measuring the knowledge base in hungary: triple helix mechanisms in a transition economy balázs...
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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
Structure of the presentation
1. Theoretical background: evolutionary triple helix relations
2. Hypotheses, research problem in the Hungarian analyses
3. Data and methods
4. Results
5. Conclusion
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.
1. Theoretical background – sub-dynamics, stochastic relations In innovation systems organized knowledge production-, diffusion-,
and control functions 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.
1. Theoretical backgroundStorper’s ‘holy trinity of technologies, organizations, and territories’
The neo-evolutionary variant of the triple-helix model.
Leydesdorff et al., 2006
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.
2. Hypotheses following previous studies
Testing two hypotheses of previous studies (Leydesdorff et. al., 2006, Leydesdorff and Fritsch, 2006)
Hypothesis 1medium-tech manufacturing can be considered as the drivers of the knowledge base of an economy more than high-tech;
Hypothesis 2knowledge-intensive services tend to uncouple the knowledge base of an economy from its geographical location.
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.
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
Level of territorial units Number of territorial units
NUTS 2 = region 7
NUTS 3 = county 19 + Budapest (capital)
NUTS 4 = subregion 167 + Budapest (capital)
3. Regions (NUTS 2) and counties (NUTS 3)
in Hungary
Source: http://en.wikipedia.org/wiki/Regions_of_Hungary
3. Data and methods
Technology dimension: NACE categories
High-tech Manufacturing 30 Manufacturing of office machinery and
computers 32 Manufacturing of radio, television and
communication equipment and apparatus 33 Manufacturing of medical precision and
optical instruments, watches and clocks Medium-high-tech Manufacturing 24 Manufacture of chemicals and chemical
products 29 Manufacture of machinery and
equipment n.e.c. 31 Manufacture of electrical machinery and
apparatus n.e.c. 34 Manufacture of motor vehicles, trailers
and semi-trailers 35 Manufacturing of other transport
equipment
Knowledge-intensive Sectors (KIS) 61 Water transport 62 Air transport 64 Post and telecommunications 65 Financial intermediation, except insurance and
pension funding 66 Insurance and pension funding, except compulsory
social security 67 Activities auxiliary to financial intermediation 70 Real estate activities 71 Renting of machinery and equipment without
operator and of personal and household goods 72 Computer and related activities 73 Research and development 74 Other business activities 80 Education 85 Health and social work 92 Recreational, cultural and sporting activities Of these sectors, 64, 72 and 73 are considered high-
tech services.
Source: Laafia, 2002: 7.
3. Data and methods
Organisational dimension
Number of employees Number of firms included in this study
Number of registered firms – 31st Dec. 2005
0 or unknown 275,202 365,861
1-9 369,869 805,209
10-19 5,976 20,870
20-49 4,921 11,046
50-249 3,733 4,860
250 or more 589 944
Total 660,290 1,228,999
Source: Hungarian Central Statistical Office (HCSO)
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
4. Results: Mutual information
Tgt Tgo Tto
Hungary 41.0 15.2 174.4
Budapest 124.1
Baranya 24.1 3.7 187.9
Bács-Kiskun 15.1 3.2 200.8
Békés 64.5 12.9 105.2
Borsod-Abaúj-Zemplén 20.7 4.1 178.3
Csongrád 15.6 3.3 148.2
Fejér 17.7 5.1 166.7
Gyor-Moson-Sopron 17.4 4.1 212.4
Hajdú-Bihar 18.1 3.2 186.5
Heves 15.4 4.3 239.5
Komárom-Esztergom 13.9 2.4 231.6
Nógrád 15.3 6.5 270.3
Pest 18.8 2.2 150.4
Somogy 31.1 4.9 197.4
Szabolcs-Szatmár-Bereg 26.7 3.7 166.9
Jász-Nagykun-Szolnok 20.5 5.4 210.0
Tolna 22.1 2.7 168.8
Vas 22.6 9.0 210.1
Veszprém 21.9 3.5 229.4
Zala 16.3 2.8 210.0
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.
4. Results: mutual information in three dimensions
Hungary -23.55 660,290
0.00 ∆T in mbits
Budapest -27.75 -9.63 229,165
Baranya -29.59 -1.13 25,308
Bács-Kiskun -41.28 -1.57 25,158
Békés -41.85 -1.20 19,003
Borsod-Abaúj-Zemplén -52.32 -2.39 30,174
Csongrád -25.26 -1.00 26,122
Fejér -39.93 -1.46 24,075
Gyor-Moson-Sopron -34.13 -1.46 28,177
Hajdú-Bihar -31.93 -1.29 26,624
Heves -42.19 -0.96 15,095
Komárom-Esztergom -49.70 -1.34 17,760
Nógrád -50.37 -0.67 8,722
Pest -33.22 -3.39 67,342
Somogy -41.87 -0.99 15,680
Szabolcs-Szatmár-Bereg -38.53 -1.19 20,422
Jász-Nagykun-Szolnok -42.04 -1.05 16,513
Tolna -33.95 -0.63 12,344
Vas -48.89 -1.07 14,490
Veszprém -43.45 -1.35 20,533
Zala -27.78 -0.70 16,538
T = T0 +i ni/N × Ti
T0= + 10.94 mbits
4. Results: Geographical decomposition of mutual information in three dimensions
T in mbits
4. Results: Contra-intuition in the North-East of HungaryRegions, counties GDP per capita
(EU25=100, %)2003
Employment rate(population aged 15-64, %)
2004
Hungary 59.9 56.8
Central Hungary 96.5 62.9
Central Transdanubia 55.4 60.3
Western Transdanubia
64.4 61.4
Southern Transdanubia
42.9 52.3
Northern Hungary 38.3 51.6
Northern Great Plain 39.1 50.4
Southern Great Plain 40.7 53.6
Source: Hungarian Central Statistical Office
4. Distribution of foreign stake in foreign
owned companies, Hungary=100 (%) Region, county 2000 2001 2002 2003 2004
Budapest 58.8 54.0 52.9 47.3 50,2
Pest 9.5 11.1 11.5 15.7 15,4
Central Hungary 68.3 65.1 64.4 63.0 65.6
Central Transdanubia 7.1 8.3 8.4 10.0 10.1
Western Transdanubia 10.8 12.4 11.9 11.9 11.6
Southern Transdanubia 2.0 1.9 2.2 1.9 1.6
Northern Hungary 4.7 4.0 4.7 5.7 4.0
Northern Great Plain 3.8 4.1 5.4 5.1 4.8
Southern Great Plain 3.3 3.2 3.0 2.4 2.3
Source: Hungarian Central Statistical Office
4. Number of R&D facilities and R&D
employees in the Hungarian Regions Region R&D places R&D employees
1996 2001 2004 1996 2001 2004
Central Hungary 710 1,199 1,255 12,831 16,924 17,535
Middle Transdanubia 64 158 158 732 1,513 1,712
Western Transdanubia 109 150 194 830 1,411 1,500
Southern Transdanubia 125 195 227 1,417 1,973 2,405
Northern Hungary 101 118 145 1,160 1,326 1,571
Northern Great Plain 162 250 280 2,213 2,489 2,873
Southern Great Plain 190 267 282 2,126 2,715 2,824
Hungary 1,461 2,337 2,541 20,859 28,351 30,420
Source: Hungarian Central Statistical Office
4. The mutual information considering the high- and medium-tech sectors at NUTS 3 level in Hungary
KISIn mbits
%change N recs Nrecs%
HT and MT inManu.in mbits %change Nrecords nrecs%
Hungary -19.28 -15.7 641,143 97.1 -3.08 351.7 19,147 2.9
Budapest -2.64 -18.9 223,325 97.5 -1.30 366.6 5,840 2.5
Baranya -0.04 -16.9 24,684 97.5 -0.14 329.3 624 2.5
Bács-Kiskun -0.03 -42.0 24,313 96.6 -0.45 886.5 845 3.4
Békés -0.03 -19.7 18,563 97.7 -0.16 351.0 440 2.3
Borsod-A.-Z. -0.07 -31.2 29,327 97.2 -0.51 633.8 847 2.8
Csongrád -0.03 -11.6 25,299 96.8 -0.09 206.4 823 3.2
Fejér -0.02 -55.8 23,299 96.8 -0.54 1172.0 776 3.2
Gyor-M.-S. -0.03 -47.9 27,327 97.0 -0.46 980.4 850 3.0
Hajdú-Bihar -0.03 -37.8 25,928 97.4 -0.30 712.4 696 2.6
Heves -0.01 -45.3 14,597 96.7 -0.29 948.5 498 3.3
Komárom-E. -0.01 -58.4 17,019 95.8 -0.53 1257.2 741 4.2
Nógrád 0.00 -49.6 8,495 97.4 -0.21 969.3 227 2.6
Pest -0.16 -51.7 64,791 96.2 -1.26 1179.0 2,551 3.8
Somogy -0.02 -21.1 15,286 97.5 -0.11 298.5 394 2.5
SzabolcsSz.-B. -0.03 -27.8 19,793 96.9 -0.23 551.7 629 3.1
Jász-N.-Sz. -0.01 -52.1 15,956 96.6 -0.37 1102.3 557 3.4
Tolna -0.01 -29.6 11,995 97.2 -0.13 632.8 349 2.8
Vas -0.01 -57.0 14,169 97.8 -0.36 1064.9 321 2.2
Veszprém -0.02 -46.3 19,888 96.9 -0.41 957.4 645 3.1
Zala -0.01 -35.2 16,074 97.2 -0.15 636.0 464 2.8
4. Contribution of high-tech services to the knowledge base
% of T
KnowledgeIntensiveservicesIn mbits
High-techServices in mbit %change
N
Hungary -19.28 -12.02 -49.0 39,415
Budapest -2.64 -13.05 35.5 18,491
Baranya -0.04 -0.14 -88.1 1,325
Bács-Kiskun -0.03 -0.91 -42.0 1,075
Békés -0.03 -0.39 -67.8 571
Borsod-Abaúj-Zemplén -0.07 -0.81 -66.0 1,387
Csongrád -0.03 -1.82 82.0 1,383
Fejér -0.02 -0.67 -54.1 1,211
Gyor-Moson-Sopron -0.03 -0.25 -83.1 1,195
Hajdú-Bihar -0.03 -0.38 -70.4 1,225
Heves -0.01 -0.21 -78.1 668
Komárom-Esztergom -0.01 -0.31 -76.5 794
Nógrád 0.00 -0.19 -71.3 332
Pest -0.16 -2.75 -18.7 5,019
Somogy -0.02 -0.58 -41.9 638
Szabolcs-Szatmár-Bereg -0.03 -0.49 -58.9 811
Jász-Nagykun-Szolnok -0.01 -0.38 -64.1 709
Tolna -0.01 -0.32 -49.7 517
Vas -0.01 -0.39 -63.3 640
Veszprém -0.02 -0.35 -74.4 836
Zala -0.01 -0.24 -65.2 586
5. Conclusions
Conclusion 1(High-tech and medium-tech industries were dealt together.)
Conclusion 2Knowledge-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.
5. Conclusions
Conclusion 3Foreign-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 4The 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.
Policy implication Hungarian system was restructured not only in terms of linkages
within the production system, but also in relation to its relevant environments.
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)
Extension of research
Innovation systems literatureUniversity-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 routinesWhat were the main forces of organizational routine’s change at
Budapest university departments: knowledge transfer from foreign-owned firms or government initiatives?
Thank you for your attention!
Balázs Lengyel - [email protected], [email protected]