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The Determinants of Venture Capital in Europe — Evidence Across Countries

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Abstract

This article analyses the determinants of European venture capital activity. The main novelty of our work is in accounting for the idiosyncrasies of the European venture capital market. In particular, we investigate whether the size of the merger and acquisition market (M&A) is important in explaining venture capital. Moreover, our work is the first that analyses the impact of the degree of information asymmetry at the macro level, the direct impact of the level of entrepreneurial activity and the impact of the unemployment rate on venture capital activity. We use aggregate data from 23 European countries for the period 1998–2003 to estimate panel data models with fixed and random effects. Our results reveal that the size of the M&A market and the market-to-book ratio have a positive impact on venture capital activity whereas the unemployment rate influences the venture capital market negatively. These results highlight the importance of the exit environment and of the degree of asymmetric information for the venture capital market.

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Notes

  1. Some related papers do not follow a reduced-form approach. For instance, Marti and Balboa (2001) emphasize venture capital funding including venture capital investments as an explanatory variable. Groh et al. (2010) use composite indices to compare the attractiveness of 27 European countries for institutional investments into the Venture Capital and Private Equity asset class. Schertler (2005) shows evidence that fund providers’ investment preferences matter for venture capital investment characteristics, using a European panel data set.

  2. Schwienbacher (2005) presents evidence regarding the differences between the European and U.S. capital markets. Early stage investments account for 50 % of the total investment for U.S. venture capitalists whereas they represent only 38 % of the total investment for European venture capitalists. Venture capitalists from the U.S. use 59 % convertibles in their investments while Europeans only use 20 %.

  3. These studies do not include the M&A market as a determinant of VC activity. However, more recently, Smith et al. (2010) and Metrick and Yasuda (2010) find that M&A exits are important in explaining the overall performance of U.S. VC funds.

  4. Table 5 shows the variables included in the studies that used a reduced form approach. The variables are divided into three groups: macroeconomic variables, entrepreneurial environment variables and technological opportunities variables.

  5. In the case of the U.S., the literature emphasizes the positive influence of the Employee Retirement Income Security Act (ERISA), also known as the «prudent man» rule of 1979, on venture capital activity. By declaring that portfolio diversification should be considered in determining the prudence of an investment, the rule opened the doors to pension funds investing in venture capital.

  6. Some governments give incentives to the creation of self-employment. This may be an additional argument for the unemployed having higher incentives to become entrepreneurs.

  7. According to NVCA and EVCA figures.

  8. Hyytinen and Toivanen (2003) argue that venture capital finance is one of the fields of finance most conducive to asymmetric information, as venture capitalists invest most often in small, technology-based ventures in emerging industries.

  9. The GLS estimator is used because the error terms \( {v_{it}} = {a_i} + {u_{it}} \) are correlated.

  10. Another estimator (used by Jeng and Wells 2000) is the between estimator, which is the OLS estimator of:

    $$ {\overline y_i} = {\beta_0} + {\beta_1}{\overline x_{i1}} + {\beta_2}{\overline x_{i2}} + \cdots + {\beta_k}{\overline x_{ik}} + {a_i} + {\overline u_i} $$

    However, use of the between estimator is questionable. It is biased when the explanatory variables are correlated with a i . In addition, if the explanatory variables are uncorrelated with a i , it is much less efficient than the random effect estimator, as it ignores information on how variables change over time.

  11. Since the dependent variable and most of the explanatory variables are expressed in percentage terms, most of the estimated coefficients indicate the change, measured in percentage points, in venture capital activity when the explanatory variable increases by one percentage point. For instance, when the unemployment rate increases by 1 percentage point, the TotalInvVC (expressed as a percentage of GDP) decreases by 0.012 percentage points. For TEA and MB, the estimated coefficients indicate the change, measured in percentage points, in venture capital activity when the variable increases by one unit.

  12. It should be noted that this last explanation does not apply in the fixed effect regressions. In these models, the unemployment rate coefficient captures within-country variations in the unemployment rate, not variations across countries. Since labour market rigidity in a given country is expected to be relatively stable over time, its effect is eliminated when we use the fixed effect estimator.

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Acknowledgments

We are grateful to one anonymous referee for very valuable comments. In addition, we gratefully acknowledge partial financial support from FCT (Fundação para a Ciência e Tecnologia), POCTI programme.

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Authors and Affiliations

  1. Departamento de Gestão and CEFAGE-UÉ, Universidade de Évora, Largo dos Colegiais, nº 2, 7000-803, Évora, Portugal

    Elisabete Gomes Santana Félix & Cesaltina Pacheco Pires

  2. Departamento de Finanças, ISCTE—Instituto Universitário de Lisboa, Av. Das Forças Armadas, Sala 236, 1649-026, Lisboa, Portugal

    Mohamed Azzim Gulamhussen

Authors
  1. Elisabete Gomes Santana Félix
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  2. Cesaltina Pacheco Pires
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  3. Mohamed Azzim Gulamhussen
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Correspondence to Elisabete Gomes Santana Félix.

Appendix

Appendix

Table 1 Description and sources of variables
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Table 2 Period of analysis and variable averages per country. The description of the variables is shown in Table 1. This table presents the period of analysis and the variable averages per country
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Table 3 Correlation matrix. The description of the variables is shown in Table 1. This table presents the correlation matrix, considering the 131 collected observations, for the variables used in the study
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Table 4 Empirical results for FundRais, TotalInvVC, HighTechInv and EarsStgInv. The description of the variables is shown in Table 1. This table shows the results for various panel data models, using the 131 observations collected by the authors. The R-squared provides the goodness of fit for each of the panel models, the Hausman test tests the null hypothesis that the unobserved effect is uncorrelated with the explanatory variables, the F test for fixed effects tests the hypothesis that all fixed effects are equal to zero, and the F and Wald tests test the joint significance of all covariates. In parentheses we present the values of the t-statistics for each variable
Full size table
Table 5 Comparison of our results with previous results in the literature. This table shows the impact of several explanatory variables on VC activity, in our study and in previous studies in the literature
Full size table

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Félix, E.G.S., Pires, C.P. & Gulamhussen, M.A. The Determinants of Venture Capital in Europe — Evidence Across Countries. J Financ Serv Res 44, 259–279 (2013). https://doi.org/10.1007/s10693-012-0146-y

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  • DOI: https://doi.org/10.1007/s10693-012-0146-y

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