The impact of venture capital on the productivity growth of European entrepreneurial firms: ‘Screening’ or ‘value added’ effect?
Highlights
► We analyze the impact of VC funding based on productivity growth before and after the first VC round. ► We also rely on a matched sample of similar non-VC-backed firms (propensity score matching). ► Productivity growth is an efficient way to isolate value-adding effects from funding in investees. ► We find that VC impact on European firms is mostly driven by investor's value added. ► Contributions: solution to reverse causality issue and separation of screening and value added
Section snippets
Executive summary
Entrepreneurial firms may lack financial resources and managerial competences which are fundamental for their economic performance, especially when they operate in high-tech industries (Gans and Stern, 2003). In this respect, venture capital (VC) is considered by both academics and practitioners as one of the key drivers of the success of entrepreneurial firms. Several previous firm-level studies investigated the relationship between VC funding and firm performance. On average, they show that
Theory and research hypotheses
Entrepreneurial firms may lack commercial and managerial competences (Teece, 1986) which limit their growth and even threaten their survival (Carpenter and Petersen, 2002a), especially when they operate in high-tech industries (Gans and Stern, 2003). Because of the technology-intensive nature of their activity, their lack of a track record and the nature of their assets (which are firm-specific and/or intangible and hence cannot be pledged as collateral), they face severe adverse selection and
The empirical models
We use the following model to test our first three hypotheses (Hypothesis 1, Hypothesis 2, Hypothesis 3, hereafter):where Prod_growthi,t is one-year productivity growth of firm i in year t. Productivity growth represents either TFP growth or partial (capital or labor) productivity growth.4 xi,t is a set of control variables. In particular, we include country
Sample selection process
The sample we used in this study is extracted from the VICO dataset. It includes data on entrepreneurial firms operating in seven European countries (Belgium, Finland, France, Germany, Italy, Spain, and the United Kingdom). VICO dataset stores information on two strata of firms, VC-backed firms and non-VC-backed (but potentially investable) ones. All firms included in the VICO dataset were: i) less than 20 years old in 2010; ii) were independent at foundation (i.e. not controlled by other
Results
Table 6 reports the results of Eq. (1). The dependent variables are TFP growth (columns I and II), capital productivity growth (columns III and IV) and labor productivity growth (columns V and VI), respectively. Columns I, III and V refer to OLS estimations, whereas II, IV and VI report GMM estimations.
As regards H1, we find that VC-backed firms do not exhibit significantly different TFP and labor productivity growth to those of their matched counterparts before the first VC round, thus
Further analyses
As explained in Section 3.1, we model VCs' screening as a process based on the ‘current’ economic performance of potentially investable entrepreneurial firms (for a similar approach see e.g. Chemmanur et al., 2011, Davila et al., 2003). Even though this idea is well grounded in both finance and entrepreneurship literature, VCs might select their portfolio firms through the assessment of their ‘potential performance’. Moreover, firm's potential performance might be influenced by the
Discussion and conclusions
The analysis of the impact of VC financing on the performance of European entrepreneurial firms is important for managers, VCs and government authorities. There are many empirical papers that have shown the superior performance of VC-backed firms after the first VC round. Nevertheless, since it is well documented that VC managers follow a very well-structured screening process, critics have often pointed out that portfolio firms perform better because they were actually better before the
Acknowledgments
This research was conducted with the support of the 7th European Framework Program (grant agreement no.: 217485). We thank the participants to the workshop organized in Milan in July 2010, to discuss the output of the VICO project, for their comments, and especially for the comments received from Massimo G. Colombo and Fabio Bertoni. We also thank Maria Alejandra Ferrer for her contribution to the construction of the database. Finally, we wish to thank two anonymous reviewers for their valuable
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