Does the US Dept. of Energy Distort the CleanTech Venture Capital Market? – a Social Network Analysis

1. Does the US Dept. of EnergyDistort the CleanTechVenture Capital Market? – a Social Network Analysis Russell Cameron Thomas @MrMeritology The 5th Biennial Atlanta Conference onScience and Innovation Policy September 26, 2013

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5. Claims of Critics (1) • DOE will distort co-investment: • “Loan guarantee programs could displace resources from non-politically motivated projectsto politically motivated ones.” • “This government involvement can distort the market signals further. For instance, the data shows that private investors tend to congregate toward government guarantee projects, independently of the merits of the projects, taking capital away from unsubsidized projects that have a better probability of success without subsidy and a more viable business plan.” Assessing the Department of Energy Loan Guarantee Program Veronique De Rugy, Mercatus Center, George Mason University, http://mercatus.org/sites/default/files/DeRugy_testimony_final.pdf

6. Claims of Critics (2) • DOE is a bad lead VC and undesirable orchestrator: • “Finally, when the government picks winners and losers in the form of a technology or a company, it often fails. First, the government does not have perfect or even better information or technology advantage over private agents. In addition, decision-makers are insulated from market signals and won’t learn important and necessary lessons about the technology or what customers want.” Assessing the Department of Energy Loan Guarantee Program Veronique De Rugy, Mercatus Center, George Mason University, http://mercatus.org/sites/default/files/DeRugy_testimony_final.pdf

7. Claims of Critics (3) • DOE investment will drive away additional VC investment in those ventures: • “…once the government subsidizes a portion of the market, the object of the subsidy becomes a safe asset. Safety in the market, however, often means low return on investments, which is likely to turn venture capitalists away. As a result, capital investments will likely dry out and innovation rates will go down.” • “…the resources that the government offers are so addictive that companies may reorient themselves away from producing what customers want, toward pleasing the government officials.” Assessing the Department of Energy Loan Guarantee Program Veronique De Rugy, Mercatus Center, George Mason University, http://mercatus.org/sites/default/files/DeRugy_testimony_final.pdf

8. What is a ‘Healthy’ Network?Benefits sought by VCs with Network Topology Implications • Resource exchange • Risk diversification • Superior selection • Reciprocal deal flow Therefore, a healthy VC co-investment network topology will exhibit evidence of all or most of these benefits

9. Different Benefits of Co-investment Have Distinct Effects on the Network Effect on the Network Benefit Sought Repeated Co-investments Few Structural Holes Effects on the Co-investment Network High Degrees High Degrees Low Eccentricity High Clustering Source: J. K. Zheng, “A social network analysis of corporate venture capital syndication,” Masters Thesis, University of Waterloo, 2004.

10. Hypotheses H1. The co-investment network structure is governed by a preferential attachment mechanism (‘rich get richer’) centered on DOE, and therefore exhibits a power law distribution in degrees. H2.Removing the DOE will significantly distort the network topology. H3.VC’s who co-invest DOE suffer a status drop in subsequent syndication transactions. H4. Ventures that receive DOE funding are less attractive to high status VCs in subsequent funding rounds.

11. Methods • Empirical social network analysis • Data: CrunchBase, 735 VCs, 1033 ventures, 637 node network • Network Analysis: • Power law analysis of degree distribution (PDF) • Statistical hypothesis testing • Stochastic simulation of network formation process

12. CleanTech VC Co-investment Network cliques 735 VCs, 1033 ventures, 637 node network US Dept. of Energy Nodes are color coded according to degree centrality—blue is lowest, red is highest, and cyan-yellow are intermediate values

13. H1: Preferential Attachment centered on DOE (Power Law distribution) Node Degree Distribution

14. H1: Preferential Attachment centered on DOE (Power Law) Distribution Fitting and Statistical Testing • Fitting distribution parameters • Discrete maximum likelihood estimators for fitting distribution parameters to data, along with the goodness-of-fit based approach to estimating the lower cutoff for the scaling region. • Testing Power Law and Power Law-with-cutoff • Likelihood ratio test compared to best fit distributions: • Log-Normal • Exponential • Stretched exponential • Gamma • p-values to evaluate validity of Likelihood ratio tests A. Clauset, C.R. Shalizi, and M.E.J. Newman, "Power-law distributions in empirical data" SIAM Review 51(4), 661-703 (2009)

15. best fit is Power Law with exponential cutoff VC Co-investment network is not governed solely by preferential attachment, centered on DOE

16. ‘Normal’ VC Network Degree Distributionalso has a Power Law with Exponential Cutoff Data source: J. K. Zheng, “A social network analysis of corporate venture capital syndication,” Masters Thesis, University of Waterloo, 2004. URL: http://hdl.handle.net/10012/854

17. H2: Removing DOE significantly distorts the network Degree Distribution with and without DOE All VCs Without DOE

18. H2: Removing DOE significantly distorts the network ⇒ Rejected Distribution of Closeness Centralitywith and without DOE The null hypothesis that the datasets have the same distribution is not rejected at the 5% level based on the Kolmogorov-Smirnov test.

19. H2: Removing DOE significantly distorts the network ⇒ Rejected Removing DOE Doesn’t Change Distribution Fits

20. H3: VCs who co-invest with DOE suffer drop in status H3: VCs who co-invest with DOE suffer drop in status ⇒ Rejected

21. H3: VCs who co-invest with DOE suffer drop in status ⇒ Rejected A VC’s degree is proxy for it’s status and reputation Pooled Distribution of Co-investors’ Degrees, Prior to co-investing with DOE vs. After

22. H3: VCs who co-invest with DOE suffer drop in status ⇒ Rejected Paired Tests, Prior to DOE vs. After (n = 19)

23. H4: Post-DOE VCs have lower status than Pre-DOE VCs ⇒ Rejected Pooled Distribution of Node Degrees, Pre-DOE VCs vs. Post-DOE VCs

24. Stochastic Simulation • 4 years simulated, 1000 ticks each • Same number of VC and ventures as empirical case • Initial VCs = 30, growing to 637 in two years (simulating market entry) • All firms scheduled for first funding with Gaussian distribution across the whole time span • Environment parameters set to match empirical case: • Size of syndicate, number of rounds, time between rounds • Experimental variables: • Probabilities for choice of VC syndication rules by lead VCs

25. Simulated Co-investment NetworkAgent Populations by Strategy Type One third of agents follow a mostly clustering strategy Syndication Rules p1: probability of selecting VC proportional to their degree p2 : probability of selecting a VC who has zero investments p3 : probability of selecting a VC proportional to their degree but who is also not already a co-investor p4 : probability of selecting a VC among near neighbors (‘friends of friends’) p5 : probability of selecting a VC without any criteria

26. Simulated vs. Empirical Empirical Simulated

27. Simulated Co-investment Network Empirical Simulated

28. Simulated vs. Empirical Degree Distributions Empirical Simulated logNode degrees logNode degrees

29. Clustering & Community Structure Nodes are color coded for community membership. 19 Communities total. Colors are unrelated.

30. Example Communities • Color coded by betweenness centrality. • Communities are named for the focal firm(s)

31. Conclusion • DOE does not distort CleanTech VC co-investment network • The CleanTech network topology is governed by a ‘healthy’ mixture of factors (in order of importance): • Preferential attachment (node degree, age, and VC size): • Resource exchange (information re: regulatory risk) • Reciprocation of deal flow • Superior selection • Clustering • Cohesion of relationships (e.g. regional and industrial VCs) • Reciprocation of deal flow • Risk Diversification • Financial diversification plays the smallest role

32. Questions?russell.thomas@meritology.com@MrMeritology

33. Appendix

34. Summary Statistics for 30 Simulation Runs Histograms of statistics for 30 realizations of the network simulation, 6000 clock cycles for each realization simulating 6 years. The corresponding statistics for the empirical data (red lines) are as follows: mean degrees = 10.12, variance degrees = 99.57, maximum degrees = 76, skewness degrees = 2.12, graph diameter = 8, size of largest connected component = 571 nodes, average node eccentricity = 6.22, Gini coefficient = 0.49.

35. Summary Statistics for 30 Simulation Runs Histograms of statistics for 30 realizations of the network simulation, 6000 clock cycles for each realization simulating 6 years. The corresponding statistics for the empirical data are as follows: mean degrees = 10.12, variance degrees = 99.57, maximum degrees = 76, skewness degrees = 2.12, graph diameter = 8, size of largest connected component = 571 nodes, average node eccentricity = 6.22, Gini coefficient = 0.49.

36. VC Syndication Process Source: Jääskeläinen, M. (2012). Venture capital syndication: Synthesis and future directions. International Journal of Management Reviews, May 2009

37. Source: J. K. Zheng, “A social network analysis of corporate venture capital syndication,” Masters Thesis, University of Waterloo, 2004.

38. References [1] R. Day, “What is the appropriate role for government in cleantech innovation?” Feb. 2012. URL: http://www.greentechmedia.com/cleantech-investing/post/what-is-the-appropriate-role-for-government-in-cleantech-innovation/ [2] J. A. Brander, R. Amit, and W. Antweiler, “Venture capital syndication: Improved venture selection vs. the valueadded hypothesis,” Journal of Economics & Management Strategy, vol. 11, no. 3, pp. 423–452, Sep. 2002. URL: http://onlinelibrary.wiley.com/doi/10.1111/j.1430-9134.2002.00423.x/abstract [3] M. Checkley, D. A. Higo ́n, and D. Angwin, “Venture capital syndication and its causal relationship with performance outcomes,” Strategic Change, vol. 19, no. 56, pp. 195–212, Aug. 2010. URL: http://onlinelibrary.wiley.com/doi/10.1002/jsc.870/abstract [4] M. Wright and A. Lockett, “The structure and management of alliances: Syndication in the venture capital industry,” Journal of Management Studies, vol. 40, no. 8, pp. 2073–2102, Dec. 2003. URL: http://onlinelibrary.wiley.com/doi/10.1046/j.1467-6486.2003.00412.x/full [5] J. Lerner, “The syndication of venture capital investments,” Financial Management, vol. 23, no. 3, pp. 16–27, Oct. 1994. URL: http://www.jstor.org/stable/3665618 [6] A. Lockett and M. Wright, “The syndication of venture capital investments,” Omega, vol. 29, no. 5, pp. 375–390, Oct. 2001. URL: http://www.sciencedirect.com/science/article/pii/S030504830100024X [7] Jääskeläinen, M. (2012). Venture capital syndication: Synthesis and future directions. International Journal of Management Reviews, May 2009. URL: http://onlinelibrary.wiley.com/doi/10.1111/j.1468-2370.2011.00325.x/full [8] Y. V. Hochberg, A. Ljungqvist, and Y. Lu, “Whom you know matters: Venture capital networks and investment performance,” The Journal of Finance, vol. 62, no. 1, pp. 251–301, Feb. 2007. URL: http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2007.01207.x/full [9] J. K. Zheng, “A social network analysis of corporate venture capital syndication,” Masters Thesis, University of Waterloo, 2004. URL: http://hdl.handle.net/10012/854 [10] CrunchBase, “CrunchBase API,” http://www.crunchbase.com/api, 2012. URL: http://www.crunchbase.com/api [11] V. De Rugy, “Assessing the Department of Energy Loan Guarantee Program”, Congressional Testimony, Mercatus Center, George Mason University, URL:http://mercatus.org/sites/default/files/DeRugy_testimony_final.pdf