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Specialization and Competition in the Venture Capital Industry

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Abstract

An important type of product differentiation in the venture capital (VC) market is industry specialization. We estimate a market structure model to assess competition among VCs—some of which specialize in a particular industry and others of which are generalists—and find that the incremental effect of additional same-type competitors increases as the number of same-type competitors increases. Furthermore, we find that the effects of generalist VCs on specialists are substantial, and larger than the effect of same-type competitors. Estimates from other industries typically show the incremental effects falling as the number of same-type competitors increases and the effects of same-type competitors as always being larger than the effects of different-type competitors. Consistent with the presence of network effects that soften competition, these patterns are more pronounced in markets that exhibit dense organizational networks among incumbent VCs. Markets with sparser incumbent networks, by contrast, exhibit competitive patterns that resemble those of other, non-networked industries.

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Notes

  1. Our work is part of an emerging literature on specialization in the VC industry. Sorenson (2008) explores the tradeoff between specialization as an exploitation strategy and exploration outside a VC’s area of expertise. Gompers et al. (2009) examine the relationship between specialization of individual human capital and VC success (without endogenizing the VC’s specialization decision). Hochberg and Westerfield (2012) compare VC fund specialization and portfolio size.

  2. In the literature on VC networks, Sorenson and Stuart (2001) explore how ties among VCs affect geographic patterns of exchange. Hochberg et al. (2007) examine the relationship between a VC’s network position and performance, while Hochberg et al. (2010) focus on the effects of networks on market entry and valuations paid to entrepreneurs. Hochberg et al. (2015) discuss various theories of network tie formation in VC, including the sharing of resources across VCs. We believe that ours is the first study to investigate differentiated competition and endogenous market structure in an industry that is networked in the way that the VC industry is.

  3. Two popular proxies used in the industrial organization literature for assessing competition are concentration indices, such as the Herfindahl, and own- and cross-price elasticities of demand. Both approaches suffer from shortcomings, and neither offers a definitive measure of competitiveness—particularly in markets with differentiated competitors. A theoretical basis for the use of the Herfindahl is a Cournot equilibrium with homogeneous firms, and thus it may not be well suited for assessing the extent of competition among differentiated competitors. While the cross-price elasticity of demand approach yields useful results for market structure simulations, it requires more detailed data than is commonly available and does not account for strategic interaction among firms in concentrated markets.

  4. The analytical framework derives from Bresnahan and Reiss (1991), who propose a simple yet flexible profit function that governs behavior in a symmetric equilibrium in market m. Bresnahan and Reiss (1991) assume that firms will participate in the market if they earn nonnegative profits. An ordered probit model is then used to estimate the parameters of their profit function. For additional development of the basic approach, see Berry (1992), Toivanen and Waterson (2005) and Seim (2006).

  5. As such, a limitation of our approach is that we cannot specifically address the potential heterogeneous impact of particular competitors within type—for example, whether some generalist VCs have more of a competitive effect than others. Indeed, to the extent that within-type heterogeneity may exist for our defined specialization strategies, this may have an impact on the value of the estimated parameters (see the discussion of this in the results section below). While we will not be able to say whether other types of heterogeneity may or may not have a similar effect, we can make statements with regard to whether this chosen measure of differentiation does matter.

  6. This specification function was chosen primarily to make the estimation tractable. Following Berry (1992) and Bresnahan and Reiss (1991), it can be interpreted as the log of a demand (market size) term multiplied by a variable profits term that depends on the number (and product types, in this case) of market competitors. There are no firm-specific factors included. The error term represents unobserved payoffs from operating as a particular type in a given market. It is assumed to be additively separable, independent of the observables (including the number of market competitors), and identical for each VC firm of the same type in a given market.

  7. The goal is to make the specification of the competitive effects as flexible as possible, while maintaining estimation feasibility. For example, in the cases where the data represent the “number” of competitors, we implicitly assume that the incremental effect of each additional competitor is the same. The specification also reflects the maximum number of VCs of each type, as discussed below.

  8. Alternatively, the set up is equivalent to assuming that the VCs have inherent types and make operating decisions that are embodied by the companies in which they make investments. As such, the specialization choice would be made upfront when the VCs initially raise the fund. With this framing, the decision can be rationalized either about operating in the market or about product-type choice; either way, we can make the inferences as described below. Empirically, we are examining the realization of this choice each period.

  9. We implicitly assume that VCs that operate in multiple geographic markets make their sector specialization decisions on a market-by-market basis.

  10. The Stackelberg game has the attractive feature that the highest payoff types will have the largest presence in the resulting market configuration. A natural alternative is a simultaneous move game; however, it has been well established that such a game has multiple equilibria, which precludes straightforward econometric estimation (see Tamer (2003)). We proceed with the Stackelberg assumption, in part relying on the finding in Mazzeo (2002) that parameter estimates are very similar across various game formulations. A unique equilibrium to this game is only ensured if the competitive effects are restricted to be negative; an assumption that we do not impose due to the possibility of benefits from cooperation in the VC context, as described below.

  11. Analytically computing the probability of each outcome is exceedingly complex in the case of three product types. As a result, a frequency simulation approach is used, whereby random draws are taken from the assumed error distribution. For each random draw, a unique simulated product-type configuration is generated for each market based on the data for that market, the parameters, and the value of the random draw. Parameters are chosen that maximize the number of times that the simulated configuration equals the observed configuration. See Mazzeo (2002) for additional details.

  12. Some progress has been made—see Ciliberto and Tamer (2009)—in more straightforward industries, such as airlines, where the total number of firms that are able to operate in a market is quite small.

  13. Most VC funds are structured as closed-end, often ten-year, limited partnerships. They are not usually traded, nor do they disclose fund valuations. The typical fund spends its first three or so years selecting companies to invest in, and then nurtures them over the next few years. In the second half of a fund's life, successful portfolio companies are exited via IPOs or trade sales to other companies, which generates capital inflows that are distributed to the fund's investors. At the end of the fund's life, any remaining portfolio holdings are sold or liquidated, and the proceeds are distributed to investors.

  14. Furthermore, Sorenson and Stuart (2001) show that VCs tend to invest locally, which provides additional support in favor of segmenting markets geographically.

  15. While entrepreneurs may consider the portfolio of past startup investments that a VC has made in other markets as well when considering the relevant expertise and specialization area of a VC, the local market portfolio of the VC is likely to be a prominent consideration.

  16. VCs also differ by geographic focus, with some that invest nationally and others that focus investment activity in a particular geographic region or regions. While geographic specialization may also represent a meaningful source of differentiation, we focus here on industry scope differentiation, which is of primary importance in the eyes of entrepreneurs that seek VC funding. As our empirical methods are not rich enough to simultaneously consider differentiation along both dimensions of specialization, we leave an exploration of the competitive effects of geographic specialization to future research.

  17. Because there are very few individual investments that are made by any single VC in a given year, it is a common convention in the VC literature to calculate proxies for characteristics such as specialization, network centrality, etc., by using some years of trailing data. Thus, specialization in year t will commonly be calculated as the industry HHI based on all investments made by the VC in years t-4 to t.

  18. As a robustness check, we collapsed the six Venture Economics categories into three broader categories: “Health” comprises biotechnology and medical; “Technology” comprises computer-related and semiconductors; and “Media” comprises communications and media. When we re-ran the structural model with a definition of VC specialization that is based on investments in these three broader categories, our empirical results were qualitatively similar to the results that are reported in the following section.

  19. We define VCs that have made only one investment over the previous five years—and are thus vacuously specialized—as fringe VCs.

  20. Bresnahan and Reiss (1991) find that the additional competitive effect of market participants dies out once there are four or five (homogeneous) firms in the industry. This finding (together with the computational issue described below) explains why papers in this literature (e.g., Mazzeo 2002; Seim 2006) focus on smaller markets.

  21. For computational reasons, markets with a very large number of participants are prohibitively difficult to estimate, since the dimensionality of the probability space for the likelihood in Eq. 7 increases very quickly as the number of market participants increases. To help alleviate concerns regarding dropping these largest VC markets, we performed a series of ordered probit estimations, whose dependent variables were the number of VCs of each type. These estimated parameters in these ordered probits were similar when we included the markets that are dropped in the structural model and when we did not, which suggests that the underlying competitive behavior that we estimate is similar in the large markets that we are forced to drop.

  22. Because of the sample restriction, our data do not represent a balanced panel in the sense that a market may enter and exit the panel based on the number of VCs that are present in a given year. In other words, 326 markets have at least one year that satisfies the sample restriction.

  23. Following Hochberg et al. (2007, 2010), we use social network analysis to measure the extent to which VCs are interconnected. Networks are represented as matrices, and are calculated for each year t based on the investments made by the VCs in a given market during the preceding five-year period. Cells reflect whether two VCs co-syndicated at least one deal during the formation period. A natural measure of how interconnected incumbents are is “density,” which is defined as the proportion of all logically possible ties that are present in a market. For example, the maximum number of ties among three incumbents is three. If only two incumbents are connected to each other, the density is 1/3 (one tie out of the three possible). With n incumbents, there are at most ½n (n − 1) ties. Let Pijm = 1 if VCs i and j have made a co-investment market m, and zero otherwise. Then market m’s density equals Σj Σi Pijm/(n (n − 1)).

  24. Recall that this market-level ordered triple will be the dependent variable of our econometric model; the resulting estimated parameters will define the attractiveness of operating as each VC type, given the specification described above.

  25. It is important to include these (0,0,0) markets in the empirical analysis, even though there are no competing VCs present. Markets with zero operating VCs help to identify the level of economic activity that is necessary to support a single VC in the market, which is critical for ultimately estimating the competitive effects. Without including these markets, we must make assumptions about initial market presence and estimate a conditional likelihood function instead (see Mazzeo 2002).

  26. Motel industry estimates are obtained from Mazzeo (2002). Telecom industry estimates are for CLECs and are obtained from Greenstein and Mazzeo (2006). Health maintenance organization (HMO) industry estimates are obtained from Dranove et al. (2003). Retail depository institution estimates are obtained from Cohen and Mazzeo (2007).

  27. Agglomeration economies – either among VCs or the startup companies in which they invest—are an additional possibility that could generate the unique pattern of estimated coefficients. Indeed, other authors have found evidence of such agglomeration economies in this context (Florida and Kenney (1988); Saxenian (1994), and Chen et al. (2010)). We are hopeful that the market-level fixed effect that we estimate from the reduced form and include in the structural model would control for these effects; however, to the extent that it does not completely, this may be an alternative explanation.

  28. As 55 % of the markets in our sample have a density of zero (i.e., no network ties amongst VCs), we use mean, rather than median, for our sample split. We obtain qualitatively similar results when segmenting in alternative fashions. We are treating the market-level network density variable as exogenous, though it might be argued that market-level network density is determined by individual VCs that decide whether to form cooperative relationships with other VCs in their markets. A model that endogenizes both sector specialization and network formation is beyond the scope of the econometric modeling in the industrial organization literature, though this is a potentially important issue that deserves its own, separate exploration.

  29. The log-likelihood for the estimation of below-mean markets is −14,378, which compares to a value of −110,021 when using the parameters at the maximum log-likelihood for above-mean markets. Similarly, the log-likelihood for the estimation of above-mean markets is −6,808, which compares to a value of −20,347 when using the parameters at the maximum log-likelihood for above-mean markets. As such, we consider the differences across market divisions to be statistically significant.

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Acknowledgment

Thanks for helpful comments and suggestions go to the editor and referees, along with Snehal Banerjee, Brett Green, Thomas Hellmann, Jiro Kondo, Brian Melzer, Josh Rauh, Paola Sapienza, Scott Stern and seminar participants at the Western Finance Association Annual Meetings, Northwestern University, the University of British Columbia, Temple University, the International Industrial Organization Conference, City University of Hong Kong, the Technion-Israel Institute of Technology and the Federal Reserve Bank of Chicago. Hochberg gratefully acknowledges funding from the Zell Center for Risk Research, the Heizer Center for Private Equity and Venture Capital and the Center for Research in Technology and Innovation at the Kellogg School of Management.

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Correspondence to Michael J. Mazzeo.

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Hochberg, Y.V., Mazzeo, M.J. & McDevitt, R.C. Specialization and Competition in the Venture Capital Industry. Rev Ind Organ 46, 323–347 (2015). https://doi.org/10.1007/s11151-015-9462-3

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