Measuring the knowledge base in Hungary : Triple Helix mechanisms in a transition economy

1. Measuring the knowledge base in Hungary:Triple Helix mechanisms in a transition economy Balázs Lengyel * and Loet Leydesdorff ** * Centre for Regional Studies Budapest Department, Hungarian Academy of Sciences ** Amsterdam School of Communications Research (ASCoR) 8th Oct. 2007, Maastricht. DIMETIC Doctoral Summer School

2. Structure of the presentation • Theoretical background: evolutionary triple helix relations • Hypotheses, research problem in the Hungarian analyses • Data and methods • Results • Conclusion

3. 1. Theoretical background Three main directions in economic geography (Boshma and Frenken, 2006): • New Economic Geography (Krugman) • Institutional Economic Geography (Saxenian, Gertler): differences in economic prosperity described by institutional differences. • Evolutionary Economic Geography (Martin, Boschma): deals with agglomeration and knowledge spill-over based on the concepts of evolutionary economics. Innovation systems: possible ground to harmonise IEG and EEG (Freeman, 1987; Nelson, 1993; Edquist, 1997; Cooke et al., 1998; Cooke, 2001; Asheim and Isaksen, 2002; Simmie, 2005) Institutional setting as given: innovation measured as output. Co-evolution: new structure of existing institutions, new institutions.

4. 1. Theoretical background – sub-dynamics, stochastic relations • In innovation systems organized knowledge production-, diffusion-, and controlfunctions are performed by different agents and relations(Etzkowitz and Leydesdorff, 2000). The different functions can be considered as sub-dynamics of the system. These sub-dynamics can be expected to interact to varying degrees. • The synergy between the industrial structure, geographical distributions, and academic traditions can be considered crucial for the strength of an innovation system (Fritsch, 2004). The distribution of the technologies in a system, the industrial organization, and the geographical spread can be considered as relatively independent sources of variation (Storper, 1997). One expects an uncertainty which can be measured as probabilistic entropy.

5. 1. Theoretical background Storper’s ‘holy trinity of technologies, organizations, and territories’ The neo-evolutionary variant of the triple-helix model. Leydesdorff et al., 2006

6. 1. Theoretical background – basic idea • When knowledge base is resulting from the synergy at the systems level, one can expect the system increasingly to ‘self-organize’ an additional feedback loop. This feedback may operate positively (that is, by reducing uncertainty in the relations) or negatively because, for example, it reinforces globalization in a previously more localized system • Etzkowitz and Leydesdorff (2000) called this additional feedback the operation of ‘a network overlay’ potentially emerging within a Triple Helix. In other words, the network of relations may turn into a configuration that can be productive, innovative, and flourishing, but not all networks can be expected to do so all the time. • Our analyses is based on this evolutionary model of Triple Helix dynamics in terms of how these relations operate: How much uncertainty is generated and/or reduced, at which level, and in which dimensions? We use an indicator of the emerging order of a knowledge-based economy and measure this order as a reduction of the uncertainty which prevails at the systems level.

7. 2. Hypotheses following previous studies Testing two hypotheses of previous studies (Leydesdorff et. al., 2006, Leydesdorff and Fritsch, 2006) Hypothesis 1 medium-tech manufacturing can be considered as the drivers of the knowledge base of an economy more than high-tech; Hypothesis 2 knowledge-intensive services tend to uncouple the knowledge base of an economy from its geographical location.

8. 2. Research problem in Hungary, hypotheses • Hungary entered transition period and faced the challenges of globalisation during the same period of time (Enyedi, 1995). Hypothesis 3: foreign-owned firms have a restructuring effect on the synergy among the three dimensions (industrial organisation, technology, geographical spread). • The differences among regions are determining for the economic prosperity: Budapest emerges as the center of the country in every sense (Barta, 2002; Varga, 2007), the rate of business R&D is higher in the Western parts while the big universities in the East are among the largest public R&D bodies (Grosz and Rechnitzer, 2005)). Hypothesis 4: the Hungarian regions are at different stages of the transition in terms of university-industry-government relations.

9. 3. Data and methods • Units of analyses: Hungarian firms;660,290 categorised in 3 dimensions (geography, technology, organisation) • Datacollection by the Hungarian Central Statistical Office • Geographical dimension: NUTS 4 (sub)regions

10. 3. Regions (NUTS 2) and counties (NUTS 3) in Hungary Source: http://en.wikipedia.org/wiki/Regions_of_Hungary

11. 3. Data and methods Technology dimension: NACE categories Source: Laafia, 2002: 7.

12. 3. Data and methods • Organisational dimension Source: Hungarian Central Statistical Office (HCSO)

13. 3. Data and methods • Uncertainty in the distribution of variable x (Shannon, 1948) Hx = − ∑x px 2log px • Two-dimensional probability distribution Hxy = − ∑x ∑y pxy 2log pxy • Mutual information in two dimension reduces the uncertainty Txy = (Hx + Hy) – Hxy • Mutual information in three dimensions can add to the uncertainty Txyz = Hx + Hy + Hz – Hxy – Hxz – Hyz + Hxyz Tgto = Hg + Ht + Ho – Hgt – Hgo – Hto + Hgto

14. 4. Results: Mutual information

15. 4. Results: mutual information in technology and organisation • Leydesdorff et al. (2006) hypothesized that the Tto might be considered as an indicator for the correlation between the maturity of the industry (Anderson and Tushman, 1991) and the specific size of the firms involved (Suárez and Utterback, 1995; Utterback and Suárez, 1993; cf. Nelson, 1994). The relatively low value of this indicator for Békés indicates that the techno-economic structure of this county is less mature than in other counties, which we can easily accept according to our expectations. Tto has the highest value in Nógrád, a similarly under-developed county in Hungary, and Budapest and Pest have relatively low values. Thus, our results do not support this hypothesis.

16. 4. Results: mutual information in three dimensions T = T0 +i ni/N × Ti T0= + 10.94 mbits

17.  T in mbits 4. Results: Geographical decomposition of mutual information in three dimensions

18. 4. Results: Contra-intuition in the North-East of Hungary Source: Hungarian Central Statistical Office

19. 4. Distribution of foreign stake in foreign owned companies, Hungary=100 (%) Source: Hungarian Central Statistical Office

20. 4. Number of R&D facilities and R&D employees in the Hungarian Regions Source: Hungarian Central Statistical Office

21. 4. The mutual information considering the high- and medium-tech sectors at NUTS 3 level in Hungary

23. % of T 4. Contribution of high-tech services to the knowledge base

25. 5. Conclusions • Conclusion 1 (High-tech and medium-tech industries were dealt together.) • Conclusion 2 Knowledge-intensive services seem to have weaker effects in uncoupling from the geographical dimension in Hungary than it was found in the Netherlands and Germany. High-tech knowledge-intensive services, mainly research and development, even have sometimes coupling effects, like it was pointed out in the former East German areas as well.

26. 5. Conclusions • Conclusion 3 Foreign-owned firms may have had a disturbing effect on triple helix mechanisms in Hungary uncoupling (more traditional) medium-tech companies from their geographical roots. In this sense, “creative destruction” by foreign-owned companies can be expected to have had determining roles in shaping university- industry- government relations. Only Budapest is an exemption, the level of integration is much higher in this metropolitan area. • Conclusion 4 The regions are at different stage in the transition in terms of university-industry-government relations. The transition from „etatistic model” to triple helix relations has not ended yet, in this sense the country is divided in three parts. Universities in the East could function in their economical surrounding as public R&D investments. The areas in the West possibly rejoined foreign innovation systems where high- and medium-tech industries are already crucial driving the knowledge base. Budapest competes with other metropolitan areas like Vienna, Munich, and perhaps Bratislava.

27. Policy implication • Hungarian system was restructured not only in terms of linkageswithin the production system, but also in relation to its relevantenvironments. • Budapest and the north-western part of the country could find a way to the European market more easily than the eastern part. • The transforming forces were largely exogenous to the Hungarian economy. • Thus, the Hungarian system may have lost control over its political economy to an extent larger than traditional economies like the Netherlands which have been able to transform and adapt their national structures more gradually (Radosevic, 2002, 2004)

28. Extension of research • Innovation systems literature University-industry-goverment relations: evolutionary or institutional triple helix? What precise role the foreign-owned firms have in the national- and regional innovation systems in transition economies? • Micro aspects: organizational routines What were the main forces of organizational routine’s change at Budapest university departments: knowledge transfer from foreign-owned firms or government initiatives?

29. Thank you for your attention! Balázs Lengyel - lengyel@rkkmta.hu, blengyel@gmail.com