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 - lengyel@rkkmta.hu, blengyel@gmail.com