where these ideas were initially discussed and much of the work was done. We are especially grateful to John Padgett, organizer of the States and Markets group at SFI for his support and insights. We have benefited from comments from the audience at seminars

Many financial markets are characterized by strong relationships and networks, rather than arm’s-length, spot-market transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company investments. VC firms that enjoy more influential network positions have significantly better fund performance, as measured by the proportion of investments that are successfully exited through an IPO or sale to another company. Similarly, the portfolio companies of betternetworked VC firms are significantly more likely to survive to subsequent financing and to eventual exit. Finally, we provide initial evidence on the evolution of VC networks.

for access to the data on MIT patents and for answering many questions about the data and

Powell and K.W. Koput, Co-PIs). We appreciate the helpful comments of Gernot

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We examine whether networks among incumbent venture capital firms help restrict entry into local VC markets in the U.S., thus improving VCs ’ bargaining power over entrepreneurs. We show that VC markets with more extensive networking among the incumbent players experience less entry. The effect is sizeable economically and robust to endogeneity concerns. Entry is accommodated if the entrant has established relationships with one or more incumbents in its home market. We also document that companies seeking venture capital raise money on worse terms in more densely networked markets, and that increased entry into a market is associated with companies receiving increased valuations.

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Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called “double semipartialing”, or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman–Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions.

Institutional investors exhibit substantial home-state bias in private equity. This effect is particularly pronounced for public pension funds, where overweighting amounts to 9.7 % of aggregate private-equity investments and 16.2 % for the average limited partner. Public pension funds ’ in-state investments underperform by 2-4 percentage points, achieving worse performance than both their own out-of-state investments and investments in their state by out-of-state investors. Overweighting in home state investments by public pension funds is greater in states with political climates characterized by more selfdealing, although local investments perform as poorly in these states as in other states. Relative to the performance of the rest of the private equity universe, overweighting and underperformance in local investments reduce public pension fund resources by $1.2 billion per year.

A Social Network Analysis of Corporate Venture Capital Syndication By Ju Kimberly Zheng The importance of social capital can be characterized by a well-known quote: it's not just what you know, but whom you know". Firms with rich social capital are more informed, more capable, and more competitive, because networks of resources are within their reach. Social capital is embedded in social networks, and social network analysis is the chief topic of this research. The network being examined contains 1126 venture capital (VC) programs, 206 of them being corporate venture programs, and the rest consisting of independent venture capital firms. Venture programs co-invest in portfolio firms following an identifiable pattern. This research attempts to explain this co-investment pattern using social network analysis. Four attributes of social networks are explored during this analysis: prominence, range, brokerage, and cohesion. The findings of the corporate venture capital network provide a number of implications for the theory of social capital. relationships in the network are indirect. Third, most prominent VCs are also the most powerful resource brokers in the network. Lastly, prominent VCs are likely to syndicate with other prominent VCs. iv Acknowledgements First and foremost, I would like to express my sincerest gratitude to my supervisor, Dr. Rod McNaughton for guiding me through this fantastic research with his valuable insights, recommendations, and encouragements. Without him, I could not have completed this thesis in such a short timeframe. Next, I would like to thank my readers, Dr. Scott Jeffrey and Dr. Cliff Blake for taking their time to read my thesis and provide constructive comments for improvements. Thanks to Dr. Brian Coz...