1. 1 The French research system: which evolution and which borders? Marie-Pierre Bès, bes@univ-tlse2.fr Frédéric Rodriguez, rorodrigue@wanadoo.fr University of Toulouse Francebes@univ-tlse2.frrorodrigue@wanadoo.fr EAEPE 2007 Conference, Porto

2. 2 Objectives Explanation of the French research organization system Analysis of series of contracts between CNRS units and firms Highlight the questions of concentration and scientific communities Test the methods used in Social Networks Analysis

3. 3 Outline 1.Some descriptive statistics highlighted by other studies focused on research partnerships : the concentration phenomena 2.Application of Social Network method : scale- free network or small world ? 3.Discussion about the old notion of scientific community (Crane, 1972) revisited with the social networks approach

4. 4 Data the name of research unit, its location (12 areas), the leader’s name, the scientific domain (classified into 8 departments), the firm’s identity, its location, the start date of the contract, the duration done by number of months + economic activity (coded by us). Number of contracts26024 Number of firms3642 Number of scientific labs1680 Average number of contracts per firm7 Average number of contracts per lab15 Average duration of a contract20 months Period1986-2000 Confidential data base of contracts of CNRS including information about :

5. 5 First Part : descriptive data

6. 6 Number of contracts per CNRS scientific department

7. 7 Evolution in number of contracts during the period 1986-2000

8. 8 Distribution of CNRS units per number of contracts during (1986-2000)

9. 9 Distribution of labs per number of contracts Number of contractsNumber of labs Share of contracts > 1691110% > 1043220% > 4415350% > 1446880%

10. 10 Distribution of economic partners per number of contracts during (1986-2000)

11. 11 Distribution of contracts per activity sector Activity sectorNumber of contracts share Industry1100442% Public836732% Service641025% Other2161% Agriculture270% Total260241

12. 12 Results from descriptive statistics Intense concentration in this data base, characterized by the presence of some labs specialized in Engineering Sciences, located in Province, engaged in some contracts with a public industrial firm. This concentration does not provide information on neither the centrality of the actors nor their positioning in the partnership chain.

13. 13 Second Part : research networks (with Ucinet Software)

14. 14 1. The Networks of CAC 40 firms Observation of the evolution of research activity of the major firms. Selection of the “CAC 40” firms = major French firms. 20% of the data base. Building of labs-firms Networks (2 “dimensions”) Hypothesis : a)the relation is an exchange of knowledge b)the network describes diffusion of knowledge

15. 15 1 st Network labs-CAC40 firms, 1988-1990

16. 16 2 nd Network public labs-CAC40 firms, 1993- 1995

17. 17 3 rd Network public labs-CAC40 firms, 1998- 2000

18. 18 2. Scientific “networks” Selection of chemistry domain Building of labs-labs Matrix through the existence of k contracts signed by the 2 labs with the same k firms. 2 kinds of ties : contracts and social indirect ties. Drawing of a meta-network i.e. the whole ties between the labs.

19. 19 Chemistry/1 st Network, 1988-1990

20. 20 Chemistry/2 nd Network, 1998-2000

21. 21 Which kind of general structure ? Some social networks are “scale-free” (Barabasi & alii, 2002) as in -Biotechnology (Gay & Doucet, 2005) Many of these networks are “small worlds” (Watts, 1998) as in -Scientific collaboration (Newman, 2001) -Innovation (Cowan, 2004) Test with our data

22. 22 Major criteria for a “small world” 1.High clustering coefficient 2.Short average path lengths 3.Weak densities 4.A Degree distribution (after smoothing plot of log-log) that follows a decline line => There are several short cuts between two isolated members.

23. 23 “Worldation” in the Chemistry networks Indicators1st network “1988-1990” 2nd network “1998-2000” Number of actors142247 Number of ties31647568 Clustering coefficient1.1781.162 Average path length1.9782.153 Connection degree>2 density0.1580.125

24. 24 log-log Plot of degree distribution Smoothing 1988-1990Smoothing 1998-2000

25. 25 conclusion The major firms’s scientific network is dominated by competition and strategy The chemistry’s network is a “small world” => existence of a scientific communities Several “small worlds” in this database as in the scientific collaboration networks the industrial research doesn’t disturb the property of scientific network.