Discussion Papers 2006. No. 50.
Regional Characteristics of Human
Resources in Hungary During the Transition
CENTRE FOR REGIONAL STUDIES
OF HUNGARIAN ACADEMY OF SCIENCES
DISCUSSION PAPERS
No. 50
Regional Characteristics of Human
Resources in Hungary During
the Transition
by
János RECHNITZER – Melinda SMAHÓ
Series editor
Zoltán GÁL
Pécs
2006
1
Discussion Papers 2006. No. 50.
Regional Characteristics of Human
Resources in Hungary During the Transition
ISSN 0238–2008
ISBN 963 9052 59 0
2006 by Centre for Regional Studies of the Hungarian Academy of Sciences
Technical editor: Ilona Csapó
Maps: Valéria Fonyódi
Printed in Hungary by Sümegi Nyomdaipari, Kereskedelmi és Szolgáltató
Ltd., Pécs.
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Discussion Papers 2006. No. 50.
Regional Characteristics of Human
Resources in Hungary During the Transition
Content
Introduction ........................................................................................................................... 7
1 Human resources and regional development .................................................................. 8
2 Human factors .............................................................................................................. 13
2.1 Schooling of the population .................................................................................. 17
2.2 Spatial structure of the schooling of the inhabitants ............................................. 21
2.3 Foreign language skills ......................................................................................... 29
2.4 Human development index ................................................................................... 32
3 Quality of life ............................................................................................................... 38
3.1 Civil society .......................................................................................................... 38
3.2 Regional and local identity ................................................................................... 43
4 The network of knowledge and the communication of information............................. 45
4.1 Primary and secondary institutions....................................................................... 45
4.2 The network of higher education and knowledge mediators ................................ 52
4.3 Regional disparities and characteristics of research and development ................. 58
4.4 Changes of the territorial structure ....................................................................... 61
4.5 Characteristics of the transition period ................................................................. 68
5 Settlement network ....................................................................................................... 74
5.1 The innovative milieu and its changes in the nineties .......................................... 74
5.2 Spread of a new skill and technology ................................................................... 81
5.3 Knowledge bases at the turn of the millennium in the urban network.................. 85
6 Trends and conclusions ................................................................................................ 91
References .......................................................................................................................... 94
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Discussion Papers 2006. No. 50.
Regional Characteristics of Human
Resources in Hungary During the Transition
List of figures
Figure 1
A different approach to regional competitiveness............................................. 10
Figure 2
Spatial factors influencing human resources .................................................. 12
Figure 3
Changes of the number of population between 2000 and 2010 ...................... 14
Figure 4
Changes of the source of labour force (population aged 15–19) in the
counties, 2000–2010 ....................................................................................... 15
Figure 5
Permanent internal migration balance per one thousand inhabitants by
counties, 2001 ................................................................................................. 16
Figure 6
Average number of school classes finished by the population aged 7 and
older, 1990, 2001 (in decreasing order by the 1990 values of the index)........ 19
Figure 7
Breakdown of the population aged 7 and older by school education,
1990 ................................................................................................................ 23
Figure 8
Breakdown of the population aged 7 and older by school education,
2001 ................................................................................................................ 24
Figure 9
Regional disparities of the college and university degree holders per one
thousand inhabitants, 2001.............................................................................. 27
Figure 10 Regional disparities of the professionals per one thousand inhabitants,
2001 ................................................................................................................ 28
Figure 11 Language skills of the population, 2001 ......................................................... 31
Figure 12 Changes of the HDI values from 1990 to l996–1997...................................... 35
Figure 13 HDI and its partial indices, 2001 (in decreasing order of HDI) ...................... 37
Figure 14 Number of non-for-profit organisations per one thousand inhabitants by
counties, 1993 ................................................................................................. 39
Figure 15 Number of non-for-profit organisations per one thousand inhabitants by
counties, 2000 ................................................................................................. 40
Figure 16 Average contribution per one inhabitant (financial donation) to the
revenues of the non-for-profit organisations by counties, 1997...................... 42
Figure 17 Number of primary school pupils per one thousand permanent
inhabitants, 2001 ............................................................................................. 48
Figure 18 Number of full-time secondary school students per one thousand
permanent inhabitants, 2001 ........................................................................... 49
Figure 19 Role of the secondary education in the towns in the provision of the
countryside, 2000 ............................................................................................. 52
Figure 20 Number of higher education students in the counties, 1990, 1996, 2001 ........... 53
Figure 21 Spatial distribution of the higher education institutions in 2002......................... 54
Figure 22 Number of full-time lecturers in the higher education institutions of the
counties, 1990, 2001......................................................................................... 55
Figure 23 Breakdown of the public body members of the HAS by science classes in
the territories of the respective regional committees, 2002 ................................ 57
Figure 24 Regional development level and the level of R & D, 1995, 2001................... 64
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Discussion Papers 2006. No. 50.
Regional Characteristics of Human
Resources in Hungary During the Transition
Figure 25 Number of applications for patents at the Hungarian Patent Office by
counties, 1992, 1997, 2002 ............................................................................... 69
Figure 26 Division of the urban network by innovation milieu, 1990 ................................. 76
Figure 27 Division of the urban network by innovation milieu, 1997.............................. 77
Figure 28 Possible groups of the Hungarian towns on the basis of the info-
communication sector ..................................................................................... 83
Figure 29 Division of the urban network by the knowledge base (towns with high
renewal capacity) ............................................................................................ 87
Figure 30 Division of the urban network by the knowledge base (towns with
limited renewal capacity) ................................................................................ 88
List of tables
Table 1
Positions of the Hungarian regions by the average number of school
classes finished by the inhabitants, 1990, 2001............................................... 20
Table 2
Development of the HDI indices at county level, 1990–2001 ........................ 34
Table 3
Changes of the values of the HDI index, 1996–2001...................................... 36
Table 4
Indices of identity in Hungary, 1992–1996..................................................... 44
Table 5
Institutions and a few indices of primary education in Hungary (1990,
2001) ............................................................................................................... 47
Table 6
Average proportion of those admitted to or students in their 4th year in
higher education institutions (A/S) by counties, 1991–1998, 1994–1998 ...... 50
Table 7
Regional distribution of the members of the public body of the Hungarian
Academy of Sciences, 2000 ................................................................................. 58
Table 8
Conditions of research and development ............................................................. 59
Table 9
Employment in research and development ..................................................... 61
Table 10
Rank-order of the R & D potential, 1995, 2001.............................................. 62
Table 11
Regional development level and the level of R & D, 1995, 2000................... 65
Table 12
R & D potential and the types of economic development............................... 67
Table 13
Amounts paid to higher education from the resources of the National
Technical Development Fund in 1996–2000, by planning-statistical
regions............................................................................................................. 71
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Introduction
The objective of regional development policies in the modern economies is the de-
crease of the disparities in the living conditions of the population. The interventions
of the regional policies aim at the transformation of the economic sectors, the in-
crease of the employment possibilities and the improvement of the different infra-
structural and institutional conditions having an impact on the living conditions. The
focus of the efforts is to guarantee possibilities by the renewal of the spatial factors
promoting development and by the safeguarding of the conditions for a catching up.
This latter factor serves spatial equalisation and the moderation of the regional dis-
parities, the effects of which of course can only be felt in the long run.
The modern regional development policies do not consider space as a single
unit; they apply the above-mentioned general objective in a differentiated approach
(Horváth, 1998). From the 1970s it became evident that the respective regions have
specific socio-economic structures, making it impossible to handle them in a uni-
form way. Only a differentiated system of objectives, tools and institutions can
allow the activation of the local resources.
The factors seen as determining regional growth were previously economic fac-
tors and human resources – including labour force –, mostly their quantitative pres-
ence, later, from the 1980s, their qualitative composition (Benko, 1999). By the
1980s it became clear that in regional development the factors could not be sepa-
rated, i.e. the economic, social, human-labour factors should not be treated sepa-
rately, on the one hand, and the human elements were more and more appreciated,
more exactly became the focal point of researches, on the other (Rechnitzer, 1993).
The last decade of the previous century demonstrated that those territories were
successful in regional development that had a larger number and complex, active
asset of human factors (Enyedi, 1997). Regional analysts and developers realised that
in different points of the space alternative cultural ands social ties had emerged, due
to the levels of schooling, professional structures, ways of life and living conditions
of the inhabitants. The linkages change, identities are different, the links to the set-
tlement and the region can be characterised by different parameters.
Work culture and the and culture of activities show definite regional character-
istics, the accumulated experiences show different levels of activity, thus the ad-
aptation to the new skills is very much differentiated in space, just because of these
social and cultural correlations (Hahne, 1985). The former paradigm of regional
development – import of the factors of production, top-down controlled transfor-
mation with a predominantly economic view – was questioned and replaced by a
new way of thinking and action. This new, shaping paradigm has two central ele-
ments: the first is bottom-up approach; the second – basically connected to and
deriving from the first – is the activation of the human resources, the promotion of
its basic elements.
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Rechnitzer, János - Smahó, Melinda :
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Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
The first part of our study focuses on the new factors of regional development, in-
cluding the elements generated by the human resources. The subsequent chapters
deal with the regional structure of a few dimensions of human resources, demon-
strating the regional characteristics that have stabilised or emerged in the 1990s and
nowadays. Finally we summarise the characteristic features, implying the trends of
regional development and thus the presumed methods of the activation of human
resources.
1 Human resources and regional development
The precise definition of the human resources, a concept heard so often that it now
almost seems a common place, is not an easy task. What is the point? It is that the
human resources, the institutions contributing to their development, and the total of
the social conditions and endowments together constitute those assets that a spatial
unit disposes of. These factors are present both in themselves and jointly, the have
impacts, they shape – reinforcing or weakening each other – the given spatial unit,
and by the multitude of the spatial units, the whole of the spatial structure (Enyedi,
1996).
The recognition of knowledge in regional science as a factor determining regional
development only started in the 1990s. The theories analysing regional development
always reflected the current paradigms of economics, so for example the neoclassical
theory dealt with the capital and labour effects (Richardson, 1969); then the export
base theory considered the role of the sectors shaping the economic structure in re-
gional growth (North, 1955). In the 1970s, the theory of endogenous development
already featured human resources among the internal factors. The focal points of the
analyses were nevertheless the endowments of the economic structure (Hahne, 1985)
or the shaping of the institutional system of regional policy, the handling of the limits
and shortcomings in this system (Stöhr, 1987).
The theories explaining the development of the national economies and the fac-
tors of their competitiveness also tried to follow and model the changing economic
environment, which led to the elaboration of “new growth theories” (EC, 2003). The
followers of the “new (endogenous) growth theory” challenged the hypotheses of the
neoclassical experts – who excluded the role of the technological changes on eco-
nomic development – and integrated technological externalities in their model. One
of their excellent representatives, Romer (1990) emphasises the importance of
knowledge creation in his growth theory. He argues that knowledge is a dominant
form of capital, and the volume of economic growth primarily depends on the accu-
mulation of knowledge. The most important feature of the knowledge-based societies
is that the creation and utilisation of knowledge is the central element of the value
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
creation processes. He also emphasises that the technological changes occur as a
consequence of the investments in new technologies and the human resources, as a
return of these investments. Accordingly, technological development is to be seen as
an endogenous factor of economic growth.
The theory of endogenous growth also supposes that all existing – codified and
implicit (“tacit”) – knowledge is freely available for the creation of the new technical
knowledge. However, this has not been reinforced by the latest researches, as the
findings revealed that the spread of the new technological knowledge – especially of
the implicit knowledge – has geographical limits (Anselin et al. 1997; Varga, 1998;
Braczyk et al. 1998; Malecki-Oinas, 1999).
According to the Schumpeterian endogenous innovation model, the businesses
carry out technological developments built on innovations in order to maximise their
profits. The model considers these technological developments as the most important
source of economic growth. This theory says that the businesses are engaged in re-
search and development activities mostly to build out temporary monopolies and
realise and extra profit (Romer, 1990).
Due to the imperfect competition, the businesses are able to realise enough profit
from their new products to cover their research and development expenses. The in-
novations involving better quality and more service content than the previous prod-
ucts are capable of replacing previous generations of the products; consequently they
secure the extra profit of the innovator. These innovations then serve as inputs for the
development activities of other businesses, contributing this way to the development
of the general technological level and to economic growth. Typical examples for
these innovations are information and communication technologies. In the Schum-
peterian model the volume of growth is determined by the returns of the research
costs, which depends on the magnitude of resources spent on innovation, the size of
the market, the profitability of the research and development activity and the market
positions of the innovators.
The theories that belong to the economic approaches of the new economic geog-
raphy try to find out what factors lead to the concentration of the economic activities
and which factors influence primarily regional competitiveness. The equilibrium
model of the new theory of economic geography, by Krugman (1991/2003) can ex-
plain not only the geographical concentration of the economic activities but also the
factors motivating the spatial reallocation of production and the transition of the in-
terregional division of labour. Krugman’s model combined the theories of Weber,
Marshal and the evolutionist economists (Nelson, Winter) in an innovatory way.
While Marshal e.g. builds his theory on the ideal of perfect competition, Krugman’s
approach also considers imperfect competition and the increasing returns. In his
model, parallel to the decrease of the transport costs, the local markets lose some of
their importance and the production becomes more and more mobile in space.
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
It comes from these theories that the competitive advantage of the advanced
economies comes form their skills of knowledge production and knowledge utilisa-
tion in the first place. These days it is knowledge which is the basis of product, proc-
ess and service innovations that also create new markets or make the production of
the existing products and services cheaper. Knowledge comes from a continuous
research and development activity that is carried out by highly skilled experts during
an efficient technology transfer and the market implementation of the new ideas.
Knowledge-based economic development is typical for half of the OECD mem-
ber states, an organisation involving the developed industrial countries. Knowledge
production and the number of those employed in knowledge industries are rapidly
increasing in these countries (OECD, 1996). The production of medium technology
intensive and high-tech product increased from 44% in 1985 to over 50% by the
turn of the millennium. The growth rate of these sectors has been significantly
higher than the GDP growth rate for years (Simmie, 2003) (Figure 1).
Figure 1
A different approach to regional competitiveness
Source: EC [2003], p. 131.
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
It is a generally accepted view that the wide spread of knowledge-based activi-
ties is playing an increasingly important role in the competitiveness of the respec-
tive countries and regions. Knowledge – as an essential component of innovation –
is part of the circulation that leads to innovation and thus to the increase of export
capacities and competitiveness. On the one hand, the export base of national and
regional economies is the main driving force of growth. On the other hand, export
and trade are most important mediators of internationally available knowledge and
the transfer of skills indispensable for innovation – and the innovation loop is
closed.
The models introduced by Hagerstrand (1952), describing the spatial spread of
innovation launched those surveys that lead to the description of the innovation
milieu where the role of the human resources is already of utmost importance
(Camagni, 1991; Rechnitzer, 1993). Only one step further is the theory of knowl-
edge regions that sees the new driving force of development in the accumulation of
skills and in the institutions and actors responsible for that, also defining a new
paradigm for regional development (Scheff, 1999; Rösch, 2000).
Meusburger (1998) devoted a comprehensive monograph to the introduction of
the regional dimension of knowledge and training, in which we get an insight not
only into the theoretical bases necessary for the survey of the new factor of economic
development but also find the most important aspects and methods for the analysis of
the human resources.
The Hungarian regional researches have neglected the analysis of knowledge of a
new element in regional development so far. Essays were written on the independent,
individual analysis of the respective elements of human resources, but these assessed
the processes of the last decade and a half or the spatial transition of certain constitu-
ent factors not systematically, but from the aspect of the sectoral factors or the tradi-
tional regional resources (Vámos, 1992; Tóth–Trócsányi, 1997, 2000).
It is a new challenge thus to analyse the spatial aspects of the human resources, as
we do not wish to describe this resource of innovation by one single factor, i.e.
population; instead we focus on the effect mechanisms and the system of factors that
mostly influence the human resources in a respective region. We divided this system
into four constituents (human factors; quality of life; knowledge and skills dissemi-
nation; settlement network), as seen in Figure 2.
Each of these elements deserves a separate study, so we have selected a few of
their components – for which territorial information is available –, and we attempt
to introduce the processes of the 1990s on the basis of these.
The first one is the human factor that can be characterised by population and
population trends, and different features of the population – in our case it is per-
haps the schooling and skills, i.e. the level of training that is the most important.
Further characteristics of the human factor are the stock of labour, activity rate, the
structure of employment and its transition, rearrangement. Our essay does not deal
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
with these latter two issues. The Human Development Index is meant to illustrate
the quality of the human resources; this index tries to find correlations between the
level of schooling of the population and the economic performance, which makes
is suitable for the definition of temporal and spatial development hierarchies.
Figure 2
Spatial factors influencing human resources
HUMAN FACTORS
QUALITY OF LIFE
- Composition of the population
- Civil society
- Level of schooling and skills
- Identity
- Labour force
- Culture
- Employment
HUMAN
RESOURCES
SETTLEMENT
NETWORK OF KNOWLEDGE
NETWORK
AND SKILLS DISSEMINATION
- Innovation environment
- Primary and secondary education
- Reception of novelties
- Higher education and further training
- Knowledge bases
- Research and development
Source: compiled by the authors.
The second block focuses on the quality of life at regional level. Nowadays the
cultural level of a respective region, the activity of the civil society or the identifica-
tion of the population with the locality or region is seen as a factor promoting devel-
opment. In our essay we analyse the latter two factors.
The third block contains the network of knowledge and skills dissemination,
because school education at different levels (primary, secondary schools and higher
education), research and development and the presence of its institutions and ex-
perts is of decisive importance in shaping the human resources of the municipali-
ties and regions.
The fourth block is on the development of the settlement network, specifically
the urban network. The settlement network is seen as an aura of the human re-
sources. Human presence and the institutions representing play a role in the devel-
opment of the settlements; on the other hand, the character and particular features
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
of the settlement also influence human actions and their circumstances. The mutual
relationship and connection is demonstrated by the example of the urban network,
partly by its restructuring in the early 1990s and by the analysis of a new knowl-
edge and technology, finally by looking at the structure of the factors carrying
knowledge.
In our essay we used the results of the Hungarian literature and the findings of
Hungarian researches, we summarised them from the aspect of the human re-
sources, and we carried out supplementary survey where we found it necessary, or
we updated the data. In our work we tried to grab the processes of the 1990s in the
first place, expanding our skills on the regional characteristics of the human re-
sources in the transition period.
2 Human factors
Hungarian regional science is in a lucky situation inasmuch as the population data
within the human factors and the tendencies that can be seen from them are fairly
well processed. The features of the population – although they are only surveyed in
details at the censuses every ten years and at the time of the micro-census at half-
time – allow a detailed spatial analysis.
The decrease of the population was a stable tendency in the last ten years, the
number of population decreased by almost 300 thousand persons since 1990. In each
region and all of the counties the number of population decreased, with the exception
of Pest and Fejér counties and the settlements in the category of three to twenty thou-
sand inhabitants. By the end of the decade, however, in not one county or region, or
settlement category did the number of live births surpass the number of deaths (9.6‰
and 13.9‰, respectively), which means that ageing is now a phenomenon affecting
the whole of the country, including the advanced and urbanised regions (Report on
the regional processes… 2001). The regional disparities have stabilised, as there is a
natural increase in Szabolcs-Szatmár-Bereg, Hajdú-Bihar and Borsod-Abaúj-Zem-
plén counties, in Transdanubia in Fejér and Veszprém counties. A high proportion of
deaths was typical of the last decade, which is especially true in the capital city, the
north Hungarian counties, but the situation is the most dramatic in the South Great
Plain. In 1990, in sixty-one per cent of the Hungarian settlements the population aged
sixty years or older outnumbered those younger than fifteen; in 1998, this was the
case in 68% of the Hungarian settlements. Ageing is a lasting tendency; nowadays it
is only the agglomeration of Budapest and the northeast part of Hungary where the
proportion of children within the total population is higher than that of the elderly
citizens.
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
The trends of the change of the population in 2000–2010 (Figure 3) reveal that in
the southern parts of Hungary (between Szentgotthárd and Gyula) the decrease of the
population will be faster than the national average, the decrease of the population
might exceed ten per cent in this decade, which can be enhanced by migration. The
pace of the population decrease will be somewhat slower in Szabolcs-Szatmár-Bereg,
Borsod-Abaúj-Zemplén and Hajdú-Bihar counties, similarly moderate tendencies
will be typical for the industrialised parts of Veszprém and Fejér counties. In the
rural areas, the population decrease will be bigger in the coming decade (reaching 10
to 12 per cent), while the urban regions will have a more moderate decrease (2–4 per
cent). Migration will contribute to the increase of the disparities (National Regional
Development Concept, Ministry of the Environment and Regional Development,
1997).
Figure 3
Changes of the number of population between 2000 and 2010
(national average = 100%)
Source: National Regional Development Concept. Ministry of Environment and Regional Develop-
ment, 1997
The regional disparities of the pool of labour force will be more striking in 2000–
2010 than before (Figure 4). The decrease of the population aged 15–59 is the most
striking in the capital city and its agglomeration, above-average decrease can also be
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
seen in Gyır-Moson-Sopron county and in Nógrád and Heves counties, closely
linked to the agglomeration of the capital city. We have already mentioned that the
South Great Plain region is characterised by a high ageing index, this is one of the
reasons why we expect a strong decrease of the number of labour force in this region.
Figure 4
Changes of the source of labour force (population aged 15–19) in the counties,
2000–2010 (national average = 100%)
Source: National Regional Development Concept. Ministry of Environment and Regional Develop-
ment, 1997.
The movement of human resources within Hungary, the migration showed differ-
ent characteristics in the transition period than in the decades before. Migration was
predominantly motivated by making a living and having better work conditions, so in
the 1990s migration to large distances was significantly reduced. Long-distance mi-
gration was slowed down, in addition to the reduction of jobs, by the lack of housing,
more exactly the striking growth in the regional disparities of housing costs. Migra-
tion now involved shorter distances, and the main targets of migration were the ur-
banised areas of Hungary (Figure 5). As a consequence of the population move-
ments, the prosperous western centres and their hinterlands attracted the population
of the eastern regions (see Rédei, 2001).
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Rechnitzer, János - Smahó, Melinda :
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Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Figure 5
Permanent internal migration balance per one thousand inhabitants
by counties, 2001
Source: Edited by the authors by HCSO data.
The age structure of the population participating in migration also changed, the
share of the younger generation increased in the 1990s. The youth attend secondary
schools and higher education institutions in these urbanised regions, having finished
their studies they settle down there, in fact, in many cases they prepare the “reloca-
tion” of their families, parents and relatives living in the eastern parts of Hungary.
The migration balance of the urban settlements turned to negative in the 1990s,
and this tendency is going on in the present decade. The outmigration of the popula-
tion form the big cities started; the Budapest agglomeration strikingly transformed
both as regards the number and the structure of its population (Csanádi–Csizmady,
2002), but more and more serious infrastructure and traffic problems also arise
(Kovács, 2001). The big cities of Hungary are also losing their population, the subur-
banisation trends affect the big cities of Transdanubia in the first place (Bajmóczy,
2000; Hardi, 2002), but the population decrease and outmigration are also palpable
in the big cities of the other regions (Timár–Váradi, 2000).
It is the active (and innovative) part of the population that leaves the rural or de-
pressed regions, leaving a growing share of elderly people and a decreasing working
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Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
population there. This raises several municipal management problems, since e.g. the
utilisation of some services decreases, at the same time the demand for other services
– especially for health and social services – increases; however, the impoverishing
municipalities are unable to afford the provision of these services.
International migration is a new tendency; it had been rather exceptional before
the systemic change. Today the number of foreigners staying in Hungary for a longer
period of time is estimated to be around 150 thousand, the number of newcomers has
stabilised at around 12–15 thousand annually (Kobolka, 2000; L. Rédei, 2001). The
majority (85%) of them arrive from European countries – half of these from Romania
–, ten percent from European Union member states, the rest from the neighbouring
countries. Another 10% of the immigrants are from Asia, mostly China (Kovács,
2000). The geographical destinations of immigration are easy to designate: on the
one hand, the border regions in the vicinity of the place of the immigration, and the
centres of such regions, and the capital city, on the other hand. In the third place, the
places of residence of friends and relatives (mostly in South Transdanubia) are the
main places where the immigrants settle down (L. Rédei, 2001).
2.1 Schooling of the population
As regards the schooling of the population, a striking tendency seen both at na-
tional and regional level in the last decade is the spectacular increase in the average
level of schooling of the population.1 The average number of finished school
classes increased by 1.1 between 1990 and 2001 – both the national and country-
side average –, which means a 13.6 per cent increase in the national average and a
slightly higher, 14 per cent increase in case of the countryside average (i.e.
theaverage calculated without Budapest). While in 1990 the Hungarian population
aged 7 or older had finished eight classes on the average, in 2001 the average
school education of the population aged 7 or older2 was almost 9.5 classes. When
1 During the census of 1990 and 2001, the schooling of the population was measured by different
methodologies: in 1990 the population aged 7 years and older was classified into different catego-
ries by the highest level of schooling finished, whereas in 2001 the highest number of finished
school classes was taken into consideration in the classification. As the data relating to the two
years cannot be directly compared in the form disseminated by the Central Statistical Office, we
used the available data to create categories that allow the comparison of the data concerning the
two censuses, and we re-grouped and corrected the existing data in these categories.
In the data series used in our survey we used the method looking at the highest level of school
finished. The started but unfinished (yet or for ever) schools of a higher class (irrespective of the
number of classes finished) was not taken into consideration, so the students of higher education in
the respective years were classified among those who had finished secondary school.
2 By population we mean the citizens aged seven years or older, as they are the part of the population
relevant in the survey of school education.
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looking at the figures of average school classes finished by counties we can see
significant territorial disparities beyond the average growth in the value of this index,
although the average number of finished school classes increased in each county
(Figure 6).
The figure immediately reveals the outstanding position of Budapest: the index
of the capital city exceeded the national average by 14.1% in 1990 and by 14.5% in
2001. In 1990 Budapest and Gyır-Moson-Sopron county showed values exceeding
the national average (8.3), the indices of Vas, Csongrád, Komárom-Esztergom,
Baranya, Fejér, Veszprém, Borsod-Abaúj-Zemplén, Zala and Pest counties were
between the national average (8.3) and the country average (8.0). In Heves, Hajdú-
Bihar, Somogy, Tolna, Békés, Nógrád, Jász-Nagykun-Szolnok, Bács-Kiskun and
Szabolcs-Szatmár-Bereg counties we find figures that did not even reach the coun-
try average.
The most dynamic development was not in Budapest in the last ten years, despite
the favourable position of the capital city. While the increase in the average number
of finished schools was less – 14% – than the country average, there were some
counties in which a growth exceeding the pace of the increase of the country average
could be observed: they are Pest (16.7 per cent), Somogy (14.9 per cent), Jász-
Nagykun-Szolnok (14.8 per cent), Csongrád (14.4 per cent), Heves (14.2 per cent)
and Fejér (14.2 per cent).
As a consequence of the uneven development during the decade, the order of the
counties changed by 2001. In 2001, in addition to Budapest and Gyır-Moson-So-
pron, Csongrád county also featured an index above the national average, but the
position of Pest and Baranya counties improved significantly, too. In Pest county the
average number of finished school classes hardly exceeded the country average in
1990, whereas in 2001 it was only 0.8% lower than the value of the national average.
The dynamic growth of Somogy, Jász-Nagykun-Szolnok and Heves counties – well
above the growth of the country average – was not enough to catch up these counties
to the level of the country average by the end of the decade. The situation signifi-
cantly worsened in Borsod-Abaúj-Zemplén county; the value exceeding the country
average in 1990 fell to below that by 2001.
The average number of finished school classes was the highest in Budapest and
the lowest in Szabolcs-Szatmár-Bereg county in both years. During the respective
decade, the difference between the highest and lowest value of average number of
finished school classes increased from 1.9-fold to 2.2-fold, which indicates the
growth of regional disparities in the relation of Budapest and Szabolcs-Szatmár-
Bereg county.
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
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Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
At the level of the largest territorial units, the regions, the growth of the average
number of finished school classes shows a more homogeneous situation than in the
case of the counties, which is explained by the different development dynamism of
the counties making the individual regions. A growth exceeding the increase of the
country average could only be seen in the South Transdanubian region, while the
dynamism of the growth of the national average was surpassed by the regions of
Central Hungary, Middle Transdanubia, North Great Plain and South Great Plain
(Table 1).
Table 1
Positions of the Hungarian regions by the average number of school classes
finished by the inhabitants, 1990, 2001
Region
1990
2001
position
Average number of
position
Average number of
finished school classes
finished school classes
Central Hungary
1.
9.05
1.
10.31
National average
–
8.34
–
9.47
West Transdanubia
2.
8.30
2.
9.42
Middle Transdanubia
3.
8.22
3.
9.36
Country average
–
8.05
–
9.18
North Hungary
4.
8.04
6.
9.08
South Transdanubia
5.
8.03
4.
9.17
South Great Plain
6.
8.00
5.
9.09
North Great Plain
7.
7.81
7.
8.89
Source: Calculation by the authors based on the county data of the censuses of 1990 and 2001. Source
of the data: county data of the census of 1990, HCSO 1992. and www.nepszamlalas.hu
In the hierarchy of the regions, both in 1990 and 2001, the Central Hungarian re-
gion ranked first as regards the average number of finished school classes. It was the
only region with indices above the national average. The following two regions,
West Transdanubia and Middle Transdanubia kept their positions between the na-
tional and the country average, a rearrangement could only be seen in the case of the
regions below the country average. The positions of South Transdanubia and South
Great Plain improved, due to their dynamic development, while the North Hungarian
region, the one closest to the country average in 1990, fell to the one but last position
by 2001. In North Hungary, the index of Borsod-Abaúj-Zemplén county fell below
the country average by 2001, in the South Great Plain region Csongrád county
showed a dynamic development, its figure surpassed the country average by 2001.
The North Great Plain – despite the spectacular development of Jász-Nagykun-
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Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Szolnok county – showed a pace of development below the growth of the national
average, it is still in the last position in the order of the regions: in all three constitu-
ent counties, the indices were below the country average both in 1990 and in 2001.
The difference of 0.3 class between the country average and the national average
remained unchanged between 1990 and 2001, which implies the moderate approach
of the country average to the national average, i.e. the slight decrease of the Buda-
pest–country disparity, in the light of the growth of the average values of the index.
The dynamism of the country average above the national average and the 5.8 per cent
decrease in the standard deviation of the national average also indicate the decrease
of the Budapest–country disparity.
In addition to the Budapest–country difference, significant territorial imbalances
can be seen in the rural space, as well, both within and among the regions. The often
more dynamic growth of the indices of the counties below the country average could
not compensate their lagging behind coming from their unfavourable starting posi-
tion, consequently most of the spatial disparities typical in the rural space in the be-
ginning of the decade continued to exist in 2001. Although the relative positions of
the regions with counties below the country average changed, the same four regions
featured the lowest average number of finished classes in 1990 as in 2001. In other
words, while the countryside is slowly catching up with Budapest – due to the above-
average indices of West Transdanubia and Middle Transdanubia, and Pest, Baranya
and Csongrád counties –, the spatial disparities of the rural space do not cease to
exist.
2.2 Spatial structure of the schooling of the inhabitants
As a result of the rise in the average number of finished classes, in the educational
structure of the population there was an increase in the share of those with higher
school education within the total population, both at national level and country
level (without Budapest). At the national level, the proportion of those without
finished primary school fell to one-quarter, the share of those who had finished 1–7
classes of the primary school dropped by 10 per cent (from 29 to 19%) between
1990 and 2001.
The four per cent decrease in the share of those with finished only eight classes in
primary school is a turning point, because the proportions of those belonging to all
other categories (more finished classes) increased: the number of those who finished
vocational and specialised secondary school increased by 5.4%, the proportion of
grammar school graduates rose by 8.6%, whereas the share of those with higher edu-
cation diploma increased by 1.3% between 1990 and 2001. After these increases,
18.4% of the population had vocational or specialised secondary school; almost 25%
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Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
of the population finished grammar school and 8.9% had finished college or univer-
sity training.
The countryside indices, calculated without the data of Budapest, show similar
tendencies, although the values of the country indices are less favourable in both
years and in all categories: the share of those countryside citizens who have lower
school education is higher than the national average, while the proportion of those
with higher level of school education is below the national average. As regards the
countryside Hungary, the proportion of those without finished primary school educa-
tion fell from 2.2 per cent to 0.6 per cent, of those with 1–7 finished classes of pri-
mary school from 31 per cent to 21 per cent, while the share of those who had only
finished eight classes of primary school fell from 32 per cent to 29 per cent from
1990 to 2001. The proportion of those who finished vocational or specialised secon-
dary school increased from 13.9% in 1990 to 19.7% in 2001, whereas those who
finished grammar school made 14.5% of the population in 1990 and 23 per cent in
2001; these figures were 5.9% in 1990 and 66.% in 2001, respectively, for the gradu-
ates of higher education institutions.
As regards the educational structure of the population, the situation was the best
in Budapest in both 1990 and 2001, as the capital city boosted with the lowest share
of those with low level of school education and the highest proportion of those with
higher level of education. In both years in question, Szabolcs-Szatmár-Bereg county
had the highest share, compared to its population, of those who had not even finished
the eight classes of primary school or only 1 to 7 classes of that. Parallel to this, in
1990 this county featured the lowest proportion of those with secondary school and
higher education leavers and in 2001 the smallest share of those who had college or
university degree.
When comparing the schooling structure of the Budapest and the countryside
population, significant disparities can be seen. The data in Figure 7. and 8. reveal
that the share of secondary school or higher education graduates within the total
population was higher in Budapest, the proportion of those with less schooling was
more significant in the countryside both in 1990 and 2001.
While in 1990 14.5 per cent of the population living in Budapest had higher edu-
cation degree, the same figure for the countryside population was not more than
5.9%. These figures increased to 19.3 per cent and 6.6 per cent, respectively, which
means that the dynamic increase in the share of higher education graduates resulted
in the fact that one-fifth of the Budapest population aged 7 years or older had higher
education diplomas, whereas in the countryside the increase of the share of the re-
spective category of population was only 0.7%.
We can see that in 1990 the category biggest in number within the population
aged seven years or older was those who had finished primary school education, both
in Budapest and the countryside. By 2001, a dramatic decline had taken place in
Budapest in the share of those with primary school education, at the same time the
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
proportions of secondary school and higher education graduates increased dynami-
cally. In the capital city, in 2001 it was already secondary school graduates who
made the biggest share of the population aged seven years or older, and the share of
those with college or university diploma almost reached the proportion of those with
not more than eight classes of primary school education. In the countryside popula-
tion, the share of the category with 1 to 7 finished classes of primary school showed
a considerable decrease, parallel to a significant increase of the categories with voca-
tional and specialised secondary school education, and with grammar school educa-
tion within the population aged seven years or older.
Comparing the processes in Budapest and the countryside, an increase is evident
in both cases in the number of those with higher level of schooling within the popu-
lation aged seven years or above. The difference is that in Budapest it was mainly
due to the rise in the proportions of grammar school and higher education graduates,
while in the countryside it was attributable to the increasing share of those with vo-
cational and specialised secondary school, or with grammar school education.
Figure 7
Breakdown of the population aged 7 and older by school education, 1990
(in per cent)
100%
90%
80%
Higher education
70%
Secondary school
60%
Vocational school
50%
8 classes of primary school
40%
30%
1 to 7 classes of primary
school
20%
No finished class of primary
school
10%
0%
Budapest
Countryside
Source: Calculation by the authors based on the data of censuses.
Source of the data: county data of the census of 1990, CSO 1992.
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Figure 8
Breakdown of the population aged 7 and older by school education, 2000
(in per cent)
100%
90%
80%
Higher education
70%
Secondary school
60%
Vocational school
50%
8 classes of primary
school
40%
1 to 7 classes of primary
school
30%
No finished class of
primary school
20%
10%
0%
Budapest
Countryside
Source: Calculation by the authors based on the data of censuses.
Source of the data: county data of the census of 2000, HCSO 2001.
In the spatial distribution of schooling it is the Budapest–country discrepancy too
that dominates. While in 1990 44.3% of those with higher education graduates and
36.4% of secondary school leavers lived in the Central Hungarian region, not more
than 29% of the population aged seven years or older lived in this region, which indi-
cates a considerable concentration. The role of Budapest is outstanding even within
the Central Hungarian region: while in 1990 Budapest was home to 19.8% of the
Hungarian population aged seven years or older, the capital city concentrated 37.7
per cent of the higher education graduates and 28.5% of those who had finished sec-
ondary school. The concentration of those with higher education diploma in the
Central Hungarian region further strengthened by 2001: in this year, almost half,
47.5% of those with higher education diplomas lived in this region, while the re-
gion’s share from the Hungarian population aged seven years or older was only
28.2%. In the region of Central Hungary, the increasing concentration is due to the
dynamic development of both Budapest and Pest county. As regards secondary
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school graduates, the share of the Central Hungarian region slightly decreased, by
approximately 2 per cent, as a consequence of two opposite processes: in Budapest
there were 4.5% less, while in Pest county 2.4% more inhabitants of this category in
2001 than in 1990.
As regards those with less schooling than secondary school, their concentration
can be seen in the countryside. While in 2001 fifteen per cent of the population aged
seven years or older lived in the North Great Plain region, one-quarter of those who
had not finished primary school lived in this region. Looking at the data at county
level, the disadvantaged position of Szabolcs-Szatmár-Bereg county is the most
striking. In 2001 Szabolcs-Szatmár-Bereg county was home to 5.6% of the popula-
tion aged seven years or older, while its share from those without finished primary
school education reached 12.8%. It is remarkable that the higher category of school-
ing, the smaller the share of the Hungarian population that lives in Szabolcs-Szatmár-
Bereg county: 12.8% of those without primary school education, 7.4% of those with
1 to 7 finished classes of primary school, almost 6% of those with finished primary
school education, 5.7% of those with vocational school or specialised secondary
school certificate, 4.3% of secondary school graduates and 3.2% of those with higher
education diploma were Szabolcs-Szatmár-Bereg county citizens in 2001. The same
phenomenon, although to a more limited extent, was typical in Tolna, Nógrád and
Hajdú-Bihar counties.
We get much information on the schooling of the population in a given region
from the share of those with higher education diploma. In 1990, the national average
of this group within the population aged 25 years or older was 10.6%, this figure
increased to 12 per cent by 2001. In the countryside the same figures were 8.3% in
1990 and 9% in 2001.
Comparing the counties and regions of Hungary, significant differences can be
seen in this respect, too. First of all we have to see the favourable position and dyna-
mism of Budapest. In 1990, one-fifth of the population aged 25 years or older had
higher education diploma in Budapest, by 2001 their share grew to one-fourth. These
numbers are more modest in the countryside of Hungary. Besides the Budapest–
country disparity, there are palpable differences within the country, as well. At the
level of the regions, the Danube River is a division line: the indices of the county in
Transdanubia were above the countryside average both in 1990 and 2001, while the
indices of the Great Hungarian Plain counties remained below that in both years.
When we look at county level data, the differences are not unambiguous. In 1990
only Budapest boasted with values higher than the national average, while in 2001
Csongrád county too showed figures in excess of the national average, due to its
dynamic development during this decade.
In 1990 the following counties had higher shares of those with higher education
diplomas within the population aged 25 years or older in the countryside: Csongrád,
Gyır-Moson-Sopron, Hajdú-Bihar, Fejér, Baranya, Veszprém, Vas, Zala, Komárom-
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Esztergom and Borsod-Abaúj-Zemplén. By the year 2001, the number of counties
with positions in between the national and the countryside average decreased: Gyır-
Moson-Sopron, Baranya, Hajdú-Bihar, Fejér and Veszprém counties kept their posi-
tions, while Pest joined this leading group. The positions of Komárom-Esztergom,
Vas, Zala and Borsod-Abaúj-Zemplén worsened, in 2001 the proportion of the higher
education graduates within the population aged 25 years or older dropped below the
countryside average in these counties.
Despite the fact that the positions of several counties worsened, in 2001 there was
at least one county in each Hungarian region – with the exception of North Hungary
– where the proportion of the higher education graduates within the population aged
25 years or older exceeded the countryside average. These are usually the counties
with higher education – primarily university – centres: Fejér, Veszprém, Gyır-Mo-
son-Sopron, Baranya, Hajdú-Bihar and Csongrád counties.
In 1990, the lowest proportions of higher education graduates within the popula-
tion aged 25 years or older could be found in Békés county (7 per cent) and Nógrád
(7.1%). By 2001, even these low values dropped considerably: the proportion of
higher education diploma holders within the population aged 25 years or older fell to
5.6% in Békés and 5.8% in Nógrád county.
Figure 9 shows the regional composition of schooling and employment, including
those in leading positions and other professionals. From the breakdown of school
education per one thousand inhabitants, the high value of the capital city is striking
(37%); together with Pest county the Central Hungarian region is home to every
second person with higher education diploma (44.7%). Apart from this proportion,
the regional disparities are not striking: the spatial structure of the higher education
graduates is more or less balanced across Hungary. The counties with university
centres stand out, also Gyır-Moson-Sopron and Vas counties in Transdanubia.
Looking at the same factor at the regional level, it is striking that the proportion of
highly educated inhabitants is higher in the South Great Plain region (11 per cent)
than in West Transdanubia where this figure is only 8.8%. This difference is inter-
esting, as one would probably think that the areas with more advanced economy and
higher income generating capacity will more definitely concentrate the proportion of
higher education graduates. This is not the case, and we even have to remind of the
anomaly that the supposedly less developed regions concentrate a higher proportion
of the highly educated inhabitants. This statement is supported by the fact that 10.7%
of all higher education graduates live in the South Great Plain region.
Figure 10 shows the spatial distribution of those in leading positions and of
intellectuals and other professionals. The combination of the two categories – those
in leading intellectual positions and other professionals – is made possible by the
fact that these two are quite similar in their regional breakdown; the proportions of
these two groups are quite the same. The role of the capital city is striking again in
the spatial structure of these occupations, as the highest figure of intellectuals per
26
Rechnitzer, János - Smahó, Melinda :
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Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Figure 9
Regional disparities of the college and university degree holders* per one
thousand inhabitants, 2001
*Data of the locally employed.
Note: number in brackets show how many counties belong to the respective category.
Source: calculation by the authors based on the HCSO data of the census of 2001.
1
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Figure 10
Regional disparities of the professionals* per one thousand inhabitants, 2001
* Data of the locally employed.
Note: Number in brackets show how many counties belong to the respective category.
Source: Calculation by the authors based on the HCSO data of the census of 2001.
2
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one thousand inhabitants can be found here. It is interesting that after Szabolcs-
Szatmár-Bereg county, it is Pest county that has the last position in this respect: it
indicates that the executive managers and the professionals are more likely to
choose Budapest as their place of residence. In regional disparities, the tendencies
mentioned above can be seen here as well: the counties of South Hungary with
large university centres stand out (Csongrád and Baranya), together with the West
Transdanubian and the Middle Transdanubian region. These regions feature values
around the national aver age. In the eastern part of Hungary the share of the
population with such occupations is lower; the number of intellectuals per one
thousand inhabitants is approximately 100–110. This is probably the consequence of
the more traditional economic structure, the high proportion of those employed in
public services and the lowest level of entrepreneurial willingness. Regional
disparities are thus clearly visible, not only as regards schooling, including higher
education, but also in those professions that are connected to intellectual activities,
i.e. management, organisation, marketing and development tasks.
2.3 Foreign language skills
The foreign language skills are basically determined by the level of schooling, but
the cultural affinity of the population is also reflected in this factor. These together
affect the quality of life. On the basis of the data of the 2001 census and informa-
tion from the 1990s, processed in a former study, we attempt to highlight the spa-
tial structure, with special reference to the changes that occurred in the 1990s
(Tóth–Trócsányi, 1997).
Among the learnt languages (i.e. languages that are not the mother languages of
the ethnic minorities), English, French and Spanish led the list in the early 1990s,
because English and Russian were more wide-spread than the languages of the ethnic
minorities (except German). There were large differences in language skills across
the country. The spread of English language was evident, which is indicated by the
fact that while in 1991 the regional difference was 14-fold between the (naturally)
leading Budapest and the holder of the least favourable position (Borsod-Abaúj-
Zemplén county), the situation changed a lot since then, the differences in English
language skills grew to 38-fold by 2001. The leading position was held by Budapest
again, the county in the last position was Nógrád. As regards the structure of
language skills, German still leads (36.1%) but English is approaching it (reaching
35.5%), all other European languages have weak positions, only Russian is worth
mentioning (6.9%), but French language is becoming more and more popular, too
(4.1%).
Budapest was the leading spatial units in Hungary in the skills of learnt languages
in 1991: 57% of those who spoke foreign language(s) lived in the capital city. The
3
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situation changed by 2001, the share of Budapest among the speakers of foreign lan-
guages dropped to 33.9%, together with Pest county to 43.7%. The regional differ-
ences are decreasing in foreign language skills. It is also true that there is a concen-
tration of the speakers of less commonly spoken languages in Budapest: Spanish
(59.0%), French (48.6%), Italian (46.5%), It is not valid for German, on the other
hand, the Budapest citizens only make 28.1% of those who speak German in Hun-
gary.
There are no striking differences between the rural areas west and east of the Da-
nube River, if we look at the proportion of those with foreign language skills. In the
two macro-regions the share of those who speak foreign languages is almost the
same; the differences can be found among the languages spoken. The nine counties
of Transdanubia are home to 38.6% of those who speak German, the highest figure
can be found in Gyır-Moson-Sopron county and the lowest in Tolna (7.4% and
2.8%, respectively). In the Great Hungarian Plain and North Hungary, on the other
hand, English is dominant; the regions in question are home to 27.8% of those who
speak English. The highest the share of English speakers is in Borsod-Abaúj-Zem-
plén county (4.9%), while in Nógrád only 1.1 of the population speak English. Also,
the county with major university centres, i.e. Csongrád or Hajdú-Bihar feature high
figures of population speaking English language (Figure 11).
We can say that theoretically every fifth citizen of Hungary speaks a foreign lan-
guage at some level, this proportion being the highest in Budapest (33.6%) and the
lowest in Szabolcs-Szatmár-Bereg county (9.3%), which means that the regional
disparity is 3.6-fold. Compared to the size of the population, in South Transdanubia
we are the most likely to find somebody with foreign language skills. Within this
region Baranya county has an outstanding position, in this county every fourth per-
son is able to communicate in some language. The next counties “rich in languages”
are Gyır-Moson-Sopron (22.1%), Pest (19.3 per cent) and Komárom-Esztergom
(18.9 per cent). In the order of the first five counties we also find Veszprém county
(18.4 per cent). On the other hand, Northeast Hungary is a “language-deficient” re-
gion, as the order of the counties from the bottom is as follows: Szabolcs-Szatmár-
Bereg (9.3 per cent), Jász-Nagykun-Szolnok (10.3 per cent), Nógrád (11.0 per cent),
Borsod (11.7 per cent) and finally Bács-Kiskun county (14.0 per cent).
4
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Figure 11
Language skills of the population, 2001
Note: number in brackets show how many counties belong to the respective category.
Source: HCSO data of the census of 2001.
5
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2.4 Human development index
The human development index (HDI) was calculated according to the principles
defined by the UNDP (the development programme of the UNO). The definition of
human development is as follows: “The creation of an environment in which the
humans can develop all of their resources, can live a productive and creative life in
harmony with their needs and interests” (A humán és a gazdasági… 2001 p. 5.).
In the spirit of this definition the human development indices are made for coun-
tries and groups of countries, on the basis of the following factors:
− long and healthy life (life expectancy);
− skills level (literacy of the adult population, and the share of those attending
the three types of schools, i.e. primary, secondary and higher education within
the respective age group);
− living standards (amount of the GDP per capita calculated at purchasing
power)3.
The average of the three elements gives HDI, basically an index the possible
maximum of which is 1.
The calculation of this index for territorial units was first done by Nemes Nagy,
József (Fóti, 2000). The author emphasises in his introductory essay that the index
“is good for demonstrating the relative development positions of the spatial units (the
counties) in comparison with each other, and it magnifies regional disparities that are
hardly palpable in international (cross-country) analyses” (ibid, p. 62.). We have to
agree with Nemes Nagy, József’s statement, i.e. that the application of the HDI index
for various spatial units is suitable for highlighting the relative differences, discrep-
ancies among the units, as well as the possible changes in these differences. On the
other hand, this index is definitely unsuitable for the definition and demonstration of
the “development level” of the territorial units within a given country, either in a
3 For each component of HDI, we calculate an index in the following way: Ii= (Xi – Xmin)/(Xmax –
Xmin), where Xmax and Xmin are the two extremes of the index, and Xi is the index for country or
regional unit “i”. The minimum and maximum values of the indices are fixed: for life expectancy
these are 25 and 85 years; for literacy rate: 0 per cent and 100 per cent; for a combined gross
schooling rate: 0 per cent and 100 per cent; for GDP per capita (at purchasing power standards):
100 Dollars and 40,000 Dollars. In our calculations, the combined gross schooling rate – similarly
to the method used by Nemes Nagy, József – was substituted by the average number of finished
school classes, and we considered the extremes of the index as zero and sixteen. The index of the
skills level is the weighed average of the adult literacy index and the index of the average number
of finished school classes, in which the literacy index is given a twofold and the average number of
finished school classes a single weight. The calculation of the index of living standards is based on
the GDP per capita (in USD at PPS) and takes places as follows: W(y) = (logy – logymin)/(logymax –
logymin). The HDI values are simple arithmetic means of the calculated indices. The values of HDI
range between 0 and 1, the higher values showing the higher level of development.
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UNDP definition or any other system of comparison, since the income index is the
only one where significant differences can be seen across the spatial figures. GDP,
used to highlight living standards, strongly differentiates the counties, whereas the
spatial disparities in the case of the other indices are less spectacular, their spatial
differentiation effect is much more limited.
The significant differences among the HDI data gradually approached each other
by the turn of the millennium (Table 2) While the difference between the highest and
the lowest figure was 31-fold (!) in 19904, it was only 1.13-fold in 2001. The national
figure of the index is increasing, too (the increase is 1.39-fold between 1990 and
2001), the main reason for which is the rising school education of the population and
the continuous growth of GDP. The average life expectancy, unfortunately, did not
increase, but its decrease is compensated by the schooling and the income positions
to a much more significant extent, while the high rate of literacy within the skills
level (99%) could be taken as stable during the 1990s.
The survey for the years 1990 and 1996–1997 did not bring any surprise (Figure
12). The capital city evidently kept its leading position, in fact not only was it able to
keep but also to slightly improve it. In the human development index, the West
Transdanubian counties and Fejér county are the leading rural counties. On the other
hand, the North Hungarian counties were unable to improve their situation; in fact,
they are more and more falling behind (strongly influenced by their weaker income
positions). It is interesting that although the South Great Plain, the South Transdanu-
bian region and Szabolcs-Szatmár-Bereg and Hajdú-Bihar counties were able to sta-
bilise their positions, their indices remained well below the national average.
In order to register further changes, we made calculations for the changes between
1996–1997 and 2001. We found that the differences in human development indices
decreased by 2001, parallel to the decrease of regional disparities, the more intensive
catching up process and the decreasing pace of growth, practically the stagnation at a
certain level in the central regions. In our opinion the change can only be seen in the
regional GDP figures, as no major regional disparities can be demonstrated in either
life expectancy or schooling level, and the literacy rate stabilised at a high level.
In Table 3 we made a comparison of the 1996/1997 and the 2001 data, according
to the volume of the changes. The national value of HDI grew by leaps from the
middle of the last decade until 2001, by almost 37%. The background of this phe-
nomenon is probably the climbing out of the economic crisis following the systemic
change, which could be seen among other things in the dynamic growth of GDP.
This strongly influenced HDI values both at the county the national level.
4 Nemes Nagy elaborated the HDI value for 1990 in his essay, at that time regional GDP data were
unavailable, he was only able to calculate with regional incomes that did not reflect the economic
performance. Also, the discrepancy between the capital city and the countryside was significant
then. This is one of the reasons why strong differences could be seen across the indices (Nemes
Nagy–Major, 1999).
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Table 2
Development of the HDI indices at county level, 1990–2001
County, region
1990
1996
1999
2001
Budapest
0.899
0.915
0.866
0.865
Pest
0.448
0.471
0.790
0.802
Central Hungary
0.673
0.693
0.838
0.847
Fejér
0.656
0.709
0.821
0.828
Komárom-Esztergom
0.517
0.562
0.805
0.802
Veszprém
0.685
0.733
0.805
0.806
Middle Transdanubia
0.619
0.668
0.812
0.814
Gyır-Moson-Sopron
0.818
0.883
0.841
0.839
Vas
0.652
0.788
0.823
0.825
Zala
0.593
0.684
0.813
0.804
West Transdanubia
0.688
0.785
0.827
0.826
Baranya
0.452
0.451
0.798
0.795
Somogy
0.324
0.280
0.789
0.784
Tolna
0.462
0.376
0.805
0.799
South Transdanubia
0.413
0.369
0.797
0.792
Borsod-Abaúj-Zemplén
0.261
0.169
0.788
0.782
Heves
0.508
0.524
0.800
0.789
Nógrád
0.419
0.218
0.776
0.772
North Hungary
0.396
0.304
0.790
0.782
Hajdú-Bihar
0.359
0.406
0.797
0.792
Jász-Nagykun-Szolnok
0.411
0.330
0.789
0.785
Szabolcs-Szatmár-Bereg
0.029
0.039
0.773
0.764
North Great Plain
0.266
0.258
0.786
0.780
Bács-Kiskun
0.295
0.322
0.796
0.787
Békés
0.519
0.543
0.796
0.789
Csongrád
0.592
0.610
0.815
0.806
South Great Plain
0.468
0.491
0.802
0.794
Total
0.584
0.595
0.817
0.814
Country (without Budapest)
0.798
Source: Nemes Nagy–Jakobi, 2003.
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Figure 12
Changes of the HDI values from 1990 to l996–1997
Source: Fóti (ed.) 1999. p. 70.
Comparing the counties to the national figure of HDI, we can make three groups
of them. The first group involves Budapest, Gyır-Moson-Sopron, Veszprém and
Vas counties, where the 1996/1997 values, well above the national average,
approached the national average by 2001. These spatial units, the most developed
ones in the middle of the 1990s, were unable to rise above the national average
spectacularly by 2001, and although their relative positions compared to the
average deteriorated seriously, the respective counties (with the exception of Vesz-
prém) still featured figures in excess of the national average in 2001. The indices of
Fejér, Heves, Csongrád, Békés, Komárom-Esztergom and Zala counties were
within the 20 per cent range around the national average in both years in question,
which means that these spatial units kept up with the national growth of the HDI.
In their case an initial position around the national average had enough reserves
that allowed them to reach values around or slightly above the national average, all
changes considered.
The third group contains the previously underdeveloped areas, such as the South
Transdanubian, the North Great Plain and, with the exception of Heves, the North
Hungarian counties, also Pest and Bács-Kiskun. These spatial units usually doubled
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their figures, their development was striking, the extent of their dynamism consid-
erably exceeded the total of the changes at national level.
We have to find the reasons – and we have to repeat in this place that in the hu-
man development index the decisive index is the growth and development of GDP,
since the life expectancy, the schooling and literacy rate are balanced and almost
uniform (Figure 13). It is clear that the capital city and the West Transdanubia,
maybe some Middle Transdanubian counties are unable to increase their GDP to a
significant extent; they are “stuck” at a stable high value where any further
progress requires significant resources. In the rest of Hungary, the growth of GDP
had a stronger impact on the catching up in HDI, so the progress of the respective
counties is more spectacular.
Table 3
Changes of the values of the HDI index, 1996–2001
2001
Well above average Average or around the average
Below average
1996
(>121%)
(80–120%)
(<80%)
Well above average
Budapest
(>121%)
Gyır-Moson-Sopron
Veszprém
Vas
Average or around the
Fejér
average (80–120%)
Heves
Csongrád
Békés
Komárom-Esztergom
Zala
Below average (<80%)
Pest
Hajdú-Bihar
Jász-Nagykun-Szolnok
Szabolcs-Szatmár-Bereg
Nógrád
Baranya
Somogy
Tolna
Bács-Kiskun
Borsod-Abaúj-Zemplén
Source: Calculation by the authors.
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3
Quality of life
Among the elements influencing the quality of the human factors that determine
the components of human resources, we cannot neglect culture, and its physical
forms, cultural heritage deriving from the existence of the cultural goods. The cul-
tural heritage is one of the potential determinants of the spatial structure. In the
Hungarian professional literature there are few examples for the analysis of culture
at regional level. It comes from the complexity of the concept of culture that its
analysis and interpretation is not an easy task. It can contain material and historical
heritage (built environment, historical locations), intellectual and social heritage
(cultural and intellectual goods and the socio-economic relations) and natural heri-
tage (natural landscape), which together and individually affect regional develop-
ment and the quality of the human resources (Czene, 2002). These factors, how-
ever, do not only characterise a spatial unit in themselves, i.e. by their existence;
their changes, dynamism also affects the regional structure. Dynamism in this place
means that the character, the features of their elements are not only occasionally
but constantly present in the spatial unit, are integrated in all elements of the space,
influencing its future condition.5
The cultural factors can thus involve a very wide range of factors, so in our essay
we cannot analyse their spatial effects. However, a few aspects of the culture-related
elements or those representing culture in some way are worth highlighting, in order
to at least refer to the regional characteristics of these components.
3.1 Civil society
In the modernising societies the local communities are more and more influential
on the development of the quality of life; their activity and influence are becoming
more and more important. The character and integration of the local community,
care can all demonstrate the internal content of the regional structure in local and
regional values, and thus can refer to the division and differences of these values.
The local communities and their regional characteristics can be best demonstrated
by the civil sector, and the organisations of the civil society. The regional presence
and spatial division of the civil organisations, such as foundations, associations or
special organised communities established for different purposes do not only repre-
5 The role of the cultural heritage is only now being recognised, the effect of these goods on spatial
development and the importance of the protection, i.e. the integration of cultural heritage in spatial
development strategies and programmes is first mentioned in the ESDP (European Spatial
Development Perspective, 2000). In Hungary, the National Regional Development Concept does
not contain development considerations concerning the cultural heritage, but the National Physical
Plan already mentions statements on this issue (Czene, 2002).
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sent the financial conditions but also of the cultural values, traditions and the integra-
tion of the inhabitants.
The dynamic growth of the number of non-for-profit organisations took place
during the systemic change. From 1989 to 1990 for example the number of such
organisations doubled, and it doubled again in the following two years. In 1992 there
were more than 30,000 self-organising social entities registered. Their number grew
above 60,000 by 2000 (Nárai, 2000).
When analysing the regional structure it is advisable to take the number of units
per one thousand inhabitants into consideration, as this well depicts the provision of a
given region and the actual activity of the population living there (1993: Figure 14;
2000: Figure 15). The dynamic growth can be seen in the growth of the number of
non-for-profit organisations per one thousand inhabitants: while in 1993 the average
of this index was 3.4 at national level, it increased to 4.7 by 2000, which means a
growth of almost 40%.
Figure 14
Number of non-for-profit organisations per one thousand
inhabitants by counties, 1993
Source: Edited by the authors, based on Nonprofit szervezetek Magyarországon 1993 [Non-for-profit
organisations in Hungary 1993]; Regional Statistical Yearbook 1992.
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Figure 15
Number of non-for-profit organisations per one thousand inhabitants
by counties, 2000
Source: Nárai, 2002.
Budapest stands out in both years in question as regards the number of non-for-
profit organisations, followed, interestingly, by the counties around the Lake Balaton
(Veszprém, Zala, Somogy). The number of non-for-profit organisations per one
thousand inhabitants shows more homogeneity than disparities in the rest of Hun-
gary. The spatial changes of the 1990s brought about equalisation and no significant
division in the number of such organisations in the countryside of Hungary
(Rechnitzer, 1998).
The density of non-for-profit organisations became balanced by the end of the
1990s, large territorial disparities could only be seen between Budapest (and the
counties around the Lake Balaton) and the countryside. It is not necessary to explain
the leading position of the capital city; it is understandable that the number of popu-
lation and the economic and public administrative weight of Budapest are attractive
for the establishment and operation of non-for-profit organisations. The counties of
Lake Balaton are “little capital cities” in this respect, which comes from the appre-
ciation of the lake as second home, more exactly as a place of recreation. The large
number of holiday home owners, who are typically from Budapest or other big cities,
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bring their local activity with them, and so the number of non-for-profit organisations
is very high around the lake. The other extreme is Fejér county, a county that can be
characterised by industrial employment, where the density of non-for-profit organi-
sations is very low (3.3 organisations per one thousand inhabitants), close to the fig-
ure of Szabolcs-Szatmár-Bereg county (3.2. organisations per one thousand inhabi-
tants).
In the 1990s a strong, almost functional relationship emerged between the ac-
tivity of non-for-profit organisations and the position in the settlement hierarchy.
The county seats and the traditional towns are the core areas for the organisation of
the civil sector, whereas in the centres with smaller population and more limited
functions the density of non-for profit organisations is strikingly decreasing. These
organisations bearing social innovations also had temporal characteristics in the
transition period, as in the first half of the 1990s the capital city and the big cities
were the places where most of these organisations were founded, followed by the
middle and small towns and large villages in the middle of the decade. Finally, in
the late 1990s, civil organisations were founded in larger numbers in villages and
other minor settlements.
The number of the non-governmental organisations itself does not reflect the
number of actually working organisations, it does not show whether the organisa-
tions in a given region are actually active or not. The financial support of the civil
sector and its regional disparities are more exact reflections of the actual operation
and of whether the inhabitant are able to support community actions and if so, with
how much money. The proportion of incomes from private resources was almost
20 per cent of the revenues of the non-governmental organisations in 1997, parallel
to the decrease of the government supports and a significant increase of the reve-
nues coming from the core activities (Nárai, 2000). On the whole, the total of the
external resources, including both state and private supports, covered 40 to 45 % of
the operational costs of the non-governmental organisations during the 1990s.
One form of the support from the inhabitants is that 1 per cent of the personal
income tax of any taxpayer can be donated to the civil initiatives. The regional
differences in the willingness of donating this amount to NGOs aptly show how
much these non-governmental organisations are integrated into the local society
and what opinion the inhabitants have about these organisations. The willingness to
donate the 1% of personal income tax does not show the amount of the money but
the fact whether the taxpayers used this opportunity at all, i.e. whether they wish to
care about the civil sector. Hungary was divided in this respect too in 1997, as in
Budapest more than one-fifth, in Gyır-Moson-Sopron county over one-quarter of
the taxpayers used their opportunity to support the civil sector, which affected al-
most one-third of the organisations. The situation was worse in the other parts of
Hungary, as the proportion of beneficiaries was lower, as was the total of the dona-
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tions. There were counties, on the other hand, where the proportion of supported
NGOs exceeded the national average, but with a more limited total amount.
The other form of supporting NGOs is direct donations, the total of which was
15 billion HUF in 1997. The amount of donations per inhabitants was 1,025
Forints, excluding Budapest. In the counties of Transdanubia the amount of finan-
cial donations exceeded the national average in each case, in the eastern part of
Hungary only Heves and Nógrád reached this level. It is natural that in the counties
with less favourable economic circumstances the support activity was lower; in
Szabolcs-Szatmár-Bereg county, for example, the amount registered was only 573
Forints.
Regional disparities are thus well visible in the support of the non-governmental
organisations as well (Figure 16). These disparities reflect the economic capacities
of the regions but not only that: the characteristics of the settlement network, the
local activity of the inhabitants, and the intellectual, cultural and primarily commu-
nity ties are all factors that affect or influence the human resources and their qual-
ity.
Figure 16
Average contribution per one inhabitant (financial donation) to the revenues
of the non-for-profit organisations by counties, 1997
Source: Nárai, 2000.
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3.2 Regional and local identity
A novel, but not actually new form of the manifestation of the quality of life is
regional identity. By identity we mean the linkages of the individuals and the
communities to the space, and the cultural, emotional and cognitive content and
manifestations of these linkages. Most of these are related to exact localities, defi-
nite, geographically designable areas (Pálné, 2000). Its tiers can be the regions, but
in the Hungarian public administration and thinking the regions have no traditions
(they were actually set up in 1996 as the units of spatial planning and program-
ming). The internal cohesion of the regions is weak, their institutional system is not
settled yet, they have no authorities, and although they have had some limited de-
velopment resources recently, the amount of these is low and the possibilities of
their use are limited. Regional identity is thus very weak. Regions as entities repre-
senting the intellectual cohesion have not been born yet, the governmental actors
are more probable to think in this spatial unit than the actors of the economy are.
The inhabitants do not show any sign of linkage to their regions; regional identity
is more a political demand than an actually palpable, existing reality (Szörényiné,
2000).
The next level of identity could be the county, as counties have considerable
traditions in both Hungarian public administration and public services. After the
systemic change the counties lost their previous spatial organising and develop-
ment functions that had been accompanied by resources, and the competency for
the distribution of the resources. In the transition period the institution-operating
function of the counties strengthened. The operation of these regional organisations
only affected a narrow layer of the population; also, the municipal governments
became legally equal to the county self-governments, i.e. there is no subordinate
relationship among them at all. Accordingly, the counties gradually lost their influ-
ence; their role and direct effects are mostly restricted to the employees of their
institutions. The influence of the counties on the inhabitants and their identity
weakened, although there are surveys which found that the role of the counties was
dominant and thus a prime factor of identity forming (Oláh, 2000). These analyses
reveal that in settlements with lower number of population and weaker functions
the demand for the county functions is stronger, whereas in bigger settlements with
more significant number of inhabitants and functions (county seats, cities) this
organising and interest representation responsibility of the counties is less typical.
The next level of identity is the level of the micro-regions. Their role was ap-
preciated in the 1990s, now they function as a new field of regional identity. The
micro-regions, each consisting of a minor centre and the surrounding, in some form
integrated set of settlements are more and more pushed in the foreground and they
are more and more visibly demonstrating their individual characteristics. The mi-
cro-regions are gradually taking over the former county linkages and have a more
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and more clear picture of the future. This makes integration and the joint thinking
and responsibilities of the stakeholders ever stronger. The identity with the micro-
regions strongly affects human resources – and vice versa, the developing human
resources are shaping regional identity, affecting this way the future of these spatial
units in a favourable way.
Table 4 shows the triumph of locality, i.e. the reverse of the previous trend. In-
stead of the regions – or the counties in this place –, it is the devotion to the local-
ity, the settlement that is in the centre of the thinking of the inhabitants (Bıhm,
2000). The spirit of the locality, the genius loci was finally released from the bottle,
allowed in the first place by the gradual construction of the self-governmental sys-
tem. Of course it was also a fact that a basic element of the bygone political system
was the suppression of the interests, because of the stigmas of certain settlements
they were often negatively discriminated, their functions were eliminated, their
developments postponed and their activity weakened. After the systemic change
the situation basically changed, the limits were eliminated, energies never seen
before broke out which gradually created a different quality of life. This of course
had a favourable effect on the development of the human resources, too (Table 4).
We are not able to give a regional picture of identity because there have not
been national level surveys conducted to find out the level of affection of the in-
habitants to their respective places. Maybe such a survey would not be worthwhile,
as the spirits of the individual places are so much different. However, it would be
reasonable to define the level and the character of identity with regions at different
development levels and their core settlements, and to interpret the components of
this identity in some form.
Table 4
Indices of identity in Hungary, 1992–1996* (in per cent)
Year
Municipality
Region
Country
Eastern
Europe
World
Europe
1992
16.2
35.6
39.1
0.2
6.2
3.7
1996
primary
48.5
5.3
38.1
0.5
4.3
3.6
secondary
21.1
12.2
20.7
1.0
16.0
5.1
* The indices of identity show how many per cent of the individuals (inhabitants) feel they are
attached to the given spatial level, territorial unit.
Source: Bıhm, 2000.
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4 The network of knowledge and the communication
of information
The spatial characteristics of the institutions and personal conditions mediating
knowledge have a fundamental effect on the human resources. We could presume
that education as a public service is spatially equally distributed, as it should be
available to each citizen. However, neither the network nor its quality is spatially
even; several differences can be seen in both factors, accordingly the distribution of
the human resources shows considerable differences, too.
4.1 Primary and secondary institutions
The primary schools are responsible for the elementary, compulsory education of
the population aged 6–14. The number of children in this age group is gradually
decreasing, by the end of the 1990s the number of primary school pupils was one-
third less than in the 1980s. The spatial disparities decreased in 1990–1994, and
then grew in the years afterwards. The reason for this is that the majority of the
district schools established in the 1980s regained their independence, as one of the
first successful actions of the local governmental system was the restoration of the
independence of the settlements’ schools. This resulted in striking developments in
the small village dominated counties of Transdanubia, i.e. Vas, Zala and Somogy.
The parameters of provisions improved – i.e. the number of pupils per one teacher
or one classroom –, the small village schools were restored (mostly in the lower
grades, only), the travel times decreased, and certain unprivileged social groups
were also able to join in the school education. On the other hand, in the small
schools (with 50–100 pupils) the personnel and equipment conditions for up-to-
date teaching are absent, the municipalities responsible for the maintenance of
these schools are unable to finance them, parallel to a constant decrease in the
number of children.
The growth of the number of primary school stopped by the middle of the dec-
ade, their number was 3,455 by 2001, which is a 8.6% increase compared to 1990. In
the handicapped North Hungarian region the school network did not expand, it actu-
ally remained at its 1990 level. Parallel to the expansion of the school network at the
national level, the number of pupils decreased in the decade of the transition. The
fifteen per cent decrease of the number of pupils reflects the decrease in the number
of population, and also features new spatial disparities. The number of pupils per one
thousand inhabitants decreased to the largest extent in those counties where the ex-
pansion of the network exceeded the average (Gyır-Moson-Sopron and Budapest),
which is probably due to the widening of the supply of education (e.g. church or
foundation schools). In the peripheral regions the situation is the opposite, i.e. the
growth of the pupils exceeded the average, but the network did not expand, i.e. the
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level of education did not improve. The spatial structure of primary schools shows a
picture opposite to the usual structure (Table 5).
The Danube River is a division line in this case, which means that the Great Hun-
garian Plain features a higher proportion of settlements with primary schools than
Transdanubia (Forray–Kozma, 1999). In the latter region, 60–80% of the settle-
ments have institutions of primary education. This comes from the settlement
structure, the already mentioned large number of small villages, the denser network
of centres or the strengthening of the suburbanisation tendencies.
The analyses mention that “the schools of East Hungary »produce« skilled labour
for West Hungary” (Forray–Kozma, 1999. p. 35.). At the same time they indicate
that the educational institutions are not only the scenes of training but also forums
of socialisation where the less favoured social groups can learn values, patterns and
future goals. If the educational units are missing – e.g. in regions dominated by
small villages or in settlements inhabited by handicapped social groups –, these
layers of the population are unable to acquire the management of the social organi-
sations, in this case the educational institutions, and the necessary conditions of
existence in these institutions. The consequence is the rapid peripherisation, falling
behind of these social groups.
The contradiction of the present situation is that in these depressed regions the
maintenance of the irreplaceable primary institutions is the responsibility of the mu-
nicipal governments that struggle with serious difficulties. At the same time, a denser
and locally available network of primary schools could significantly contribute to the
social integration and the concomitant better conditions of the human resources (Fig-
ure 17).
Secondary education rearranged faster and more drastically than primary educa-
tion did, as more pressure was put on this level in the period of transition. The pro-
portion of grammar school training, a training finishing with final exams and offering
the chance of higher education studies significantly increased, as a change of profile
took place in these institutions from several aspects. The institutions found resources
by expanding downwards (six- and eight-class grammar schools); also, the former
skilled worker training schools started to run new vocational and grammar school
classes (Figure 18).
The proportions of students in different types of secondary schools did not
change until the 1980s: one-fifth of primary school leavers continued their studies
in grammar schools, one-third of them in vocational schools and half of them in
skilled worked training institutions. In the 1990s these proportions fundamentally
changed, the training forms finishing with a school leaving certificate became
dominant and the share of those participating in short skilled worked training
courses significantly decreased. From 1990 to 1997, the proportion of students
attending institutions giving a certificate increased from 56.5% to 74.6% within the
total of the secondary school students (Jelentés a területi folyamatok… 2001).
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Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Table 5
Institutions and a few indices of primary education in Hungary, 1990, 2001
County
Number of primary
Change
Number of primary
Change
schools
(in per cent)
school pupils per one
(in per cent)
thousand inhabitants
1990
2001
1990
2001
Budapest
353
397
112.5
91.9
73.3
79.8
Pest
267
317
118.7
111.4
95.1
85.4
Central Hungary
620
714
115.2
98.1
81.6
83.1
Fejér
146
152
104.1
118.2
99.1
83.8
Komárom-
111
122
109.9
113.2
96.1
84.9
Esztergom
Veszprém
158
164
103.8
118.8
93.8
79.0
Middle
415
438
105.5
117.0
96.5
82.4
Transdanubia
Gyır-Moson-
182
213
117.0
116.0
88.2
76.0
Sopron
Vas
119
133
111.8
109.5
91.3
83.3
Zala
137
143
104.4
111.7
87.9
78.7
West Transdanubia
438
489
111.6
112.9
88.9
78.7
Baranya
176
177
100.6
106.1
91.2
85.9
Somogy
175
174
99.4
107.8
94.0
87.2
Tolna
98
115
117.3
113.4
93.3
82.2
South Transdanubia
449
466
103.8
108.5
92.7
85.4
Borsod-Abaúj-
361
367
101.7
116.2
104.1
89.6
Zemplén
Heves
146
146
100.0
107.4
90.9
84.6
Nógrád
125
125
100.0
108.2
92.7
85.7
North Hungary
632
638
100.9
112.6
98.9
87.8
Hajdú-Bihar
160
184
115.0
116.6
104.2
89.4
Jász-Nagykun-
124
144
116.1
116.0
99.5
85.8
Szolnok
Szabolcs-Szatmár-
256
268
104.7
127.0
111.1
87.5
Bereg
North Great Plain
540
596
110.4
120.3
105.5
87.8
Bács-Kiskun
202
209
103.5
110.4
97.4
88.2
Békés
110
166
150.9
106.2
92.8
87.4
Csongrád
142
136
95.8
103.9
90.5
87.1
South Great Plain
454
511
112.6
107.1
93.9
87.7
Total
3548
3852
108.6
109.0
92.6
84.9
Country total
3195
3455
108.1
113.1
96.6
85.4
(without Budapest)
Source: Calculation by the authors based on Regional Statistical Yearbook 1990, p. 69. and
Hungarian Statistical Yearbook 2001, p. 226.
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Figure 17
Number of primary school pupils per one thousand permanent
inhabitants*, 2001
* Including special institutions for mentally handicapped children.
Source: HCSO T-STAR 2001.
In the secondary school network, regional disparities can also be shown. The
capital city kept its leading position, not only because of the large number of
population (Budapest provides the agglomeration as well), the traditional
institutional network, a better informed management able to influence the whole
educational policy, but also as a consequence of concentration of institutions who
recruit students from all over Hungary. Csongrád and Gyır-Moson-Sopron
counties stand out with their networks of secondary educational institution, while
the institutional networks of Bács-Kiskun, Somogy, Komárom-Esztergom,
Veszprém and Békés lag behind the national average. In the North Great Plain
counties, i.e. Hajdú-Bihar and Szabolcs-Szatmár-Bereg, but also in Pest and
Baranya counties the network of grammar schools is dominant, the number of
vocational schools is lower, the reason for which is that there is hardly anything to
train the students wishing to make final exams for (Forray–Kozma, 1999). In
Transdanubia it is Zala, Fejér and Somogy counties where the number of Figure
18institutions offering grammar school training is lower than the national average.
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Figure 18
Number of full-time secondary school students per one thousand permanent
inhabitants, 2001*
* Including the relevant classes of grammar schools with six or eight classes.
Note: the numbers in brackets indicate the number of micro-regions in the relevant
categories.
Source: HCSO T-Star 2001.
The reason for this is that formerly the number of skilled worker training insti-
tutions was significant in the first two counties, and the functions of these schools
were taken over by the vocational schools; in the latter case, the reasons are to be
found in the weaker network built out (large county with few centres).
The transition of the structure of secondary education, as we have seen, was mo-
tivated by the acquisition of the school leaving certificate (and also by the continua-
tion of the studies in higher education institution for most of the students). The prob-
ability of being admitted to a higher education institution is shown by Table 6, dem-
onstrating the proportion of those admitted in the average of several years. It is
striking that Gyır-Moson-Sopron and Szabolcs-Szatmár-Bereg have the same posi-
tion in this respect, the probability is almost the same that the students finishing sec-
ondary schools in these two counties are admitted to higher education institutions; in
fact, Szabolcs-Szatmár-Bereg county even surpassed the privileged west Hungarian
23
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
county on the basis of the last five years. The spatial structure in this respect is het-
erogeneous, there are considerable and striking disparities among the counties, as
counties from both the Great Hungarian Plain and Transdanubia can be found among
the leaders and the less successful counties alike.In general we can state that there
are significant differences in Hungary as regards the chances to be admitted to
higher education institutions. The institutions in the respective spatial units are
unable to drastically change the structure and quality of their education, so the pre-
sent structure is durable and very much stable, although the chances are improving
on the whole for secondary school students to continue their studies at higher edu-
cation institutions.
Table 6
Average proportion of those admitted to or students in their 4th year in higher
education institutions (A/S) by counties, 1991–1998, 1994–1998
County
1991–1998
1994–1998
Order
A/S
Order
A/S
Gyır-Moson-Sopron
1
32.98
2
35.53
Szabolcs-Szatmár-Bereg
2
32.97
1
35.67
Bács-Kiskun
3
32.29
4
33.29
Hajdú-Bihar
4
31.15
3
33.81
Vas
5
31.02
5
32.59
Csongrád
7
30.56
7
32.33
Borsod-Abaúj-Zemplén
8
30.38
8
32.23
Zala
9
29.31
13
29.85
Baranya
10
29.08
10
30.55
Veszprém
11
28.96
11
30.43
Tolna
12
28.94
9
30.74
Jász-Nagykun-Szolnok
13
28.25
12
30.01
Békés
14
27.00
14
28.51
Komárom-Esztergom
15
26.63
15
28.25
Fejér
16
26.41
16
27.39
Pest
17
25.99
17
27.52
Somogy
18
25.71
18
26.72
Nógrád
19
23.00
19
24.44
Budapest
20
22.84
20
24.01
Average of the counties
27.76
29.33
Source: Halász–Lannert, 2000.
24
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Figure 19
Role of the secondary education in the towns in the provision of the countryside,
2000
Key: Number of the countryside population “served” compared to the population of the town: 1 – At
least twice as much; 2 – One and half time – twice as much; 3 – The same number or one and half
times higher; 4 – Half or the same number; 5 – Less than half; 6 – The town does not have non-
resident students; 7 – The number of countryside population “served” is negative; 8 – there is no
secondary school.
Source: Enyedi–Horváth, 2002 p. 252.
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
4.2 The network of higher education and knowledge mediators
The role of higher education is dominant in the development of human resources.
While in the early 1990s 15.1% of the population aged 20–24 studied at higher
education institution, and the proportion of full-time students was 11.3%, in 2001
the same figure for the respective age group was 42.9%, and 23.8% of this genera-
tion participated in full-time training. While in 1990 there were about 102,000 stu-
dents participating in higher education, their number grew to 3.42-fold by 2001
(349,000 students), within that, the growth of students participating in full-time
training was 2.51-fold. Actually 250,000 students had to be given new forms of
training (e.g. post-secondary, remote training), new institutions had to be organ-
ised, new specialisations and the training of new professions had to be launched.6
The reforms started in the 1980s, already, but the systemic change created a more
open system of higher education, more sensitively reacting to the economic and
social processes and also a system with a bigger independence – together with its
more and more complex contradictions.
The regional breakdown of the growth of the number of students is not even (Fig-
ure 20). Budapest kept its leading position, although the share of all enrolled students
in the institutions of Budapest fell from 44.1% in 1990 to 38.5% in 2001, and within
that the participants of full-time training were not more than 42.1%. A balanced
spread of the higher education institutions was typical in the 1990s, which led to the
decrease of the spatial disparities on the whole (Forray–Kozma, 1999). Every
county seat or second order centre that had any pride and also some traditions in
higher education tried to build out or develop their positions in some way, as a
result of which there are 42 settlements in Hungary now with higher education
institution (Figure 21).
We can witness a spectacular growth of the number of students and of the places
and supply of training, as the new institutions received a large mass of students and
offer almost all forms of training (post-secondary, graduate, professional further
training, doctoral school). The counties home to institutions established in the 1990s
mainly on local initiatives (mostly as foundations or organisations created as outlets
of some Budapest institutions in the county seat, gradually becoming independent)
have outstanding positions (Fejér, Komárom-Esztergom, Jász-Nagykun-Szolnok,
Békés). In the development of the existing networks some counties or their centres
are also very dynamic (Heves, Veszprém, Gyır-Moson-Sopron, Baranya, Pest and
Szabolcs-Szatmár-Bereg).7 The division line of development might be 1996: from
1990 until 1996 the number of students had been steadily growing, but in the
6 The register of the Hungarian Accreditation Board follows the university and college majors,
whose number is continuously growing, partly due to the competition of the institutions and partly
because of the more and more specialised demands (www.mab.hu).
7 Only taking growth exceeding the national average into consideration.
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Rechnitzer, János - Smahó, Melinda :
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Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
counties listed above and their centres of higher education the number of students
grew by leaps again. There is a long list of reasons; the dominant is the appearance
of several market elements in training (e.g. paying courses, remote training), also,
the demand for certain professions grew extremely rapidly (e.g. for economists,
ICT experts, lawyers, communication experts etc.), whereas some profession lost
their popularity steadily (e.g. teachers) of temporarily (e.g. training of engineers).
Some institutions were able to better adapt to these market effects than the others,
so a new, spatially quite even structure of higher education can be seen now in
Hungary, assisted by the integration of several state-owned higher education insti-
tutions in the late 1990s (in 2000).
By the late 1990s the number of those participating in higher education more than
tripled. The question is what human resources this spectacular growth is built on.
Higher education requires well-trained experts and trainers, so it can be set as a re-
quirement that the lecturers should be experts with adequate qualifications (profes-
sional qualifications, scientific degrees). The comparison of the staff of lecturers in
1990 and 2001 reveal thought-provoking characteristics (Figure 22).
Figure 20
Number of higher education students in the counties, 1990, 1996, 2001
(students per 1,000 inhabitants)
Source: Calculation by the authors based on HCSO data.
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Figure 21
Spatial distribution of the higher education institutions in 2002
Note: we did not feature all institutions in the map, as we only considered the units down to the
faculty level, excluding institutions and other smaller forms of higher education (for
example centres of consultancy).
Source: Ministry of Education, Department of Statistics.
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Rechnitzer, János - Smahó, Melinda :
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Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Figure 22
Number of full-time lecturers in the higher education institutions of the
counties, 1990, 2001
Source: Calculation by the authors. Source of the data: Statistical Bulletin. Felsıoktatás 1990/91;
Felsıoktatás 2000/2001. Ministry of Education.
While the number of students multiplied, the set of lecturers remained intact
both in its number and structure, or their number even slightly decreased (by 5.9%)
during the decade of great reforms.8 The number of senior lecturers (university or
college professors, associate professors) actually did not change much, and the
structure of the set of such lecturers did not change significantly, either. Parallel to
the decrease of the share of the capital city in higher education, there was only a
slight decrease in the proportion and number of the senior lecturers of the Budapest
institutions (50.7% in 1990 and 47.7% in 2001). The newly employed lecturers
actually substitute those leaving the institutions, so the larger number of students is
8 On the basis of statistics published by the Ministry of Education, the number of full-time lecturers
decreased, while the data published by the Hungarian Central Statistical Office reveal a growth in
the number of lecturers. According to the oral statement of the HCSO, the difference is due to the
fact that the HCSO data contain not only the data of the full-time lecturers.
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served by the same number of lecturers as before.9 The structure of the lecturers’
status did not become better, as in the larger university centres the growth of the
proportion of the senior lecturers did not keep up with the increase in the number of
students. In the new centres of higher education, the number of lecturers was
adjusted to the growth of the number of students. Presently it is frequent that the
senior lecturers travel, commute from the capital city or some large regional centres
of higher education to the new institutions. The everyday lecturing is done by the
local lecturers who usually do not (yet) possess scientific degrees. However, the
qualification system of higher education requires that the persons responsible for the
majors or the subjects should be senior lecturers, which is the case formally, in paper
– but in practice this requirement is only met with difficulties in the new institutions
and majors.
This leads to survival of the intellectual leading and organising role of the capital
city in the long run, in fact, the intellectual resources are further concentrated, as the
capital city did not become an “exporter” but remained an “importer” of intellectual
goods in the 1990s. The new centres of higher education in the countryside can only
become the intellectual centres and promoters of their regions with great difficulties,
as they have an extremely low proportion of qualified personnel, and they are
engaged with lecturing; also, those who are responsible for majors and professions
(at least formally), are usually not more than “commuters”, in the professional jargon
they are called “intercity professors”. The regional disparities, the concentration of
the highly qualified experts in the capital city can be best demonstrated by the spatial
distribution of the members of the public body of the Hungarian Academy of
Sciences (Table 7).
The distribution of the members of the public body of the Hungarian Academy of
Sciences according to their place of residence shows a clear dominance of Budapest,
supporting our statement above that the capital city is the greatest “supplier” and also
the receiver of the highly qualified intellectual resources these days. The regional
disparities can be further elaborated by highlighting the structural problems, i.e.
looking at the number of members in the different science classes of the public body,
in a breakdown by the regional academic committees (Figure 23).
9 Personal fluctuation is strengthened by the fact that the number of PhD doctors was 3.300 in the
new qualification system, and a total of 6.500 people received scientific degrees, habilitation
(university private lecturer status) was achieved by 2.150 persons (www.mab.hu). These figures
show that the majority of the scientific qualifications are absorbed by higher education; they
actually secure the continuous supply of qualified personnel.
30
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
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Rechnitzer, János - Smahó, Melinda :
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Table 7
Regional distribution of the members of the public body of the Hungarian
Academy of Sciences, 2000
Region
D.Sc.
C.Sc.
Ph.D.
Total
(persons)
(persons)
(persons)
persons
in %
Budapest and its hinterland
1537
4138
524
6199
65.7
Debrecen and its hinterland
153
534
130
817
8.7
Miskolc and its hinterland
43
273
49
365
3.8
Pécs and its hinterland
98
370
55
523
5.5
Szeged and its hinterland
210
532
104
846
9.0
Veszprém and its hinterland
90
375
54
519
5.4
No data available
26
118
28
172
1.9
Total
2157
6340
944
9441
100.0
Source: Data on the non academy doctor members of the public body of the Hungarian Academy of
Sciences (as of 24 September 2000.) Research Organisation Institute of HAS, Budapest.
The structure is stable, in other words: it is extremely conservative. Looking at the
structure of the regional committees, the number of qualified persons in the tradi-
tional university and higher education professions is outstanding. For example, in the
Veszprém Academic Committee working in North Transdanubia, the number of
agricultural experts (Keszthely, Mosonmagyaróvár), and of chemists (Veszprém) is
the highest. The proportion of economists and lawyers is low among the members of
the public body (8.2 per cent), although such trainings at university level are done in
three places of the region (Gyır, Sopron and Veszprém), parallel to college level
training in four places (Dunaújváros, Tatabánya, Székesfehérvár and Szombathely).
This phenomenon is typical in all academic regions, which implies the inner content
of the regional disparities of the intellectual resources and thereby the lasting, long-
term contradictions of the Hungarian human resources.
4.3 Regional disparities and characteristics of research and
development
In regional policy there is a more and more definite demand to integrate technology
development both into its objectives and into the tools and institutional system of
implementation. It has been recognised that the competitiveness of a given region
can only be enhanced by the more and more sophisticated systems of research and
development. It is absolutely necessary to explore all forms of research and devel-
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
opment at regional level and to activate them, partly to strengthen the regional en-
dowments and partly to adjust them to the general processes of R & D. The na-
tional monopoly of science and technology has ceased to exist; its gradual decen-
tralisation has started by the promotion of regionalisation. This resulted both in the
transformation of the institutional system and the more equal spread of the central
resources, the elaboration of new financing models and the novel measurement of
the efficiency of developments.
The transformation had a deep impact on research and development, similarly to
the other sectors of the economy. The elimination or transformation of the system of
large state-owned companies tore apart the previous economic and R & D co-opera-
tions. The decreasing number of orders from companies at the turn of the 1980s and
1990s, the declining national income and then the consolidation of the state budget,
together with the concomitant consolidation of higher education and the academic
sector postponed the fall of the R & D sector to the end of the decade (Table 8).
While the GDP has been continuously increasing since 1994, although at a slowing
pace, the decrease of the share of R & D reached its nadir in 1996. In 1987 the R & D
expenditure made 2.6% of the GDP, in 1989 it was still 2.0%, then it fell to 0.7% of
the GDP by 1996. This figure stagnated until 1999, since then the positions of R & D
have improved by 0.1% annually, exceeding 1% by now, still lagging far behind both
the Hungarian figures of the late 1980s and the present figures of the European Un-
ion.
Table 8
Conditions of research and development
Year
GDP index (1989=100)
R & D expenses in % of GDP
1989
100
2.0
1990
94
1.6
1991
83
1.1
1992
80
1.1
1993
79
1.0
1994
81
0.9
1995
82
0.8
1996
81
0.7
1997
85
0.7
1998
88
0.7
1999
90
0.7
2000
95
0.8
2001
98
0.9
Source: Calculation by the authors based on HCSO data.
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Rechnitzer, János - Smahó, Melinda :
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The positions of the scientific researches in the last decade were characterised by
the narrowing down of the resources on the one hand, and a considerable restructur-
ing, on the other (Magyarország 1990–2001, 2002). The available – human and fi-
nancial – resources decreased in the time of the decline or stagnation of the economic
performance, later stagnated in the years following the start of the economic growth,
and still later they moderately grew. The present level – approximately 1 per cent of
R & D expenditure from the GDP – is far below the European average and only half
of the Hungarian figure ten years ago.
The restructuring in the field of research and development resulted in the decrease
of the state financed researches, on the one hand, and the relative strengthening of R
& D in the company sector and even more in higher education, on the other. The
share of R & D employment – parallel to a significant decline in total employment –
decreased significantly, although not to the same extent as the resources, later it stag-
nated. The total of R & D expenditure grew significantly in numbers, but it did not
reach the extent of the inflation. The most significant financial source of R & D ex-
penses is still state budget. In the second half of the decade – as a consequence of the
foreign investments and the accession process to the European Union, e.g. the par-
ticipation in the 5th and 6th framework programme of the EU –, the role of foreign and
international organisations is also visible now in financing.
In 1989 almost half of the R & D expenditure was used in state-owned research
institutes and other research places, 38 per cent in the business sector and the rest in
higher education. The share of budgetary sector has continuously decreased, the rea-
sons for which are the transformation of certain research institutes into business
ventures or the closedown of some institutes, and the decrease of the budgetary sup-
port. The proportion of budgetary organisations fell to less than 30%, the share of
businesses grew to 44%, that of higher education to 28%.
As regards the number of employees, the shares of the business and the higher
education sectors changed the most dramatically (Table 9). The number of employ-
ment in the state-owned R & D sector fell to one-third by the middle of the decade,
and its share from the whole sector also fell from 42% to 29%. This situation did not
change much until the turn of the millennium. Higher education suffered a more
moderate loss of employment, and by the beginning of the new decade it had 10%
more researchers, but 5% less auxiliary staff than in the late 1980s. The proportion of
research and development staff in higher education grew from 26% to 40% in 12
years. In the public sector, the number of researchers fell to almost a half in the pe-
riod in question, while their proportion from the total number of researchers moder-
ately decreased. The fall of the number of auxiliary staff was larger than that of the
researchers and developers, so the share of R & D staff grew from 48% in 1989 to
64% by now.
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Rechnitzer, János - Smahó, Melinda :
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Table 9
Employment in research and development*
Branches
R & D staff
number (thousand people)
proportion (per cent)
1989
1995
2001
1989
1995
2001
Budgetary sector
15.9
7.7
7.8
37.6
39.3
33.9
of which: research and development
7.4
3.9
4.7
36.3
37.1
32.0
Business sector
17.7
5.6
6.8
41.8
28.6
29.6
of which: research and development
7.7
2.6
4.1
37.7
24.8
27.9
Higher education
8.7
6.3
8.4
20.6
32.1
36.5
of which: research and development
5.3
4.0
5.9
26.0
38.1
40.1
Total:
42.3
19.6
23.0
100.0
100.0
100.0
of which: research and development
20.4
10.5
14.7
100.0
100.0
100.0
* Number calculated for the full-time employees in proportion with the time spent on research and
development activities
Source: calculation by the authors based on Magyarország 1990–2001 [2002] p. 70. and Hungarian
Statistical Yearbook 2001. p. 513.
The impact of the transformed system of scientific qualifications is now visible;
the number of those with such qualifications is 49% higher now than it was in 1989.
Almost 60% of those in this category – doctors of science, candidates of science,
PhD-s – work in research and development places. The structure of the activity trans-
formed, too. As regards expenditure, in the late 1980s experimental researches, in the
mid–1990s basic and applied researches, in 2001 experimental researches once again
were the primary activity.
4.4 Changes of the territorial structure
In our survey we were able to calculate the first spatial order on the basis of three
parameters for 1995 (number of R & D places, R & D expenditure per one inhabi-
tant, number of researchers and developers per 10,000 inhabitants), so we can
relate to the deepest crisis of the Hungarian R & D capacities and performance. If
we add up the positions, we get a sort of order for the R & D potential of the
respective countries for the given year. The second order was made on the basis of
the last available data, the data for 2001. Although a six-year period is a relatively
short time for monitoring the major changes (Table 10).
35
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Table 10
Rank-order of the R & D potential, 1995, 2001
Final
County
R & D places R & D expen- Research and devel-
Total
order
(1996)
diture per one
opment staff per of positions
inhabitant
10,000 inhabitant
1995
1.
Budapest
1.
1.
1.
3.
2.
Csongrád
2.
2.
2.
6.
3.
Hajdú-Bihar
4.
3.
3.
10.
4.
Baranya
3.
7.
4.
14.
5.
Gyır-Moson-Sopron
6.
5.
5.
16.
6.
Veszprém
8.
4.
6.
18.
7.
Pest
7.
8.
8.
23.
8.
Borsod-Abaúj-Zemplén
5.
11.
7.
23.
9.
Fejér
12.
6.
12.
30.
10.
Szabolcs-Szatmár-Bereg
9.
10.
11.
30.
11.
Heves
10.
12.
10.
32.
12.
Jász-Nagykun-Szolnok
13.
13.
8.
34.
13.
Békés
16.
9.
15.
40.
14.
Vas
11.
17.
14.
42.
15.
Somogy
17.
14.
13.
44.
16.
Bács-Kiskun
14.
15.
16.
45.
17.
Tolna
18.
16.
18.
52.
18.
Zala
15.
19.
19.
53.
19.
Komárom-Esztergom
18.
18.
20.
56.
20.
Nógrád
20.
20.
16.
56.
2001
1.
Budapest
1.
1.
1.
3.
2.
Csongrád
2.
2.
2.
6.
3.
Hajdú-Bihar
3.
4.
3.
10.
4.
Gyır-Moson-Sopron
6.
3.
5.
14.
5.
Baranya
5.
6.
4.
15.
6.
Veszprém
7.
5.
7.
19.
7.
Pest
4.
8.
8.
20.
8.
Fejér
11.
7.
6.
24.
9.
Borsod-Abaúj-Zemplén
8.
13.
9.
30.
10.
Bács-Kiskun
10.
10.
13.
33.
11.
Somogy
13.
12.
10.
35.
12.
Békés
12.
9.
15.
36.
13.
Heves
14.
14.
11.
39.
14.
Szabolcs-Szatmár-Bereg
9.
17.
14.
40.
15.
Jász-Nagykun-Szolnok
17.
11.
17.
45.
16.
Vas
15.
18.
12.
45.
17.
Komárom-Esztergom
16.
15.
16.
47.
18.
Zala
18.
16.
18.
52.
19.
Tolna
19.
20.
19.
58.
20.
Nógrád
20.
19.
20.
59.
Source: Bulletin of the West Transdanubian Research Institution, CRS of HAS, 152/d.
36
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
The table shows that hardly any change occurred in the relative R & D potential
that had emerged by the middle of the last decade. Breaking down the order into
groups of five we can see that for the majority of the counties maybe the position
within the respective group of five changed somewhat.
The same five counties can be seen in the first – best – group: Gyır-Moson-
Sopron and Baranya changed positions, due to the relatively better R & D
expenditure proportions of the previous county. This is attributable to the revival of
the business R & D (e.g. by the Audi automotive company) and also to the expansion
of the higher education capacities. The improvement of the position in the R & D
expenditure projects that the total R & D potential of the county will improve, too.
This makes it possible that Gyır-Moson-Sopron will come up to position three, if the
present trends continue.
In the group containing the weakest five counties there are no more than two
changes. On the one hand, Komárom-Esztergom and Tolna county changed positions
17. and 19., now Komárom-Esztergom has the better position; on the other hand, a
new actor showed up at position 16: Bács-Kiskun county was replaced by Vas
county. As regards the change of the positions in the order, the position of three
counties changed considerably for the better and also three for the worse; the total
position of Bács-Kiskun improved by 12 scores, moving the county from position 16
to 10, while the positions of Somogy and Komárom-Esztergom improved by 9 scores
in both cases. For Somogy county it meant two, for Komárom-Esztergom four
positions advance in the order. The biggest decline was shown by Jász-Nagykun-
Szolnok and Szabolcs-Szatmár-Bereg counties: the total of the scores increased by
10 for Jász-Nagykun-Szolnok, so it fell 4 positions back in the hierarchy, while
Szabolcs-Szatmár-Bereg’s total of scores increased by 11, putting the county three
places back in the order.
While the orders highlighted in Table 10 only measure the relative potentials and
the changes of those, the two-dimension scaling of the county R & D performance
within the county GDP and economic development level (specific regional GDP)
better reflects the positions of R & D within the respective counties. Also, it has
important messages for the R & D and regional policy (Figure 24 and Table 11). If
we feature the two indices on the two axes of a coordinate system, the four fields
represent four basically different groups of counties, as regards their R & D potential.
The horizontal axis of the system demonstrates the R & D performance of the
respective counties compared to the county GDPs, while the vertical axis was used to
show the specific economic performance of the respective counties compared to the
national average.
There are two possible solutions to set a dividing axis. The first is to compare
the value of both factors to their average; the other is the use of a theoretical
division line. We used the latter in our analysis. In the case of the R & D
performance, the value of 1.0% within the GDP was the limit above which a
37
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
38
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Table 11
Regional development level and the level of R & D, 1995, 2000
County*
1995
2000
R & D expenditure County GDP in
L: low
R & D expenditure in County GDP in
L: low
in per cent of the
per cent of the
H: high
per cent of the county per cent of the
H: high
county GDP
national GDP
GDP
national GDP
Budapest (1)
1.44
181
HH
1.50
196
HH
Pest (2)
0.46
73
LL
0.41
80
LL
Central Hungary
1.28
144
HH
1.29
154
HH
Fejér (3)
0.46
99
LL
0.36
115
LH
Komárom-Esztergom (4)
0.03
87
LL
0.08
82
LL
Veszprém (5)
0.74
84
LL
0.59
81
LL
Middle Transdanubia
0.44
91
LL
0.36
94
LL
Gyır-Moson-Sopron (6)
0.43
109
LH
0.34
130
LH
Vas (7)
0.07
107
LH
0.09
118
LH
Zala (8)
0.02
92
LL
0.03
89
LL
West Transdanubia
0.22
103
LH
0.20
115
LH
Baranya (9)
0.46
80
LL
0.84
78
LL
Somogy (10)
0.21
76
LL
0.19
68
LL
Tolna (11)
0.14
92
LL
0.00
88
LL
South Transdanubia
0.29
82
LL
0.41
77
LL
39
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Continuing Table 11
County*
1995
2000
R & D expenditure County GDP in
L: low
R & D expenditure in County GDP in
L: low
in per cent of the
per cent of the
H: high
per cent of the county per cent of the
H: high
county GDP
national GDP
GDP
national GDP
Borsod-Abaúj-Zemplén (12)
0.29
76
LL
0.27
66
LL
Heves ( 13)
0.26
75
LL
0.27
72
LL
Nógrád (14)
0.02
59
LL
0.02
54
LL
North Hungary
0.24
73
LL
0.23
66
LL
Hajdú-Bihar (15)
0.82
78
LL
1.19
71
HL
Jász-Nagykun-Szolnok (16)
0.24
77
LL
0.25
67
LL
Szabolcs-Szatmár-Bereg (17)
0.38
61
LL
0.31
54
LL
North Great Plain
0.51
71
LL
0.64
64
LL
Bács-Kiskun (18)
0.18
79
LL
0.30
69
LL
Békés (19)
0.33
78
LL
0.29
67
LL
Csongrád (20)
1.36
93
HL
1.29
85
HL
South Great Plain
0.64
83
LL
0.65
74
LL
Total
0.76
0.79
* The numbers in the brackets are the identification numbers of the counties used in Figure 24.
Source: Calculation by the authors based on the Hungarian Statistical Yearbook 1995 (p. 458.)
and 2001 (p. 518.).
40
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
county has relatively favourable R & D performance or potential by the Hungarian
standards. Below the 1.0% level, the R & D positions of the respective county are
moderate or weak. In the case of the specific GDP values, we set the value of 100 as
the limit above which a county has strong, below which weak it has positions. The
coordinate system thus features the following four groups:
− strong economic potential and favourable R & D capacities (upper right field);
− weak economic potential and favourable R & D capacities (lower right field);
− strong economic potential and moderate R & D capacities (upper left field);
− weak economic potential and moderate R & D capacities (lower left field).
The applied two-dimensional scaling shows a rather homogeneous picture of the
economic development and R & D positions of the Hungarian counties (Table 12).
Three-quarters of the counties can be found in field 4, both in 1995 and 2001. The
figure also shows that in these counties the stagnation or moderate growth of the R
& D performance, a decline of the economic potential compared to the average is
typical. This tendency will remain typical in the coming years, despite the
increasing spatial disparities of the economic development measured with the GDP
and the catching-up programmes of regional development policy. It comes from the
fact that the foreign direct investments arriving at the more advanced counties in
the middle of the 1990s implement at least the supplementary investments
necessary to secure the competitiveness of the counties, whereas the counties with
a shortage of capital received less capital injections compared to the more
developed counties in the last years (with the exception of the multinational retail
networks).
Table 12
R & D potential and the types of economic development
III. Strong economy, moderate R & D
I. Strong economy, intensive R & D
Fejér, Gyır-Moson-Sopron, Vas
Budapest
IV. Weak economy, moderate R & D
II. Weak economy, intensive R & D
Baranya, Bács-Kiskun, Békés, Borsod-Abaúj-
Csongrád, Hajdú-Bihar
Zemplén, Heves, Jász-Nagykun-Szolnok,
Komárom-Esztergom, Pest, Nógrád, Somogy,
Szabolcs-Szatmár-Bereg, Tolna, Veszprém, Zala
Source: Table 10.
In the Hungarian circumstances it is only Budapest that belongs to the ‘strong
economy–favourable R & D performance’ category. In the figure we did not show
41
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
the values of the capital city, as in this case the national average calculated with the
capital city would have narrowed down the other categories, and the changes of the
positions of the respective counties between the two years in question would have
blurred. The good R & D potential is unfortunately coupled with weak economic
performance in Csongrád and Hajdú-Bihar counties, as the good R & D performance
is incapable of improving the economic performance to a level that increases the
overall relative economic positions of the respective counties.
In the case of three counties – Fejér, Gyır-Moson-Sopron and Vas – we can see a
disharmony between the R & D capacity and the relatively advanced economic per-
formance. It is not surprising that the regional development programmes of both
West Transdanubia and Middle Transdanubia treat the development of the innova-
tion milieu as a selected priority and both regions have worked out their regional
innovation strategies.
The figure shows that the positions of two counties changed considerably over the
period in question, which means that they moved from one field of the coordinate
system to another. Hajdú-Bihar county moved from field 4 – with a worsening eco-
nomic potential – to the sector with favourable R & D capacities. Fejér county, also
from sector 4, moved to the sector that implies good economic performance, besides
worsening R & D performance.
As a summary we can say that in the short run the good R & D capacities did not
affect the growth of the county GDP values in Hungary, and vice versa, the out-
standing economic performance – by Hungarian standards – is not founded by the
increase of the R & D capacities.
4.5 Characteristics of the transition period
The concentration of the resources of research and development in the capital city
did not decrease; in fact, it increased in the years of the transition. The activities,
organisations and also the information carrying the innovations are all concentrated
in Budapest. The significant organisational changes taking place in the research
and development sector hit the capital city too, nevertheless Budapest kept its
leading position in this sector.
As an example let us take a look at the applications for patents, which well illus-
trates the spatial structure of research and development. While in 1992 almost 2,700
applications for patents were submitted to the Hungarian Patent Office, the number
of applications continuously decreased over the decade, reaching the nadir in proba-
bly 1998. The national tendency of the change in the number of applications for pat-
ents could also be seen in most of the counties, while the spatial distribution of the
applications shows a very much differentiated picture (Figure 25).
42
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
Figure 25
Number of applications for patents at the Hungarian Patent Office by counties,
1992, 1997, 2002
* The Data of Pest county without Budapest.
Source: Edited by the authors based on the data of the Hungarian Patent Office.
In absolute terms, the most dramatic changes took place in the case of Budapest
in the previous decade: while in 1992 a total of 1,544 applications for patents were
submitted in capital city, in 1997 this number dropped to less than half, and the num-
ber applications submitted in 2002 was just over that in 1992. Parallel to the changes
following the national tendencies, the concentration of Budapest was typical all the
time, although its scale decreased somewhat: in 1992, 57% of the applications for
patents were submitted by Budapest persons or organisations, by 1997 this figure
decreased to 55%, and to 50% by 2002.
The number of applications for patents per one million inhabitants was 262 in
1992; this figure fell back to 130 by 1997 and then started to rise again after the na-
dir, to reach 154 applications per one million inhabitants by 2002.
The total number of research and development units decreased, the majority of
them was reorganised. Those organisations that were closed down were usually the
ones outside Budapest, in the countryside centres. A limited number of research and
development businesses appeared, as in a few centres (Gyır, Székesfehérvár) the
43
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
developers of the large businesses founded smaller organisations, several of whom
still successfully operate, having expanded their activities.
A few large multinational companies have located or are planning to locate re-
search and development units to Hungary, and these units are usually concentrated in
Budapest and its region, too. Their connections to the universities, to higher educa-
tion and to other research units are still weak, is has only been initiated in Budapest
and a few other cities of Hungary so far that innovation parks should be organised
parallel to the universities, creating thereby a considerable intellectual concentration
that also entails economic and regional development effects. In the rural higher edu-
cation and economic centres the will to found innovation and technology centres is
given, but the resources are missing, the organisational structure and the develop-
ment directions are unsettled yet and the permanent interest conflicts of the stake-
holders set back the initiatives.
While the renewal of the economic structure was faster and more successful in the
western and north-western parts of Hungary, these regions were and still are in a very
bad situation as regards research and development and also higher education. The
only progressive connection that we can see is the one between the restructuring of
the economy of the capital city and the research and development and higher educa-
tion basis of Budapest – due to the large-scale concentration –, but such a phenome-
non is completely missing in the non-capital city centres, in fact, just the opposite is
the typical case.
The renewal of the territorial structure of Hungary and the performance of the
economy is not connected directly to research and development and higher education
in the transition in Hungary (Table 13). It is clear that not these capacities are the
main motivators of location, the attractors of foreign and Hungarian direct invest-
ments or the generators of the restructuring of the economy. Probably the first phase
after the establishment of the market economy, the phase of quantitative growth and
restructuring, is going to be followed by another phase when the relationships among
the economic units and the resources of science higher education are gradually es-
tablished. A few signs of this can already be seen in the case of Budapest (e.g. the
location of research and development centres and the increasing volume of orders for
R & D).
In the rural centres of science and higher education, the available capacities allow
a faster territorial development and can also offer more favourable conditions for the
restructuring of the local and regional economies. It is evident, on the other hand, that
regional and local resources (of local governments, economic organisations and in-
terest representations) are inadequate for the realisation of these efforts.
The local and regional self-governments have attributed so far a varying signifi-
cance to the settlement and regional development possibilities lying in higher
education and, to a more limited extent, scientific research. What we can say is that
44
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
those smaller centres were more determined to assist the development of this sector
Table 13
Amounts paid to higher education from the resources of the National Technical
Development Fund in 1996–2000, by planning-statistical regions
Planning-statistical region
1996
1997
1998
1999
2000
1996–2000
Share of regions
Million
Share
from GDP
HUF
(%)
(%)
Central Hungary
154.2
716.7
246.4
199.6
234.8
1551.8
55.60
43.11
Middle Transdanubia
8.7
60.5
9.8
12.8
8.6
100.3
3.59
11.08
West Transdanubia
9.5
13
23.1
16.6
7.2
69.4
2.49
11.23
South Transdanubia
28.1
145.2
32.7
11.7
22.7
240.5
8.62
7.26
North Hungary
13.3
45.2
6.4
17.3
18
100.1
3.60
8.15
North Great Plain
22.7
275.6
34.8
22.4
32.6
388.1
13.91
9.61
South Great Plain
29.6
185.7
29.1
44
51.5
339.9
12.19
9.56
Total
266.1
1441.8
382.2
324.5
375.4
2791.1
100.00
100.00
Source: Positions of the research and development in higher education. Ministry of Education,
Budapest, 2001
45
Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
(mostly by the provision of establishments and to a lesser extent by the donation
of financial means) that usually did not have higher education at all or where
higher education had been one-sided – or where the effect of the personal relations
reinforced the significant traditions (a few university towns). The bigger centres are
slower to recognise that the development of the scientific and higher education ca-
pacities have a considerable impact on the future of the respective settlements.
Today it is demonstrated also in Hungary that the existence of higher education in
an adequate volume does have an impact on the local economy (Hardi–Rechnitzer,
2003). Regional and local actors are interested in the short-term results, whereas
the effect of research and development and its institutions can be seen in a longer
period of time, so enthusiasm cannot replace either the resources or the continuous
lobbying for the development of the institutions.
Regional policy and science and technology policy have not had much in
common in the past fifteen years. Both policies were engaged with the creation of
their own identities, thus neither regional policy had concrete messages for science
and higher education nor the shaping science and technology policy was interested in
spatial structure and regional processes.
The National Regional Development Concept of Hungary (1998) mentioned the
spatial structure of research and development and higher education in Hungary10, but
there were no comprehensive researches behind the development paths designated in
the Concept. In addition, the Concept was more rejected than accepted, due to the
lack of professional reconciliations.
10 The Concept puts the centres that harmonise research and development and the development of
businesses into three categories. The first group is made up by the regional innovation centres
where the organisation of science parks is desirable: such centres are Pécs, Szeged, Miskolc, De-
brecen, Sopron and Veszprém. The second category involves the innovation centres that “possess
comparative advantages” as links of the chain. These are Mosonmagyaróvár, Keszthely and
Gödöllı. The third group are the junctions of industrial restructuring, where technology centres di-
rectly assisting the region-specific production and its services should be located, together with in-
dustrial parks and de-centres of higher education: they are Gyır, Dunaújváros, Székesfehérvár,
Tatabánya, Szombathely, Zalaegerszeg, Kaposvár, Kecskemét, Nyíregyháza, Szolnok, Eger and
Békéscsaba. The text of the concept and the map in its annexes do not correspond to each other, it
is actually not explained what the authors mean under the certain categories, probably what they
did was the simple categorisation of the university centres and sub-centres and the colleges. Since
then, however, the structure of higher education has changed after the structural integrations. Fi-
nally, the authors did not give specific research directions; they were not connected to the business
structures of the respective regions. In our opinion this categorisation, the ideas of this “institutional
models” is not feasible enough professionally. The research and development chapter of the Na-
tional Regional Development Concept of Hungary is a good example for the lack of adequate analy-
ses and strategy for the territorial structure of this activity, accordingly the development sugges-
tions are inadequate, too, as they are not built on the inner correlations of the regions and are not
connected to the renewal of technology policy itself, either.
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Rechnitzer, János - Smahó, Melinda :
Regional Characteristics of Human Resources in Hungary During the Transition.
Pécs : Centre for Regional Studies, 2006. 97. p. Discussion Papers, No. 50.
At certain organisational developments the weak sings of the assertion of spatial
aspects could be seen, such as in the establishment of the institutions of the Bay Zol-
tán Research Foundation, the instrument centres of OTKA (Országos Tudományos
Kutatási Alap, National Scientific Research Fund) and the de-centres of the European
Union 5th Framework Programme (Gyır, Veszprém, Pécs, Miskolc, Debrecen, Szar-
vas and Szeged), but these were not parts of a conceptual approach but were aiming
at the managing the lack of it.
At the development concepts of higher education and its new industrial system,
regional aspects (e.g. lack of certain fields of science, accessibility, concentration of
capacities) were not given much emphasis; these concepts were not built on the de-
velopment and renewal ideas of the regional economies. At the transformation of the
institutional and tools system of regional development (e.g. decentralised resources),
research and development and higher education capacities were not taken into con-
sideration, or if they happened to be, without resources allocated to them.
A careful initiative was made in the last third of the 1990s for the regionalisation
of research and development, when the county chambers of commerce were given
limited resources by the former National Technical Development Committee. The
few years of experience does not allow us to draw far-reaching conclusions. The
support of research and development at regional level was raised again in 2004, in
connection with the act on the innovation fund. Besides the regional development
councils, regional innovation councils could be created. They can handle decentral-
ised innovation resources on the basis of the principles set by the regional level, and
their activity can be assisted by the newly established regional innovation agencies.
A considerable shift towards regionalisation and also decentralisation was made this
way, the results of which will probably become visible in the long run.
The institutional system of regional development was gradually built out both at
county and regional level. The development concepts and programmes of the coun-
ties and primarily of the regions deal with research and development; they specify
the demands and plan the enlargement of the institutions, mainly in the regional cen-
tres and the locations of the research and development institutes. The research and
development ideas appearing in the concepts11 are not integrated into an adequate
organisational system, they are nut supported by resources, so research and develop-
ment appears as a rather general objective of regional development and not as part of
a structure promoting the internal renewal of the regional economy. The concepts
cannot fit research and development into the institutional level, either, because there
are no adequate models and alternatives for solutions for this in the Hungarian re-
gional development practice. Also, the international programmes do not take this
segment into consideration yet.
11 Those county development concepts have chapters on research and development where there are
universities or significant higher education institutions in the county. In other concept we can read
general statements, most of which specify demands but not exact development ideas.
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On the other hand, it is a definite achievement that there is an expressed demand
for the support of research and development at territorial (regional) level. However,
without models and suitable partners (governmental or regional bodies) and interest
representation, no specific tools and institutional system has been created so far in
the spatial units, accordingly the concepts and programme are mainly concentrating
on the conservation or the minimum development of the existing structures.
5 Settlement network
Analysing the relationship of the settlement network and the human resources we
can see that the settlements, their institutions, operation, the character of their
economy and society, their communities, values and a lot of their other factors
have an impact of the people’s lives and also their capacities. The central settle-
ments concentrate the most important actors of the economy, build out their local
and regional institutions that attract and also transform the population. On the other
hand, the centres with a smaller range of functions gradually lose their human re-
sources, decreasing thereby their economic capacity, which may lead to the further
deterioration of the functions.
We could go on with the list of consequences; instead we only say that it is im-
portant to look at the economic, social and institutional structures determined by
the settlement network, and on the basis of this to search their impacts on the
structure and regional characteristics of the human resources. In the subsequent
pages we concentrate on the urban network within the total of the settlements. The
cities and towns are the determinants and focal points of regional processes. The
inner movements, the restructuring, the hierarchy and spatial division of the urban
network, the transformation of some of its old functions and the appearance and
spread of new attractions do not only describe the regional processes but can also
highlight the role and influence of the respective elements of human resources.
5.1 The innovative milieu and its changes in the nineties
First we try to introduce the rearrangement of the urban network in the nineties, in
order to illustrate the factors that actually impact the regional processes, and repre-
sent the renewal and innovation milieu of the regional processes.
Is there a shift in the urban network in the 1990s? We can only give a precise
and scientifically supported answer to this question if we measure the structure at
the two dates with the same parameters. The comparison is hard to make, never-
theless we can make a provisional comparison of the beginning and the last third of
the nineties (Rechnitzer, 1993, 2002).
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On the basis of the analyses made we can draw the following conclusions on the
rearrangement of the urban network and the factors promoting it (Figure 26 and
27). The first conclusion is that by the end of the decade, among the elements of
the network, the integration and interdependence of the factors and institutions
representing the modern business and economic services is more expressed. These
new urban functions are interrelated, and as a consequence of the consumer ca-
pacities motivated by the economic potential, they appear in the cities in an ever
larger number and improving quality. By the turn of the millennium they reached a
certain mass both as regards their supply and their spatial spread, and they became
dominant elements of the quality and division of the urban network.
The second conclusion is that while in the early nineties it was the traditional
centre functions, i.e. mostly institutions connected to public services (education,
health care, justice and public administration) that primarily influenced the urban
network and its division, by the late 1990s the business and economic services and
market factors became the prime factors. The elements making the network built
on public services thus lost some of their importance, and they were replaced by
the functions connected to the economy, or the functions related to market and
consumption, and also the towns as the places of population concentration and
attraction.
The third characteristic is that, besides the market and consumption factors,
very closely related to them, the accessibility, good transport location of the towns
was highly appreciated. The better the access to a town, the stronger attraction it
has on its region and the wider range of services the town integrates, the richer the
supply of its consumer market becomes.
The fourth statement is that while in the early nineties the human resources, es-
pecially the schooling of the population was a dominant factor in the development
of the network, schooling, including the strong influence of those with higher edu-
cation degrees, lost its importance by the end of the decade. The structure in this
respect may have become more balanced, and the economic factors – including
incomes or elements representing consumption – may induce bigger disparities
than human resources do. Human resources have probably become more homogene-
ous within the urban network.
The change of the division of the urban network is striking: of the 164 towns
examined in 1990, 97 towns (59% of the total) shifted in one direction or the other.
The number of stable and active towns increased: they are usually the county seats,
large cities and some middle towns with old traditions. These centres are the strong
points of the network; they have established their regional functions and are home
to 35.2% of the total population of Hungary, according to our survey.
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The group of towns with special functions rearranged, too. It is not the mixed
functions but the concentration of the individual, outstanding local characteristics
that separate those towns whose situation is good due to several factors. The long-
term development, on the other hand, is exposed to economic booms or seasonality
effects, so the development strategies have to include the expansion of the con-
sumption and service functions of the respective town. A large-scale rearrangement
took place in the last years in the set of towns in transition, able to improve their
positions. Several large towns managed to get out of the category of the unstable
settlements (e.g. Hódmezıvásárhely, Eger and Szekszárd), also, many traditional
middle towns reinforced and stabilised their situation (such as Baja, Keszthely or
Mosonmagyaróvár).
There was a mass “intrusion” of the small and middle towns established be-
tween 1971 and 1988 into the category of transforming towns able to improve their
situation, representing the third stability level12 of the urban network. In the begin-
ning of the 1990s, most of these towns could be considered as lagging centres,
lacking innovation, but they were able to strengthen their regional organising func-
tions by the last third of the decade. The transition is more striking in the small
towns of the territories west of the Békéscsaba–Salgótarján line; east of this divi-
sion line only a few towns, usually the already mentioned centres of Szabolcs-
Szatmár-Bereg county, were able to make this progress to another category.
A larger number of small and medium sized towns founded in the 1970s and
1980s were able to improve their situation, receive new service institutions and
also develop their regional positions. Probably by the early 1990s the institutional
systems and the hinterlands of these towns had been established, which contributed
to the appearance of further, new functions and at the same time the stabilisation of
their human resources base.
The group of the ex-socialist and industrial towns, which existed at the time of the
first survey, although with poor and one-sided innovations, disintegrated. Some of
them now belong to the new industrial towns (like Paks, Százhalombatta or
Tiszaújváros), some of them managed to renew themselves, probably due to their
county seat functions (Szekszárd and Miskolc), and it is only Szolnok that made a
relatively minor step, as it now belongs to the category of the towns in transition.
The group of towns without innovations fell apart in the nineties, too. A signifi-
cant set of towns (those founded between 1971 and 1988) made a move to the
group of the transforming, stabilising towns. It means that now there is a group of
towns that managed to survive the shock of the transition, the market functions and
the innovative institutions representing them are gaining a more and more impor-
tant role in their urban functions. Nevertheless there are some “static” towns,
12 The first level is the capital city, level 2 is made up by the regional centres, level 3 are the meso-
centres and level 4 are the micro-regional centres.
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mostly in the Trans-Tisza region (the territories of Hungary east of the Tisza River)
and in North Hungary, so in the small towns of Békés, Hajdú-Bihar, Jász-Nagykun-
Szolnok, and Borsod-Abaúj-Zemplén the wave of the national modernisation cannot
be seen. Although there are new institutions, elements of the market economy and
units of the new services, their weigh is still low, moderate compared to the total of
the characteristics of the respective towns. It is interesting that in Transdanubia
there are only four static towns in this respect (Csorna, Szigetvár, Mohács and
Tolna).
The small towns that were given the town rank in the 1990s (there are 53 such
towns now in Hungary) do not automatically belong to those falling behind; in fact,
these new small centres are definitely divided, different factors are dominant in
them. Besides the new holiday resorts, one industrial town and four small towns
are among the towns in transition (Csepreg, Aszód, Pécsvárad and Máriapócs).
Twenty-six towns are in a transition situation, most of them in Transdanubia, in the
agglomeration of Budapest, in the region between the Danube and the Tisza Rivers
– the ones falling behind in the Trans-Tisza region again. The majority of the set-
tlements that were awarded the town rank in the 1990s thus did not contribute sig-
nificantly to the modernisation of the urban network as a whole; in fact, they con-
siderably deteriorated its quality. This mass award of new towns, not considerate
enough, only increased the tensions in the network. The criteria of the award of
town rank are connected to the institutional infrastructure, to quantitative charac-
teristics and not to the presence of the elements of modernisation (Csapó–Kocsis,
1997; Kara , 1998).
The analyses revealed that the transport relations played a significant role in the
modernisation of the individual elements of the network. If we compare the distances
of the settlements from the motorway network at the different times and the positions
of the towns, we get a characteristic structure. The correlation between the two things
is not unequivocal and indirect. The developing towns within those in transformation
make the majority in the stripe of the best access, within 30 minutes from the motor-
ways. Probably these are the towns where the structural changes will accelerate in the
future, where market services and their institutions will play an increasingly impor-
tant role and that will become more attractive not only for the economic units but
also for the inhabitants.
In the early 1990s a large-scale rearrangement took place in the Hungarian urban
network. The institutions of market economy showed a spectacular development, by
the end of the nineties they became the dominant factors in shaping the total of the
settlement network. The big cities, with a larger population, considerable hinterland,
mature and multi-level institutional system and income generating capacity were able
to better react to the factors bearing modernisation, thus they were able to stabilise
their situation and also expand their regional organising functions.
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In the network, the towns built on one particular function or one particular eco-
nomic (industrial) activity are separated from each other. The traditional middle
towns and the small and medium sized towns founded in the 1970s and 1980 gradu-
ally got rid of the negative economic impacts of the systemic change. They were able
to integrate the institutions of the market economy and accommodate those institu-
tions of the service sector that mediate modernisation.
In the towns in transition situation, without a stable structure and institutional
system – many of whom gained the town rank by administrative measures –, the
building out of the institutional system carrying modernisation is still accidental, the
economic structure is fragile, the income positions are less favourable, so the future
development of these towns is uncertain. In the group of those falling behind we see
a larger number of centres that were awarded the town rank in the nineties (or in the
last twenty-thirty years in the Trans-Tisza region). There were achievements in the
development of the market institutions and services and the economic base as a
whole, but the pace of these developments lagged behind the development of the
whole of the network, making the falling behind of these towns striking and palpable.
The majority of these towns can be found in agricultural areas or industrial crisis
regions, so they are not able either to absorb energy from or give energy to their hin-
terlands.
To sum it up, the urban network was not static in the 1990s, the total of the urban
system and the majority of the individual towns tried to adapt to the market econ-
omy, partly by the integration of institutions and partly by the expansion of the new
regional functions. In the major part of the network, the shock typical for the early
1990s has gradually disappeared by now. Ever larger groups of small and medium
sized towns in all regions of Hungary have either regained their functions or are pre-
paring for the reception of new functions. In the nineties, the dilution of the network
started, which increased the density of the urban network and accordingly the com-
petition for the new functions in certain regions, while in other places, due to the
backward situation of some regions, the new towns have not been able so far to be-
come the mediators of the institutional systems of modernisation. The large towns
became the evident winners of the network, the successful towns – at a different pace
and at different times –; in these towns the new institutional system of the market
economy has emerged and stabilised, so the regional effects of these towns have
gained a new dimension, primarily built on consumption; also, they concentrated the
human resources.
Based on the experiences of the nineties, the projected trend of the future devel-
opment of the urban network is the further strengthening of the large cities, the ex-
pansion of their functions and thereby the ever sharper competition among each
other. The traditional middle towns and the small towns founded before the mid–
1980s are expected to stabilise their situation, and also to increase their micro-re-
gional, in the better case meso-regional functions. We also think that the circle of
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towns with special functions is likely to expand, i.e. the increase in the number of
holiday resorts, agglomeration towns (also around the regional centres) and as a –
now visible – new phenomenon, of border towns. Finally, the revival of the small
towns may be spectacular in those regions where the conditions of a long-term eco-
nomic growth are given, allowing these towns to join in the regional networks that
are more and more definitely shaped by the economies of the large towns. In those
regions where the large towns are unable to strengthen the development of the net-
work, are engaged with the rearrangement and stabilisation of their own structures,
the small towns will continuously stagnate, parallel to the quiet decrease of their
institutions and services. Probably new awards of town rank as an administrative and
political “act” will be necessary in the future too. There are still regions in Hungary
with a deficiency of towns, but the newly recognised towns will only be able to have
an impact on their micro-regions – or they already have an impact –, and will not
influence significantly the development of the whole urban network. Competition
and the most intensive integration into the European network will remain typical of
the large towns; these towns will remain the primary factors in the development of
the network and the determinants of the structures at regional level.
5.2 Spread of a new skill and technology
In our further analyses of the Hungarian urban network we tried to explore what
differences and similarities can be found between the socio-economic development
level of the towns and a new knowledge technology system, the provision with
info-communication infrastructure13 (Csizmadia–Grosz–Rechnitzer, 2001).
As regards the development level of the towns in the field of info-communica-
tion technology, the findings of the survey of the available infrastructure and info-
communication services allowed the separation of several different categories. The
factors making these categories are manifold, but there are a few especially impor-
tant characteristics. One of the most important features is that the development
level of the towns in info-communication technology is influenced by the size of
the given town, together with the – historically developed – central functions and
roles in cause and effect relationship with the size. The traditional centres of Hun-
gary – county seats, towns with county rank, regional centres –, that had been the
13 We analysed the info-communication infrastructure through 12 variants, which are the following:
secondary and higher education institutions offering ICT training; number of info-communication
businesses in manufacturing industry; number of businesses related to products and non-material
production; businesses interested in the media economy; internet service forms; domain names;
number of telephone subscriptions; share of business lines within all telephone lines; number of
mobile phone service subscribers.
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centres of the socio-economic development for a long time, also have an out-
standing position within Hungary as regards the provision of info-communication
services (Figure 28).
These towns concentrate the majority of the businesses active in the info-commu-
nication sector (both in manufacturing and the services sector), the number and qual-
ity of the available communication infrastructure (both the traditional and most up-
to-date, internet-based ones), and the level of the connected services is well above the
services available in the small and medium sized towns. They also have a consider-
able advantage in the field of ICT training. A special group of them are the tradi-
tional regional centres (Debrecen, Szeged, Pécs and maybe Miskolc); their special
positions are reinforced by their role in university and scientific research and the
institutions of these researches.
Somewhat behind the traditional regional centres but above the level of the
county seats and the micro-regional centres we find a distinct group, the group of the
towns that are new or partial regional centres. On the basis of the info-communica-
tion indices, in addition to Gyır, Kecskemét, Székesfehérvár and Nyíregyháza, we
can mention Veszprém, mostly due to its university traditions and research bases.
The county seats and the micro-regional centres can be classified into two basic
categories. One category is made up by the county seats and towns with county
rank, at the next level of the hierarchy, that have traditionally played and still play
a considerable role in the settlement structure. In the second group we find a few
middle-sized towns in the vicinity of Budapest: Vác, Szentendre, Budaörs,
Gödöllı, Budakeszi, Dunakeszi, Érd and Esztergom. Their dynamic development
is definitely the consequence of the proximity of the capital city, the suburbanisa-
tion tendencies gaining a momentum in the last decade and, among other things,
also the spread of the info-communication sector.
Mainly in the Budapest agglomeration, but also in North Transdanubia – a re-
gion advanced both in social and economic sense –, and also in the South Great
Plain region there are several active, dynamically developing small and medium
sized towns. In these settlements both the quantity of the info-communication in-
frastructure and the activity of the businesses in this sector exceed the national
average.
Regarding their activity and mobility, a homogeneous group of towns are the
so-called holiday or bathing towns, but their special position is attributable much
more to their specific economic functions than their provision with info-communi-
cation infrastructure.
Most of the small and medium sized towns – approximately a hundred of them
– do not show any particular activity in the reception of info-communication tech-
nology, in their case a lower level of provision, well below the national average is
typical. Finally there is a significant number of towns (58) that are in the group
characterised by lagging development and falling behind; they are definitely
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concentrated in the north-eastern part of Hungary (of course there are a few such
towns in other regions of Hungary too).
We can rightly say that the info-communication infrastructure of the Hungarian
towns is primarily determined by the size of the towns (differences among the tra-
ditional large cities, the county seats and the small and medium sized enterprises),
their geographical location (disparities of the dynamically developing, the stagnat-
ing and lagging, and the backward regions) and also their special features (e.g. the
dominance of the holiday resort functions, belonging to the agglomeration of the
capital city, university and higher education centres). We cannot show a close rela-
tionship between the time of the award of the town status and the development
level in information and communication technologies. Leaving the regional and
micro-regional centres out of consideration, among the towns given the towns rank
in the last decade there are active towns, static ones and lagging small towns as
well.
Of the 251 towns of Hungary, only 20–25 can be seen as definitely developed,
if we consider the development level of the information and communication tech-
nologies, the available infrastructure and the supply of such services – however,
they are home to some 40% of the urban population of Hungary without Budapest
(2 million people). A further one million inhabitants live in the 70–75 relatively
active small and medium sized towns that are trying to catch up with the former
group, whereas in the other, more than 150 towns we cannot actually talk about a
strong presence of info-communication sector – in fact, 60 of these towns are defi-
nitely backward and still falling behind. It means that the number of urban popula-
tion left out of the development of the information and communication sector is
almost 2 million.
As regards the info-communication infrastructure, the spatial structure and the
settlement structure of Hungary are more or less divided; there are disequilibria in
this respect. The majority of the most advanced towns – regional and micro-re-
gional centres – can be found in North Transdanubia and the agglomeration ring
surrounding Budapest, whereas the southern and eastern parts of Hungary (South
Transdanubia and the territories east of the Danube River) are in a less favourable
position. This is underlined by the geographical location of the backward towns, as
most of them are concentrated in North Hungary. From the aspect of info-commu-
nication infrastructure, the most deprived areas are the regions lying east of the
Salgótarján–Szolnok–Békéscsaba line.
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5.3 Knowledge bases at the turn of the millennium in the urban
network
The study of the knowledge-based renewal capacity of the urban network can raise
several questions. The issues can be the definition of the content of knowledge-
based renewal, and the choice of the indices and factors suitable for its description.
Another issue can be time – should we analyse the renewal in the network in a time
series? We can think about the methods of analysis, the well-known limits of the
analyses or the difficulties that we encounter during the application. Last but not
least our conclusions may lead to disagreements, as the towns see the reality in the
different ways and comprehend the reasons and explanations differently. In fact,
their analysis of the situation may considerably differ from what the academics put
down in this study.
If we want to get a clear picture of the endowments of a given settlement in
connection with the many forms of renewal capacities, we need a single model that
carries
1. the dominant material factors of innovation (e.g. economic development
level of the households and the organisations, institutional system, em-
ployment and unemployment);
2. endowments offered by the human resources (e.g. the level of schooling,
proportion of the highly qualified segment of the labour market, higher
education and research and development); and naturally
3. the sings of innovative behaviour at the local level, besides the presence of
the adequate supporting institutional system (e.g. the presence of institu-
tions supporting patents and innovations, the weight of the R & D sector).
We put all these factors into five groups of analysis and examined them for a
total of 251 towns. 1. The index of the economic development level summarises the
data of the inhabitants, the local economic actors and the institutions and organisa-
tions involved in the local economic activities. In the creation of the new, complex
variable, the indices measuring the income positions of the households and the
dynamics of the employment play the most significant role. 2. The four indices of
the main component ‘schooling and management’ sum up the segments of the la-
bour market data of the census of 2001. The index reflects the existence of the eco-
nomic and state roles and functions built on advanced, highly qualified professional
and especially managerial jobs. In the establishment of this variable the main role
is played evidently by the indices measuring the proportion of the professionals,
within this that of the intellectuals in leading positions. 3. The index of the main
component ‘social activity’ is made up by the data measuring the willingness of
participation in the referendum of the EU accession, the organisational and financ-
ing (support) background of the civil society and the complexity of the local social
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publicity. The main factors in this variable are the indices measuring the electoral
behaviour and the weight of the non-for-profit sector. 4. The dimension of human
resources reflects the institutional and human weight, the development level of the
higher education sector. In the concise variable, the most significant component is
the proportion of the qualified lecturers in leading positions in higher education,
and the urban parameters measuring the faculties of higher education, and secon-
dary institutions. 5. The main component ‘innovation’ aggregates four indices di-
rectly, and a total of eleven indices indirectly. The structure of the indices shows that
this dimension describes primarily the local innovative activities and the supporting
real physical and digital institutional system, the service milieu in the background.
The biggest importance was attributed to the indices depicting innovative initiatives
patented over the last ten years, the development level of the information and com-
munication technologies and the density of the network-based digital services.
We analysed the innovation potential of the towns on the basis of the data of the
five main components, and the aggregated values of these became the elements of
clustering. In this two-step action we managed to separate eleven groups that show
a relative homogeneity in the “space” defined by the axes of economy, schooling,
society, human resources and innovation, and are also markedly different from each
other (Figures 29 and 30).
The three big towns of cluster 1 (Szeged, Pécs, Debrecen) as traditional regional
centres are in the focal point of higher education and innovation processes, with
good labour market and economic parameters. These centres, that are home to
more than 10 per cent of the total non-Budapest urban population of Hungary (just
five million inhabitants) are the most innovative members of the Hungarian urban
network. Their primary feature, in addition to the characteristics reflecting high,
above-average level of schooling and the presence of a knowledge-oriented labour
market, is the outstandingly high average values of human resources and innovation
indices. With regard to the development dimensions connected to innovation,
cluster 2 is made up by big towns with central roles, belonging to the cutting edge
(Miskolc and Nyíregyháza) that only lag behind in their economic parameters.
These two towns have 6 per cent of the total non-Budapest urban population, but
their economic data are only around the average, even if we take the total of the
Hungarian urban network into consideration. If they improve their less favourable
economic potentials, they will have everything that is needed to have more inten-
sive and successful innovative functions. Members of cluster 3, i.e. Gyır, Székes-
fehérvár and Kecskemét (7 per cent of the respective urban population) are among
the towns with excellent innovation capacities if we consider the total set of towns
in Hungary. Actually the number of innovative initiatives is high, the institutional
system is given, but the adequate human potential is less developed, there are no
university bases or they only have a short history. Another common feature sepa-
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rating these towns from others is the presence of very developed economic factors,
and the significant share of the foreign capital.
Figure 29
Division of the urban network by the knowledge base
(towns with high renewal capacity)
Legend: 1 – Regional centres with complex structure I. (3 towns – 533 thousand persons – 10.8%); 2
– Regional centres with complex structure II., with less favourable economic parameters (2 towns
– 303 thousand persons – 6.1 per cent); 3 – Centres with a shaping innovation potential and built
on strong economic foundations (3 towns – 342 thousand persons – 6.9 per cent); 4 – Centres
with considerable higher education and human resources base (6 towns – 389 thousand persons –
7.9 per cent); 5 – Sub-regional centres with good endowments (4 towns – 198 thousand persons –
4.0 per cent); 6 – Towns with predominantly higher education orientation (3 towns – 86 thousand
persons – 1.7 per cent); 12 – towns in the Budapest agglomeration, with outstanding economic
and labour market conditions (2 towns – 36 thousand persons – 0.7 per cent).
The size of the circles indicating the towns are proportionate with their number of population, the
percentage figure indicates their share from the urban population of Hungary calculated without
Budapest.
Source: West Transdanubian Research Institute, CRS of HAS, 2003.
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Figure 30
Division of the urban network by the knowledge base
(towns with limited renewal capacity)
Legend: 7 – Towns in a transition situation, with innovation potential (20 towns – 636 thousand
persons – 12.9 per cent); 8 – Towns in a transition situation, with less innovation potential (23
towns – 324 thousand persons – 6.6 per cent); 9 – Average level of urban development with low
human resources base and innovation potential (59 towns – 770 thousand persons – 15.6 per
cent); 10 – Towns less developed than the average (59 towns – 764 thousand persons – 15.5 per
cent); 11 – Towns with definitely unfavourable endowments (67 towns – 545 thousand persons –
11.1 per cent).
The size of the circles indicating the towns are proportionate with their number of population, the
percentage figure indicates their share from the urban population of Hungary calculated without
Budapest.
Source: West Transdanubian Research Institute, CRS of HAS, 2003.
The members of cluster 4 are secondary centres with considerably dynamism
(for example Sopron, Szombathely or Eger), it is mainly their higher education
functions that put them into a cluster of selected importance from the aspect of
innovation. The six towns of this cluster are home to 8 per cent of the non-Buda-
pest urban population, they have above-average endowments in all indices but their
rate of innovation is much more moderate compared to the previous clusters. The
factors connected to human resources, higher education are already given and the
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economic and social conditions are also favourable. The presence of the institu-
tional system supporting and serving innovation is not complete, and the proportion
of actually implemented innovative initiatives is low. The towns of cluster 5 (for
example Szentendre, Zalaegerszeg and Szolnok) cannot beconsidered by any
means as settlements with central functions from the aspect of innovation features
and higher education and research capacities. They concentrate 4 per cent of the
urban population. Their specific feature is the well organised civil society on the
one hand, and the high schooling indices and the good indices of knowledge-ori-
ented leading professional positions for to the number of inhabitants, on the other.
A separate cluster (6) is made up by the three middle towns with smaller popula-
tion, oriented mainly for higher education (Gyöngyös, Keszthely and Gödöllı).
Their development indices measuring the background factors of renewal are good,
but the development indices show a large-scale dissonance. Although the presence
of higher education and the proportion of the share of the adequate local human re-
sources are outstandingly high, the number of local innovative actions is only
around the average. The complexity of the institutional system of innovation is also
much more moderate than in the previous clusters.
Two settlements, two new organising centres of the agglomeration ring around
the capital city are Budaörs and Budakeszi, not fitting into the classification at all
(cluster 12). Their common feature is their excellent economic and labour market
conditions, good innovation potential and moderate human (higher education and R
& D) parameters. As regards the economic and schooling data and the indices
measuring the presence of the developed knowledge-based and qualification-ori-
ented positions on the local labour market, Budaörs and Budakeszi are far above
the level of the other small and medium sized towns.
The set of towns in the second line from the aspect of renewal capacity involves
43 settlements scattered all over Hungary, making two clusters. Their total popula-
tion is 960 thousand inhabitants, which means that they are home to one-fifth of the
non-Budapest urban population of Hungary. The members of cluster 7 typically
show a moderate level of development in all indices of categorisation, and the hu-
man and innovative capacities are somewhat above the average values. The former
and present industrial centres, most of the ex-socialist towns and the county seats in
less favoured position can be grouped here. They were prone to significant transfor-
mation, restructuring impacts, which carries the possibilities of a dual development,
depending on their reactions. Cluster 8 is the collective group of holiday resorts,
settlements built on the tourism potential, micro-regional centres, and newly created
industrial centres. These towns have favourable economic and labour market oppor-
tunities. Within the employees, the proportion of professionals, of intellectuals in
leading positions exceeds the urban average. On the other hand, they do not feature
above-average indices in the human and institutional segment, extremely important
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for the innovative capacity. Although their economic parameters are above those in
cluster 7, they lag behind in the factors related to innovation.
More than 180 towns of Hungary – with a total population over two million –
would make a single cluster in a simple case, whose basic feature is underdevel-
opment. The separation of the following three groups could actually be explained
by the grades of backwardness. In these groups it is not the human elements, not
the heterogeneity of the institutional system of innovation and not even the pres-
ence of the different levels of higher education that differentiates. Their lagging in
the promotion and support of innovation, the renewal of the local economy and
society is universally serious. On the other hand, the economic potential, the level
of schooling, the viability of the civil society and the complexity of local publicity
definitely disintegrates this large block into three groups at different levels of de-
velopment. The members of cluster 9 have average economic and social indices,
unfavourable innovation potentials and a lack of adequate human resources. In
cluster 10 the unfavourable innovation, higher education and R & D endowments
are accompanied by moderately developed economic and social characteristics.
The last group, cluster 11 features the least favoured towns of the Hungarian urban
network, with universally bad development indices and a total lack of innovative
capacity: they are the marginal towns.
If we look at the economy, social life and the structure of the labour market, the
251 towns of Hungary can be put into two blocks of more or less the same size: the
share of the towns with below-average and above-average parameters is more or
less the same. On the other hand, the two main components playing a significant
role in the innovation potential, one measuring the human resources–higher educa-
tion–research sector and the innovative environment, and the other one mapping
the actual results, show a much more homogeneous picture. This highlights the fact
that the majority of the towns show a significant lagging behind the “innovation
elite”. As regards human resources, 78 per cent of the Hungarian towns are below
the aggregate average of the towns, in connection with innovation 76% of them are
below that.
As the hierarchy of the clusters more or less followed the differences in the de-
velopment level, it was also suitable for grabbing the “macro-structure” or the ur-
ban innovation potential. The data of the distribution show that in 75% of the 251
towns we cannot see good endowments in any component of the innovation con-
figurations. Towns belonging to clusters 9–11 do not possess the economic, social,
education and research capacities founding the local renewal processes, or the
functions built on these capacities. We have another large block (clusters 7 and 8).
These two groups can be called the “second line”, as they already have average or
slightly above-average parameters. In 17 per cent of the towns we can see several
foundations of the further development, as regards the institutional system and the
human elements. Really advanced, already marked innovation potential and the
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closely related economic–schooling–social parameters can only be found in a very
narrow “minority” of the Hungarian towns. If we analyse Hungary with the factors
that we compiled and applied, we can say that not more than 8% of the towns of
Hungary can be said to have mature innovation potential.
In the knowledge-based division of the urban network in Hungary the size plays
a dominant role. In most of the Hungarian towns with less than forty thousand in-
habitants the background conditions that could create an innovative environment
for the economic and social actors of the region are missing. In the case of small
and medium sized towns, the geographical differences are big. In North Hungary
and the two regions of the Great Hungarian Plain a few big towns of outstanding
“performance” are accompanied by a backward, relatively underdeveloped urban
network. The elements of development are concentrated in the big towns. In the
other four regions, the “segregation of the settlements built on similarities” shows a
more balanced distribution. In these regions there is a much larger set of towns that
are around average or in slightly above-average position as regards the renewal
capacity. In the northern and eastern parts of Hungary the biggest problem is not
underdevelopment but the low number of towns that are able to catch up. In most
cases those settlements have considerable renewal capacities that have had centu-
ries of urban traditions (among the big cities of the Hungarian countryside, only
one is “younger” than six hundred years) (Csizmadia–Grosz–Rechnitzer, 2004).
6 Trends and conclusions
We can say that human resources as a new dimension of economic and social de-
velopment is a notion very complex in content, thus its analysis takes a complex,
painstaking effort. In a regional approach this complexity is even more visible, as
we compare regions of different characteristics where it does matter what devel-
opment path they have gone through and during that what endowments they have
accumulated. In the territorial units there are values or carriers of values all the
time that, knowingly or not, contribute to the development of the quantity and even
more the quality of the human resources. The way and extent of this contribution
depends on several factors, such as the geographical location of the given territorial
unit, its economic structure, the institutional system that it has created and its op-
erators, the historical development of the settlement, the accumulated knowledge,
the composition of the inhabitants, the image of the territorial unit, its development
concepts and ideas, the political activity and many other elements. We cannot and
must not analyse and assess the human resources of the spatial units on the basis of
only a few factors. We have to strive for complexity, the mapping of the compli-
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cated network of the factors and only when we have done that it is reasonable to
draw conclusions and define development objectives.
Consequently, in addition to the survey of the simpler or more complicated cor-
relations of the traditional evaluations of data in the regional analyses of human
resources, it is necessary to apply sampling procedures, case studies and other
methods of sociology. The complex methods allow the exploration of the more
subtle correlations of the factors and the display of their synergies.
The few Hungarian analyses demonstrate that there is a strong stochastic, but
not functional correlation between the regional structures of human resources and
the economy in the decade of the transition. The regional economic structure as a
whole has an impact on the territorial structure of human resources; it influences
the spatial structure of human resources, but the more sophisticated, the more sub-
tle picture we want to make of the situation, the larger number of factors we find,
some of which are not dependant on the economic potential. We can state that the
extensive indices of human resources (e.g. employment, level of training and
schooling, certain measurable skills) are more closely related to the economic en-
dowments of the spatial units. However, the other indices that can be called inten-
sive (quality of life, social activity, culture) are determined not only by the eco-
nomic conditions but also the inner resources (hidden energies) of the given spatial
unit, having a significant impact thereby on the quality and also the regional divi-
sion of human resources.
On the basis of this we can also register that the regional structures of human
resources were stable in the nineties, they did not change considerably in the dec-
ade of the transition, no territorial restructuring took place in this respect.
No convergence took place among the regions, equalisation processes cannot be
seen; to the opposite, disparities of new type emerged. The new knowledge and
skills show a definite concentration in the capital city and to a lesser extent in the
regional centres, and they filter down the lower levels of the settlement hierarchy
slowly, so to say stealthily, many times accidentally. In addition to the capital city–
countryside dichotomy, a west–east discrepancy can also be seen in the case of the
human resources, i.e. the western, mostly border regions were more active in con-
centrating and expanding their capacities than the eastern, Great Plain regions of
Hungary were. At the same time it is also clear that the traditional large centres of
the eastern parts of Hungary are able to stabilise institutions representing the
knowledge base, their output is of large volume, but in many cases they train hu-
man resources for the western parts of Hungary, due to the migration.
A similarly growing gap can be seen between the economic performance and
the institutions determining the knowledge base, the latter were unable to integrate
into the economy at the local level. Also, the importance of the position occupied
in the settlement hierarchy is still valid, as the central or peripheral location influ-
enced the existence and the activity level of the human resources. Besides the va-
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lidity of this general conclusion of course there are several – maybe more and more
– new/old constituents of development that do not necessarily follow the deter-
mined paths of development, slowly disintegrating and eroding this way the present
regional structure.
The breakout points are described in the development concepts and pro-
grammes, both at regional and national level. The regions have recognised that
without the comprehensive development of their human resources they will not be
able to improve their conditions. The development of human resources appears in
many ways, with different emphases and objectives in the future objectives and
action plans. Maybe it would be possible to implement the objectives of the regions
if they had more independence, more freedom in the assertion of the regional wills
– and this could accelerate the decrease of the spatial disparities.
Among the recommendations we have to mention that the objectives defined in
the regional development concepts, and the programmes based on these objectives
should be given an opportunity for implementation, because the regional, bottom-
up initiatives can be much more successful than a few actions launched at national
level.
In those regions that have a high concentration of human resources, the related
institutional system is mature and there is a demand by the economy for resources
of high quality, the knowledge region programmes should be worked out. The ob-
jective of the knowledge region programmes is to strengthen and fill up with con-
tent the inner co-operations of the existing institutional network, handle the exist-
ing disparities at regional level, create new types of network connections and
thereby activate an ever larger mass of stakeholders for regional development.
The renewal of the human resources takes a long time, it has a national policy
level (population-, education-, social- and science policy) but the regional level is
also important, where the establishment and integration of the institutional frame-
works can be done. In addition, we must not neglect the local level where the qual-
ity and conditions of life and the foundation of the environment fostering innova-
tion can be the goals. In other words, each level has their space of action and com-
petence, but they can only have an impact together. The negligence of the regional
and the local level, the lack of recognition and the inadequate support of these spa-
tial levels will further deepen the territorial disparities, making the long-term and
spatially even distribution of the human resources impossible.
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Regional Characteristics of Human
Resources in Hungary During the Transition
The Discussion Papers series of the Centre for Regional Studies of the Hungarian
Academy of Sciences was launched in 1986 to publish summaries of research findings on
regional and urban development.
The series has 5 or 6 issues a year. It will be of interest to geographers, economists, so-
ciologists, experts of law and political sciences, historians and everybody else who is, in
one way or another, engaged in the research of spatial aspects of socio-economic develop-
ment and planning.
The series is published by the Centre for Regional Studies.
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P.O. Box 199, 7601 PÉCS, HUNGARY
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Discussion Papers 2006. No. 50.
Regional Characteristics of Human
Resources in Hungary During the Transition
Papers published in the Discussion Papers series
Discussion Papers /Specials
BENKİNÉ LODNER, Dorottya (ed.) (1988): Environmental Control and Policy: Pro-
ceedings of the Hungarian–Polish Seminar in the Theoretical Problems of Envi-
ronmental Control and Policy
OROSZ, Éva (ed.) (1988): Spatial Organisation and Regional Development Papers of the
6th Polish–Hungarian geographical Seminar
DURÓ, Annamária (ed.) (1993): Spatial Research and the Social–Political Changes: Papers
of the 7th Polish–Hungarian Seminar
DURÓ, Annamária (ed.) (1999): Spatial Research in Support of the European Integration.
Proceedings of the 11th Polish–Hungarian Geographical Seminar (Mátraháza,
Hungary 17–22 September, 1998)
GÁL, Zoltán (ed.) (2001): Role of the Regions in the Enlarging European Union
HORVÁTH, Gyula (ed.) (2002): Regional Challenges of the Transition in Bulgaria and
Hungary
KOVÁCS, András Donát (ed.) (2004): New Aspects of Regional Transformation and the
Urban-Rural Relationship
BARANYI, Béla (ed.) (2005): Hungarian–Romanian and Hungarian–Ukrainian border regions as
areas of co-operation along the external borders of Europe
Discussion Papers
No. 1
OROSZ, Éva (1986): Critical Issues in the Development of Hungarian Public
Health with Special Regard to Spatial Differences
No. 2
ENYEDI, György – ZENTAI, Viola (1986): Environmental Policy in Hungary
No. 3
HAJDÚ, Zoltán (1987): Administrative Division and Administrative Geography
in Hungary
No. 4
SIKOS T., Tamás (1987): Investigations of Social Infrastructure in Rural Settle-
ments of Borsod County
No. 5
HORVÁTH, Gyula (1987): Development of the Regional Management of the
Economy in East-Central Europe
No. 6
PÁLNÉ KOVÁCS, Ilona (1988): Chance of Local Independence in Hungary
No. 7
FARAGÓ, László – HRUBI, László (1988): Development Possibilities of Back-
ward Areas in Hungary
No. 8
SZÖRÉNYINÉ KUKORELLI, Irén (1990): Role of the Accessibility in De-
velopment and Functioning of Settlements
No. 9
ENYEDI, György (1990): New Basis for Regional and Urban Policies in East-
Central Europe
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Discussion Papers 2006. No. 50.
Regional Characteristics of Human
Resources in Hungary During the Transition
No. 10
RECHNITZER, János (1990): Regional Spread of Computer Technology in
Hungary
No. 11
SIKOS T., Tamás (1992): Types of Social Infrastructure in Hungary (to be not
published)
No. 12
HORVÁTH, Gyula – HRUBI, László (1992): Restructuring and Regional Policy
in Hungary
No. 13
ERDİSI, Ferenc (1992): Transportation Effects on Spatial Structure of Hungary
No. 14
PÁLNÉ KOVÁCS, Ilona (1992): The Basic Political and Structural Problems in
the Workings of Local Governments in Hungary
No. 15
PFEIL, Edit (1992): Local Governments and System Change. The Case of a Re-
gional Centre
No. 16
HORVÁTH, Gyula (1992): Culture and Urban Development (The Case of Pécs)
No. 17
HAJDÚ, Zoltán (1993): Settlement Network Development Policy in Hungary in
the Period of State Socialism (1949–1985)
No. 18
KOVÁCS, Teréz (1993): Borderland Situation as It Is Seen by a Sociologist
No. 19
HRUBI, L. – KRAFTNÉ SOMOGYI, Gabriella (eds.) (1994): Small and me-
dium-sized firms and the role of private industry in Hungary
No. 20
BENKİNÉ Lodner, Dorottya (1995): The Legal-Administrative Questions of
Environmental Protection in the Republic of Hungary
No. 21
ENYEDI, György (1998): Transformation in Central European Postsocialist Cit-
ies
No. 22
HAJDÚ, Zoltán (1998): Changes in the Politico-Geographical Position of Hun-
gary in the 20th Century
No. 23
HORVÁTH, Gyula (1998): Regional and Cohesion Policy in Hungary
No. 24
BUDAY-SÁNTHA, Attila (1998): Sustainable Agricultural Development in the
Region of the Lake Balaton
No. 25
LADOS, Mihály (1998): Future Perspective for Local Government Finance in
Hungary
No. 26
NAGY, Erika (1999): Fall and Revival of City Centre Retailing: Planning an
Urban Function in Leicester, Britain
No. 27
BELUSZKY, Pál (1999): The Hungarian Urban Network at the End of the Sec-
ond Millennium
No. 28
RÁCZ, Lajos (1999): Climate History of Hungary Since the 16th Century: Past,
Present and Future
No. 29
RAVE, Simone (1999): Regional Development in Hungary and Its Preparation
for the Structural Funds
No. 30
BARTA, Györgyi (1999): Industrial Restructuring in the Budapest Agglomera-
tion
No. 31
BARANYI, Béla–BALCSÓK, István–DANCS, László–MEZİ, Barna (1999):
Borderland Situation and Peripherality in the North-Eastern Part of the Great
Hungarian Plain
No. 32
RECHNITZER, János (2000): The Features of the Transition of Hungary’s Re-
gional System
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Regional Characteristics of Human
Resources in Hungary During the Transition
No. 33
MURÁNYI, István–PÉTER, Judit–SZARVÁK, Tibor–SZOBOSZLAI, Zsolt
(2000): Civil Organisations and Regional Identity in the South Hungarian Great
Plain
No. 34
KOVÁCS, Teréz (2001): Rural Development in Hungary
No. 35
PÁLNÉ, Kovács Ilona (2001): Regional Development and Governance in Hun-
gary
No. 36
NAGY, Imre (2001): Cross-Border Co-operation in the Border Region of the
Southern Great Plain of Hungary
No. 37
BELUSZKY, Pál (2002): The Spatial Differences of Modernisation in Hungary
at the Beginning of the 20th Century
No. 38
BARANYI, Béla (2002): Before Schengen – Ready for Schengen. Euroregional
Organisations and New Interregional Formations at the Eastern Borders of Hun-
gary
No. 39
KERESZTÉLY, Krisztina (2002): The Role of the State in the Urban Develop-
ment of Budapest
No. 40
HORVÁTH, Gyula (2002): Report on the Research Results of the Centre for
Regional Studies of the Hungarian Academy of Sciences
No. 41
SZIRMAI, Viktoria – A. GERGELY, András – BARÁTH, Gabriella–
MOLNÁR, Balázs – SZÉPVÖLGYI, Ákos (2003): The City and its Environ-
ment: Competition and/or Co-operation? (A Hungarian Case Study)
No. 42
CSATÁRI, Bálint–KANALAS, Imre–NAGY, Gábor –SZARVÁK, Tibor
(2004): Regions in Information Society – a Hungarian Case-Study
No. 43
FARAGÓ, László (2004): The General Theory of Public (Spatial) Planning (The
Social Technique for Creating the Future)
No. 44
HAJDÚ, Zoltán (2004): Carpathian Basin and the Development of the Hungarian
Landscape Theory Until 1948
No. 45
GÁL, Zoltán (2004): Spatial Development and the Expanding European Integra-
tion of the Hungarian Banking System
No. 46
BELUSZKY, Pál – GYİRI, Róbert (2005): The Hungarian Urban Network in
the Beginning of the 20th Century
No. 47
G. FEKETE, Éva (2005): Long-term Unemployment and Its Alleviation in Rural
Areas
No. 48
SOMLYÓDYNÉ PFEIL, Edit (2006): Changes in The Organisational
Framework of Cooperation Within Urban Areas in Hungary
No. 49
MEZEI, István (2006): Chances of Hungarian–Slovak Cross-Border Relations
75