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Direct and indirect effects of private- and government-sponsored venture capital


Starting from the discourse on the impact of private and governmental venture capital investments, we examine the effects of different types of venture capital on firms’ sales, employment and investment. Our results show that both private and governmental venture capital investments boost firm sales with a delay of 2–3 years. The results suggest that VC impacts sales primarily through efficiency gains and to some extent, investments in physical capital investments, whereas no employment effects can be traced. Finally, we find indications of governmental VC investors being more prone to make follow-up investments in stagnating, non-growing firms than private investors.

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  1. 1.

    In addition to VC, other forms of start-up financing can also play a role, such as subsidized loans, grants, incubators, and crowdfunding, as well as policies/conditions which stimulate the demand for VC (as emphasized by, for example, Callagher et al. 2015).

  2. 2.

    In the European context, it is believed that the lower level of R&D spending (2% of GDP) compared to the USA (2.6% of GDP) may partially reflect the relatively small European VC market (European Commission 2010; p. 22). To close the European–US VC gap, the European Commission implemented the Risk Capital Action Plan in 1998 (European Commission 1998) to stimulate stock market openness, increase the flexibility of labor markets and provide a set of tax incentives.

  3. 3.

    In case of a “later-stage VC gap,” GVCs can also play an important role if PVCs avoid start-ups that need VC and have the potential to become sustainable businesses, but that do not offer the high growth potential demanded by PVCs.

  4. 4.

    An early analysis of the Swedish GVC market was undertaken by the Parliamentary Audit Office (1996). It found that most GVC investments had been failures and that the GVCs lacked knowledge and skills in board work and management. GVC policies have recently been reformed, with the aim to direct more focus on the early investment stages and increase coordination with private investors (SOU 2015:64; Prop., 2015/16:110).

  5. 5.

    Other arguments in favor of GVC intervention include their “counter-cyclical role” (see Gompers and Lerner 2003; Robinson and Sensoy 2013; Lerner and Watson 2008), and the creation of positive externalities in the form of increased entrepreneurial and innovative activity (Lerner 2010).

  6. 6.

    This conclusion has been reached in studies using several different metrics of firm performance, including exits (Brander et al. 2010, 2015; Cumming et al. 2014; Tykvova and Walz 2007), patents (Bertoni and Tykvova 2015), productivity (Alperovych et al. 2015) and growth as measured by employment or sales (Grilli and Murtinu 2014).

  7. 7.

    Other research on GVC has investigated whether GVCs behave differently as investors compared to other types of GVC, for example, with regard to the types of firms which they invest in Bertoni et al. (2011). Another vein of research has focused on the macroeconomic impact of GVC on the VC market, in particular addressing the question of whether GVC investment “crowds out” private VCs (Leleux and Surlemont 2003; Brander et al. 2015; Cumming and Macintosh 2006; Cozzarin et al. 2015).

  8. 8.

    These include publicly owned pension funds such as the 6th AP Fund and VCs that are independently run but funded partially with public funds, such as the VC Stockholm Innovation and Growth (what is known as a “hybrid” governmental–private VC).

  9. 9.

    Almi Invest makes nearly all of its investments together with private investors and business angels. Further, Tillväxtanalys (2016) states that 35 percent of Almi’s co-investments are made with business angels, which are not observed in our data. For this reason, the share of “GVC only” companies is likely to be overestimated somewhat.

  10. 10.

    Typically, larger firms are more prone to become targets of leveraged buyouts or growth capital.

  11. 11.

    For recent applications of matching methods in the VC literature, see Croce et al. (2013), Cumming et al. (2014), and Grilli and Murtinu (2014).

  12. 12.

    Corresponding results for the employment and capital equations are excluded here because of space constraints but can be found in “Appendix”.

  13. 13.

    One advantage of CEM compared to PSM is that CEM not only considers the first moment but also higher moments.

  14. 14.

    The expression for kit and lit is typically derived from cost minimization. It gives the optimal amount of kit and lit required to produce a given output. To avoid circularity in the model, we do not use sales in the demand equations, but rather log(value added) to proxy for firm size.

  15. 15.

    It relies on (long) differencing and is not burdened by the problem of (weak) moment conditions, and has good short panel properties.

  16. 16.

    Although standard deviations estimated in SEM and SUR models are asymptotically identical, they differ in finite samples. There is no reason to expect that one is better than the other, however.

  17. 17.

    We may note that the insignificant results implicitly suggest that the impact of different types of VC may not differ dramatically.

  18. 18.

    As indicated by the matching results in Table 6, firm capital intensity is included as one of the matching variables.

  19. 19.

    In line with subsequent labor demand estimations, the dynamic models is based on a standard labor demand model (or more generally, demand for factors of production) with adjustment costs (Cahuc and Zylberberg 2004; Hijzen and Swaim 2008).

  20. 20.

    See Han et al. (2014).

  21. 21.

    The employment regressions analyzed in Table 11 are performed using the Han–Philips Fixed Effects Dynamic Panel Data estimator. The estimated model is based on a standard labor demand model with adjustment costs (Cahuc and Zylberberg 2004; Hijzen and Swaim 2008).

  22. 22.

    The only exception from the nonsignificant patterns is for MVC; using the FE estimator, 3 years after receiving VC we note a negative and significant employment effect.

  23. 23.

    As a robustness test, employment regressions are additionally estimated using both the Arellano and Bond (1991) estimator and the Blundell and Bond (1998) system GMM estimator with similar results. Results are available on request.

  24. 24.

    Full set of estimations are available on request.

  25. 25.

    The SEM models do not allow for firm-level fixed effects; in order to compensate for this to some extent, we apply industry fixed effects at the 2-digit level throughout all SEM models.

  26. 26.

    For MVC, we in the SEM estimations in Table 12 were not able to achieve model convergence for the period-by-period analysis.

  27. 27.

    We may note that the insignificance of MVC to some extent may be attributed to relatively few observations in this group; approximately 20% of the firms receive MVC.

  28. 28.

    For MVC, we in the SEM estimations in Table 11 were not able to make get convergence for the period-by-period analysis.

  29. 29.

    The SEM models do not allow for firm-level fixed effects, in order to some extent compensate for this we apply industry fixed effects at the 2-digit level throughout all SEM models.


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Funding was provided by the Swedish Competition Agency.

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Correspondence to Daniel Halvarsson.

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See Table 15 and Fig. 7.

Table 15 Mean and median values for the second control group (capital and employment equations)
Fig. 7

Distributions of matching variables for treated and control groups, employment and capital equations

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Engberg, E., Tingvall, P.G. & Halvarsson, D. Direct and indirect effects of private- and government-sponsored venture capital. Empir Econ 60, 701–735 (2021).

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  • Venture capital
  • Start-ups
  • Firm growth
  • Investments
  • Governmental venture capital

JEL Classification

  • C21
  • C23
  • D22
  • G24
  • G28
  • L25
  • L26
  • H44