Abstract
In this paper, we examine the trade off between different effects of the availability of venture capital on the speed of technological progress in an industry. We consider an evolutionary industry simulation model based on Nelson and Winter (1982), in which R&D efforts of an incumbent firm generate technological know-how embodied in key R&D employees, who might use this know-how to found a spinoff of the incumbent. Venture capital is needed to finance a spinoff, so that the expected profits from founding a spinoff depend on how easily venture capital can be acquired. Accordingly, thick venture capital markets might have two opposing effects. First, incentives of firms to invest in R&D might be reduced and, second, if spinoff formation results in technological spillovers between the parent firm and the spinoffs, the generation of spinoff firms might positively influence the future efficiency of the incumbent’s innovation efforts. We study the manner in which this tradeoff influences the effect of venture capital on innovation expenditures, speed of technological change and evolution of industry concentration in several scenarios with different industry characteristics.
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Notes
As discussed in more detail in the final section of this paper, empirical evidence reported in Chatterji (2007) suggests that the relevant type of knowledge inherited by the spinoffs is not necessarily technological knowledge.
The amount of early stage venture financing is, for example, one of the key indicators in the country reports of the European Innovation Scoreboard published annually by the European Commission.
There is some theoretical work on the effects of the threat of outgoing technological spillovers due to employee mobility on firm’s innovative efforts. Starting with Pakes and Nitzan (1983), different aspects of the optimal design and welfare effects of contracts for key R&D employees under the threat of exit of that personnel (e.g., Anand and Galetovic 2000; Baccara and Razin 2004) and the incentives of firms to commercialize ideas internally rather than allowing for spinoffs (e.g., Anton and Yao 1995; Cassiman and Ueda 2006) have been studied. However, effects on ex-ante incentives for investing in innovative activities are not the focus of these streams of literature. Gersbach and Schmutzler (2003) show in a static game theoretic model that potential worker mobility has negative effects on equilibrium investments in innovative activities, and Böhm and Colombo (2006) point out that, in a general equilibrium framework, potential worker mobility discourages the adoption of better technologies by the incumbent firms.
This could also be a group of senior employees that consider leaving the firm in order to found a spinoff.
For a more systematic discussion of the influences of venture capitalists on the establishment and performance of new ventures, see, e.g., Stuart and Sorenson (2003).
For the extreme case γ = 1, spinoff founders obtain venture capital without any sharing of profits. Obviously, this is not a realistic scenario and should be considered as a theoretical benchmark only.
This would occur whenever the entry of spinoffs does not cause positive spillovers, or the latter are not strong enough to counterbalance the negative incentive effect on existing firms’ R&D investment.
Various sources of agglomeration economies have been highlighted in the literature (see Hanson 2001 for a detailed survey). On the one hand, Henderson (1974, 1988) focuses on the positive spillovers between firms in the same industry sharing the same location, and Lucas (1988) stresses the positive spillovers on the local workforce stemming from the accumulation of human capital. Black and Henderson (1999) combine both channels, showing that industry agglomeration increases the productivity of all firms in a local industry, and that labor agglomeration makes all local workers, regardless of industry, more productive. On the other hand, Krugman (1991) argues that industry agglomeration stems from the cost and demand linkages between firms: firms benefit from being closer to large consumer markets and vertically connected industries. At the same time, workers benefit from common labor markets as agglomeration reduces the risk of unemployment and increases the incentives to invest in industry-specific skills, since with several potential employers the risk of lock-in diminishes (see, e.g., Diamond and Simon 1990; Krugman 1991; Rotemberg and Saloner 1990). Finally, a more sociological literature (Sorenson and Audia 2000; Sorenson 2003; Stuart and Sorenson 2003) highlights the role of firm co-location in leveraging the necessary social and professional ties to mobilize essential resources for the formation of new ventures.
In their analysis of the laser industry Klepper and Sleeper (2005) conclude that ‘nearly all the spinoffs initially produced lasers that their parents had previously produced.’ [p. 1297].
Several factors may determine the extent of the knowledge loss. For instance, if most R&D activities are carried out in teams, the impact on the firm’s efforts if an employee leaves the firm may be limited. Moreover, in general, the incumbent’s incentives are affected by the costs of training a new R&D worker and rebuilding the lost knowledge base.
Although in our simulation setup we assume that all knowledge is embedded in the employee and therefore lost by the incumbent following spinoff formation, relaxing this assumption alters the results of the paper in an entirely obvious way.
An interesting addition to our analysis would be to embed the main ingredients of this paper setup (i.e., the interplay between strategic and dynamic effects) in more specialized frameworks, able to account for the characteristics and evolution of specific industries. In this respect, a particularly interesting case is that of the computer industry, for which Malerba et al. (1999, 2001) provide an explicitly ‘history friendly’ evolutionary model that pays great attention to the empirical reality and the specific evolution of the industry, detailing the role of demand in determining market structure and the rate of technological advance, the consequences for firms of the introduction of a new technology or the opening of a new market, as well as the role of entry conditions, and especially that of venture capital markets backing new firms.
Here, venture capital is taken as the essential complementary asset needed for spinoff formation. This is clearly a simplification, as there are certainly other relevant factors that are necessary for the generation of spinoffs, and that may have different and more complicated (strategic and direct) effects. In a more general framework, one should carefully identify the expected effects of a larger availability of the relevant complementary assets on the founding rates of spinoffs. See, for instance, Stuart and Sorenson (2003).
Differences in the availability of venture capital may be apparent in cross-country comparisons; for instance, private equity markets are much more developed in the U.S.A. than in most European countries. However, the difficulties in identifying and successfully controlling for all other relevant differences between countries may render quite problematic a cross-country analysis. An alternative approach may be to concentrate on a single country, comparing the availability of venture capital across time (exploiting structural changes influencing the availability of private equity), or across different areas of a country (exploiting the fact that specific venture capital resources appear to be quite localized; see, e.g., Gompers 1995; Sorenson and Stuart 2001; Shane and Cable 2002). Although a wide number of controls at the regional, industry and firm levels will remain necessary, it may be possible that the complexity of the problem can be reduced when looking at a single country due to a greater homogeneity in institutional and social characteristics.
References
Alchian A (1963) Reliability of progress curves in airframe production. Econometrica 31:679–693
Anand B, Galetovic A (2000) Weak property rights and holdup in R&D. J Econ Manage Strategy 9:615–642
Anton JJ, Yao DA (1995) Start-ups, spin-offs, and internal projects. J Law Econ Organ 11:362–378
Audretsch D, Feldman M (1996) R&D spillovers and the geography of innovation and production. Am Econ Rev 86:630–640
Baccara M, Razin R (2004) Curb your innovation: corporate conservatism in the presence of imperfect intellectual property rights. CEPR Discussion Paper No 4466
Böhm V, Colombo L (2006) Technology choice with externalities—a general equilibrium approach. Discussion Paper of the Department of Economics—Bielefeld University, No 541
Black D, Henderson V (1999) A theory of urban growth. J Polit Econ 107:252–284
Bottazzi L, Da Rin M, Hellmann T (2004) The changing face of the European venture capital industry: facts and analysis. J Priv Equity 7:26–53
Cassiman B, Ueda M (2006) Optimal project rejection and new firm start-ups. Manage Sci 52:262–275
Chatterjee S, Rossi-Hansberg E (2007) Spin-offs and the market for ideas. NBER Working Paper 13198
Chatterji AK (2007) Spawned with a silver spoon? Entrepreneurial performance and innovation in the medical device industry. mimeo, Duke University—Fuqua School of Business
Colombo L, Dawid H (2008) Complementary assets, start-ups and incentives to innovate. DISCE Working Paper, No 80, Catholic University—Milano
Dahl MS, Pedersen COR, Dalum B (2003) Entry by spinoff in a high-tech cluster. DRUID Working Paper No 03-11
Dawid H (2006) Agent-based models of innovation and technological change. In Tesfatsion L, Judd K (eds) Handbook of computational economics, vol 2. Agent-Based Computational Economics, North-Holland, pp. 1235–1272
Diamond C, Simon C (1990) Industrial specialization and increasing returns to labor. J Labor Econ 8:175–201
Dorfman NS (1983) Route 128: the development of a regional high technology economy. Res Policy 12:299–316
Dosi G, Marsili O, Orsenigo L, Salvatore R (1995) Learning, market selection and the evolution of industrial structures. Small Bus Econ 7:411–436
Gersbach H, Schmutzler A (2003) Endogenous spillovers and incentives to innovate. Econ Theory 21:59–79
Gompers PA (1995) Optimal investment, monitoring, and the staging of venture capital. J Finance 50:1461–1489
Gompers P, Lerner J, Scharfstein D (2005) Entrepreneurial spawning: public corporations and the genesis of new ventures, 1986–1999. J Finance 60(2):577–614
Hanson GH (2001) Scale economies and the geographic concentration of industry. Journal of Economic Geography 1:255–276
Hellmann T, Puri M (2002) Venture capital and the professionalization of start-up firms: empirical evidence. J Finance 57:169–197
Henderson JV (1974) The sizes and types of cities. Am Econ Rev 64:640–656
Henderson JV (1988) Urban development: theory, fact, and illusion. New York: Oxford University Press
Jaffee J, McKendrick DG (2006) The cumulative and temporal effects of spin-outs on the performance of parent firms (Progenitors) in the hard disk drive industry. Working Paper, Marshall School of Business, University of Southern California
Jaffe A, Trajtenberg M, Henderson R (1993) Geographic localization of knowledge spillovers as evidenced by patent citations. Q J Econ 108:577–598
Klepper S (1996) Entry, exit, growth, and innovation over the product life cycle. Am Econ Rev 86:562–583
Klepper S (2001) Employee startups in high-tech industries. Ind Corp Change 10(3):639–674
Klepper S (2004) Agglomeration through spinoffs: how Detriot became the capital of the U.S. automobile industry. Mimeo, Carnegie Mellon University
Klepper S, Sleeper S (2005) Entry by spinoffs. Manage Sci 51(8):1291–1306
Klepper S, Thompson P (2006) Intra-industry spinoffs. Mimeo, Carnegie Mellon University
Krugman P (1991) Increasing returns and economic geography. J Polit Econ 99:483–499
Lucas RE (1988) The mechanics of economic development. J Monet Econ 22:3–42
Malerba F, Orsenigo L (1996) The dynamics and evolution of industries. Ind Corp Change 5:51–87
Malerba F, Nelson R, Orsenigo L, Winter S (1999) History-friendly models of industry evolution: the computer industry. Ind Corp Change 8:3–40
Malerba F, Nelson R, Orsenigo L, Winter S (2001) Competition and industrial policies in a “History Friendly” model of the evolution of the computer industry. Int J Ind Organ 19:635–664
Nelson RR, Winter S (1982) An evolutionary theory of economic change. Belknap, Cambridge
Pakes A, Nitzan S (1983) Optimum contracts for research personnel, research employment and the establishment of “Rival” enterprises. J Labor Econ 1:345–365
Philipps DJ (2002) A genealogical approach to organizational life chances: the parent-progeny transfer among Silicon Valley law firms, 19461-996. Adm Sci Q 47:474–506
Rotemberg JJ, Saloner G (1990), Competition and human capital accumulation: a theory of interregional specialization and trade. NBER Working paper no. 3228
Shane S, Cable D (2002) Social relationships and the financing of new ventures. Manage Sci 48:364–381
Sorenson O (2003) Social networks and industrial geography. J Evol Econ 13:513–527
Sorenson O, Audia PG (2000) The social structure of entrepreneurial activity: geographic concentration of footwear production in the United States, 1940–1989. Am J Sociol 106(2):424–461
Sorenson O, Stuart TE (2001) Syndication networks and the spatial distribution of venture capital investments. Am J Sociol 106:1546–1588
Stuart TE, Sorenson O (2003) The geography of opportunity: spatial heterogeneity in founding rates and the performance of biotechnology firms. Res Policy 32:229–253
Acknowledgements
We thank Jasmina Arifovic, Uwe Cantner and Domenico Delli Gatti for useful comments, and especially an anonymous referee for very helpful suggestions. The usual disclaimers apply. Financial support from the German Science Foundation (DFG) under grant GRK1134/1: International Research and Training Group ’Economic Behavior and Interaction Models (EBIM)’ is gratefully acknowledged.
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Appendix
Appendix
We test our results for robustness by means of a parameter sensitivity test. Table 2 shows the parameters determining the basic structure of our model, which remain fixed in all simulations. We randomly generate 50 different profiles of the parameters given in Table 3. Each parameter is drawn from a bounded uniform distribution (its specified range can be seen in Table 3).
Each particular setting for our control parameters is run over all 50 profiles. All results are found to be very robust under all settings, namely 50 different runs with profiles based on the shown parameter ranges.
The following tables show the results of the Wilcoxon signed-rank test. We test whether the medians for \(\bar X^{in}_T\), the average innovation costs per capital after 200 periods, \(\bar X^{im}_T\), the average imitation costs per capital after 200 periods, and \(\bar A_T\), the productivity after 200 periods, are the same for γ = 0 and γ = 1 (Table 4), for γ = 0 and γ = 0.5 (Table 5) and, finally, for γ = 0.5 and γ = 1 (Table 6). The null hypothesis is that the two samples are drawn from a single population, and therefore that the means are equal. The “*” labels indicate that the null can be rejected at a 95% level, while “n.s.” means that the null hypothesis can not be rejected at that significance level. The signs in brackets hint at the direction of the difference between the means.
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Colombo, L., Dawid, H. & Kabus, K. When do thick venture capital markets foster innovation? An evolutionary analysis. J Evol Econ 22, 79–108 (2012). https://doi.org/10.1007/s00191-010-0206-0
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DOI: https://doi.org/10.1007/s00191-010-0206-0