Abstract
In this paper we explore the effectiveness of selected research and innovation policies among EU countries. Using data from the AEGIS database and information from the 2015 Bruegel’s Partnership report on research and innovation polices in EU countries, we compare and contrast the response of knowledge intensive firms to a sample of policies. We find that the impact of the policies in our sample varies not only across countries but also across the age of affected firms in those countries. We conclude our analysis with a call for policy makers to begin to investigate the incidence of the impact of their research and innovation policies as measured not only by the age of firms but also by other dimensions.
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Notes
The triple helix is a conceptual and analytical model of university, industry, and government relationships, policies and dynamic interactions (see Link and Tassey 1989; Etzkowitz and Leydesdorff 1999). Within the triple helix, universities play a critical role in these dynamic relationships and interactions (see Cunningham 2015; Cunningham and Harney 2006). More recently, scientists in the principal investigator role are seen as key actors at the micro level in forging these dynamic interactions within the triple helix (see Mangematin et al. 2014)
This group of policies may have its antecedents in the replication of innovation policies between the United States and Europe (Gulbrandsen and Etzkowitz 1999).
We have written about the AEGIS database numerous times. Duplication of background informational text is inevitable.
According to AEGIS (2012, p. 4): “Knowledge-intensive entrepreneurship is [the] core interface between two interdependent systems: the knowledge generation and diffusion system, on the one hand, and the productive system, on the other.”
For an institutional overview of innovation policies in Germany, for example, see Audretsch and Lehmann (2016).
The firms in the AEGIS database are not a random sample of European firms. In order to have a large enough sample to study firms in all countries, firms in smaller countries (e.g., Croatia and the Czech Republic) were sampled at a higher rate than firms in larger countries (e.g., France and Germany). To account for this non-random sampling, unless otherwise noted, sample weights are used in the analyses. As described in Caloghirou et al. (2011), the sampling process was challenging due to the desire to have adequate representation of smaller countries and across industries. The final sampling frame consisted of 202,286 firms, and the database includes survey information on 4004 firms. See Link and Swann (2016).
Many of the policies in the Bruegel Partnership report relate to the creation of technology-related infrastructure, such as the existence of centers of excellence or cluster formations. Information on the use of such infrastructures is not captured by the knowledge intensive entrepreneurial firms in the AEGIS database.
Leyden and Link (2015) refer to such technology and innovation polices as examples of public sector entrepreneurship.
See Table 2 for response codes and note that responses to Intellectual Property Rights Measures are dichotomous rather than in terms of a 5-point Likert scale.
Among the 9 countries that we considered in this paper, there is heterogeneity in policy applicability as shown in Table 1.
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Cunningham, J.A., Link, A.N. Exploring the effectiveness of research and innovation policies among European Union countries. Int Entrep Manag J 12, 415–425 (2016). https://doi.org/10.1007/s11365-016-0394-7
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DOI: https://doi.org/10.1007/s11365-016-0394-7