Targeting biomed cluster from a mature pharma industry: the Medicon Valley experience

Abstract

This paper analyses the emergence and development of the Medicon Valley biomedicine cluster from an evolutionary perspective. We focus on the co-evolution and synergies among the drivers of the cluster's emergence. Targeting biomed involves some challenges and complexities, which are particularly strong in this case, owing to its specificities (bi-national nature, predominant context of horizontal policies, pre-existent pharma industry). The main aim is to analyse the role of the different institutional players in this policy process. The study suggests that targeting emerges from regional coordination bodies. It also reveals a smart specialisation strategy ‘avant la lettre’ and shows a number of critical insights for the implementation of this policy approach.

Keywords:

• targeted policies
• life-cycle industry
• cluster
• biomed
• smart specialisation
• Medicon Valley

Acknowledgements

The authors would like to thank the valuable collaboration of the people and institutions (see the Appendix) that have collaborated in the field research. We are grateful to Michele Mastroeni for helping us revise the paper. This paper is written within the framework of TARGET, a research project supported by the European Union 7th Framework Programme.

Notes

Horizontal policy supports R&D and innovation without specifying a priori sector, technology, or product class (Teubal 1997). This policy focuses on creating a favourable environment for innovation and is mainly based on the existence of market failures. In contrast, a targeted policy focuses on a particular sector or technology. It requires sectoral specificities and the existence of strong policy capabilities for designing and implementing the strategy (Teubal 1997, 2002; Avnimelech and Teubal 2008).

Smart specialisation is about specialising in a smart way, concentrating resources on key priorities based on the specificity of the industry structures and knowledge bases of a region (Foray, David, and Hall 2009; Foray 2011; McCann and Ortega-Argilés 2011).

The initial name was Medicon Valley Alliance.

It launched initiatives such as funding joint university–industry PhD programmes or the Life Science Ambassadors Program to collaborate with 12 world-leading life science clusters.

Private foundations contribute significantly to fund public research. In fact, they allocated €40 million during the period 2002–2005. Moreover, some large (mainly pharma) companies are owned by foundations, which play a relevant role in Denmark.

It should be noted that this entity only started investing in small companies from the 1990s.

They were more focused on funding research centres and concentrating resources on universities.

In fact, 146 biotech start-ups were created in Denmark in the period 1997–2006, creating posts for 2600 employees meaning an employment increase of 30% (Gestrelius 2008).

The weight of the cluster differs greatly in each country. The Danish part of the cluster represents about the 92% of life sciences activity in that country, while the Swedish side represents only about the 19% (Gestrelius, Sandström, and Dolk 2008; Sandström, Bergqvist, and Dolk 2011).

As the definition of medical company is wider since 2006, the data for the period 2003–2006 are not absolutely comparable. So, the employment variation is estimated studying the companies included in 2003 plus the ones started between 2003 and 2006 (Gestrelius, Sandström, and Dolk 2008).

Based on Vinnova definitions, pharma activities refer to drug production (not biotech) segment. Biotech activities consist of biotech tools and supplies. Bio-pharma activities include drug discovery/development, drug delivery and bioproduction (healthcare related) segments. Biotech-medtech activities comprise in vitro diagnostics and biotech medical technology. Bio-pharma-medtech includes CROs.

According to Biopolis (Domínguez 2007) terms, policy-directed funding includes explicit policy decision-making about installing a specific instrument. It can include biotechnology-specific policy instruments (that have been specifically established to stimulate biotechnology) and generic policy instruments (not dedicated to a specific technology, but all technologies, thus also including biotechnology). Non-policy-directed funding of research includes funding which is part of the structural governmental support for scientific education, research and research infrastructure.

The mean for EU27 amount to 4% and 5%, respectively.