized as limited partnerships, accurate information on their death is not publicly available. I treat a VC as dead if it no longer makes investments based on two reasons: the core business of VCs is to invest in private companies; and VCs are able to raise new funds for future investments if their previous funds perform well. I also check the online business news (e.g. the Company Insight Center from the BusinessWeek) to see if the discontinuation of the investments is caused by name changes or acquisitions. Although the definition of death used here is not perfect, it serves as a valid proxy for the real death.15  Main independent variables The two dimensions of heterogeneity – experience and performance – can be either general or relevant for the portfolio company’s industry or location. Due to the high correlations among general experience (performance), experience (performance) in the portfolio company’s industry, and state, and the similar impact they have on syndication formation, I only report the heterogeneity in VCs’ experience (performance) in the portfolio company’s industry. The following paragraphs describe how I construct the main independent variables in greater detail. The definition of other independent variables can be found in Table 1.  I apply the coefficient of variation measure to continuous variables to capture the degree of heterogeneity (Beckman and Haunschild, 2002). For each VC in a syndicate, I calculate as its experience the number of companies it funded in the portfolio company’s industry in the past five years.  15  16  For each syndicate, EXP HETER is equal to the standard deviation of VCs’  The sample for analysis starts from 1995 to 2005. To check the death of a VC, I extend the sample to include VC  investments made in 2006 and 2007, based on which I calculate the last year of investment for each VC. If a VC’s year of investment is its last year of investment, I record DEATH as 1 and the VC firm drops out of the sample. 16  Alternatively, the experience can be captured by the number of companies a general partner, who sits on the  company’s board, has funded in the past five years. Unfortunately, information on individual partners in VentureXpert is highly incomplete. The variable here is a VC firm’s experience in the portfolio company’s industry. If a VC firm only allocates partners who are experienced in the portfolio companies’ industry as directors of the  22  Table 1 The Definition of Variables Variable Name  Definition  REALIZED  A dummy variable equal to 1 if the syndicate is realized.  TOTAL SYNDICATES  The number of realized and hypothetical syndicates each company has.  EXIT  A dummy variable equal to 1 if the portfolio company has an IPO or gets acquired by other companies.  EXP MEAN  For a syndicate, it is the average of VCs’ relevant industry experience. The relevant industry experience is calculated as the number of companies a VC funded in the portfolio company’s industry in the past five years.  EXP HETER  For a syndicate, it is the standard deviation of VCs’ relevant industry experience divided by their average relevant industry experience.  PERF MEAN  For a syndicate, it is the average of VCs’ relevant industry performance. The relevant industry performance is calculated as the number of successful exits a VC had divided by the number of companies the VC funded in the portfolio company’s industry in the past five years.  PERF HETER  For a syndicate, it is the standard deviation of VCs’ relevant industry performance divided by their average relevant industry performance.  CTRY HETER  For a syndicate, it is the entropy measure of the diversification of VCs’ countries of origin. It is calculated as  p ln p where pi is the  i i i  proportion of VCs in country i in each syndicate. TYPE HETER  For a syndicate, it is the entropy measure of the diversification of VCs’ types. It is calculated as  p ln p where pi is the proportion of VCs  i i i  of type i in each syndicate. DEGREE MEAN  For a syndicate, it is the average of VCs’ normalized degree centrality scores in a 5-year window.  DEGREE HETER  For a syndicate, it is the standard deviation of VCs’ normalized degree centrality scores divided by their average normalized degree centrality scores in a 5-year window.  EIGENVEC MEAN  For a syndicate, it is the average of VCs’ normalized eigenvector centrality scores in a 5-year window.  EIGENVEC HETER  For a syndicate, it is the standard deviation of VCs’ normalized eigenvector centrality scores divided by their average normalized eigenvector centrality scores in a 5-year window.  DIST VC  The average distance between any two VCs in a syndicate.  portfolio company, which is highly possible, the measures of a VC firm’s industry-relevant experience should be highly correlated with the experience of partners sitting on the board.  23  Variable Name  Definition  DIST HETER  For a syndicate, it is the standard deviation of the distances between each VC and the portfolio company divided by the average VC-company distances.  PRIOR RELATION  The number of pairs of VCs that syndicated in the past five years divided by the total number of pairs of VCs in a syndicate.  RDAMT  The amount of money (in thousand dollars) a company received in the first financing round.  SYNDICATION SIZE  The number of VCs in a syndicate.  DEATH  A dummy variable equal to 1 if a VC no longer makes investments in the future.  NEW DEAL  The number of first round deals VCs make in the following year.  INDUDIV  For each VC, it is the entropy measure of its own industry diversification in the following year. It is calculated as  p ln p where pi is the  i i i  proportion of its investments in each industry i in the following year. PARTEXP MEAN  For a VC in a given year, it is the average experience of its syndication partners. The experience is calculated as the number of companies each partner funded in the past five years.  PARTEXP HETER  For a VC in a given year, it is the standard deviation of its syndication partners’ experience divided by their average experience.  PARTPERF MEAN  For a VC in a given year, it is the average performance of its syndication partners. The performance is calculated as the number of successful exits each partner had divided by the number of companies it funded in the past five years.  PARTPERF HETER  For a VC in a given year, it is the standard deviation of its syndication partners’ performance divided by their average performance.  PARTCTRY HETER  For a VC in a given year, it is the entropy measure of the diversification of the VC’s syndication partners’ countries of origin. It is calculated as   pi ln pi where pi is the proportion of syndication partners in country i. i  PARTTYPE HETER  For a VC in a given year, it is the entropy measure of the diversification of the  VC’s  syndication  partners’  types.  It  is  calculated  as    pi ln pi where pi is the proportion of syndication partners of type i. i  VCEXP  For a VC in a given year, it counts the number of companies the VC funded in the past five years.  experience divided by their average experience. Similarly, to obtain the heterogeneity in VCs’ performance, I first calculate as a VC’s performance the ratio of the number of successful exits a  24  VC has in the portfolio company’s industry to the total number of companies the VC funded in the portfolio company’s industry in the past five years (e.g. for a VC that funded 5 companies in biotechnology with 2 of them either listed in the stock market or acquired by other companies, its exit ratio in biotechnology is 0.4). For each syndicate, PERF HETER is equal to the standard deviation of VCs’ performance divided by their average performance.  To capture the degree of heterogeneity of categorical variables, I follow Jacquemin and Berry (1979) and apply the entropy measure to VCs’ types17 and countries of origin18. Jacquemin and Berry (1979) show a numerical example to compare two measures of corporate diversification in different industries, entropy measure and the Herfindahl Index. They suggest that for a small change in industry diversification, there will be an increase in the entropy measure while the Herfindahl Index largely ignores the change i.e. the entropy measure is more sensitive to changes in diversification than the Herfindahl Index. As the entropy measure weights the proportion of each category, denoted by pi , by the logarithm of 1 / pi , the entropy measure of VCs’ types and countries of origin therefore increases with the diversification in VCs’ types and countries of origin within a syndicate. I calculate CTRY HETER as   pi ln pi , in which pi is the i  proportion of VCs in country i in a syndicate. I calculate TYPE HETER as   pi ln pi i  where p i is the proportion of VCs of type i within a syndicate.  17  Based on the information from VentureXpert and VCs’ websites, I classify VCs into seven types: (1) Private  equity firms; (2) Corporations; (3) Banks, insurance companies, asset management companies, and real estate companies; (4) Consulting firms, marketing firms, law firms, and accounting firms; (5) Angel networks and; (6) Governments, university endowments, nonprofit organizations, and incubators. 18  Based on the information from VCs’ websites, I record the 48 countries (or regions) of VCs. They are: Argentina,  Australia, Bahrain, Belgium, Brazil, Canada, Cayman Islands, China, Chile, Czech Republic, Denmark, Finland, France, Germany, Greece, Hong Kong (China), Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Kuwait, Luxembourg, Malaysia, Mauritius, Mexico, Monaco, Netherlands, New Zealand, Norway, Philippines, Poland, Portugal, Puerto Rico, Russia, Singapore, Slovenia, South Africa, South Korea, Spain, Swaziland, Sweden, Switzerland, Taiwan, United Kingdom, and United States.  25  2.4 Empirical Analysis  2.4.1 Syndication formation Table 2 reports summary statistics of the sample for syndication formation. The unit of analysis is a syndicate. There are 156, 407 syndicates with 2% of them being realized. The average number of syndicates for each company is 64. On average, each VC in a syndicate funded 17 companies in the portfolio company’s industry in the past five years. The average heterogeneity in VCs’ relevant industry experience in a syndicate is 1.05. On average, 11% of a VC’s investments had either IPOs or sales to other companies in the past five years. The heterogeneity in VCs’ performance is very close to that in VCs’ experience in a syndicate, due to the normalization of the standard deviation by the means. The correlation coefficient between heterogeneity in VCs’ performance and heterogeneity in VCs’ experience is about 0.39 as shown in Table 3, suggesting that they are not completely mutually exclusive. The entropy measures show that there is higher diversification in VCs’ types than that in VCs’ countries of origin in a syndicate. The positive correlation between the heterogeneity in VCs’ types and VCs’ countries of origin shows an interesting phenomenon: international VCs that invest in U.S. private companies tend to be multinational corporations and financial institutions.  26  Table 2 Descriptive Statistics of Samples This sample has 156,407 realized and hypothetical VC syndicates. The syndicated investments are made to 2,451 U.S. companies that received the first round financing from 1,669 VCs between 1995 and 2005. Variable  No. of Obs.  Mean  Std. Dev.  Min  Max  REALIZED  156407  0.02  0.12  0  1  TOTAL SYNDICATES  2451  64  87  3  855  EXP MEAN  156407  17  18  0  201  EXP SD  156407  17  21  0  192  EXP HETER  156407  1.05  0.48  0  3  PERF MEAN  156407  0.11  0.10  0  2  PERF SD  156196  0.11  0.11  0  1.41  PERF HETER  156196  0.99  0.66  0  3  CTRY HETER  150512  0.19  0.33  0  1.74  TYPE HETER  145858  0.36  0.38  0  1.6  DEGREE HETER  156407  1.02  0.47  0  3  EIGENVEC HETER  156407  1.01  0.47  0  3  DIST VC  126322  1021.06  1251.92  0  4376.26  DIST HETER  118852  0.99  0.60  0  2.63  PRIOR RELATION  156407  0.19  0.31  0  1  Table 3 Correlation Matrix of Key Variables The matrix is abased on the sample of 156,407 realized and hypothetical VC syndicates. All of the correlation coefficients are significant at the 1% level. Index  Variable Name  1  1  REALIZED  1  2  EXP HETER  -0.04  1  3  PERF HETER  -0.03  0.39  1  4  CTRY HETER  -0.02  0.13  0.06  1  5  TYPE HETER  -0.03  0.16  0.11  0.18  1  6  DEGREE -0.04  0.74  0.36  0.12  0.17  1  HETER  -0.04  0.71  0.35  0.12  0.15  0.97  1  8  DIST HETER  -0.04  0.13  0.13  0.02  0.21  0.12  0.12  1  9  DIST VC  -0.03  0.06  0.01  -0.01  0.13  0.07  0.09  0.43  1  10  PRIOR 0.06  -0.28  -0.14  -0.10  -0.05  -0.36  -0.42  -0.07  -0.18  HETER 7  2  3  4  5  6  7  8  9  10  EIGENVEC  RELATION  1  27  I use the Conditional Logit model as the main econometric model, which groups the realized and hypothetical syndicates for each portfolio company. This is similar to performing portfolio company fixed effects in logistic regressions when the data has a panel structure. As the Conditional Logit model calculates the likelihood of syndication formation for each company, the interpretation of the results should be: within a company, how heterogeneity in VCs affects the formation of syndication for the company. Formally, the Conditional Logit model is shown as follows: X denotes key explanatory variables and Z denotes control variables; portfolio company is indexed by i and each syndicate is indexed by j: Pr( REALIZED  1| X ij , Z ij )   exp( i  X ij   Z ij  ) 1  exp( i  X ij   Z ij  )  Table 4 reports the baseline results of syndication formation. Column (1) presents the cross sectional evidence based on a regular logistic regression on four main independent variables only. Heterogeneity in VCs’ experience, performance, types, and countries of origin has a negative and significant impact on syndication formation. Column (2) presents regression results after controlling for the fixed effects of the size of the syndication, the industry, state, and year of the first round financing of the portfolio company. The predictions in Column (2) are consistent with those in Column (1). Column (3) applies the Conditional Logit model and obtains the within company evidence: for a company, the heterogeneity in VCs’ experience, performance, types, and countries of origin decreases with the likelihood of syndication formation. Due to the lack of variation in the syndication size, industry, state, and year of the first round financing within a portfolio company, I cannot control for the fixed effects of these variables. I use Column (3) as the main model specification to study syndication formation.19  19  The reason for not including in the regression the average of the experience (performance) of VCs in a syndicate  is that the heterogeneity measure is already a de-mean measure. In unreported regressions, I control for the averages and obtain the same predictions from the heterogeneity variables.  28  Table 4 The Base Model The regressions in this table are based on the sample of 156,407 realized and hypothetical VC syndicates. The unit of analysis is a syndicate. The first two columns report regression results from the Logit model. The last column reports regression results based on the Conditional Logit model, in which observations are grouped at the portfolio company level. Robust and clustered standard errors at the portfolio company level are reported in parentheses. I use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively.  VARIABLES EXP HETER PERF HETER CTRY HETER TYPE HETER  (1)  (2)  (3)  REALIZED  REALIZED  REALIZED  Cross sectional  Cross sectional  Panel FE  -0.394***  -0.235***  -0.258***  (0.0463)  (0.0474)  (0.0484)  -0.233***  -0.155***  -0.181***  (0.0350)  (0.0356)  (0.0363)  -0.452***  -0.325***  -0.661***  (0.0769)  (0.0806)  (0.0887)  -0.416***  -0.125*  -0.118**  (0.0602)  (0.0656)  (0.0592)  LN RDAMT  -0.0325 (0.0245)  SYNDICATION SIZE FE  NO  YES  NA  YEAR FE  NO  YES  NA  INDUSTRY FE  NO  YES  NA  STATE FE  NO  YES  NA  COMPANY FE  NO  NO  YES  CONSTANT  -3.324***  -4.294***  (0.0470)  (0.221)  Log likelihood  -11738.36  -10460.99  -7980.61  No. of Obs.  145650  143426  142674  To check whether or not the empirical predications are mainly driven by an artifact of a particular matching method, I perform the following robustness checks on the “Single Deviation Method”: First, to select “potential investors”, I relax the “inclusion criteria” by focusing on any two of the three criteria at a time.  29  Second, I apply the “Multiple Deviation Method” to syndicates formed by three VCs, in which I replace any two VCs in a realized syndicate with any other two “potential investors”. Third, some companies have more hypothetical syndicates than others due to the “popularity” of their industries, states, or the time of financing. To check whether different numbers of hypothetical syndicates for each company affect the results, I form two sub-samples – one of syndicates with two investors, and the other with three investors. I then replace each VC in the realized syndicate with one randomly selected VC from the pool of “potential investors”, leading to the same number of hypothetical syndicates for each company in the two respective sub-samples. I apply the Rare Event Logit model (King and Zeng, 1999a, 1999b), which corrects for the oversampling of the true events (i.e. 33% or 25% of the syndicates are realized in the two respective sub-samples), and the Conditional Logit model to replicate the baseline analysis shown in Column (3) of Table 4.  Fourth, the “Single Deviation Method” treats each VC in a syndicate equally and replaces every VC by a potential investor. This is consistent with the focus of this paper, which studies how VCs select each other to form a syndicate. However, if there exists a lead VC who initiates the deal and selects its co-investors, the lead VC should not be replaced in the hypothetical syndicates. VentureXpert does not report who are the lead investors and prior research provides a few ways of identifying the lead VCs.20 I first identify the VC with the biggest equity share in a  20  Bottazzi et al. (2007) used the survey instrument to directly ask whether or not the VC is the syndicate lead.  Others have used different criteria to identify lead investors if they do not have a direct measure. For example, Sorensen (2007) assigns VCs with the largest total investment in the company as the lead VCs. Similarly, Hochberg et al. (2007) define a lead VC as the one making the largest investments in each financing round and change to VCs’ cumulative investment in the company if there are ties. Gompers (1996) classifies lead VCs as those sitting on the board for the longest time and turns to the biggest equity holder if there are ties. Brander et al. (2002) equals the first investor in the company as the lead investor. Sorenson and Stuart (2001) take the first investor in a company as the lead investor. If there are multiple investors in the first round, they will treat the VC that invests in every subsequent round as the lead VC.  30  financing round as the lead VC. If the information on VCs’ amount of investment is missing or there are ties of shares, I then identify the VC that sits on the board of the portfolio company, and prefers to be the deal originator as the lead VC. Altogether, I am able to identify lead VCs for about 60% of companies in the whole sample. When constructing hypothetical syndicates for these companies, I keep the lead VC in every syndicate.  The results I obtain from the above samples are consistent with those from the base model. The regression tables are not reported due to the limited space and are available upon request.  2.4.2 Other control variables The management literature suggests that firms prefer alliance partners with similar social status (Brass et al, 2004; Chung, Singh, and Lee, 2000; Podolny, 1994). A proxy used in the literature for social status is a firm’s network centrality, which captures how connected a firm is in a market. There are different measures of network centralities and I focus on two measures here: the degree centrality (Freeman, 1979) which directly measures the number of connections each firm has, and the eigenvector centrality (Bonacich, 1972) which may have the biggest economic impact on VCs’ performance (Hochberg et al., 2007).  To measure a VC’s network centrality, I first define the connection as the syndication among VCs. Using the social network analysis software Ucinet, I obtain a VC’s degree centrality and eigenvector centrality in a five-year window. The degree centrality counts the number of unique VCs a VC has syndicated with. To ensure the comparability of VCs’ centralities across years, the degree centrality is further normalized by the maximum possible degree centrality a VC can have in the network. The eigenvector centrality (Bonacich, 1972) not only counts the number of syndication partners a VC has, but puts more weights on the syndication with those partners who are well connected in the network. It is also normalized by the maximum possible eigenvector centrality a VC can have in the network. Conceptually, the degree centrality captures the  31  “quantity” of connections a VC has, while the eigenvector centrality captures the “quality” of connections a VC has.  For each syndicate, the heterogeneity in VCs’ degree centrality defined as DEGREE HETER is the standard deviation of the normalized degree centrality scores of VCs divided by the average normalized degree centrality scores in a 5-year window. Similarly, the heterogeneity in VCs’ eigenvector centrality defined as EIGENVEC HETER is calculated as the standard deviation of the normalized eigenvector centrality scores of VCs divided by the average normalized eigenvector centrality scores in a 5-year window.  Columns (1) and (2) in Table 5 report the empirical results of degree centrality and eigenvector centrality, respectively. Heterogeneity in VCs’ network centrality, which is an indicator of their social status, is negatively associated with the syndication formation. After controlling for the heterogeneity in VCs’ network centralities, the heterogeneity in VCs’ experience and performance still have similar predictions to those in the base model, although their coefficients become smaller due to their high correlations with VCs’ network centralities.  Column (3) in Table 5 further explores how geographic distances affect the syndication formation. I consider two types of distances here: the distances among VCs in a syndicate, and the distances between each VC and the portfolio company. I match the zip codes with their corresponding longitude and latitude scores, based on which I calculate the distances. I only have the longitude and latitude scores of U.S. zip codes, so the analysis in Column (3) is based on the syndicates formed by U.S. VCs and international VCs with U.S. offices. To calculate the distances among VCs denoted by DIST VC, I first calculate the distances between any two VCs in a syndicate and then take the average of them. For VCs with multiple zip codes, I use the shortest distances between them. For the second type of VC-company distances, I first calculate the distances between each VC and the portfolio company and use the shortest VC-company  32  distances if a VC has multiple zip codes. The heterogeneity in VC-company distances DIST HETER is the standard deviation of the VC-company distances in a syndicate divided by the average VC-company distances in the syndicate. Column (3) reports the regression results that syndication is more likely to be formed among VCs located close to each other. Meanwhile, similar VC-company distances can facilitate the formation of syndication among these VCs. Table 5 Network Centralities and Geographic Distances The regressions in this table are based on the sample of 156,407 realized and hypothetical VC syndicates. The unit of analysis is a syndicate. All of the columns report regression results based on the Conditional Logit model, in which observations are grouped at the portfolio company level. Robust and clustered standard errors at the portfolio company level are reported in parentheses. I use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. (1)  (2)  (3)  VARIABLES  REALIZED  REALIZED  REALIZED  EXP HETER  -0.142**  -0.0979*  -0.238***  (0.0586)  (0.0578)  (0.0528)  -0.168***  -0.160***  -0.158***  (0.0362)  (0.0362)  (0.0398)  PERF HETER DEGREE HETER  -0.215*** (0.0598)  EIGENVEC HETER  -0.304*** (0.0583)  DIST VC  -0.0001*** (2.48e-05)  DIST HETER  -0.319*** (0.0510)  CTRY HETER  -0.650***  -0.639***  -0.362***  (0.0886)  (0.0887)  (0.103)  -0.110*  -0.111*  -0.0705  (0.0592)  (0.0592)  (0.0664)  Log likelihood  -7975.34  -7969.44  -6519.17  No. of Obs.  142674  142674  105980  TYPE HETER  33  Table 6 Prior Relations Columns (1), (2), and (3) report regression results based on the sample of 156,407 realized and hypothetical VC syndicates. Column (4) is based on the sample of syndicates in which VCs did not syndicate with each other before (PRIOR RELATION equal to 0), while Column (5) is based on the sample of syndicates in which VCs syndicated before (PRIOR RELATION greater than 0). All of the regressions are based on the Conditional Logit model, in which observations are grouped at the portfolio company level. Robust and clustered standard errors at the portfolio company level are reported in parentheses. I use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively.  VARIABLES  EXP HETER PERF HETER PRIOR  (1)  (2)  (3)  (4)  (5)  REALIZED  REALIZED  REALIZED  REALIZED  REALIZED  Full  Full  Full  PRIOR  PRIOR  RELATION=0  RELATION>0  -0.0826  -0.140***  -0.129**  0.184*  (0.0526)  (0.0507)  (0.0612)  (0.104)  -0.139***  -0.157***  -0.151***  -0.0576  (0.0373)  (0.0357)  (0.0443)  (0.0768)  1.150***  1.166***  1.171***  1.674***  (0.0629)  (0.0627)  (0.0612)  (0.197)  -0.508***  -0.504***  -0.514***  -0.802***  -0.0954  (0.0910)  (0.0910)  (0.0909)  (0.130)  (0.136)  -0.122**  -0.122**  -0.128**  -0.0505  -0.301***  (0.0605)  (0.0604)  (0.0603)  (0.0840)  (0.0982)  Log likelihood  -7833.84  -7858.04  -7834.99  -3954.09  -3042.33  No. of Obs.  142674  142942  142674  62543  45899  RELATION CTRY HETER TYPE HETER  Another important explanation for tie formation is the prior ties among firms (Gulati and Gargiulo, 1999; Podolny, 1994). Although prior ties have strong explanatory power for future tie formation, it cannot explain: how is the tie formed at the first time when firms do not have any prior ties? To explore this question, I construct PRIOR RELATION for each syndicate, which is the number of pairs of VCs that syndicated before divided by the maximum number of pairs of VCs in the syndicate. Base on this variable, I form two sub-samples of syndicates – one sample of “new relations” including syndicates formed by VCs who did not syndicate with each other before (PRIOR RELATION equal to 0), and the other sample of “repeated relations” including  34  syndicates formed by VCs who syndicated before (PRIOR RELATION greater than 0). I then replicate the baseline analysis in the two sub-samples, respectively.  Prior syndication among VCs has a positive and significant impact on their future syndication formation as shown in Column (1) of Table 6. After controlling for the prior syndication, the coefficient of heterogeneity in VCs’ experience becomes insignificant due to its correlation with heterogeneity in performance. Therefore, when heterogeneity in experience and performance are introduced separately to regressions in Column (2) and Column (3), both of them remain statistically significant. Columns (4) and (5) report the regression results based on the sub-samples of “new relations” and “repeated relations”, respectively. VCs are less likely to form syndication with “strangers” who have different levels of experience, performance, and come from different countries. Given that VCs syndicated with each other before, heterogeneity in experience can no longer prevent different VCs from forming syndication together. The different impacts of heterogeneity in VCs’ experience in these two sub-samples suggest that: First, the criteria for choosing alliance partners from those with which a VC co-invested before can be quite different from the criteria used for choosing alliance partners from the strangers. Second, VCs can build trust among each other through prior co-investments, which help reduce transaction costs incurred in heterogeneous syndicates, making it possible for VCs with different levels of experience to form future syndication. Third, the positive assortative matching is most salient among VCs who form syndication for the first time, compared with a selected sample in which VCs choose syndication partners from those they already syndicated with before. 2.4.3 The performance of syndication To study the performance of syndication, I focus on the realized syndicates only. The unit of analysis is a portfolio company (or a realized syndicate). Table 7 describes the sample of 2,451 companies that received the first round financing from the VC syndicates between 1995 and 2005. 22% of them provided VCs with successful exits either through IPOs or sales to other  35  companies. On average, each VC in a realized syndicate funded 15 companies in the portfolio company’s industry in five years before the syndicate was formed. The average heterogeneity in the experience of VCs in a realized syndicate is 0.9, which is 14% lower than that in the whole sample of realized and hypothetical syndicates. The average exit rate for each VC in a realized syndicate is 11%, which is the same as that in the whole sample. The heterogeneity in VCs’ performance and other VCs’ characteristics is also smaller than that in the whole sample. Companies received on average $7.89 million in the first rounds from three VCs. In 1995, 128 companies received their first round financing from VC syndicates. The number increased steadily and peaked at 587 companies in 2000. After reaching its lowest point in 2002, the number of companies funded by VC syndicates began to increase again. Computer related industry attracted more than half of the total VC investments, followed by Communications and Media, and Medical/Health/Life Science. More than half of the VC investments were received by companies located in California, followed by Massachusetts, New York and Texas. Table 8 reports the correlation matrix of the key variables.  Table 7 Sample for Portfolio Companies’ Performance This sample has 2,451 U.S. companies that received the first round financing from 1,669 VCs between 1995 and 2005. This table also reports the number of companies funded in each industry and the four largest states with the most VC investments. Variable  No. of Obs.  Mean  Std. Dev.  Min  Max  EXIT  2451  0.22  0.42  0  1  EXP MEAN  2451  15  18  0  149  EXP SD  2451  13  19  0  185  EXP HETER  2451  0.90  0.52  0  3  PERF MEAN  2451  0.11  0.12  0  1.05  PERF SD  2445  0.10  0.13  0  1.41  PERF HETER  2445  0.82  0.68  0  3  CTRY HETER  2378  0.14  0.28  0  1.6  TYPE HETER  2327  0.28  0.35  0  1.33  DEGREE HETER  2451  0.88  0.48  0  3  EIGENVEC HETER  2451  0.87  0.50  0  3  36  Variable  No. of Obs.  Mean  Std. Dev.  Min  Max  DIST VC  2119  753.81  1130.69  0  4337.72  DIST HETER  2007  0.81  0.61  0  2.33  PRIOR RELATION  2451  0.33  0.41  0  1  RDAMT  2417  7888.56  12058.64  50  339045  SYNDICATION SIZE  2451  3  1  2  12  Categorical Variable  Frequency  Percent  Non-High-Technology  146  5.96  Biotechnology  78  3.18  Communications and Media  450  18.36  Computer Related  1394  56.87  Medical/Health/Life Science  194  7.92  Semiconductors/Other Electronics  189  7.71  California  1393  56.83  Massachusetts  329  13.42  New York  150  6.12  Texas  122  4.98  Industry of companies:  State of companies:  To study the performance of syndication denoted by EXIT, which equals 1 if the portfolio company financed by the syndication has an IPO or sale to another company, I first perform the baseline analysis of EXIT and then address the selection issues. The empirical results in Table 9 are based on the regular logistic regressions. Since the unit of analysis is a company, I cannot control for company fixed effects any more. I therefore control for dummy variables of companies’ states, industries, sizes of syndication, and years of the first financing rounds. Column (1) shows that companies funded by VCs with different levels of performance are less likely to have IPOs or sales to other companies. I then explore whether the control variables that affect the syndication formation have any implications for the performance of syndication. Interestingly, I find that heterogeneity in VCs’ network centrality (in Columns (2) and (3)), heterogeneity in VC-company distances (in Column (4)), average distance among VCs (in Column (4)), and prior relations among syndication partners (in Column (5)) do not have a statistically significant impact on the performance of syndication, once the syndication is  37  formed.21 Another consistent finding in these regressions is the positive impact of the amount of financing a company received in the first round on the likelihood of having an IPO or sale in the future. One possible explanation is that companies with sufficient funds to start with may develop faster and are more likely to survive unexpected financial difficulties. Another explanation can be the amount of financing a company can obtain simply proxies for its quality which leads to better future performance.  Table 8 Sample for the Performance of Syndication The matrix is abased on the sample of 2,451 U.S. companies. I use * to denote significance at the 10% level.  21  Index  1  2  3  4  5  6  7  8  1  1  2  0  1  3  -0.03*  0.36*  1  4  0.02  0.13*  0.06*  1  5  0.01  0.17*  0.11*  0.16*  1  6  0.01  0.69*  0.31*  0.13*  0.17*  1  7  0.01  0.67*  0.30*  0.13*  0.17*  0.96*  1  8  0.01  0.15*  0.09*  0.05*  0.19*  0.13*  0.13*  1  9  0.01  0.08*  0.04*  0.05*  0.15*  0.09*  0.10*  0.43*  1  10  0.00  -0.31*  -0.09*  -0.11*  -0.15*  -0.39*  -0.43*  -0.13*  -0.18*  Index  Variable Name  Index  Variable Name  1  EXIT  6  DEGREE HETER  2  EXP HETER  7  EIGENVEC HETER  3  PERF HETER  8  DIST HETER  4  CTRY HETER  9  DIST VC  5  TYPE HETER  10  PRIOR RELATION  9  10  1  I also run regressions in which I only include these control variables and obtain statistically insignificant results.  38  Table 9 The Performance of Syndication This sample has 2,451 U.S. companies that received the first round financing between 1995 and 2005. The unit of analysis is a portfolio company (or a realized syndicate). All regressions are based on the Logit model, which controls for fixed effects of portfolio company’s state, industry, syndicate size, and year of the first round financing. I use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. (1)  (2)  (3)  (4)  (5)  VARIABLES  EXIT  EXIT  EXIT  EXIT  EXIT  EXP HETER  -0.0382  -0.0209  -0.003  0.0049  -0.0382  (0.116)  (0.146)  (0.143)  (0.129)  (0.119)  -0.194**  -0.199**  -0.193**  -0.265***  -0.194**  (0.0896)  (0.0907)  (0.0910)  (0.0991)  (0.0896)  PERF HETER EXP MEAN PERF MEAN  -0.0003  -0.0023  -0.0014  0.0013  -0.0003  (0.004)  (0.0051)  (0.0054)  (0.0043)  (0.0043)  -0.274  -0.353  -0.339  -0.412  -0.274  (0.495)  (0.513)  (0.520)  (0.540)  (0.502)  DEGREE HETER  -0.0203 (0.158)  DEGREE MEAN  0.0124 (0.0204)  EIGENVEC HETER  -0.0555 (0.155)  EIGENVEC MEAN  0.005 (0.0202)  DIST VC  -1.33e-05 (5.90e-05)  DIST HETER  0.0463 (0.114)  PRIOR RELATION  0.0001 (0.155)  CTRY HETER  0.0840  0.0946  0.0915  0.0077  0.0840  (0.196)  (0.196)  (0.196)  (0.287)  (0.196)  0.0307  0.0291  0.0344  0.0184  0.0307  (0.161)  (0.161)  (0.161)  (0.181)  (0.162)  0.160**  0.158**  0.157**  0.161**  0.160**  (0.0626)  (0.0627)  (0.0629)  (0.0679)  (0.0626)  YES  YES  YES  YES  YES  YEAR FE  YES  YES  YES  YES  YES  INDUSTRY FE  YES  YES  YES  YES  YES  TYPE HETER LOG RDAMT SYNDICATION  SIZE  FE  39  (2)  (3)  (4)  (5)  VARIABLES  EXIT  (1)  EXIT  EXIT  EXIT  EXIT  STATE FE  YES  YES  YES  YES  YES  CONSTANT  -2.766***  -2.746***  -2.727***  -2.706***  -2.766***  (0.564)  (0.570)  (0.571)  (0.615)  (0.566)  Log likelihood  -1093.77  -1093.57  -1093.65  -920.30  -1093.77  No. of Obs.  2258  2258  2258  1925  2258  Two types of selection may bias the results of performance. The first type is the selection among VCs who tend to syndicate with partners who are similar. To study how heterogeneity among VCs affects the performance of their syndication, we need to base the analysis on the realized syndicates that are in general less heterogeneous than those not realized. The second type is the more well-known selection between VCs and start-ups (Sorensen, 2007), in which top tier VCs invest in top tier start-ups.  To address the selection among VCs, I apply the Heckman two-step procedure: In the selection equation, I study how the heterogeneity among VCs affects the syndication formation; In the EXIT equation, given that the syndication is formed, I study how the heterogeneity among VCs affects the performance of syndication. The model identification comes from two measures of geographic distances: the average distances among VCs in a syndicate measured by DIST VC, and the heterogeneity in VC-company distances in a syndicate measured by DIST HETER. Geographic proximity among investors can increase the likelihood of forming syndication among them. After the syndication is formed, simply locating close to each other should not directly affect the success of their investments. Although companies located close to their investors can have better performance (Tian, 2008), the heterogeneity in the VC-company distances should not have a direct impact on the company’s performance after the syndication is formed. I apply the STATA’s Heckprob model which implements the Heckman two-step procedure when the dependent variable of the EXIT equation is a dummy variable. Formally, the Heckprob model combines the syndication formation model in its first stage and the following Probit model in its second stage:  40  Pr( EXIT  1 | X ij , Z ij )  1   ( X ij   Z ij  )  The likelihood function of the full model is shown as follows: Let H 1 denote all of the independent variables in the selection equation and H 2 denote all of the independent variables in the EXIT equation. DIST VC and DIST HETER are included in H 1 but not in H 2 to make the function identifiable. L    iS ; yi  0      iS ; yi  0  i  i  ln{ 2 ( H i2   offset i , H i1  offset i ,  )}  ln{ 2 ( H i2   offset i , H i1  offset i ,  )}     i ln{1  ( H i1  offset i )} iS  Column (1) in Table 10 reports the results of the selection equation. Both DIST VC and DIST HETER are negatively associated with the syndication formation. Column (2) shows that after controlling for the selection among VCs, heterogeneity in VCs’ performance remains negative and statistically significant. Rho, the correlation coefficient of the error terms from the two equations, is not significant, suggesting that the results of the performance of syndication are unlikely to be driven by the selection among VCs.  To address the selection between VCs and companies, I apply the instrumental variable approach proposed by Ackerberg and Botticini (2002). The theoretical motivation is that VC investments are usually concentrated in certain industries and location. As a result, characteristics of such industries and location can capture the local availability of venture capital, entrepreneurial talents, and other unobservable local characteristics. In fact, the local availability of certain firm characteristics has served as a popular instrument in finance literature (see Berger et al., 2005 and Bottazzi et al., 2007).22 Such local characteristics have a direct impact on the availability of syndication partners and portfolio companies, and therefore the matching between a portfolio company and a syndicate of VCs. After the syndicates are formed and investments made, such 22  For example, Berger et al. (2005) use the median size of the bank in the local market to instrument for the size of  a bank. Bottazzi et al. (2007) instrument a partner’s business experience by the number deals made by a VC firm relative to the total number of deals made in the company’s country  41  local characteristics should not directly affect the success of a particular company.  To implement Instrument Variable (IV) estimation, I construct portfolio companies’ local markets as an interaction between companies’ industries and states, resulting in 75 local markets for the entire sample. I then test which local markets have predictive power to explain the endogenous variable (i.e. heterogeneity in VCs’ past performance). In a regression where the endogenous variable is regressed upon 74 dummy variables of local markets as well as all relevant exogenous variables, 14 out of 74 local markets obtain significant coefficients. Based on a LM test, dummy variables with non-significant coefficients are redundant and they do not improve the asymptotic efficiency of the estimation. Furthermore, including all 74 dummy variables as instruments for one endogenous variable can not satisfy the overidentifying restrictions. Therefore, in the first step of IV estimation, I regress the endogenous variable on 14 dummy variables while control for all relevant exogenous variables. To check whether the instruments are valid, I perform three types of tests. First, an F test of joint significance of 14 instruments generates an F statistic of F (14, 2257) = 65.23, suggesting that these 14 variables are jointly significant. Second, including the 14 instruments improve the R-square of the first step IV estimation from 0.1285 to 0.1433 (i.e. 12%). A Kleibergen-Paap test for weak identification rejects the null hypothesis that these instruments are only weakly correlated with the endogenous regressor. Third, to test whether the instruments are correlated with the error terms, I perform the overidentifying restrictions test. The Hansen J statistic (J statistic= 18.282 and Chi-sq (13) P-value= 0.1471) suggests that we cannot reject the null hypothesis that the instruments are exogenous to the dependent variable in the main regression (i.e. exit). In the second step of the IV estimation, I regress the performance of a portfolio company on the predicated value of heterogeneity in VCs’ performance and all other relevant variables, shown in Column (3) of Table 10. Consistent with the original findings, the IV estimation reports a  42  negative impact of heterogeneity on the performance of syndication.23 Table 10 Selection Issues of the Performance of Syndication This sample has 2,451 U.S. companies that received the first round financing between 1995 and 2005. The unit of analysis is a portfolio company (or a realized syndicate). Columns (1) and (2) report regression results from the Heckporb model which implements the Heckman two-step procedure when dependent variables are dummy variables. Column (1) reports the regression results of the selection equation with REALIZED as its dependent variable. Column (2) reports the regression results of the EXIT equation with EXIT as its dependent variable. Column (3) reports the EXIT equation based on the Ackerberg and Botticini Approach. The selection equation regresses PERF HETER on 14 dummy variables, which are constructed as the interaction of the portfolio companies’ states and industries, and all of the control variables used in Column (3). I use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively. (1) VARIABLES  EXP HETER PERF HETER EXP MEAN PERF MEAN CTRY HETER TYPE HETER LN RDAMT DIST VC  (2)  (3)  REALIZED  EXIT  EXIT  HECKPROB:  HECKPROB:  A & B IV:  SELECTION Eq.  EXIT Eq.  EXIT Eq.  -0.0956***  -0.0176  (0.0224)  (0.0827)  -0.0557***  -0.155***  -0.2236**  (0.0167)  (0.0569)  (0.1143)  0.0001  0.0006  -0.0002  (0.0006)  (0.0023)  (0.0005)  0.0160  -0.185  0.1268  (0.0946)  (0.299)  (0.1166)  -0.121***  0.0027  0.0304  (0.0464)  (0.167)  (0.0338)  -0.0237  0.0081  0.0147  (0.0301)  (0.104)  (0.0273)  -0.0220*  0.0864**  0.0259**  (0.0113)  (0.0407)  (0.0103)  -3.98e-05*** (9.68e-06)  DIST HETER  -0.105*** (0.0195)  23  SYNDICATION SIZE FE  YES  YES  YES  YEAR FE  YES  YES  YES  If EXP HETER is included in the regression, it obtains a positive sign due to its correlation with PERF HETER.  Whether or not EXP HETER is included, PERF HETER has a negative and statistically significant coefficient.  43  (2)  (3)  REALIZED  (1)  EXIT  EXIT  HECKPROB:  HECKPROB:  A & B IV:  SELECTION Eq.  EXIT Eq.  EXIT Eq.  INDUSTRY FE  YES  YES  NO  STATE FE  YES  YES  NO  CONSTANT  -2.040***  -2.057**  -0.0182  (0.102)  (0.986)  (0.0823)  VARIABLES  Rho  0.2038 (0.4133)  Log likelihood  -9715.5  -9715.5  R-squared No. of Obs.  0.2105 115816  115816  2295  Table 11 Sample for VCs’ Survival This sample has 3,380 VCs that co-invested with at least two other VCs in any year between 1995 and 2005. There are 12,434 VC-year observations. This table also reports the top three countries with the most VC investments in the U.S., and the three most popular types of VCs. Variable  No. of Obs.  Mean  Std. Dev.  Min  Max  DEATH  3380  0.51  0.50  0  1  NEW DEAL  12434  1  3  0  83  INDUDIV  12434  0.48  0.51  0  1.79  VCEXP  12434  15  28  0  515  PARTEXP MEAN  12434  43  30  0  324  PARTEXP HETER  12434  1.16  0.41  0  3.39  PARTPERF MEAN  12434  0.13  0.07  0  0.91  VCs’ Characteristics:  Syndication Partners’ Characteristics:  PARTPERF HETER  12433  0.97  0.46  0  3.16  LOCAL HETER  10651  1.95  0.41  0  3.83  LOCAL EXP  11783  16.24  7.87  0  102.8  PARTCTRY HETER  12325  0.38  0.38  0  2.06  PARTTYPE HETER  11654  0.60  0.36  0  1.52  Categorical Variable  Frequency  Percent  1995  478  3.84  1996  566  4.55  1997  733  5.90  1998  860  6.92  Year of syndication:  44  Variable  No. of Obs.  Mean  1999  1339  10.77  2000  1891  15.21  2001  1514  12.18  2002  1368  11.00  2003  1206  9.70  2004  1268  10.20  2005  1211  9.74  United States  2603  77.13  United Kingdom  122  3.61  Canada  85  2.52  Std. Dev.  Min  Max  VCs’ country of origin:  VCs’ type: Private Equity Firm  1989  58.93  Corporations  630  18.67  Financial Institutions  432  12.80  Table 12 Correlation Matrix for VCs’ Survival The matrix is based on the sample of 12,434 VC-year observations. I use * to denote significance at the 10% level. Index  Variable Name  1  2  3  4  5  6  7  1  DEATH  1  2  NEW DEAL  -0.18*  1  3  INDUDIV  -0.38*  0.45*  1  4  VCEXP  -0.16*  0.43*  0.43*  1  5  PARTEXP HETER  -0.06*  0.05*  0.11*  0.07*  1  6  PARTPERF HETER  -0.02*  0.04*  0.01  -0.07*  0.39*  1  7  PARTCTRY HETER  -0.07*  0.04*  0.11*  0.17*  0.24*  0.11*  1  8  PARTTYPE HETER  -0.07*  0.09*  0.13*  0.15*  0.34*  0.15*  0.30*  8  1  2.4.4 The survival of VCs The analysis of VCs’ survival is based on the panel data of 12,434 VC-year observations between 1995 and 2005. Table 11 describes the sample. About half of the VCs in the sample went out of business. On average, VCs invest in one new deal in the following year and 15 companies on average in the past five years. In each year, each syndication partner funded an average 43 companies in the past five years. The big difference between the average of VCs’  45  own experience and the average of partners’ experience is mainly due to the oversampling of experienced VCs as syndication partners, given the fact that more experienced VCs syndicate more often. There are more observations during the internet bubble years where many new VC firms were established, thus creating more syndication opportunities. About 77% of the VCs are from the U.S., followed by U.K. and Canada. More than half of the VCs are independent VCs and another 30% are captive VCs affiliated with corporations or financial institutions. Table 12 reports the correlation matrix of the key variables.  It is interesting to examine how heterogeneous syndication partners affect VC funds’ performance. However, VC funds are usually organized as 10 year close-end funds, and the sample period is not long enough to study their performance. Therefore, I only focus on the survival of VCs. Taking advantage of the panel structure, I apply the fixed effects OLS model24 where I control for VC fixed effects, and year fixed effects to reduce the autocorrelations of error terms over time. The interpretation of the coefficients after controlling for VC fixed effects is: within a VC firm, how changes in its partners’ heterogeneity affect the VC’s survival. The econometric model is illustrated as follows: Yit    X it   vi   it Yit is DEATH for VC i at year t. X it includes the measures of heterogeneity among VC i’s syndication partners, as well as other time-varying characteristics of VC i. vi is VC i’s individual effect. Its correlation with  it determines whether fixed effects or random effects model should be used.  Before studying VCs’ survival, Columns (1) and (2) in Table 13 suggest two possible  24  The fixed effects logistic model does not achieve convergence.  46  mechanisms through which heterogeneous partners may help VCs to survive. First, learning from partners with diverse experience, VCs may be able to make more investments. I construct NEW DEAL as the number of the first round deals (i.e. new investments instead of following on rounds) VCs make in the following year. The regression results in Column (1) suggest that the heterogeneity in partners’ experience (performance)25 is positively associated with the number of the first round deals in the following year. Second, learning from partners with diverse experience may help VCs to diversify their investments in their preferred industries. I construct the entropy measure INDUDIV to capture the extent to which VCs diversify their investments into certain industries. Technically, the entropy measure of diversification does not simply count the number of industries VCs invest in, but focuses more on if there is an even distribution of VCs’ investments in their preferred industries. Practically, it is difficult for VCs to invest in industries where they do not have prior business experience. However, keeping a balanced investment portfolio in their preferred industries may make VCs less vulnerable to the shocks to a particular industry. Column (2) shows that heterogeneity in syndication partners’ experience is positively associated with VCs’ industry diversification. The results in Columns (1) and (2) are based on the cross sectional evidence only. The coefficients of heterogeneity no longer reach statistical significance when the within firm effects are examined. Column (3) reports the positive impact of heterogeneity in partners’ experience on a VC’s survival based on the cross sectional evidence, while Column (4) shows the within firm evidence that for a VC firm, increase in heterogeneity in its syndication partners’ experience has a positive impact on the VC’s survival. In addition to the heterogeneity in partners’ experience, VCs’ own experience also helps their future survival.  I perform several robustness checks to mitigate the concerns with the measurement of VCs’ death. First, I focus on a sample of VC-year observations from 1995 to 2002. As the VC activities are available until the end of 2007, the death is defined as no future investments in at 25  The insignificant coefficient of PARTEXP HETER is due to its correlation with PARTPERF HETER. When  included separately in regressions, both of them are positive and significant.  47  least five years. The positive impact of partners’ heterogeneity on VCs’ survival remains the same. Second, captive VCs, for which VC investments are not the core business, may only make VC deals occasionally. The discontinuation of their VC investments may be caused by factors unrelated to their VC activities. Therefore, I replicate the analysis in Column (4) in a sample of independent U.S. VCs in Column (6) and obtain similar results. Third, the number of syndication partners may be inflated during the internet bubble years. VCs’ learning from partners that they syndicated with during the bubble period may be different from those they co-invested with in other years. To check whether the results are affected by the syndication in the bubble years, I replicate the analysis on a sample that excludes VC-year observations in 1999 and 2000. I obtain similar results (in unreported regressions).  26  Some selection issues may bias the above results. First, there is positive assortative matching among VCs, as shown in the first part of this paper. Implementing the Heckman two-step procedure is inappropriate due to the different data structure used for the syndication formation and for the VCs’ survival. I therefore control for VCs’ own experience and apply VC fixed effects to mitigate such concern. Second, anticipating heterogeneous partners may help survival in the future, VCs may self-select into more heterogeneous syndicates. If such selection is based on VCs’ experience over time or time-invariant characteristics, controlling for VCs’ experience and VC fixed effects should reduce such concern. If the selection is based on VCs’ time-varying  26  As the dataset used in this section has VC-year as a unit of analysis, VC’s experience in portfolio companies’  industry is difficult to identify. I then tried four measures to capture the industry-specific heterogeneity: 1) I construct a dataset with VC-year-deal as the unit of analysis, based on which I first calculate the heterogeneity of syndication partners’ experience in the deal’s industry and then take the average of heterogeneity across deals for a VC in a given year; 2) I assign each VC a core industry, in which the VC make the most investments in the sample period, and then calculate the heterogeneity in partners’ experience in the VC’s core industry; 3) I calculate the distribution of each partner’s investments in each industry and then measure the extent to which these partners share similar industry preferences; and 4) I construct the heterogeneity in partners’ experience in each of the six industries. I use heterogeneity in partners’ experience in each industry and the sum of the heterogeneity across industries. These measures have quite similar predictions to the baseline analysis in Column (4).  48  characteristics, I need some extra exogenous variation to identify the impact of heterogeneity.27 To construct instrumental variables that capture the local characteristics of VCs, I first define local available VCs as all of the “live” VCs located in the VC’s state whenever the location of the VC is known but exclude the VC itself and its syndication partners in that year. The first instrument, denoted by LOCAL HETER, measures the heterogeneity of local available VCs for each VC in a given year. The second instrument, denoted by LOCAL EXP, measures the average experience of local available VCs. The theoretical motivation of the instruments is that the local availability of VCs can affect VCs’ choices of syndication partners. However, the local VCs that the VC does not syndicate with should not directly affect the survival of this VC. I have tried the following tests to check the validity of the two instruments. First, both instruments should have significant impacts on heterogeneity in syndication partners’ experience. Column (1) of Table 14 reports the regression results, showing a negative and significant correlation between the heterogeneity in other local VCs and the endogenous variable. An F test of the joint significance of the instruments generates an F statistic of F (2, 7279) = 42.15. Second, to check whether they are strong instruments, I perform a Kleibergen-Paap test, which rejects the null hypothesis that the instruments are only weakly correlated with the endogenous regressor. Third, to test whether the instruments are correlated with the error terms, I perform the overidentifying restrictions test. The Hansen J statistic (J statistic= 1.560 and Chi-sq (1) P-value= 0.2116) suggests that we cannot reject the null hypothesis that the instruments are exogenous to the dependent variable in the main regression (i.e. survival of VCs). In the second step of the IV estimation, I regress the VCs’ survival on the predicated value of heterogeneity in syndication partners’ experience and all other relevant variables, shown in Column (2) of Table 14. Consistent with the original findings, the IV estimation reports a positive impact of partner  27  This type of selection can also be interpreted as “active” learning that VCs actively seek partners with diverse  experience for more learning opportunities, as opposed to the “random” learning that VCs learn from the available partners who happen to have diverse experience.  49  heterogeneity on VCs’ survival.  28  Table 13 VCs’ Survival This table is based on the sample of 12,434 VC-year observations. I use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively.  VARIABLES  (1)  (2)  (3)  (4)  (5)  (6)  NEW  INDUDIV  DEATH  DEATH  DEATH  DEATH  FULL  FULL  FULL  FULL  YEAR<2003  U.S. & IVC  0.0892  0.0964***  -0.0405***  -0.0344***  -0.0376***  -0.0288***  (0.0632)  (0.0106)  (0.0074)  (0.0086)  (0.0099)  (0.0098)  0.219***  -0.0031  -0.0082  0.0041  0.0044  0.0127  (0.0583)  (0.0097)  (0.0069)  (0.0077)  (0.0084)  (0.009)  -0.0003  -0.0002  -1.00e-05  -2.67e-05  4.25e-05  -9.22e-05  (0.0009)  (0.0002)  (0.0001)  (0.0001)  (0.0002)  (0.0001)  -0.115  0.214***  0.0275  0.0943*  0.109**  0.211***  (0.382)  (0.0638)  (0.0456)  (0.0508)  (0.0549)  (0.0608)  0.0495***  0.0074***  -0.0025***  -0.0027***  -0.0025***  -0.003***  (0.0008)  (0.0002)  (0.0002)  (0.0002)  (0.0002)  (0.0003)  -0.0161*  -0.0056  -0.0248**  (0.0095)  (0.0110)  (0.0115)  -0.0307***  -0.0320***  -0.0226**  (0.0097)  (0.0110)  (0.0110)  DEAL PARTEXP HETER PARTPERF HETER PARTEXP MEAN PARTPERF MEAN VCEXP PARTCTRY HETER PARTTYPE HETER VC FE  NO  NO  NO  YES  YES  YES  YEAR FE  YES  YES  YES  YES  YES  YES  28  I also tried another set of instrumental variables, following Ackerberg and Botticini (2002). I construct each VC’s  local markets as an interaction of the VC’s state (where the largest proportion of the VC’s past investments is made) with the year of syndication. In the first step IV estimation, I regress the heterogeneity in syndication partners on these instruments as well as all relevant control variables. In the second step of IV estimation, I regress VCs’ survival on the predicated values of heterogeneity in syndication partners with other controls. The IV estimation generates a significant and negative coefficient on heterogeneity in syndication partners, consistent with those reported in the Table 13 and Table 14.  50  (1)  (2)  (3)  (4)  (5)  (6)  NEW  INDUDIV  DEATH  DEATH  DEATH  DEATH  FULL  FULL  FULL  FULL  YEAR<2003  U.S. & IVC  -0.0874  0.152***  0.358***  0.212***  0.258***  0.146***  (0.116)  (0.0193)  (0.0146)  (0.0159)  (0.0176)  (0.0188)  R-squared  0.2769  0.2276  0.0399  0.1275  0.1102  0.1133  No. of Obs.  12433  12433  12433  11622  8275  6602  VARIABLES  DEAL CONSTANT  Table 14 Selection Issues of VCs’ Survival This table is based on the sample of 12,434 VC-year observations. Column (1) reports the regression results of the selection equation with PARTEXP HETER as its dependent variable and LOCAL HETER and LOCAL MEAN as two instruments. Column (2) reports the regression results of the DEATH equation with DEATH as its dependent variable. All regressions apply fixed effects OLS models with robust standard errors. I use ***, **, and * to denote significance at the 1%, 5%, and 10% level (two-sided), respectively.  VARIABLES  LOCAL HETER  (1)  (2)  PARTEXP HETER  DEATH  IV APPROACH:  IV APPROACH:  SELECTION Eq.  DEATH Eq.  -0.151 *** (0.0187)  LOCAL MEAN  -0.0025 (0.0016)  PARTEXP HETER  -0.167** (0.0781)  PARTPERF HETER PARTEXP MEAN PARTPERF MEAN VCEXP  0.315***  0.0497*29  (0.0132)  (0.026)  0.0028***  0.0005*  (0.0002)  (0.0003)  -0.8702***  0.018  (0.0880)  (0.092)  0.00002  -0.0025***  (0.0001)  (0.0003)  29  The positive coefficient of PARTPERF HETER is purely driven by its correlation (the correlation coefficient is 0.39) with PARTEXP HETER. PARTPERF HETER is not significant when included alone and becomes significant once PARTEXP HETER is introduced to the regression. The coefficient of PARTEXP HETER remains negative and significant with or without PARTPERF HETER.  51  (1)  (2)  PARTEXP HETER  DEATH  IV APPROACH:  IV APPROACH:  SELECTION Eq.  DEATH Eq.  0.252***  0.0029  (0.015)  (0.0221)  VC FE  YES  YES  YEAR FE  YES  YES  R-squared  0.2966  0.0981  No. of Obs.  9160  9160  VARIABLES  PARTTYPE HETER  2.5 Conclusions Syndication among firms is common. It has recently received more attention from researchers in economics and finance. This paper contributes to the literature by examining how VCs select syndication partners, and how their preferences for partners affect their successful exits from portfolio companies, and eventually, their survival.  Based on a comprehensive dataset of venture capital investments made to U.S. companies from 1990 to 2005, this paper shows VCs have strong preferences for partners who are similar to themselves. Although the preferences for similarity can generate immediate benefits for VCs, syndication with more heterogeneous partners can help VCs to survive in the future. This paper introduces another type of selection among VCs when forming syndication, in addition to the more well-known selection between VCs and portfolio companies.  Although VC industry is an attractive research context, the implications from this study are quite general. When firms form alliances, they tend to ally with others who are similar to them and easy to get along with. 30  30  The preferences for similarity may lead to successful alliances  For example, Fernando, Gatchev, and Spindt (2005) showed the existence of positive assortative matching  between equity issuers and underwriters that higher ability underwriters are matched with higher quality issuers. Rhodes-Kropf and Robinson (2008) also showed evidence of assortative matching in the market of mergers and acquisitions that acquirers tend to buy targets with similar market-to-book ratios.  52  captured by their immediate financial returns. In contrast, firms allying with partners who are different need to bear additional costs and spend more effort to make the alliance work. However, the valuable learning opportunities and different perspectives brought by the diverse partners may help firms to survive in the long run.  53  2.6 References: Ackerberg, D. and M. Botticini (2002), “Endogenous matching and the empirical determinants of contract form”, Journal of Political Economy, Vol. 110, No. 3, pp. 564-591 Admati, A.R. and P. Pfeiderer (1994), “Robust financial contracting and the role of venture capitalist”, Journal of Finance, Vol. 49, No.2, pp. 371-402 Baker, M. and P. Gompers (2003), “The determinants of board structure at the initial public offering”, Journal of Law and Economics, Vol. 46, pp. 569-598. Beckman, C. M. and P. R. Haunschild (2002), “Network learning: The effects of partners’ heterogeneity of experience on corporate acquisitions”, Administrative Science Quarterly, Vol. 47, No. 1, pp. 92-124 Berger, A., N. Miller, M. Petersen, R. Rajan, and J. Stein (2005), “Does function follow organizational form? Evidence from the lending practices of large and small banks,” Journal of Financial Economics, Vol. 76, No. 2. pp. 237-69 Bonacich, P. (1972), “Factoring and weighting approaches to status scores and clique identification,” Journal of Mathematical Sociology, Vol. 2, pp. 113-120 Borgatti, S. P., M. G. Everett, and L. C. Freeman (2002), Ucinet 6 for Windows: Software for Social Network Analysis, Harvard: Analytic Technologies Bottazzi, L., M. Da Rin, and T. Hellmann (2008), “Who are the active investors? Evidence from venture capital”, Journal of Financial Economics, Vol. 89, No. 3, pp. 488-512 Brander, J., R. Amit, and W. Antweiler (2002), “Venture capital syndication: Improved venture selection versus the value-added hypothesis”, Journal of Economics and Management Strategy, Vol. 11, pp. 423-452 Brass, D. J., J. Galaskiewicz, H. R. Greve, and W. Tsai (2004), “Taking stock of networks and organizations: A multilevel perspective”, Academy of Management Journal, Vol. 47, No. 6, pp.795-817 Casamatta, C. and C. Haritchabalet (2007), “Experience, screening and syndication in venture capital investments”, Journal of Financial Intermediation, Vol. 16, No.3, pp. 368-398. Cestone, G., J. Lerner, and L. White (2006), “The design of syndicates in venture capital”, working paper, Harvard Business School.  54  Chung, S., H. Singh, and K. Lee (2000), “Complementarity, status similarity and social capital as drivers of alliance formation”, Strategic Management Journal, Vol. 21, No. 1, pp. 1 - 22 Egan, E. J. (2007), “Matching firm names for research in business”, working paper, University of British Columbia Fernando, C. S., V. A. Gatchev, and P. A. Spindt (2005), “Wanna dance? How firms and underwriters choose each other”, Journal of Finance, Vol. 60, No. 5, pp. 2437-2469 Freeman, L. C. (1979), “Centrality in social networks: conceptual clarification”, Social Networks, Vol. 1, pp. 215-239 Gompers, P. (1996), “Grandstanding in the venture capital industry”, Journal of Financial Economics, Vol. 42, pp. 133-156 Gompers, P. and J. Lerner (1999), The Venture Capital Cycle, Cambridge: MIT Press, 1999 Gorman, M., and W. Sahlman (1989), “What do venture capitalists do?” Journal of Business Venturing, Vol. 4, No. 4, pp. 231-248 Greene, W. H. (2000), Econometric Analysis, Prentice-Hall, Inc. Gulati, R. and M. Gargiulo (1999), “Where do interorganizational networks come from?” American Journal of Sociology, Vol. 104, No. 5, pp. 1439-1493 Hambrick, D. C., T. S. Cho, and M. Chen (1996), “The influence of top management team heterogeneity on firms’ competitive moves”, Administrative Science Quarterly, Vol. 41, No. 4, pp. 659-684 Hellmann, T. and M. Puri (2002), “Venture capital and the professionalization of start-up firms: Empirical evidence”, Journal of Finance, Vol. 57, No. 1, pp.169–197 Hochberg, Y, A. Ljungqvist, and Y. Lu (2007), “Venture capital networks and investment performance”, Journal of Finance, Vol. 62, No. 1, pp. 251-301 Hochberg, Y, A. Ljungqvist, and Y. Lu (2009), “Networking as a barrier to entry and the competitive supply of venture capital”, working paper Hoffman, L.R. and N.R.F. Maier (1961), “Quality and acceptance of problem solutions by members of homogeneous and heterogeneous groups”, Journal of Abnormal and Social  55  Psychology, Vol. 62, No. 2, pp.401-407 Hsu, D. S. (2004), “What do entrepreneurs pay for venture capital affiliation?” Journal of Finance, Vol. 59, No. 4 pp. 1805-1844 Jacquemin, A.P. and C. H. Berry (1979), “Entropy Measure of Diversification and Corporate Growth”, Journal of Industrial Economics, Vol. 27, No. 4, pp. 359-369 Kaplan, S. and A. Schoar (2005), “Private equity performance: Returns, persistence, and capital flows”, Journal of Finance, Vol. 60, No.4, pp. 1791-1823 Kaplan, S. N. and P. Stromberg (2004), “Characteristics, contracts, and actions: evidence from venture capitalist analyses’, Journal of Finance, Vol. 59, No. 5, pp. 2177-2210 King, G. and L. Zeng (1999a), “Logistic regression in rare events data,” Department of Government, Harvard University, available from http://GKing.Harvard.Edu King, G. and L. Zeng (1999b), “Estimating absolute, relative, and attributable risks in case-control studies,” Department of Government, Harvard University, available from http://GKing.Harvard.Edu Kogut, B., P. Urso, and G. Walker (2007), “Emergent Properties of a New Financial Market: American Venture Capital Syndication, 1960–2005”, Management Science, Vol. 53, No. 7, pp. 1181–1198 Lerner, J. (1994), “The syndication of venture capital investments”, Financial Management, Vol. 23, No. 3, pp. 16-27 Lerner, J. (1995), “Venture capitalists and the oversight of private firms”, Journal of Finance, Vol. 50, No. 1, pp. 302-318 Lindsey, L. (2008), “Blurring firm boundaries: the role of venture capital in strategic alliances”, Journal of Finance, Vol. 63, No. 3, pp. 1137-1168 McPherson, M., L. Smith-Lovin, and J. M. Cook (2001), “Birds of a feather: homophily in social networks”, Annual Review of Sociology, Vol. 27, pp. 415-444 Megginson, W. and K. Weiss (1991), “Venture capital certification in initial public offerings”, Journal of Finance, Vol. 46, No. 3, pp. 879-903  56  Phalippou, L. and O. Gottschalg (2007), “The performance of private equity funds”, working paper Podolny, J. M. (1994), “Market uncertainty and the social character of economic exchange”, Administrative Science Quarterly, Vol. 39, No. 3, pp. 458-483. Rhodes-Kropf, M. and D. T. Robinson (2008), “The Market for Mergers and the Boundaries of the Firm”, Journal of Finance, Vol. 63, No. 3, pp. 1169-1211. Roth, A.E. and M.A.O. Sotomayor (1990), Two-Sided Matching, Cambridge, UK and New York: Cambridge University Press Rotemberg, J. J. and G. Saloner (1995), “Overt interfunctional conflict (and its reduction through business strategy)”, RAND Journal of Economics, Vol. 26, No. 4, Symposium on the Economics of Organization (Winter, 1995), pp. 630-653 Sorensen, M. (2007), “How smart is smart money: A two-sided matching model of venture capital”, Journal of Finance, Vol. 62, No. 6, pp. 2725-2762 Sorensen, M. (2008), “Learning by investing: evidence from venture capital”, working paper Sorenson, O. and T. E. Stuart (2001), “Syndication networks and the spatial distribution of venture capital investments,” American Journal of Sociology, Vol. 106, pp. 1546-1586 Sorenson, O. and T. E. Stuart (2008), “Bringing the context back in: Settings and the search for syndicate partners in venture capital investment networks.” Administrative Science Quarterly, Vol. 53, No. 2, pp. 266-294 Tian, X. (2008), “Geography, Staging, and Venture Capital Financing”, working paper Van den Steen, E. (2004), “The costs and benefits of homogeneity, with an application to culture crash”, working paper Wooldridge, J. M. (2002), Econometric Analysis of Cross Section and Panel Data, the MIT Press  57  3 INTERNATIONAL PATTERNS OF OWNERSHIP STRUCTURE CHOICES OF START-UPS: DOES THE QUALITY OF LAW MATTER?31  3.1 Introduction  Ownership concentration has been an important subject in the corporate governance literature (Demsetz and Villalonga, 2001; La Porta, Lopez-de-Silanes, Shleifer, and Vishny, 1998; La Porta, Lopez-de-Silanes, Shleifer, and Vishny, 1999b). The determinants or optimal design of ownership structure and the impacts of various ownership structures on firm performance have been studied extensively in the context of publicly traded companies. The consequences of ownership concentration, however, are ambiguous. On the one hand, highly concentrated ownership provides the largest equity holders with more rights to deal with corporate matters, probably leading to an efficient governance structure. On the other hand, a high degree of ownership concentration may cause minority shareholders to fear expropriation of their investment or violation of their rights by the large shareholders and thus reduce their willingness to invest unless the legal system provides them with sufficient protection. Without effective legal protection of investors, external financing may be less available to firms.  The macro-effects of having a high quality legal system have been explored by prior research. The general conclusion was that a high quality legal system, typically defined as a common law system with effective enforcement and norms of law and order in the population, can lead to more valuable capital markets and more dispersed ownership (see Glaeser, Johnson, and Shleifer, 2001; Djankov et al., 2003; and Demirguc-Kunt and Levine 2001). Less is known, however, about the effect that the quality of the legal system has on specific segments of the population of enterprises in a country. Exceptions were the work of La Porta et al. (1998) dealing with ownership concentration of large publicly traded companies and the work of Lerner and Schoar 31  A version of this chapter has been submitted for publication. Du, Q. and Vertinsky, I. International Patters of  Ownership Structure Choices of Start-ups: Does the Quality of Law Matter?  58  (2005) on ownership structures of private equity investees. Despite the importance of the contributions of founding small and medium sized enterprises (SMEs) to economic growth (Berger and Udell, 1998), to our knowledge, no study has considered the ownership structures at the founding stage of small and medium firms that are not backed by private equity firms. This segment of new enterprises contains the majority of start-ups, both by value and number. For example, 94.5% of U.S. nonfarm, nonfinancial, nonreal-estate small businesses or $1582.4 billion in monetary value belong to this segment (Berger and Udeall, 1998). Indeed private equity backed investments constitute only a very modest share of the value of all investments in most countries. Only 1.85% of U.S. nonfarm, nonfinancial, nonreal-estate small businesses or $31 billion in monetary value are funded by venture capital firms (Berger and Udeall, 1998). Our paper fills this gap in the literature by developing and empirically testing an analytical framework which takes into consideration the specific differentiating characteristics of firms in this segment and the specific nature of their interactions with different types of funding sources.  In their seminal paper La Porta et al. (1998) hypothesized that a higher quality legal system is likely to encourage a dispersed ownership structure. They argued that in a high quality legal system, minority shareholder’s rights are well protected. They are, therefore, willing to invest. Consequently, we would expect a dispersed ownership structure of enterprises in countries with high quality legal system. They found support for their hypothesis using data from large public corporations.  Lerner and Schoar (2005) studying private equity backed investees concluded that in a low quality legal system private equity firms will substitute for the lack of effective protection from the legal system by acquiring majority positions in the enterprises they invested in. Thus founders face higher costs (including loss of control) of securing external funding from private equity firms where the legal system in place offers lower protection to investors. The implication is that ownership structures of investees are likely to be more concentrated in lower quality legal  59  systems. They tested their framework with data obtained from private equity groups operating internationally.  The implications of our theoretical framework suggest that in de novo founding of small and medium firms, most of which are not backed by established private equity firms, founders are more likely to attract co-owners and secure internal capital from all start-up owners in environments with lower legal protection. In environments with strong legal protection, they are more likely to raise external debt capital and retain full ownership at founding.  We test our prediction using data from the Adult Population Survey of the Global Entrepreneurship Monitor project from 2001 to 2004. The empirical setting we use has several distinct advantages compared to the studies of La Porta et al. (1998) and Lerner and Schoar (2005). Studying ownership structure choices at founding allows for a less biased estimation of the impact of legal systems. The ownership structures of established firms can dramatically differ from their initial ownership choices as they evolve over time. Furthermore, ownership structure and firm’s characteristics (firm size and performance for example) are endogenously determined (Bitler, Moskowitz, and Vissing-Jorgensen, 2005; Cassar, 2004). In addition, studying ownership structure decisions at founding reduces problems of survival bias, a serous problem given the high mortality rate of start-ups. Finally, we use in our econometric analysis micro level data that enables us to analyze ownership preferences of individual entrepreneurs and estimate more accurately the ownership concentration of start-ups in the country. We also are able to examine and control for the impacts of founder’s and firm’s characteristics on ownership structure choices. In addition, our study also finds that entrepreneurs’ demographic characteristics can affect their choices of ownership structures.  This paper is organized as follows: Section 3.2 briefly reviews the literature on how the legal system plays a role in economic decisions. Section 3.3 outlines the theoretical framework.  60  Methodology and data are discussed in Section 3.4 and empirical findings are presented in Section 3.5. Section 3.6 concludes the paper.  3.2 Does Law Matter?  This section briefly reviews the existing literature that establishes the association between the legal system and investors’ protection. Our theoretical framework examining the mechanism through which a legal system affects entrepreneurs’ choices of ownership will be presented in the next section. La Porta et al. (1998) argued that legal origins and legal enforcement are the two key measures of a legal system with respect to the protection of investors.  According to La Porta et al. (1998, 1999, 2003), there are two broad origins of legal systems common law and civil law systems. Common law originated in England. Civil law has its origins in the Roman Empire with three representative legal families: French, German and Scandinavian.32 These legal families were then transplanted to many other countries through conquest, colonization or voluntary adoption. Although each country’s legal system has developed over time and borrowing from other legal families is possible, the essential features of the legal origin remained intact.  Two types of explanations were advanced to suggest why legal origins signal different levels of protection of minority investors. The “judicial” explanation is based on the fact that in the common law system, judges can make their decisions based on general rules or precedent judgment, but are not limited to them in making choices, while in civil law countries, judges cannot make decisions beyond what is prescribed by the legal rules (Glaeser and Shleifer, 2002). La Porta et al. (1999a) also provided a “political” explanation that the legal differences are explained by the “relative power of the king and the property owners”. As early as the 17th 32  Russian law is originated from civil law but does not belong to the three common legal families. Therefore, it is  treated as a separate category in the analysis.  61  century, the crown in England lost some control of the courts which were guided by parliament where the voice of property owners was dominant. As the power of parliament increased, the protection of investors gradually expanded. This was not the case in France or Germany where the government remained in control of the courts and legislators.  The effectiveness of the legal system in protecting investors depends not only on the decisions of the courts but also on the effective enforcement of these decisions. The quality of legal implementation is reflected both in the resources and effectiveness of enforcement institutions, and the propensity of citizens to obey laws. For example, transparency in financial reporting and bureaucracy increase trust in the society. Higher voluntary compliance with laws and lower use of litigation to resolve private disputes reduce the burden on the legal system.  3.3 The Quality of the Legal System and Financing and Ownership Decision by Founders of Start-ups  De novo start-ups have some distinct characteristics. They are typically small privately held firms. Information opacity and asymmetries in information held by founders and potential investors are acute as public records of performance (or indeed any records of performance) are not available. Thus obtaining external financing is a challenge irrespective of the level of protection offered by the legal system to investors. Indeed in most cases financing is one of the key factors affecting ownership structures and rights or control allocation within start-ups.  In our framework we assume that there are two sources of capital to finance start-ups at founding stage: internal capital and external capital. Internal capital mainly refers to the equity capital obtained from start-up founders and their co-owners while external capital mainly refers to the debt capital raised by entrepreneurs from banks. The division of internal capital and external capital is reasonable because the major sources of financing for start-ups are from principal  62  owners and banks, and external equity financing, such as venture capital, is very limited at founding stage (Berger and Udell, 1998).  In a good legal system, the law could protect well both internal and external capital investors. It can be true that start-up founders prefer internal capital financing because co-owners could add some extra value by advising, monitoring the business, and providing access to networks. It can also be true that start-up founders prefer external capital financing to maintain absolute control of start-ups. We can argue that in equilibrium, given a particular level of quality of legal protection, there is some fixed ratio of the amount of internal capital to the amount of external capital used by start-up founders to finance their ventures.  In a poor legal system, the weak investor protection makes it more difficult to raise external capital than in a good legal system. External capital investors, i.e. lenders do not have access to information, monitoring, and control as much as equity holders and have to rely on bankruptcy laws and contract enforcement to protect their interests. Therefore, in a poor legal system, external capital investors anticipate a higher default rate from entrepreneurs, leading to a higher cost of external capital. On the other hand, start-up founders may succeed in securing internal capital from other possible owners to finance their ventures. Although the investor protection offered by the legal system is inadequate, once becoming co-owners, internal capital investors can rely on their ownership shares and control as a substitute for the ineffective legal system. Prior research has documented the existence of substitution for the poor legal protection in private companies. For example, Bergman and Nicolaievsky (2007) observed that in Mexico the law provides only scant protection to investors, leaving a need for investors to contractually “opt out” of the legal system and obtain protection provided by investees privately (pp. 739). Lerner and Schoar (2005) studying private equity investments in developing countries found that poor legal systems constrained the ability of private equity partners to write sophisticated contracts that can separate control rights from cash flow rights and offer adequate protection for investors.  63  In such cases private equity investors may seek majority ownership to gain control as a substitute for the lack of legal protection. In our case of start-ups at founding stage, acquiring a larger stake is less financially prohibitive, thus the “substitution mechanisms” can be more effective than that in large public companies.  We predict that in equilibrium, start-ups may have more owners in a poor legal system than in a good legal system. Given the limited number of owners for start-ups at founding stage, we are more likely to observe partial ownership structure in a poor legal system than in a good one.  3.4 Data and Methodology  The data used to test our hypotheses was derived from the GEM (Global Entrepreneurship Monitor) project database. GEM is an annual assessment of entrepreneurial activities at the country level. GEM adopts a broad definition of entrepreneurial activities that include any general start-up activities and are not limited to high technology sectors, although high technology start-ups and venture capital backed start-ups are important constituents of entrepreneurship. Our dataset included start-up founders from 19 countries or regions in 2001, which expanded to 31 countries or regions by 2004. Each country or region participating in GEM project conducted an Adult Population Survey to get a random sample of no less than 2,000 individuals. Individuals were asked to report their demographic characteristics, status of employment, and characteristics of the start-ups. This paper makes use of the sub-sample of respondents who identified themselves as independent founders of start-ups. To be included in our sample, only founders who have made significant commitments and took a significant action to develop their business (e.g. buying equipment and renting space) were included. The final sample has 9,561 founders of independent start-ups in the surveys conducted between 2001 and 2004. To check the robustness of our results, we also conducted country level analysis, in which we calculated the percentage of entrepreneurs who expected to fully own the business in each  64  country and explored the relationship between this percentage and legal and economic variables. The country level analysis is comparable to prior research that focused on large publicly traded companies (La Porta et al., 1998). Table 15 presents the country composition in our sample and their legal origins.  Table 15 Country Composition and Legal Origin Country  No. of Obs.  Percentage  Year  Australia  216  2.26  2002, 2003, 2004  Canada  193  2.02  2001, 2002, 2003  Hong Kong  65  0.68  2002, 2003, 2004  India  356  3.72  2001, 2002  Ireland  87  0.91  2002, 2004  Israel  96  1  2001, 2002, 2004  New Zealand  165  1.73  2001, 2004  Singapore  286  2.99  2001, 2002, 2003, 2004  South Africa  343  3.59  2002, 2004  Thailand  131  1.37  2002  Uganda  148  1.55  2003  United Kingdom  989  10.34  2002, 2003, 2004  United States  747  7.81  2001, 2002, 2003, 2004  English Origin Total  3822  39.97  Argentina  494  5.17  2001, 2002, 2003, 2004  Belgium  110  1.15  2002, 2003, 2004  Brazil  284  2.97  2003, 2004  Chile  330  3.45  2002, 2003  France  70  0.73  2001, 2002, 2003, 2004  Greece  68  0.71  2003, 2004  Italy  103  1.08  2001, 2002, 2004  Jordan  194  2.03  2004  Mexico  115  1.2  2002  Netherlands  111  1.16  2002, 2003, 2004  Peru  476  4.98  2004  Portugal  23  0.24  2001, 2004  Spain  687  7.19  2002, 2003, 2004  French Origin Total  3065  32.06  China  211  2.21  2002, 2003  Croatia  83  0.87  2002, 2003, 2004  65  Note:  Country  No. of Obs.  Percentage  Year  Germany  750  7.84  2001, 2002, 2003, 2004  Hungary  127  1.33  2001, 2002, 2004  Japan  45  0.47  2001, 2002, 2003, 2004  Korea  138  1.44  2001, 2002  Poland  118  1.23  2001, 2002, 2004  Slovenia  61  0.64  2002, 2003, 2004  Switzerland  105  1.1  2002, 2003  Taiwan  33  0.35  2002  German Origin Total  1671  17.48  Denmark  116  1.21  2001, 2002, 2003, 2004  Finland  91  0.95  2001, 2002, 2003, 2004  Iceland  184  1.92  2002, 2003, 2004  Norway  238  2.49  2001, 2002, 2003, 2004  Sweden  353  3.69  2001, 2002, 2003, 2004  Scandinavian Origin Total  982  10.26  Country  No. of Obs.  Percentage  Year  Russia  21  0.22  2002  The  sources  of  countries’  legal  origins  are  La  Porta  et  al  (1998)  and  http://www.nationmaster.com/graph-T/gov_leg_ori  To characterize the enforcement quality in the legal system, we used several indexes developed by the World Bank between 2004 and 2005 based on the series of work from La Porta et al. Although the GEM surveys were conducted between 2001 and 2004, we do not expect the legal enforcement to change significantly over such a short horizon. The four legal enforcement variables “Legal Rights of Borrowers and Lenders”, “Disclosure Index”, “Director Liability Index”, and “Shareholder’s Suits Index” capture different dimensions of legal enforcement of judicial decisions. “Legal Rights of Borrowers and Lenders” directly captures how collateral and bankruptcy laws facilitate lending and can be used to directly test whether better protection of creditors leads to sole ownership. “Disclosure Index” measures how transparent the transactions are and thus how the legal system mitigates the asymmetric information problem. “Director Liability Index” measures the liabilities of self-dealing while “Shareholder’s Suits Index” captures the ease for shareholders to sue officers and directors for their misconducts.  66  The above four legal enforcement variables are not mutually exclusive. They are correlated to each other and to the legal origins as well. Common law countries usually have better legal enforcement in terms of higher “Legal Rights of Borrowers and Lenders”, “Investor Protection Index”, “Disclosure Index”, and “Director Liability Index”. In a good legal system, the expropriation of investors’ holdings is discouraged and investors are well protected. When the laws are not protective, different types of investors are affected to different extents.  Our micro-level data allows us to examine the effects of entrepreneur level and company level characteristics on ownership preferences. AGE acts as a proxy for experience. The more experienced the entrepreneurs are, the more likely they are to choose full ownership because they perceive themselves more capable of managing their firms on their own. Old age may also mean the dislike of complexity brought by shared ownership.  The effect of GENDER on ownership preference is more ambiguous. DeMartino and Barbato (2003) show that female and male entrepreneurs may have different career motivations. Female entrepreneurs prefer flexibility and balance between work and family while male entrepreneurs tend to choose careers where they can accumulate wealth. On one hand, female entrepreneurs may want to fully own their business because they enjoy the flexibility of being the sole owner of their businesses. On the other hand, female entrepreneurs may be more motivated to look for business partners to share the workload so that they can have more time for family obligations.  The effect of EDUCATION on ownership preferences is also ambiguous. On one hand, higher education may act as a positive signal to secure loans so that sole ownership is made more possible (Bates, 1990). On the other hand, higher education also acts as a positive signal that attracts business partners, potentially facilitating a partial ownership structure.  67  Table 16 Description of the Legal Variables Variables  Description & Sources  COMMLAW  dummy  variable  common  law  which  origin;  equals  equals  0  1  if  the  if  the  country’s  country’s  legal  legal  system  system  has  has civil  law origin. Source: La Porta et al (1998) “Law and Finance” and http://www.nationmaster.com/graph-T/gov_leg_ori LEGALR  continuous  variable  bankruptcy  laws  from  0  facilitate  to  10  lending.  to  measure  The  how  bigger  well  the  collateral  value,  the  and better  the laws facilitate lending. Source: World Bank website, “doing business” section DISCLS  This  is  variable  the from  Disclosure 0  to  10  Index  from  to  measure  the  World  how  Bank.  transparent  It are  is  a  the  continuous transactions.  The bigger the value, the more transparent the transactions. Source: World Bank website, “doing business” section DIRLIA  This  is  continuous  the  Director  variable  Liability  from  0  to  Index  from  10  to  the  measure  World  Bank.  “the  liability  It of  is  a  self-  dealing”. The bigger the value, the more liabilities of self-dealing. Source: World Bank website, “doing business” section SUITS  This  is  continuous to  sue  the  Shareholder  variable officers  from and  Suits 0  Index  from  the  to  10  to  measure  directors  for  misconduct”.  World the The  Bank.  It  “shareholders’ bigger  the  is  a  ability value,  the more ease the shareholders will have to suit directors. Source: World Bank website, “doing business” section  Personal wealth has been regarded an important factor in entrepreneurial finance. Due to the lack of information on wealth, we use INCOME as a proxy. Again, wealth could also have ambiguous effects on ownership choice as wealthy entrepreneurs are able to attract both internal and external investors.  The ambiguity on how gender, education, and income affect entrepreneurs’ ownership preferences will be resolved empirically in this study.  The effect of risk preference on ownership choice is straightforward. If an entrepreneur is RISK AVERSE, the desire of risk-sharing will lead to partial ownership.  68  Table 17 Description of Other Variables Variables  Description  SOLEOWN  dummy variable which equals 1 if an entrepreneur fully owns the business; equals 0 if the entrepreneur partly owns the business.  OWNERS  continuous  variable  which  indicates  how  many  people  including  the  interviewed entrepreneurs will own and manage the new business. OWNER4  categorical variable which equals 1 if the expected number of owners of the new business is 1; equal 2 if the expected number of owners is 2; equals 3 if the expected number of owners between 3 and 5; equals 4 if the expected number of owners is above 6.  AGE  continuous variable between 18 and 64.  GENDER  dummy variable which equals 1 if male; equals 0 if female.  INCOME  categorical variable which equals 1 if the income belongs to the lowest 33 percentile; equals 2 if income belongs to the middle 33 percentile; equals 3 if income belongs to upper 33 percentile.  EDUCATION  categorical variable which equals 1 if the educational attainment is some secondary;  equals  2  if  secondary  degree  obtained;  equals  3  if  post  secondary education and equals 4 if graduate education. NETWORK  dummy  variable  which  equals  1  if  the  interviewed  entrepreneurs  know  someone personally who started a business since the past 2 years; equals 0 otherwise. RISK AVERSE  dummy  variable  which  equals  1  if  fear  of  failure  would  prevent  the  interviewed entrepreneurs from starting a business; equals 0 otherwise. INDUSTRY  categorical  variable  which  equals  1000  if  the  business  is  in  agriculture,  forest, and hunting; 2000 if mining and construction; 3000 if manufacturing, 4000 if transportation, communication, and utilities; 5000 if wholesale and repair; 6000 if retail, hotel, and restaurant; 7000 if finance, insurance, real estate; 8000 if bus services; 9000 if education and social services; and 10000 if consumer services. NO. of JOBS  continuous variable which is a log of the number of jobs in the next 5 years.  LNGNI  GNI per capita taken average from the past five years.  GDPGR  Annual GDP per capital growth rate taken average from the past five years.  Well-connected entrepreneurs have better access to information and advice about the entrepreneurial process (Hoang and Antoncic, 2003). The entrepreneurial NETWORK also makes it easier for these entrepreneurs to find suitable business partners and increase the likelihood of a partial ownership structure.  Apart from the entrepreneurs’ personal characteristics, INDUSTRY type is also assumed to have  69  an impact on ownership choices. Traditional manufacturing industry usually requires significant commitments of capital, making sole ownership for many infeasible. Some high-tech (software for example) or consulting firms that are human capital intensive may not require large investments, making sole ownership more likely.  FIRM SIZE is also an important factor in ownership determination as the size of commitment affects the ability of a single founder to obtain debt financing or to self finance the start-up. Different measures are used to capture firm size, such as firm’s equity, asset or number of employees. Nascent start-ups usually have not recruited enough employees yet and a measure of expected number of employees in the future can be used to proxy for firm size.  The level and growth rate (GDPGR) of GNP per capita (LNGNP) are both introduced in the regressions. Higher GNP per capita is correlated with financial market sophistication that we showed to be negatively correlated with higher concentration of ownership.  Table 18 describes our sample. Among these 9,561 start-up founders, 52% chose to fully own their businesses. About 40% of the entrepreneurs in our sample operate their businesses in countries with common law systems. Within the civil law family, French civil law systems have the biggest number of start-up entrepreneurs, followed by German, Scandinavian, and Russian civil law system. In our sample, start-up investors enjoy an average “Legal rights” rating of 6.22 (out of 10), an average “Disclosure Index” of 6.89 (out of 10), an average “Director Liability” of 5.57(out of 10), and an average “Shareholder’s suits” of 6.48 (out of 10). Contrasting with the depiction of entrepreneurs of optimists, only 19% of them declare to be risk averse. Given the importance of networking, 67% of the entrepreneurs were connected to others with entrepreneurial experience.  70  Table 18 Descriptive Statistics of Variables Variable  No. of Obs.  Mean  Standard Deviation  Soleown  9561  0.52  0.50  Owners  9535  2.36  16.14  Owners4  9535  1.73  0.87  Common Law  9561  0.40  0.49  French  9561  0.32  0.47  German  9561  0.17  0.38  Scandinavian  9561  0.10  0.30  Russia  9561  0.00  0.05  Legal rights  9561  6.22  2.23  Disclosure Index  9561  6.89  2.23  Director liability  9561  5.57  2.23  Shareholder's suits  9561  6.48  1.70  Risk averse  9423  0.19  0.39  Network  9431  0.67  0.47  Income  8666  2.07  0.80  Education  9452  2.25  1.00  Gender  9561  0.64  0.48  Age  9561  37.01  11.12  Log of GNI/Capita  9528  9.24  1.26  GDP growth rate  9528  2.16  1.73  Expected No. of jobs  9350  154.70  5877.56  Agriculture, forest, hunting, fishing  420  Mining, construction  504  Manufacturing  765  Transportation, communication, utils  458  Wholesale repair  704  Retail, hotel, restaurant  2766  Finance, insurance, real estate  378  Bus services  1557  Education & social services  659  Consumer services  724  Year 2001  853  Year 2002  3228  Year 2003  2112  Year 2004  3368  71  The average start-up entrepreneur is in the middle-income group and has obtained a secondary degree. About 64% of entrepreneurs are males with the average age of 37. The average GNI per capita in our sample is about $9,240 with an average annual GDP growth rate of 2.16%. For the start-ups in our sample, the average number of employees they plan to hire within 5 years is 155. They are concentrated in “Retail, hotel, restaurant”, followed by “Bus services”, “Manufacturing”, and “Consumer services”.  The main research question of this paper is to determine the relationship between the founders’ tendency to choose sole ownership rather than enter into partnership and the quality of protection offered by the legal system. We estimate a binary dependent variable model to predict the probability of using one choice against the other (Greene, 2002). Five sets of variables are used to explain ownership choices.  The first set of variables contains legal origins and legal enforcement variables. The second set of variables includes personal characteristics of start-up entrepreneurs. The third set of variables is used to control for differences in macroeconomic environments. The fourth set of variables controls for firm specific characteristics. We also control for time fixed effects.  The estimated function therefore becomes: Prob (sole ownership=1) = f (Legal system, Personal characteristics, Macroeconomics, Firm characteristics, Time fixed effects)  Since the data includes samples of individuals from different countries, there are potential correlations of error terms within each country (Greene, 2002) so in our model standard errors are clustered on the country level.  72  3.5 Econometric Analysis  Individual Level Analysis The results of Logit regressions are reported in Table 19 to show the effects of the explanatory variables on ownership choices. In Table 19, we use legal origin variables to capture the quality of the legal system while in Table 20 we report regression results using legal enforcement variables. The first two regressions in Table 19 focus only on legal origins and macroeconomic variables, controlling for time fixed effects and clustering standard errors on country level. We find that entrepreneurs in common law countries are more likely to have sole ownership. Individual specific variables are introduced in the third and fourth regressions. Again, legal origin variables remained significant. Entrepreneurs with more income, more education, who are more risk averse and having more access to entrepreneurial networks are more likely to choose partial ownership while older entrepreneurs prefer full ownership. Gender, however, does not have a significant coefficient. The fifth and sixth regressions introduce firm specific variables in addition to entrepreneurs’ characteristics. The effects of legal origins remained robust except for the coefficients of German and Russian legal systems. Almost all personal characteristics had the same impact on the choice of ownership except that gender has a strong and positive impact in this regression, indicating that male entrepreneurs are more likely to have sole ownership. Firm size measured by the expected number of jobs in the future had a significant positive effect on the tendencies to choose partial ownership. Industry fixed effects are also controlled. The log of GNI per capita and GDP are not significant after controlling for individual and firm characteristics.  73  Table 19 Regressions on Legal Origins (1)  (2)  (3)  (4)  (5)  (6)  VARIABLES  SOLEOWN  SOLEOWN  SOLEOWN  SOLEOWN  SOLEOWN  SOLEOWN  Common Law  0.48***  0.45**  0.44**  (3.72)  (3.38)  (3.24)  French German Scandinavian Russia  -0.61***  -0.57***  -0.57***  (-4.38)  (-4.12)  (-4.13)  -0.26*  -0.19  -0.15  (-2.14)  (-1.62)  (-1.20)  -0.56**  -0.56**  -0.61***  (-3.42)  (-3.38)  (-3.82)  -0.25†  0.09  0.11  (-1.66) Risk averse Network Income Education Gender Age Log of GNI/Cap GDP growth rate  (0.96)  (1.05)  -0.22***  -0.22***  -0.25***  -0.25***  (-3.88)  (-3.87)  (-4.31)  (-4.35)  -0.25***  -0.28***  -0.21**  -0.23***  (-4.29)  (-5.16)  (-3.70)  (-4.45)  -0.10*  -0.11**  -0.09*  -0.10*  (-2.53)  (-2.73)  (-2.04)  (-2.23)  -0.12***  -0.12***  -0.13***  -0.12***  (-4.52)  (-4.59)  (-4.26)  (-4.29)  0.04  0.04  0.11*  0.12*  (0.67)  (0.76)  (2.18)  (2.35)  0.02***  0.02***  0.02***  0.02***  (5.87)  (5.97)  (5.84)  (5.90)  -0.09†  -0.09†  -0.05  -0.05  -0.05  -0.05  (-1.82)  (-1.85)  (-1.15)  (-1.36)  (-1.16)  (-1.15)  0.01  -0.01  0.01  -0.02  0.02  -0.01  (0.56)  (-0.67)  (0.29)  (-0.71)  (0.66)  (-0.28)  -0.14***  -0.14***  (-8.34)  (-8.50)  YES  YES  Exp. No. of jobs Industry FE Year FE  YES  YES  YES  YES  YES  YES  Constant  0.76†  1.35 **  0.44  0.99**  0.37  0.88*  (1.66)  (2.69)  (1.10)  (2.79)  (0.84)  (2.03)  pseudolikelihood  -6511.01  -6496.82  -5767.50  -5751.18  -5302.10  -5282.14  No. of obs.  9528  9528  8527  8527  7985  7985  Log  74  Table 20 reports the results of regressions on legal enforcement variables. Efficient legal systems measured by “Legal rights of borrowers and lenders” and “Shareholders’ Suits” are more likely to encourage full ownership of start-ups. The variable “Legal rights of borrowers and lenders” serves as a direct test of our theoretical framework: if the external investors, i.e. lenders are not well protected in a poor legal system, it becomes more difficult for entrepreneurs to borrow, leading to more internal capital financing and more dispersed ownership structures in a poor legal system. The predictive power of “Legal rights of borrowers and lenders” is the strongest among the four types of legal enforcement. As shown in the fifth column, after introducing “Legal rights of borrowers and lenders” the other three measures of legal enforcement become statistically insignificant. Access to networks, income and age display patterns consistent with the results reported in Table 19 showing that entrepreneurs with higher income and network access would prefer partial ownership while older entrepreneurs would choose sole ownership.  Because some of our individual level variables could affect access to and ability to use the legal system to obtain protection of rights, we have also tested whether interactions of the quality of the legal system with personal characteristics have any effect on the ownership choice. We expected that the education and income would interact with the quality of the legal system as more educated and wealthier founders are able to use a good legal system more effectively but the interaction coefficients were insignificant.  75  Table 20 Regressions on Legal Enforcement (1)  (2)  (3)  (4)  (5)  (6)  VARIABLES  SOLEOWN  SOLEOWN  SOLEOWN  SOLEOWN  SOLEOWN  SOLEOWN  Legal Rights  0.11**  0.11**  0.03  (2.61)  (2.66)  (0.70)  0.03  -0.02  -0.04  (0.71)  (-0.71)  (-1.45)  0.01  -0.03  -0.07*  (0.32)  (-0.93)  (-2.21)  0.07*  0.05  -0.01  (1.96)  (1.33)  (-0.32)  Disclosure Index Director Liability Shareholder’s Suits Common Law  0.66** (3.25)  Risk Averse  -0.24 ***  -0.25***  -0.24***  -0.24***  -0.25***  -0.27***  (-4.02)  (-4.12)  (-4.23)  (-4.07)  (-4.14)  (-4.57)  -0.23***  -0.24***  -0.25**  -0.25***  -0.25***  -0.22***  (-4.07)  (-3.80)  (-3.78)  (-4.08)  (-4.44)  (-4.01)  -0.10*  -0.09*  -0.10*  -0.09*  -0.10*  -0.11*  (-2.20)  (-2.02)  (-2.21)  (-2.03)  (-2.50)  (-2.58)  -0.10**  -0.10**  -0.09**  -0.10***  -0.10**  -0.13***  (-3.14)  (-3.19)  (-2.82)  (-3.37)  (-3.44)  (-4.74)  Gender  0.11*  0.10*  0.09†  0.10*  0.11*  0.11*  (2.15)  (2.03)  (1.92)  (1.98)  (2.14)  (2.16)  Age  0.02***  0.02***  0.02***  0.02***  0.02***  0.02***  (5.70)  (5.82)  (5.83)  (6.17)  (5.84)  (5.80)  -0.16**  -0.03  -0.03  -0.06  -0.17**  -0.03  (-2.61)  (-0.51)  (-0.60)  (-1.04)  (-3.33)  (-0.54)  -0.01  0.02  0.02  0.02  -0.01  0.01  (-0.20)  (0.76)  (0.78)  (0.79)  (-0.21)  (0.52)  -0.14***  -0.14***  -0.14***  -0.14***  -0.14***  -0.14***  (-8.92)  (-8.60)  (-8.61)  (-8.73)  (-9.31)  (-8.61)  YES  YES  YES  YES  YES  YES  Network Income Education  Log of GNI/Capita GDP growth rate Expected No. of jobs Industry fixed effects Year fixed effects  YES  YES  YES  YES  YES  YES  Constant  1.04*  0.24  0.38  0.16  0.96†  0.72  (2.01)  (0.36)  (0.62)  (0.26)  (1.84)  (1.48)  Log pseudolikelihood  -5313.36  -5337.62  -5340.31  -5308.22  -5308.22  -5284.04  No. of obs.  7985  7985  7985  7985  7985  7985  76  Table 21 Robustness Checks VARIABLES Common Law  (1)  (2)  (3)  (4)  (5)  (6)  (7)  SOLEOWN  SOLEOWN  OWNER4  OWNER4  OWNER4  OWNER4  SOLEOWN  PROBIT  PROBIT  OLS  OLS  OPROBIT  OPROBIT  LOGIT  0.27**  -0.14*  -0.21*  0.51***  (3.27)  (-2.28)  (-2.45)  (0.12)  French German Scandinavian Russia  -0.35***  0.16*  0.23**  (-4.17)  (2.63)  (2.83)  -0.09  0.03  0.06  (-1.21)  (0.52)  (0.75)  -0.38***  0.28**  0.37**  (-3.84)  (3.21)  (3.32)  0.07  0.13*  0.19**  (1.01)  (2.52)  (2.65)  Trust  0.60 (0.57)  Risk Averse  -0.16***  -0.16***  0.09**  0.09***  0.13***  0.13***  -0.25***  (-4.36)  (-4.40)  (3.38)  (3.63)  (3.63)  (3.86)  (.06)  -0.13**  -0.15***  0.09***  0.09***  0.13***  0.13***  -0.17***  (-3.72)  (-4.47)  (3.89)  (4.06)  (4.01)  (4.22)  (0.06)  -0.06*  -0.06*  0.03  0.03*  0.04†  0.04†  -0.11**  (-2.05)  (-2.24)  (1.54)  (1.70)  (1.67)  (1.84)  (.05)  Education  -0.08***  -0.08***  0.06***  0.06***  0.08***  0.08***  -.014***  (-4.26)  (-4.30)  (4.61)  (4.64)  (4.65)  (4.69)  (0.03)  Gender  0.07*  0.08*  0.01  0.00  -0.01  -0.01  0.13**  (2.18)  (2.36)  (0.20)  (0.04)  (-0.25)  (-0.42)  (0.05)  0.01***  0.01***  -0.01***  -0.01***  -0.01***  -0.01***  0.02**  (5.92)  (5.97)  (-3.91)  (-3.81)  (-4.50)  (-4.41)  (0.003)  -0.03  -0.03  0.02  0.01  0.03  0.01  -0.07  (-1.18)  (-1.17)  (0.85)  (0.31)  (0.98)  (0.51)  (0.06)  0.01  -0.01  -0.01  -0.00  -0.01  0.00  -0.01  (0.65)  (-0.30)  (-0.60)  (-0.07)  (-0.57)  (0.01)  (.05)  Exp. No. of jobs  -0.09***  -0.09***  0.07***  0.07***  0.10***  0.10***  -0.13***  (-8.49)  (-8.65)  (10.09)  (9.92)  (10.50)  (10.45)  (0.02)  Industry FE  YES  YES  YES  YES  YES  YES  YES  Year FE  YES  YES  YES  YES  YES  YES  YES  Constant  0.23  0.55*  1.50***  1.43***  0.43  (0.84)  (2.03)  (7.54)  (7.74)  (0.50)  pseudolikelihood  -5301.95  -5281.91  No. of obs.  7985  7985  Network Income  Age Log of GNI/Cap GDP growth rate  Log 7980  7980  -8666.28  -8645.68  -4752.99  7980  7980  7157  77  A series of robustness checks have been done in Table 21. In Column (1) and (2) of Table 21, Probit instead of Logit regression was used and generate the same prediction. For other regressions from Column (3) to Column (6), the dependent variable changes from a binary variable to a categorical variable. The bigger the value of the categorical variable, the more owners there are in the start-ups. Both Ordinary Least Square models and Ordered Probit models are estimated for the categorical dependent variables. In the OLS and Ordered Probit regressions, entrepreneurs in countries belonging to French and Scandinavian law show a tendency to have more partners. Income, risk aversion, education and network access show consistent results as before but lose some significance while gender loses its explanatory power.  In Column (7) of Table 21, we examine whether one type of “social capital” among people – trust, has any impact on start-up entrepreneurs’ choices of ownership structures. We obtain data from the World Value Survey which reports people’s values and cultural changes all over the world. As the surveys of start-up ownership structures used in this paper were conducted in 2001 and 2004, we choose the World Value Survey conducted in 1995 and 2000 to avoid the possible reverse causality bias in regressions. Survey respondents answer two similar questions about trust: “Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?” in the 1995 survey, and “Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people” in the 2000 survey. We are able to obtain the data of trust for 31 countries in our original sample. For each country, we calculate the percentage of respondents who choose “Most people can be trusted” as an indicator of general trust among people. If a country participates in both the 1995 and 2000 surveys, we take the average of the scores of trust. Column (7) of Table 21 reports the regression of ownership structures on legal origins, trust, and other control variables. The regression results suggest that general trust among people do not have a direct impact on entrepreneurs’ choices of ownership structures.  78  Country Level Analysis The country level analysis can be seen as either a complementary analysis or robustness check of the individual level analysis. The country index of preference for sole ownership was defined as the percentage of entrepreneurs with sole ownership (Sole Ownership Preference Index). Table 22 shows the regressions explaining Sole Ownership Preference Index as a function of two legal origins and five legal families. Table 22 presents the results of the country level regressions. Column (1) shows the significant and positive relationship between common law countries and the percentage of sole owners in a country without controlling for country level variables. The coefficient of common law does not reach conventional statistical significance after control variables are introduced in Column (2). Column (3) and column (4) replicate the regressions in Column (1) and Column (2) except that the legal origin variable is replaced by memberships in the five legal families. Only the French law family and the Scandinavian family show significant results without the controls and only French law family remains significant with controls. The insignificance of legal variables is partly due to the limited number of observations.  Our results should be interpreted with some caution. First, direct examination of contracts and fuller details about informal strategies used by both founders and investors to deal with risks is needed to validate our conclusions. Second, future detailed case studies of financing and ownership structure decisions under high and low quality legal systems as well as longitudinal studies of changes in behaviors that occurred in systems which have transitioned from low quality to high quality legal system will provide a fuller account of the role that protection offered by the legal system has on founders’ and investors’ behaviors.  79  Table 22 Country Level Regressions  Common Law  (1)  (2)  (3)  (4)  SOLEOWN  SOLEOWN  SOLEOWN  SOLEOWN  RATIO  RATIO  RATIO  RATIO  0.06†  0.06  (1.74)  (1.67) -0.08†  -0.07†  (-1.92)  (-1.84)  -0.02  -0.02  (-0.38)  (-0.41)  -0.13*  -0.09  (-2.33)  (-1.59)  0.02  -0.02  (0.23)  (-0.20)  French German Scandinavian Russia Log of GNI/Cap GDP growth rate Constant  0.01  0.01  (1.09)  (0.63)  -0.03*  -0.03†  (-2.33)  (-1.94)  0.49***  0.73***  0.55***  0.77***  (24.92)  (5.94)  (19.22)  (5.88)  Adj. R-Square  0.05  0.17  0.09  0.15  No. of Obs.  42  41  42  41  3.6 Discussions and Conclusions  The focus of this paper was the relationship between the quality of the protection offered by the legal system to investors and the propensity of start-up founders to opt for sole ownership. The issue of concentration of ownership received significant attention in the law and economics, and finance literatures. La Porta et al. (1998) highlighted the importance of the quality of the legal protection offered by a country to minority shareholders to the development of its financial markets. They argued that ownership concentration is negatively related to effective legal protection. Without adequate protection minority shareholders are likely to be discouraged from investing. La Porta et al. (1998) found evidence from samples of large publicly owned firms to support their theoretical arguments.  80  Our theoretical framework suggests that the relatively low cost of external financing in a good legal system encourages start-up founders to finance their start-ups by external debt financing while retain full control of their ventures. Without means of effective protection, financial institutions are less willing to lend the required capital as the risks of recovering their investments are higher. As a result, start-up founders tend to attract possible co-owners from whom they secure internal capital, at the expense of giving up some ownership and control of the ventures.  Our results shed a light on an important area of entrepreneurial research which has received relatively little attention – the relationship between the choices of start-up founders with respect to modes of financing and ownership structures, and the quality of the legal system. Our results suggest that there is a significant relationship. The findings which imply that the costs of equity financing of start-ups are less sensitive to the quality of the law have interesting ramifications. They may imply that in financing start-ups protection mechanisms which do not depend on the legal system are available and may play a significant role in investment decisions. There is need for future research to better understand the nature and consequences of these substitutes for protection offered to investors by a legal system. Given the evidence that founders, especially of family businesses, prefer to retain ownership and control, improvement in the quality of the legal system and thus the availability of debt financing are likely to stimulate the emergence of entrepreneurial ventures.  81  3.7 References Bates, T. 1990. Entrepreneur Human Capital Inputs and Small Business Longevity, The Review of Economics and Statistics, 72(4): 551-559. Berger, A., and G. Udell. 1998. The Economics of Small Business Finance: The Roles of Private Equity and Debt Markets in the Financial Growth Cycles. Journal of Banking & Finance, 22: 613-673. Bergman, N. K. and Daniel Nicolaievsky. 2007. Investor protection and the Coasian view. Journal of Financial Economics, 84(3):738-771. Bitler, M., T. Moskowitz, and A. Vissing-Jorgensen. 2005. Testing Agency Theory with Entrepreneur Effort and Wealth. The Journal of Finance, 60 (2):539-576. Bottzzi, L., M. Da Rin, and T. Hellmann. 2008. What Role of Legal System in Financial Intermediation? Theory and Evidence. Journal of Financial Intermediation, Forthcoming Burkart, M., and F. Panunzi. 2006. Agency Conflicts, Ownership Concentration, and Legal Shareholder Protection. Journal of Financial Intermediation, 15 (1): 1–31 Carter, N., W. Gartner, and P. Reynolds. 1996. Exploring Start-Up Event Sequences. Journal of Business Venturing, 11 (3): 151-166. Cassar, G. 2004. The financing of business start-ups. Journal of Business Venturing, 19 (2): 261–283. DeMartino, R., and R. Barbato. 2003. Differences between women and men MBA entrepreneurs: exploring family flexibility and wealth creation as career motivators. Journal of Business Venturing, 18 (6): 815–832. Demsetz, H., and B. Villalonga. 2001. Ownership structure and corporate performance. Journal of Corporate Finance, 7: 209–233. Demirguc-Kunt, A. and R. Levine. 2001. Financial Structure and Economic Growth: A Cross-Country Comparison of Banks, Markets, and Development. Cambridge, MA: MIT Press. Djankov, S., R. La Porta, F. Lopez-De-Silanes, and A. Shleifer. 2003. Courts. The Quarterly Journal of Economics, 118 (2): 453-517 Djankov, S., C. McLiesh, and A. Shleifer. 2007. Private Credit in 129 Countries. Journal of  82  Financial Economics, 84 (2): 299-329. Glaeser, E., S. Johnson, and A. Shleifer. 2001. Coase versus the Coasinas. The Quarterly Journal of Economics, 116 (3): 853-899. Glaeser, E., and A. Shleifer. 2002. Legal Origins. The Quarterly Journal of Economics, 117 (4): 1193-1229. Greene, W. 2002. Econometric Analysis. 5th edition, Prentice Hall Hoang, H., and B. Antoncic. 2003. Network-based research in entrepreneurship: A critical review. Journal of Business Venturing, 18 (2): 165–187 Kaufmann, D., A. Kraay, and M. Mastruzzi. 2003. Governance Matters III: Governance Indicators for 1996-2002. working paper La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. Vishny. 1997. Legal Determinants of External Finance. The Journal of Finance, 52 (3): 1131-1150. La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. Vishny. 1998. Law and Finance. Journal of Political Economy, 106 (6): 1113-1155. La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. Vishny. 1999a. The quality of government. Journal of Law, Economics and Organization, 15 (1): 222-279. La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. Vishny, 1999b. Corporate Ownership around the World. The Journal of Finance, 54 (2): 471-517. La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. Vishny. 2000. Investor Protection and Corporate Governance. The Journal of Financial Economics, 58 (1-2): 3-27. Levi, M., Li, K., and Zhang, F. 2009. Deal or No Deal: Hormones and the M&A Game, working paper Lerner, J. and A. Schoar, A. 2005. Does Legal Enforcement Affect Financial Transactions? The Contractual Channel in Private Equity. The Quarterly Journal of Economics, 120 (1): 223-246. Shleifer, A. and D. Wolfenzon. 2002. Investor Protection and Equity Markets. Journal of Financial Economics, 66 (1): 3-27. World Bank, World Development Indicators (Washington, DC: The World Bank, 2004)  83  4 BORN LEADERS: THE RELATIVE-AGE EFFECT AND MANAGERIAL SUCCESS33  4.1 Introduction  There is mounting empirical evidence that summer-born children are at a disadvantage as a result of being up to a year younger than other classmates in their school grade. Summer-born children are in this position due to the fact that the cutoff dates for admission into school generally fall at the end of summer. Therefore, those just born before the grade entry cutoff date during the summer months are up to a year younger and less physically and intellectually developed than their classmates. The disadvantage faced by summer-born children has been shown to exist throughout school, and even to affect the success at entering university. This well-documented condition has become known as the “relative-age effect” or the “birth-date effect”.  This paper investigates whether a relative-age or birth-date effect extends to the selection and performance of CEOs of S&P 500 companies. Given the high level of corporate achievement such positions represent, and the relatively fierce competition faced in reaching such positions, CEOs represent an ideal context for studying the evidence for such an effect, and to investigate whether it extends well beyond school and into adulthood.  In order to investigate the possible presence of a relative-age effect among highly successful U.S. CEOs we construct a birth-date dataset for the CEOs of S&P 500 companies between 1992 and 2006. Based on the prevailing cutoffs of school entry in the United States, we distinguish four birth seasons with the summer season being from July to September. We obtain the names of the CEOs from ExecuComp, and their birth-date and educational background from the Biography Resource Center. We were able to identify birth-dates and education backgrounds of 321 33  A version of this chapter has been submitted for publication. Du, Q., Gao, H., and Levi, M. Born Leaders: The  Relative-Age Effect and Managerial Success.  84  CEOs.34 Stock price data were obtained from CRSP and accounting data from Compustat.  The principal findings are as follows. First, we find that non-summer born individuals have a significantly higher chance of becoming a CEO of an S&P 500 company. This occurs relative to both an equal division of births across seasons, and relative to the actual seasonal pattern of births. The effect is robust to measurement of relative age within school grade that allows for the different grade-entry cut-off dates in different US states. Second, conditional on becoming a CEO, those who were born in the summer add higher value to their company whether this be considered via Tobin’s Q involving market and book value of assets, or the market to book value of equity, M/B. We find that having a summer-born CEO increases Tobin’s Q and M/B at a p-value of less than one-percent. Third, we demonstrate the return from a policy of forming a portfolio based on buying companies with summer-born CEOs and selling short companies with non-summer-born CEOs. These portfolios are reset once each year. This is shown to generate an annual abnormal return of 8.32 percent. This indicates that financial markets are yet to realize the effect of CEO birth season on asset returns.  We also investigate whether the relative-age effect shows up in CEO compensation. We find that summer-born CEOs receive unconditionally higher compensation, but after controlling for firm characteristics like firm performance, size, risk, and other unobservable firm characteristics, the season of CEO birth has no impact on compensation. This is consistent with a lack of recognition of the possible influence of birth-date on managerial performance.  Some variation exists in the school-year cutoff dates applicable in different states. As a result, CEOs born in the summer may not have been the youngest students in their grade year. Therefore, to check the robustness of our results, we construct three additional variables to capture CEOs’ relative ages in their respective age cohorts. Based on a sub-sample of CEOs 34  We address the sample selection issue later. We do not expect our sample of birth-dates to be biased in any  obvious way.  85  where their state of birth and their state’s grade cutoff date are both available, we are able to evaluate: First, how many days older a CEO is relative to the hypothetically youngest classmate (who would be born 1 day before the cutoff day and therefore be the youngest possible student in the grade). Based on this we can determine whether or not a CEO was born in the latest possible quarter for enrollment in the school year; Third, we can compare CEO success according to quarter of birth based on state specific school cutoff dates. The robustness tests using these three alternative measures of CEO relative age generate consistent predictions.  We provide an explanation of the empirical relevance of season of birth both for the disproportionately higher number of non-summer born CEOs, and for the better performance of CEOs born in summer. The explanation does not rely on there being any eventual difference in management skills in adulthood according to the season of a person’s birth. The explanation relies only on the advantage that non-summer born have in being selected for activities that provide them with experiences which benefit them in the competition to become CEOs, and on the need for summer-born to distinguish themselves among their cohort. A simple model is used to demonstrate how both of these effects occur.  The paper is organized as follows. Section 4.2 discusses the extensive literature on the relative-age or birth-date effect. Section 4.3 considers the relevant season of birth. Section 4.4 explains how birth-date data for S&P 500 CEOs were collected and organized. In Section 4.5 we consider the prevalence and performance of summer-born CEOs and perform robustness checks to support our findings. Section 4.6 turns to the explanation for what we have found. Section 4.7 of the paper concludes.  86  4.2 The Relative-age or Birth-date Effect It is probably fair to say that of the literally hundreds of research papers documenting the relative-age or birth-date effect, the vast majority focus on sport.35 Sport is an activity in which children are grouped by age, usually in one-year categories, in order to control for age-related differences in physical and intellectual development. The relevance of relative-age for long-run success in sports has been documented in just about every sport played, from soccer, to basketball, to ice hockey, to baseball, to tennis. Taking sports in no particular order, we can begin with soccer where a study by Glamser and Vincent (2004) showed that a disproportionate number of elite youth soccer players in the United States were born early in the school year, making them generally older than their teammates. The effects of relative-age continue into professional soccer, as has been shown by Ashworth and Heyndels (2007) who examined wage levels and showed that players born late after the cutoff date – making them older than teammates – earned more than other professional soccer players. The importance of the relative-age effect in ice hockey has been studied by Baker and Logan (2007) who considered the selection order in the annual National Hockey League (NHL) draft. As in this paper, athletes were divided into four quartiles by the season of their birth. It was determined that relative age still plays a role at the time of the NHL draft. This study followed an earlier investigation of ice-hockey success by Barnsley, Thompson, and Barnsley (1985). In this study the birth months of Ontario Hockey League and Western Hockey League players were organized by month. The authors showed an almost straight line decline in numbers reaching these leagues, which are just below the NHL in prestige, from team members born in January – 16 percent of the players – to those born in December, about 4 percent. In English-speaking Canada, the school year cutoff is December 31, so children born in January are the oldest and those born in December are the youngest. 35  However, as we shall see, the birth-date effect has been documented in other endeavors, including academic  success. See Bedard and Dhuey (2006).  87  With height being a highly favored characteristic in basketball it is little surprise that a relative-age effect has been observed. Esteva, Dobnic, and Puigdellivol (2008) examined the birth-dates of Spanish youth and professional players as well as National Basketball Association (NBA) players in the 2004/05 season, and the best 50 players in NBA history. By sorting birth-dates into four categories, or seasons, and applying a chi-quadrate test, a relative-age effect was identified which extended right up to the all-time very top NBA professionals. A more extensive study of the relative-age effect in basketball involving over 150,000 male players and 100,000 female players in France by Delorme and Raspaud (2008) confirmed the importance of the calendar quarter of the year of birth. The developmental benefit enjoyed by those born in the first few months of the selection year has been documented in baseball by Thompson, Barnsley, and Stebelsky (1991). The relative-age effect is confirmed in a population of 837 major league baseball players, again showing that season of birth can have a lifelong effect. Another sport where the relative-age effect has been shown to survive into adulthood is tennis. This involved investigation of birth-dates of nationally ranked junior tennis players in the United States by Giacomini (1999). Even in a non-team sport such as tennis, those born earlier in a year are older and enjoy advantages. The importance of the relative-age effect has been shown to extend well beyond sport, and to be found in academic performance throughout school and even in the likelihood of attending university. For example, Bedard and Dhuey (2006) have documented the effect across OECD countries: older children perform several percentiles better than younger class members. Other country-specific studies confirm these effects, including the impact on income. 36 Most importantly for the evidence presented later in this paper, it has also been shown that relative-age has a significant effect on high school leadership activities. To quote the abstract from one 36  For example, for the situation in Japan see Kawaguchi (2006). For Germany see Jurges and Schneider (2008).  For Britain, see Hutchison and Sharp (1999). The issue of disadvantaged summer-born children recently gained so much attention in Britain that the Education Secretary launched a review in January 2008 to see what might be done to help them.  88  research paper in this area, “…school entry cutoffs induce systematic within grade variation in student maturity, which in turn generates differences in leadership activity. We find that the relatively oldest students are 4-11 percent more likely to be high school leaders,” (Dhuey and Lipscomb, 2008). 4.3 The Relevant Seasons of Birth The predominant practice in the research cited above is to divide the year into the four calendar quarters. The youngest in class are taken as those born in the summer, July through September, the third quarter of the year. The oldest are taken as those born in the fourth quarter. This is based on the prevailing cutoffs for entry for either kindergarten or first grade. In this regard it is worthy to note that thirty-seven of the U.S. states plus Puerto Rico have kindergarten entry cutoff dates between August 31 and October 16.37 This would make the children born in July and August among the youngest in class, with September born in many cases also being younger. Those born after mid-October through the end of the year would be among the oldest. However, the fourth quarter could contain some of the younger students. Nevertheless we would expect the number of younger children to be relatively higher in the third quarter than the fourth quarter. The ranking in terms of average age is not in doubt for the first and second quarters, with these containing the second and third youngest children. We follow the universal convention in the relative-age research of classifying the seasons as each of the four calendar quarters. However, as a robustness check we also present results based on excluding the month of possible age ambiguity, September. We do this by considering pairs of months rather than calendar quarters: the youngest in class have July and August birthdays. We again find the relative-age effect to be present. As a yet further robustness check, for those situations where we have CEO state of birth we also recalculate relative age according to the hypothetically youngest possible student to be admitted to the CEO’s  37  Education Commission of the States, State Notes Kindergarten. The relevant, detailed data can be found at  http://www.ecs.org/clearinghouse/50/00/5000.htm.  89  grade.38 That is, relative age is recalculated, where possible, to reflect heterogeneity of states’ cut-off ages for grade entry. Our conclusions are shown to be robust to these alternative measures of relative age. 4.4 Data Employed: CEO Birth-dates and Firm Characteristics In order to investigate the possible presence of a relative-age effect among highly successful U.S. CEOs we construct a birth-date dataset for the CEOs of S&P 500 companies between 1992 and 2006. Based on ExecuComp, we first identify the names of the CEOs, and then search for their birth-date and educational background in Biography Resource Center. Biography Resource Center is a database providing comprehensive biography information of notable individuals in business, art, government, and other endeavors. We were able to identify birth-dates and education backgrounds of 321 CEOs. Although the source we employ does not provide the birth information of all CEOs we do not expect our sample of birth-dates to be biased in any obvious way. Bias would require the likelihood of birth-date information being available to differ according to the quarter of the year of birth. We can think of no reason why this would happen.  Stock price data were obtained from CRSP and accounting data from Compustat. The sample we use containing the 321 CEOs for which we have birth-dates provides 2168 firm-year observations.  As for the financial data, following Gompers, Ishii and Metrick (2003) and others we measure Tobin’s Q as the market value of assets divided by book value of assets. We also measure the market-to-book (M/B) ratio as the market value of equity over book value of equity. The market value of equity (MV Equity) is computed as the yearly closing share price multiplied by the number of shares outstanding. We define Volatility as the stock return standard deviation using  38  Our sample size is reduced in the construction of this more precise measure because we lack birth states for some  CEOs, or because some CEOs were born outside the United States or in states with flexible admissions.  90  monthly returns of the preceding three years. Return on assets (ROA) is measured as the ratio of operational income before depreciation over total assets. We compute Leverage as the ratio of long-term debt over total assets. Capital Expenditure is the firm’s yearly capital expenditure normalized by total assets. MBA is a dummy variable, taking value of one if the CEO holds an MBA degree and zero otherwise. Summer is a dummy variable which equals one if the CEO is born in the summer, consisting of July, August and September when calendar data are considered (Relative age allowing for state differences involves a more complex structure as we shall explain). All the monetary variables in the sample are measured in 2006-constant dollars. To ensure outliers in the data are not driving our results, we winsorize all the continuous variables at the 1st and 99th percentiles.  Panel A of Table 23 reports the characteristics of the firms in our sample. The median firm has a Tobin’s Q of 1.49 and its M/B is 2.56. During the sample period the firms are performing well with a median Stock Return of 17.3% and ROA of 13.2%. Moreover, the median firm is moderately levered with a Leverage of 16.1%; its market value of equity is $12.71 billion; it makes Capital Expenditure of 4.3% and experiences Volatility of 0.083. FirmSize is defined as the natural logarithm of market value of equity. The mean and median values for CEOage are respectively 56.7 and 57. The MBA dummy takes an average value of 0.39, indicating that 39% of the sample CEOs have an MBA.  In Panel B of Table 23, we further describe firms’ characteristics classified by their CEO birth seasons. The firms with summer-born CEOs tend to have higher market valuation, better performance, bigger stock volatility and capital expenditure, but smaller market value of equity and lower financial leverage. Table 23 reports the correlation matrix of explanatory variables. All the correlation coefficients are below 0.5 in magnitude.  91  Table 23 Descriptive Statistics Panel A: Descriptive Statistic of Firm Characteristics The sample consists of 2168 firm-year observations managed by 321 CEOs from 1992 to 2006. All the sample firms are S&P 500 companies. We obtain stock price data from CRSP and accounting data from Compustat, and collect the CEO birth data from the Biography Resource Center. Following Gompers et al. (2003), Tobin’s Q is computed as the market value of assets divided by book value of assets. M/B is the ratio of market value of equity over book value of equity. Summer is a dummy variable which is equal to 1 if the CEO was born in July, August, or September. RelativeAge is how many days a student is older than the hypothetical youngest student in the class. Latestqt is a dummy variable which is equal to 1 if RelativeAge is less than 90 days (roughly one quarter). Relativeqt is a categorical variable and is constructed based on quartiles of RelativeAge. Relativeqt is equal to 1 if the values of RelativeAge fall into the first quartile of RelativeAge, equal to 2 if the second quartile, equal to 3 if the third quartile, and equal to 4 if the last quartile. StockReturn is the annual stock return of the firm. Volatility is the stock return standard deviation based on the monthly return of past three years. ROA is the accounting return of assets, obtained as the ratio of earnings before interest and taxes to total assets. Leverage is the ratio of long-term debt (book value) over total assets. MV Equity ($billion) refers to the market value of equity computed as the yearly closing stock price multiplied by the number of shares outstanding. Capital Expenditure is the firm’s yearly capital expenditure normalized by total assets. CEOage measures the age of the CEO. MBA is a dummy variable, taking the value of one if the CEO has an MBA degree and zero otherwise. All the dollar-value variables are measured in 2006-constant dollars. Mean  Std  5th Pct  Median  95th Pct  No. of Obs.  Tobin’s Q  2.18  1.82  0.98  1.49  6.06  2168  M/B  3.76  3.65  0.88  2.56  11.84  2168  Summer  0.24  0.43  0  0  1  2168  RelativeAge  176  97  26  179  321  1475  Latestqt  0.24  0.43  0  0  1  1475  Relativeqt  2.50  1.12  1  2  4  1475  Stock Return  18.4%  30.9%  -30.8%  17.3%  69.3%  2168  Volatility  0.092  0.042  0.045  0.083  0.17  2168  ROA  14.1%  8.3%  2.4%  13.2%  30.3%  2168  Leverage  18.3%  13.5%  0.1%  16.1%  42.1%  2168  MV Equity ($B)  32.34  52.25  2.31  12.71  130.17  2168  Capital Expenditure  5.2%  4.4%  0  4.3%  14.3%  2168  CEOage  56.7  6.48  46  57  67  2168  MBA  0.39  0.48  0  0  1  2168  92  Panel B: Firm Characteristics Classified by CEO Birth Season The sample consists of 2168 firm-year observations managed by 321 CEOs from 1992 to 2006. All the sample firms are S&P 500 companies. In the sample, 384 firm-year observations are managed by 66 CEOs who are born in the summer season, which includes July, August and September. The middle column and final column give the difference of the two means and the two medians, respectively. The tests of means are based on t-statistics; the tests of medians are based on Wilcoxon signed tests. The notation ***, ** and * denote statistical significance at the 1%, 5% and 10% level, respectively.  Mean  Median  Summer  Non Summer  Difference  Summer  Non Summer  Difference  (1)  (2)  (1)-(2)  (4)  (5)  (4)-(5)  Tobin’s Q  2.32  2.13  0.19**  1.46  1.50  -0.04  M/B  3.76  3.75  0.01  2.44  2.57  -0.13  Stock Return  0.21  0.18  0.04**  0.19  0.17  0.02*  Volatility  0.10  0.09  0.01***  0.09  0.08  0.01***  ROA  0.15  0.14  0.01*  0.13  0.13  0.00  Leverage  0.17  0.19  -0.02***  0.14  0.16  -0.02***  MV Equity  30.65  32.34  -1.69  10.01  12.71  -2.7***  0.06  0.05  0.01***  0.045  0.043  0.002**  CEOage  56.32  56.93  -0.61*  57.00  57.00  0.00  MBA  0.34  0.41  -0.07***  0.00  0.00  0.00***  Capital Expenditure  Table 24 Correlation Matrix The sample consists of 2168 firm-year observations managed by 321 CEOs from 1992 to 2006. Variables used in this matrix are defined in Panel A of Table 23. Summer takes the value of one if the CEO is born in the summer, and zero otherwise. FirmSize is defined as the natural logarithm of market value of equity. Correlations with absolute value greater than 0.1 are significant at the 1% level. (1) Summer (1)  (2)  (3)  (4)  (5)  (6)  (7)  (8)  1  ROA (2)  0.04  1  FirmSize (3)  -0.06  -0.07  1  CEOage (4)  -0.04  -0.06  0.16  1  Volatility (5)  0.08  -0.06  -0.24  -0.16  1  MBA (6)  -0.06  0.02  -0.02  -0.17  -0.07  1  Leverage (7)  -0.09  -0.26  0.19  -0.02  -0.09  0.05  1  0.06  0.46  -0.02  -0.04  0.00  -0.06  -0.07  1  0.05  0.02  -0.19  -0.07  0.1  -0.03  -0.05  -0.01  Capital Expenditure (8) Stock Return (9)  (9)  1  93  4.5 The Prevalence and Performance of CEOs by Birth Season  A. Distribution of CEOs by Birth Seasons  Figure 1 shows the number of CEOs sorted by birth season. For convenience, January to March, April to June, July to September, and October to December are respectively referred to as winter, spring, summer and fall. We see 66 of the 321 S&P 500 CEOs were born in the summer, the smallest number among the seasons. The season with the most CEOs is winter with 92, followed by fall with 82 CEOs and spring with 81 CEOs. This is close to what we would expect from a relative-age effect, particularly where there is some blurring between summer and fall around the variation across U.S. states with regard to September. Most importantly, we find that only around 20% of the sample CEOs were born in the summer.  Figure 1 Number of CEOs by Birth Season This figure reports the number of CEOs classified by their birth seasons. The sample consists of 321 CEOs of S&P 500 companies from 1992 to 2006. Spring includes April, May, and June. Summer includes July, August, and September. Fall includes October, November, and December. Winter includes January, February, and March.  94  In determining the statistical significance of the number of summer born in the dataset of 321 CEOs we define a summer born dummy, summerCEO, as one if the CEO is born in the summer and zero otherwise. With the null-hypothesis that the population birth number is uniformly distributed across the four seasons, we conduct a two-tail t-test versus the null that summerCEO = 0.25. The null is rejected with a corresponding p-value of 0.05.  Figure 2 Season of Birth: CEO Sample versus U.S. Population This figure compares the proportion of CEOs born in the four seasons to that of the U.S. population. The CEO sample consists of 321 CEOs of S&P 500 companies from 1992 to 2006. The U.S. population sample consists of all births from 2000 to 2005. Spring includes April, May, and June. Summer includes July, August, and September. Fall includes October, November, and December. Winter includes January, February, and March.  The test above assumes that the distribution of births is uniform throughout the year. In actual fact, however, United States births follow a seasonal pattern. In particular, by examining birth information of the U.S. population from 1978 to 1987 Nunnikhoven (1992) showed that there is above-average birth frequency in the summer. This means that if we compare the seasonal summer-born CEO numbers with the actual summer-born numbers, the presence of a particularly low number will be more striking.  95  In order to judge the relevance of comparing CEO birth frequencies with the actual birth seasonality we consider birth information for the U.S. population between 2000 and 2005 from the National Center for Health Statistics (NCHS).39 NCHS provides information on the number of births each month from 2000. In Figure 2, we compare the proportion of CEOs born in the four seasons with the proportions for the U.S. population. Consistent with Nunnikhoven (1992), summer has higher birth frequency for the population than other seasons, with 26.3% of births being in the summer. It is apparent that the result that fewer CEOs are born in the summer is not because of the seasonality of births. Indeed, when we re-do the t-test with the null hypothesis: summerCEO=0.263 the null hypothesis is rejected and the corresponding p-value is 0.02. This greater significance is no surprise since we are comparing 20% of CEOs to 26.3% of the population born in summer rather than 25%.  B. Season of CEO Birth and Market Valuation  In this section, we investigate the relationship between the season of CEO birth and firms’ valuations. We control for potentially important firm characteristics, including ROA, FirmSize, Volatility, Leverage, and Capital Expenditure. We also include MBA and CEOage to control for CEO education background and age: Betrand and Schoar (2003) have shown that these two variables significantly influence managerial style. Moreover, we use year dummies to capture macroeconomic and time trend effects, as well as firm fixed effect to control for unobserved heterogeneity and industry effects. Specifically, we estimate the following regression:  Valuationit  a0  a1Summerit  a2 ROAit 1  a3 FirmSizeit 1  a4CEOageit a5Volatilityit 1  a6 MBAit  a7 Leverageit 1  a8CaptialExpenditureit 1 (1) YearDummy  Firm Fixed Effect   it  where i indexes firms and t indexes years. The dependent variable is the firm’s valuation, 39  The birth information can be found at http://www.cdc.gov/nchs/births.htm.  96  measured by Tobin’s Q or by the M/B ratio. The primary independent variable of interest is Summer, a dummy which equals one if the firm’s CEO is born in the summer and zero otherwise. Estimating a significant positive coefficient for a1 would reject the null hypothesis that CEOs born in the summer are equally successful as CEOs born in the other seasons.  Table 25 shows a positive relation between firms’ valuations and the Summer dummy; the relation is both statistically and economically significant. The dependent variable in Column (1) is Tobin’s Q, where the coefficient of Summer is 0.36 and is significant at the 1% level. The interpretation of this coefficient is as follows: since we control for firm fixed effects, the result indicates that, within a firm, when the CEO is summer born so the dummy, Summer, goes from zero to one, Tobin’s Q increases by 0.36 compared to the sample median of 1.49. The median firm in our sample has a market value of assets (equity and total debt in 2006 dollars) of approximately $30.4 billion, and so the positive effect on Tobin’s Q of 0.36 means an enhanced firm value of about $7.34 (0.36×30.4/1.49) billion. Clearly, this result is very economically significant.  The M/B ratio is considered as an alternative measure of the effect of a CEO being summer born in Column (2). The variable Summer has a coefficient of 0.55 which is significant at the 1% level. This indicates that for a firm, if its CEO was born in the summer so that the dummy, Summer, goes from zero to one, there is an associated increase in M/B of 0.55. Since our median sample firm has a market value of equity of $12.7 billion and M/B of 2.56, an increase in M/B of 0.55 is associated with an increase in firm value of approximately $2.73 (0.55×12.7/2.56) billion. Again, this is economically significant.  The coefficients on the control variables in the regression are generally consistent with those in the existing literature. As shown in the table, firms tend to have higher market valuation  97  Table 25 Season of CEO Birth and Firms’ Valuation The sample consists of 2168 firm-year observations from 1992 to 2006. In the sample, 384 firm-year observations are managed by the CEOs who are born in the summer season. The summer season includes July, August and September. The dependent variables are Tobin’s Q and M/B ratio. Tobin’s Q is computed as the market value of assets divided by book value of assets. M/B is measured as the ratio of market value of equity over book value of equity. Summer takes the value of one if the CEO is born in the summer, and zero otherwise. Column (1) reports the results of the regression with Tobin’s Q as the dependent variable, while Column (2) reports the regression results in which M/B is the dependent variable. Both regressions use an OLS model controlling for firm fixed effects. P-values are reported in brackets. We use the notation ***, ** and * to denote statistical significance at the 1%, 5% and 10% level, respectively.  VARIABLES Summer ROA FirmSize CEOage Volatility  (1)  (2)  Tobin’s Q  M/B  0.36***  0.55**  (0.003)  (0.05)  9.05***  15.7***  (0.000)  (0.000)  0.12***  0.33***  (0.01)  (0.002)  -0.02***  -0.04***  (0.001)  (0.008)  4.02***  4.25  (0.000)  (0.12)  -0.27***  -0.73***  (0.01)  (0.004)  0.07  1.21  (0.88)  (0.16)  -0.46  -2.88  (0.66)  (0.24)  Year Dummy  Yes  Yes  Firm Fixed Effect  Yes  Yes  0.31***  -0.23***  MBA Degree Leverage Capital Expenditure  Constant  (0.000)  (0.000)  N  2168  2168  Adjusted R2  15%  12%  when they have better accounting performance, when they are bigger firms, and when they experience higher stock volatility. Older CEOs tend to lower market valuations. Surprisingly, MBA degrees are associated with lower market valuation. The negative coefficient of MBA may  98  share the same cause of the underperformance of non-summer born CEOs: Only exceptionally capable people can manage to become CEOs without an MBA degree and conditional on becoming CEOs, those without MBA degrees are better at increasing firms’ valuation. Most importantly, the results in Table 25 strongly support the relative-age effect in the context of senior level corporate management, with CEOs born in the summer creating higher market value than CEOs born in other seasons.  C. Season of CEO Birth and Stock Returns  This section investigates the relation between the season of CEO birth and stock returns. In order to do this, we construct portfolios of companies based on the CEOs’ birth seasons. In particular, we construct a Spring Portfolio, a Summer Portfolio, a Fall Portfolio and a Winter Portfolio. We also construct a non-Summer Portfolio as a value-weighted portfolio of stocks of companies whose CEOs are born in the spring, fall, and winter. All of these portfolios are reset once every year according to the birth-dates of the CEOs.  Figure 3 shows the stock performance of portfolios being selected by CEO birth season. We find that an investment of $1 in the Summer Portfolio on January 1, 1992, the beginning of our data period, would have grown to $27.4 by December 31, 2006. In contrast, a $1 investment in the non-Summer Portfolio would have grown to $14.8 over this period. This is equivalent to annualized returns of 24.7% for the Summer Portfolio and 19.7% for the non-Summer portfolio, a difference of approximately 5% per year. The Winter, Spring, and Fall Portfolios perform very similarly to each other; their annualized returns are 21.2%, 18.6%, and 19.2%, respectively. It is apparent that stocks of companies with CEOs born in the summer outperform stocks of companies with CEOs born in the other seasons.  To ensure that differences in riskiness or “style” of the portfolios are not driving the performance  99  differences, we run the following four factor model of Carhart (1997) over the daily portfolio returns: Rt    1RMRFt  2 SMBt  3 HMLt  4 Momentumt   t (2) where Rt is the excess return of a certain portfolio relative to the risk-free rate on day t. The first factor RMRFt is the day t value-weighted market return minus the risk-free rate. Figure 3 Performance of Portfolios by CEO Birth Season This figure shows the stock performance of the portfolios classified by CEO birth seasons. The Y axis represents the dollar value by December 31, 2006 for a $1 investment in the portfolio on January 1, 1992. The corresponding equivalent annualized rates of return are reported in parentheses. The sample consists of 2168 firm-year observations from 1992 to 2006. In the sample, 384 firm-year observations are managed by the CEOs who are born in the summer season. We construct stock portfolios based on the CEO’s birth season. In particular, Spring Portfolio, Summer Portfolio, Fall Portfolio, Winter Portfolio, and non-Summer Portfolio are the value-weighted portfolios of stocks whose CEOs are born in the spring, summer, fall, winter, and non-summer respectively.  The second and third factors, SMBt and HMLt , are based on Fama and French (1993) and represent the difference in returns between portfolios of small versus large firms, and between portfolios  of  high  and  low  book-to-market  ratios,  respectively.  The  momentum  100  factor, Momentumt , follows Carhart (1997) and captures predictability of immediate past returns toward future returns.40 The parameter  measures the excess daily return of the portfolio relative to the four factors. To demonstrate the economic significance we also report the annualized excess return which involves multiplying the  parameter by 252 trading days of the year. Table 26 Season of CEO Birth and Firms’ Stock Performance The sample consists of 2168 firm-year observations from 1992 to 2006. In the sample, 384 firm-year observations are managed by the CEOs who are born in the summer season. We construct stock portfolios based on the CEO’s birth season, Spring Portfolio, Summer Portfolio, Fall Portfolio, Winter Portfolio, and non-Summer Portfolio. These are value-weighted portfolios of stocks whose CEOs are born in the spring, summer, fall, winter, and non-summer, respectively. All of these portfolios are reset every year. We estimate four-factor regressions of value-weighted daily returns for portfolios classified by the CEO’s birth season. The first row contains the results when we use the portfolio that buys the summer portfolio and sells short the non-summer portfolio. Similarly, the second, third and four columns report the results of buying the summer portfolio and selling short spring, fall, and winter portfolios, respectively. The explanatory variables are RMRF, SMB, HML, and Momentum. There variables are the returns to zero-investment portfolios capturing market, size, book-to-market, and momentum effects, respectively (Fama and French (1993) and Carhart (1997)). P-values are reported in brackets. We use ***, **, and * to denote statistical significance at the 1%, 5% and 10% level, respectively.   Summer Non-summer Summer  0.033*** (0.004) 0.044***  Spring  (0.002)  Summer  0.03***  Fall  (0.003)  Summer  0.026**  Winter  (0.012)   Annualized 8.32% 11.09% 7.56% 6.55%  RMRF  SMB  HML  Momentum  0.05***  0.18***  -0.55***  -0.03**  (0.000)  (0.000)  (0.000)  (0.02)  0.08***  0.18***  -0.81***  -0.06***  (0.000)  (0.000)  (0.000)  (0.000)  0.08***  0.22***  -0.46***  -0.01  (0.000)  (0.000)  (0.000)  (0.36)  -0.02  0.09***  -0.43***  -0.01  (0.19)  (0.000)  (0.000)  (0.39)  The first row of Table 26 shows the results of estimating Equation (2) where the dependent variable Rt is the daily return difference between the Summer and non-Summer Portfolios. Thus, 40  These factors are from Kenneth French’s website, http://mba.tuck.dartmouth.edu/pages/faculty/ken.french.  101  the variable  is the abnormal return on a zero-investment strategy that buys the Summer Portfolio and sells short the non-Summer Portfolio. For this specification, the  parameter is 0.033% per day, or approximately 8.32% per year. This point estimate is statistically significant at the 1% level.  The second row of Table 26 reports the excess return from buying the Summer Portfolio and shorting the Spring Portfolio. The  coefficient is 0.044% and is significant at the 1% level, indicating that the annual abnormal return is about 11.09%. In the remaining two rows, the dependent variables are the daily return difference between the Summer Portfolio and the Fall Portfolio, and the return difference between the Summer Portfolio and the Winter Portfolio. The   parameters are 0.03% and 0.026%, respectively, and are statistically significant at the 5% level. This economically important result indicates annual abnormal returns of 7.56% and 6.55%, respectively.  Overall, the results in this section show that CEOs born in the summer generate higher stock returns than other CEOs. The stock return difference is both statistically and economically significant. If shareholders fully understood the relative-age effect, then the portfolios sorted by CEO birth seasons should not have generated any abnormal returns. Our results suggest that the stock market has not recognized the importance of CEO birth season in predicting managerial success.  Another natural question is whether summer-born CEOs receive higher compensation than others. Consistent with corporate theory regarding compensation, we found no difference in compensation between summer-born CEOs and CEOs born in other seasons. Standard principal-agent theory predicts that a CEO’s compensation contract should be based only on observable factors such as firm size, performance and risk (see Holmstrom and Milgrom (1987) for example). Unobservable/unverifiable factors, like CEO talent, should have no impact on  102  CEO compensation, even though CEO talent might contribute to performance. In other words, as long as we properly control for observable firm characteristics, CEO birth season should have no influence on CEO compensation.  Employing data from ExecuComp, we calculate the CEO’s total annual compensation as the sum of the CEO’s yearly salary, bonus, payouts from long-term incentive plans, the value of restricted stock granted, the Black-Scholes value of stock options granted, and all other compensation (ExecuComp Item TDC1). In order to determine the importance of CEO compensation, we first regress Ln(TDC1) on the Summer dummy defined in Section IV, as well as year dummies and industry dummies of Fama and French (1997)’s 48 industries. The coefficient on Summer is 0.15 and is significant at the 1% level, indicating that when we do not control for firm-specific characteristics, summer-born CEOs are paid more than other CEOs. In the second step, we add control variables for FirmSize, Stock Return, Volatility, ROA, Leverage, M/B, Capital Expenditure, CEOage, MBA degree, year fixed effects, and firm fixed effects. The coefficient on Summer declines to 0.07 and is insignificant. This result is consistent with the agency-based argument expressed earlier that, conditional on similar firm performance and other characteristics, summer-born CEOs’ compensation should be similar to that of CEOs born in other seasons.  Unlike July and August, September can add ambiguity regarding school entry-date cutoff. If a school’s cutoff date is in early September, then children born in September could be among the oldest within their class. To address this possibility, we include only July and August in the summer season and replicate all the empirical analysis. Our results are qualitatively similar. Based on this new definition of summer, we find that (1) 14% of CEOs are born in the summer relative to 17.6% of the U.S. population, (2) summer-born CEOs are associated with $4.3 billion higher market value of equity, and (3) the portfolio strategy of buying stock of summer-born CEOs and short selling stock of non-summer-born CEOs generates annual abnormal returns of  103  9%.  To the extent that different states apply different school cutoff dates, the relative age effect may not be properly captured by the dummy variable we have used to indicate summer birth. Therefore, as a further check on the robustness of our results, we construct another variable which takes account of the different states’ school-year cutoff dates. This involves taking the following steps. We begin by obtaining information, where available, for the state of birth for each U.S. born CEO. We use the state of birth as a proxy for the state where they first enroll in school. We then obtain the school cutoff dates for different states from Bedard and Dhuey (2006), Appendix 2, which is adapted to our data and shown in Table 27. To obtain a more accurate measure of relative age than our summer dummy, we calculate how many days a student is older than the hypothetical youngest student (who was born 1 day before the cutoff day, i.e. the youngest student allowed to enroll) in the class year. To illustrate how we construct the relative age variable, let us consider two students A and B with birth-dates respectively on May 1, 1942 and December 1, 1942. Assume both of them need to enroll in elementary school at the age of 7 and that the school cutoff date is September 1. In September 1949, student A starts elementary school and is 122 days older than the hypothetical youngest student in the class (who was born on August 31, 1942). Student B, however, enrolls in the next year on September 1, 1950 and is 275 days older than the hypothetical youngest student in the class year.  104  Table 27 U.S. School Cutoff Dates This table reports the school cutoff dates for the U.S. states where the CEOs in our sample were born. We also report the distribution of CEOs according to their states of birth as well as the corresponding percentages. The source of the data is from Bedard and Dhuey (2006) (see Appendix 2: page 1471, QJE). Since all CEOs in our sample were born before 1965, we only report the cutoff dates used by schools before 1976 since some states began to change their cutoff dates after 1976. Missing values in school cutoff dates are caused by the fact that a unique school cutoff date does not exist in some states or the cutoff dates are simply unavailable. U.S. State  School cutoff dates  Number of CEOs  Percentage  Alabama  1-Oct  10  0.53  Arkansas  1-Nov  16  0.85  California  1-Dec  76  4.03  Colorado  NA  2  0.11  Connecticut  1-Jan  16  0.85  Delaware  1-Jan  9  0.48  Florida  1-Feb  24  1.27  Georgia  1-Jan  2  0.11  Hawaii  1-Jan  7  0.37  Idaho  15-Oct  4  0.21  Illinois  1-Dec  128  6.79  Indiana  NA  78  4.14  Iowa  15-Oct  48  2.55  Kansas  1-Sep  13  0.69  Kentucky  1-Jan  4  0.21  Louisiana  1-Jan  11  0.58  Maryland  NA  30  1.59  Massachusetts  NA  84  4.46  Michigan  1-Dec  47  2.49  Minnesota  1-Sep  34  1.8  Mississippi  1-Jan  47  2.49  Missouri  1-Oct  53  2.81  Montana  15-Sep  9  0.48  Nebraska  15-Oct  17  0.9  Nevada  1-Oct  9  0.48  New Hampshire  1-Oct  5  0.27  New Jersey  NA  121  6.42  New Mexico  1-Sep  9  0.48  New York  1-Dec  346  18.37  North Carolina  15-Oct  46  2.44  North Dakota  1-Nov  6  0.32  105  U.S. State  School cutoff dates  Number of CEOs  Percentage  Ohio  1-Oct  116  6.16  Oklahoma  1-Nov  15  0.8  Oregon  15-Nov  12  0.64  Pennsylvania  1-Feb  146  7.75  Rhode Island  1-Jan  17  0.9  South Carolina  1-Nov  9  0.48  South Dakota  1-Nov  26  1.38  Tennessee  1-Nov  7  0.37  Texas  1-Sep  57  3.03  Utah  NA  31  1.65  Virginia  1-Oct  38  2.02  Washington  NA  46  2.44  West Virginia  1-Nov  15  0.8  Wisconsin  1-Dec  36  1.91  Wyoming  15-Sep  2  0.11  1,884  100  Total  In Figure 4, we show the distribution of the relative age of CEOs in our sample. We divide the relative age into four ranges with the first range including the youngest CEOs in their class year and the fourth range including the oldest CEOs in their class year. The number of CEOs, whose relative age falls into the first range, is the second smallest. This suggests that the chance of becoming a CEO for those who are the youngest in their class year is smaller than average. In Table 28, we use the calculated relative age instead of the summer born dummy variable to replicate the econometric analysis we have done in Table 25 concerning the effect on company valuation. The coefficient on relative age is negative and statistically significant, suggesting that firms headed by CEOs who were relatively older than their classmates tend to have lower market valuation, which is consistent with our previous conclusions.  106  Figure 4 Number of CEOs by Relative Age This figure reports the number of CEOs classified by their RelativeAge. The sample consists of 220 CEOs of S&P 500 companies from 1992 to 2006, whose RelativeAge data are available.  In the baseline analysis, we use the variable Summer to capture whether CEOs were relatively younger than their classmates. Since we have information on CEOs’ relative age for a subset of CEOs, we are able to identify whether CEOs were born in the latest possible quarters for school enrollment in their respective age cohorts in the subset of CEOs. We construct a variable called Latestqt as a dummy variable to indicate whether a CEO was born in the latest possible quarter for school enrollment (i.e. less than 90 days older than the youngest possible student in class). In Table 29, we test whether the empirical impact we have identified thus far still exists if we use Latestqt as the independent variable of interest. Regression results in Table 29 suggest that, firms managed by CEOs born in the latest possible quarter for school enrollment are more likely to receive higher market valuation.  If all states had the same October 1 birth-date requirement for enrollment in the school year, our definition of summer would perfectly coincide with the third calendar quarter: those born July 1  107  though September 30 would be the youngest in class. Similarly, those born October 1 through December 31 would be the oldest, and so on. We would then be able use calendar quarter of birth to see how quartile of age in class is related to CEO performance. However, the fact that there exists variation in school cutoffs dates across states means that to study the effect of relative quarter of birth in a school grade, we need to create four “relative quarters” based on the quartiles of RelativeAge. We define the first quartile of RelativeAge less than 92 days older than the hypothetical youngest student in class. The second quartile of RelativeAge is more than 92 but less than 183 days older than the hypothetical youngest student in class. The third quartile of RelativeAge is 183 to 273 days older than the hypothetical youngest student in class, and the fourth quartile of RelativeAge is 274 to 364 days older than the hypothetical youngest student in class. Compared with Latestqt, relative quarters have four categories and enable us to compare the value created by CEOs born in each relative quarter with that by CEOs born in the latest possible quarter for school enrollment. This is shown in Table 30. Compared with the first relative quarter, CEOs born in the second, third, or fourth relative quarters are associated with lower market valuation of the firms they manage. The prediction from Table 30 is consistent with the baseline results: firms tend to have higher market valuation if their CEOs were relatively younger than their classmates. Not surprisingly, CEOs born in the third relative quarter tend to perform worse than CEOs born in the second relative quarter due to the fact that CEOs born in the third relative quarter are older than those born in the second relative quarter. There may be some ambiguity in the fourth relative quarter which has a smaller negative impact than the third relative quarter. This would occur if some parents push their children to go to school one year earlier if they were born shortly after the school cutoff dates. Nevertheless, the economic magnitude of the coefficient of the fourth relative quarter is still twice as big as that of the second relative quarter.  108  Table 28 Relative Age and Firms’ Valuation The sample consists of 1466 firm-year observations from 1992 to 2006 conditional on that the values of RelativeAge are known. The dependent variables are Tobin’s Q and M/B ratio. Tobin’s Q is computed as the market value of assets divided by book value of assets. M/B is measured as the ratio of market value of equity over book value of equity. RelativeAge is how many days a student is older than the hypothetical youngest student in the class. Column (1) reports the results of the regression with Tobin’s Q as the dependent variable, while Column (2) reports the regression results in which M/B is the dependent variable. Both regressions use an OLS model controlling for firm fixed effects. P-values are reported in brackets. We use the notation ***, ** and * to denote statistical significance at the 1%, 5% and 10% level, respectively.  RelativeAge  (1) Tobin’s Q  (2) M/B  -0.002***  -0.007***  (0.005)  (0.001)  8.592***  12.665***  (0.000)  (0.000)  -0.486***  -0.888***  (0.000)  (0.000)  -0.044***  -0.101***  (0.000)  (0.000)  1.425  -1.980  (0.291)  (0.538)  -0.255*  -0.688*  (0.099)  (0.062)  -0.618  -1.427  (0.192)  (0.206)  0.671  -0.231  (0.591)  (0.938)  Year Dummy  Yes  Yes  Firm Fixed Effect  Yes  Yes  8.554***  18.172***  (0.000)  (0.000)  N  1466  1466  Adjusted R2  15.7%  10.3%  ROA FirmSize CEOage Volatility MBA Degree Leverage Capital Expenditure  Constant  109  Table 29 The Latest Possible Quarter and Firms’ Valuation The sample consists of 1466 firm-year observations from 1992 to 2006 conditional on that the values of RelativeAge are known. The dependent variables are Tobin’s Q and M/B ratio. Tobin’s Q is computed as the market value of assets divided by book value of assets. M/B is measured as the ratio of market value of equity over book value of equity. Latestqt is a dummy variable which is equal to 1 if RelativeAge is less than 90 days (roughly one quarter). Columns (1) reports the results of the regression with Tobin’s Q as the dependent variable, while Column (2) reports the regression results in which M/B is the dependent variable. Both regressions use an OLS model controlling for firm fixed effects. P-values are reported in brackets. We use the notation ***, ** and * to denote statistical significance at the 1%, 5% and 10% level, respectively.  Latestqt  (1) Tobin’s Q  (2) M/B  0.57***  1.56***  (0.003)  (0.001)  8.62***  12.73***  (0.000)  (0.000)  -0.51***  -0.94***  (0.000)  (0.000)  -0.047***  -0.11***  (0.000)  (0.000)  1.25  -2.46  (0.353)  (0.445)  -0.26*  -0.72*  (0.091)  (0.051)  -0.70  -1.66  (0.140)  (0.142)  0.43  -0.92  (0.733)  (0.756)  Year Dummy  Yes  Yes  Firm Fixed Effect  Yes  Yes  8.41***  17.71***  (0.000)  (0.000)  N  1466  1466  Adjusted R2  15.7%  10.3%  ROA FirmSize CEOage Volatility MBA Degree Leverage Capital Expenditure  Constant  Since we are unable to obtain birth-dates for all CEOs in S&P 500 companies, we compare the characteristics of firms and their CEOs whose birth-dates are available with those whose birth-dates are not available. We find that firms with known CEO birth-dates are similar to those  110  with unknown CEO birth-dates on all variables considered except for market capitalization. We find that the average market capitalization of firms with known CEOs’ birth information is larger than those without CEOs’ birth information: coverage of CEO information is better for bigger firms to which more public attention is paid.41  Table 30 Relative Quarter and Firms’ Valuation The sample consists of 1466 firm-year observations from 1992 to 2006 conditional on that the values of RelativeAge are known. The dependent variables are Tobin’s Q and M/B ratio. Tobin’s Q is computed as the market value of assets divided by book value of assets. M/B is measured as the ratio of market value of equity over book value of equity. Relativeqt2 is a dummy variable and has the value of 1 if CEOs’ relative ages fall into the second quartile of RelativeAge. Similarly, Relativeqt3 and Relativeqt4 are both dummy variables and have the value of 1 if CEOs’ relative ages fall into the third or fourth quartile of RelativeAge, respectively. Columns (1) reports the results of the regression with Tobin’s Q as the dependent variable, while Column (2) reports the regression results in which M/B is the dependent variable. Both regressions use an OLS model controlling for firm fixed effects. P-values are reported in brackets. We use the notation ***, ** and * to denote statistical significance at the 1%, 5% and 10% level, respectively.  Relativeqt 2 Relativeqt 3 Relativeqt 4 ROA FirmSize CEOage Volatility  41  (1) Tobin’s Q  (2) M/B  -0.43*  -1.03*  (0.058)  (0.055)  -1.04***  -2.55***  (0.000)  (0.000)  -0.83***  -2.12***  (0.001)  (0.000)  8.69***  12.89***  (0.000)  (0.000)  -0.46***  -0.83***  (0.000)  (0.000)  -0.053***  -0.12***  (0.000)  (0.000)  1.41  -2.06  (0.296)  (0.520)  The percentage differences in the average values of key variables in the sample with versus the sample without  information on CEOs’ birth-dates are: CEO’s age (2 percent); M/B ratio (1 percent); Tobin’s Q (5 percent); market capitalization (185 percent); return on assets (9 percent); capital expenditure normalized by total asset (7 percent); leverage ratio (1 percent); stock return (4 percent); and stock return volatility (9 percent).  111  (1) Tobin’s Q  (2) M/B  -0.17  -0.52  (0.272)  (0.157)  -0.66  -1.53  (0.160)  (0.173)  0.41  -0.86  (0.745)  (0.771)  Year Dummy  Yes  Yes  Firm Fixed Effect  Yes  Yes  8.99***  19.02***  (0.000)  (0.000)  N  1466  1466  Adjusted R2  16.7%  11.2%  MBA Degree Leverage Capital Expenditure  Constant  4.6 Relative Age, CEO Birth-dates and Birthday-related Performance The two essential empirical results we have obtained, namely that the number of summer-born CEOs is disproportionately small, but that those summer-born who do become CEOs show better corporate performance, follow from a simple selection model based on leadership experience early in school. To be selected while young for activities which will play a role in achieving leadership positions when older - though the experience itself or even just from signaling by having been selected - a threshold of demonstrated performance is required. The threshold of demonstrated performance can be met by a combination of development, that while young is related to age, and through raw, innate ability. Non-summer born who achieve the threshold and thereby enjoy the leadership-related activities achieve this relatively more through development than through innate ability. On the other hand, the summer born who manage to overcome their relative age disadvantage enjoy the leadership-related activities relatively more through innate ability than through development. Via their greater development, more non-summer born have the opportunity to enjoy leadership- related activities and so more of them become CEOs. However, as adults, the development levels of the two groups converge - a  112  few months of age makes less and less difference over time – so performance depends only on innate ability. The summer born have more of this.  Symbolically, the main features of the argument above can be expressed as follows. Gaining leadership experience when young requires meeting a threshold, T, of demonstrated performance, pi, where pN is the performance of non-summer born, and pS is the performance of summer born. pi is a function of both innate ability and development. The probability, Pi(T), of meeting or exceeding the required threshold of demonstrated performance depends jointly on development, Di, and ability, Ai, i.e.   Pi (T )   f { p i ( Di , Ai )}dp i , T  i  N, S where f pi pi , , 0 p i Di Ai  When young, DN >DS, so ceteris paribus, non-summer born have higher probability of achieving the target threshold, PN(T)>PS(T). That is, more non-summer born are selected for leadership-related activities that affect the chance of becoming a CEO.  However, some summer born do achieve the target threshold because they are particularly able. They have outstanding innate ability within their cohort so that for them, AS>AN by enough to compensate for DN>DS. This innate ability of those summer born meeting the threshold for enjoying leadership experience results in higher performance when development levels converge, DN=DS=D, later in life. Then demonstrated performance depends only on innate ability, and AS>AN. That is, performance conditional on becoming a CEO is given by pi(Ai|D).  113  4.7 Conclusion This paper contributes to a growing literature about the relative-age effect, whereby younger, summer-born children are at a disadvantage versus their older classmates born in other seasons. Based on a sample of S&P 500 CEOs, we document that CEOs born in the summer are underrepresented. However, CEOs born in the summer are associated with higher market valuation and better stock performance. These conclusions have been reached based on conventional measurement of calendar quarters. They have also been confirmed taking care of the fact that different states use different cutoff dates to determine the group of students admitted to a grade; calendar birth-dates do not correspond exactly to students’ relative ages in class. In order to make allowance for this we first defined summer born based on just two months, July and August, rather than on the calendar quarter including September. Conclusions were not affected using this better defined measure of summer born. We also used the state level data to refine the measure of relative age in class. Using relative age data generates the same disproportionate number of non-summer born CEOs and superior performance of summer born CEOs.  We have provided an explanation for finding fewer summer-born CEOs but at the same time better performing summer-born CEOs. The explanation depends on the advantage that non-summer born individuals have in securing top jobs due to the leadership experiences they have enjoyed through their education, and the need for summer-born CEOs to be particularly capable within their cohort to overcome the disadvantages they face. The argument is similar to that often raised in the context of the superior ability of successful female managers. They are underrepresented in top corporate positions, but it is frequently argued that those who do succeed in achieving such positions are often better than their male counterparts.  114  4.8 References Ashworth, John, and Bruno Heyndels, “Selection Bias and Peer Effects in Team Sports,” Journal of Sports Economics, 8 (2007), 355-377. Baker, Joseph, and A. Jane Logan, “Developmental Contexts and Sporting Success: Birth Date and Birthplace Effects in National Hockey League Draftees 2000-2005,” British Journal of Sports Medicine, 41(2007), 515-517. Barnsley, Roger H., A. H. Thompson, and P. E. Barnsley, “Hockey Success and Birth-date: The Relative Age Effect,” Canadian Association of Health, Physical Education, and Recreation Journal, 51(1985), 23-28. Bedard, Kelly, and Elizabeth Dhuey, “The Persistence of Early Childhood Maturity: International Evidence of Long-Run Age Effects,” Quarterly Journal of Economics, 121 (2006), 1437-1472. Bertrand, Marianne, and Antoinette Schoar, “Managing with Style: The Effect of Managers on Firm Policies,” Quarterly Journal of Economics, 118(2003), 1169-1208. Carhart, Mark M., “On the Persistence in Mutual Fund Performance,” Journal of Finance, 52(1997), 57-82. Delorme, N., and M. Raspaud, “The Relative Age Effect in Young French Basketball Players: A Study on the Whole Population,” Scandinavian Journal of Medicine & Science in Sports, 2008, 1-8. Dhuey, Elizabeth, and Stephen Lipscomb, “What Makes a Leader? Relative Age and High School Leadership,” Economics of Education Review, 27(2008), 173-183. Esteva, Santiago, Francek Drobnic, and Jordi Puigdellivol, “Birth-date and Basketball Success,” FIBA Assist Magazine, 18(2008), 64-66. Fama, Eugene F., and Kenneth R. French, “Common Risk Factors in the Returns on Bonds and Stocks,” Journal of Financial Economics, 33(1993), 3-53. Giacomini, C. P., “Association of Birth-date with Success of Nationally Ranked Junior Tennis Players in the United States,” Perceptual and Motor Skills, 89(1999), 381-386. Glamser, Francis D., and John Vincent, “The Relative Age Effect among Elite American Youth Soccer Players,” Journal of Sport Behavior, 27(2004), 31-38.  115  Gompers, Paul, Joy Ishii, and Andrew Metrick, “Corporate Governance and Equity Prices,” Quarterly Journal of Economics, 118 (2003), 107-155. Holmstrom, Bengt, and Paul Milgrom, “Aggregation and Linearity in the Provision of Intertemporal Incentives.” Econometrica, 55(1987), 303-328. Hutchison, Dougal, and Caroline Sharp, “A Lasting Legacy? The Persistence of Season of Birth Effects,” National Foundation for Educational Research London U.K. working paper, 1999 Jürges, Hendrik, and Kerstin Schneider, “What Can Go Wrong Will Go Wrong: Birthday Effects in Early Tracking in the German School System,” University of Connecticut Unpublished manuscript, 2008 Kawaguchi, Daiji, “The Effect of Age at School Entry on Education and Income,” ESRI Discussion Paper Series 162, 2006 Nunnikhoven, Thomas S., “A Birthday Problem Solution for Nonuniform Birth Frequencies,” The American Statistician, 46(1992), 270-274. Thompson, Angus H., Roger H Barnsley, and George Stebelsky, “Born to Play Ball! The Relative Age Effect and Major League Baseball,” Sociology of Sport Journal, 8(1991), 146-151.  116  5 CONCLUDING CHAPTER  5.1 Conclusion and Discussions  Syndication is common practice among financial institutions. Investment bank syndicates, which underwrite most of the equity issuance businesses, serve as an effective organization form to alleviate the moral hazard problem among banks (Pichler and Wilhelm, 2001). Syndicated loans are also very common and have experienced a strong growth to over $1 trillion recently (see Sufi, 2007: page 629). In venture capital investments, around 60% of venture capital backed start-up companies received at least one syndicated financing rounds in the past two decades in the U.S. My first chapter contributes to the literature of venture capital by examining how VCs select syndication partners, and how their preferences for partners affect their successful exits from portfolio companies, and eventually, their survival.  Based on a comprehensive dataset of venture capital investments made to U.S. companies from 1990 to 2005, this paper finds that VCs strongly prefer similar partners. The preference for similarity may bring immediate financial benefits for VCs because companies funded by less heterogeneous syndicates are more likely to have IPOs or get acquired by other companies. Why not VCs simply syndicate with similar partners all the time? Subsequent analysis shows VCs whose partners are more heterogeneous are more likely to make more investments, diversify their portfolio, and enjoy a higher chance of survival.  Although VC industry is an attractive research context, the implications from this study are quite general and may also apply to other contexts such as alliances and social networks. There are also some limitations with this study. We do not have information about the financing contracts, from which we can have a better idea of how syndication was formed as a result of the bargaining among VCs. Such knowledge could improve the matching algorism to predict the  117  formation of syndication. Due to the relatively short sample period used in this study and the relatively young industry we are focusing on, it is difficult to examine how investor heterogeneity affects the long term performance of VC funds. The availability of more detailed information of financing contracts and more data will benefit future study on the above two questions. Another interesting question would be to look at the dynamics of venture capital networks and see what implications it has on the performance of VC funds.  Start-up companies have distinct characteristics from publicly traded companies and have limited choices of financing sources. The second chapter studies how the quality of a legal system affects start-up entrepreneurs’ choices of financing and the ownership structures. Prior work (La Porta et al.,1998) provided evidence on how the legal protection of investors affect the development of the financial markets and ownership concentration of large publicly traded companies. There has been no evidence shown in the context of start-ups which are mostly not high-tech companies and therefore not targets for venture capitalists. Our results suggest that the costs of equity financing of start-ups may be less sensitive to the quality of the law than debt financing. Given a decrease in the quality of law, shareholders could still use their ownership and control of company to substitute for the poor legal protection and making the ownership of start-ups less concentrated.  Given the evidence that start-up founders prefer to retain the ownership and control of their companies, one policy implication of this study can be that the improvement in the legal protection which may increase the supply of debt financing could lead to more entrepreneurial businesses. The limitation of this study is that we do not have detailed information on financing contracts and have little knowledge of the use of debt or equity on the firm level. Future studies may gather more information on entrepreneurs’ financing strategies as well as develop longitudinal data to capture how ownership structure evolves over time.  118  The third chapter focuses on CEOs of S&P 500 companies. Specifically, we study who are more likely to join the elite club of CEOs of S&P 500 companies and conditional on becoming a CEO, what makes a successful CEO. The cutoff dates of school admissions group together children with age differences up to one year, with summer-born children being younger than their non-summer-born classmates. Summer-born children on average are given fewer opportunities of gaining leadership related experience, and therefore are less likely to be selected as CEOs. Some exceptionally capable summer-born children, however, manage to gain such experience, and eventually become CEOs and add more value to their companies.  While the relative-age effect and the disadvantage faced by summer-born children have been extensively in the education literature, there has been no evidence on whether the relative-age effect plays any role in the selection and performance of CEOs. We find both statistically significant and economically important impact of the relative-age effect. The evidence we have shown in this paper is consistent with the argument in the context of female managers. Although there are fewer female executives, it is frequently argued that those who do manage to obtain top corporate positions may be better than their male counterparts. As the impact of relative-age effect is likely to be present in other contexts as well, we plan to explore how the relative-age effect is likely to affect individuals’ decision making, their risk preferences, and career choices in our future studies.  119  5.2 References Pichler, P. and W. Wilhelm (2001), “A Theory of the Syndicate: Form Follows Function”, Journal of Finance, Vol. 56, No. 6, pp. 2237-2264 Sufi, A. (2007), “Information asymmetry and financing arrangements: evidence from syndicated loans”, Journal of Finance, Vol. 62, No. 2, pp. 629-668  120  

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