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The patterns of venture capital investment in Europe

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Abstract

We study the investment patterns of different types of venture capital (VC) investors in Europe: independent VC, corporate VC, bank-affiliated VC and governmental VC. We rely on a unique dataset that covers 1663 first VC investments made by 846 investors in 737 young high-tech entrepreneurial ventures in seven European countries. We compare the relative specialization indices of the different VC investor types across several dimensions that characterize investee companies: industry, age, size, stage of development, distance from the investor and country. Our findings indicate that VC investor types in Europe differ substantially in their investment patterns when compared to one another and that, in terms of investment patterns, governmental VC investors appear to be the most distinct type of VC investor. The investment patterns of different VC investors are stable over time and similar across different European countries. Finally, the investment patterns of the different VC investor types in Europe are significantly different from those observed in the USA.

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Notes

  1. The use of VC investments to GDP to gauge VC market development is discussed by Cumming (2011).

  2. The value of M j for the 6 dimensions is as follows: M 1 = 6 (industry), M 2 = 4 (age), M 3 = 4 (size), M 4 = 4 (distance), M 5 = 3 (stage), M 6 = 2 (country). Thus, we have a total of 23 distinct categories for the 6 dimensions.

  3. In our sample, the number of IVC investments in ICT manufacturing (\(N_{1,1}^{1}\)) is 163; the total number of IVC investments (\(\mathop \sum \nolimits_{k = 1}^{{M_{j} }} N_{j,k}^{1}\)) is 918; the number of VC investments in ICT manufacturing (\(\mathop \sum \nolimits_{i = 1}^{4} N_{1,1}^{i}\)) is 284, and the total number of VC investments (\(\mathop \sum \nolimits_{i = 1}^{4} \mathop \sum \nolimits_{k = 1}^{{M_{j} }} N_{j,k}^{i}\)) is 1,663. \({\text{BI}}_{1,1}^{1}\) is therefore equal to 163/918/(284/1,663) = 1.040.

  4. For instance, in our sample, the number of IVC investments is larger than the number of CVC investments by a factor of 5.6 (918 vs. 165), which means that the same measurement error would have an impact on CVC’s BI that is larger by a factor of 5.6 than its impact on IVC’s BI.

  5. In our data, the TBI correlates with the original BI at 95.69 %. The transformation that we adopt to compute TBI is common in the literature, but other transformations are also possible. For example, the Balassa index can be subjected to a logarithmic transformation (Vollrath 1991) or a hyperbolic tangent transformation (Grupp 1994). We replicated our analyses using these alternative transformation methods. The TBI that was used here correlates with both Grupp’s (1994) and Vollrath’s (1991) specifications at a 99 % level, and the results we obtained are virtually the same. For the sake of synthesis, we do not report the results obtained under these different transformations, which are available upon request.

  6. A full description of the database is provided by Bertoni and Martí Pellón (2011).

  7. There is generally a close correspondence between the type of VC investor and the origin of the funds it invests. Captive investors generally invest funds obtained from their parent company (CVC or BVC) or public sources (GVC). See Mayer et al. (2005).

  8. ICT manufacturing includes the following industries: electronic components, computers, telecommunications equipment and electronic, medical and optical instruments.

  9. For instance, Schertler and Tykvová (2010) found that approximately two-thirds of global VC deals between 2000 and 2008 included only domestic investors.

  10. Using Eq. (2), we obtain that a TBI of 0.046 corresponds to a BI of 1.097, which implies that IVC’s share of VC investments in companies aged 3–5 years is 9.7 % greater than the overall share of IVC investments out of the total number of VC investments.

  11. As a robustness check of the following analysis, we used Kendall’s tau rank correlation instead of Spearman’s rank correlation. The results are robust and available from the authors upon request.

  12. To avoid small numbers, we reclassified the industry dimension from 6 to 5 categories, which brings the total number of categories from 23 to 22.

  13. We do not consider here the investee company’s stage of development at the time of the VC investment because the classification is not entirely comparable across the Thomson One and VICO datasets.

  14. There are 6 categories for the industry dimension, 4 categories for the age dimension and 4 VC investor types.

  15. “It’s not the people you know. It’s where you are.” The New York Times, 10/22/2006.

  16. One possible reason for this result is that GVC investors provide limited value-enhancing services to investee companies (Luukkonen et al. 2013). In accordance with this view, the effects appear to be more positive when GVC investors syndicate with private VC investors. For instance, while analyzing a large sample of VC-backed companies in 25 countries, Brander et al. (2014) documented that these syndicated investments have outperformed other types of VC investments in terms of the total amount of investment obtained by companies and the likelihood of successful exit (i.e., through IPOs and third-party acquisitions). Bertoni and Tykvová (2015) found similar results with regard to the patenting activity of young European biotechnology and pharmaceutical companies. In Europe, however, GVC investors are unlikely to form syndicates, due to the divergence of their objectives and investment specialization patterns from those of private investors. For an overview of worldwide evidence on GVC investments, see Colombo et al. (2014b).

  17. Investment vehicles founded by a regional or national government are often statutorily prevented or otherwise discouraged from investing outside of regional or national borders. The obvious reason for this prohibition is that policy-makers would find it difficult to explain to taxpayers in one region or country why their money is being used to support companies in another region or country. SITRA, a Finnish GVC investor, is an interesting exception. SITRA invests a portion of VC funds outside of Finland, claiming that the objective of these cross-border investments is to create a window to the international VC market and learn about new investment practices. At the end of 2010, the international portion of the assets managed by SITRA had a book value of 42 million Euros, corresponding to 6 % of total assets (SITRA 2011).

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Acknowledgments

We acknowledge support from the 7th EU Framework Programme VICO project on “Financing Entrepreneurial Ventures in Europe: Impact on Innovation, Employment Growth, and Competitiveness” (Contract no. 217485).

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  1. Department of Economics, Finance and Control, EMLYON Business School, 23 Avenue Guy de Collongue, 69134, Ecully, France

    Fabio Bertoni & Anita Quas

  2. Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy

    Massimo G. Colombo

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Bertoni, F., Colombo, M.G. & Quas, A. The patterns of venture capital investment in Europe. Small Bus Econ 45, 543–560 (2015). https://doi.org/10.1007/s11187-015-9662-0

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