Vehicle Industry and Competitiveness of Regions in Central and

Transcription

Vehicle Industry and Competitiveness
of Regions in Central and Eastern Europe
Edited by
János Rechnitzer – Melinda Smahó
SZÉCHENYI ISTVÁN UNIVERSITY
UNIVERSITAS-GYŐR Nonprofit Ltd.
Győr, 2012
Szerzők: Barta, Györgyi; Csizmadia, Zoltán; Dusek, Tamás; Füzi, Anita; Gombos,
Szandra; Józsa, László; Kollár, Katalin; Lados, Mihály; Lengyel, Imre; Lukovics,
Miklós; Nárai, Márta; Rechnitzer, János; Savanya, Péter; Smahó, Melinda; Tóth,
Tamás; 2012
ISBN 978-615-5298-02-8
All rights reserved by the authors, Széchenyi István University and the publisher as copyright
proprietors, yet non-commercial multiplication and distribution are allowed in constant form with
the indication of the publisher. Any kind of alteration, adaptation, abbreviation or commercial
multiplication and distribution are only allowed with the prior written permission of the authors,
Széchenyi István University the publisher.
Published by the UNIVERSITAS-GYŐR Nonprofit Ltd. (UNIVERSITAS) Győr, 2012
Legally responsible publisher: managing director of the UNIVERSITAS
Layout editor: Nagy, Zoltán.
Printed by the Palatia Nyomda és Kiadó Kft. Legally responsible manager: Radek, József.
This book has been published as a result of the project „TAMOP-4.2.1/B-09/1/KONV-2010-0003:
Mobility and Environment: Research in the fields of motor vehicle industry, energetics and
environment in the Middle- and West-Transdanubian Regions of Hungary. The Project is
supported by the European Union and co-financed by the European Regional Development
Fund”
CONTENTS
Part I.
Positions of Vehicle Industry in Central and Eastern Europe
János Rechnitzer – Melinda Smahó: Economic Effects of the Vehicle and
Automotive Industry in the Regions of Central and Eastern Europe
and Hungary .............................................................................................................. 7
Györgyi Barta: Central and EasternEuropean Automotive Industry in European
Context..................................................................................................................... 33
Melinda Smahó: System of Knowledge Transfer in the Automotive Industry.............. 71
Anita Füzi – Szandra Gombos – Tamás Tóth: Location Factors of Automotive
Industry in Central and Eastern Europe ................................................................. 108
Part II.
Competitiveness of Regions and Production Centres
Imre Lengyel: Competitiveness of Regions of Central and Eastern European
Countries ................................................................................................................
Miklós Lukovics – Péter Savanya: Competitiveness of the Visegrád
Countries’ Counties from the Aspect of Automotive Industry ..............................
Tamás Dusek: Competitiveness of Automotive Centres in Central and
Eastern Europe .......................................................................................................
Mihály Lados – Katalin Kollár: Local Economic Development and the
Automotive Industry in Győr .................................................................................
129
165
196
228
Part III.
Characteristics of the Supplier Network
Zoltán Csizmadia: Features and Spatial Differentation of Supplier Networks
in Automotive Industry .......................................................................................... 251
Márta Nárai: Innovation activities of companies related to car manufacturing,
automotive industry ............................................................................................... 268
László Józsa: Analysis and Development Strategies – Summarized Exploratory
Research of Company Performance....................................................................... 283
List of Contributors ..................................................................................................... 301
PART I.
POSITIONS OF VEHICLE INDUSTRY
IN CENTRAL AND EASTERN EUROPE
ECONOMIC EFFECTS OF THE VEHICLE AND
AUTOMOTIVE INDUSTRY IN THE REGIONS OF
CENTRAL AND EASTERN EUROPE AND HUNGARY
JÁNOS RECHNITZER – MELINDA SMAHÓ
Keywords:
vehicle industry automotive industry Central and Western Transdanubian region Central and
Eastern European competition area supplier network
Within the framework of the project „Mobility and Environment: Research in the fields of motor
vehicle industry, energetics and environment in the Middle- and West-Transdanubian Regions of
Hungary, TAMOP-4.2.1./B-09/1/KONV-2010-0003” one of the sub-programmes – in two part
programmes – has undertaken to examine the social, economical and location factors of the
vehicle industry as well as the emerging of the supplier’s network. The aim of the research was
presenting the economic and regional contexts, which influence the development of the vehicle
industry (and within it the passenger car (automotive) production), in Central and Eastern
Europe on the one hand, and – within this larger region – in the Central and Western
Transdanubian region on the other hand. Furthermore, the research aimed to show the effect of
this industrial sector on the future development trends of the two domestic regions. In this way,
the Hungarian territorial units as well as their centres can be positioned in the larger region,
which holds up a more and more spectacular industrial specialisation. Thus, also the main nodes
of the development strategy based on the vehicle industry can be drawn out by integrating the
results explored in other dimensions (e.g. innovation activity and the business environment of
suppliers) of economic contexts
Concept of the research
Preliminary research has proven that in the three hundred-kilometre circle of the GyőrEsztergom-Szentgotthárd triangle several millions of engines are produced and between
500 and 600 thousand vehicles are assembled yearly. Thus, until the start of the 2010’s
a Central and Eastern European automotive manufacturing large region had evolved
where parallel factories can be found and each of them are owned by a different economic organisation. The aim of the research was presenting the economic and regional
contexts, which influence the development of the vehicle industry (and within it the
passenger car (automotive) production), in Central and Eastern Europe on the one hand,
and – within this larger region – in the Central and Western Transdanubian region on
the other hand. Furthermore, the research aimed to show the effect of this industrial
sector on the future development trends of the two domestic regions. Earlier research
confirmed that the production capacities of the domestic vehicle (and within it the
automotive) industry are concentrated in these two regions, and also the supplier network can be considered as important. The two regions can be found in a shaping Central
8
and Eastern European automotive industry competition area, therefore it is essential to
position the Hungarian regional units and their centres in a larger region which holds up
a more and more spectacular and sector-dependent industrial specialisation.
One direction of the research covered the comprehensive presentation of the vehicle
production in Central and Eastern Europe, and – inside of this large region – the evaluation as well as the positioning of the Hungarian regions’ competition area. The objective
was to map the vehicle production capacities mainly in the Central and Eastern European region1 (Germany, Austria, Slovenia, Czech Republic, Slovakia, Poland, Hungary,
Romania), but sometimes in other Eastern and Southern European countries as well
(Croatia, Serbia, Romania, Bulgaria). The aim of the analysis was to recognize and
compare the development trends, product structure and production capacities of the
vehicle industry facilities settled in the regions of the mentioned countries on the one
hand and incidentally to evaluate their supplier networks on the other hand. The
research invesigated the local, regional economical as well as social effects induced by
the location of the vehicle industry; furthermore it explored the characteristics and specialities of the granting system initiated in order to attract and accept the sector in the
territorial units in question (country, region, settlement).
It was elaborated the competitiveness analysis of regions as well as vehicle production centres of the Central and Eastern European growth area. By this general competitiveness analysis, factors affecting general economic development were identified,
recorded and compared, thus the position of regions and vehicle industry centres could
be measured as well as assimilated. The competitiveness analysis was supplemented by
the reviewing of location factors of the vehicle (automotive) industry in the analysed
regions and vehicle industry centres. Within its framework, the national-, regional- and
local-level economic incentives, granting and institutional systems as factors shaping
the market environment, as well as the factors determining the spatial location and concentration of vehicle industry (clustering) were also analysed. Thus, the main nodes of
the development strategy based on the vehicle industry could be drawn out by
integrating the results explored in other dimensions (e.g. innovation activity and the
business environment of suppliers) of economic contexts.
Another direction of the research was to map the supplier firms located in the
Central and Western Transdanubian regions, which are mainly connected to the vehicle
(automotive industry) and the evaluation of their innovation and development circumstances as well as management systems. The research registered the production capacities (enterprises) connected to vehicle (automotive) industry and examined their general
economic characteristics (production, employment, plants, and main location factors). It
was evaluated the role such sectors play inside of the whole of the two regions’
economy by defining the factors on the one hand, which are beneficiary from the aspect
1
The research region defined as Central and Eastern Europe includes the following countries:
Germany, Austria, Poland, Czech Republic, Slovakia, Slovenia, Hungary, Romania. The multivariable statistical analysis exclusively included the figures of such countries while in further
investigations other countries were also involved in addition to those mentioned already.
Economic Effects of the Vehicle and Automotive Industry in the Regions …
9
of accepting the sector and on the other hand, in relation to which further developments
are necessary – based on the location- and general business evaluation.
We evaluated the role of such sectors in the integrity of the two regions’ economy
by defining the factors on the first hand, which are beneficiary from the aspect of
accepting the sector and on the other hand, in relation to which further developments is
necessary – based on the settlement and general business evaluation.
Questionnaires (118 companies) and – in case of the most important enterprises –
deep interviews (43 companies) were carried out in order to examine the innovation
activity as well as the renewal ability of the supplier enterprises by searching those
internal and external factors, which shape their product development and market position. In connection with this, it was analysed to what extent these firms have been integrated into the local economy of the settlement, area and region, as well as their their
co-operation and co-operation needs towards the economic capacities and development
institution systems (universities, research organisations, innovation services, stock of
professionals, etc.) being available in the settlement, area and region. It were evaluated
the flows of networking of the enterprises connected to the vehicle (automotive)
industry so far as well as their future networking opportunities – defined via surveys.
The examination also covered the analysis of the business environment and
management system of the enterprises related to the observed sector. Most of the supplier firms related to the vehicle (automotive) industry are small and medium-sized
enterprises, which are able to establish a modern company organisation and management system as well as to shape the market connections in a planned way at their own
level, thus, differently. The objective of the research was to map the market and business milieu of the supplier enterprises of the vehicle (automotive) industry to evaluate
their company management and organisation systems, as well as to elaborate recommendations on the development and continuous renewal of the organisation and the
relations. Based on the research results, development directions were defined for the
vehicle (automotive) industry at national, regional and local level, regarding the renewal
of the business environment, as well as the expansion of the supply-elements of education and training.
International contexts and regional positioning of the vehicle
(automotive) industry
The executed research proved clearly that in the past twenty years in Central and
Eastern Europe the vehicle industry has developed dynamically (Barta 2012; Losoncz
2012). Within this, the automotive industry became from a net importer to a net exporter
because while in 2006 300 thousand vehicles were produced, by 2012, already 1.1
million vehicles are planned. The investments in the automotive industry were realised
mainly via direct foreign investments. The cheap and qualified labour force as well as
the appearance at new markets favoured this, but the geographical vicinity of the
Western European markets should not be forgotten either. Most of the automotive
10
industry investments in the Central and Eastern European region were green-field
investment developments and the settlement of first-circle suppliers started at the same
time as they were established and by this, the development of the vehicle industry value
chain (pyramid) (Figure 1). The second- and third-circle suppliers where the enterprises
of the countries where the plants are located represent larger and larger proportions,
started to be settled only gradually, later in time but mainly after the turn of the
millennium or they are established nowadays.
FIGURE 1
System of relations among the car manufacturers and their suppliers
OEMs
Engines
Bodies (design)
Car assembly
Sales (marketing)
Shifting value added
Squeezing to
cut costs
OEM
Fising input in a form of
technological know-how
Tier 1 suppliers
Automotive systems
(e.g. interior, steering)
Squeezing to
cut costs
Alliances
Joint ventures
M&A
Capital links
OEM
Tier 1
Consolidation
Tier 1
Consolidation
Tier 1
Bottom-up pressure
resulting from rising
material costs
Tier 2, 3, ... suppliers
Individual parts
and modules
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Source: The Automotive Sector in CEE… (2007, 9) based on Smahó (2012).
The consequence of the economic crisis that started in 2008 was a decline in the
examined countries but by 2010, the Central and Eastern European region reached its
previous level. It can be considered as a result that the automotive factories were not
closed which can be owed to the flexibility of the producers and the work and
organisation experiences of the past 10–15 years but the adaptation of the employees
should not be neglected either. In the countries of the region, some governments had
taken measures as well in order to protect the dynamic sector, which appeared in the
form of incentive purchases supporting the revitalisation of the market. The purchase
incentives of the Western European countries (wreck car programmes) aimed at
vehicles with lower consumption and better parameters regarding the burden to the
environment; because the production of such types are the most important in the Central
and Eastern European region therefore the demand of the production of such
manufacturers (Hyundai – Kia, Fiat) did not decrease.
In addition to all this, the governments of the large region – similarly to the
countries of Western Europe – consider that the vehicle and automotive industry plays
an important role. When providing the grants, they drew the attention clearly to that;
they expect the maintenance of employment, the preservation of work places and plants,
as well as the reconsideration of production plans. The European adaptation strategy
was more successful than that in North America (Table 1).
11
TABLE 1
Strengthening of Central and Eastern Europe (CEE)* in global automotive industry
Number of vehicles produced
(1000 units)
Share of countries in CEE and global
automobile manufacturing
CEE=100%
Germany
Austria
Czech Republic
Poland
Slovakia
Romania
Slovenia
Hungary
Serbia
Aggregate
Russia
Turkey
Global aggregate
Global=100%
2000
2010
2000
2010
2010
5,527
141
456
505
182
78
123
137
13
7,162
1,206
431
58,374
5,906
105
1,076
869
557
351
211
168
18
9,261
1,403
1,094
77,858
77.2
1.9
6.4
7.1
2.5
1.1
1.7
1.9
0.2
100.0
63.8
1.1
11.6
9.4
6.0
3.8
2.3
1.8
0.2
100.0
7.6
0.1
1.4
1.1
0.8
0.5
0.3
0.2
0.0
12.0
1.8
1.4
100.0
* In our research the CEE region does not include Russia and Turkey but it includes Germany and
Austria.
Source: Barta (2012).
In the Central and Eastern European region, the production of the automotive
industry increased by up to 30% in the 2010’s but it was a bit behind the global
production dynamism (33.4%). The dominance of the companies in Germany is
decisive but the proportion of work distribution between Germany and the other
countries of Central and Eastern European region changed in the past 10 years; the
German share was reduced from 72:23% to 64:36%, which shows the production
potential increase of the examined region.
The global reorganisation of the vehicle- and automotive industry could be observed
in the past decade, which could be shown in Europe as well because the four most
important car producing countries operated 70 factories and makes in 1950 while only
six remained of them by 2008. The restructuring happened differently by each country
but based on the trends of world economy the consequences of this were amalgamation,
the establishment of strategic alliances, and the consolidation of the market of suppliers.
Out of such transformation types, relocation of complex car factories affected the
Central and Eastern European region, because the relocation of production started from
the high-cost regions, new markets appeared, the competition became stronger, and
several new functions were integrated into the motor vehicles via research and
development, while the expectations from the products of consumers also grew.
Thus, Central and Eastern Europe became a winner of the global production and
market reorganisation starting from the 1990’s. The relocation of the automotive
12
industry started and by today, the following four groups can be separated based on their
linkage to the European node areas, the features of the owners’ role and work
distribution:
− The EU members in Central and Eastern Europe: Poland, Czech Republic,
Slovakia, Hungary, Slovenia;
− Romania and Bulgaria – that joined later;
− Turkey, which concluded a free-trade treaty with the EU in the hope of later
accession;
− Ukraine and Russia, which are tied mainly to the Russian node region but they
hope to develop closer relations with the EU. This latter is more applicable to
Croatia and Serbia.
The countries of Central and Eastern Europe are defined as a strongly integrated,
peripheral market while the remaining countries (Turkey, Ukraine, and Russia) are
classified into the peripheral region. In the countries of the Central and Eastern
European region the slow but systematic embedment of foreign companies is going on
but at different intensities. The various multinational companies follow different
strategies each just because of the partial differences in location factors, in capabilities
of the countries as well as in grant systems supporting firms’ location and operations.
The research has proven that from among the location factors the following factors
are more favourable in the large region than in the parent countries:
− lower costs of wages (between 1996–2005 the hourly wages may have doubled;
but the Romanian wages constitute only 8% and the Czech ones only 16% of the
German wages);
− vicinity to the Western European market;
− outstanding work culture in several countries in machine production, and in
certain countries in automotive industry or in the larger vehicle industry;
− the transportation and site infrastructure that can be considered favourable –
mainly connected to the earlier industrial centres;
− in addition, the gradually developing system of state grants.
The Central and Eastern European region has considerable vehicle and automotive
industry traditions (Hardi 2012). Thus there are countries which had production
traditions already before 1989 with their own technology and development and
traditions from before the Second World War (Czech Republic, Eastern Germany,
partly Hungary), there are countries where there was production based on western
licences (Poland, Yugoslavia and Romania), component producers where assembly did
not exist before that is to say supplier countries (Hungary, Bulgaria). After reviewing
the development of vehicle and automotive industry in each country it could be stated
that before the change of the political system there were no important vehicle and
automotive industry centres or zones built on them. In each country in only one or in a
few large industrial centres vehicle and automotive industry or some segment of this
industry was dominant but they did not become such an industrial base, which could
13
have served the development or renewal of the sector in the long run. However, they
played an important role in that the larger centres started with a competitive advantage
after the change of the political system in gaining vehicle and automotive industry firms
arriving from the countries of Western Europe and initiating brown and/or green-field
investments. Among the location factors, the historical past in vehicle and automotive
industry production or earlier location conditions played an important role as well but
such factors do not have to be considered as primary settlement aspects in the Central
and Eastern European region.
An unbelievable mass of knowledge is accumulated in the vehicle industry the
content, features and place of which occupied in the production process has changed
much as well (Smahó 2012). During the research it was examined that changes in the
production systems how repositioned the knowledge hubs and how required to exceed
and reorganise the previous knowledge flow. The production and principally
development needs and the knowledge system built on them as well as the institutional
framework have been restructured also because of the fast change in the requirements
from the vehicles. While in the 50’s-60’s of the last decade the developments were
realised almost completely in production centres, by the expansion of the Toyota
method, they were gradually relocated to primary suppliers and as far as it concerns the
adapting production that has been spreading today already decisive development
functions go into this circle already. Three special features of the vehicle industry
innovation could be explored. The first one is that as a consequence of the transformation of the value chain, the suppliers play a more and more important role in the
development process, the second one is that a deep technological change characterises
the sector, the result of which is the spreading of modular systems and by this the
product scale is widening to a great extent. In the end, the third trend is that more and
more innovation and development appear in lower-category (cost and function)
vehicles, moreover, their new solutions are transferred into higher-category vehicles. It
could be justified that at this increased development speed the suppliers would settle
closer to car factories, assembly plants in order to establish the direct relations but at the
same time there is strong concentration in course in the supplier network itself, which
increases the innovation potential. The enterprises specialised clearly to development
are connected to this circle in a more and more intensive way, which participate in the
complex development of the suppliers but of the production facility as well.
During the regional analysis of research and development activities, as the highest
level of knowledge production and transfer, we could state that institutions are
established gradually after the appearance of vehicle industry units in the Central and
Eastern European region. The Czech Republic, with its most considerable production
traditions, is outstanding in the region with its research and development institutions
(development units, university education, research base); however, in each country of
Central and Eastern Europe it can be observed that systems serving the functional
renewal of vehicle industry appear. The differences between the countries can be felt in
this regard as well, there are really developing (Slovakia, Poland), possessing good
skills, conditions but also there are that develop slower (Hungary) (Table 2).
14
TABLE 2
Automotive industry research and development capacities in some countries
of Central and Eastern Europe (2010)
Category
Scientific and technology park
University centre
Excellence centre
Technology development centre
Research centre / Research institute
Centre providing engineering services
Test centre
Innovation centre
Totally
Slovenia
Slovakia
Hungary
Romania
–
6
–
63
4
8
1
3
85
1
1
7
9
8
6
2
2
36
–
3
–
13
–
3
–
1
20
–
2
1
6
3
4
–
–
16
Source: Smahó (2012).
We expanded the analysis of the location factors of vehicle industry regarding the
countries of Central and Eastern Europe as well via a general, country-level comparison
analysis (Füzi–Gombos–Tóth 2012). Our objective was to systematize the location factors characteristic to the sector, by following the trends specified in the literature and
highlighting especially hard (measurable) factors such as vehicle industry traditions,
logistics, transportation (infrastructural) conditions, the potential supplier environment,
the tax system, the labour market and its costs and in the end, the business milieu, which
can be measured by the speed of foundation. It could be stated that there were vehicle
industry traditions in almost all the countries of Central and Eastern Europe, which
might have given guidance for settlement but which had an effect only informally upon
decision-making or which, could be felt only if certain location conditions were used.
Certain elements of the business environment (level of corruption, speed of company
foundation, the blue- and white-collar labour market, the taxation system, the transportation infrastructure, and the potential supplier networks) are very different in the
countries of the region. Within each factor, large differences can be observed which
may inspire the location choice, but it is important how a cost benefit regarded as
favourable (e.g. blue- and white-collar labour force) can compensate the unfavourable
circumstances existing in other factors (e.g. bureaucracy, low level of research and
development, the under-developed supplier network) which – as other research confirmed this – change slowly in these countries.
In Table 3 we defined the ranking of the countries based on the examined six location factors. There are no big differences in this ranking compared to preliminary
research. The leading role of Germany and Austria is clear but the latter country have
quite bad position in the ranking with two factors therefore they can enter into competition with the upcoming, dynamical countries of the region. They are the Czech Republic
and Poland that more and more break away with the former eastern block and supply
better and better location conditions. Hungary and Slovakia move together with Slovenia with a medium-level but uneven location factor offer at the same time. However,
15
Romania, Bulgaria and Croatia are positioned at the end of the ranking by offering the
least favourable choice in almost all the factors. In the countries of Central and Eastern
Europe in the general – thus not soft – location factors the differences are spectacular,
the competition for the vehicle industry companies using modern technology can be felt
clearly within the region. The countries of Central and Eastern European region battle
for replacing each other.
TABLE 3
Ranking of regions based on location factors
Vehicle Economic Taxation
industry environsystem
traditions
ment
Germany
Austria
Czech Republic
Poland
Hungary
Slovenia
Slovakia
Romania
Bulgaria
Croatia
1
2
3
4
5
8
6
7
9
10
2
1
5
4
6
3
7
10
9
8
6
10
8
4
9
3
7
5
1
2
Labour
market
1
5
4
2
3
9
7
6
8
10
Infrastruc- Supplier
ture
network
1
2
3
8
5
4
6
10
9
7
1
2
3
5
4
8
6
7
9
10
Totally
12
22
26
27
32
35
39
45
45
47
Source: Füzi–Gombos–Tóth (2012).
Research was carried out on the role played by the state in connection with the
vehicle industry and more closely, automotive industry in the countries of Central and
Eastern Europe (Pájer 2012). It can be stated that in the member states of the European
Union in the region – in accordance with community laws – the state grant system is
almost the same the general objective of which is to create jobs, to renew the industrial
structure and to help the development and integration of peripheral regions (Table 4).
Minimal differences can be observed in the methods, value limits of each grant, as well
as in the procedural systems; there are also differences in the sector trends of the grants
as well as in their size in the preferred regions. Differences can be registered in case of
Slovenia and Slovakia. With the first country, the regional preference is clear while in
the second one the employment criterion are coupled with higher granting intensity and
the system of transferring real estate is simpler. In Serbia that is not the member of the
European Union, the state grant is connected to volume size of the investment and it is
connected more to long-lasting tax allowances. In the case of the member states of the
European Union located in Central and Eastern Europe the more important vehicle and
automotive industry large investments (exceeding 100 million euro) must be announced
to the European Commission when a grant is allocated and are subject to authorisation.
The system, the information need, the process as well as the time scheduling of
judgement need renewing because this procedure reduces the competitiveness of the
16
17
countries of the region compared to the non-union member states located in other
regions, such as Eastern Europe and South-Eastern Europe.
When analysing the competitiveness of the central and eastern europen countries we
must not forget whether the incoming multinational companies can insure the models of
quality production on the first hand and whether each country is able to serve the
increasing competitive factors on the other hand. Research has proven clearly that the
incoming big companies bring some of the elements of their domestic work and
production models (educational structure, work organisation, supplier networks, service
needs) into the receiving country and they build up their activities in the receiving
countries’ regions based on such factors. It can be experienced that among the countries
of the region – primarily it is true for the Visegrád Countries – there are not big
differences in the location factors therefore their competitive position is almost
identical. The difference may come from the fact how much they are able to adapt the
above production and work models and what development opportunities they offer to
vehicle and automotive industry and to each of its companies.
The research directed attention to the safety of the workplace, which means to what
extent the presence of qualified labour force is guaranteed and how its extension can be
insured in adapting it to the development. It can be experienced that increasing lack of
skilled labour force should be expected in the countries of the region, especially in the
regions preferred by the vehicle and automotive industry and this can hinder the
expansion of production capacities or the evolvement of their more diversified structure.
During the research it was suggested how the appearance of the Central and Eastern
European region in the automotive and vehicle industry can affect the industry of
Western Europe, whether the tensions can acuminate between the regions and at the
same time between the countries which might have political and economic
consequences. It can be proven that the competition cannot be shown between Germany
and the countries of Central and Eastern Europe but between the peripheral countries in
Southern Europe and the region examined by our research. This proves that the import
to the German automotive industry might not have increased globally but it has been
restructured because the countries of the Central and Eastern European region have
increased their export jointly to Germany more than Spain and Portugal have increased
it (between 1995 and 2005 from 9% to 37%).
All the above is confirmed by that the value of foreign working capital inflow into
the vehicle and automotive industry was 17 billion euro until 2006 of which the share of
Hungary (28.8%), Poland (30.3 %) and the Czech Republic (28.9%) can be defined
respectively. Near the assembly factories, the largest suppliers have appeared as well
and by this, the sector has developed dynamically because in the large region the share
of the sector in the industrial added value increased from 5.8% to 7.3% between 2000
and 2005.
The countries of the Central and Eastern European region started to compete for
receiving the vehicle and automotive industry investments. As we have marked, the
state grants show almost identical systems and structures but there are still essential
differences in the taxation systems and disparities in the economic political incentives of
18
the sector can be observed as well. On the whole, it can not be found any considerable
differences between the countries, however, the political climate, the economic-politic
balance, the qualification level of the available labour force, the complexity of the
training and educational system, as well as the administrative environment and the site
offer of the country in question can be essential factors in location choice and development.
Competitiveness of regions and production centres
The objective of the other big block of research is to define the parameters – then based
on them create categories – of specified regions (NUTS 2) and centres (NUTS 3 and
centres) within the Central and Eastern European region, which characterise the regional
units from the aspect of receiving the vehicle and automotive industry. The aim of the
ranking and categorizing is to demonstrate the position of Hungarian regions and
centres. Thus the objective is on the first hand to specify those economic, social and
other factors, which strongly determine the position and ranking of the examined
regional units, and on the other hand – by positioning Hungarian regions and centres –
to give recommendations for improving their position and increasing their competitiveness.
Our analyses contributed to the evaluation of the theoretical models of competitiveness and to the research connected to it to the extent that new elements could be
integrated into the existing model (Lengyel 2012). The basic categories of classical
work productivity and employment were refined owing to which social capital elements
as well as the parameters relating to the traded sector (sectors producing for export)
were integrated into the model in addition to the basic factors of research and development, factors displaying human capital and those grasping the working capital.
In the first phase of the research – that we can call regional dimension – we compared the NUTS 2 units of eight countries, which mean 93 regions and there 91 vehicle
and automotive industry economic organisations are operated. The analyses were based
on 25 variables which were evaluated by various mathematical and statistical methods.
By analysing the factors of competitiveness such as work productivity and
employment it could be stated that the region is strongly differentiated i.e. a clearly
delimited fault line shows up there. The segregation can be characterised by the fact that
regions of the developed Western European market economies and the upcoming
regions of Central and Eastern Europe separate definitely from each other. In the former
group, high employment rate is coupled with high productivity, while in the latter one
low employment rate is accompanied by lower productivity.
Our research area – the Western and Central Transdanubian regions – may be in a
more favourable position compared to the regions of the former socialist countries, it
may be closer to the fault line existing between the two country groups; it is not far
from the Czech regions, regarding both aspects (Figure 2). On the contrary to this, the
remaining Hungarian regions – except for the capital region because it is closing up to
19
the Western European type – were ranked to the peripheries of the larger region by
getting much behind. It could be proved that the presence of the vehicle and automotive
industry companies does not mean a differentiating factor in the complex competitiveness and the clear influence of such sectors cannot be caught in the act on either
employment or work productivity.
FIGURE 2
Types of regions by competitiveness principal component
Source: Lengyel (2012).
From deeper examinations – the analyses of factors affecting competitiveness – it
could be stated that two factors define the position of the regions in competitiveness.
The first one is called human capital – this factor includes the development level of the
labour force, the ability to attract labour force and the existence of patents –, which
divides the large region to a great extent by showing a more sophisticated picture in its
differentiation. Our examined regions are closer again to the values of the Czech and
Polish regions, which are not far from the limit values. However, the remaining Hungarian regions were far behind, they are characterised by the same values as the
peripheral Roman regions. The other factor is research and development – R&D expenditures, the proportion of employees in the high-tech sector, generation of fixed capital,
winner framework programmes –, which symbolises the presence of knowledge-based
economy and innovative sectors and by this, it spreads the Central and Eastern Euro-
20
pean region to a larger extent. In this “mushroom-shaped” division the two regions of
our research area move together but they are more behind the Czech and Polish regions.
The analysis strengthens the Hungarian – or it can be said, Central European – feature
again that the capital region is characteristically differs from the remaining regions – in
our case Budapest is segregated specifically spectacularly –, and it is closer to the
developed regions, the cap of the „mushroom”. In the „stem”, as the block falling behind, the remaining Hungarian regions can be found indicating that their research
potential is unfavourable and by this their competitiveness is specifically weak in
Central and Eastern Europe.
The next level of the spatial features of the vehicle and automotive industry is the
definition of the position of those regional units, which include production facilities
themselves or may be potential areas of operation for them (catchment areas)
(Lukovics–Savanya 2012; Dusek 2012). We narrowed the research area (Central and
Eastern Europe) because the analyses proved that it is more appropriate to evaluate the
competitive situation of the Hungarian regions in relation to the Visegrád Countries
more in details. The reason for this is that a vehicle and automotive industry potential
has evolved in these four countries and it is developed continuously via both the
capacity increase of the facilities and the building up of the supplier networks. The
location factor offer of the four countries has become more favourable (production
culture, presence of suppliers, special professional training, transportation infrastructure,
system of state grants), but it should not be forgotten that the mentioned countries have
almost the same conditions in the offer of such factors. Therefore the competition is
more intensive for expanding the facilities, receiving suppliers or building the suppliers’
network in this company group. The research directed attention to how the vehicle and
automotive industry zones are shaped in the Visegrád Countries, that is to say the
further industrial capacities have been built that can be found near certain larger
factories, assembly facilities belonging to this sector and by this a future large region of
vehicle and automotive industry is shaping, which might cover several smaller spatial
concentrations that are separated regarding their character.
In the spirit of the previous findings, also the examinations were continued in two
directions. The first one was the comparison of the NUTS3 area units of the Visegrád
Countries by defining the dimensions of their competitiveness but the other direction –
with a newer narrowing – is only the analysis of the vehicle and automotive industry
centres, showing how the sector affects their development and exploring their position
compared to one another.
The first direction of deeper examinations at regional level is thus the NUTS3 level
(in the group of examined countries we observed 108 units) where we aimed at using
the indicators that we had elaborated earlier for the competitiveness. It should be
remarked that the deeper we go down in the regional consideration, the more difficult it
will be to find uniform figures that can be compared. Therefore, it was necessary to
supplement the factors belonging to the basic category of competitiveness with those
factors that are related only partially to their interpretation sometimes.
21
The analyses realised via multidimensional scaling showed that firstly the average
ranking number of Hungarian counties is the highest in the country group, therefore,
their situation is less favourable all in all compared to the counties of the other three
countries (Figure 3). The cluster analyses made the image more detailed by that they
defined development groups, i.e. the counties represented the same level in development
therefore their competitiveness can be judged the same as well.
FIGURE 3
Competitive ranking of countries
Source: Lukovics–Savanya (2012).
The counties of the four countries are strongly different in this relation. There are
quite moderate differences among the Czech counties thus they were concentrated into
groups with „relatively strong” and „strong on the average” competitiveness. The Polish
counties are already more different, they were ranked into the „weaker than the
average” and „weaker” groups, while in case of the Slovak counties high duplicity can
be observed already: their competitiveness is either very weak or rather falling behind.
The Hungarian counties are also different; most of them are in one of the categories
with weak competitiveness. Two of the six examined counties (Győr-Moson-Sopron
22
and Komárom-Esztergom) were ranked into the group with relatively strong competitiveness, two of them (Fejér and Vas) belonged to the cluster with features stronger than
the average, while two of them have the values of areas with weaker than the average
(Zala and Veszprém).
The second direction of research was also based on the NUTS3 regional units of the
Visegrád Countries and the former analysis was supplemented by a comparison made
between the group of regions with or without vehicle and automotive industry (Dusek
2012). We wished to test by this analysis – in addition to the analyses made earlier –
whether the sector contributes to the development of the area and if yes what level its
intensity can be in each country.
It can be stated that the competitiveness of regions possessing vehicle and automotive industry is much stronger than the competitiveness of the counties – more exactly,
centres and their surroundings – that do not concentrate the companies of the sector.
This better competitiveness represents an important attractive force to the further companies connected to vehicle production thus they can achieve constantly higher dynamism. Competitiveness can differentiate also countries which can be analysed deeper in
accordance with development differences of the regions with and without vehicle and
automotive industry. The research statements become deeper by that we break down
these counties based on whether they are town or non-town regions (Table 5).
TABLE 5
Competitiveness of the sub-regions with and without vehicle factories by countries
Country
Average of sub-regions
with a vehicle factory
Average of sub-regions
without a vehicle factory
Average of all the
sub-regions
640
474
520
684
548
577
412
338
404
410
609
412
365
509
444
Czech Republic
Poland
Hungary
Slovakia
Together
Source: Dusek (2012).
The towns having vehicle and automotive industry were able to realise higher GDP
increase while they showed stronger migration and flat building values near lower
unemployment. The counties (sub-regions) without vehicle and automotive industry
were behind the other ones in relation to both the towns and regions without towns in all
of the examined parameters. It can be confirmed thus that the presence of the vehicle
and automotive industry in one or another county (sub-region) influences the economic
flows favourably, furthermore, strengthens the town functions and increases their
attraction at the same time.
Location conditions as well as the situation of centres with vehicle industry compared to one another were analysed, and within them, the three centres of the Western
and Central Transdanubian regions were positioned (Filep–Tömböly 2012). It can be
23
shown that the towns receiving the vehicle and automotive industry vary considerably
in their size, characteristics, functions and influence on the region (catchment areas,
number of institutions, their region-organising effects). During the location process do
not these aspects were decisive because the around fifty towns where vehicle industry is
registered move on the wide scale of small, medium and large towns. The similarity is
important in the fact that the institutions of industrial infrastructure were built gradually
(industrial park, innovation centres), the transportation connections (railway junction
point, vicinity of an airport) are favourable, and the higher education institutions are on
the spot or at available vicinity and the centres play regional or local organisation roles
as well. Furthermore it can be stated that centres in Hungary are not in an unfavourable
position based on such functions. The location conditions of the three centres (Győr,
Esztergom, Szentgotthárd) are renewed continuously therefore they are able to serve the
operation of the vehicle production and offer urbanisation advantages to that (Figure 4).
The research examining the contexts of the vehicle and automotive industry and the
regional development strategies draws the attention to the fact that the sector group has
not been integrated directly to the development concepts of the 46 NUTS 2 regions of
the Central and Eastern European region (Tóth 2012). The development level of the
regions varies to a large extent in the larger region therefore the development gravity
centres are really differentiated.
As far as the capitals and regions possessing important economic potential the supporting of development should be underlined which targets the intensive improvement of
higher education as well as research and development potentials. This can be favourable to
the vehicle and automotive industry of each country but it is known from other research
that such potentials can be integrated into the development very slowly and mainly the
skilled labour force can mean improved attraction and resource offer.
In less developed regions rather infrastructure development is emphasised and
within this, the more favourable shaping of the location factors are aimed at, which
might offer opportunities for capacity increase and for the location of the newer members of the supplier network. The research found little reference for vehicle and automotive industry in the lagging regions, after all the aim is to shape the availability of
such regions and to insure employment at least at a minimal level. The developments of
the regions in the Central and Eastern European area count less with vehicle and automotive industry or its capacity increases but the larger region is very divided, there are
ever deepening differences between the regions thus the capitals and their surroundings
as well as the centres showing growth potential can have the chance to receive vehicle
and automotive industry or the activities connected to it.
From among the country studies the chapter covering Slovakia publishes interesting
and instructive results (Kovács 2012). The Slovak economic policy shaped the vehicle
and automotive industry developments consciously and three car factories settled into
three centres after the beginning of the third millennium. The location policy is
instructive itself as well, after all the features of the country, the regions and the centres
in question were exploited, by applying a wide granting system. Slovakia exploited
favourably its EU membership, by introducing the common currency it achieved
24
FIGURE 4
Vehicle industry centres and their production potential in the Visegrád Countries
Poznan
Poznan
Warszawa
Warszawa
Polkowice
Polkowice
Jelcz-Laskowice
Jelcz-Laskowice
Starachowice
Starachowice
Walbrzych
Walbrzych
Mlada
Mlada Boleslav
Boleslav
Praha
Praha
Vrchlabi
Vrchlabi
Kvasiny
Kvasiny
Kolín
Kolín
Gliwice
Gliwice
Nosovice
Nosovice
Tychy
Tychy
Niepolomice
Niepolomice
Bielsko-Biala
Bielsko-Biala
Žilina
Žilina
Bratislava
Bratislava
Trnava
Trnava
Győr
Győr
personal car
commercial vehicle
engine
Edited by Tamás Hardi, 2012.
Szentgotthárd
Szentgotthárd
Esztergom
Esztergom
25
economic stability which favoured and which favours the emerging assembly facilities
that wish to build markets. It is instructive that the supplier networks were built
continuously, their directions were defined (electric systems, internal appliances, drive
gears, body elements other driving structures), then the transparent granting policy was
also established to them. Owing to this the location of first-level suppliers – mainly
companies that were organised by foreign ownership – started fast in the Slovak
economy which was followed by the appearance of the second and third level suppliers
the majority of which are owned by Slovak owners. By the automotive industry centres
but in other industrial centres as well (Nitra, Banska Bistrica) the suppliers appeared and
the cluster establishment was started by them. The research draws the attention to the
weaknesses of the Slovak automotive industry which can be observed in the moderate
participation of the university and research and development facilities and institutions
on the first hand and in the concentration of production and supplier networks in the
western part of the country – as the reproduction of regional disproportioning – on the
other hand. The Slovak example proves that the targeted location of the vehicle and
automotive industry can contribute largely to the renewal of an emerging economy.
Characteristics of the supplier network in Hungary
At the presentation of the research concept it was mentioned that a questionnaire with
118 companies was carried out in order to examine the supplier network of the Hungarian vehicle industry. Almost two third (28.9%) of the examined firms were located in
the selected two regions (Western and Central Transdanubia) but the other regions of
the country were represented in the necessary proportion as well. 94 percent of the
enterprises were established after 1990 and within this, more than half of the companies
(57.4 %) were launched between 1990 and 2000. The sample is characterised mainly by
medium and large enterprises (59.4%), and only 30.1 percent of the enterprises
examined by us were established via green-field investment. The proportion of Hungarian ownership came out above 50 %, and in case of 53.0 percent of the examined
organisations the share of activities connected to vehicle industry was above 75 %
therefore it can be stated that the sample represents the Hungarian vehicle industry to
the necessary extent.
We supplemented the questionnaire with the deep-interview examination of 43
vehicle industry suppliers located in the Central and Western Transdanubian regions
(Józsa 2012). It could be stated based on the primary research that the examined
suppliers did not have strong domestic competitors therefore they play an important role
in the sector as they state. By systematizing the strengths of the enterprises, the
favourable location factors are outstanding (social capital, geographical position,
proximity, favourable business environment, professional knowledge, stable employee
background), the modern business aspect (flexibility, quality, specialisation, modern
company management systems), and the continuous innovation constraint (low-series
production, providing service supplements, success orientation, reliability, participation
26
in the development). The analyses covered the weaknesses of the suppliers as well.
Such weaknesses are resulted from the quantitative and qualitative factors of the labour
force (local lack of professionals, lack of foreign language skills, underdeveloped work
place culture), the weaknesses of the economic environment (bureaucracy in administration, changing legal regulations, lack of grants, tenders) and in the end from the
increased competition (vacuum effect of big companies, geographical distance, size of
companies). The organisation of business relations does not mean a problem in case of
companies, which belong to a parent company seated in a foreign country; the market
organisation is solved in their case and similarly they do not have to deal with development – or only to a minimal extent. Nevertheless, in the case of suppliers in Hungarian
ownership or which do not connect to larger international companies, the organisation
of markets demands considerable forces, they build their markets mainly on earlier
business relations. These organisations are prevented in that they do not have the necessary capital either for the insurance of continuous and good-quality raw material supply
nor or the continuous development. The enterprises supplement the lack of capital via
intensive resource collection where they request the Hungarian grants allowed to
enterprises as well as the innovation-promoting tools but they can receive ever less
because of the lack of state resources. It is a generally expressed weakness that either
the quantity for the quality of the skilled labour force are not insured in the two
examined regions (skill level, work culture, language skills) for the constant company
expansion and development. However, despite all of these, the development directions
were defined by most of the companies, thus they aim at the increase of the market
share, the widening of the production profile to which they connect production, plant
and labour force expansion. In order to realise these goals, they define strategic objectives such as strengthening of customer relations, reduction of dependence relations,
guaranteeing financial stability as well as implementing product and production
development. According to the survey, it could be stated clearly that – in spite of the
economic crisis – the supplier firms in the vehicle industry are development-oriented,
they have defined future objectives and for them, they are able to mobilise the necessary
tool system, however at varying intensity; but they also consider the enlargement of the
national-level and regional granting systems as necessary.
The first block of the questionnaire related to the development and operation of the
network systems of the vehicle industry enterprises (Csizmadia 2012). It could be stated
from the examinations that in this sector population the statements concerning to other
Hungarian supplier relations are valid as well. The difference is only that the
participants of the examination are surprisingly close to the central players of the chains
(producers or first-tier suppliers), and at the same time, they are also the members of the
networks in some ways. Unfortunately, the relations are not multi-complex, they
concentrate only to one activity. With one third of the enterprises of the sample the
harmonised relations can be shown definitely, which relate to purchasing, research and
development or the joint application of other (service provider) functions. The first
marks of arranging into a network can be recognised with the vehicle industry
enterprises but these networks cannot be defined at regional level (regional dimension),
27
they appear rather in the sector focus yet. In the case of the examined two regions the
organisation of regional networks can be recognised the reason for which is that the
enterprises located there entered the chain earlier (1990’s), their relations with the
foreign companies and large automotive companies are stronger therefore they have
more favourable conditions for building strategic alliances than organisations with a
similar profile located in other parts of the country.
The relation analysis established with the environment of the enterprises showed
that the economic chambers as well as the local municipalities are the players that the
enterprises have intensive and continuous co-operation with (Reisinger 2012). At the
enterprises of the examined regions the relations with higher educational institutions can
be either recognised or felt (research orders, consulting) but in such co-operation it is
not good organisation or consciousness that are remarkable but it aims mainly to insure
the following generation of professionals. The research underlined that settlements
receiving the enterprises, as primary location factors do not influence the organisations,
and they do not have sensible influence, effect on either the operations of the enterprises
or on their further development or on the shaping of their relations. Most of the settlements or their municipalities can live only indirectly (reputation, attraction of other
enterprises) with the presence of the suppliers, and vice versa, the enterprises themselves are unable to have constructive strengths from the settlements (in relation to
education, training in a better case).
Innovation activity can be considered as a special form of relations, which characterises 90% of the organisations (Nárai 2012). Most of the innovation activities cover
product development, as well as product and production renewal but organisational and
marketing innovation can be observed as well. At the same time one third of the organisations consider innovation as the decisive, definitive factor of the competitiveness. On
the contrary they consider market relations, cheap but skilled labour force or standing
on several feet as more important. However, it can be shown that the enterprises having
more capital strength, employing larger numbers of more skilled labour force and possessing wider supplier relations are more willing to innovate.
The second big block of the questionnaire analysed the business and market operations of the supplier enterprises. When searching the conditions of strategic thinking it
could be stated that at most companies the conscious and organised system of future
shaping could be recognised. In most cases strategic aspect can be shown only in the
short run and it is realised with the involvement of a narrow circle of company
management (Bencsik 2012). In the two regions the time dimension of this strategic
aspect thinking covers a longer distance just because of the longer company history and
the more arborescent. The relations between ownership structure and strategic thinking
could be also shown based on the research. The sample proved that the strategic
thinking of Hungarian-owned companies with less capital strength is weaker than the
planning activities of foreign-owned companies with more capital resources but in the
latter case strategy building takes place especially centralised – at the parent company.
The company size and the capital strength play a decisive part in conscious future
shaping.
28
The next block examined the components of the successful enterprise (Eisingerné
2012). By the aid of the questionnaire, the research could identify the criterion, which
defines the competitiveness of companies in the vehicle industry. Such criterion are the
favourable price, the safe partnership relations, the high and stable quality, the
outstanding productivity, the wide product choice and the good adaptation ability; the
organisation is able to stay in the competition if such factors are present jointly and the
organisation can owe its successful operation of such factors at the same time. The
Hungarian-owned suppliers established in the Central and Western Transdanubian
regions are able to fulfil only a few of the previous factors. Thus they are able to operate
as Tier 2 suppliers constantly; there are only a few of them that can achieve the success
of being Tier 1 supplier. The chances of small and medium-sized enterprises are clearly
moderate, only their very small number is able to break out, establish successful
enterprises and close up to the foreign-owned companies.
When examining the relations between the company size and management it could
be shown that in case of companies which are more centralised and belong to a larger
international organisation the information system might be more developed but they are
less able to react fast (Ercsey 2012a). The way of seeing the things, as well as the mentality and relations of the company management are decisive in the efficiency of leadership; in this regard the two examined regions show a more favourable image than other
Hungarian regions. The situation of Hungarian-owned, medium-sized organisations
with less hierarchical ranking is similarly favourable because they are able to react to
changes faster than the stronger, very centralised enterprises. Such advantages can be
shown in information management as well and the enterprises underlined the role of this
function in several regions, which shows the movement toward more developed company management.
When examining the organisational systems of marketing activity it could be shown
that the existence of this function depends largely of the size of the company (Ercsey
2012b). In smaller organisations, marketing is part of the company management but in
regions with stronger market influences and older production traditions (e.g. Western
Transdanubia) marketing is managed as an outstanding activity. The customer
satisfaction is a decisive element of the marketing strategy of all the enterprises but
flexibility does not characterise the organisations.
Neither costs nor the profit but the client’s constraint predominate the price and
business policies; not the principle of return play the decisive part in price calculation
but the needs of the clients that are ruling. The suppliers are exposed to assembly
facilities (OEM) and this dependence cannot be reduced either by innovation or cooperation opportunities (Lőre 2012).
29
Recommended development principles and directions
The research has proven that the vehicle and automotive industry is a fast-developing,
dynamic sector of the Central and Eastern European area for the development
initiatives of which countries possessing almost identical location conditions as well as
regions and centres with already more differentiated in location factors – compete with
each other. In this ever-accelerating market space the Hungarian regions and large
centres have to strengthen their position. Moreover, they have to develop it in a way
that they can assist the renewals of the existing vehicle and automotive industry bases
on the first hand and on the other hand they can promote the establishment of new
capacities – that wish to connect partially to the existing ones. The two development
trends have shared elements therefore we have to aim at underlining such shared
elements in our recommendations.
It became clear from the research that the regions and centres could be the winners
of the vehicle and automotive industry developments of the coming five-eight years,
which consciously shape the location factors. By establishing the location environment
the undisturbed operation and development of the sector can be insured while the
market relations are also growing. According to the research findings, the qualification
level and the available quantity of the labour force are the most important starting
points for the future development. The fast renewal of skilled worker education,
introduction of dual training in higher education, the preparation for the entrance to the
labour market, maintaining the favourable elements of the work culture and establishing
newer ones (such as especially foreign language skills, the development of
communication skills, adaptive ability at the work place, shaping of participation in
team work) are the most important conditions for the future development of the vehicle
and automotive industry. It would be appropriate to think and develop in regional
dimension with secondary level (skilled labour) training which might mean the
harmonisation of education capacities on the first hand and on the other hand, the more
reasonable shaping of work distribution – and resources – at the same time.
Concerning the development of education, not only the secondary level training
should be renewed but also much attention should be paid to higher education. In
universities, in addition to transferring new knowledge, strengthening language training,
developing innovation activity, a basic competition requirement is that the basis of
research and development should be built and renewed in a versatile way. It would be
appropriate to establish the network of higher education institutions that should be
connected to the vehicle and automotive industry to generate and promote educational,
training and research connections between them. The number of specialisation would be
worth increasing and the professional engrossment would be worth widening via
international relations and their networks.
In addition to higher education and the research and development built on it,
organisations supporting innovation were established in the two regions, which have
considerable experiences in the sector of small and medium-sized enterprises (SME’s)
in connection with supporting technical, product and activity development as well as
30
their process. It can be experienced that such innovation centres and their organisations
can be found in almost all centres accepting vehicle and automotive industry companies
or more important suppliers. It would be beneficiary if the sector could play a more
important role in their activity, and enterprises, which are connected to vehicle
production or which wish to develop into this direction would be treated with more
attention. Decentralised innovation-supporting tools (funds) would be necessary for this,
which were available at spatial (regional) level and could be distributed based on the
needs of the towns accepting vehicle industry or their organisations.
In the Central and Western Transdanubian region the industrial infrastructure is at a
high level therefore the location conditions are insured, which can accept the units of the
vehicle and automotive industry. However, the network of industrial parks does not
provide their offers in a harmonised way, there are no specialised location systems, and
the centres rather compete instead of co-operating. The joint offer and the market
participation do not appear. By distributing the suppliers in a more reasonable manner –
labour force, transport costs, specialised knowledge, places of training and education –
considerable reduction of costs can be achieved, which could result in further economic
effects (establishment of newer enterprises, improving transportation connections, renewal
of the environment of settlements, vivifying smaller region effects), and at the same time
it could improve the competitive position of the two Hungarian regions in the Central and
Eastern European region.
It can be experienced that in the Central and Eastern European large region supplier
concentrations are shaping around each vehicle industry centres. They are the natural and
reasonable trends of industrial development. It is the task of regional policy to give
incentives to the generation and then to the continuous operation of such concentrations at
national or regional or local level. In addition to the support of the above mentioned site
offer, the encouragement of innovation process, and the supply of professionals, it will be
a future task to strengthen the establishment of network systems. However, the number of
clusters connected to vehicle and automotive industry may be high in Hungary and the
enterprises of the two examined regions joined such organisations, but these groups are
not able to find their own, special character, they have not become the resources of
renewal of the sector yet. The level of establishment should be exceeded in case of the
clusters (establishment, construction, planning of the organisation) to which clear central
and regional support, stable organisational systems and clear development objectives are
necessary – that can be achieved.
Last but not least it should be underlined that in the two region a zone of vehicle- and
automotive industry emerges – spontaneously at the moment – for the targeted shaping of
which the large-scale co-operation of the players would be necessary (state, enterprises,
local municipalities, bridge organisations, educational and higher educational institutions,
research and development organisations, interest representation organisations, etc.). The
location factors cannot be developed in a segregated way any longer, the only local
renewal of labour force basis will not produce any results as well as the individual
development of transportation and infrastructural systems and various public services
(health, education, public institutions) will not do either. In the developing vehicle and
31
automotive industry region they must be shaped in a harmonised and targeted way –
based on development plans. The resources and institutions, as well as local and sectoral
development efforts must be harmonised and interconnected by preserving the
individual features, as well as the abilities and skills of the centres and regions. The
Hungarian regions can acquire competitive advantages in the Central and Eastern
European region only via a conscious, long-term, professional and future-oriented
shaping of the vehicle and automotive industry in the coming five-eight years.
References
Studies which were written in frame of the project „TAMOP-4.2.1/B-09/1/KONV-20100003: Mobility and Environment: Research in the fields of motor vehicle industry, energetics
and environment in the Middle- and West-Transdanubian Regions of Hungary”:
Barta, Györgyi (2012) Central and Eastern European automotive industry in European context.
Manuscript, Győr.
Bencsik, Andrea (2012) A beszállítói hálózatok menedzsmentjének és vállalati stratégiájának
értékelése. In: Rechnitzer, János – Smahó, Melinda (szerk.) (2012) A járműipari beszállítói
hálózat Kelet-Közép-Európában és Magyarországon. Széchenyi István Egyetem Universitas
Nonprofit Kft., Győr, pp. 101–131.
Csizmadia, Zoltán (2012) Features and spatial differentation of supplier networks in automotive
industry. Manuscript, Győr.
Dusek, Tamás (2012) Competitiveness of automotive centres in Central and Eastern Europe.
Manuscript, Győr.
Eisingerné Balassa, Boglárka (2012) A beszállítói hálózat sikerességének vizsgálata. In:
Rechnitzer, János – Smahó, Melinda (szerk.) (2012) A járműipari beszállítói hálózat KeletKözép-Európában és Magyarországon. Széchenyi István Egyetem Universitas Nonprofit Kft.,
Győr, pp. 163–177.
Ercsey, Ida (2012a) A beszállító vállalatok vállalatirányítási rendszerének elemzése. In:
Rechnitzer, János – Smahó, Melinda (szerk.) (2012) A járműipari beszállítói hálózat KeletKözép-Európában és Magyarországon. Széchenyi István Egyetem Universitas Nonprofit Kft.,
Győr, pp. 132–145.
Ercsey, Ida (2012b) A beszállító vállalatok marketingtevékenységének elemzése. In: Rechnitzer,
János – Smahó, Melinda (szerk.) (2012) A járműipari beszállítói hálózat Kelet-KözépEurópában és Magyarországon. Széchenyi István Egyetem Universitas Nonprofit Kft., Győr,
pp. 146–162.
Filep, Bálint – Tömböly, Teodóra (2012) Nyugat- és Közép-Dunántúl járműipari központjainak
pozicionálása a kelet-közép-európai városhálózatban. In: Rechnitzer, János – Smahó, Melinda
(szerk.) (2012) Járműipar és regionális versenyképesség. Nyugat- és Közép-Dunántúl a keletközép-európai térségben. Széchenyi István Egyetem Universitas Nonprofit Kft., Győr, pp.
294–313.
Füzi, Anita – Gombos, Szandra – Tóth, Tamás (2012) Location factors of automotive industry in
Central and Eastern Europe. Manuscript Győr.
Hardi, Tamás (2012) A közúti járműgyártás szerepe a kelet-közép- és délkelet-európai ipari
térségek kialakulásában. In: Rechnitzer, János – Smahó, Melinda (szerk.) (2012) Járműipar
és regionális versenyképesség. Nyugat- és Közép-Dunántúl a kelet-közép-európai térségben.
Széchenyi István Egyetem Universitas Nonprofit Kft., Győr, pp. 99–108.
Józsa, László (2012) Analysis and development strategies – summarized exploratory research of
company performance. Manuscript, Győr.
32
Kovács, Zoltán (2012) A szlovák autóipar elemzése. In: Rechnitzer, János – Smahó, Melinda
(szerk.) (2012) Járműipar és regionális versenyképesség. Nyugat- és Közép-Dunántúl a keletközép-európai térségben. Széchenyi István Egyetem Universitas Nonprofit Kft., Győr, pp.
364–386.
Lados, Mihály – Kollár, Katalin (2012) Local economic development and the automotive industry
in Győr. Manuscript, Győr.
Lengyel, Imre (2012) Competitiveness of regions of Central and Eastern European countries.
Manuscript, Győr.
Lőre, Vendel (2012) A járműipari beszállítók ár- és üzletpolitikájának elemzése. In: Rechnitzer,
János – Smahó, Melinda (szerk.) (2012) A járműipari beszállítói hálózat Kelet-KözépEurópában és Magyarországon. Széchenyi István Egyetem Universitas Nonprofit Kft., Győr,
pp. 178–189.
Losoncz Miklós (2012) A kelet-közép-európai járműipar piaci környezete. In: Rechnitzer, János –
Smahó, Melinda (szerk.) (2012) Járműipar és regionális versenyképesség. Nyugat- és KözépDunántúl a kelet-közép-európai térségben. Széchenyi István Egyetem Universitas Nonprofit
Kft., Győr, pp. 64–98.
Lukovics, Miklós – Savanya, Péter (2012) Competitiveness of the Visegrád Countries’ counties
from the aspect of the automotive industry. Manuscript, Győr.
Nárai, Márta (2012) Innovation activities of companies related to car manufacturing, automotive
industry. Manuscript, Győr.
Pájer, Ivett (2012) A járműipari beruházások állami támogatásai a kelet-közép-európai térség
országaiban. In: Rechnitzer, János – Smahó, Melinda (szerk.) (2012) Járműipar és regionális
versenyképesség. Nyugat- és Közép-Dunántúl a kelet-közép-európai térségben. Széchenyi
István Egyetem Universitas Nonprofit Kft., Győr, pp. 164–188.
Reisinger, Adrienn (2012) A járműipari beszállítók kapcsolati hálója. In: Rechnitzer János –
Smahó Melinda (szerk.) (2012) A járműipari beszállítói hálózat Kelet-Közép-Európában és
Magyarországon. Széchenyi István Egyetem Universitas Nonprofit Kft., Győr, pp. 62–80.
Smahó, Melinda (2012) System of knowledge transfer and the automotive industry. Manuscript,
Győr.
Tóth, Péter (2012): Regionális fejlesztési stratégiák és a járműgyártás kapcsolata Kelet-KözépEurópában. In: Rechnitzer, János – Smahó, Melinda (szerk.) (2012) Járműipar és regionális
versenyképesség. Nyugat- és Közép-Dunántúl a kelet-közép-európai térségben. Széchenyi
István Egyetem Universitas Nonprofit Kft., Győr, pp. 341–363.
CENTRAL AND EASTERN EUROPEAN
AUTOMOTIVE INDUSTRY IN EUROPEAN CONTEXT
GYÖRGYI BARTA
Keywords:
integration economic recession technological advancement centre-periphery low-wage work
model relocation
Abstract: The globally organised automotive industry is one of the most essential sectors in the
European economy, in terms of scale, its role in employment, its driving role in the economy, as
well as its predominance in the field of research and development. In this study three dimensions
of the recent history of the European automotive industry are discussed and highlighted: the
development stage of it starting from the 1990s, the effect of globalisation and the economic
integration of the Central and Eastern European countries after the change of the political
system; the years of economic recession which have meant a dip in the development of the
automotive industry; as well as the anticipated changes in the foreseeable future which are
expected to revolutionise the role and structure of the automotive industry. The second part of this
study examines the integration process of the Central and Eastern European region regarding its
effects on the automotive industry. The process is characterised by a centre-periphery relation.
However, in the ECE countries, the automotive industry is almost exclusively linked to foreign
direct investment (FDI). This relationship, however, has been changing, and the question is
whether these significant developments will open chances for these countries to narrow the gap
between them and the more developed part of the world.
Introduction
Automobile manufacturing is an extremely important sector in Europe. One third of the
vehicles produced in the world are manufactured there; after Asia, Europe is the second
largest car and motor manufacturer in the world. The European automotive industry
employs 6 million people directly, and a further 12 million jobs are associated with this
sector indirectly. The automotive industry generates 3% of GDP in Europe, and it represents 7% of the aggregate industrial production as well as 5% of European exports.
The last ninety years of the European automotive industry’s history can be divided
into four periods: the initial stage between 1920 and 1930, the period of dynamic
advancement and progress following World War II, and the slow-growth period
between 1960 and 1970, and finally the period starting with the 1990s which is considered on the one hand as the evolution of globalisation, and, on the other hand, provided opportunities for Central and Eastern Europe to catch up with the West European
automotive industry after the change of the political system. This region, with its
automotive industry established chiefly by the investment of foreign capital, has
developed from net importer to net exporter within two decades, and the Central and
34
Györgyi Barta
Eastern European countries have become absolute winners of the development of the
global and the European automotive industry. It should be clarified at this point which
countries are referred to when Central Europe or Central and Eastern Europe are
discussed (this region is collectively referred to as Central and Eastern Europe,
abbreviated here as CEE). A determinant role for the sake of Central Europe is played
by Germany – due to its size and economic leadership. Austria also belongs to this
region but is not so much interested in the automotive industry. The Central and Eastern
European regions encompass the Czech Republic, (Southern) Poland, Slovakia,
Hungary, Slovenia, Romania and Bulgaria, as well as Croatia and Serbia. This study
discusses the automotive industry of this region. (Other surveys include also Russia and
Turkey in this region).
The CEE region generated 12% of world automotive production in 2010. The major
share was represented by the German-owned automotive industry, but ratios have
changed significantly between Germany and the CEE countries during the past 10 years
from 77:23% to 64:36%, i.e. without Germany. A total of 4.4% of the world’s vehicle
production is now manufactured in the CEE countries (three-fourth of it being produced
in the Czech Republic, Poland and Slovakia).
Foreign direct investment and the restructuring referred to above, as well as relocation of production sites have triggered a professional debate within the CEE region:
How much can the automotive industrial performance of countries receiving foreign
working capital investment be considered an economic achievement of their own?
Where are the limits of development? Does the development of the automotive industry
assist Central and Eastern European countries in closing the gap to the more developed
world? Even in the investing countries doubts were raised: Will relocation cause job
losses in the core territories or not?
This study consists of two parts:
− In the first part, the global status of the automotive industry as well as its
development is discussed. The literature dealing with this popular topic is abundant. Within the framework of this brief study two issues are set in focus: the
economic and territorial effects of the economic recession which are discussed in
detail; in addition, it was impossible to omit a very exciting issue, namely that
the automotive industry will step into a new era soon, which will see a radical
transformation of the structure of the automotive industry throughout the world.
− The second part provides an analysis of the integration of Central and Eastern
European countries into the car manufacturing sector of Europe and the world, as
well as presenting an assessment of this region’s chances of catching up. The
situation of the countries forming the periphery of Europe is examined within the
framework of the centre-periphery relationship.
Central and Eastern European Automotive Industry in European Context
35
Global importance and dynamics of the automotive industry
Global status and development of the automotive industry
The automotive industry in the world and in Europe
In 2006, the world’s automotive industry was concentrated in three large regions:
Western Europe, North America and Japan. Combined, they produced nearly two thirds
(62.9%) of all the vehicles in the world. However, regarding the volume of production,
they were closely followed by China. By 2010, the world’s automotive industrial
production pattern had radically changed: Owing to the boom-like growth in China,
over half of the world’s vehicles were produced in Asia (while Japan’s share declined
notably). The share of European production decreased somewhat (the referenced
research includes Russia and Turkey in this group), but still accounting for one-fourth
of the world’s vehicles manufactured. The share of North America (consisting of the
USA and Canada) decreased dramatically, but the share of South America grew.
Obviously, the focus of the automotive industry shifted from the developed part of the
world into the group of emerging and developing countries: The ratio of 63%:37%
recorded in 2007 changed to 43%:57% in 2010 as for the number of vehicles produced
in the respective world regions (Table 1) (Halesiak et al. 2007).
The European automotive industry – besides its characteristic big share in passenger
car production – takes an essential role in employment: In this sector, 6 million people
are directly employed (1.2 million people are employed in the automotive industry; 4.8
million are employed by suppliers), and indirectly nearly 12 million jobs are associated
with this industry – both at large companies and small and medium sized enterprises.
This sector operates Europe’s most extensive privately owned research and development units, into which approximately 20 thousand million Euros have been invested
until now; additionally, it is the key driving force of innovation. The automotive
industry generates 3% of the EU’s GDP, it accounts for 7% of overall industrial
production and for 8% of the aggregate government expenditure of the Member States
of the European Union, and it represents 5% of total European exports.
The Central and Eastern European automotive industry is developing rapidly. This
region has changed from a net importing area to a net exporting region. In 2006, over
300,000 vehicles were produced, more than those sold in the region, and for 2012, 1.1
million vehicles are scheduled to be manufactured. Development projects in the
automotive industry are mainly associated with foreign direct investment (FDI). In
principle, this region is attractive for FDI due to lower wages and highly skilled labour.
In addition, it is also considered to be a promising new market.
The majority of industrial automotive investments have manifested themselves in
green-field development projects. The geographical vicinity to West-European markets
is a significant factor to be considered when new productions sites are being planned.
The CEE region is an excellent choice for lower cost production, which is an important
criterion in the intense global competition. New assembly plants were followed by
36
Györgyi Barta
suppliers and subcontractors. Later on, a relocation of more complex activities with
higher added value took place.
TABLE 1
Regional distribution of the automotive industry (OEM*)
Region
Vehicles produced, in%
North America
South America
Western Europe
Central and Eastern Europe**
Africa
Asia and Oceania
Of which:
Japan
China
South Korea
India
Total
2007
2010
22.9
4.6
23.4
7.4
0.9
40.8
12.6
8.6
17.9
7.4
0.8
52.7
16.6
10.4
5.5
2.7
100.0
12.4
23.5
5.5
4.5
100.0
*OEM = original equipment manufacturer (complex vehicle factories).
**CEE excluding Germany and Austria and including Russia and Turkey.
Source: Own calculations, based on OICA statistical figures, and Halesiak et al. (2007, 6).
Evolutionary periods of the automotive industry,
a chronology of processes
Periods of consolidation and deconsolidation alternated in the past ninety years of the
European automotive industry. What does consolidation mean? Industrial consolidation
means the reduction in the numbers of domestic manufacturers or domestic brands in a
particular country. Market consolidation means the decrease in the numbers of manufacturers and brands on the market of a particular country. Consolidation, therefore,
refers to the concentration of (all) domestic manufacturers or domestic and all car
brands produced in a particular country (while production and profitability is constantly
increasing). Deconsolidation indicates the appearance of new (domestic or foreign)
players and new car brands (Diez–Becker 2010).
In the history of the world’s automotive industry, the number of global players has
grown continually. In the 1960–1970s, the appearance of Japanese manufacturers on the
American and European markets meant the beginning of a new era, and in the 1980–
1990s they were followed by Korean automobile manufacturers. Statistical figures
already indicate that the next era will feature the dominance of Chinese manufacturers
(Figure 1).
37
FIGURE 1
Production trends (number of vehicles) in the automotive industry of some countries
surveyed between 1950 and 2010
16 000 000
14 000 000
12 000 000
10 000 000
8 000 000
6 000 000
4 000 000
2 000 000
United Kingdom
France
Germany
Italy
Sweden
Japan
2010
2000
1990
1980
1970
1960
1950
0
USA
Source: SMMT, Motor Industry of Great Britain (2003).
Consolidation and deconsolidation in the European automobile industry
The European automobile industry went through four major stages:
− The 1920–1930s can be considered as the first consolidation stage (this is the era
of industrial pioneers and the first mergers).
− The second stage comprised the 1950s, the years of prosperity following World
War II.
− The third stage began in the 1960s, when the fast pace of economic growth began to slow down and the market of sellers changed into a market of customers.
− The fourth consolidation stage commenced in the 1990s as a response to the
challenges posed by globalisation.
More details about the fourth phase
The fourth phase was characterised by two essential changes: The establishment of the
European Single Market (from 1986), and the change of the political system in Eastern
Europe. The majority of Eastern European, formerly socialist countries joined the EU.
These changes enhanced the European economic integration. However, globalisation
38
Györgyi Barta
created a keener competition also in the European market. The political and economic
transformation of the Eastern European countries gave ample space for the settlement
and extension of lower cost production in the automotive industry. This era cannot only
be characterised by the withdrawal of players from the market (nevertheless there were
some examples of these as well: Chiefly, the car brands formerly produced in the
socialist countries disappeared, such as Trabant, Wartburg, etc.), because also company
fusions occurred (Škoda, Seat – VW).
Production of the automotive industry in the Central and Eastern European region
increased by nearly 30% in the decade under review, falling only slightly behind the
dynamism of global production (world production increased by 33.4%). Within the CEE
countries, German companies play a dominant role (the extent of this predominant role
is much larger if the territorial distribution of the companies by proprietorship is
examined. This will be discussed in detail below). The division of labour between
Germany and the CEE countries changed during the 10 years under review: from
77:23% to 64:36%, from which every CEE country benefited. In the aggregate, the CEE
region contributes about 12% to world production (on the basis of the number of
vehicles manufactured) (Table 2).
TABLE 2
Strengthening of Central and Eastern Europe (CEE*) in global automobile
manufacturing
Number of vehicles produced
(1000 units)
Share of countries in CEE and global
automobile manufacturing
CEE=100%
Germany
Austria
Czech Republic
Poland
Slovakia
Romania
Slovenia
Hungary
Serbia
Aggregate
Russia
Turkey
Global aggregate
Global=100%
2000
2010
2000
2010
2010
5,527
141
456
505
182
78
123
137
13
7,162
1,206
431
58,374
5,906
105
1,076
869
557
351
211
168
18
9,261
1,403
1,094
77,858
77.2
1.9
6.4
7.1
2.5
1.1
1.7
1.9
0.2
100.0
63.8
1.1
11.6
9.4
6.0
3.8
2.3
1.8
0.2
100.0
7.6
0.1
1.4
1.1
0.8
0.5
0.3
0.2
0.0
12.0
1.8
1.4
100.0
* In our study, the CEE region does not include Russia and Turkey, but includes Germany and
Austria.
Source: Own calculation based on the figures of OICA statistics.
39
Impact of the recession: Transformation of the European automotive industry
and its alternative options
The European automotive industry had gone through some profound restructuring well
before the outbreak of the recession (2008–2010). Several problems triggered the
restructuring: European automotive industry suffered from
− market saturation (in West Europe every third person had an automobile in
1993);
− a significant decline in demand (chiefly due to traffic congestion in cities);
− oversized production capacities;
− increasing raw material and fuel prices (mainly due to the increasing price of
crude oil);
− technological lag (due to insufficient expenditure in R&D);
− a relatively high level of production costs.
Prior to the outbreak of the recession, new markets and new production areas were
established (for instance in the CEE region, in China and India) and production costs
were significantly cut back by the relocation into countries offering lower labour costs.
The recession affected the European automotive industry quite severely, both in the
West and in the East – although to differing degrees (Table 3).
While global production in 2010 was again already in excess of the level recorded in
the period preceding the crisis, in Europe the recovery was slow. However, the CEE
region (including Germany) had a mitigating effect on the decline of European
production, so much so that in this region the number of vehicles produced in 2008 was
still not decreasing, and by 2010 output levels approximated those recorded in the year
directly preceding the onset of the recession (Figure 2). The lowest point of production
in Germany was reached in the first quarter of 2009, then, following a continual
increase in the second half of 2010, another setback occurred.
TABLE 3
Slow-down of automotive production in the years of the economic recession
Changes in the number of vehicles produced (%)
2008 vs. 2007
Global production*
Europe’s production
CEE
96
95
100
2009 vs. 2008
87
77
87
*OICA members.
Source: Own calculation based on the figures of OICA statistics.
2010 vs. 2007
106
84
97
40
Györgyi Barta
FIGURE 2
Change in the production of automotive industry, 2007–2010*
(number of the produced vehicles)
25 000 000
20 000 000
15 000 000
10 000 000
5 000 000
Central and Eastern Europe
Europa
2010
2009
2008
2007
0
Japan
USA
Source: Calculated by Szabolcs Szabó based on OICA figures.
In other CEE countries, production still showed significant growth in 2008 (with the
exception of Austria). In 2009, the volume of automobile manufacturing dropped considerably in Hungary and Slovakia, in addition to Austria. However, the Czech Republic
– as the most significant automobile manufacturer in the region after Germany – posted
a positive year-end balance even in the second year of the general recession. By 2010,
each country increased its production considerably compared to the production in the
previous year – with the exception of Austria and Serbia. As a consequence, production
levels recorded in 2007, i.e. in the year before the outbreak of the crisis was exceeded
by the Czech Republic, Poland, Romania and Slovenia, while Germany approximated it.
However, Hungary, Austria and Serbia achieved merely half or two thirds of this level
(Figure 3, Table 4).
Accordingly, the figures show that it was in 2009 that automobile production really
dropped. Nevertheless, by 2010, the CEE region by and large managed to re-achieve the
output posted preceding the crisis. The fact that, for the European automotive industry,
this was a relatively minor and shorter recession was the result of a concerted effort by
the EU, the governments concerned, the automobile manufacturers and the trade unions.
It was not generally attributable to a domestic market effect as vehicle-purchasing
dropped dramatically: Between 2007 and 2009 sales of new automobiles in Great
Britain dropped by 21%, in Spain by 53%, in Romania by 60%, in Hungary by 63%, in
Ireland by 71% and in Iceland by 87% (Figure 4).
41
FIGURE 3
Change in production of the automotive industry in CEE countries, 2007–2010
%
150
100
50
-100
Germany
Slovenia
Czech Republic
Hungary
Poland
Austria
Slovakia
CEE
Romania
n.é. = quarter
* Data pertaining only to ACEA members (figures for 2010 are estimated only).
Source: Calculated by Szabolcs Szabó on the basis of ACEA data.
TABLE 4
Change in the number of vehicles produced during the crisis in CEE
Country
Variation in the number of vehicles produced (%)
2008 vs. 2007
Germany
Czech Republic
Poland
Slovakia
Romania
Slovenia
Hungary
Austria
Serbia
97
101
119
101
101
100
118
66
117
Source: Own calculation based on OICA figures.
2009 vs. 2008
86
104
92
80
121
108
62
48
144
2010 vs. 2009
113
110
99
121
118
99
159
55
79
2010 III. n.é.
2010 II. n.é.
2010 I n.év
2009 IV. n.é.
2009 III. n.é.
2009 II. n.é.
2009 I n.év
2008 IV. n.é.
2008 III. n.é.
2008 II. n.é.
-50
2008 I n.év
0
42
Györgyi Barta
FIGURE 4
Change in the number of cars registered in CEE countries between 1990 and 2010
(Previous year = 100%)
%
30
20
10
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
-10
1991
0
-20
-30
-40
-50
-60
-70-100
Germany
Germany
Slovenia
Czech Republic
Hungary
Poland
Romania
Austria
Slovakia
CEE
Austria
Romania
CEE
Source: Calculated by Szabolcs Szabó based on data of ACEA.
Annually, 16.7–17.7 million vehicles were sold in the EU between 1998 and 2008.
In January 2009, this figure decreased by 3.5 million. In the first quarter of 2009, the
number of new automobiles registered declined by 35.6% – compared to the previous
year: minus 31.1% in Western Europe and minus 48.7% in the new EU member states
(Jürgens–Krzywdzinski 2009).
Approximately 5000 European suppliers also experienced difficulties. Demand
dropped by half, and a sizeable number of SMEs went bankrupt.
However, the success of the crisis-mitigating strategy is owed mostly to changes in
employment during the years of recession. In the aggregate the impact of the recession
on employment was also extremely high, but we still do not have a comprehensive
view. Approximately 10% of the 12 million jobs were lost in the European automotive
industry in 2009, which struck Germany most (until the middle of 2009, some 50,000
jobs at the manufacturers and 20,000 jobs at suppliers disappeared). (6) Employment in
Slovakian, Polish and Romanian automotive companies dropped in absolute figures as
well, while in other countries it was stagnant or employment rose only at a low pace. (It
should be noted here that regarding this issue the available data are either deficient or
unreliable.) (Figure 5).
43
FIGURE 5
Changes in employment in CEE countries between 2002 and 2010
%
25
20
15
10
5
2010
2009
2008
2007
2006
2005
2004
2003
-5
2002
0
-10
-15
-20
Germany
Romania
Czech Republic
Slovenia
Poland
Hungary
Slovakia
Austria
Source: Calculated by Szabolcs Szabó based on data of ACEA.
At the end of 2009 and at the beginning of 2010, Europe seemed to have started to
recover from the global financial and economic crisis, but currently “clouds of another
crisis” are gathering over Europe. The effect of the recession varies, and prospects for
revival and growth differ in each country. Prior to the economic and financial crisis, the
increase in Germany’s and Sweden’s exports has driven economic growth. The Central
European countries were able to turn the opportunities inherent in the export of cheap
commodities to their advantage, while other countries, such as Italy and France could
demonstrate a certain economic growth deriving from an increase in domestic consumption; Great Britain intensified its financial activities, and Spain raised its investments in the construction industry. Financial and wage deregulation in European countries also fostered growth; however, the gaps in incomes became even wider, especially
in England, Portugal, Spain and Greece. These processes were just the opposite in the
Scandinavian countries, in Germany and in France. At the onset of the recession, the
exporting countries were those who suffered most. Afterwards it hit those countries
where domestic consumption used to be the driving force of economic growth. At that
time, however, the exporting countries’ situation (Sweden, Germany) already started to
improve. For most European countries – with the exception of Germany and Switzerland – the recession was far from coming to an end, and it is also difficult to predict
how European markets or consumption will develop in the coming years.
44
Györgyi Barta
It is worth dwelling on the crisis-mitigating strategies of some European countries
(EU), of the governments (of major automobile manufacturing countries) and of companies that proved to be successful and enriched the CEE region with some useful
experience.
Corporate and governmental interventions and their effects during the crisis
According to automobile manufacturers the decrease in the sale of vehicles had less
adverse effects on how they formed their strategies than the freezing of bank loans.
Automobile factories reduced production and they sold their stock of vehicles already
produced. Hiring was suspended; permanent staff was put in a “parking” status temporarily; the contracts of temporary employees were not renewed, and production plans
were rescheduled. The strategy was to decrease production to a level which is sustainable without bank loans.
It is deemed to be a great achievement that no car factory was closed down until the
summer of 2010 (with the exception of a Fiat factory in Sicily, when assembly of the
Panda model was relocated to Poland). In France, PSA and Renault survived due to
state subsidies. (It was before the crisis that a VW factory was closed down in Belgium,
and PSA shut down a plant in Great Britain.) Even American parts manufacturers provided support to allow for as few factories be shut down in Europe as possible, in order
to preserve their export markets in Europe. Ford closed down one factory in Bordeaux,
and Land Rover and Jaguar were sold well before the outbreak of the crisis to Indian
Tata, and Volvo was sold to China. Before the crisis set in it was already doubtful if
Opel/Vauxhall could survive. GM intended to sell those factories (which it did not
allow to export to markets it wanted to export its American products to). Thus Opel
could not benefit from the export successes its German competitors achieved in Asia.
All in all, the European automobile manufacturers managed to adapt to the crisis,
there was hardly any plant closed and even the rate of dismissals was relatively low.
They managed to quickly implement temporary solutions (flexible work-time introduced during the past decade and annual scheduling of work-time were perfectly
utilised: days and holidays were given off adjusted to production needs, or even paid in
cash, the overtime was either prolonged or shortened according to necessities). Automobile manufacturers managed to pull through the period of recession much better than
their suppliers or subcontractors. The suppliers’ situation was made even more difficult
by delayed payments by the automobile manufacturers. To counterbalance this, the
governments endeavoured to provide assistance to the SME sector by encouraging
banks to lend money.
Nevertheless, these measures did not prove to be sufficient. Governments and the
EU had to take some action as well. This happened on three fields:
− Banks lent loans again to enable continuation of production and development
projects, but they promoted only the production of environmentally friendly cars;
− Sales of less polluting premium-category cars were also promoted in countries
where such cars were produced;
45
− Governments supported partial financing of wages of workers temporarily sent
on mandatory vacation.
Although similar actions and measures were taken by the governments of various
countries, they failed to harmonise them. Germany and France took the necessary steps
at the very beginning of the crisis and made efforts to provide assistance to the extent
needed and possible. Italy first refused to support an allowance or premium for purchasing new cars (“scrappage allowance”), but later it became inclined to do so. Great
Britain and Spain, perceiving the impending collapse of the market, started their scrappage allowances at the very moment when it was terminated in Germany and France.
State support for buying new cars had such a great influence in Germany and France
that sales increased even during the years of recession. State subsidy also had an
influence on the customer choice of cars purchased: In 2009 the proportional rate of
small passenger cars increased, while sales of luxury cars, four-wheel-drive cars and
vans decreased. This also had a significant effect on automobile manufacturing.
Hyundai-Kia and Fiat benefited from their cheaper car production in the CEE. In Germany, state subsidies were available for purchasing of premium cars as well.
Accordingly, governments have become important players within the automotive
market again, although this role had never ceased before, either. Governments – even
those that earlier benefited especially from the free market – expressly expected the
manufacturing companies to maintain actual employment levels and production sites in
return for government subsidies. Moreover, in some countries it was an explicit demand
that production plans of companies had to be redrawn. All of this basically breached EU
regulations. It is attributable to the application of those sales promoting premiums in the
European countries, as well as to the fact that only few countries turned towards the
“new economy”, that European markets survived the recession in a better shape than
that of North-America.
Further opportunities for recovering from the crisis,
new short term directions of development
Efforts of automobile factories
Further opportunities remained, of course, for those who were able to survive the crisis.
Automobile manufacturers are interested in various directions of development: They try
to improve their competitiveness, they consider the merger with companies in trouble,
they forge strategic alliances, they try to achieve larger shares in prospering markets,
and they launch new products to meet new demand while (slowly) decreasing the emission of pollutants (Freyssenet 2010).
Cost reduction:
− The ratio of social security contributions was cut back in Germany and France.
Germany introduced a “social” VAT (basically a VAT increase), which meant
that social contributions were partly passed from employers to consumers. Ger-
46
Györgyi Barta
man products became more competitive this way, and imported goods became
less competitive on the German market.
− French automobile manufacturers were successful in convincing the government
to reducing the tax on industrial activities.
− A bill on a carbon tax – which had already been introduced in many European
countries – was not passed in Sweden. France, having been the leader in the
combat against climate change, eventually gave up imposing the tax as well.
− The EU levied a tax on the import of second-hand cars.
Structural changes:
− The bankruptcy of an automobile manufacturer opens opportunities for others.
Fiat acquired parts of Chrysler, GM sold Saab (which recently went bankrupt),
and Ford sold Volvo. The government of the United States bailed out GM, thus it
managed to sustain Opel/Vauxhall for the time being.
− European manufacturers forged new alliances and joint ventures with Japanese,
American, Indian and Chinese automobile companies. VW acquired a share of
20% in Suzuki/Maruti. Fiat acquired 35% of Chrysler. Renault-Nissan reinforced
the capital relationships with Daimler. Renault is extending its business in India
(Ssang Yong). PSA concluded contracts with Mitsubishi and Chinese companies
(Dongfeng, Changan).
Seeking New Markets:
−
European automobile manufacturers were seeking to expand in the markets of
the emerging and developing economies. VW extended on the markets of: Brazil, China, Russia, India; PSA is present in Brazil and China, but it does not have
an important role anywhere. At the same time it appeared on the Russian market
and in India with Mitsubishi. Renault was unable to reinforce its situation in
Brazil. Korean Samsung could get a foothold in the Chinese market, but its expansion in India remained uncertain. Fiat is present in Brazil, but it was unable to
get into either the Indian or the Chinese markets. It is trying to expand in Russia,
too. CEE – as a consumer market – is less significant, but for large automobile
manufacturers it is not negligible.
Technological development:
− Automobiles are required to produce less CO2 emission: Fiat is developing cars
driven by gas and agro-fuels (e.g. biomass), but it deals less with electric cars.
Progressive programmes are seen at VW, PSA, Daimler and BMW: First petrol
driven engines are being optimised, hybrid cars are being introduced, and at a
later stage, electric cars followed by fuel cell cars. In their opinion, electric cars
will remain a marginal solution even after a relatively long period of time.
Nevertheless they do not cancel the introduction of electric cars from their
agenda.
47
Governmental interventions
Setting up Clusters:
− Cooperation between small and medium sized enterprises and R&D institutes
must be developed systematically, and cluster-based innovation centres are to be
established. Local and regional R&D infrastructures must be reorganised and
enlarged. Technical research and education in automotive engineering are scattered among locations and various players and they are not well integrated. Everywhere, a regional academic base (universities, research institutes) is indispensable for the setting up of clusters. Local R&D activities are themselves attractive for other investments or projects. For regional clusters the goal is to establish relationships between diverse competencies and technologies in the hightech sectors (IT, communications, software development, space research, chemical industry), and to set up competence and vocational or professional education
centres (Blöcker et al. 2009).
Requirements for a qualified labour force:
− Research carried out in the West-European automotive industry has found that
there is a lack of experts in the fields of electronics, accumulator technology,
new electronic systems, integrated technical development, quality management,
management, business development and financial innovation management.
− There are debates whether a considerable shift will take place in the automotive
industry from the need for highly qualified labour towards less qualified; or if it
will be just the opposite, and the proportion of qualified employees will increase
in this sector in the future. According to a survey, the following opinions were
predominant:
• Cheap labour will be less important in the automotive industry;
• Qualified labour will remain the determining human resource in the future;
• Standardised work processes will be enhanced;
• The need for up-to-date technical knowledge will increase; the life cycle of
knowledge will be shortened;
• Profound technical knowledge and practical skills will remain important in the
future;
• Furthermore, it will be important to acquire skills and knowledge of more than
one profession, i.e. the need for multidisciplinarity will increase;
• Managerial and organisational skills will become more important;
• Labour mobility and the need for flexibility will increase;
• Access to professional qualifications will be widened;
• Vocational training will increasingly take place outside the company.
National R&D policies related to the European automotive industry:
− European governments follow different strategies. In Sweden, experiments are
carried out with cars driven by agro-fuels, in Italy gas driven cars (CNG and
LPG) have long been a choice. R&D subsidies provided by the governments in
48
Györgyi Barta
France, Great Britain, Spain, Portugal, Denmark, Ireland and Switzerland are focused on electric cars. Germany adopted “technical neutrality”. It is generally
accepted that CO2 emission is to be decreased, and consumers should be allowed
the chance to choose. However, every strategy can be countered by soaring
mineral oil prices (which would foster the market chances of hybrid cars). It is
also commonly known that the ever increasing number of cars in the emerging
countries with large populations, especially China and India, will eventually not
find enough mineral oil supplies to drive them. This, and air pollution, is why
China is especially interested in the manufacturing of electric cars.
Although in some years, the recession will probably be considered merely a historical episode it is likely that we are approaching another “car revolution”. Electric cars do
not only represent a different type of motorisation, but a new kind car construction as
well; it may entail a radical reformation and simplification of design, production and
distribution – once the problem of energy storage is solved in a more satisfying manner.
The social background, geographical location and economic capacities of the global
automotive industry are not synchronised with the development of new markets and
new manufacturing countries. New cars will probably not be petrol driven, and the
inhabitants of large, emerging countries will not merely be consumers, and users of the
new cars but they will be involved in their production as well. A new competitive
situation is about to evolve in the automotive industry. The recession is not over yet, but
competition for the markets of the future is already in full swing.
The future: We have come to a junction
Two different scenarios have been created for the future of the automotive industry until
2025: one is the green revolution and the other one is the revolution of mobility (Diez–
Becker 2010).
Green revolution
To achieve the target of reducing CO2 emission experiments are being carried out with
bio-fuels, hybrid cars, electric cars and hydrogen fuels. Current results are not yet
suitable to base manufacturing in commercial volumes on. Probably, hybrid technology
will play a major role. Electric cars are expected to be sold in larger quantities from
around 2020, and perhaps by 2030 these will be the cheapest, provided the accumulator
technology achieves a breakthrough. Biomass and waste as fuel will also play an important role (second generation bio-fuel). Hydrogen is expected to be the fuel leading to
electric cars (via fuel-cells), which will become essential in the period between 2030
and 2050, and although by 2050 electric cars will dominate the market, heavy vehicles
will still consume liquid fuels. Obviously, an exact concept has yet to be found concerning the dominant technologies of the future.
A new social value has appeared in politics which has become increasingly open for
the concept of innovative and alternative mobility. “Green revolution” refers to the fact
49
that an increasing political pressure has urged the introduction of environmentally
friendly cars. Accordingly, they are politicians who insist on accelerating a “green technological development”, a significant decrease in CO2 emission of cars, while
threatening the manufacturers with punitive action if they fail to make all reasonable
efforts to achieve such targets. The technology of electric drives will probably play a
major role in this.
If this path of development is chosen, the European automotive industry will need
10 to 15 years to introduce advanced “green technologies”. The automotive industry
will have to make considerable financial investments in R&D in order to adapt production concepts and restructure value chains. Financing the implementation of new technologies will probably be a bottleneck for some companies. As a consequence, access to
the necessary funding will be a selective factor regarding the survival of some automobile manufacturers.
Consolidation will be low, whereas new competitors can be expected to enter the
marketplace. For some European automobile manufacturers there will be fundamental
financial problems to cope with. If the scenario of the “green revolution” will come true
it will entirely restructure European automobile manufacturing in the future.
“Mobility revolution”
In this scenario the main impetus comes from the consumer. This version is based on
the assumption that the majority of drivers will not own cars in the future, but will participate in car pools or car sharing and use cars only temporarily when they need them.
Besides the ecological aspects, financial considerations will be more important as
buying and running a car is becoming increasingly expensive. This is already a trend,
accelerated by growing urbanisation which makes the use of one’s own car more and
more complicated. Cities with very advanced public transport systems (or alternative
vehicles such as electric bicycles) facilitate individual mobility without the use of cars.
Another, perhaps smaller segment of mobility will still rely on using cars or other
means of transport, such as trains or aeroplanes.
According to this scenario, the automotive industry would be perfectly restructured.
Until now automobile manufacturers used to be system leaders in the automotive
industrial value chain, but they would lose this role to mobility providers who would, in
turn, establish contact with the consumers directly. Market acquisition potential would
no longer be associated with car brands, but with mobility brands.
To sum it up: The automotive industry has come close to a turning point. The question is who will survive? Who will sustain itself until the next development stage in this
industrial sector?
The European automotive industry – its scale, productivity and brand portfolio as
well as the ownership structure – is extremely heterogeneous. This heterogeneity challenges the long term survival of European automobile manufacturing. Each manufacturer is to seek and find the answers itself as to what product to manufacture and what
services to render in order that its position be competitive in the future. What sources
50
Györgyi Barta
can they use and how will they be able to restructure the manufacturing value chain
efficiently? A business model sustainable in the long run is what is to be formulated and
implemented.
In the case of the “green revolution”, this model is relatively simple: It “merely”
means to further improve the current business model. The product, i.e. the car, is in its
focus, which will reach the customer through the standard sales channels and which will
perhaps be produced in altered structures of the value chain.
The vision of the other scenario – “Mobility Revolution” – entails a total reformation. Until now, it was the automotive industry that took the role of the developer and
the manufacturer as well; with the “mobility revolution”, however, the leading positions
in the value chain will be taken over by service-providing companies. But: Is there sufficient inner potential for such a transformation within the automotive industry? Or will
new service providers be sought outside this industrial sector?
Central and Eastern European region
Who are the competitors?
This study discusses issues related to the Central and Eastern European automobile
manufacturing – in a European context. It fits the research project to analyse the role of
the automotive industry in the Northern Transdanubian region of Hungary. The
common base to approaching this topic, and the difference at the same time, is that the
status and effects of the automotive industry are examined at different territorial levels.
However, the question is: Is this “territorial” base relevant?
The automotive industry is a strongly integrated sector involving a complex chain of
global “Tier 1” subcontractors and finishing brand producers (OEM). Such companies
are the leaders of the automotive industrial value chain. Obviously, the development of
the sector highly depends on the short and long term strategies and decisions of such
companies. The interesting question is: What are the effects of company-level decisions
at the various territorial and regional levels?
It makes sense and it is relevant to survey the automotive industry at diverse territorial or regional levels, but this cannot be separated from the aspect of how a particular
company is related to a country or region: i.e. it is important to know where the headquarters of the company is; which nation the owner belongs to; and which activity of the
company or element of the value chain is settled in or relocated to a particular country
or region. Obviously these aspects determine the perceptible or measurable effects in a
particular region. If we select any territorial level as the target of our analysis, the global
relationships of the companies operating in a particular region must not be neglected
either.
Many different answers may be given to the question – who are the competitors? –
posed in the subtitle. In principle, the large automobile manufacturing companies compete with one another, but also countries are in competition with one another – mobi-
51
lising their own resources – to attract or anchor some segment of the automotive
industry within their borders. Even regions smaller or larger than a country compete for
investors, due to the European Union regional policy. Every territorial level has different opportunities and tools to further their interests; and, of course, the impact of the
automotive industry operating in a particular region will be different according to the
developmental level of that particular region.
In this section the automotive industry of the Central and Eastern European region is
examined at different levels: globally and in Europe, focusing on the group of Central
and Eastern European countries. The European regions are presented as they evolved in
terms of the development of the automotive industry. This is not intended as a comprehensive and detailed statistical description. But the typical problems and correlations
related to individual regional levels are highlighted: mainly centre-periphery correlations between Western and Eastern Europe; the competition among the CEE countries
for direct foreign investments, and the impact of the already existing automotive
industry on the countries examined. The chapter begins with a brief presentation of
automobile factories, mainly European car factories, but disregarding individual corporate problems and issues as they would be beyond the scope of this study.
Global players are changing:
Automotive industrial companies in the world and in Europe
The prediction made at the beginning of the 1990s, according to which the number of
automobile manufacturers will decrease significantly (maybe 6 will remain all over the
world) – has not yet come true. At present more than 100 legally and financially independent companies are engaged in car manufacturing throughout the world. Deducting
small-series manufacturers (below 1000 cars/year), there are still 60 to 70 companies.
The number of integrated automobile manufacturers is approximately 32, of which the
13 largest can be considered global companies, and 8 companies are partially global,
while 9 are engaged primarily on national markets (Tables 5 and 6).
A low level of globalisation, high production and sales revenues characterise large
countries such as Russia, India and China; while a high level of globalisation coupled
with high production and sales revenues characterise the following automobile manufacturers (alphabetically): BMW, Daimler, Fiat/Chrysler, Ford, Fuji (Subaru), GM,
Honda, Hyundai, Mitsubishi, PSA, Renault-Nissan, Toyota, VW.
The number of European automobile companies has decreased significantly over the
last sixty years: In the 1950s there were still 70 vehicle manufacturing companies and
brands of which altogether 6 were left by the year 2008. This dramatic consolidation
occurred in the 1970–1980s, and it was not a result of globalisation. In the consolidation
of the European automotive industry general and specific features are mixed in each
country:
52
Györgyi Barta
TABLE 5
Ranking of companies that produced over one million automotive vehicles in 2008
Ranking
Corporate Group
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Total
Toyota
General Motors
VW
Ford
Honda
Nissan
PSA
Hyundai
Suzuki
Fiat
Renault
Daimler
Chrysler
BMW
Kia
Mazda
Mitsubishi
Number of vehicles
9,237,780
8,282,803
6,437,414
5,407,000
3,912,300
3,395,065
3,325,407
2,777,137
2,623,567
2,524,325
2,417,351
2,174,299
1,893,068
1,439,918
1,395,324
1,349,274
1,309,231
69,561,356
Source: OICA (2009). In: Diez–Becker (2010, 14; fig. 4).
TABLE 6
Consolidation of German, French, British and Italian automotive industry: changing
numbers of independent manufacturers
Period
Germany
France
Great Britain
Italy
1950–1960
1970
1980
1990
2000
2008
Independent vehicle
manufacturing
companies in 2008
11
10
5
5
5
3
BMW
Daimler
VW
20
5
3
2
2
2
PSA
Renault
20
6
4
1
0
0
19
6
3
2
1
1
Fiat
Source: OICA (2009). In: Diez–Becker (2010, 17; fig. 6).
53
− In Germany, all non-military industry was almost totally destroyed at the end of
World War II; due to low income levels, the manufacturing of small cars became
essential in the 1950s, and the disciplining force of a liberal economic policy
became noticeable.
− French automobile manufacturing was characterised earlier by a relatively high
demand for luxury cars. After World War II, adaptation caused difficulties to
many automobile manufacturers, and eventually led to several close-downs.
Unlike most of the German automotive companies, the French government’s
participation in their automotive industry was always significant, and it hampered the adaptation to market requirements. However, French vehicle companies have always been willing to cooperate with other – even non-European
vehicle companies (Toyota, Nissan, SsangYong, AvtoVAZ).
− The disappearance of British automotive companies was linked to the economic
policy in Great Britain. Deindustrialisation was the most intensive in this European country. The automotive industry shrank significantly, which was partly
attributable to the trade unions which firmly resisted the often painful structural
reforms. The changes in economic policy implemented in the 1980s came too
late, and as a consequence, an independent British automobile industry practically ceased to exist.
− Fiat was so firmly locked into the Italian market that it made entering of other
vehicle manufacturers close to impossible, unless they were able to target some
gaps (luxury cars, sports cars, etc.). The Fiat company was further strengthened
by governmental interventions, too, for instance in subsidies for underdeveloped
Italian regions. This prevented, for example, the break-through of Japanese cars
on this market until the 1990s. In the year 2000, GM acquired a share of 10% in
Fiat. Globalisation weakened the position of Fiat, especially on the global
market.
The proportional share of the largest automotive OEMs in 2006 on the basis of the
number of vehicles produced was the following: GM 13.1%, Toyota 11.8%, Ford 9.2%,
Renault-Nissan 8.4%, VW Group 8.3%, Daimler/Chrysler 6.7%, Hyundai-Kia 5.6%,
Honda 5.4%, PSA 4.9%, Fiat 3.4% (Source: Companies’ annual reports (OICA). In:
Halesiak et al. (2007, 7; fig. 2).
Major problems of automobile manufacturing companies included – before the
recession – increasing production costs (global price war, increasing oil prices, higher
R&D expenses) and decreasing profitability (decreasing attractiveness for investors,
especially in the USA).
Some automobile concerns reacted to market changes with restructuring even before
the recession:
− As of the 1980s, the automobile manufacturers made attempts to reduce risks, or
share costs by mergers, strategic alliances, and the establishment of joint
ventures.
54
Györgyi Barta
− Brand automobile manufacturers (OEM) paid higher attention to market
strategies, which extended to passing over part of costs to the suppliers. By this a
more transparent and simpler production organisation was set up and they
managed to even cut costs.
− Until 2005 and in this restructuring process, Japan led the automotive industry.
Their role was imitated both in the American and in the West-European markets.
Their success was built upon perfectly organised production processes, on clear
production positions and better understanding of economic tendencies.
− At that time, automobile manufacturing in the United States of America had to
cope with difficulties: due to the fact that the domestic market was cyclic, they
lost market shares to Japan, and mainly because it was inflexible (they insisted
on cars with high fuel consumption). In addition, the finish of American cars was
below Japanese and European.
− Although Europeans and Americans partially walked in similar shoes, the former
showed a somewhat better performance than the Americans. Many players are
present on the European automotive market which means advantages and disadvantages alike. They had to achieve considerable cost reduction which led to
relocation. The winner of this process was unequivocally the CEE region.
Correlations between West and Central Eastern Europe – centre-periphery
relationships in the automotive industry
Normally two statements are conceptualised referring to centre-periphery correlations:
− This is a system of relationships, since it is impossible to consider the centre
without speaking about the periphery as well. And then again, it is the special
relationship with the core region, i.e. the centre, which actually makes a region
the periphery. The centre predominates in this relationship, taking mainly
advantages of, and benefits from, the periphery. Decision making – which affects
the situation of, and the potentials for, development in the periphery – takes
place at the centre. To emphasise this correlation is essential as it often occurs
that a periphery is taken out of this context and dealt with individually. For the
purpose of this study we attempt to examine the situation and problems of both
poles with special regard to the European automotive industry. A rarely posed
question also emerged: Does the improvement of the situation in the periphery
have negative effects on the centre (Domanski–Lung 2009; Diez–Becker 2010;
Özatagan 2011)?
− When examining a short period of a centre-periphery system it might appear to
be stable, and it rarely occurs that the centre becomes periphery, and vice versa.
However, studying some past decades in the history of the automotive industry
provides evidence that there have been such changes. In certain countries, some
regions have drifted to the periphery of a core territory, while a periphery also
has had certain zones being nearer to, or farther form, the centre, and even
55
between these zones there have been movements. It must be underscored that
even in the case of a positive shift, a centre will always be sharply differentiated
from the periphery. As a consequence, the factors determining and separating the
poles will generally remain quite stable. A periphery situation is a condition (e.g.
the CEE region, Hungary’s situation within the automotive industry), which will
always remain obvious.
Features of a division of labour
First of all, it must be mentioned that Germany is part of the core territory of the
automotive industry in Europe, while the periphery includes Central and Eastern
European, formerly socialist countries, and some studies list here Russia and Turkey as
well (and in both countries the role of the automotive industry is important, too). The
core territory is within the area of the EU15, but, basically, it is composed of only four
countries, namely Germany, France, Italy and England that have had a traditional
automotive industry. In the 1970–1980s, Spain used to be the largest country at
Europe’s periphery. At the same time, the United Kingdom suffered significant losses
of its former position. Today it does not own any major car brand. A new division of
labour has been established in Europe, including companies that relocated to Central
and Eastern Europe.
In the European automotive industry, the CEE regions form the European periphery,
which has had a significant role in parts production and will have a not so insignificant
role in. Although some of the former socialist countries had their own automotive
industry, the progress following the change of the political system has been associated
with remarkable foreign direct investment (FDI) in this sector. The majority of such
investments were of the green-field type, but through privatisation and, subsequently,
the acquisition of inherited socialist automobile factories, or other companies, they were
also linked to brown-field investments. Two main reasons led to the fact that the foreign
capital investment directed to the automotive industry selected these countries: It was
seeking new markets for its products as well as cheap production conditions. Not only
lower wages, but also the quality, the reliability and geographical closeness of a
workforce were important aspects in the selection of new production sites (Figure 6).
The automotive industry of the European periphery is not a homogeneous one. Four
groups of countries can be distinguished in accordance with the extent of integration
associated with the European core territory (more precisely the links to the EU), the
features of automotive industrial ownership and the position taken in the division of
labour:
− Central-European EU member states: Poland, the Czech Republic, Slovakia,
Hungary, Slovenia;
− Romania and Bulgaria – as countries involved in the accession at a later date;
− Turkey, which concluded a treaty on free trade with the EU in the hope of
accession at a later date;
56
Györgyi Barta
− Ukraine and Russia, which currently have more links to the Russian core
territory, but hope to establish closer relationships with the EU. This latter
prospect actually applies more to Croatia and Serbia (Halesiak et al. 2007).
FIGURE 6
Comparative advantages of the CEE countries in the automotive industrial sector
Central Europe
South Eastern Europe
Source: Own calculations based on Halesiak et al. (2007, 16; fig. 18).
57
The countries of Central and Eastern Europe (Poland, The Czech Republic,
Slovakia, Hungary and Slovenia) are considered as being strongly integrated peripheral
markets (their situation is comparable to that of Mexico in NAFTA). The Central and
Eastern European (CEE) peripheral regions include Romania and Bulgaria, as well as
the potential future EU members, Croatia and Serbia. In the automotive industry of the
CEE region, a slow embedding of foreign companies has taken place; the production
strategies of the investing transnational automotive companies facilitate gradual
growing of added value in this region, and also R&D institutes have appeared –
preferring development to research, however. At the same time it is typical for the
automotive industry of this region that labour intensive activities with low added value
are performed by low-wage labour. In the manufacturing value chain the automotive
industry of the CEE has focused on assembling macro components (modules), and on
the production of low cost generic components meant for export. In the course of
increasing production, a higher rate of specialisation has evolved, nevertheless the
centre-periphery relationship has remained stable, i. e. the peripheral situation of the
CEE remains unchanged.
Low-cost work model
In 1992, Pyke and Sengenberger formulated the theory of “high-road – low road”
(Pyke–Sengenberger 1992) which depicts the two forms of development experienced by
industrial districts in global competition. Low-road can be interpreted as a wagereducing solution as it is seeking low-wage labour and a deregulated labour market.
While the high-road model, which can be referred to as a quality-work model, is based
on the improvement of effectiveness and on innovation. This, however, results in a
rising wage level, the improvement of social conditions and a better protection of workers’ rights.
These industry sociologists put quality production in focus as the fundamental element of industrial restructuring. The quality work model is based on institutionalised
interest representation, the sustainment of high-standard employment and wage level, an
inner flexibility of a corporate organisation (this also applies to work organisation) and
a long term investment in vocational education. While in the low-wage work model the
workers’ interest representation is weak, the privileges of the employer are strong. It is a
general practice that the labour force is employed for a short term, temporarily, or provisionally, and in this employment system high fluctuation is typical, the labour market
is uncertain, the market pressure on employment is high, the wages are low and finally
the company’s interest in the vocational training and the development of competencies
of its employees is minimal.
The quality work model encourages long term investment in vocational training, and
the enhancement of competencies, which are preconditions to the security of employment. This entails high inner flexibility as well. The strategy of cheap work is based on
low labour costs and semi-skilled work. Semi-skilled workers’ low wages are only sustainable under the conditions of harsh competition on the labour market. Flexibility can
58
Györgyi Barta
be achieved by changing the staff number as seen necessary (dismissal, temporary work,
occasional work).
Basically, the two types of work models in the centre and the periphery of the automotive industry move the development in Europe in this field.
Closing up and relocation
The development of the automotive industry in the CEE commenced at the beginning of
the 1990s and it is still in progress. First, the Western investors acquired a sequence of
post-socialist companies (VW in the Czech Republic, in Slovakia and Poland; Fiat in
Poland; Renault in Slovenia). Afterwards these companies were transformed into not
much more than suppliers. Initially they produced only for the CEE markets. When
cost-cutting became inevitable due to the keen competition between West-European
vehicle manufacturers, the focus was mostly directed at CEE locations. VW and Fiat
modernised their plants in the CEE, with the intention of manufacturing products for
West-European markets. VW, GM and Toyota started parts production in the CEE
(chiefly engines and gear-boxes). Qualified, but still cheap labour, governmental incentives for investments (special economic zones, tax allowances) attracted more and more
investments to this region. In the first years of the new millennium, a new wave of
automotive investments – mainly Korean and French – began, which in turn led to an
increasing investment by suppliers.
General tendencies in the development of CEE plants were: First older models were
manufactured, or partially or completely assembled there. As of the second half of the
1990s, some progress was made: With the modernisation of technologies at affiliate
companies and divisions (Škoda in Bohemia, VW in Poznan), and by adopting standardised systems, the competencies of the CEE plants were extended (with technical
process functions, logistics and sales). The product range also expanded: besides small
cars also the bigger models appeared, i. e. the compact and premium category.
Moreover, aggregates with high added value were produced (such as engines), and even
exports to the West grew (over 90% of the manufactured vehicles were exported,
mainly to Western Europe, at the end of the 1990s).
For the CEE countries, relocation has been a positive process because it helped
catching up technologically with Western Europe. Improvements in the quality of
products as well as the enhancement of competencies have been highly appreciated
benefits. Nevertheless, the process of catching up remains restricted, as the automotive
industry in CEE is still characterised by labour-intensive production which does not
require a high technological level. On the one hand, R&D and design remained in most
cases at the headquarters of the automotive manufacturers (with the exceptions of Škoda
VW and Renault Dacia). On the other hand, threatening competitors of the CEE region,
that is countries farther to the East (Russia, Ukraine, and especially Asian countries),
have strengthened, although the impetus to relocate plants from the CEE region farther
to the East was weak for automotive companies. Investments in China have not
weakened the automotive industry of the CEE. Nevertheless the advantage of CEE is
59
decreasing continuously as a consequence of increasing wage levels (German investors
have already complained about this trend in Poland, and they are said to be favouring
more and more the Ukraine and China).
After 2005, the increasing lack of qualified labour in the CEE region squeezed the
labour market, and wages began to rise. Between 1995 and 2006 the hourly wages in
euro doubled, although the wages in Romania were 8%, and the wages in The Czech
Republic were 19% of the wages paid in Germany. In 2004, with the accession to the
EU and with increasing FDI, the migration of labour was facilitated. This exerted some
pressure on companies. In 2006 and in 2007, workers went on strike in VW, MAN,
Toyota and GM factories in Poland demanding higher pay and better working conditions. Similar actions took place at VW Škoda in Prague, or at Dacia in Romania and at
Suzuki in Hungary.
No matter what progress the automotive industry in the CEE region makes, the
competencies regarding innovation and decision making will remain in the hands of the
core territories. At the same time this suggests that the current localisation of creativity,
knowledge accumulation and decision making and dependent situation of the peripheral
regions will remain unchanged. As a consequence, what is now periphery will remain
periphery. It is not impossible, however, that a country will turn from periphery into a
core territory, and that a country will disappear from the periphery and will be replaced
by another peripheral country.
An important issue regarding the development of CEE is whether Western Europe
will transfer the model of quality work to CEE. Some case studies suggest that although
certain elements (qualification structure, work organisation, work time organisation) are
taken by foreign investors to CEE, the work model established differs from the domestic one, primarily in terms of the partnership approach between management and employees. Obviously, it is impossible to introduce the same work model to a recipient
country 1:1 owing to the recipient country’s different social/economic environment.
Therefore hybrid models are developed. It is also true that escaping from domestic constraints is an explicit aim of investors.
In relation to foreign direct investment, or relocation, three essential effects are to
be examined: What is the situation with workplace security, vocational training and the
employees’ interest representation at automotive companies with foreign owners in the
CEE region?
The quality work model focuses on workplace security and has already appeared in
some CEE companies when, for instance, at the time of recession the majority of qualified labour was not dismissed by companies. This situation was made, however, more
difficult to handle by the increasing shortage of skilled labour. It must be noted here,
however, that the group of qualified labour composes a minority. Uncertainty of employment is more typical for the majority of semi-skilled workers (on probation, and
workers employed temporarily to satisfy increased production needs) than in Western
Europe (in many West European automotive factories, temporary or provisional employment is much more restricted).
60
Györgyi Barta
The other important issue is vocational training, because it is a long term investment
that helps employees keep their jobs, at least with many companies. In a low-cost work
model neither the employer nor the employee are interested in investing in vocational
training. The question is: What is the situation in CEE when it comes to vocational
training? Due to the collapse of the socialist vocational training system, in CEE professional or vocational education is still insufficient. It is somewhat compensated by the
training organised at subsidiaries of multinational companies, or by the participation of
affiliate companies in external professional education (e.g. at a university). Of course,
there exist state education systems in CEE as well. A certain duality has developed in
this respect, i. e. state and corporate education or training facilities. These are some
examples: Bosch in the Czech Republic, FSO in Warsaw and Fiat in Tychy are involved
in local professional or vocational training. There are two cases in Hungary: Audi in
Győr and Bosch in Miskolc. These companies opened departments at the local universities. In the Czech Republic an agreement has been made between the German and the
Czech Chambers of Industry for the purpose of promoting and supporting the local
vocational and professional training). The general opinion is that professional or vocational training is still an unresolved issue in CEE, especially since foreign corporations
require more skilled labour. In Hungary, a lack of qualified labour has become apparent
well before the recession (Palkovics et al. 2009).
The third aspect is the issue of labour representation. Although the socialist heritage
is different in each country (for instance, in Poland trade unions were stronger than
anywhere else), in general motivation to set up a trade union seems to be weak. Especially small and medium sized enterprises (SMEs) reject cooperation with trade unions.
The relationship between large companies and trade unions is different. There are some
examples of powerful representation of employees’ interests, for instance at Volvo, at
VW or at German Mahle Group (a big automotive supplier). In other cases, following
initial conflicts (Fiat, GM in Poland), cooperation between management and trade
unions proceeded at a slow pace. With Japanese companies, the source of tension was
originally the setting up of trade unions in the first place (Toyota in Poland, Suzuki in
Hungary). However later, relationships normalised. In the case of brown-field investments, some trade unions were “inherited”, while in the case of green-field investments
there were no trade unions at all at the beginning. With these newly incorporated companies, the establishment of trade unions and their functioning entailed many conflicts
(Jürgens–Krzywdzinski 2009).
Losses to the core territories? Relocations from Western Europe
Disputes about the division of labour between the European west and east started at the
beginning of the 1990s, due to relocations. Germany was criticised for letting its
economy turn into a “bazar economy”, i. e. the economy dealt with the final manufacturing of products only, while the components were produced in low-wage countries.
These criticisms were targeted principally at the automotive industry. It was not just
about activities relocated to other countries, but also about the loss of competencies at
61
the core regions. This was actually a minor problem in the case of big integrated vehicle
manufacturers. It caused more problems at the suppliers’ side. As a result of this situation the proportion of labour costs compared to the aggregate costs declined considerably. As a consequence, relocation had become a determining competitive factor
among large automotive companies. German automotive suppliers relocated 25–38% of
their production abroad between 1997 and 2001, mainly to CEE and China, and not
merely production but also an increasing proportion of technical services were also
relocated.
The change in the division of labour among European countries gave rise to hot
debates in Western Europe, especially in Germany. As a result of these political debates,
the EU Council of Ministers made the decision to cease promoting relocations in 2006.
At the same time, detailed analyses – with the exception of some cases raising media
attention – have stated that there was only a relatively small rate of shifting in the westeast division of labour, and its impact on employment in the West is hardly detectable.
The question is whether the integration of the CEE region into the European automotive
production network really “hollows out” the West-European automotive industry –
regarding employment and the further development of the quality-work model. And:
Does relocation and, in general, foreign direct investment in the western and eastern
regions of Europe lead to a decrease of economic differences or will they increase the
labour market competition?
When examining the impact of relocation on the core countries and, in particular, on
their labour markets, Germany should be separated from all other countries. The statistical figures clearly indicate that in Germany relocation did not hurt the domestic
economy at all. The level of industrial employment in Germany did not decline in the
1990s. German automotive factories relocated those activities requiring low qualification mainly to low-wage countries, and, generally, the positive development of employment in the German automotive industry did not change. However, in other countries –
in Portugal, Belgium, Great Britain – job losses due to relocation were significant. It has
to be noted that those countries either do not belong to the core territory of European
automobile manufacturing, or their development in the automotive industry made them
lose their status of being central to the European automotive market, or their status was
typical of peripheral countries to begin with. Obviously, the competition is not between
Germany and the countries of the peripheral CEE region, but between the SouthEuropean peripheral countries and the CEE. In practice, South-European countries
became losers of relocation – Portugal, Spain, Greece, or even the southern part of Italy.
The considerable increase in the import volume of vehicles is another indicator of
what impact relocation has had. It is undoubted that there was a certain increase in the
import volume, but in the case of Germany it was not too significant compared to
France or Italy. In the aggregate, German imports did not grow spectacularly, but the
special structure of German imports did change: Import of parts from CEE grew
considerably (between 1995 and 2005 from 9% to 37%), while the rate of automobile
parts imported from Spain or Portugal dropped by half. This means that Germany – the
62
Györgyi Barta
only country in Western Europe – fundamentally changed the territorial structure of
production and imports – by involving the CEE region.
There are several reasons why relocation did not affect the German automotive
industry adversely. First, the economic situation in Germany improved from the middle
of the year 2000 because very stringent market reforms were introduced. This reduced
the political constraint associated with any relocation towards low-wage regions. But
the economic situation may vary and it will. Second, Germany profited from its unrivalled premium-car brands. The premium product market is less price-sensitive and it
supports the “Made in Germany” label. Third, the fact that Germany started from a
leading position enhanced the advantage of German companies in CEE in the price
competition compared to other West European countries. From the end of the 1990s,
Japanese, Korean and French automobile manufacturers also appeared in the CEE region, probably diminishing the German advantage. However, German industrial strategic planning is second to none.
The automotive industry of
Central and Eastern European countries
In 2007, 2.3 million persons were employed in the automotive industry, 80% of them
worked in the EU15 countries, and 20% were employed in the new EU member states.
Additionally, the number of people employed by automotive industrial suppliers and
service providers in Europe was approximately 10 million.
Severe structural problems emerged in the automotive industry in the first years of
2000: dropping sales, increasing material costs and R&D expenses and soaring oil
prices. The automotive industry responded to these problems with overall restructuring:
strategic partnerships were established, simpler – leaner – organisations were set up,
higher pressure was exerted on suppliers and relocations towards regions with lower
production costs were commenced. One of the main winners of this process was the
Central and Eastern European (CEE) region.
Investors were motivated by several main factors, when they chose the Central and
Eastern European region for their investments: They sought a region with low manufacturing costs, as well as new markets for their products. In addition, they needed ample
space for new top-of-the-art factories they couldn’t find in their home countries. Foreign
direct investment was a key factor in the development of the automotive industry in CEE.
Until 2006, FDI by the automotive industry was almost 17 thousand million euro. Until
2006, it was primarily Hungary, Poland and the Czech Republic where foreign
investments flowed into the automotive industry, then Slovakia joint these three countries,
and at present, it is virtually these four countries that have received 90% of all foreign
automotive industrial investments in the CEE region (Table 7).
63
TABLE 7
FDI in the automotive industry of CEE in 2006 (%)
Country
Romania
Slovenia
Hungary
Poland
Czech Republic
Slovakia*
Total
Share of FDI
6.6
1.3
28.9
30.3
28.9
4.0
100.0
Note: Automotive industrial investments in Slovakia increased after 2006.
Source: Halesiak et al. (2007, 23; fig. 21).
The CEE region has attracted the largest global automobile manufacturers into these
countries:
− Poland: Fiat, VW, Opel, GM; MAN, Volvo, Scania – bus manufacturing;
− The Czech Republic: VW/Škoda, Hyundai, Toyota, PSA; bus manufacturing:
Iveco (Italian);
− Slovakia: VW, Kia, PSA;
− Hungary: VW-Audi, Suzuki (this was the first FDI in post-socialist countries);
− Other Balkan countries: Renault (Romania).
In the second stage, the ‘Tier 1’ foreign suppliers also settled in the CEE region,
mostly in the Visegrád countries.
The ten largest suppliers of the world that settled in the CEE (2005) were:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
Bosch (Germany) > Hungary, the Czech Republic, Poland, Romania, Slovakia;
Denso (Japan) > Hungary, the Czech Republic, Poland;
Delphi (USA) > Hungary, the Czech Republic, Poland, Slovakia;
Johnson Controls (USA) > Hungary, the Czech Republic, Poland, Slovakia;
Slovenia;
Magna (Canada) > Slovakia, the Czech Republic, Poland;
Aisin Seiki (Japan) > the Czech Republic;
Lear (USA) > Hungary, the Czech Republic, Poland, Romania, Slovakia
Visteon (USA) > Hungary, the Czech Republic, Poland, Slovakia;
Faurecia (French) > the Czech Republic, Poland, Romania, Slovakia;
TRW (USA) > the Czech Republic, Poland, Romania.
(Source: Companies’ websites. UniCredit [2007]).
In general, the EEC business environment was suitable for foreign investments. But
although this region offered a great number of cheap and relatively well-qualified
labour, productiveness lagged behind West-European averages (Table 8).
Wage differences have decreased gradually between Western and Eastern Europe:
Wages are rising fast in the CEE region (in 2007 the average wage level was 2.5 times
64
Györgyi Barta
higher in the automotive industry than it was in the year 2000; that is 250%). This,
however, had a bad impact on productivity figures, since the Western subsidiaries in
CEE were not (allowed to be) innovative enough.
The automotive industry is developing in the most dynamical way in Central and
Eastern European countries; its share in the industrially added value grew from 5.8% to
7.3% between 2000 and 2005 (Figure 7).
TABLE 8
Labour costs and productivity in the automotive industry in the Western and Eastern
parts of Europe, in 2006
Country
Work productivity (gross added
value/number of employees)
Hungary
Poland
Czech Republic
Slovakia
Bulgaria
Romania
Slovenia
EU25
EU25=100
(EU15=106)
EUR/employees
Labour cost
EUR /
employees
75
61
69
69
34
37
82
100
37.2
22.8
23.2
20.3
5.6
7.2
23.4
58.0
13.8
9.5
10.5
10.1
3.7
4.8
15.8
45.0
Work productivity
and wages
(summarised)
(%)
268.5
239.8
220.1
199.7
151.5
148.6
147.9
129.0
Source: Eurostat, UniCredit, New Europe Research Network. In: Halesiak et al. (2007, 28).
%
FIGURE 7
Development of the Central and Eastern European automotive industry, 2000–2005
Gross value added in Euro (at price rates of 2005)
14
12
10
8
6
4
2
0
2000
2001
2002
2003
2004
2005
Note: Share of automotive industry from the industrial added value: 5.8%
(2000); 5.8% (2001); 6.3% (2002); 6.7% (2003); 7.1% (2004); 7.3% (2005).
Source: Halesiak et al. (2007, 11; fig. 7).
65
Some renowned automotive brands have established factories here during the past
two decades, mainly as green-field investments. They are strategically located so that
the majority were settled in a zone of 500 km from the boundary to Western Europe
(Figure 8). This was done quite deliberately for several reasons (distance, infrastructure,
existing affiliates and clusters).
FIGURE 8
Location of vehicle assembly plants in CEE
Poland
Czech Republic
Slovakia
Hungary
Slovenia
Romania
Croatia
Serbia
Bulgaria
Foreign owners
Local owners
Source: Own calculations based on Halesiak et al. (2007, 5).
It should be mentioned again that this region will become increasingly important as
a new market for the global automotive industry. Currently about 45 million vehicles
run in CEE countries. Whereas market saturation (number of vehicles per the number of
inhabitants) is far behind Western Europe (in CEE, the ratio is 20 vehicles per 100
inhabitants, in Western Europe it is 50 vehicles per 100 persons), thus with faster
economic growth it can also be expected that due to higher personal incomes car
ownership and car usage rates will increase dynamically. It should be added that in this
66
Györgyi Barta
region the average age of first and second hand cars is also higher, thus the replacement
demand will be bigger, too.
The automotive industry is an essential element in the development of the economy
in the EEC region (Tables 9 and 10).
In large countries, the effect of the automotive industry is smaller (Poland,
Romania), in some countries hardly any effects are detectable (Croatia, Bulgaria), while
in the Czech Republic and Slovakia the automotive industry has become a predominant
factor, not only in industry but also in the whole economy. At the time of the recession,
due to the considerable setback in many countries (in Slovakia, Germany, Poland and
Hungary) the GDP share of this sector also decreased, but not in the Czech Republic
and Romania, where growth remained unbroken even during the crisis (Figure 9).
TABLE 9
Proportion of the automotive industry in the economy of countries of CEE,
in 2005, % (based on the increase in gross added value, %)
Country
Czech Republic
Hungary
Slovakia
Romania
Poland
Slovenia
Croatia
Bulgaria
Total
Proportional share
of automotive
industry in GDP
3.1
2.3
2.2
1.7
1.2
1.1
0.2
0.1
–
Proportional share
of automotive
industry in
industrial GDP
11.8
10.2
9.5
7.6
6.5
4.3
1.2
0.4
–
Proportional share of
automotive industry in
the automotive industrial
GDP of the CEE region
29
18
8
13
28
3
1
100
Source: Own calculation based on Halesiak et al. (2007, 12; fig. 8–9).
TABLE 10
Change in proportional rate of automotive industry in industry in CEE, %
Country
2007
2008
2009
Germany
Czech Republic
Poland
Slovakia
Romania
Slovenia
Hungary
Austria
CEE
21.4
14.5
7.9
29.2
5.5
9.1
10.8
4.4
18.6
22.1
16.8
8.6
37.6
7.8
11.7
8.0
7.0
19.2
15.1
23.1
7.4
30.7
8.3
14.3
7.8
7.1
14.1
Source: Eurostat.
67
FIGURE 9
Proportional rate of the automotive sector in the industrial production
in the countries of CEE, between 2005 and 2009, %
%
40,0
35,0
30,0
25,0
20,0
15,0
10,0
5,0
Germany
Slovenia
Czeh Republic
Hungary
Poland
Austria
Slovakia
CEE
2009
2008
2007
2005
0,0
Romania
Source: Edited by Szabolcs Szabó based on Eurostat figures.
Concluding these thoughts it is still worth posing the question of whether CEE
countries are competitors to one another in the automotive industry? The answer is yes
and no.
Central and Eastern European countries (here only the post-socialist countries are
meant) have similar characteristics (countries with small areas, a socialist heritage,
capital shortage, middle rate development), i.e. they mostly possess the same incitements to attract or not attract investors. Undoubtedly they make remarkable sacrifices to
acquire foreign investments: They develop their infrastructure (mostly, construct motorways), participate in investments abroad with governmental support, they develop
professional and vocational training to satisfy the demands of investors, they create
investment-friendly taxation schemes, tax holidays included. Big subsidies for
greenfield-investments are also the rule. Two examples are provided regarding the different taxation systems by countries, and the dates of amending the taxe schedules, and
supports received from local governments (Tables 11 and 12) (Varga 2011; Kemenczei
2009, 2010).
Similar- type factors of attractiveness are created here and there, but they all fall into
the same category. The economic policy of the Visegrád countries also shows remarkable differences regarding the importance attributed to foreign investments. As a general
conclusion, CEE countries have to take serious and constant efforts to attract foreign
68
Györgyi Barta
capital but basically they are unable to step over their limits, thus more or less they offer
similarly attractive factors for investing countries.
It might also be interesting to consider, that for historical reasons or other, CEE is
basically the strategic playfield of (mostly) German automotive giants, for better or
worse.
In the statements of the automotive industry investing and capital recipient countries
appear not individualised, as these countries become integrated during the globalisation
into the international division of labour. For instance, the parts and components
manufactured in Hungary are used by the Slovakian manufacturer to assemble the finished
product. The geographical position of the Visegrád countries enables foreign investors to
see these countries as a cluster. Therefore these countries are much more involved in cooperation rather than being competitors to one another (Sipos 2010).
Incorporated companies with a high share of foreign capital are mostly deemed as
remote business units of transnational companies (Ansani–Singer 1992). The scale,
structure and time of investments are determined by the strategies of the investing companies. It is the investor who decides on what role to give to the recipient country. The
major investors in the CEE region are German automotive companies that follow their
own long-range strategies in planning, building, production, vocational training, etc.
TABLE 11
Business taxes in Visegrád countries, %
Tax type
Corporate tax, surtax
Local tax on industrial
activities
Tax on dividends,
Health Contribution
Hungary
Czech Republic
Poland
Slovakia
2003
2008
2003
2008
2003
2008
2003
2008
18
16
4
31
21
27
19
25
19
2
2
None
None
None
None
None
None
20
25–35
14
15
15
15
19
15
None
Source: Kemenczei (2009, 34).
TABLE 12
Forms of support provided by the local municipalities for multinational companies
settled in the countries of CEE (% of the companies)
Czech Republic
Reduction, cancellation of local tax
Reduction, cancellation of rental fees
Provision of land at low price or free
Taking over of problematic real estates
Fast administration
Source: Gelei–Venter–Gémesi (2011).
35.1
2.7
1 8.9
0.0
8.1
Poland
16.3
0.9
1.3
5.3
7.9
Hungary
21.4
3.8
4.4
3.1
26.3
69
Conclusions
The Central and Eastern European region was the main winner of the global and
European development in the automotive industry. Dynamic development commenced
with an annual growth of 20% in added value production, and currently the CEE
automotive industry generates 10% of the industrial added value production. The CEE
region generated 16% of Europe’s production in 2010.
The CEE automotive industry is export oriented, although imports also increased
significantly, several countries of the region (the Visegrád countries and Slovenia)
became net exporters (Figure 10).
This rapid growth could happen only with significant foreign direct investment.
The usage of cars also increased rapidly in this period in the CEE, thus this region
not only became an essential manufacturing region but also it became an important
market.
The development of the CEE automotive industry is a success story. This region is
expected to remain the development area of the West-European automotive industry, for
the differences in manufacturing costs between the western and eastern parts of Europe
diminish slowly, the region is stable and reliable, and keen competition keeps forcing
the western companies to further extend their businesses in Eastern Europe although
some relocation of production farther to the East is also anticipated.
FIGURE 10
Net exporters of CEE in 2006
Key: 1 – Turkey; 2 – Poland; 3 – Slovenia; 4 – Czech Republic; 5 – Hungary; 6 – Slovakia;
7 – Croatia; 8 – Romania; 9 – Bulgaria.
Source: Own editing based on UniCredit New Europe Research Network. In: Halesiak et al.
(2007, 22; fig. 25).
70
Györgyi Barta
References
ACEA (European Automobile Manufacturers’ Association):
http://www.acea.be/images/uploads/files/20100930 Production EU27 1006 III MV.pdf;
http://www.acea.be/images/uploads/files/20101223 ER 1012 2010 III Q3.pdf
[11 August 2011].
Ansani, J. A. – Singer, H. W. (1992) Rich and Poor Countries. Routledge, London.
Blöcker, A. – Meissner, H-R. – Jürgens, U. (2009) Anticipation of Change in the Automotive
Industry. Analysis of Automotive Regions. Study 3.
Diez, W. – Becker, D. (2010) Brand and Ownership Concentration in the European Automotive
Industry. Possible Scenarios for 2025. Institute for Automotive Research, KPMG Europe
LLP.
Domanski, B. – Lung, Y. (2009) The Changing Face of the European Periphery in the
Automotive Industry. – European Urban and Regional Studies. 16. pp. 5–10.
Eurostat: http:// epp.eurostat.ec.europa.eu/portal/page/portal/prodcom/data/excel_files_nace
[10 August 2011].
Freyssenet, M. (2010) Transformation and Choices of European Automobile Industry Post–
Global Financial Crisis. GERPISA, CNRS Paris.
http://freyssenet.com/files/Transformation%20and%20Choices%20of%20European%20Auto
mobile%20Industry%20Post%20Global%20Financial%20Crisis%20-%20site.pdf
[10 August 2011].
Gelei A. – Venter L. – Gémesi K. (2011) A multinacionális vállalatok a járműgyártás iparágban.
In: Chickán A. (szerk.) A multinacionális vállalatok hatása a hazai versenyre és a versenyképességre. Budapesti Corvinus Egyetem Vállalatgazdaságtan Intézet, Budapest. pp. 179–231.
Halesiak, A. – Mrowczynski, K. – Ferrazzi, M. – Orame, A. (2007) The Automotive sector in
CEE: What’s next? UniCredit Group New Europe Research Network, Wien/Mailand.
Jürgens, U. – Krzywdzinski, M. (2009) Changing East-West Division of Labour in the European
Automotive Industry. – European Urban and Regional Studies. 1. pp. 27–42.
Kemenczei N. (2009) Az állami támogatások szerepe a magyar autóiparban. Doktori értekezés,
BME Gazdálkodás- és Szervezéstudományi Doktori Iskola, Budapest.
Kemenczei N. (2010) Hova húz az autóipar Magyarországon? – Külgazdaság. 3–4. pp. 48–63.
Motor Industry of Great Britain (2003) World Automotive Statistics, SMMT, London.
OICA (International Organization of Motor Vehicle Manufacturers):
http://oica.net/category/production-statistics [13 August 2011].
Özatagan, G. (2011) Shifts in value chain governance and upgrading in the European periphery of
automotive production: evidence from Bursa, Turkey. – Environment and Planning. 4. pp.
885–903.
Palkovics, L. – Vadvári, T. – Nádai, L. (2009) Developing New Curriculum for Vehicle Mechatronics based on Industry Demands. Budapest University of Technology and Economics,
Budapest.
Pyke, F. – Sengenberger, W. (1992) Industrial Districts and Local Economic Regeneration:
Research and Policy Issues. In: Pyke, F. – Sengenberger, W. (eds) Industrial Districts and
Local Economic Integration. International Labour Organization, Geneva. pp. 3–30.
Sipos M. (2010) A járműipar Szlovákiában. Nemzetgazdasági Minisztérium Tudásgazdaság
Főosztály, Stratégiai Műhely UniCredit Group.
Varga Gy. (2011) Tépelődés az autóúton. – Népszabadság. 2 June. p. 12.
SYSTEM OF KNOWLEDGE TRANSFER
IN THE AUTOMOTIVE INDUSTRY
MELINDA SMAHÓ
Keywords:
automobile industry research and development functional upgrading
The study investigates the knowledge-based process and knowledge-transfer system of the
automotive industry introducing the relevant theoretial concepts on the one hand, and analysing the
knowledge-based process of the Central and Eastern European (CEE) automotive industry on the
other hand. The theoretical-historical literature review confirmed that knowledge and/or its any
form appears and plays a role in the production paradigms of the automotive industry, and at the
same time, its characteristics differenciates the production system according to more dimensions.
Dramatic changes that have taken place for the last two decades have caused modification in the
value chain’s structure and have shortened the product life cycle. Furthermore, they predicts a turnround in the direction of the innovation. Thus, the main points of the production and development
tasks have been changing that have led to the restructuring of the international labour division of the
automotive industry on the one hand, and to the launch of upgrading process in the automotive
industry of the CEE countries. The research states that the building and broadening of R&D
capacities have started in the analysed Central and Eastern European countries’ automotive
industry. This process incorporates both the starting up of foreign companies’ R&D units and the
revival and development of the domestic research centers.
Introduction
According to the sectoral innovation approach there exist systematic and significant
differences between the innovative attitudes of individual industrial sectors, for
instance, in the pace of technical change as well as in the field of the organisation of
innovative activities (Reinstaller–Unterlass 2008). Automobile industry, being one of
the keystones of the economy in Europe, belongs to the so-called medium-high tech
industries. Its future competitiveness is highly determined by the innovation system
supporting the creation of new and attractive products (product innovation) on the one
hand, and their high-standard and effective production (process innovation) on the
other. Innovation in automobile industry may be interpreted largely as a collaborative
work, i.e. the outcome of cooperation between the car manufacturing companies and
their technologically experienced Tier1 suppliers (Sofka et al. 2008). A peculiarity of
this sector is that the production of components, having high added value, being capital
intensive and requiring research and development, is combined with the manufacturing
of parts and components having low added value and being labour intensive
(Fortwengel 2011). At the end of the first decade of the new millennium 63 per cent of
the companies engaged in automobile industry in the EU25 may be considered as
72
Melinda Smahó
knowledge-producing, while 37 per cent is knowledge-applier, which, compared with
other sectors of industry, may be esteemed as a favourable proportion (Reinstaller–
Unterlass 2008). The number of patents applyed by automobile industry also suggests
the innovative nature of this sector. In 2008, nearly 6300 patent applications derived
from the European automobile industry, which made more than half (54%) of the
applications received by the European Patent Office in 2008. The rest of patent
applications were received from Japan (22%), came from the United States of America
(16%), and arrived from China/Taiwan and South Korea (less than 1%) (ACEA 2010).
On the basis of the foregoing it may be assumed that the role of knowledge as well
as knowledge based processes in automobile industry is not negligible at all. The
objective of this study is to reveal the knowledge based process and knowledge transfer
system of vehicle (automotive) industry); on the one hand through the presentation of
theoretical relations, on the other hand through analysing the knowledge based process
applied in automobile industry throughout the Central and Eastern European
Countries. The first chapter of this study examines the role knowledge plays in the
production system of automobile industry. Following the review of the main – in
particular knowledge related – peculiarities of production paradigms in chronological
order, comes a deeper analysis and comparison of some production system. The second
chapter investigates the actual trends of knowledge transfer in automobile industry, on
the one hand by approaching and revealing the characteristics of research and
development and innovation, on the other hand by investigating the suppliers’ status and
roles taken in knowledge based process. The third chapter discusses the geographical
dimensions of knowledge transfer in automobile industry, relevant to two Western
European countries (Germany, Austria) and six Central and Eastern European countries
(Poland, the Czech Republic, Slovakia, Slovenia, Hungary and Romania).
Role of knowledge in the production system of automobile industry
Historical overview
In the small-scale industrial and craft manufacturing system, at the turn of the 19th and
the 20th centuries, knowledge can be traced in the form of the craftsmen’s professional
competence. They would design and manufacture the components and parts of cars,
conforming to the individual demands and requirements of customers, as independent
entrepreneurs. As for professional work, their roles were predominant, while the car
manufacturing undertakings engaged in the assembling of components and parts took
“merely” the role of a co-ordinator. Heuristic knowledge production serving for discovery and understanding was therefore restricted to only few craftsmen or undertakings, considering the fact that at that time there existed no collective knowledge base
that other producers or professionals could have also extended, constructed or improved; moreover, even the formal technical education requisit for engineers or technicians was also lacking. At the same time, craftsmen – in the course of their work –
System of Knowledge Transfer in the Automotive Industry
73
cumulated more and more tacit knowledge that they acquired empirically over the years;
they shared such knowledge with their assistants and apprentices during their collaboration and the process of working together, thus the latter had the opportunity to learn the
craft by observation or “by watching” (learning by doing). The craftsmen’s resources
(professional knowledge, capital) perfectly fitting for individual manufacturing, however, proved insufficient for the pursuance of systematic research and development
activities, and limited the technical improvement within the framework of this paradigm
(Havas 2010; Wibbelink–Heng 2000).
At the outset of the 20th century it was Henry Ford, who recognised the absolute
importance and inherent opportunities of technical development. In his factory, opened
in 1910, he applied machine tools that were much more developed than those used
earlier; and such tools were combined in 1913 with another technical innovation, the
conveyor belt, produced based on József Galamb’s designs, who was Hungarian by
origin. The use of modern machinery facilitated the mass production of standardised,
interchangeable and easy-to-assemble parts, which was accompanied by the decrease of
specific or per unit time and cost indicators. The greatness of Ford’s achievement did
not lie in the development of individual techniques and technologies but in the effective
combination of already existing technology elements in a mass production system
(Wibbelink–Heng 2000; http://www.hfmgv.org/ exhibits/hf/#fmc).
This novel, much more developed production technology, however, was coupled
with the masses of unskilled workers. In accordance with Taylor’s theories regarding
the division of labour, anyone could be able to briefly learn the simplified operations of
disintegrated work processes, and by this the workers became easily replaceable or
substitutable. Engineering work was characterised by strong specialisation: first
development and production was separated then product and production development
parted within the field of development and eventually product developers became
specialised in the designing of certain parts (Havas 2010). Owing to the knowledge
accumulated within a corporation and the inner economies of scale, Ford was always
able to develop the technology of mass production faster at its suppliers. Car manufacturing factories adapting the principles of mass production paradigms were, even later
on, able to take their leading positions in respect of technology over the suppliers, so
that the parts were typically designed by their own engineers, and assigned the suppliers
with merely some morsel of subtasks, impeding knowledge drain or leakage by this
(Havas 2010; Wibbelink–Heng 2000).
From the middle of the 80’s the ‘Fordist’ production system was superseded by the
Toyota production scheme (lean production)1 developed by Eiji Toyoda and Taiichi
Ohno, which spread not only throughout Japan, but it captivated the car manufacturing
companies and suppliers in Western Europe and in North America as well, and all over
the world it determined – and still keeps determining – the car manufacturing trends
(Cséfalvay 2004). In fact, the Japanese transformed or re-tailored and further developed
the system of Fordist mass production, so that the production system was adjusted to
their culture and domestic market. The global success of the Toyota-method (and lean
production) was attributable largely to the fact that following World War II the
74
Melinda Smahó
technology and knowledge transfer was directed from the automobile industry of the
USA and Western Europe to Japan, and – in relation to this – to the progress made in
the field of organisational learning. At that time Japanese government would encourage
car manufacturing companies to bring the knowledge and technology related to car
production from the West. Technology import was carried out on the one hand in the
framework of associations and co-operations with British, French and American car
manufacturing companies on the other hand indirectly by way of “reverse engineering
activities” – i.e. through dismantling and studying the cars produced by competitors
(Haak 2006; Wibbelink–Heng 2000).
In factories operating in accordance with the principles of lean production, teams of
skilled workers are engaged in the resolution of variegated problems and endeavour to
manufacture products of high quality. Employees are trained and educated for a long
time and their works are often rotated inside the corporate organisation – for the purpose
of enabling their learning processes – and – in line with the Toyota management’s
philosophy – they are constantly involved in the improvement and development of the
system (kaizen). The fundament of Kaizen philosophy is a kind of corporate culture
where the employees may – without fearing of punishment – focus attention on
imperfections or flaws and may identify problems and subsequently they work
collectively on the resolution thereof taking advantage of benefits deriving from
knowledge sharing and knowledge flow. If collective work is successful individual
knowledge will bring about a new standard applicable to the company as a whole,
consequently this progress may be interpreted as one of the processes of organisational
learning. One of the key elements of the paradigm is the reduction of waste which shall
be understood not only in material, physical sense, but also to the waste considered by
the system as being the “worst”, to reworking, that is to the wasting of human resources
(Haak 2006; SAP 2003). Additionally the Toyota-method opened another channel of
knowledge flow, that is the one between companies by the Toyota company promising
guaranteed orders and the sharing of surplus profit deriving from cost reduction for the
sake of the company’s economic production (cost reduction) to its suppliers, who were
willing to adapt and introduce the production principles – especially the just-in-time
system forming a part thereof (Haak 2006). With the Toyota-method the knowledge
flow appeared formally moreover it was promoted between car factories and their
suppliers – but of course, with a restricted and controlled scope. This production system
is an alloy of all the advantages that are inherent in handicraft production and the
Fordist mass production, obviating the high costs that the former entails and the
inflexibility that characterise the latter (Haak 2006).
The flexible manufacturing or mass customization, evolving at the end of the 1980s
and at the outset of the 1990’s, may be construed as the extension of the principles of
lean production to certain extent. Mass customization of production is enabled by the
advancements in production-supporting-technology by allowing the use and sharing of
the common data base of parts and products, as well as production capacities and
problem data among those involved. By interpreting this knowledge as a resource and
by utilizing this potential the companies are able to react more swiftly to the changes in
75
market conditions, which means a competitive advantage for them (Henriksen–
Rolstadås 2010; SAP 2003).
At the end of the 1990’s and at the beginning of the 2000’s technology had dramatic
impact on productiveness. With the information becoming ubiquitous customers
advanced to “crowned kings”, and at present the (car manufacturing) companies’
success depends highly on the swift and efficient reaction and adaption to the “kings’ ”
ever changing demands. Two major features of adaptive production paradigm are flexibility and fastness that the companies may achieve through integrated solutions which
adjust the supply chain perfectly to the companies’ own operational processes, production machinery and operative systems (SAP 2003). Knowledge flow between car manufacturing companies and their suppliers is not merely a potential or stimulating factor
but also a requirement, being prerequisite for the smooth operating of the value chain
system.
Knowledge base and knowledge based process
On the basis of the foregoing analysis it is obvious that knowledge appears and also
plays a role in every paradigm, nevertheless its peculiarities strongly differentiate production system along several dimensions (Table 1). Innovational process at companies
in the automobile industry are vigorously formed by their knowledge base which is
different in each industrial sector, but it also depends on the corporate strategy. As a
matter of fact, in the knowledge base of particular industrial sectors the explicit and tacit
knowledge is present in a differently proportioned composition; furthermore the potentials for the codification and restriction of knowledge are also different. In synthetic
knowledge base the application of the knowledge in-hand or a new combination of the
attainable knowledge lead to innovation. Research and development activities and the
interrelations between the university and the industry are not very significant and in
principal they focus on applied researches, on product and production development. As
a result of inductive process knowledge is developed in the course of testing, experiments, and practical work. In principal, the source of knowledge is experience, the
learning-by-doing, therefore the knowledge created in synthetic knowledge base is
chiefly of tacit type (Cooke et al. 2007). As opposed to the foregoing, in the case of
analytical knowledge base the production of scientific knowledge and the access to
knowledge source are of determining significance. Fundamental and applied research as
well as constant technological development can be equally found at corporate R&D
divisions on the one hand and at universities as well as at research institutes on the other
hand. University-industry relationship is strong and is based on scientific co-operation;
the existence of academic spin-off companies are frequent. Knowledge input and output
are codified to a greater extent than in the case of the other knowledge base, at the same
time, in analytical knowledge base for the application and use of codified knowledge, in
most of the cases tacit knowledge is needed. Production of new knowledge takes place
with the usage and application of existing studies, scientific principles and methods,
while the output of formally organised knowledge creation process is codified in the
76
Melinda Smahó
77
78
Melinda Smahó
form of scientific studies, reports or patent applications. The aim of research and
development activities is the production of scientific discoveries, technical and technological innovations which will be patented later on (Cooke et al. 2007). Although basically every company has both synthetic and analytical knowledge base, in the case of
the handicraft production the former while in the case of mass production the latter one
dominates, and the background of lean and adaptive production schemes is ensured by
the synthesis of the two mentioned knowledge base (Henriksen–Rolstadås 2010).
To suggest differences, without the claim for completeness, some examples are
demonstrated below. In the case of mass production a large and central staff is
responsible for development and quality issues; quality control is grounded on explicit
knowledge and at the end of the production process (e.g. after a car is ready) it is carried
out in large scales. Learning process take place on the basis of directions or commands,
however in the course of this to make the workers understand and observe the quality
principles means or might mean a problem. Supplier relations also rely on explicit
knowledge, at the same time, they are formal (e.g. tender, well-defined requirements,
documentation). This is because suppliers are selected on the basis of objective criteria,
thus the car manufacturers endeavour to keep an arm’s length distance from their
potential suppliers.
On the contrary, in the case of lean production quality control is much less
centralised, and even the labour force is involved in it: the operators working on the
production line may stop production if any quality flaw is detected. In the lean
management system, tacit knowledge plays a predominant role, the sharing of which
takes place on the one hand between persons close to customers (i.e. the sales) and the
manufacturer on the other hand between the manufacturer and the supplier. At the same
time it is indispensable and is challenging to make this knowledge explicit – and a part
of the analytical knowledge base – since the sales and marketing divisions give input to
the research&development department and to the production, while decision making
regarding the producibility of new products requires conciliation among the research
and development, the production unit and the suppliers. To the smooth functioning of
such processes the fast and undisturbed flow of codified knowledge is indispensable
(Henriksen–Rolstadås 2010).
The major principles included in Table 1 above are shaded by the fact that research
and development at lean companies may be centralised, minor corrections and
improvements however are better to be decentralised, i.e. to allocate them to local units
being closer to consumers and suppliers. This may be explained by the fact that the
linear or radical innovation associated with the centralised research and development is
largely based upon explicit knowledge, the spatial spreading of which is not limited. As
opposed to this the fundament of incremental innovation is formed by tacit knowledge,
to the attainment of which the car manufacturers must be close in space to the
consumers and suppliers. In the case of lean management the relationship between the
car factory and the Tier 1 suppliers is crucial; Tier 1 suppliers play a central role in the
process of knowledge creation and knowledge transfer, considering the fact that they are
expected by the car manufacturers to keep coming up with new and improved solutions.
79
This, however, requires access to R&D institutes and the analytical knowledge base, but
also a very close relationship is needed within the development teams and with the
synthetic knowledge base thereof (Henriksen–Rolstadås 2010).
Nowadays adaptive production focuses not merely on knowledge creation and
knowledge transfer but much more on the fast adaptation of knowledge and this is
largely promoted by the application of information technologies (Henriksen–Rolstadås
2010). The symbiotic co-operation between car manufacturers and suppliers is prerequisite for the alignment of production process, which requires knowledge transfer
among the partners via standards, requirements and informal relationships. Territorial
determination of knowledge flow, at the same time, might be mitigated by the application of info-communicational technologies.
Time is required for the acceptance and adaptation of the principles of a newly
created production paradigm, thus the coexistence of characteristics of different production systems often occurs at companies (Henriksen–Rolstadås 2010). The following
chapters discuss the deeper correlations inherent in the lean and adaptive production
schemes – and their alloys – being commonly accepted nowadays in the developed
countries.
Current tendencies knowledge transfer in automobile industry
Characteristics of research & development and innovation
in automobile industry
The innovational peculiarities of automobile industry, which predominate nowadays,
developed as a consequence of and influenced by the dramatic changes that have
occurred in this sector since the beginning of the 1990’s as for the economic and social
conditions and circumstances as well as the ever changing consumer demands and
requirements, and they show significant differences compared to the peculiarities that
used to characterise the previous period. The first such change was the transformation
in the structure of the value chain, which had or has been accompanied by decreasing
production and development depth at car manufacturing companies. At present,
relatively low is the number of internationally active OEMs in automobile industry, who
design, produce and sell the car on his own, as a complete product. The major part of
the units – the so called modules – constituting nearly 65% of the added value of a
modern car – is produced by the multi-tier supplier system, and this proportion –
according to the experts’ estimations – is expected to reach 78% by 2020 (Fortwengel
2011; Kremlicka et al. 2011; Sofka et al. 2008; Kinkel–Zanker 2007). Outsourcing of the
production processes was accompanied by the outsourcing of the related development
activities to the suppliers – in principal to Tier1 suppliers. While two-third of the
developments were performed by the OEMs in 2000, in 2010, this ratio was only
around 50%, and by 2020 it is estimated – according to the forecasts – to drop to onethird; that is within some years two-third of the developments in automobile industry
80
Melinda Smahó
will be performed by and will fall under the responsibility of suppliers (Figure 1)
(Kremlicka et al. 2011; Kinkel–Zanker 2007). Considering the division of labour per
field of speciality, in the technological development of cars with conventional driving
the supplier companies are also involved in – moreover they are responsible for –
development, while the development of fuel cell technology – at least for the time being
– is concentrated in the hand of OEMs (Kinkel–Zanker 2007).
FIGURE 1
OEMs and suppliers’ participation in value creation
Source: Kremlicka et al. (2011, 3).
The second essential factor forming the innovational process in automobile industry
is the profound technological change that took place during the past two decades and
nowadays may be considered as nearly constant. At the same time, the new technological potentials emerge more often outside the traditional fields of speciality, in particular
in the fields of electronics, the software, the alternative driving systems of motors and
alternative fuels, as well as new materials and new production technologies. Development targets include primarily higher safety, higher comfort, the increasing performance
as well as environment friendly features. On the short run, for instance in the case of
motor developments, the incremental improvement of combustion engines, while on the
medium-term hybrid drive and on the long run the fuel cell technology is expected to
forge forward (Reinstaller–Unterlass 2008; Kinkel–Zanker 2007).
As a consequence of the accelerated technological advancement the service life
cycle of models has declined drastically – from 10 years to 3 to 6 years. Parallel with
81
this the concept of model updating has also changed; nowadays it is not the development of a completely new vehicle, but “merely” a facelift is meant by it. That is design
of the old model is mostly retained and the alterations target primarily the details of
design and extend to modifications that are not at all directly visible for customers.
Japan shall be treated as an exception in this respect; there car manufacturers still count
with longer service lives, whereas their customers still require significant and substantial improvements not merely a facelift. Another typical strategy to put “new models”
on the market is the extension and differentiation of the product range, including the
satisfaction of customers’ demands via niche models and by this to reveal new niche
markets as well. This is facilitated and made economical by the fact that in high
proportion identical (global) underframes and modules are integrated in different
models; since the research and development expenditure regarding certain parts that
may be built-in more than one model, may be projected to a higher serial number, thus
their specific value can be drastically reduced (Kremlicka et al. 2011; Kinkel–Zanker
2007). However there have already been overlapping among the cars of corporate
groups assembling more than one brands, the new production technology developed by
the Volkswagen Group, namely the “baukasten principle” (MQB, Modulare
Querbaukasten) has facilitated since 2012 the integration of standardised components in
the case of 30 or 40 models to an extent that two apparently different cars are built, 60
to 70 per cent, of the same parts. This is expected to mean 30% cost saving for the
company, and from 2020 it will enable the production of 50 different Audi, Seat, Škoda
and Volkswagen models. This is a remarkable sum, regarding the annual capacity of the
holding scheduled for 10 million cars, and at the same time it is an immense price- and
competitive advantage (Becker 2010; Autógyári kannibalizmus 2011).
The third direction of changes is the so-called Low Cost-High Tech trend, which can
be derived from the transformation of social and economic relations and the consumers’
demands. The population of young urban customers has become a consumer category
with growing significance and they raise higher expectations against low category cars
than usual in respect of the technical solutions and accessories that the small cheap cars
are generally equipped with. In principal in countries with low purchasing power –
where the segment of the ultra-low-cost car has appeared again – the car manufacturers
can manage to satisfy these demands cost effectively only through extremely smart
innovations. High technical requirements coupled with low retail prices – and depressed
manufacturing costs – result in the reversal of the former innovational trends: innovative
solutions are not any longer put from the higher category cars, after a certain time, into
lower category vehicles, but quite the contrary (Figure 2) (Kremlicka et al. 2011).
Let us examine how these features and tendencies of innovation affect the
knowledge based process, system of motivation and relations of the role players of
automobile industry!
82
Melinda Smahó
FIGURE 2
Reversal dispersion of automobile industrial innovation
Innovation pressure
Luxury
NEW
Large
Medium
Small
Basic
CLASSIC
ULCC
Price pressure
Classical direction of innovation
New direction of innovation
Note: ULCC = ultra-low-cost car
Source: Kremlicka et al. (2011, 5).
Suppliers and their knowledge based process
Automobile industry is a typical example to the so-called quasi-hierarchical value
chains, in which the lead firms – the OEMs in automobile industry – organise and
control the value chain by virtue of their corporate and market power. They decide on
which suppliers to involve in the network and which to exclude therefrom, furthermore,
it is their competence to define the characteristics of supplied parts, as well as the
production, transportation and quality control process relevant to such parts, not only
against the direct suppliers but also all along the supply chain (Figure 3) (Humphrey–
Schmitz 2002; Pavlínek–Ženka 2010).
Fairly large burden is laid upon suppliers due to their intensive involvement in value
creating process and to the shortening of the product’s life cycle. As a consequence of
shorter product life cycle the development cycle of supplied parts or components also
became shorter, namely from 40 moths at the beginning of the 1990’s by today it has
been reduced to approx. 20 months. Besides faster development of innovative products
it is more and more expected from the suppliers to solve pre-financing of the research
and development activities, and they are also supposed to undertake the risk of a
83
possible failure. As a matter of fact R&D work is often not directly appreciated but
based on the number of pieces actually sold from a particular product, which encumbers
the planning of the returns on an investment. Additionally, suppliers are expected to
assume the risk of product liability passed to them by the OEMs, moreover they are to
achieve certain rate of cost reduction as well. The latter – at least theoretically – may be
accomplished through the synergies related to developments and the returns to scale. In
practice the suppliers are to save costs on production due to the increasing financial
requirements of the development for the sake of surviving (Kinkel–Zanker 2007). This
might encourage them to the introduction of new technologies, or even to the relocation
of their business to another geographical area in order to take advantage of the benefits
concomitant with such relocation (namely, the lower wages, state subsidies, tax
benefits) (Szalavetz 2010).
FIGURE 3
System of relations among the car manufacturers and their suppliers;
Structure of the value chain
OEMs
Engines
Bodies (design)
Car assembly
Sales (marketing)
Squeezing to
cut costs
OEM
Fising input in a form of
technological know-how
Tier 1 suppliers
Automotive systems
(e.g. interior, steering)
Squeezing to
cut costs
Alliances
Joint ventures
M&A
Capital links
OEM
Tier 1
Consolidation
Tier 1
Consolidation
Tier 1
Bottom-up pressure
resulting from rising
material costs
Tier 2, 3, ... suppliers
Individual parts
and modules
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Tier 2
Source: The Automotive Sector in CEE… (2007, 9).
Requirements raised against the suppliers pertaining to management and production
– nevertheless the increasing standardisation and the large scale mounting of identical
parts – are growing more and more serious, whereas it is necessary to conform to a great
number of customer- and variant-specific demands. Their situation is further hampered
by the fact that OEMs drastically cut back on the number of suppliers by outsourcing
their production process (former Tier 1 suppliers become Tier 2 suppliers), which they
reason with their assumption that only few suppliers possess the competencies and
capacities they expect and which are requisite for the development and production of
complete vehicle modules. Furthermore, OEMs hope to achieve through concentration
the reduction of transaction costs and the realisation of economies of scale advantages at
the remaining suppliers (Kinkel–Zanker 2007).
In fact, only Tier 1 suppliers have the appropriate innovational competencies, they
are the ones who are to be capable of accomplishing radical (strategic) product and tech-
84
Melinda Smahó
nological innovations, additionally, they are expected to produce their own parts, to
assemble the complex module associated with the parts as well as to manage the supplier chain related to this module – in compliance with the OEM’s demands and
requirements (Gelei–Venter–Gémesi 2011). According to the logic of modular production it is indispensable that the OEMs and the Tier 1 suppliers shall be located
geographically close to one another (Pavlínek–Ženka–Žížalová 2010), therefore with the
relocation of the car manufacturers – in order to establish the module-competence –
often, the suppliers are encouraged/forced to global presence, and they regularly acquire
their competitors, along with their technologies, as a best practice (Kremlicka et al.
2011). Tier 2 suppliers have only product-competences, which covers the implementation of incremental innovations i.e. the development of the specifications or production
technologies (for instance in the case of a model change) of the manufactured product
(Figure 4) (Gelei–Venter–Gémesi 2011).
Although less and less suppliers are capable of meeting the ever increasing requirements, those who persist are given higher recognition and are respected by the car
manufacturers as equal strategic partners. Consequently the competition between
suppliers has become strained; the companies growingly emphasise the importance of
direct contact with the car manufacturers and when they make decisions regarding the
location of their production sites they upgrade the importance of the large car manufacturers’ territorial vicinity (Kinkel–Zanker 2007), as well as the essence of informal
knowledge flow.
Innovational competence
TIER1
Product competence
TIER2
Capacity competence
TIER3 and below
Management of
module product
supplier network
+
Strategic innovation
Incremental innovation
+
Managing relations with own suppliers
Price, compliance with specifications, quality,
standard of services, volume, flexibility, reliability
Source: Own editing based on Gelei–Venter–Gémesi (2011, 186–190).
Product complexity, profit
FIGURE 4
Pyramid of supplier competences
85
At the same time not only conventional suppliers may benefit from the outsourcing
of development functions but also the companies rendering development services.
These development companies have appeared as new, independent role players in automobile industry hence they may enter into service of any – or even more than one – car
factories or suppliers at a time. As third parties they are increasingly involved in the cooperation between the suppliers and OEMs and with their technical knowledge they
support and assist the participants of the development network significantly, furthermore they often undertake the role of the “lead firm” in the development process of
modules and components, including also the responsibility for the coordination of development networks. In Germany, the number of employees at such companies has
quadrupled in the period between 1998 and 2003, which – at least in Germany –
suggests their growing significance. To such an extent that nowadays such companies
undertake the complete development of vehicles marketed on gap markets. At the same
time, their future role cannot be sufficiently estimated, since the system suppliers taking
over more and more development functions from the car manufacturers are not
interested in transferring or outsourcing the developments, on the contrary, they take all
efforts to retain them, to enhance and reinforce their own key know-how (Kinkel–
Zanker 2007).
Geographical dimensions of the knowledge transfer
in automobile industry: Central and Eastern-Europe
Knowledge flow and international division of labour
Theories reasoning the new international division of labour consider peripheries not
merely as territories exploited by the centre and playing exclusively raw material
supplying functions but also as regions having productive functions as well (Fortwengel
2011). Moreover with the shortening of the product life cycle and with the acceleration
of the learning process of affiliate companies relocated to the peripheries the
geographical division of labour may not be simplified any longer so that the production
of mature products with low added value is put to the peripheries, while new products
with high added value are manufactured by the centre (Szalavetz 2010). In this respect
the theory of the Global Commodity Chains (GCC) emphasizes the role of the lead
firms in this industrial sector, and it sees the path of development in the union with them
– in the case of automobile industry with the OEMs. However the theory of the Global
Value Chains (GVC) examines the patterns and imprints of international division of
labour in the era of geographically fragmented production process with geographically
scattered role players, taking the unequal distribution of benefits also into consideration.
This theory sets upgrading into focus and distinguishes four forms of it. Product
upgrading refers to the case when companies shift to the direction of manufacturing
products being more sophisticated than the former ones, while process upgrading
means increased efficiency, which may be implemented either through the introduction
86
Melinda Smahó
of new technologies or through production system restructuring. Intra-chain upgrading
or functional upgrading involves the extension of the current scope of a company’s
activities by adopting new functions either preceding or following the existing ones in
the chain (e.g. besides production designing, marketing, research and development,
etc.). Inter-chain upgrading occurs when companies apply or utilise the competences
acquired in the course of one of the functions in another sector (Fortwengel 2011).
The quasi hierarchical value chains typical to automobile industry are favourable for
product and process upgrading while they hinder or even impede functional upgrading.
An exception from the latter is when the suppliers are involved in the research and
development functions, which appears as a new function at them. The suppliers’ product
and process upgrading are carried forward by the product and process standards
specified by the lead firms (OEMs), while in the case of the process modernisation also
the expected cost reduction may mean a considerable incentive for the incremental
improvement of the production procedures. However the upgrading potentials in the
case of small supplier companies being at the bottom of the hierarchy are remarkably
restricted due to the strong concentration of companies (Pavlínek–Ženka 2011).
In the first half of the 1990’s the automobile industry in Western Europe and in
Central and Eastern Europe was characterised by an international division of labour
analogous to the duality of centre and periphery. Owing to direct foreign capital investments in the green and brown field production units established in the countries of Central and Eastern Europe (Poland, the Czech Republic, Slovakia, Hungary, Slovenia,
Romania) at the outset only small and cheap “low-tech” models were manufactured
(e.g. Fiat Seicento in Poland), and the products were manufactured by means of modern
technology conforming to the world standard. In the first period of the investments in
automobile industry no product upgrading took place, but a very significant technological development was carried out.
From the end of the 1990s the OEMs changed their strategies, redefined the roles of
the countries in Central and Eastern Europe, and more and more export oriented
assembling and component manufacturing functions were located there. These plants
equipped with world standard technologies shifted to the manufacturing of high-tech
products representing high added value (e.g. the VW Tuareg of premium category, or
the Porsche Cayenne manufacturing in Bratislava), of course, at much cheaper wage
costs than those paid in the Western-European factories. This process can be perfectly
traced in the change of the rate of products of low, medium and high added value.
Between 1996 and 2006 considering the collective output of the Czech Republic,
Hungary, Poland and Slovakia the rate of products with low added value dropped from
26.1% to 23.9% while that of those with high added value increased from 14.1% to
32.3%. The most spectacular change occurred in Poland where the rate of automotive
industrial products with high added value grew from 4% in 1996 by 2006 to 33.3 per
cent. Thus the initial technological upgrading was also supplemented from the end of
the 1990’s with product upgrading (Fortwengel 2011).
All these have affected the purchasing strategies of the traditional car manufacturing
companies as well as their changes. Parallel with their extension in Central and Eastern
87
Europe the OEMs have increasingly expected from their suppliers to follow them and to
settle their sites in the vicinity of the newly established assembly plants to serve them.
This “follow sourcing” strategy has led to the restructuring of global automobile
industry and to the appearance of the so-called global suppliers. The geographical
expansion of supplier companies towards the Central and Eastern European countries is
truly reflected in the development of the number of automotive industrial companies
(Table 2).
TABLE 2
Number of vehicle industrial companies (NACE 34) in some European Countries
Country
Germany
Austria
Czech Republic
Poland
Slovakia
Hungary
Slovenia
Romania
Number of Companies (pc)
1999
2000
2002
2007
Change (%)
(2007/1999)
2,308
206
288
1,646
41
194
124
218
2,283
193
341
1,145
53
202
126
300
2,558
237
573
1,070
76
396
96
352
2,483
307
491
1,328
141
409
104
402
107.6
149.0
170.5
80.7
343.9
210.8
83.9
184.4
Source: Eurostat, Structural Business Statistics.
Since the end of the 1990’s the number of automotive industrial companies have set
out to increase intensely in nearly every reviewed CEE countries. Slovakia, where
almost three-and-a-half-times growth was achieved in the period between 1999 and
2007, showed the most remarkable change. In Hungary, the number of automotive
industrial companies more than doubled, but the Czech Republic (170%) and Romania
(184%) could also record a growth of similar order of magnitude. Considering the
absolute data and the orders of magnitude, Germany and Poland constitute a separate
category, and in both countries the “waving” of the number of companies in a minor or
greater extent is observable. The question to be posed here is what quality tendencies
are hidden behind the increasing number of companies: only the labour intensive
productive activities have been relocated to the CEE countries, to utilise the benefits
deriving from lower wage rates, or functional upgrading has taken place and knowledge
intensive research and development activities have also been introduced? Before giving
an answer to this question let us take a closer look at the automotive industrial research
and development potentials of Germany and Austria belonging to the central region of
European car manufacturing.
Altogether 91 car or motor manufacturing factories (OEM) can be found in the eight
countries examined in the framework of this study, and the mentioned factories are
concentrated in 54 regions2. Regarding the GDP proportional research and development
figures a sharp demarcation line is perceptible between the regions of Germany and
Austria, and those of the Central and Eastern European countries. It is obvious that the
88
Melinda Smahó
car factories (OEMs) of Germany and Austria – with the exception of five German
factories – are located in regions where the GDP proportionate R&D expenditure is over
1%. Moreover, almost the same applies to the Czech Republic, where merely one or two
regions having car factories (Moravskoslezsko) are below the 1% limit (Figure 5).
FIGURE 5
OEMs location and R&D expenditure (2007) in per cent of GDP
Total expenditure of R&D
(GERD) in % of GDP
3,01 - 6,81
2,00 - 3,00
1,01 - 1,99
0,51 - 1,01
0,00 - 0,50
(10)
(13)
(25)
(15)
(30)
Source: Own editing based on Eurostat and ACEA figures.
Map by Tamás Hardi.
The research and development data relevant to the automotive industry also support
the highly prominent role of Germany (Tables 3 and 4). On the basis of turnover automobile industry is the largest sector in the country; the research and development
expenditure of 18 billion Euro in this sector is over one-third of the national value and
nearly one-fourth of the added value of this segment. In 2007, the R&D expenditure of
the automotive industry in Germany represented almost 70% of the same value of the
EU (Pavlínek–Ženka–Žížalová 2010), and even Braunschweig, a region of Europe being
the most specialised to automotive industry is also located here (Eurostat 2010).
Twenty-one research and development centres closely related to the OEMs are functioning in the country, which represent 42% of the R&D institutes operated by the
European OEMs and Tier 0.5 suppliers. German car manufacturers and their suppliers
are world leaders in the field of innovation with their over 3500 registered patents a
year. The background for this is provided by the high standard higher educational
89
90
Melinda Smahó
TABLE 4
Expenditure per person engaged in research and development
Country
Germany
Austria
Czech Republic
Poland
Slovakia
Hungary
Slovenia
Romania
Expenditure per person engaged in research and
development (thousand EUR/person)
2002
2007
174.4
175.1
69.0
n.a.
6.3
17.0
35.7
3.1
211.5
156.3
89.1
24.1
41.7
61.8
53.4
0.7
Change
(%)
121.3
89.3
129.2
n.a.
666.7
363.9
149.5
21.3
Source: Own calculation on the basis of Eurostat, Structural Business Statistics figures.
system of the country, with over 100 universities and colleges giving excellently
qualified labour force to this industrial sector. The number of people working in
automotive industrial research and development is in excess of 83 thousand persons,
which makes almost 10% of the employees in this sector. The automotive industrial
R&D expenditure per person engaged in research and development is also the highest
here, namely it was over 211 thousand Euros in 2007. Beyond this, several innovational
clusters integrate the requirements, achievements and challenges of this industrial
sector, scientific research and education in scientific fields, related to automobile
industry (Figure 6). The innovation political incentives and supports granted to the
automobile industry also significantly contribute to its success and achievements (The
Automotive Industry in Germany… 2008).
Germany’s leading position in automobile industrial research and development was
confirmed also by the ranking of the EU’s Investment Scoreboard in 2010, which listed
the first one-thousand European companies having the highest expenditure of research
and development in the years of 2010. Volkswagen Holding, based in Germany, leads
this ranking, and among the altogether 43 companies in the list all carrying out their
businesses in automobile industry further 19 – i.e. altogether 20 – are also based in
Germany (e.g. Daimler, Bosch, BMW, Continental, Porsche, etc.) (Annex 1). In
accordance with its automotive industrial R&D indices Austria can be ranked between
Germany and the CEE countries however it showed great differences in scale compred
to both (Tables 3 and 4). In the European ranking of the EU Investment Scoreboard two
Austria based automobile industrial companies are listed (Miba, KTM Power Sports),
and with this it may be stated that more than half of the 43 automobile industrial
companies (22) have German or Austrian headquarters (Annex 1). On the basis of the
foregoing it may be assumed that these two countries, having advanced automotive
industrial research and development potentials and significant corporate headquarters –
in the course of the extension of the companies towards the Central and Eastern
European countries –, have (also) functioned as knowledge exporting countries.
Source: The Automotive Industry in Germany… (2008, 5).
FIGURE 6
Network of competence centres related to automobile industry in Germany
91
92
Melinda Smahó
Functional upgrading, research and development
Functional upgrading may be measured by means of several different methods and
index figures. Pavlínek and Ženka (2010) consider the rate of R&D expenditure
compared to added value, as well as the number of R&D employees in proportion to all
other employees as the index numbers of functional upgrading. At the same time,
according to global commodity chain and global value chain theories the research and
development activities and the existence of research centres suggest functional
upgrading process (Fortwengel 2011). In the following we attempt to discover the
Central and Eastern European process by alloying these two approaches.
The research and development figures of the six CEE countries in 2007 unequivocally suggest the leading role of the Czech Republic (Tables 3 and 4). The number of
employees of the country engaged in automotive industrial research and development
activities is in excess of that of Austria, but the volume of research and development
expenditure (290 million EUR), and the rate thereof in proportion to the added value
(6.7%) were also here the highest. In the period between 1997 and 2008 the automobile
industrial research and development expenditure of the Czech Republic quadrupled and
exceeded the aggregate rate achieved in Hungary, Poland, Slovakia and Slovenia (Pavlínek–Ženka–Žížalová 2010). In the case of the R&D expenditure of Slovakia, Hungary
and Slovenia and the rate of them projected to one employee a drastic increase can be
experienced, however it is coupled with a very low base value as initial point. In Romania automotive industrial research and development unequivocally dropped and declined. Despite the increasing of research and development expenditure the rate thereof
in proportion to the added value showed a declining tendency in almost each country,
which – with the exception of Romania – can be explained by the fact that the pace of
increase in added value surpassed that of the R&D expenditure. Narrowly defined
automobile industry, in 2010, amounted to 39.1% of the total industrial research and
development expenditure in the Czech Republic, while the same ratio reached 16.3% in
Hungary, 11.7% in Poland and 3.5% in Slovakia (Pavlínek–Ženka–Žížalová 2010).
The Skoda Auto gives over three-fourth of automobile industrial research and
development expenditure in the Czech Republic. On the background of this on the one
hand is the agreement (1991) made between the Czech government and the Volkswagen
company in accordance with which the new owner is obliged to retain the Skoda brand,
as a consequence of which at the outset of the 1990s the R&D capacities characterising
the period before 1989 were maintained, and later new research and development
functions were introduced in the factory to support the adaptation of the VW technology
and along with this to promote the manufacturing of Skoda models as well as to extend
its product range. On the other hand the Mladá Boleslav region, hosting the Skoda Auto,
had one of the largest and best qualified labour force bases in Central and Eastern
Europe. Relying on the advantages of cheap and well-qualified labour force VW
introduced some routine research and development functions (e.g. computer aided
designing) in the factory of the Czech Republic at the beginning of the 1990’s
(Pavlínek–Ženka–Žížalová 2010).
93
Before 1989, in Central and Eastern Europe only the Czech Republic and EastGermany designed and developed individually its passenger cars, the car factories in the
rest of the CEE countries worked in accordance with West-European technological
licences. In the Czech Republic the majority of corporate automotive industrial research
centres were established before 1989 and following the transformation of the regime
they went into the hands of foreign large companies or domestic and foreign joint
ventures. The new owners recognised the “virtues” of know-how and excellently
qualified researchers and developers, therefore they retained these research centres
moreover they established new research and development units in the country. Between
1995 and 2007 the number of large automobile industrial R&D centres employing over
100 persons increased from one to five, while the number of small units having a staff
of less than 20 persons grew from 35 to 88 (Pavlínek–Ženka–Žížalová 2010).
The automobile industrial research and development activities are either co-located
with factories or are carried out in stand-alone R&D centres (Szalavetz 2010). In 2006,
the Visegrad countries had on the aggregate 40 automotive industrial research centres
(Figure 7), from among which the staff number of 26 was over 50 persons. The
majority of R&D centres was concentrated in the Czech Republic and Poland, while
they were moderately present in Hungary and in principle in Slovakia. Over half of the
centres were established after 2004, which also suggests that the establishment of
automotive industrial research and development in Central and Eastern Europe – with
the exception of the Czech Republic and the former East-Germany – was only a recent
process (Pavlínek–Domański–Guzik 2009).
Transnational companies may apply manifold strategies in the course of the
territorial distribution of the research and development activities. In the majority of
cases the routine type, applied research and development activities are placed to the
periphery (CEE), while the basic research and higher grade R&D functions are located
in the mother countries of transnational companies, and carried out by specialised
research centres. The so-called multi-local strategy is more advanced but occurs less
frequently, when the research centre situated in either of the CEE countries is
specialised in the designing and manufacturing of a unique component (becomes
product specialised), serving and supplying the whole company, or at least the European
division thereof. This latter strategy is built upon the utilisation of the specialised
professional knowledge of individual affiliate companies. In spite of the foregoing the
majority of research and development activities are still concentrated in WesternEuropean automobile industrial centres, taking advantage of the synergy effects and
obviating the parallel research between different factories (Pavlínek–Domański–Guzik
2009).
Engineers employed in local factories established in CEE countries were, at the
beginning, assigned only with technical support or and process engineering tasks, but
later they were allowed to participate in the designing of locally produced cars and
components. Nowadays each car factory employs engineers assigned with process
engineering tasks, testing and other routine research and development activities,
although they are not in all cases referred to as researchers. More essential research and
94
Melinda Smahó
FIGURE 7
Major OEMs and R&D centres in the Visegrad Countries, 2006
Słupsk
POLAND
Solec Kujawski
Sady
Bolechowo
Warsaw
Poznań
Grójec
Polkowice
Wałbrzych
Wrocław
Lublin
Starachowice
Jelcz
Mladá Boleslav Vrchlabí
Kvasiny
Gliwice
Prague Kolín
Nový
Vysoké Mohel- Jičín
CZECH REPUBLIC Mýto
nice
Kopřivnice
České Budějovice
Plzeň
Częstochowa
Mielec
Tychy Cracow
Niepołomice
Skawina
Andrychów
Sanok
Žilina
Trenčín
Kechnec
Trnava SLOVAKIA
Bratislava
Esztergom
Pilisszentiván
1
Győr
2
3
Budapest
Szentgotthárd
HUNGARY
4
5
6
Note: 1 – PC assembly plant; 2 – commercial vehicle assembly plant; 3 – engine plant; 4 –
transmission plant; 5 – major automotive R&D centres with component plant; 6 – major
automotive R&D centres without component plant.
Source: Pavlínek–Domański–Guzik (2009, 47).
95
development activities, at the same time, are performed by the individual research
centres. Relocation of the automotive industrial research and development activities is
strongly motivated by the fact that the investors have recognised the advantages
inherent in the differences of highly qualified engineers’ and researchers’ wages in
Western European and Central and Eastern European countries – although they possess
nearly identical level of knowledge – as well as the opportunities to gain extra profit.
Consequently numerous global companies have established research and development
facilities in CEE countries (Table 5). TIER 1 supplier category – and also the related
module development functions – are mainly dominated by companies with foreign
owners, while the domestic, Tier 2 and 3 suppliers product and functional upgrading
process are restricted and pushed into background (Gentile-Lüdecke–Giroud 2012;
Pavlínek–Ženka 2010). Besides the corporate research and development in respect of
automobile industrial R&D the relations between universities and the industry, as well
as the role of automobile industry related Centres of Excellence are also determining
(Szalavetz 2010).
TABLE 5
Research and development facilities of major automobile industrial companies
in some countries of Central and Eastern Europe
Country
Investors
Poland
Delphi, Faurecia, TRW Automotive, Volvo, Remy Automotive,
Valeo, Volkswagen
Bosch, Mercedes-Benz, TRW Automotive, Valeo, Visteon,
Ricardo
Audi, Bosch, Denso, Magna-Steyr, Visteon, Knorr-Bremse,
Continental, Thyssen-Krupp
PSA, Volkswagen, Johnson Controls, Visteon
Czech Republic
Hungary
Slovakia
Source: The Automotive Sector in CEE… (2007, 26).
Initially companies from the United States of America established research and
development centres in Poland, and still nowadays their dominance is perceptible,
however the ice is broken nowadays and also the German VW is represented in this
country. The largest research centre of the country is the Delphi in Krakow and the
TRW in Częstochowa, and the former employs 560 while the latter 160 engineers
(Domański–Gwosdz 2009).
Within Central and Eastern Europe automobile industrial research and development
and technological centres can be found in the largest numbers in the Czech Republic,
which can be reasoned with the strong engineering traditions and the high standard of
technical higher education. The number of students to be graduated as engineers is
estimated to be 79 thousand, and from among them annually 17 thousand are graduated.
In seven towns of the country at altogether nine universities there are courses related to
automotive industry, for instance at the second largest technical university of Europe,
the Czech Technical University (CTU). The mentioned universities closely co-operate
96
Melinda Smahó
with the role players of automobile industry and the projects implemented in the
framework of such co-operations further enhance the standard of education (Figure 8)
(Czechinvest 2009).
In the case of Slovenia we may witness a dynamic increase of research and
development capacities in the period between 2002 and 2007: the number of persons
employed in R&D grew 1.6 times, while the R&D expenditure became 2.4 times higher
(Tables 3 and 4). The favourable process is uninterrupted which is indicated by the fact
that by 2010 the country could exhibit 85 automobile industrial research and
development facilities mainly operated by the business sector, among which there were
63 technological centres (Table 6, Figure 9). In Slovenia the automobile industrial
research and development have been primarily orientated to the satisfaction of market
demands and to achieving higher and higher profit. Due to fast changes research and
development capacities proved to be scarce, therefore the resources of universities (2
faculties at the University of Ljubljana and 2 faculties at the University of Maribor)
have also been utilised. Accordingly, by today, the aggregate number of registered
researchers and developers employed in the Slovenian automobile industry is in excess
of 1000 persons (ACSEE 2010).
FIGURE 8
Vehicle industry related university faculties in the Czech Republic
Source: Czechinvest (2009, 12).
97
TABLE 6
Automobile industrial research and development capacities in some countries
of Central and Eastern Europe (2010)
Category
Slovenia
Scientific and Technological Park
University Centre
Centre of Excellence
Technology Developing Centre
Research Centre/ Research Institute
Centre rendering engineering services
Testing Centre
Innovation Centre
Total
Slovakia
6
63
4
8
1
3
85
1
1
7
9
8
6
2
2
36
Hungary
Romania
3
3
2
1
6
3
4
1
20
16
13
Source: own editing in accordance with ACSEE 2010, 15–16.
FIGURE 9
Distribution of some CEE countries’ R&D capacities per proprietor (2010)
Source: Own editing based on ACSEE 2010, 16
In the recent years supporting the automobile industrial research and development
and innovation has received also in Slovakia a prominent role and governmental
promotion. The major direction of developments is e-mobility that is the construction of
infrastructure for electric cars. In this topic research has been performed at the technical
universities of Bratislava and Kosiče, as well as at the University of Žilina. Further
automotive industrial research is in progress at the Academy of Fine Arts and Design in
Bratislava, and at the Alexander Dubček University in Trenčin. Besides the universities
the Slovakian R&D centres, the institutes of the Slovakian Academy of Sciences, as
98
Melinda Smahó
well as the research and development centres of global companies, altogether 36
research facilities form the base of automobile industrial researches. Half of these are
operated by the corporate sector, one-fourth belongs to higher education, and the other
one-fourth of these facilities is run by the government. Another feature that makes the
Slovakian automotive industrial knowledge transfer interesting is that the three car
factories located in the country and having different cultural backgrounds (the German
Volkswagen, the Korean KIA Motors and the French PSA Peugeot Citroën) have
separate supplier networks, not allowing any opportunities for “transversal” knowledge
flows (ACSEE 2010; SARIO 2011).
In Hungary, the research and development facilities owned by foreign companies
(Table 5), some domestic companies (e.g. Rába Futómű Kft., Borsodi Műhely), and the
universities and academic research institutes are the major role players of the knowledge
based processes in automotive industry. Inside the Universities from among the regional
university knowledge centres (RET) established in 2006 those dealing with automotive
industrial research deserve special attention, such as the Széchenyi István University
(SZE) Automotive Industrial Regional University Knowledge Centre (JRET), or the
Budapest University of Technology and Economics (BMGE) Electronic Vehicle and
Vehicle Steering Knowledge Centre (EJJT). The role players and system of relations in
the two knowledge based networks constituted by the participation of these regional
university knowledge centres are demonstrated in Figure 10. Although no direct
research and development and innovational co-operation have been established between
these two networks, the higher educational and academic institutes along with some
corporations (e.g. Audi) connect these role players directly (Csonka 2009).
Today (2010) Hungary has 20 automotive industrial research facilities, four-fifth of
which is associated with the business sector (Table 6, Figure 9). Unequivocally, the
regions of Western Transdanubia (and especially Győr-Moson-Sopron County) and
Central Transdanubia are the ones that lead in corporate automotive industrial researches (Figure 11). In the Western Transdanubian region between 2005 and 2009
there were 12 automotive industrial companies that maintained research facilities. The
number of these dropped to 7 by 2009, where 304 persons were employed as
researchers and developers. During the same period the companies in this region belonging to the automotive industrial sector spent 19 billion Hungarian Forints (at market
price) on research and development, but in 2008 this sum declined to 4.8 billion HUF,
and in 2009 this amount was estimated to be 3.8 billion HUF (Table 7) (A járműipar
helyzete… 2011).
In Romania, between 2002 and 2007, a significant decline in the research and
development capacities occurred (Tables 3 and 4). In 2010, 16 automobile industrial
research facilities are recordable in the country and nearly two-third is engaged in the
business sector. Outsourcing of research and development to the local suppliers was
launched by the Dacia, and afterwards foreign suppliers also established research and
development activities in the country, to satisfy the Dacia’s demands. Concurrently, the
research and development centre, built by the Renault in Titu – from a loan received
from the European Investment Bank –, is engaged in testing and the improvement of
99
Renault technologies. With respect to research and development the strengths of the
Romanian automobile industry include the traditions of technical education, the
established Romanian R&D network, as well as the research centre established in the
country by the Renault parallel with the relocation of the production. However among
the weaknesses we shall highlight the fact that the potentials of universities and R&D
institutes situated far from the car manufacturers are not utilised, and universities and
research institutes are underfinanced (ACSEE 2010).
FIGURE 10
Knowledge-based automobile industrial co-operations in Hungary
Foreign countries
Legend:
The size of lines and arrows indicates
the direction and significance of information flow.
JRET relations
EJJT relations
Other research and development,
and other cooperations
The size of the sign indicates the partner’s size.
Academic sphere, research institute
Hungarian enterprise
Foreign enterprise in Hungary
Foreign enterprise
Higher educational institution
Note: the narrowest arrow: haphazard co-operation; arrow with medium thickness: prototype,
product or procedural innovation; strongest link: co-operation for the implementation of
frequent complex R&D tasks. The arrows are pointing at the beneficiary.
Source: Csonka (2009, 101).
100
Melinda Smahó
FIGURE 11
R&D expenditure at vehicle industrial companies and the rate thereof in proportion to
gross added value in Hungarian regions, 2008
%
10,0
Million HUF
5000
4500
14,0
4000
12,0
3500
3000
10,0
2500
8,0
2000
6,0
1500
4,0
1000
500
0
2,0
0,0
Central
Hungary
Central
Transdanubia
R&D expenditure
Southern
Transdanubia
Western
Transdanubia
Northern
Hungary
Northern
Great
Plain
Southern
Great
Plain
In percentage of the automotive industrial gross value added
Source: A járműipar helyzete… (2011, 33).
TABLE 7
R&D expenditure at vehicle industrial companies in Western Transdanubia
Year
2005
2006
2007
2008
2009
Average of
2005–2009
Million HUF
Its share
Rate of
investments
(%)
Expenditure per
person engaged
in research and
development,
thousand HUF*
30.3
25.9
40.8
29.8
26.3
7.3
2.6
8.2
12.2
17.8
9,100.1
9,666.3
26,144.2
19,316.2
16,698.9
31.2
10.4
16,449.4
From aggregate national
economy
From the aggregate automotive industrial companies
1,911.0
2,213.6
6,274.6
4,809.7
3,757.3
67.6
56.2
64.0
53.8
41.3
3,793.2
54.8
* On the basis of calculated staff number.
Source: A járműipar helyzete … (2011, 35).
Existence of knowledge flows directed to Central and Eastern Europe as well as
their effects are confirmed by some scientific studies. Pavlínek and Ženka (2010) have
established – relying on the analysis of 490 Czech automobile industrial companies in
terms of their upgrading process – that between 1998 and 2006 several notable but very
selective and uneven industrial upgrading process were carried out in the automobile
101
industry of the country. Product, process and functional upgrading process occurred as
well, from among which the most essential one, the functional upgrading, was
detectable at one-fifth of the studied companies. As a result of functional upgrading the
Czech Republic has improved its position taken in the automobile industrial value
chain, and could mitigate the distance between it and Germany, as well as the countries
considered as the semi-periphery of car manufacturing, it has still been unable to
overcome its peripheral situation (Pavlínek –Ženka 2010).
In the case of Poland, Gentile-Lüdecke and Giroud (2012) drew the conclusion from
the analysis of 380 automobile industrial companies (141 affiliate companies with foreign
proprietor, and 239 domestic suppliers), that the companies with foreign owners enjoying
higher autonomy in their decisions regarding the suppliers and manufacturing products
for the wider (Central and Eastern) European market, are much more committed to the
knowledge transfer towards the domestic suppliers, than those working for the local
market. The knowledge acquired from the foreign affiliate companies with high
probability improves the domestic suppliers’ performance, but really strong ties in
relationship are needed to improve and develop co-operation and mutual understanding
being prerequisite for corporate growing. Despite these, the knowledge received from the
foreign affiliates and the innovativeness of suppliers are not significantly interdependent:
to produce new knowledge the suppliers need to rely on their own research and
development facilities and opportunities (Gentile-Lüdecke–Giroud 2012).
It may be established in accordance with the data and correlations revealed in this
chapter that the appearance of foreign automotive industrial companies has contributed
remarkably to the upgrading of the automotive industry in Central and Eastern European
countries, in respect of products, technologies and functions alike. In the surveyed CEE
countries the development and expansion of the automotive industrial research and
development capacities were commenced, which, besides the settlement of research
facilities belonging to foreign companies also extended to the activating and developing
of domestic research facilities’ capacities. However the process of functional upgrading
is still selective and uneven, it has been launched and hopefully it will be continued in
the future as well.
Summary
The objective of this study was to give an overview of the system of automotive
industrial knowledge transfer and knowledge based process from several views. It was
verified, through a historical overview about the role of knowledge in automobile
industrial production systems, that knowledge, or any form of it appears and plays role
in each paradigm, however its features strongly differentiate the production systems
along more than one dimension. In the manufacturing systems of handicraft-small-scale
industry the craftsmen’s professional knowledge was predominant, while the new
technical procedures introduced in the Fordist mass production were coupled with
Taylor’s work organisational principles and masses of unskilled workers. However in
102
Melinda Smahó
the lean production scheme the professional knowledge and problem solving abilities
are revaluated and appreciated again, and not only do the knowledge flows appear but
also they are promoted between car manufacturers and suppliers. Adaptive production
requires fastness, flexibility and perfect adaptation are also achieved through and
enabled by knowledge based process and the supporting information technologies.
Since the beginning of the 1990’s dramatic changes have taken place in automobile
industry leaving their marks also on the innovational features of this sector. Outsourcing
and modularisation concomitant with the lean production resulted in the structural change
of the value chain, as a consequence of which an increasing proportion of production as
well as research and development tasks are delegated to the suppliers. At the same time
fast and constant technological changes take place in automobile industry: the life cycle of
products is shortened, there are more and more opportunities for product differentiation
and cost reduction, furthermore new technological potentials emerge outside the
traditional fields of expertise (e.g. electronics, software, engines/motors with alternative
drive, alternative fuels, new materials and new production technologies). The third
direction of changes, the so-called Low Cost-High Tech trend, projects the prospect of the
“reversal” in the innovational direction.
Intensive joining in value creating process and the shortened product life-cycle lay
huge burdens on suppliers, while OEMs raise increasingly serious requirements (such as
permanent cost reduction, pre-financing of research work, conformance to an increasing
number of specific needs and demands) towards them – mainly towards the Tier 1
suppliers with innovational competences. Emergence of companies – as potential
competitors – being independent of OEMs and providing development services has
made their situation even more difficult.
Shifting of the focus points in production and development tasks has led to the
change in international division of labour within this industrial sector as well as to the
transformation of the system of relationships and to the initiation of modernisation
processes in automobile industry of Central and Eastern Europe being reckoned with as
the periphery of European car manufacturing. As a consequence of green and brown
field foreign investments technologies of world standard and new management
knowledge streamed into the automobile industry of CEE countries. Process upgrading
was followed by the modernisation of the product structure, which is indicated by the
increasing proportion of high added value products. The third step is functional
upgrading that is the commencement of research and development activities’ relocation.
In the second half of the past decade, in Central and Eastern European countries, not
merely the number of automotive industrial research and development centres was
increased but also the extent and seriousness of tasks performed or taken over by them
grew. Research and development functions that the CEE countries performed, at the
same time, have been typically retained as routine, or applied research tasks, while the
fundamental or base research has been further on carried out in the traditional
automobile industrial centres of more developed countries. A strong motivating factor
to relocate the R&D functions to Central and Eastern Europe is the notable difference in
the wages paid to researchers and developers possessing similarly high standard
103
knowledge and qualifications in Western-Europe and in the CEE countries. Regarding
each surveyed country it may be stated that in them by today the research and
development facilities of companies with foreign owner, the domestic companies, as
well as the universities and academic research institutes have become the major role
players in the knowledge based process of automotive industry.
In the field of functional upgrading the Czech Republic is worth to be highlighted from
among the surveyed CEE countries. Its traditions regarding vehicle developing (keeping
up the research and development units that existed even before 1989 after they have been
acquired by foreign owners), as well as its excellent technical higher education provide a
good ground for research and development activities. As a result of functional upgrading
the Czech Republic has improved its position taken in the automobile industrial value
chain, and could mitigate the distance between it and Germany, as well as the countries
considered as the semi-periphery of car manufacturing, it has still been unable to
overcome its peripheral situation. On the basis of research results for each surveyed
Central and Eastern European countries it may be stated that to smaller or greater extent
the functional upgrading process has been launched in automotive industry, but they have
still not succeeded to break out from their peripheral positions. In my perception,
however, they have already “found the good way” and what they should do is to proceed
on the path of knowledge based upgrading; their competitive advantages should be
grounded upon knowledge in order that they can face successfully with all future
tendencies that are anticipated in automotive industry (BRIC countries forging ahead,
diminishing the wage cost based competitive advantages).
In the surveyed CEE countries the establishment and expansion of automotive
industrial research and development capacities have been initiated, which have included
– in addition to the settlement of research facilities owned by foreign companies – the
capacity activating and developing at domestic research facilities as well. However the
process of functional upgrading is still selective and uneven but at least it has
commenced, and hopefully will continue.
Note
1
The difference between the Lean Production and the Toyota production system is that as long as
the former may be applied to the production system of a company belonging to any sector, the
Toyota production system represents the Toyota Corporation’s production management system
exclusively (Haak 2006).
2
The eight countries examined in this study encompass altogether 93 NUTS2 regions.
104
Melinda Smahó
References
ACEA (2010): Automotive Sector Tops R&D Investment Scoreboard.
http://www.acea.be/news/news_detail/automotive_sector_tops_rd_investment_scoreboard/.
ACSEE (2010): Švač, Vladimír–Chudoba, Štefan, Bárta–Jozef, Bušen, Dušan–Mihalič, Branko–
Antal, Attila–Borsellino, Diego–Haba, Cristian-Gyozo– Madzharov Nikolay–Stancheva
Stela–Vratonjic Dejan (2010): Innovation Trends and Challenges and Cooperation
Possibilities with R&D in Automotive Industry. Automotive Cluster, West Slovakia in
Trnava, http://www.autoclusters.eu/index.php/download. [11 January 2012].
Autógyári kannibalizmus (2011), http://nol.hu/gazdasag/autogyari_kannibalizmus
The Automotive Sector in CEE: What’s next? Analysis by the Unicredit Group New Europe
Research Network, 2007. december. http://www.docstoc.com/docs/88989545/TheAutomotive-sector-in-CEE---Whats-next. [6 January 2012].
The Automotive Industry in Germany – Driving Performance through Technology. Industry
Overview. http://www.german-business-portal.info/GBP/Navigation/en/Business-Location/
Manufacturing%20Industries/automotive-industry,did=325954.html. [13 January 2012].
Becker, Helmut (2010) Darwins Gesetz in der Automobilindustrie. Springer, Heidelberg
Cooke, Philip – Laurentis, Clara De – Tödtling, Franz – Trippl, Michaela (2007): Regional
Knowledge Economies. Markets, Clusters and Innovation. New Horizons in Regonal Science,
Edward Elgar, Cheltenham, UK, Northampton, MA, USA.
Czechinvest (2009): Automotive Industry in the Czech Republic.
http://www.czechinvest.org/en/1automotive-industry. [7 January 2012].
Cséfalvay, Zoltán (2004) Globalizáció 2.0. Esélyek és veszélyek. Nemzeti Tankönyvkiadó,
Budapest.
Csonka, László (2009) Hálózatok az autóiparban: tanulás a kutatás-fejlesztés és innováció
érdekében. Külgazdaság, LIII. évf., 2009. július-augusztus, 89–109. pp.
Domański, Bolesław–Gwosdz, Krzysztof (2009): Toward a More Embedded Production System?
Automotive Supply Networks and Localized Capabilities in Poland. Growth and Change,
Vol. 40 No. 3. pp. 452–482.
Eurostat (2010) Eurostat regional yearbook 2010, Structural Business Statistics.
http://epp.eurostat.ec.europa.eu/portal/page/portal/product_details/publication?p_product_cod
e=KS-HA-10-001-06. [8 January 2012].
Fortwengel, Johann (2011) Upgrading through Integration? The Case of the Central Eastern
European Automotive Industry. Transcience Journal Vol 2, No 1.
Gelei, Andrea – Venter, Lóránt – Gémesi, Katalin (2011): A multinacionális vállalatok a járműgyártás iparágban. In: Chikán Attila (szerk): A multinacionális vállalatok hatása a hazai versenyre és a versenyképességre. BCE Versenyképesség Kutató Központ, Budapest. pp. 179–
232. http://www.versenykepesseg.uni-corvinus.hu/fileadmin/user_upload/hu/kutatokozpontok
/versenykepesseg/tanulmanyok_pdf-ben/Multik_egyben_elektronikus.pdf. [8 January 2012].
Gentile-Lüdecke, Simona – Giroud, Axéle (2012) Knowledge Transfer from TNCs and
Upgrading of Domestic Firms: The Polish Automotive Sector. World Development, Vol. 40,
Issue 4, April 2012, 796–807. pp. http://www.sciencedirect.com/science/article/pii/S030575
0X11002464. [15 January 2012].
Haak, René (2006) Implementing Process Innovation – The Case of the Toyota Production
System. In: Herstatt, Cornelius – Stockstrom, Christoph – Tschirky, Hugo – Nagahira, Akio
(eds.) (2006) Management of Technology and Innovation in Japan. Springer Verlag, Berlin –
Heidelberg, Part II, pp. 185–203.
Havas Attila (2010) A járműipar kutatás-fejlesztési, innovációs és tudásmenedzsment modelljei és
stratégiái. Az oktatási ágazatban hasznosítható tanulságok. Oktatáskutató és Fejlesztő Intézet,
Budapest.
http://tamop311.ofi.hu/szakmai-program/agazati-tanulmany. [23 August 2011].
105
Henriksen, Bjørnar – Rolstadås, Asbjørn (2010) Knowledge and manufacturing strategy – how
different manufacturing paradigms have different requirements to knowledge. Examples from
the automotive industry. – International Journal of Production Research. 8. pp. 2413–2430.
Humphrey, John – Schmitz, Hubert (2002) How does insertion in global value chains affect
upgrading in industrial clusters? Institute of Development Studies, University of Sussex,
Brighton, UK, cdi.mecon.gov.ar/biblio/docelec/dp3012.pdf. [23 January 2012].
Humphrey, John – Schmitz, Hubert (2002) How does insertion in global value chains affect
upgrading in industrial clusters? http://ideas.repec.org/a/taf/regstd/v36y2002i9p10171027.html. [17 January 2012].
A járműipar helyzete… (2011) A járműipar helyzete és szerepe a Nyugat-Dunántúlon. Központi
Statisztikai Hivatal, Budapest.
Kinkel, Steffen – Zanker, Christoph (2007) Globale Produktionsstrategien in der Automobilzulieferindustrie. Springer, Berlin–Heidelberg, 4. fejezet. pp. 31–72.
Kremlicka, Robert – Mayer, Stephan – Bittner, Gerhard – Reich, Georg (2011) Megatrends in der
Automobilindustrie und ihre Auswirkungen auf den AC Centrope. Wirtschaftsagentur, Wien.
Pavlínek, Petr – Domański, Bolesław – Guzik, Robert (2009) Industrial Upgrading through
Foreign Direct Investment in Central European Automotive Manufacturing. – European
Urban and Regional Studies. 1. pp. 43–63.
Pavlínek, Petr – Ženka, Jan (2010) Upgrading in the automotive industry: firm-level evidence
from Central Europe. – Journal of Economic Geography. No 4.
Pavlínek, Petr – Ženka, Jan – Žížalová (2010) Functional upgrading through research and
development in the Czech automotive industry. XXIIsjezd České Geografické Společnosti
Ostrava 2010, http://konference.osu.cz/cgsostrava2010/dok/Sbornik_CGS/Socioekonomicka_
geografie/Functional_upgrading_through_research.pdf. [13 January 2012].
Reinstaller, Andreas – Unterlass, Fabian (2008) Sectoral Innovation Watch Synthesis Report.
What is the right strategy for more innovation in Europe? Drivers and challenges for
innovation performance at the sector level. Europa INNOVA paper N°8. Austrian Institute for
Economic Research (WIFO), European Communities.
archive.europe-innova.eu/ servlet/Doc?cid=10396&lg=EN. [17 January 2012].
SAP (2003) Manufacturing Strategy: An Adaptive Perspective. SAP White Paper mySAP SCM.
http://mthink.com/content/manufacturing-strategy-adaptive-perspective. [12 January 2012].
SARIO (2011) Automotive Industry. SARIO, Slovak Investment and Trade Development
Agency, http://www.sario.sk/?automotive-industry. [10 January 2012].
Sofka, Wolfgang – Grimpe, Christoph – Leheyda, Nina – Rammer, Christian – Schmiele, Anja
(2008) Sectoral Innovation Systems in Europe: Monitoring, Analysing Trends and Identifying
Challenges. Sector Report – Automotive Sector. Mannehim, 8. May 2008.
Szalavetz, Andrea (2010) The Hungarian automotive sector – a comparative CEE perspective
with special emphasis on structural change. http://ec.ut.ee/eaces2010/artiklid/SzalavetzThe%20Hungarian%20automotive%20sector.pdf
Wibbelink R. P. – Heng M. S. H. (2000) Evolution of Organizational Structure and Strategy of
the Automobile Industry. Serie Research Memoranda, Vrije Universiteit Amsterdam Faculteit
der Economische Wetenschappen en Econometrie, April 2000.
106
Melinda Smahó
ANNEX 1
Ranking of the European automotive industrial companies as per R&D investments
(2010)
Rank (1000
European
companies)
Company name
Country
R&D
Staff number R&D investment
Ranking
Investment (persons) per one employee
(R&D
(million
(thousand
investment
EUR)
EUR/person)
per person)
1
Volkswagen
Germany
6,258
351,907
17.8
3
Daimler
Germany
4,852
258,120
18.8
7
6
7
Robert Bosch
Germany
3,824
276,418
13.8
11
12
BMW
Germany
2,773
94,446
29.4
5
15
Peugeot (PSA)
France
2,402
198,220
12.1
17
17
Fiat
Italy
1,936
196,723
9.8
22
20
Renault
France
1,728
124,749
13.9
10
23
Continental
Germany
1,525
142,695
10.7
18
31
Porsche
Germany
924
148,199
6.2
31
45
ZF
Germany
621
62,558
9.9
21
48
Valeo
France
557
57,930
9.6
23
49
Michelin
France
545
110,007
5.0
33
69
Hella
Germany
323
22,852
14.1
9
74
MAHLE
Germany
310
44,151
7.0
27
94
Rheinmetall
Germany
214
20,079
10.7
19
97
126
Behr
Spyker Cars
Germany
Holland
209
154
16,522
3,888
12.6
39.7
16
4
131
Pirelli
Italy
150
30,329
4.9
34
143
ZF Lenksysteme
Germany
140
10,480
13.3
14
152
GKN
United
Kingdom
135
35,096
3.9
38
166
Burelle
France
120
15,682
7.6
25
195
Eberspaecher
Germany
98
5,637
17.3
8
257
IMMSI
Italy
63
8,057
7.8
24
320
ElringKlinger
Germany
46
4,453
10.3
20
384
Grammer
Germany
33
7,745
4.3
37
28
4,153
6.7
29
23
4,723
4.8
36
26
425
Haldex
Sweden
Germany
470
WET Automotive
Systems
471
Miba
Austria
23
3,064
7.4
481
KTM Power Sports Austria
22
1,594
13.8
12
486
Montupet
22
3,202
6.7
28
499
Carraro
Italy
21
4,014
5.1
32
504
MGI Coutier
France
20
4,122
4.9
35
536
Veritas
Germany
18
2,829
6.3
30
625
TI Fluid Systems
14
15,690
0.9
42
657
Nokian Tyres
United
Kingdom
Finland
13
3,338
3.8
39
France
107
Count. Annex 1
Rank (1000
European
companies)
Company name
Country
722
Brembo
Italy
Italy
729
Cobra Automotive
Technologies
Hymer
837
10
5,880
1.7
41
10
781
13.0
15
Germany
7
2,591
2.7
40
Antonov
United
Kingdom
7
27
252.3
1
Kassbohrer
Gelaendefahrzeug
Germany
6
478
13.4
13
Torotrak
6
53
111.4
2
5
12,352
0.4
43
5
86
54.0
3
848
873
R&D
Staff number R&D investment
Ranking
Investment (persons) per one employee
(R&D
(million
(thousand
investment
EUR)
EUR/person)
per person)
974
CIE Automotive
United
Kingdom
Spain
989
Twintec
Germany
903
Source: Own editing in accordance with the figures of the EU Investment Scoreboard 2011.
LOCATION FACTORS OF AUTOMOTIVE INDUSTRY
IN CENTRAL AND EASTERN EUROPE
ANITA FÜZI – SZANDRA GOMBOS – TAMÁS TÓTH
Keywords:
capital flow location indicators regional development level
By writing of this study we had an objective to set up a model which is able to explain the location
decisions in the Central and Eastern European region. As an initial presumption we have
connected the local capital flow to the regional competitiveness and have analysed the location
factors behind the decision makings. After uncovering the theoretical background we set up a 6
factors model which consists of the industrial traditions, business environment, labour market,
taxation, infrastructure and local supplier network. As a final conclusion we have tried to set up
ranking with the 10 analysed countries.
Introduction
The purpose of our study is to identify the economic indicators which are able to infuence
the industrial location decisions. The focus of the analyses is on the Central and Eastern
European region compared to the control group, the developed Western European German
and Austrian markets. In the first part of the study we build up a general competitiveness
report among the regional countries, the basis of which is the stock and flow of yearly
foreign direct invested money. After collecting these macroeconomic details we tried to
collect the location indicators and set up a model that explains the flow of capital. Except
for the industrial traditions and local supplier network we could provide general economic
figures but in this two areas we had to choose a leading industrial sector. We have
choosen the automitive industry because beside its leading position it has a tight
connection to the German and Austrian market and has made a huge contribution to the
regional economic performance.
Flow of capital
Economic literature offers a wealth of possibilities to measure competitiveness, considered
as a general economic index. It is widely spread especially in the field of finance. The most
common method is to follow the flow of direct international capital investments. This clearly
describes the appeal of an economy (Lengyel 2003). During the past two decades since the
significant changes in the regime of the Eastern-European countries a general flow of capital
can be seen. Its main driver is cost efficient production. By the beginning of the 90s
Western-European companies reached the inner boundaries of their growth. Its result was
Location Factors of Automotive Industry in Central and Eastern Europe
109
that they opened towards Eastern Europe – they found new markets and outsourced a
part of the production for cost efficiency reasons (Lemoine 1998; Kinkel–Zanker 2007).
The opening of new markets in the region happened on a different timescale depending on the development and predictability of an economy. Table 1 gives a summarizing overview of this process, which took 20 years. In this context the international
direct capital investment is shown in separate regions, differentiating between the current substance and the inflow per year. The chart shows that the performance level of
the German and Austrian economy is far higher than any Eastern European countries.
Both of the two countries have the highest indexes in terms of current substance and
inflow per year. However the CEEC’s appeal has sharply risen. The Czech Republic,
Poland and Hungary strictly fall into line with the top, as the other countries of the
region tend to increase their competitiveness (Pavlinek 2004).
It is worth examining the proportion of the capital inflow to the GDP, which can act
as a guideline by estimating the growth potential of an economy. Based on the above
mentioned facts it can be claimed that Germany and Austria are still able to increase
their national economy’s growth potential, while there is a significant potential in
CEEC, which can be used under stable economic circumstances.
However to determine general competitiveness we choose direct capital investment,
competitiveness and deployment factors depending on the characteristics of the industry. An area from the angle of competitiveness can be attractive for a multinational
company, which deals with services – while for other reasons (like human resources or
infrastructure) is not satisfying for a vehicle factory. The next chapters of the study deal
with the production sector and the indicators of deployment in the automotive industry,
taking into consideration the advantages and disadvantages as well as the future of the
developed and the transformed countries.
TABLE 1
Foreign direct investment stock and flow
Flow (million USD)
2001–2005
2006–2010
Austria
Bulgaria
Czech Republic
Croatia
Poland
Hungary
Germany
Romania
Slovakia
Slovenia
36,029
182
1,580
1,407
4,769
5,633
156,179
21
540
2,129
99,917
1,279
10,375
1,915
28,509
13,627
580,308
907
2,668
4,574
Source: Own countruction after World Bank (2011).
Stock (2010)
(million USD)
GDP %
170,581
1,575
14,018
5,416
30,983
19,423
1,394,225
1,455
3,316
7,318
45.0
3.3
7.3
8.9
6.6
15.1
42.5
0.9
3.8
15.6
110
Anita Füzi – Szandra Gombos – Tamás Tóth
Location indicators
Both the theories and the practice oriented models emphasize the identification of the
deployment factors, and their analysis, because on one hand it helps the regions to keep
their automotive industrial companies and on another hand it helps to find new investors.
Bossak és Bienkowski (2004) conducted research on the deployment factors of the
manufacturers:
−
−
−
−
−
−
−
−
−
−
−
−
−
−
low transaction costs,
low investment risk,
developed market of capital,
ensured ownership,
high input into R&D,
developed infrastructure,
liberal economic policy,
no barriers to enter or to leave the market,
institutions, which help innovation, are available
low taxes and incidental expenses,
well-educated experts,
expanded local market,
stable political and economic circumstances,
positive vision about the development of the country.
In the case of companies operating in the field of manufacturing vehicles special
factors also count, like the number of suppliers with ISO 9000/2000 standard, the distance from the centres, the availability of raw material, the guarantees given by the
government, the operating clusters, as well as the cooperation between the role players
of the industry, the universities, the R&D institutions and the consultancies.
According to research by Murray et. al (1999) the relevant location factors for vehicle
manufacturers can be categorized into 3 groups. Those indicators belonging to the first
group, which influence the level of the operating costs, are, for example salaries (the
average and the minimal), overheads, price of raw materials, upcoming costs due to real
estate, and taxes. Furthermore work productivity, niveau and availability of infrastructure
belong to the first group. Following that there is the regulation environment, the distance
from the markets, demographical characteristics, and the volume of urbanization. The
third group contains the factors regarding the standard of living, like the condition of the
natural environment, education opportunities and crime rate.
The German Investment Agency also recited most of the above mentioned factors in
its study of 2008. According to the study of this institution the following points should
be considered:
− nearness of the markets,
− properly educated human resources,
− R&D institutions,
111
− the development of R&D support,
− availability of other manufacturers and suppliers in connection with vehicles and
their market position,
− infrastructure,
− stable investment environment, and different motivation systems.
KPMG also conducted research in this field in 2009. Its main goal was to examine
the deployment strategies of the vehicle industrial suppliers. It says there are 4 main
factors to observe, which appear on a different scale in a different country: the nearness
of the markets, the costs, the ability for innovation (meaning the advantages or
disadvantages of a given location), and finally the low political, economic and social
risks (KPMG 2009).
Werner (2003) emphasizes the nearness of the markets (like the EU) in his study, the
advantages ensured by the government, the well-educated workers, and the favorable
economic expectancies. These expectations are influenced by many factors, which is
why the indicator described by Werner (2003) is a summarizing category, and its
elements should be identified individually.
The Allen & Overy (2008) study concentrates on the CEEC region. Within this
framework the taxation system, the availability of the EU structural and cohesive
system, adequate human resources, the transportation infrastructure, the availability of
the buyers and the stable economy are given importance.
Rechnitzer et al. (2003) divides the factors in two big groups and named them hard
and soft deployment factors (Table 2).
Based on the available literature we strived to design a model which simply and clearly
describes the motivations by the deployment, and takes into consideration the factors,
which help make the decision. In the following we examine 6 different deployment factors
(industrial traditions, economic environment, taxation system, infrastructure, human
resources, supplier network), which explain the process of the flow of capital.
TABLE 2
Classification of location factors
Hard locaion factors
Soft location factors
Industrial traditions
Logistic, and infrastructiral network
Potential local suppliers
Taxation system
Labour market
Business environment
Attractiveness of the region, city
Value of free time
Cultural factors
Quality of government
Living environment
R&D basis
Opportunity for industrial cooperations
Innovation potencial
Source: Own construction after Rechnitzer et al. (2003).
112
Industrial traditions
The automotive industry has great traditions in the CEEC area, which can be a baseline by
the choice of the location both in the case of a West-European and a Far East company
(ACEA 2011). European and Asian car manufacturers built spare-part plants and assembly
capacities based on the competitive advantages of the region. One of the most important
competitive advantages is the ability to adopt new production technologies, so it is good to
examine the automotive industrial traditions in each country, which was a stable basis for
the largest car manufacturers.
The former Czechslovakia had the strongest traditions in this field: the Skoda car
industry was established in 1899, and by 1990 it had become the the biggest and oldest car
manufacturer among the CEE countries (Werner 2003). It was the first which specialized
in designing vehicles. The Tatra factory produces vans. It is also a prominant company in
this region. The Trnavské automobilové závody (TAZ, manufacturer of trucks), and the
Bratislavské automobilové závody (BAZ) operating with Skoda license models are the
determining companies in the Czech Republic (Jakubiak-Kolesar et al. 2008).
Poland also has great traditions: the first Fiat factory was established in the 1930s.
Inexpensive and well educated human resources, a large home market and a highly
qualified human capital were available – all of these factors contributed to give the
country an acknowledged and preferred position on the market (KPMG 2007)
In Yugoslavia an engine factory was established in 1929, which operated with
licenses. Another important year is 1954, when the production of cars began, based on the
Fiat license (ibid).
Before World War II. in Slovenia the first vehicles were produced in the capital city.
The Avtomontaža factory manufactured buses, followed by the production of vans. At
that time the Avtomontaža was already dealing with international companies. Nowadays
these partnerships are still alive. The production of cars began in 1954 in Novo Mesto.
Another milestone is that they started to manufacture caravans and commercial vehicles
together with the French Renault (ACEA 2011).
Romania has a 60-year-old past in terms of car manufacturing. It began with the
production of Dacia models based on Renault licenses in 1967. Car manufacturing was
launched in 1927 in Bulgaria. Later on the activity was expanded to assemblage based
on western and soviet licenses (ibid).
In the case of Hungary the story of the Rába Magyar Vagon- és Gépgyár (nowadays it
is called Rába Holding Rt.) is significant. Győr was an excellent location for establishing
larger works, because there was an important railway crossing and 4 rivers meet in the
city. Following the establishment of the factory its first main product was railway
carriages, and they also began to make vans and cars. The other prominent car
manufacturer was Ikarusz, which was the biggest coach manufacturer in Europe with its
15,000 buses per year in the ’90s.
The roots of the automotive industry in the CEE region origin can be traced back to
the first decades of the 20th century. Its dynamic development and competitiveness
were halted by World War I. and II. and the economic policies of the Soviet Union.
113
Socialist industrialization considered the automotive industrial traditions, which played a
determining role in the life of every country concerned. They wanted the countries to
manufacture their own cars, which could be exported through the use of Western-European
and Asian licenses.
Despite great support this industry decreased after the fall of Communism, and in
order to turn this process around, foreign capital was needed (Husan 1997). Assembly
industry was installed upon its own production capacities in the greenfield investment
framework. Thanks to these efforts the automotive industrial districts came alive after
the fall of Communism and development could be experienced again. The investors
were foreign companies like Fiat, Citroen, Renault. They had already domiciled automotive industrial factories in the region during socialism. Their activity is still operating
in the 21th century.
Table 3 gives an overview of the role-players of the CEEC’s automotive manufacturers, emphasizing the timing of their establishment. The operation of the companies in
brackets is over, or due to a transaction (fusion, acquisition) they lost their independence. The data of the chart exemplify that the Czech Republic, Poland and Hungary
have the greatest traditions. In these countries the automotive industry played an important role during the communist era. Their industrial positions remained strong. Such
a positive process can not be seen in Romania, which has added little value to its GDP
since the fall of communism. The greenfield investments were replaced by brownfield
investments. The volume of foreign capital flow to Slovakia decreased, because the only
car manufacturer, Renault, was present before the end of communist regime. Unlike the
other 2 countries Slovakia had no automotive industry at all – after communism
Volkswagen, PSA Peugeot – Citroen and Hyundai-Kia abruptly appeared.
TABLE 3
Vehicle manufacturing companies in Central and Eastern Europe
Estimation of vehiche industry companies
Before 1990
Czech Republic
Between 1990 and 2000
Slovakia
Fiat, Volkswagen AG,
SOR
Solaris, Opel-GM,
Volkswagen, MAN,
Scania, Volvo
(MÁVAG), Rába, (Ikarusz) Suzuki, Audi, GM
Dacia-Renault, ARO,
(Daewoo)
(MARTA, Citroen)
–
Volkswagen
Slovenia
Renault
Poland
Hungary
Romania
Tedom, Tatra, Avia Ashok
Leyland Motors, Skoda
Fiat, (FSO)
Source: Own construction (2012).
–
After 2000
Toyota Peugeot
Citroen, Hyundai
Toyota
Mercedes-Benz
Ford
PSA PeugeotCitroen, HyundaiKia
–
114
One of the most important competitive disadvantages of the CEEC is that the economic
and social culture does not follow western trends at all, so the instability of the economic
environment causes a relevant competitive disadvantage on a global scale. Independent
studies mention corruption and white-collar criminality as primary sources of risk – but
there are also difficulties in a company start-up (PWC 2007). Table 4 is a ranking by the
World Bank which shows the elements of a business-friendly environment (the ranks can
be seen in the brackets) with the number of company start-ups from 2009 assigned.
TABLE 4
Austria
Bulgaria
Czech Republic
Croatia
Poland
Hungary
Germany
Romania
Slovakia
Slovenia
Business-friendly environment
ranking (2011)
Registered business set up
(2009)
32
59
64
80
62
51
19
72
48
37
3,228
35,545
21,717
7,800
14,434
42,951
64,840
56,698
15,825
5,836
Source: Own construction after World Bank (2011).
Corruption
The global flow of capital has a relevant barrier; corruption, which seems to be invincible. In an analysis of the economic circumstances corruption can not be ignored, because its negative effect can be so efficient that no other factor can compensate it.
Corruption is especially strong in the public sphere – there is no countable transaction time and the financial planning is also lax in the fields of public procurements and
other licensing areas. The low salaries of governmental employees encourage bribery to
become a daily habit. Due to the fact that society takes no serious steps to fight it, corruption and its most common form, bribery, blossom in the CEEC. Besides the critical
mass government agencies should oppose corruption – unfortunately many members of
this sphere are also involved in it. Proof of this is a survey of Transparency International
(2011), which examined the measures against corruption in different European countries. Almost all of the participants received negative qualifications.
Corruption interrupts the normal process of corporate procurement in the B2B
relations of the CEE region – it particularly disagrees with the culture of the WesternEuropean and American parent company. The counteraction of subordination in the
115
private sector is not the states’ responsibility – it belongs to the internal controlling
division of a company (Transparency International 2011).
Table 5 shows the continuously up-dated corruption index collected by Transparency International. It clearly shows the different attitude of the West and East.
Investors should decide about the volume of risk taking – not only monetary, but in the
terms of the measurement represented.
TABLE 5
Corruption index and ranking, 2011
Country
Austria
Bulgaria
Czech Republic
Croatia
Poland
Hungary
Germany
Romania
Slovakia
Slovenia
Ranking
Index
15
73
53
62
41
50
15
69
59
27
7,9
3,6
4,6
4,1
5,3
4,7
7,9
3,7
4,3
6,4
Source: Own construction after Transparency International (2011).
Business start-up
A corner stone of certain and predictable economic environment is the simplicity of the
company start-up process. The main goals for the company incentives of government
agencies can be the destruction of the formation constraints, the minimization of the
authority processes and transit time.
Table 6 shows that the CEEC pay particular attention to ensure a business-friendly
environment – so they have simplified the process of the start-up. Although large enterprises
are less sensitive to such monetary and temporal inputs, a dynamic development of the
SMEs can be observed thanks to these actions.
Labour market
Blue-collar workers
The low wage demand of blue-collar workers was what helped the outsourcing trend of
the automotive industry to rise sharply. In the frame of the socialist systems high
standards of education were hard to reach. Obligate employment removed the market’s
regulation and selection ability. Total employment induced inner unemployment, which
collapsed in the face of the real market causing mass unemployment. This shock was
116
TABLE 6
Corporate set-up process, 2012
Austria
Bulgaria
Czech Republic
Croatia
Poland
Hungary
Germany
Romania
Slovakia
Slovenia
Austria
Time to set up a business
(days)
Process to set up a business
(steps)
28
18
7
20
15
4
32
14
13
18
6
8
4
6
9
9
4
6
6
7
6
2
also a possibility for investors: they had the chance to choose the most appropriate
employees. Their main characteristics were low wage demand, middle education, high
productivity (MacNeill–Chanaron 2005).
The differences between western and eastern wages are still present. There is no
compulsory minimum wage in Austria, whereas in Germany its level is determined by
profession and education and these differences can be felt all over Europe. Another
typical feature of the CEEC is that the unions place pressure on the companies and on
the government, which results in a minimum wage which can not be substituted by the
market’s selective power, such as already happened in Western-European countries
(World Bank 2011).
Table 7 contains the minimal wages of CEEC. The range itself gives information,
furthermore compared to the average wage and connected to the corporate added value
it represents the competitiveness of the local blue-collar workers. Based on these facts
we can claim that the officially determined minimum wages dispersion is high. In accordance with the productivity it makes the added value predictable (World Bank 2011).
White-collar workers
As previously mentioned while the mass of inexpensive blue-collar workers is among
the most attractive indicators of the 90’s, the main base of the location factors is
educated labour in the 21th century. Traditional CEE education is high-level
(particularly in the Czech Republic and Hungary), and has become available for a wide
range of society. The result is that investors can easily find the right white-collar
workers. This class is a stable and reliable segment of the market, and above all their
wage demand is not much higher than the wage demand of the non-educated employees
(Gauselmann–Knell–Johannes 2010).
117
TABLE 7
Minimum wages on the labour market, 2011
Monthly minimal wage
(€)
Minimal wage to
average wage (%)
Minimal wage to
value added (%)
123
319
381
349
281
157
317
748
40.4
35.0
37.8
35.7
38.8
30.5
33.5
43.5
22
21
32
27
25
24
23
37
Bulgaria
Czech Republic
Croatia
Poland
Hungary
Romania
Slovakia
Slovenia
Source: Own construction after World Bank, Eurostat (2011).
In today’s innovative economic environment a national economy can not keep its
competitive advantage only because of low wages. In knowledge intensive industries,
like automotive industry the human element is challenging the governments. The right
education system and strategy can ensure a competitive advantage for a country on a
global scale. The modernization and customization of higher education can form a base
for the investments. The educational expenses in table 8 orient to the performance of the
national economy in each region. The participants spend 4–5% of the GDP on public
education – from pre-school to university education (OECD 2011).
TABLE 8
Portion of graduates on the labour market, 2009
Number of
graduates
in a year
Austria
Bulgaria
Czech Republic
Croatia
Poland
Hungary
Germany
Romania
Slovakia
Slovenia
52,157
57,803
96,207
31,693
574,972
68,158
466,196
310,886
75,364
18,103
Proportion of
graduates to the
population (%)
0.62
0.76
0.92
0.72
1.51
0.68
0.57
1.45
1.39
0.88
Source: Own construction after World Bank, Eurostat (2011).
Graduates
in real areas
(%)
26
25
26
24
21
20
30
22
23
25
Education expenditure to GDP
(2008) (%)
5.5
4.6
4.1
4.3
5.1
5.1
4.6
n/a
3.6
5.2
118
For the choice of a car manufacturer’s location the availability of graduates is
important. The conclusions of table 8 is that there is no strong correlation between the
number of fresh graduates and the volume of foreign capital input. Yet the education of
work craft labour is an important task in each country – if it wants to prevail on the
global market. The efforts taken to strengthen higher education in the CEEC can be seen
from the rate of graduates. We have to admit that there is a lack of economic and
engineer experts.
Besides this positive process we have to mention the differences of the demand and
supply sides of the labour market in the CEE region, which can be felt in highereducation. Putting reforms into effect and the reconstruction of the educational system
requires serious effort from the decision-makers and executives. The conformity is the
only way to get and sustain the competitive advantage (OECD 2007).
Taxation
The indicatiors related to human resources admittedly play very important role in the
location decisions of industrial companies but from the point of view of cash flow and
financial return we have to examine some fiscal aspects as well like the tax system of
the analysed country. The tax burden settled by the state is measurable with exact
figures but to show the real indexes it is essential to take into account different taxes and
rates. Although the European Union enforce a unified tax system since its establishment
its implementation has failed so far and all of the member states operate with their own
different taxation systems. Those new member CEE states stand out where taxation is
so complicated and intransparent that it makes financial planning more difficult
(relating the investments) both in the short and long run (Limpók 2010).
A department of the World Bank is continuously following up the changes of the
mentioned national economies and examines the total tax burden separated into 3
classes (World Bank 2011). According to the table 9 we can identify that in the
developed welfare countries (Austria, Germany) we can meet the ordinary high burdens
and in the CEE region we are faced with govenments with hardly 30 percent total tax
rates (Bulgaria, Croatia). Hungary and Czech Republic stand out among the CEE region
countries using a high total tax burden that seems unattractive from the perspective of
investors but as we previously presented the FDI figures actually show the opposite.
The reason for the relatively attractive business environment is that in the last 15-20
years the goverments of the analysed countries have provided tax benefits for the
investor companies that could reduce the burden thus making the country more
attractive for investing foreign capital. This practice had a visible outcome, however as
the directives of the EU forbid it so the method can not be applied in the future.
We can summarize that although the tax policies of the analysed countries are
different both in their theoretical and practical approach we can not see a close
connection between the foreign direct investments and the total tax burdens. If we
examine the developing routes of the different countries we can not expect a single EU
taxation system in the near future because the goverments would loose one of the most
119
important fiscal instrument with which they could regulate the operation of internal
markets. The expectations of the EU, however, sharply separate the concept of
regulation and interventions so we will not be able to rely on a technique in the future
with which the goverments would be able to intervene in the operation of a sector
prefering this way an invester company.
TABLE 9
Corporate taxes, 2011 (%)
Austria
Bulgaria
Czech Republic
Croatia
Poland
Hungary
Germany
Romania
Slovakia
Slovenia
Taxes on profit
Taxes on work
Other taxes
Total tax rate
15.0
4.9
7.5
11.5
17.4
14.8
19.0
10.4
7.2
14.1
34.8
19.2
38.4
19.4
23.6
34.1
21.8
31.8
39.6
18.2
3.4
4.1
3.2
1.5
2.6
3.5
5.9
2.2
2.0
2.4
53.1
28.1
49.1
32.3
43.6
52.4
46.7
44.4
48.8
34.7
Probably the most essential factor of the taxation policy is a predictability in a long
run that can facilitate the checking of the cash flow and fosters the influx of the foreign
direct investments. Both the European Union and its member states must enforce the
single taxation system in the future because with this common policy the affected
regions could become more competitive from the viewpoint of foreign investors.
Infrastructure
Due to the intensive material flow the industry places serious pressure on logistics and
transport. The existence of appropriate transport connections, railway and motorway
networks and airports are basic requirements. The efficiency and competitiveness of
production is determined by the availability of remote sales markets, transaction costs
and contact with the different headquaters (Klauber 2008). One of the most
determinative elements of the location decisions is the availability of the sites because
in this way the competitiveness is raised inside the industry. The easy availability and
the right intermodal connections can boost the influx of foreign direct investment and
place into focus the time factor because it brings the purchase and the sales markets
closer together and ensures more space for the workforce mobility.
Though examining the quality and quantity criterions of the road and railway
infrastructure we can conclude that CEE has a perceived competitive disadvantage
compared to Western Europe.
120
The analysed Central and Eastern European countries have noticeably different
highway supply figures which are the table 10. We can see that the pre-accession funds
had a positive effect on motorway construction, the CEE economies could connect to
the European area and its availability was improved so they could become a potencial
site for Western European and Asian multinational companies. According to the
Eurostat figures of 2009 Hungary has a 1.273 km long motorway network which is the
best result in the region with Romania in the worst position with 321 km. Besides the
quantitative data we should investigate the changing of lengths of motorway. Among
the CEE countries this value has tripled in Hungary in the last 10 year period but
Croatia and Romania were able to exceed these figures owing to construction between
1999 and 2009.
TABLE 10
Total length of motorways between 1998 and 2009, km
Austria
Bulgaria
Czech Republic
Croatia
Poland
Hungary
Germany
Romania
Slovakia
Slovenia
1999
2004
2009
Change (%)
1999=100
1,634
324
499
382
448
11,515
317
113
399
295
1,677
331
546
742
569
12,174
552
228
483
316
1,696
418
729
1,097
1,273
12,813
849
321
747
391
4
29
46
18
184
11
168
184
87
33
Source: Own construction after Eurostat (2011).
The density of the motorway lines (table 11) is concentrated mainly in the capital
city deistricts that results in a crossing of the roads. In the location decisions the
distance to the capital cities was determinative in almost every CEE countries: in the
case of this is demonstrated by the location of Audi in Győr, in Slovakia Volkswagen in
Bratislava and Skoda in Mlada Boleslav in Czech Republic.
When examining the railway networks we can conclude that the density of the
network is relatively low in the Central and Eastern European region, beside which the
trains are old and in poor condition. The proportion of electrified lines is also low and is
in need of modernization. However the lines between their own and other Western
European capital cities are satisfactory so the automotive industry companies place
particular importance on the proximity of railway junctions.
121
TABLE 11
Density of motorways and railway network, 2008
Density of motorway lines
(km/1000 km2)
Austria
Bulgaria
Czech Republic
Croatia
Poland
Hungary
Germany
Romania
Slovakia
Slovenia
20.7
3.9
9.3
20.1
2.7
13.7
35.9
1.4
8.5
38.6
Density of railway lines
(km/1000 km2)
70
37
122
49
62
79
106
45
73
61
Source: Own construction after Eurostat (2011).
Local supplier network
In the industrial area is of fundamental importance whether there is a competitive market
for local suppliers within the sector and whether there is an opportunity to build it up or
not. One of the main principles in the industrial production is that the end-stage product
manufacturing plants produce only essential components and they purchase the other parts
from the suppliers. These manufacturers have specific needs and expectations from their
partners and have strict technical requirements and deadlines (Klauber 2008). The finished
product manufacturing plant does the assembly function schedules the procurement and
organizes the logistic tasks. This special manufacturing organization results in a very
competitive production where the supplier are organized in a multilevel system
highlighted the outsourcing and specification functions in the 21 century.
The CEE region became a target area by the multinational inverstors in the last 2
decades and could integrate to the supplier pyramid. The region has a competitive
advantage through the cheep and flexible workforce and because of the fast availability
of the sales markets (Gyukics-Klauber et al. 2011).
The supplier companies located in the region have built up a pyramid of at least 3
levels. Most of these corporations are subsidiaries in the CEE region. We could hardly
find locally owned companies. The second and lower levels are available, however, and
they hold many benefits but only for the partners which are able to fulfill the conditions.
The quality is not negotiable as the end product manufacturers place very strict
requirements on the area of flexible delivery and production. The competition among
the part suppliers is excessively heavy as they could be replaced anytime which
subsequently continuously generates a chance to decrease the purchase prices. Primarily
those companies are able to survive and ask for higher sales prices which produce complex, special highly innovated products and do so by applying systems of quality stan-
122
dards (Gyukics-Klauber et al. 2011). Chart 12 shows the proportion of ISO certificated
companies in the analiysed countries. We can conclude that this region can not meet the
quality requirements so far and the dispersion is also remarkably high among these
figures.
TABLE 12
ISO certification ownership, 2009
ISO certificated companies proportion (%)
Bulgaria
Czech Republic
Croatia
Poland
Hungary
Romania
Slovakia
Slovenia
19,9
43,5
16,5
17,3
39,4
26,1
28,6
28,0
The proximity of the suppliers also makes the programming of the production more
flexible and easier as well as the logistics and purchasing functions so that numerous
suppliers want to locate close to its main sales market. Table 13 gives a summary of the
10 biggest automotive supplier companies in the CEE region detailing their activities
and locations.
The key for success is the presence of innovation and the build-up of tight collaborative strategies. It is excessively important in location decisions to find the strategically
appropriate supplier partner. The key for long term partnership is R&D potential and
technological development. The automotive industry dictates one of the fastest technical
progresses in the industrial sector and the claims are continuously changing so it is easy
to loose the market if someone can not keep up. Table 14 summarizes the regional R&D
activities, the most widely used index of which is the expenditure to GDP besides which
we often apply the number of hired researchers per million people.
There are some extremes in the supplier networks of the CEE region. The located
Western European and Asian companies usually bring our own suppliers and rely little
on the local network. Sometimes the local companies do not force the partnership even
with the multinational company located in its region (Klauber 2008). The main reasons
for the low number of business relationships are the lack of capital and the language and
communication deficiencies.
Beside the low activity, numerous corporations want to integrate to the supplier
pyramid. One of the most fashionable solutions are clusters which are organized from
inside as a buttom-up model. This organization is not so widespread in the Central and
Eastern European region but has serious traditions in the Western part of Europe. For
example these clusters have their own management and budget in Germany and Austria
and are used in decentralized decision-making processes. The clusters as business forms
123
are not so popular in Hungary as there is a low willingness for cooperation in social and
business areas as well (Grosz 2005).
TABLE 13
Top 10 vehicle industry supplier of the CEE region
Romania
Slovakia
Bosch
(Germany)
Automotive electronics,
Chassis, Break systems
X
X
X
X
X
Denso
(Japan)
Air conditioning
X
X
X
X
Delphi
(USA)
Integrated systems, modules
X
X
X
X
Johnson Controls
(USA)
Seat, door technics,
Dashboard
X
X
X
X
Magna
(Canada)
Chassis,
Seats, lighting systems
X
X
Aisin Seiki
(Japan)
Gear shift,
clutch
X
Lear
(USA)
Seats,
Electronic systems
X
X
X
Visteon
(USA)
Inside accessories,
Driving systems
X
X
X
Faurecia (France)
Seats,
Exhausting
X
X
X
TRW
(USA)
Break systems,
Steering wheels
X
X
X
Source: Own construction after Unicredit Group (2011).
X
X
X
X
X
Slovenia
Hungary
Countries
Poland
Profile
Czech Republic
Company
X
124
TABLE 14
R&D activity, 2008
Austria
Bulgaria
Czech Republic
Croatia
Poland
Hungary
Germany
Romania
Slovakia
Slovenia
R&D expenditure
(GDP %)
Number of researchers
(per million people)
2.66
0.49
1.47
0.90
0.61
0.96
2.54
0.59
0.47
1.66
4 123
1 499
2 886
1 514
1 623
1 733
3 532
908
2 331
3 490
Conclusions
We have itemized the indicators which play an important role in location decisions in
the study but an investor’s decision can not be based solely on the review of objective
factors. Subjective indicators, the governmental and local governmental lobby often
overwrites the return and risk which can be expressed with figures and in turn the
calculable, long-term sustainable economic environment can compensate for the shortterm competitive disadvantages which stem from other factors’ adverse effects (Schwab
2010).
During the decision making process regarding enterprise location the economic
environment and the economic region could be attractive but in the examination of the
above mentioned factors we also have to calculate up the status of the location’s
saturation. Practically, the existence of a labour market with a stable base is pointless in
the long run as well as a well developed infrastructural environment in the region, if
previously settled industry has used up the labour force and the infrastructure is also at
the top of its utilization. The saturation process can redraw the economic map of a state
and can open gates for regions with lower industrial efficiency earlier.
Consequently, decisions are made by considering the objective and subjective, real
and human fields but the result of the process is strongly influenced and deformed by
the saturation data and the governmental lobby. The capital’s flow clearly observes the
direction from west to east, in turn the meso level and regional centres saturate,
developing through this the economic map of the vehicle industry.
To complete the study we set up a ranking for all six location factors which shows the
achievement of the examined ten countries in each category (Table 15).
125
TABLE 15
Ranking of regions after location factors
Industrial
Business Taxation
traditions environment
Germany
1
2
6
Austria
2
1
10
3
5
8
Czech Republic
Poland
4
4
4
Hungary
5
6
9
Slovenia
8
3
3
Slovakia
6
7
7
Croatia
10
8
2
Romania
7
10
5
Bulgaria
9
9
1
Labour
market
1
5
4
2
3
9
7
8
6
10
Infrastructure
1
2
3
8
5
4
7
6
10
9
Supplier
system
1
2
3
5
4
8
6
10
7
9
Total
12
22
26
27
32
35
40
44
45
47
Source: Own construction (2012).
The table shows that with an exception of the tax load in the case of all location factors
Germany and Austria in the top position, thus proving the capital flow processes presented
at the beginning of the study. The Central and Eastern European region can be competitive
on the global market first and foremost because of its blue and white collar labour force
with low wage demands and favorable tax system but its uncertain economic environment
can be unattractive to foreign capital investment. It is gratifying that the real direction of
location in the vehicle industry and the capital’s flow are consistent with the conclusions
of our model which proves that we have choosen correctly the factors of the analyses.
References
ACEA European Automobile Manufacturer’s Association (2011) http://www.acea.be/
Allan & Overy (2008) http://www.allanovery.com/
Bossak, J. W. – Bieńkowski, W. (2004) „Międzynarodowa zdolność konkurencyjna kraju i
przedsiębiorstw”. Szkoła Główna Handlowa, Warszawa.
Eurostat (2011) http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home/
Gauselmann, A. – Knell, M. – Stephan, J. (2010) Investment motives of FDI into Central East
Europe. – 11th Bi-Annual Conference of European Association for Comparative Economic
Studies, Comparing Responses to Global Instability, 26-28 August, 2010, Tartu.
Grosz A. (2005) Klaszteresedés és klaszterorientált politika Magyarországon – potenciális
autóipari klaszterek az észak-dunántúli térségben. Doktori értekezés. Győr–Pécs.
Gyukics R. – Klauber M. – Palócz É. – Páczi É. – Vakhal P. (2011) A magyar kis és
középvállalatok beszállítói szerepének erősítéséről szóló stratégia kidolgozása a gép- és
gépjárműipari ágazatban: a jelenlegi helyzet tanulságai és a lehetőségek kihasználásának
eszközei. Kopint Konjunktúra Kutatási Alapítvány, Budapest.
Husan, R. (1997) Industrial policy and economic transformation: The case of the Polish motor
industry. – Europe-Asia Studies 1. pp. 125–39.
Invest in Germany (2008) The Automotive Industry in Germany – Driving Performance Through
Technology. Invest in Germany GmbH, Berlin.
126
Jakubiak, M. – Kolesar, P. – Izvorski, I. – Kurekova, L. (2008) The Automotive Industry in the
Slovak Republic: Recent Developments and Impact on Growth. Working Paper No. 29.
Commission on Growth and Development.
Kinkel, S. – Zanker, C. (2007) Globale Produktionsstrategien in der Automobilzulieferindustrie.
Springer-Verlag Berlin Heidelberg, Heidelberg.
Klauber M. (2008) A járműipari ágazati stratégia kialakítását megalapozó szakmai átvilágító
tanulmány. Kopint-Tárki Konjuktúrakutató Intézet, Budapest.
KPMG (2009) Global location strategy for automotive suppliers. KPMG.
KPMG (2007) The Automotive Industry in Central and Eastern Europe. KPMG.
Lemoine, F. (1998) Integrating CEE. Working Paper No. 107, BRIE Working Paper Series,
Berkeley Roundtable on the International Economy, UC Berkeley.
Lengyel I. (2003) Verseny és területi fejlődés: térségek versenyképessége Magyarországon.
JatePress, Szeged.
Limpók V. (2010) A működőtőke és az adópolitika kapcsolata, különös tekintettel Magyarországra és Ausztriára. Doktori értekezés. Széchenyi István Egyetem, RGDI, Győr.
OECD (2007) Entrepreneurship and Higher Education. OECD.
OECD (2011) Local Economic and Employment Development (LEED). OECD Publishing, Paris.
MacNeill, S. – Chanaron, J. (2005) Trends and drivers. – International Journal of Automotive
Technology and Management. 5. pp. 83–106.
Murray, M. N. – Dowell, P. – Mayes, D. T. (1999) The Location Decision of Automotive
Suppliers in Tennesse and the Southeast. Center for Business and Economic Research College
of Business Administration, University of Tennessee, Research paper.
Pavlinek, P. (2004) Regional development implications of foreign dierct investment in Central
Europe. – European Urban and Regional Studies, 11. pp. 47–70.
PWC (2007) Eastern Influx. Automotive manufacturing in Central and Easter Europe. PWC.
Rechnitzer J. – Edelényi B. – Németh K. – Smahó M. (2003) Új típusú telepítési tényezők és a
gazdasági szereplők térpreferenciái az ezredfordulón. – NYUTI közlemények 152/b. MTA
RKK NYUTI, Győr.
Schwab, K. (2010) The Global Competetiveness Report 2010-2011. World Economic Forum.
Transparency International (2011) Progress report.
Unicredit Group (2007) The automotive sector in CEE. Unicredit Group.
Werner, R. (2003) Location, Cheap Labor and Government Incentives: A Case Study of
Automotive Investment in Central Europe Since 1989. Columbia University School.
World Bank (2011) Doing Business. World Bank.
PART II.
COMPETITIVENESS OF REGIONS
AND PRODUCTION CENTRES
COMPETITIVENESS OF REGIONS
OF CENTRAL AND EASTERN EUROPEAN
COUNTRIES
IMRE LENGYEL
Keywords:
regional competitiveness endogenous development human capital
Nowadays the competition between regions and consequently the examination of regional
competitiveness has become a research question of outstanding importance. In our study we will
first look at the definition of competitiveness and the frames of interpretation related to its
definition, then we will focus on the models of competitiveness and the questions of its
measurement. We update the pyramid model of regional competitiveness, which rests on
endogenous development theories, and integrate the viewpoints of the region’s key sectors,
clusters, so that it may be applied in case of car industry as well. Afterwards we will proceed to
analyse the competitiveness of 93 NUTS2 level regions of 8 Central and Eastern European
countries with the help of an empirical data base, using multivariable statistical methods.
Introduction
Nowadays the increase of global competition can be observed in almost all markets, as a
consequence of which the economic role of countries has weakened in comparison to how
it used to be, and the value of functional (nodal) regions has been raised. The companies
of the global industrial sectors plan in groups of countries with respect to product markets,
sales; while in course of the organization of input markets and production they are
thinking in sub-national regions, generally cities and their surrounding areas. The
companies taking part in global competition have realized that the sources of their
competitive advantages are concentrated in space; therefore they have to take steps to
strengthen these advantages locally. This competition of industrial sectors resulted in the
raising of the value of the economic role of regions, which can be observed on the one
hand in the rivalry, special competition between regions, and on the other hand in the
increased business capitalization of the agglomeration advantages resulting from spatial
concentration. Holding one’s ground permanently in the competition between regions
emphasized the concept of competitiveness.
Nowadays the investigation of the competition between regions has become one of
the major questions of regional science, generating vivid disputes. According to the
well-known opinion of Krugman (1994) there is no competition between countries,
since in the specialization of labour emerging according to comparative advantages, all
countries will be winners with the standard of living improving everywhere. Therefore
also in case of regions, the increasing rate of productivity and not competitiveness is
130
Imre Lengyel
going to be the determining factor. On the other hand, according to Porter (2007) the
competition between regions can be observed, but even here, similarly to the
competition of industrial sectors, the competitive advantages, in other words, absolute
advantages became important, since nowadays the comparative advantages hardly
prevail. As he states: “Competitiveness depends on the productivity with which a
location uses its human, capital, and natural resources. Productivity sets the sustainable
standard of living” (Porter 2008, 3).
It seems to be an accepted fact in regional science that the competition between
regions exists, but its characteristics differ both from the competition between companies
and the competition between countries (Batey–Friedrich 2000; Chesire 2003; Malecki
2002). Capello (2007a, xviii) states that “regions compete on absolute rather than
comparative advantage”. The consequences of regional competition are similar to the
result of the competition between countries: the standard of living, employment and wages
increase in the successfully competing regions, new investments appear, talented and
creative young people, businessmen move there, etc. (Malecki 2004; Polenske 2004). Due
to the recognition of these factors success in competition and the examination of
competitiveness have become major research questions in the recent decades.
The theoretical and practical studies dealing with the investigation of regional
competitiveness can be classified under three main topics, which are built on each other
in the integrated, complex approach of competitiveness (Barkley 2008):
− How can we define competitiveness and the factors that influence it?
− By what indicators can productivity be measured?
− How can productivity be improved?
In the European Union car industry is one of the highlighted sectors, in which the EU
can preserve its current competitive advantage. European car industry is characterised by
the concentration of strategic sections, centres in a few developed regions, while a part of
the executing, assembling works have already been outsourced, e.g. to the regions of postsocialist countries in Central and Eastern Europe. Therefore car industry is important not
only for the developed, but also for the developing convergence regions, as it may
contribute to the improvement of their competitiveness.
In our study we will first look at the definition of competitiveness and the frames of
interpretation related to its definition, then we will focus on the models of competitiveness and the questions of its measurement. We will update the pyramid model of
regional competitiveness, which does not rest only on endogenous development
theories, but also integrates the viewpoints of the region’s key sectors, clusters, so that it
may be applied in case of car industry as well. Afterwards we will proceed to analyse
the competitiveness of 93 NUTS2 level regions of 8 Central and Eastern European
countries with the help of an empirical data base, using multivariable statistical
methods.
Competitiveness of Regions of Central and Eastern European Countries
131
Definition of competitiveness and its coming into prominence
Nowadays the definition of competitiveness overlaps the theoretical and the practical,
economic-political categories of both economic growth and economic development
(Camagni–Capello 2010; Lengyel 2009a). Besides the many theoretical works which
would be able to fill a library, it is sufficient to mention the surveys dealing with the
countries’ competitive rankings appearing in yearly publications (IMD 2010; WEF
2010), and one of the key areas of the EU’s regional policy (one of the aims of the
2007–2013 programming period is to improve regional competitiveness and employment), the European Regional Competitiveness Report first published in 2010 (Annoni–
Kozovska 2010).
It seems that a kind of joint “rebirth” of the concepts of economic growth and development lies behind the “fashion” of the concept of competitiveness: competitiveness
is an economic growth which entails sustainable social and environmental development.
This new, complex view is well presented by the fact that Roberta Capello (2007a) in
her textbook entitled ‘Regional Economics’ associates the various modern trends of
local development and regional growth with territorial competitiveness as a key concept. Whereas in the period of 1960–1990, in case of the traditional growth models,
growth was measured by the indicators of wages and employment, or productivity and
standard of living, from the 1990s onwards the improvement of competitiveness was
unequivocally considered. Competitiveness unifies the idea of productivity (as economic effectiveness) favoured by Krugman and Porter with the expectation of the joint
improvement of employment and standard of living.
With the increase of globalization the socio-economic background conditions have
changed, the effects of which the traditional neoclassical trends were no longer able to
describe properly. It is important to note that the non-traditional factor availability (innovation, territorial capital), and the endogenous territorial elements have become major
growth factors, partly as a consequence of regional competition (Capello 2007b;
Camagni 2009; Rechnitzer–Smahó 2011). It is also important that competitiveness has
unequivocally become the key concept in the interpretation of regional economic
growth. It also follows from this that although in certain cases (Keynesian) central
governmental interventions are necessary, beyond this, to improve competitiveness
unique, multi-sectored, integrated economic development strategies have to be
developed, organized bottom-up, and built on endogenous characteristics in every region (Lengyel 2009b).
Competitiveness is an umbrella term difficult to define, it expresses a tendency to
compete, ability for competition, and a capacity for gaining a position and maintain
permanent stand in competition, which is primarily indicated by success (measured in
some way), the size of market share, and the increase of profitability. Regional economic development essentially means the programs aimed at the improvement of a
particular region’s competitiveness, the encouragement of especially those workplaces
which come into being in the business sector meeting demands outside of the region
(Lengyel 2009a).
132
Imre Lengyel
In the course of the years many concepts of competitiveness were formed which
spring from diverse opinions. From an economic point of view, the competitiveness of
territorial units, i.e. countries and regions can be measured by the productivity of the
inputs, as Krugman (1994) and Porter (2008) also said. Competitiveness of regions and
cities may be well described by the widely recognized definition of Storper (1997, 20):
“The ability of an (urban) economy to attract and maintain firms with stable or rising
market shares in an activity while maintaining or increasing standards of living for
those who participate in it.” However, definitions of competitiveness are elusive, since
they usually cover forms of regional economic growth accompanied by rising standards
of living in the region.
However, as opposed to the economic view, in regional science it can be considered
generally accepted that the competitiveness of regions, cities is more than the productivity of inputs, since it essentially means a regional economic growth, as a result of
which the average standard of living in the region improves (Camagni 2002; Lukovics
2009; Malecki 2002). Labour productivity can be also high if many people work for
very low wages (e.g. in mining industry), or if the number of permanently unemployed
people is high, like it can be observed in dual-structured developing countries. This
however means only short term success, because the social expense of one-sided economic production will be very high in a few years’ time. The recognition that welfare
should be extended to everyone, not only its participants, has already been made in the
study of the countries’ competitiveness. Welfare can extend to a greater part of society
if the employment rate is high, since sustainable and high standard of living can only be
attained with high employment rate. Therefore besides the total factor (capital and labour) productivity which demonstrate economic growth, employment rate is also an
important measure of competitiveness.
On the basis of the above, nowadays regional competitiveness consists of two different, contradictory economic categories; expressing the joint expectation of productivity and employment. Built on this approach, the standard notion of competiveness is
widely accepted as (EC 1999, 75): “the ability of companies, industries, regions,
nations and supra-national regions to generate, while being exposed to international
competition, relatively high income and employment levels”. In other words “high and
rising standards of living and high rates of employment on a sustainable basis” (EC
2001, 37). The European Competitiveness Reports also adopt this approach (EC 2008,
15): “competitiveness is understood to mean a sustained rise in the standards of living
of a nation or region and as low a level of involuntary unemployment, as possible”.
In our study we also apply the standard concept of competitiveness, on which the
pyramid model we took as a basis is built. This model systematizes the impact factors of
exceedingly complex processes affecting welfare, labour productivity and employment.
In our empirical study we also apply the pyramid model updated on the basis of the
results of the newest theoretical trends.
133
Measurement of competitiveness: a further development
of the pyramid model
Productivity and employment are the two basic indicators of regional competitiveness, but
these well-known economic categories as certain the results of past processes, and do not
refer to ability, i.e. the prospective future change of competitiveness. Therefore we also
have to investigate those factors on which the future growth of both productivity and
employment depends in the middle and long run.
In case of standard competitiveness relatively high income (measured by GDP per
capita) and relatively high employment level (shown by the employment rate) constitute
the two major factors. These two factors can be measured separately as well, but a
connection between them can be demonstrated in a well-known way, since the GDP per
capita can be divided into three multiplication components:
GDP
GDP
total
population
=
employment
employment
*
working-age
population
*
working-age
population
total population
The third factor (working-age population / total population) changes slowly over
time and is rather a demographic than economic term. These remarks suggest that
measuring regional competitiveness can be traced back to two interdependent economic
categories:
Regional income ≅ Labor productivity × Employment rate.
It follows from the above that regional competitiveness has no single accentuated
indicator, cannot be described with one factor; it rather means an aggregation of
relatively well measurable and obvious economic categories which are closely related to
each other. The categories include the economic growth expected by economists
(GDP/capita) and labour productivity, as well as employment held important by
regionalists. Not only the current magnitude of the indicators is of interest, but also their
change in time. If we set aside the consideration of the age composition of a given
region, three basic indicators remain:
− the magnitude of the regional GDP per capita, and its rate of growth;
− labour productivity in the region, and its rate of growth;
− employment rate in the region, and its change.
It is generally accepted that in case of the above indicators not only the absolute
level, but also the rate of change shall also be taken into consideration, as a result of
which competitiveness is:
− from the static approach: the magnitude of the three economic categories in a
given year;
− from the dynamic approach: the rate in which the three categories change in a
given period of time.
134
Imre Lengyel
It is also accepted that the approach of regional competitiveness is primarily relative, i.e. regional units are correlated to each other. A region may also be correlated to
one of its former situations observed in an earlier time period, but the change measured
in comparison to its former position will not show whether in comparison to the other
competing regions this is much or little.
The improvement of a region’s competitiveness is not an objective, but a means of
economic development. Namely the logical structure of a region’s development is the
following:
− Target: to increase the population’s quality of life, standard of living, prosperity,
welfare;
− Means: to strengthen a region’s competitiveness, which requires the improvement of productivity;
− Basis: to utilize and strengthen the capabilities, abilities of a region.
The rate of growth of productivity primarily depends on technological change,
partly on the development of innovations, and partly on the implementation of innovations (technology transfer), which enable companies to strengthen and stabilize their
competitive advantages (Vas 2009). The growth of productivity, and therefore the improvement of competitiveness are based decisively on the abilities of a region. It is not
important in which industrial sectors the regions compete, what is important is how they
compete, what company and industrial sector strategies they use (Porter 2008). In this
line of thought competitiveness is only a means, which promotes the permanent improvement of the quality of life, the average standard of living of a region’s population.
FIGURE 1
Decomposing regional prosperity
Prosperity
Domestic
Purchasing
Power
- Standard of living
- Inequality
Per Capita Income
- Consumption taxes
- Local prices
~ Efficiency of local
industries
~ Level of local market
competition
Labor Productivity
- Skills
- Capital stock
- Total factor productivity
Labor Utilization
- Working hours
- Unemployment
- Workforce participation rate
~ Population age profile
Source: Porter (2007, 7).
135
Studying the elements of economic growth, Porter (2007) interpreted the factors
affecting the quality of life, standard of living, welfare in harmony with the concept of
standard competitiveness (Figure 1). The population’s prosperity, standard of living, as
the target of the improvement of competitiveness, is on the one hand dependant on the
income per capita, which is determined by labour productivity and the utilization of
work force (essentially: employment). On the other hand, standard of living depends on
the type of region, and also on the level of purchase power in the region, i.e. the average
standard of living generated by the produced income (in a less developed region it is
generally cheaper to make a living; public services, properties, etc. are less expensive).
Therefore we have to estimate on the basis of the purchase power parity what kind of
standard of living can be sustained from a given income. The countries’ and region’
performances are compared on the basis of the purchase power parity also in the EU,
but it is also important within a given country, in case of different types of regions,
areas how the local purchase power influences the standard of living.
Our study reviewing the competitiveness of Central and Eastern European regions is
built on the pyramidal model since it is coherent with the above-mentioned findings,
and is established on the basis on the inputs- outputs – outcomes relationship (Lengyel
2004, 2009a). Outcomes are the standard of living, the prosperity of any region depends
on its competitiveness. Outputs are the basic competitiveness indicators: per capita
Gross Regional Product (GRP), labor productivity and employment rate. Sources of
competitiveness, inputs influencing regional competitiveness can be divided into two
groups of direct and indirect components. Of particular importance are competitiveness
factors with a direct and short-term influence on economic output, labor productivity
and employment rates. But social, economic, environmental and cultural processes and
parameters, the so-called ‘success determinants’, with an indirect, long-term impact on
competitiveness are also to be taken into account.
Three levels can be distinguished with regard to the targets of regional development
programming and the various characteristics and factors influencing competitiveness:
− Revealed competitiveness (or basic categories) (ex post indicators, output): these
output categories measure competitiveness and include income, labor
productivity and employment rate.
− Competitiveness factors (ex ante factors): input factors with an immediate impact
on revealed competitiveness categories. These can be used to influence regional
competitiveness by means of institutions in short-term programming periods.
− Success determinants (social and environmental backgrounds): input determinants with an indirect impact on basic categories and competitiveness factors.
These determinants take shape over a longer period of time and their significance
reaches beyond regional policy-making.
The pyramidal model has been adopted by many authors in international literature
(Berumen 2008; Gardiner–Martin–Tyler 2004; Resch 2008; Sinabell 2011; Snieska–
Bruneckiené 2009), since “this model is useful to inform the development of the determinants of economic viability and self-containment for geographical economies” (Pike–
136
Imre Lengyel
Champion–Coombes–Humphrey–Tomaney 2006, 26). “This is an aggregate notion, …,
in a regional context, labour productivity is the outcome of a variety of determinants
(including the sort of regional assets alluded to above). Many of these regional factors
and assets also determine a region’s overall employment rate. Together, labor productivity and employment rate are measures of what might be called ‘revealed competitiveness’, and both are central components of a region’s economic performance and its
prosperity (as measured, say, by GDP per capita), though obviously of themselves they
say little about the underlying regional attributes (sources of competitiveness) on which
they depend” (Gardiner–Martin–Tyler 2004, 1049). As it can be perceived in the
pyramidal model, “more recent analytical review has sought to identify the interrelated
factors that drivel competitiveness” (Pike–Rodrígues-Pose–Tomaney 2006, 112).
Kitson, Martin and Tyler (2004) also measure regional competiveness by the three
related indicators: productivity, employment and standard of living. According to them
competitiveness is both influenced by hard and soft elements. Hard elements consist of
well-measurable economic, demographic, infrastructural, etc. factors, while soft elements include quality, hard to measure characteristics. In systematizing the sources of a
region’s competitive advantages they highlighted six factors, in case of which the frame
of interpretation is provided by the concept of “capital”: productive capital, human
capital, social-institutional capital, cultural capital, infrastructural capital, intellectual/creative capital. While productive capital is relatively well-measurable, serious
disputes of interpretation and measurability can be expected in case of human capital.
Furthermore, not only the measurement but also the definition of cultural capital, or
social-institutional capital is yet in the experimental phase. It is also of importance that
it is not enough to look at the measurable factors in case of the particular capital types, it
would also be good to estimate the quality elements (network relationships, trust etc.),
because in today’s knowledge-based economy these have become the motive forces of
development.
We have renewed the pyramidal model on the basis of the above thoughts, starting
from the growth theory, and taking into account the thoughts of Porter (2007), Parkinson (2006), as well as those of Kitson, Martin and Tyler (2004). Growth theories are
traditionally based on the dual factors of capital and labour, to which technology and the
human factor were added later. Nowadays, however, other viewpoints have also
emerged in the analysis of endogenous growth and development, which are becoming
increasingly important in regional trends.
Stimson, Robson and Shyy (2009) modelled regional endogenous growth in the nonmetropolitan regions of Australia. They considered 27 independent variables in five
factor groups: the structure and size of an industrial sector, unemployment, human
capital and income, occupational shifts and know-how, effects of choosing coastal and
island locations, and proximity to the metropolitan area.
Stimson, Stough and Salazar (2009) suggested a new conceptual model framework
for regional endogenous development. Endogenous development as a dependent
variable is measured by two indicators, on the one hand by the change of employment
or income, and on the other hand by the changing of the employment-based location
137
quotient (LQ). Explanatory variables include the availability of resources, estimated by
13 indicators, and market fit, measured by 4 indicators. In their model they use more
indicators to consider the quality of leadership, institutions and entrepreneurship as
well.
In my opinion, in the theoretical literature on regional competitiveness and in regional political documents besides the well-measureable, hard economic and infrastructural indicators, hard-to-measure, soft indicators are increasingly gaining ground,
especially innovation and knowledge (Lukovics 2006; Rechnitzer 2008). Similar to the
way described in case of the theories of growth, regional competitiveness studies are
increasingly influenced by endogenous growth and development theories, in which
human capital, social capital play an important part (Lengyel 2011).
The modifications of the pyramid model can be traced back to endogenous growth
and development theories, and consist of the redefinition of the competitiveness factors
(Figure 2):
(ou Targ
tco et
me
s)
FIGURE 2
The renewed pyramid model of regional competitiveness
c o Re v
mp ea
eti led
tiv
en
ess
Quality of life
Standard of living
Regional performance
Gross Regional Product
de Suc
ter ce
mi ss
na
nts
Co
mp
e
fac t itive
tor ne
ss
s
Labour productivity
Research and
technological
development
Human
capital
Employment rate
Productive
capital and
FDI
Traded sectors
and clusters
Social capital
and
institutions
Economic
structure
Innovative activity
and entrepreneurship
Regional
accessibility and
infrastructure
Skills of
workforce
Social structure
Decision centres
Environment
Regional culture
Source: based on Lengyel (2004, 2011).
138
Imre Lengyel
a) Research and technological development (RTD): determines the competitiveness
of companies in a decisive way, because innovations and the introduction of new
technologies and new products can become competitive advantages. Innovations
can come from outside of a region (technology transfer, know-how), or they can
be the own developments of the companies operating in the region. The permanent growth of a region’s competitiveness is primarily facilitated by the effective
R&D activity in the region.
b) Human capital (HC): an efficient educational and training system determining
the standard, qualification of human capital, as well as the related entrepreneurship has become important in the formation of the differences in regional competitiveness. Not primarily the quantitative characteristics of the work force, but
rather its know-how, attitude, risk-taking have become of critical importance. As
a consequence of quick technological and market changes, frequent re-trainings,
life-long studying became prominent, which calls attention to the importance of
the adaptability of human capital.
c) Productive capital and foreign direct investments (PC-FDI): The regions’
economic development is strongly connected to their ability to draw and sustain
a successful production activity. The existing working capital is one of the
depositaries of productivity. Incoming FDI increase employment (one of the
basic categories of regional competitiveness) on the one hand in a direct way, by
generating new productive capacity, and on the other hand in an indirect way, by
improving the competitiveness of local companies working as suppliers,
subcontractors, outside workers, sub-agents.
d) Traded sectors and clusters (TSC): the income flowing into the region is
generated in the traded sector, therefore these sectors are of major importance, as
the economic base (export base) model also states. But local sectors also
contribute as subcontractors, local business partners to the success of the
companies participating in global competition, i.e. the formation of networks and
clusters increases regional competitiveness, income, and improves employment.
e) Social capital and institutions (SCI): are of basic importance in regional economic growth, since besides “tangible” elements (such as infrastructure for
example), intangible assets also play a part in development. Social capital is
especially important from the point of view of regional development, which is
built on the characteristics of inter-company cooperation, cultural traditions and
attitudes, aggregated experience, behavioural patterns, risk management, creativity etc. An efficient economy requires not only institutions (economic organizations, the organizations of employees, administrative institutes) in general, but
also an efficient system of relationships built on trust between them, which can
be strengthened by civil social organizations (e.g. churches, non-profit organizations).
139
The renewed pyramidal model builds both on endogenous growth and development
theories. The factors taken as a basis in case of endogenous growth theories appear in
the model, as well: capital (productive capital and FDI in the model), labour (human
capital in the model), and technology (research and technological development in the
model). However, the social capital stated in endogenous development theories, and the
clusters playing an important part in the updated economic base model also came to be
included in the pyramidal model’s competitiveness factors.
Similarly to the regional growth theories, for the investigation of the relations between
revealed competitiveness (RC) and the competitiveness factors, it is possible to draw up
the Regional Competitiveness Function (RCF):
RC = f (RTD, HC, PC-FDI, TSC, SCI)
RCF fundamentally expresses the relationships between revealed competitiveness
(RC) measured by three basic categories and the competitiveness factors influencing it,
complementing the thoughts of traditional regional economic growth with the newest
findings of endogenous growth and development trends. The importance of the traded
sector and clusters in regional specialization was pointed out by Porter (2003, 2008),
Stimson, Robson and Shyy (2009). In the meantime, sociological research called the
attention to social capital (and territorial capital), which among others was also specially
highlighted by Camagni (2009), Faggian and McCann (2009), Florida (2002) and
Glaeser (2008).
In the course of the empirical study of the regions of Central and Eastern European
countries the renewed pyramidal model is taken as a starting point. Not only basic
categories, revealed competitiveness shall be analysed with the help of multivariable
statistical procedures, but also the background processes described by the competitiveness
factors.
The empirical study of the regional competitiveness of
Central and Eastern European countries
In the course of the empirical study the competitiveness of the NUTS2 level regions of
eight countries has been analysed, altogether 93 regions, touching on 91 car and motor
factories operating there. The distribution of the 93 regions between the countries is
disproportioned, since Germany’s 39 regions represent an outstanding proportion,
whereas the number of Slovenia’s regions (2) is very small:
−
−
−
−
−
−
−
Austria 9 regions (6 car and motor factories);
Czech Republic 8 regions (11 car and motor factories);
Poland 16 regions (16 car and motor factories);
Hungary 7 regions (4 car and motor factories);
Germany 39 regions (46 car and motor factories);
Romania 8 regions (4 car and motor factories);
Slovakia 4 regions (3 car and motor factories);
140
Imre Lengyel
− Slovenia 2 regions (1 car and motor factory).
The objectives of the empirical study:
− the typifying of regions on the basis of their similarity;
− the comparison of regions according to their competitiveness, accentuating the
possible role of car factories;
− the demonstration of the extent to which the utilized indicators, indicator groups
influence regional competitiveness.
Our study follows the rationale of the renewed pyramidal model. The basic categories show the competitiveness attained in the pas period, as ex post indicators. On the
one hand, the competitiveness factors express their contribution to the basic categories.
On the other hand, they refer to the ‘ability’, the future potential, as ex ante indicators:
how regional competitiveness is expected to be modified by their development in the
near future. We tried to compile the database of the empirical analysis according to the
redefined pyramidal model. Unfortunately, as it often occurs in the course of international studies, the data supply of the countries differs, e.g. Germany provides the data
related to qualifications for NUTS1 level regions, instead of NUTS2.
In many cases the supply of data is also incomplete, or in case of the appearance of
new regions there are no older data. A part of soft type information (e.g. information
related social capital) is not included in public and verifiable databases. Only partial
information is available about the car industry, the number of car factories per region.
As a result of the above we were not able to conduct a full-scale analysis of all the competitiveness factors with indicators following the rationale of the pyramidal model. In
spite of this, we are of the opinion that regional competitiveness can be investigated
with the existing indicators, and interesting and important correspondences can be
pointed out. In the course of the gathering of data1 we primarily relied on the Eurostat
database and the publicly released indicators of cohesion reports no. 4 and 5. For the
computerized investigations the SPSS–18 program pack was used.
Our database utilized for the empirical study consists of (Table 1):
− 4 indicators expressing basic categories;
− 21 indicators describing competitiveness factors.
In the course of the examination of empirical data more methods were used:
− standardization: with hierarchical clustering and multidimensional scaling;
− principal component analysis: to form a common scale from the 3 basic categories;
− factor analysis: to filter dominant factors on the basis of the competitiveness
factors;
− multivariable linear regression: to demonstrate the competitiveness factors
influencing regional competitiveness.
141
TABLE 1
Indicators of empirical investigation
Code
Denomination
Source
Basic categories
eugdp08
empr1509
dispinc07
labprod07
Regional gross domestic product (PPS per inhabitant in% of the EU27 average),
2008,%
Employment rate of the age group 15–64, 2007,%
Disposable income of private households (Purchasing power standard based on
final consumption per inhabitant), 2007
Labour productivity in industry and services (GVA per employee, in the
average of EU27), 2007,%
Eurostat
Eurostat
Eurostat
CR5
Research and Technological Development
gerd07
emphigh08
fp707
pat1607
lisbind08
Total intramural R&D expenditure (GERD), percentage of GDP,
2007,%
Employment in high-technology sectors within the number of total employed,
2008,%
7th Framework Program, average funding per head (EU27= 100),%
Patent applications to the European Patent Office (EPO), average 2006–2007,
per inhabitant
Lisbon Index (0–100), 2008
Eurostat
CR5
CR5
CR5
CR5
Human Capital
adedu08
tertedu34
age25–64
weeklyh10
mwork78
gfcf07
Population aged 25–64 with tertiary education (ISCED 5–6), 2008,%
Population aged 30–34 with a tertiary education (ISCED 5–6), 2008,%
The proportion of people aged 25–64 in the total population, 2004,%
The number of average weekly hours worked (in full-time job), 2010, hour
That proportion of people from the active age population who moved into the
region from outside in the past two years (from within the EU, 2007–2008,%
Productive Capital and FDI
Gross fixed capital formation per inhabitant (all NACE activities), 2007, Euro
CR5
CR5
CR4
Eurostat
CR5
Eurostat
Traded Sectors and Clusters
indust05
serv05
Employment in industry (% of total employment), 2005,%
Employment in services (% of total employment), 2005,%
adedutr08
eudev07
povrisk08
Participation of adults aged 25–64 in education and training, 2008,%
EU Human Development Index (0–100), 2007,%
The proportion of the population subjected to poverty even after receiving social
benefits, 2008,%
Unemployment rate, 2009,%
Population aged 25–64 with low education, (ISCED 1–2), 2008,%
Share of long-term unemployment (12 months and more), percentage of total
unemployment, 2009,%
Youth unemployment rate, 2008,%
UN Human Poverty Index (between 0–100), 2007
CR4
CR4
Social Capital and Institutes
unempr09
lowedu08
lunempr09
unempy08
unhump07
Sources: Own compilation.
CR5
CR5
CR5
Eurostat
CR5
Eurostat
CR5
CR5
142
Imre Lengyel
Classifying of regions
The groupings generated on the basis of the similarities of the 93 regions, the typifying
of the regions was examined by clustering and multidimensional scaling. In both cases
25 indicators were used (see Table 1), i.e. 4 basic competitiveness categories and 21
competitiveness factors were considered, performing standardization per indicator.
In case of cluster analysis a hierarchical procedure was chosen, which contracts
similar regions on the basis of one tree structure until only one group remains; the steps
of the procedure can be illustrated in a dendrogram. In the course of this procedure we
can choose in a slightly arbitrary way the groups at which step shall be considered as the
subject of our study, in this case the 6 types were accepted after step 10 (Table 2). There
was one outlier: Voralberg (AT 34) which constituted an independent type until the very
last step.
The six clusters form characteristic types:
− Cluster 1: all Hungarian, Polish, Czech and Slovakian regions, except the capital
regions, with 31 car factories in 31 regions,
− Cluster 2: the Romanian regions, except the capital region, with 4 car factories in
7 regions,
− Cluster 3: the Czech, Slovakian, Hungarian, Polish Romanian capital regions,
with 3 car factories in 5 regions,
− Cluster 4: German metropolitan (Hamburg, Bremen etc.) regions and the region
of Vienna, with 11 car factories in 6 regions,
− Cluster 5: East-German (post-socialist) regions, 10 car factories in 9 regions,
− Cluster 6: the two Slovenian, and the rest of the Austrian and German regions,
with 32 car factories in 34 regions.
On the basis of the spatial separation of regional types established by clustering, the
use of the 25 indicators compiled for the study of regional competitiveness, it can be
stated that the types are determined by national characteristics (Figure 3). The regions
of the post-socialist countries (except Slovenia and Romania) are present only in two
clusters, in clusters 1 and 3, with the capital regions belonging to the latter. The regions
of Romania, except the capital, have unique characteristics, creating a separate group
(Cluster 2). The German, Austrian and Slovenian regions also constitute graphically
separate groups, the ’East-German post-socialist’ regions belong to the independent
Cluster 5, while the rest are very similar to each other, except a few metropolitan
regions (Cluster 4).
Car factories can be found in each cluster, i.e. no region-specific location can be
demonstrated. In case of the two greater number types, in the 31 post-socialist country
regions (in Cluster 1) 31 car factories are operating in 16 regions, while in Cluster 6,
listing 34 regions, 32 car factories can be found in 20 regions. Thus there is an car
factory roughly in every second region, the most, 5–5 car factories can be found in the
Polish Dolnoslaskie and the Czech Severovychod regions, the car factories in the same
region obviously belong to different world companies. The car manufacturers’ seats,
143
strategic divisions are located almost only in West-German regions, while in the other
regions there are assembly plants, sites with low level decision independence. Let us
note that the number of car and motor factories exceeds the number of regions in two
countries, in Germany (46 factories in 39 regions) and the Czech Republic (11 factories
in 8 regions).
TABLE 2
Types of hierarchical clustering for regions
1
2
3
4
5
6
SK03
SK04
HU31
HU32
HU33
HU23
PL11
PL21
PL63
PL42
PL51
PL43
PL61
PL62
PL41
PL31
PL52
PL22
PL33
PL32
PL34
CZ03
CZ05
CZ06
CZ07
CZ02
HU21
HU22
CZ08
SK02
CZ04
RO11
RO42
RO12
RO21
RO41
RO22
RO31
CZ01
SK01
HU10
PL12
RO32
DE60
AT13
DE50
DE12
DE21
DE91
DE42
DEG0
DED1
DE80
DEE0
DE41
DED2
DED3
DE30
SI01
SI02
AT11
AT12
AT21
AT22
AT31
AT33
AT32
DE93
DEF0
DE92
DEA1
DEA5
DEC0
DE73
DEB1
DE94
DEA3
DE22
DE27
DE24
DEA4
DE71
DEA2
DE11
DE14
DE13
DE23
DE72
DEB3
DE26
DE25
DEB2
Source: Own compilation.
144
Imre Lengyel
FIGURE 3
Types of clustering for regions
Clustering highlights similarity, so on the basis of the 25 indicators similar historical
courses seem to show up, picturing the long-term dominance of the socio-culturalhistorical roots between countries. A powerful spatial separation can be observed; the
regions making up the individual clusters constitute “bands” from west to east. The
regions of the post-socialist countries, including the East-German provinces, detach
themselves from the rest, with the only exceptions of Slovenia and Romania. The
Hungarian regions are in Cluster 1, except for Central Hungary, which is listed in
Cluster 3. The effect of the urbanization agglomeration advantages can also be
observed (Capello 2007a, Lengyel–Rechnitzer 2004), on the one hand, the capital regions
of the post-socialist countries constitute a separate group, and on the other hand the
German (Hamburg, Bremen etc.) and Austrian (Vienna) metropolises also detach
themselves (Clusters 3 and 4) from the rest. The 25 indicators describing competitiveness and the factors influencing it probably indicate basic institutional and social
settlement, which can change only in the course of a longer time period.
The similarities between regions were also examined by multidimensional scaling,
using a PROXSCAL procedure. In a two dimensional point figure mainly similar shapes
can be observed for hierarchical clustering, whereas the different types’ relationship to
each other, their location, proximities and similarities are also pictured (Figure 4).
145
FIGURE 4
Position of regions by multidimensional scaling
In the figure the regions of the post-socialist countries detach themselves from the
German and Austrian regions (Voralberg, AT34 is an outlier here as well), only the
Slovenian regions integrate into the latter, and the capital regions got close to them
(Prague, CZ01 “positioning” from outside). The multidimensional typifying made on
the basis of 25 indicators pictures different courses of development, and similarly to
clustering, it pinpoints the socio-economic-historical background and past impact still
subsisting today. It is very important to note that the regions do not mix, the regions
within the same country showing similar characteristics are located in each other’s
proximity, only the capitals are detached. That is to say that the characteristics,
institutional background, etc. of a given country still determine regional characteristics.
The differences between countries are stronger than the differences within the countries.
The Hungarian regions can be found in three groups: Central Transdanubia (HU21)
and Western Transdanubia (HU22) together with certain Polish regions got close to
German and Austrian regions. Central Hungary (HU10) is also on the border between
the post-socialist countries’ regions and those of Germany, while the remaining four
Hungarian regions form a separate group, which is the farthest from that of the
developed German regions. While in the course of clustering six Hungarian regions
146
Imre Lengyel
were classified in one cluster, multidimensional scaling has thrown light on the
Hungarian regions’ different path of development: the characteristics of the Central
Transdanubia (HU21) and Western Transdanubia (HU22) regions are close to those of
certain German, Austrian and Slovenian regions, as well as to those of Central Hungary.
While Southern Transdanubia (HU23), Northern-Hungary (HU31), Northern Great
Plain (HU32) and Southern Great Plain (HU33) constitute a separate group, they differ
most from the German and Austrian regions. This confirms the results of other studies:
while the economics of three Hungarian regions integrated into the economy of the EU,
the other four regions are still very far from this (Lengyel–Leydesdorff 2011).
In the pyramidal model the basic categories are the effects, and the competitiveness
factors are the causes, however, they are in obvious interaction with each other.
Calculating separately and illustrating together the one dimensional scaling of the 21
competitiveness factors and the four basic categories it is possible to see whether the
specific characteristics of the regions are prevalent, i.e. whether there are dominant
background processes, or the results of the two different scaling are randomly diffused
(Figure 5).
FIGURE 5
Positions of regions by one-dimensional scaling
147
There seems to be a strong connection between the two scales calculated from the
two different indicator groups: the one dimensional projection of the regions according
to basic categories resulted in a figure similar to that of the scaling calculated from the
21 competitiveness factors. The linear correlation of the two data rows is -0,906, which
means that they move closely together. The polynomial regression curve fitting on the
points is:
y= 0,1754 x2 – 0,9529 x – 0,0771, where R2=0,8359.
On the basis of the results of typifying and scaling utilizing competiveness indicators it is probable that regions form groups in the long run on the basis of their specified
social-historical characteristics. These types are not random: the regions of a country
generally cluster in one place, are similar to each other, and only partly mix with the
regions of other countries. Only the capitals of the post-socialist countries and the
Slovenian regions can get close to the German and Austrian regions. The distribution of
car factories, as it was shown in the course of clustering, is not dependent on regional
types, since there are divisions in every group, in about every second region.
Revealed competitiveness
Revealed competitiveness is measured by basic categories. As it was demonstrated GDP
per capita can be broken down using the decomposition method: to the product of labour productivity, employment rate and age composition (the latter is usually left out).
The available income of the households is also listed among these indicators (as it appears in the reviewed up-to-date specialised literature), which shows the level of welfare, standard of living of those living in the given region. These indicators determine
competitiveness not separately, but together. As mentioned before, competitiveness can
be regarded as the renewal and augmented interpretation of economic growth, since in
the latter case generally only one indicator, the GDP is taken as a basis.
From the decomposition of the GDP it follows that labour productivity and
employment are the two basic indicators of competitiveness. On the basis of these two
indicators the situation of the 93 regions shows interesting, although well-known and
anticipated correspondences (Figure 6). The linear correlation of the two data rows is
+0,842, which means that they move closely together. The regression curve fitting to
the points is:
y=19,443 ln (x) – 19,477, where R2=0,7376.
On the basis of labour productivity and employment the two groups of regions can
be well divided into groups above and below the CZ02 – SI01 – RO32 – HU10 line.
The group above the line includes all German and Austrian regions, as well as the
Czech, Romanian, Hungarian, Polish and Slovenian capital regions, and the two
Slovenian regions. While the group below the line consists of all the other regions of the
post-socialist countries. Similar spatial correspondences were pointed out on the basis
of these indicators like in the course of typifying, certain regional types distinctly detach
from each other, especially depending on the characteristics of the countries.
148
Imre Lengyel
FIGURE 6
Connection between employment rate and labour productivity
It is also demonstrated that among the post-socialist countries employment is high,
about 65%, in the Czech regions, followed by several Polish regions, while in the
Romanian and Hungarian regions employment is much lower even in case of similar
labour productivity. Among the 93 regions, employment rate is the lowest in four
Hungarian regions: Northern Great Plain (48,1%), Northern Hungary (48,6%), Southern
Transdanubia (52,1%) and Southern Great Plain (53,%). While in the other two regions,
in Central Transdanubia (57,8%) and Western Transdanubia (59,7%) employment is a
little higher, but even so it qualifies as very low. With respect to labour productivity
(which is compared to the average of EU=27 on purchase power parity) the 5 regions of
the lowest value include two Romanian, two Hungarian (Southern Great Plain 46,5%
and Northern Great Plain 48,4%) and one of the Polish regions. Neither Central
Transdanubia (56,1%) nor Western Transdanubia (58,5%) reaches 60% of the EUaverage. Consequently, according to both basic indicators of competitiveness, the com–
petitiveness of four Hungarian regions is very weak, while the other two regions (Cent–
ral Transdanubia and Western Transdanubia) are in a slightly better position only due to
their higher employment rate.
149
It is a basic question whether the car and motor factories of the regions influence the
employment rate and the level of labour productivity. The correlation between the
number of car factories and the other two indicators (0.14 with employment rate, and
0.12 with labour productivity) shows that they are not moving together. I.e. the
influence of car industry is not detectable either in employment or labour productivity.
There must obviously be some influence, but on the one hand, the number of car
factories is not sufficient to demonstrate this, and on the other hand, in the regions
where there is no car industry, other industries play a key role in the development of
both employment and labour productivity.
To perform further calculations a common competitiveness indicator is formed from
the three basic categories, and to contract the information contained by the basic
categories principal component analysis is applied (Lengyel 2011). From the four basic
categories, GDP per capita will be ignored. With the help of the three indicators on the
right side of the decomposition equation, labour productivity (labprod07), the employment
rate of people aged 25–64 (empr1509) and the available income of households
(dispinc07), a principal component (RC) is established with the use of principal
component analysis, which shall later be considered as a dependent variable:
− RC contains 92,8% of the information of the 3 indicators;
− Communalities: labprod07: 0,938; empr1509: 0,883 and dispinc07: 0,961.
This principal component shall hereinafter be referred to as competitiveness principal
component, an indicator of revealed competitiveness (RC). The indicator values are
dispersed around the interval of zero, therefore the regions of negative values may be
regarded as regions of weak competitiveness, while those of positive values are
considered as regions of strong competitiveness.
The values of regions according to the competitiveness principal component, as
types specified by factor values, show sharp spatial characteristics (Figure 7). A
coherent area, the ’Alps-area’ can be observed, which consists of South-German and
North-Austrian regions of the strongest competitiveness. The other German and
Austrian (and one of the Slovenian) regions, which may be regarded as the “middle
mountains” connected to the Alps, constitute the second group (including Prague and
Bratislava), which can still be regarded as being of strong competitiveness. The “hillcountry” situated east from the Alps comprise the third group, consisting of mainly
Czech regions, which means just one or two smaller hills the further we get from the
Alps. The fourth group is the plain, with regions of very weak competitiveness. The
competitiveness principal component shows that the competitiveness of the regions
depends strongly on their geographical proximity and distance from the “core”.
The majority of the post-socialist countries’ regions (except Slovenia and the Czech
Republic), comprising a coherent area, can be found in the fourth type of regions with
the weakest competitiveness, only the capitals and some industrial regions could make it
into the third type. On the basis of the factor values Northern Great Plain, Northern
Hungary and Southern Great Plain stand at the three last positions among the 93
regions, followed by two Romanian regions and Southern Transdanubia. Consequently,
150
Imre Lengyel
these four Hungarian regions are numbered among the weakest, the last six regions with
respect to revealed competitiveness, as well.
Car factories are distributed unevenly among regional types according to the
competitiveness principal component: in the 17 regions of the first, strong competitiveness
type there are 21 car factories, in the 34 regions of the second type there are 33 factories,
in the 16 regions of the third type there are 26 factories, while in the 25 regions of the
fourth type there are 11 factories. Consequently, in the regions of strong competitiveness
there are relatively many, while in the regions of weaker competitiveness there are few car
factories. On the basis of this it can be supposed that the European (especially German)
car manufacturers regard transport distance as an important factor to be considered, on the
one hand between the parent company and the plant, and on the other hand the plant and
the West-European markets.
The competitiveness principal component and the level of economic development
(GDP/capita) are strongly related (Figure 8): the linear correlation of the two data rows
is +0,8752, showing that they move strongly together. The regression curve fitting to
the points is:
y=2,0706 ln (x) – 9,0873, where R2=0,8752.
FIGURE 7
Types of regions by competitiveness principal component
151
FIGURE 8
Connection between competitiveness principal component and GDP per capita
Examining the regions together on the basis of the two indicators, the competitiveness principal component and the level of economic output (GDP/capita) it can be also
pointed out that the German and Austrian regions detach themselves from the other
regions. The least developed regions of the weakest competitiveness include both
Central Hungary and the other six Hungarian regions, located in the bottom left quarter
in the company of Romanian and Polish regions.
Examining the regions together on the basis of the two indicators, the competitiveness principal component and the level of economic output (GDP/capita) it can be also
pointed out that the German and Austrian regions detach themselves from the other
regions. The least developed regions of the weakest competitiveness include both
Central Hungary and the other six Hungarian regions, located in the bottom left quarter
in the company of Romanian and Polish regions.
The EU regional competitiveness index also publishes the relative competitiveness
positions of the 27 member states’ regions on a scale of 0–100 (Annoni–Kozovska
2010). There is a very close relationship between the competitiveness principal component and the EU’s competitiveness index (Figure 9): the linear correlation of the two
152
Imre Lengyel
data rows is +0,8738, meaning that they move closely together. The linear regression
line fitting to the points is:
y= 0,0499 x – 2,7014, where R2=0,8738.
There are differences between the competitiveness principal component and the EU
regional competitiveness index, but the closeness of the correlation is showed by the
fact that these differences are not considerable. The competitiveness principal component assigns greater importance to the employment rate, while the EU regional competitiveness index processes a multitude of indicators (e.g. infrastructure, institutional
system, etc.) following Porter’s methodology (Annoni–Kozovska 2010). However, the
earlier observations can be repeated here as well: the competitiveness of the German
and Austrian regions separate from the rest, followed by the other countries’ capital
regions and the Slovenian regions (one of the two is obviously a capital region here as
well). The EU’s regional competitiveness index of the four Hungarian regions of less
competitiveness is between 27–29% on the scale of 0–100, while Central Transdanubia
and Western Transdanubia scored 36,4% and 37,4% respectively, and even Central
Hungary attained only 56,4%, besides several Romanian regions.
FIGURE 9
Connection between competitiveness principal component and EU regional
competitiveness index
153
Up to now we have demonstrated the competitiveness of regions on the basis of data
available for last year, i.e. from a static approach. It is worth to examine the change of
the three basic categories, as dynamic indicators: the changes in the employment rate of
people aged 20–64, in 2000–2008 (empl08-00), the growth of productivity within the
sector (in the EU27’s average), in 2007/2000 (prodgr07/00), the available income of
households (PPCS, on the basis of the final consumption per capita), in 2007/2000
(disp07/06). A principal component was generated by principal component analysis,
which we regard as dynamic dependent variable:
− The principal component contains 75,4% of the information of the 3 dynamic
indicators;
− Communalities: empl08-00: 0,66; prodgr07/00: 0,777 and disp07/06: 0,826.
In the upper left quarter there are German and Austrian regions of strong position,
but weak dynamics (Figure 10). The change of the indicators of German and Austrian
regions with strong competitiveness is much less than that of the other regions, which is
understandable, because high level employment for instance cannot be continuously
increased. The regions of Prague and Bratislava are located in the upper right quarter,
which can be considered strong according to both dimensions, but the regions of
Warsaw and Budapest (Central Hungary) are not far from the border of this quarter
either. The bottom left quarter, which is considered weak according to both dimensions,
includes the Polish regions and Central Transdanubia (although on the edge of the
quarter), the positions of which worsened in the past decade, as it was shown by several
studies. In the bottom right quarter there are five Hungarian regions of weak
competitiveness, which however have somewhat improved their situation, noting that
the dynamic value of Western Transdanubia is only 0,24. The Romanian regions are the
most dynamic, who started obviously at a very low value, but their growth accelerated
in 2000–2008.
Factors influencing competitiveness: Factor analysis
and regression analysis
The five competitiveness factors of the pyramidal model could be characterised by a
very different number of indicators, therefore the relations between the competitiveness
factors and revealed competitiveness shall not be examined separately. It may be noted
that multicollinearity can also occur among the indicators of the five competitiveness
factors, which makes correct statistical analyses more difficult (Szakálné Kanó 2008).
Instead of considering which indicator belongs to which basic factor, independent
factors were formed by compacting the information included in the 21 indicators by
factor analysis, among which there is no multicollinearity, the remaining members are
distributed normally, and there is no homoscedasticity either. Then a multivariable
linear regression analysis was performed with these factors, taking into consideration
the competitiveness principal component (RC), as dependent variable calculated from
154
Imre Lengyel
the three basic categories. It is the advantage of this method that it makes the testing of
the pyramidal model’s structure possible, as well. Its disadvantage is that the meaning
of the individual factors generated in the process has to be explained afterwards with the
help of the indicators included in them, and the factor structure can differ from the
competitiveness factors of the pyramidal model.
FIGURE 10
Connection between static and dynamic competitiveness principal component
Sources: Own compilation.
By performing a factor analysis on the basis of the 21 indicators five factors were
generated, which contain 81,5% of the information included in the indicators. Varimax
rotation was applied on the factors to form the components of the individual indicators.
From among the rotated components of the factors in the absolute value the values
above 0,5 were taken into consideration (Table 3).
The economic interpretation and factor weight of the 5 factors are the following:
− Factor 1: Human capital: human development, workforce attraction and patents
(HCD), factor weight: 18,873. Human development, people moving in, high
patent announcements shape this factor positively, while the proportion of people
of active age and the number of hours worked affect it negatively.
155
TABLE 3
Factors and their components
Factors
Denomination
Components
Factor 1: HCD Human capital: human development, workforce attraction and patents
eudev07
EU Human Development Index (0–100), 2007,%
0,701
mwork78
That proportion of people from the active age population who
0,684
moved into the region from outside in the past two years (from
within the EU, 2007–2008,%
pat1607
Patent applications to the European Patent Office (EPO),
0,614
average 2006–2007, per inhabitant
age25–64
The proportion of people aged 25–64 in the total population,
–0,819
2004,%
The number of average weekly hours worked (in full-time job),
weeklyh10
–0,906
2010, hour
Factor 2: RTD
fp707
gerd07
emphigh08
lisbind08
gfcf07
Research and Technological Development
7th Framework Programme, average funding per head
(EU27=100),%
Total intramural R&D expenditure (GERD), percentage of
GDP, 2007,%
Employment in high-technology sectors within the number of
total employed, 2008,%
Lisbon Index (0–100), 2008
Gross fixed capital formation per inhabitant (all NACE
activities), 2007, Euro
Factor 3: SCP
povrisk08
lowedu08
unhump07
unempr09
unempy08
0,820
0,642
0,602
0,544
Social Capital: Poverty
The proportion of the population subjected to poverty even
after receiving social benefits, 2008,%
Population aged 25–64 with low education (ISCED 1–2),
2008,%
UN Human Poverty Index (between 0–100), 2007
Factor 4: SCU
lunempr09
0,866
–0,733
–0,869
–0,915
Social Capital: Unemployment
Share of long-term unemployment (12 months and more),
percentage of total unemployment, 2009,%
Unemployment rate, 2009,%
Youth unemployment rate, 2008,%
Factor 5: HCH
Human Capital: High Education
tertedu34
Population aged 30–34 with a tertiary education (ISCED 5–6),
2008,%
adedu08
Population aged 25–64 with tertiary education (ISCED 5–6),
2008,%
indust05
Employment in industry (% of total employment), 2005,%
0,965
0,955
0,688
0,741
0,684
–0,881
156
Imre Lengyel
− Factor 2: Research and technological development (RTD), factor weight:
17,901. The high share of the expenses spent on R&D, the high proportion of
people employed in the high-tech sector, and high fixed capital generation
constitute this factor.
− Factor 3: Social capital: poverty (SCP), factor weight: 17,224. The factor
comprising high poverty ratio, low education.
− Factor 4: Social capital: unemployment (SCU), factor weight: 15,265. This
factor is made up of the unemployed, among them the high ratio of permanently
unemployed and young unemployed people.
− Factor 5: Human capital: high education (HCH), factor weight: 12,306. The
high ratio of highly qualified people has a positive effect on this factor, while the
ratio of people employed in industry has a negative effect on it.
From the 21 indicators 19 are connected to one of the factors, two were left out: the
proportion of the people employed in services and the proportion of people participating
in education and courses from the population aged 25–64. The three competitiveness
factors of the pyramidal model appeared also in the factors: research and technological
development, human capital and social capital (the latter divided into two-two parts
respectively). From the competitiveness factors those two were not represented to which
the appropriate number of measurable indicators was not found: working capital and
FDI, and the traded sectors and clusters (one of their indicators joined a connected factor). Only Factor 1, human capital: human development and the proportion of people of
inactive age factor became “mixed”, into which one indicator of social capital and one
of research-development were also included besides the characteristics of human capital. Consequently, the pyramidal model seems to be appropriate for the systemization of
factors influencing competitiveness.
The results of the factor analysis can be analysed in themselves as well, however,
our main aim at present is to demonstrate to what extent the competitiveness principal
component (RC) as dependent variable is explained by the 5 factors as independent
variables. In case of the multivariable linear regression the 5 factors explain 93,5%
(R2=0,935) of the dependent variable’s (RC) dispersion. Examining integration the
Durbin-Watson test is 1,571, which signifies weak negative autocorrelation by a 5%
significance level.
On the basis of the calculations the following model was generated:
RCi = + 0,691 HCDi + 0,439 RTDi + 0,322 SCPi – 0,334 SCUi + 0,22 HCHi + Ei
The regression coherence shows what effect a factor has on regional competitiveness, e.g. one unit improvement of HCD results in 0,691 improvement of the dependent
variable (RC). The equation demonstrates that regional competitiveness is largely determined by human capital and research-development. While in case of social capital
poverty moves in a similar direction to competitiveness, it moves in inverse ratio to
unemployment. This relationship also shows that regional competitiveness is really
close to the field of endogenous development, since it is moved by slow spatial social
processes. While the proportion of people with high qualifications may improve in a
157
decade or two, the modification of more characteristics of the social capital in a given
case requires a time period of more generations.
Factor 1 (human capital: human development, workforce attraction and patents)
exerts the greatest influence on regional competitiveness. This means the high standard
of human capital, since in Europe the developed metropolises are generally the destinations of migration, which provide workplaces and high income. However, Factor 1 is
influenced in inverse direction by the proportion of active aged people (25–64 years
old) and the average weekly hours worked, probably because there are less working
hours in the competitive regions, and the proportion of young and elderly people is
higher.
The spatial distribution of the values of Factor 1 (human capital: human development, workforce attraction and patents) shows a west-east slope (Figure 11). Here, too,
the German regions are at the top, but in a different way compared to that of the competitiveness principal component: almost two thirds of the German regions constitute
the strongest group, especially in the western and central parts of the country. The second group also includes German and Austrian regions, while in the third group German
and Austrian regions (Vienna and Carinthia) appear besides the regions of post-socialist
countries. The weakest type consists of Polish and Romanian regions, but Czech
(including Prague), Slovakian (Bratislava) and the Slovenian region also belong here.
FIGURE 11
Types of regions by human capital factor
158
Imre Lengyel
It becomes also apparent that there is hardly any difference between the 7 Hungarian
regions according to Factor 1, from the international point of view regional differences
perceived in Hungary are less conspicuous in this indicator group. Car factories are
relatively evenly distributed in the regional types according to human capital factor: in
the 23 regions of the first type of strong competitiveness there are 19 car factories, in
the second type’s 21 regions there are 27, in the third type’s 34 regions there are 30,
while in the fourth type’s 15 regions there are 16 factories.
Examining the relation between the competitiveness principal component and Factor
1 results in the delineation of two types of regions (Figure 12). In the right upper quarter there are only German and Austrian regions, while in the left bottom quarter there
are the regions of the post-socialist countries (with the exception of a few capital
regions). This also means that the previously observed two regional types, moving on
two different tracks of development, detach from each other even according to Factor 1.
Considering Factor 1, the Hungarian regions are in a much better position in comparison to their revealed competitiveness, since they come directly after the German and
Austrian regions. Consequently, the human factors at home are more developed than
what is shown by revealed competitiveness (Lengyel–Ságvári 2011).
FIGURE 12
Connection between competitiveness principal component and human capital factor
159
Factor 2 also has a serious impact on regional competitiveness: assistances won
from the EU research funds, gross expenses spent on R&D, the number of people
employed in the high-tech sectors. It can be unequivocally stated that regional competitiveness depends largely on the magnitude of R&D, the expansion of knowledgebased, innovative economies (Bajmócy–Szakálné Kanó 2009). The types of regions
according to the human capital factor are spatially much more dispersed than they used
to be (Figure 13). It can be observed here as well, that the German and Austrian regions
are at the top (with Prague and one Slovenian region), but they are much less in number,
and form an “island”, not a block. The German and Austrian regions dominate also in
case of type 2, plus out of the 7 Hungarian regions 5 are listed here (together with
Bucharest and Vienna), and 2 out of 4 Slovakian regions, too. The third type can be
found almost consistently in all countries, while the fourth group includes Polish and
German regions.
FIGURE 13
Types of regions by R&D factor
It is a characteristic feature of the R&D activities that they are spatially concentrated,
and with their global connections they are connected not to their direct neighbours, but to
professionally outstanding partners located anywhere in space. Among the regional types
according to Factor 2 car factories display concentration characteristics: in the 13 regions
of the first type of strong competitiveness there are 22 car factories, in the 23 regions of
type two there are 19 factories, in the 48 regions of type three there are 46 factories,
160
Imre Lengyel
while in the 9 regions of type four there are 4 factories. Consequently, in those German
and Austrian regions where there is a high portion of assistance won from EU research
funds, gross expenses spent on R&D, and people employed in the high-tech sectors,
there are significantly more car factories.
Examining the connections between the competitiveness principal component and
Factor 2 results in a spatial structure slightly different than what it used to be earlier
(Figure 14). The German and Austrian regions of strong competitiveness are dispersed
in a very wide band according to Factor 2, and part of them is even in a situation similar
to the regions of the post-socialist countries. The latter regions can rather be found in a
block, in the bottom left quarter. Considering the 93 regions the Hungarian regions are
situated in the middle, leading the field among the post-socialist countries’ regions.
Consequently, considering Factor 2, the Hungarian regions are in a much better position
in comparison to their revealed competitiveness, overtaking among others German and
Austrian regions.
FIGURE 14
Connection between competitiveness principal component and R&D factor
161
The investigation of the 21 factors influencing competitiveness with the help of
factor analysis and regression analysis points out that human capital and research and
technological development have a very serious influence on regional development.
Whereas considering human capital the German and Austrian regions excel, on the basis
of research and technological development more regions of the post-socialist countries
reach the middle field. According to these two factors the Hungarian regions belong to
the middle field, the leading group of the post-socialist countries’ regions.
Summary
In our study the newest trends connected to regional competitiveness were reviewed,
from which the theories of endogenous growth and development were highlighted.
Nowadays these trends describe the growth and development taking place under the
conditions of global competition, therefore in the course of economic development
aimed at the improvement of regional competitiveness, the development of a strategy
built on local characteristics, organized from below is required. Human capital and
social capital constitute the most important factors, which though may be centrally
encouraged, are intrinsically connected to a specific place and may be exploited locally.
The redefinition of the pyramidal model was introduced to interpret, measure the
concept of regional competitiveness and demonstrate its influencing factors, in which
besides human and social capital, traded sectors are also included. Multivariable
statistical procedures were applied to demonstrate the correspondences, examine the
database compiled from the data of the 93 regions of the 8 Central and Eastern
European countries. Due to the difficulty of obtaining international data, the database
generally contains data from the years 2008 and 2007, i.e. shows the situation before the
global crisis.
From the results we point out that the competitiveness of the German, Austrian and
Slovenian regions is in every respect considerably stronger than that of the other
countries’ regions, only the capital regions may be numbered among them. Regions of
strong competitiveness cluster spatially, and the regions of the following type are located
in their neighbourhood, in their geographical proximity. With respect to the Hungarian
regions, with the exception of Central Hungary all the other Hungarian regions belong to
the regions of the weakest competitiveness in almost every respect. Four of our regions
(Southern Transdanubia, Northern Hungary, Northern Great Plain and Southern Great
Plain) constitute a separate group, they are the lasts not only in employment, but they are
of the weakest competitiveness according to the competitiveness principal component,
falling behind even the Romanian and Polish regions. The situations of Central
Transdanubia and Western Transdanubia are slightly better; their competitiveness
approaches that of the medium Czech regions. The spatial distribution of car factories is
more or less even in the three stronger types, whereas there are few factories in the regions
of the weakest competitiveness.
162
Imre Lengyel
The results of the factor analysis and the regression analysis show that although the
competitiveness of the domestic regions is weak, on the basis of human capital and
R&D, the factors determining future competitiveness, there is hope for their situation to
improve quickly. In other words, although both employment and labour productivity are
of a low level in the domestic regions, the network of research institutes and the
preparedness of the work force would enable a significantly quicker rated economic
growth. The revealed competitiveness of the Hungarian regions lags behind in
comparison to the regions of the post-socialist countries, but overtakes them on the
basis of the mentioned potential development factors. Consequently, the potential
conditions of the improvement of regional competitiveness are given; the question is
whether the national economic, regional development policy can properly take
advantage of them.
References
Annoni, P. – Kozovska, K. (2010) EU Regional Competitiveness Index. European Commission
Joint Research Centre, Luxembourg.
Bajmócy, Z. – Szakálné Kanó, I. (2009) Measuring the Innovation Performance of Hungarian
Subregions. – Bajmócy, Z. – Lengyel, I. (eds) Regional Competitiveness, Innovation and
Environment. JATEPress, Szeged. pp. 99–121.
Barkley, D. L. (2008) Evaluations of Regional Competitiveness: Making a case for case studies. –
The Review of Regional Studies. 2. pp. 212–143.
Batey, P. – Friedrich, P. (2000) Aspects of Regional Competition. – Batey, P.– Friedrich, P. (eds)
Regional Competition. Springer, Berlin. pp. 3–33.
Berumen, S. A. (eds) (2008) Cambio tecnológico e innovación en las empresas. ESIC Editorial,
Madrid.
Camagni, R. (2002) On the Concept of Territorial Competitiveness: Sound or Misleading? –
Urban Studies. 13. pp. 2395–2411.
Camagni, R. (2009) Territorial capital and regional development. – Capello, R.–Nijkamp, P. (eds)
Handbook of regional growth and development theories. Edward Elgar, Cheltenham. pp.
118–132.
Camagni, R. – Capello, R. (2010) Macroeconomic and territorial policies for regional competitiveness: An EU perspectives. – Regional Science Policy & Practise. 1. pp. 1–19.
Capello, R. (2007a) Regional economics. Routledge, London and New York.
Capello, R. (2007b) A forecasting territorial model of regional growth: the MASST model. – The
Annals of Regional Science. pp. 753–787.
Chesire, P.C. (2003) Territorial Competition: Lessons for (Innovation) Policy. – Bröcker, J.–
Dohse, D.–Soltwedel, R. (eds) Innovation Clusters and Interregional Competition. Springer,
Berlin. pp. 331–346.
EC (1999) Sixth Periodic Report on the Social and Economic Situation and Development of
Regions in the European Union. European Commission, Luxembourg.
EC (2001) Second Report on Economic and Social Cohesion. European Commission, Brussels
EC (2008) European Competitiveness Report 2008. European Commission, Brussels.
Faggian, A. – McCann, P. (2009) Human capital and regional development. – Capello, R.–
Nijkamp, P. (eds) Handbook of regional growth and development theories. Edward Elgar,
Cheltenham. pp. 133–151.
Florida, R. (2002) The Rise of the Creative Class: And How It's Transforming Work, Leisure,
Community and Everyday Life. Basic Books, New York.
163
Gardiner, B. – Martin, R. – Tyler, P. (2004) Competitiveness, Productivity and Economic Growth
across the European Regions. – Regional Studies. 9. 1045–1068.
Glaeser, E. L. (2008) Cities, Agglomeration, and Spatial Equilibrium. Oxford University Press,
Oxford.
IMD (2010) The World Competitiveness Yearbook. IMD, Lausanne.
Kitson, M. – Martin, R. – Tyler, P. (2004) Regional Competitiveness: An elusive yet key
concept? – Regional Studies. 9. pp. 991–999.
Krugman, P. (1994) Competitiveness: A dangerous obsession. – Foreign Affairs. 2. pp. 28–44.
Lengyel, B. – Leydesdorff, L. (2011) Regional innovation systems in Hungary: The failing
synergy at the national level. – Regional Studies. 5. pp. 677–693.
Lengyel, B. – Ságvári, B. (2011) Creative occupations and regional development in Hungary:
mobility of talent in a one-centred transition economy. – European Planning Studies. 12. pp.
2073–2093.
Lengyel, I. (2004) The Pyramid Model: Enhancing Regional Competitiveness in Hungary. – Acta
Oeconomica. 3. pp. 323–342.
Lengyel, I. (2009a) Bottom-up Regional Economic Development: Competition, Competitiveness
and Clusters. – Bajmócy, Z. – Lengyel, I. (eds) Regional Competitiveness, Innovation and
Environment. JATEPress, Szeged. pp. 13–38.
Lengyel, I. (2009b) Knowledge-based local economic development for enhancing
competitiveness in lagging areas of Europe: The case of the University of Szeged. – Varga, A.
(eds) Universities, Knowledge Transfer and Regional Development: Geography,
Entrepreneurship and Policy. Edward Elgar, Cheltenham–Northampton. pp. 322–349.
Lengyel, I. (2011) Types of competitiveness of Hungarian microregions: Empirical testing of the
regional competitiveness function. – DIME workshop on „Regional innovation and growth:
Theory, empirics, and policy analysis”. Pécs, march 31 – april 1. p. 37.
http://www.krti.ktk.pte.hu/files/tiny_mce/File/Konferencia/2011/Lengyel.pdf
Lengyel, I. – Rechnitzer, J. (2004) Regionális gazdaságtan (Hungarian). Dialóg Campus,
Budapest–Pécs.
Lukovics, M. (2006) A Possible Method of Measuring the Competitiveness of Hungarian
Counties. – Gazdálkodás, Vol. I. Speciel edition, No. 17. pp. 54–62.
Lukovics, M. (2009) Measuring Regional Disparities on Competitiveness Basis. – Bajmócy, Z. –
Lengyel, I. (eds): Regional Competitiveness, Innovation and Environment. JATEPress, Szeged.
39–53.
Malecki, E. J. (2002) Hard and Soft Networks for Urban Competitiveness. – Urban Studies. 5–6.
929–945.
Malecki, E. J. (2004) Joykeing for position: What it Means and Why It Matters to Regional
Development Policy When Places Compete. – Regional Studies. 9. pp. 1101–1120.
Parkinson, M. et al. (2006) State of the English Cities. A Research Study. Volume 1. Office of the
Deputy Prime Minister, London.
Pike, A. – Champion, A. – Coombes, MG. – Humphrey, L. – Tomaney, J. (2006) The economic
viability and self-containment of geographical economies: a framework for analysis. Office of
the Deputy of Prime Minister, London.
Pike, A. – Rodrígues-Pose, A. – Tomaney, J. (2006) Local and regional development. Routledge,
New York.
Polenske, K. R. (2004) Competition, Collaboration and Cooperation: An Uneasy Triangle in
Networks of Firms and Regions. – Regional Studies. 9. 1029–1043.
Porter, M. E. (2003) The Economic Performance of Regions. – Regional Studies. 6–7. 549–578.
Porter, M. E (2007) Competitiveness: implications for Central Europe and the Czech Republic.
Paper presented in Prague, 22 October.
Porter, M. E (2008) Regional Competitiveness in a Global Economy. The Summit for American
Prosperity, The Brookings Institution, Washington.
Rechnitzer, J. (1998) Területi stratégiák (Hungarian). Dialóg Campus, Budapest–Pécs.
164
Imre Lengyel
Rechnitzer, J. – Smahó, M. (2011) Területi politika (Hungarian). Akadémiai Kiadó, Budapest.
Resch, J. (eds) (2008) Handbuch Lernende Regionen Grundlagen. Österreichisches Institut für
Erwachsenenbildung (ÖIEB), Wien.
Sinabell, F. (eds) (2011) Indikatoren für die Auswirkungen des Programms der Ländlichen
Entwicklung 2007/2013 in Österreich. Österreichisches Institut für Wirtschaftsforschung,
Statistik Austria, Universität für Bodenkultur Wien, Wien.
Snieska, V. – Bruneckiené, J. (2009) Measurement of Lithuanian Regions by Regional
Competitiveness Index. – Engineering Economics. 1. pp. 45–57.
Stimson, R. – Robson, A. – Shyy, T-K. (2009) Modeling regional endogenous growth: an
application to the non-metropolitan regions of Australia. – The Annals of Regional Science.
pp. 379–398.
Stimson, R. – Stough, R. R. – Salazar, W. (2009) Leadership and Institutions in Regional
Endogenous Development. Edward Elgar, Cheltenham.
Storper, M. (1997) The regional world. The Guilford Press, New York.
Szakálné Kanó, I. (2008) Regressziószámítás alkalmazása kistérségi adatokon (Hungarian). –
Lengyel, I. – Lukovics, M. (eds) Kérdőjelek a régiók gazdasági fejlődésében. JATEPress,
Szeged. pp. 264–287.
Vas, Zs. (2009) Role of Proximity in Regional Clusters: Evidence from the Software Industry. –
Bajmócy, Z. – Lengyel, I. (eds) Regional Competitiveness, Innovation and Environment.
JATEPress, Szeged. pp. 162–182.
WEF (2010) The Global Competitiveness Report 2010–2011. World Economic Forum, Genf.
COMPETITIVENESS OF THE VISEGRÁD
COUNTRIES’ COUNTIES FROM THE ASPECT
OF AUTOMOTIVE INDUSTRY
MIKLÓS LUKOVICS – PÉTER SAVANYA
Keywords:
automotive industry economic area localization Visegrád Countries competitive ranking of
NUTS3 territorial units
The car industrial regions of the Visegrád Countries (Czech Republic, Poland, Hungary and
Slovakia), encompassing the area of Central and Eastern Europe, form an integral part of the new
structure of European car manufacturing which is expanding due to economic integration. The
localization of this industry is being shaped by numerous global and industry specific factors in the
expanding economic space: supplier networks that are becoming global, regionally organized
manufacturing structures, and macro-regionally integrated markets. The dominant participants of
this industrial sector’s concentration, the car manufacturers and their strategically important
technological and supplier partners lead the networks being built in the sector, in which the process
of outsourcing and specialization are observable both at the level of companies, certain sector
activities, and the spatiality of the industrial sector. The competitiveness of the regions, the available
resources, and the characteristics of the company competition edge in a given region interpreted by
Porter can essentially determine the role of car industrial territories in the sector’s structure.
Therefore the measurement and comparison of regional competitiveness raises important questions
from the viewpoint of automotive industry as well, either the positions of the car industrial districts
of given regions are evaluated in the sector, or the concepts of economic development going in this
direction. The purpose of the present essay’s section on automotive industry is to provide a
theoretical frame to consider how the results of the competitiveness analysis of the counties of the
given countries can be interpreted in assessing the relative competitiveness of the car industrial
regions of Central and Eastern Europe.
Introduction
The globalization processes of markets and industrial sectors, which characterised the
past three decades, constitute a generally accepted statement in regional economics
(Lengyel 2003). The development of logistics and IC technologies enabled the
formation of (global) production systems spanning great geographical distances, while
also on the side of consumption and market new consumer and cultural trends crossing
geographical borders prevail (Lengyel 2010).
According to several approaches the process of globalization has fundamentally
rearranged the economic-geographical look of automotive industry. Some of the
doubtlessly common attributes of industrial sectors participating in globalization is the
establishment of global production systems and the organization of market structures
spanning borders (Dicken 2007). The establishment of global production chains and
166
Miklós Lukovics – Péter Savanya
integrated markets were catalyzed by the expansion of commercial and investment
activities liberalized within the frames of international agreements, and parallel to this,
by the expansive institutionalization of economic integrations (e.g. ASEAN, EU,
NAFTA). The multinational activities of car industries and the vindication of their
business interests played an active part in the building of economic integrations and
their legal institutionalization (Heribert 2007). The increasing practice of outsourcing
and the establishment of value chains based on new, cooperative connections are other
common characteristic features of these industrial sectors (Bieserbroek–Sturgeon 2010;
Chanaron–MacNeill 2005). As a result of this, the car industrial supply companies of the
developed countries increased their activity in both commerce and capital outsourcing,
while on the other side, in the developing countries the industrial sectors performing
supplier activities underwent an enormous (mainly quantitative) development
(Humphrey–Memedovic 2003; USITC 2010). Industrial suppliers operating in developed
countries became global corporations, with multinational presence and global potential to
serve a wide circle of affiliated companies (Bieserbroe –Sturgeon 2010; Chanaron–
MacNeill 2005).
In the economic mapping of automotive industry, the investigation of globally
organizing sector production systems and integrated market structures, however, a
conceptual definition has to be introduced that will assist us in the interpretation of the
sector’s described characteristics. Dicken (2007) and Florida–Sturgeon (2000, 19-20) in
the conceptual definition of global economy differentiates between internationalization
processes and globalization processes. Internationalization processes mean that
economic activities cross borders, go beyond the institutional system of national
economies. This can be interpreted as a quantitative change, within the frames of which
the geographical borders (districts) of economic activities expand, making connections
between (national) economies. On the other hand, the processes of globalization can be
portrayed as a qualitative change. Moving beyond internationalization, (international)
economic activities crossing national borders form functionally integrated systems.
Within the frames of globalization, the activity of the individual economic actors
(corporations) constitutes an internationally coordinated (organized) process system.
Besides the above mentioned processes, however, the structure of automotive
industry is characterised by numerous facts that established value chains built on
regionally defined markets and functioning in global-regional production connections in
the industrial sector.
− Concentration: It is a characteristic feature of automotive industry that it is
marked by an extremely concentrated corporeal structure: a few giant
corporations established in the course of acquisitions and purchases dominate the
industrial sector.1 Parallel to the increased concentration of corporations and the
car industrial sector, the standardization of technological and business practices
of the industrial sector can be observed. The planning of conceptions built on a
common platform, and the modular production practice of certain model-designs
diversified by markets and segments built on this, or the application of JIT
production systems can be mentioned here. In the competition between car
The Competitiveness of the Visegrád Countries’ Counties from the Aspect…
167
manufacturers the efficient and system-based process management became a key
factor in every field of corporeal activity (technologies, product and design
development, production technologies, production and market organization,
marketing innovations, the observation of industrial sector and partnertechnologies, the adaptation of sector’s best practice) (Lorentzen 2011).
− Production capacities installed in the markets: The end-product manufacturing
phases of the production processes – which in this case mean the final assembly
(manufacturing) of the automobiles – and in a wider sense the manufacturing of
the component-modules are installed in the vicinity of the distribution’s target
markets. The reasons for this are partly political, and can partly be explained by
the markets’ peculiarities, as it will be seen below. The fullness of markets, the
proliferation of motorization, the “produce where you can sell” market behaviour
of car manufacturers resulted in the trend that the corporations spread the production infrastructures to more countries of the world than they had done before.
− Regionally organizing industrial sector structures: Automotive industry is
characterized by strong regional market differentiation. Although automotive
industry has increasingly progressed towards global integration since the 1980s,
the integration resulted in a powerful regional sector pattern.
Concentration characterizes mainly the sector’s business structure, and the company
strategies determining the sector’s laws of movement. The production capacities
installed in the markets and the regionally organizing industrial sector structures
established the economic-geographical look of the industrial sector as the mirror images
of each other, in a parallel way.
The automotive industry of Europe, and thus the automotive industry of Central and
Eastern Europe, the Visegrád Countries, is determined by these economic and industrial
sector power lines (macro-regional economic integration, outsourcing and activity
localization). The establishment of the institution system of the EFTA (European Free
Trade Association), Central-Europe’s European economic integration starting in the
1990s and gradually expanding to the east has made these countries’ economic
resources relevantly available for car manufacturers.2 Car manufacturers installed their
production-assembly units based on the workforce available in the region which is
considerably cheaper than the national base, channelling them into the production and
market structure of European automotive industry, as well as into the macro-regional
supplier systems built on global resources.
Macro-regions in automotive industry
As defined by Bieserbroek et al. (2009) the economic map of the world’s automotive
industry shows a quasi temporary picture between globalization processes and national
markets, which corresponds to the previously described conceptual definition. On
account of globalization changes, the national type position of the industrial sector
(connected to national character) has transformed in certain parts into a globally inte-
168
grated system. These global integration processes embedded the actors of automotive
industry into a network of globally impacting production, market systems. This global
system is constituted by stably established regional systems (regions growing out from
the integrations of national economies) at an operative level. The diversity of market
demands, and the growth oriented pressure of reaction-adaptation requires the elaboration of new models and solutions meeting the demands of a defined market.3 Perhaps
the primal motive for the establishment of regionally organized sector structures is the
process of macro-regional economic integrations, the merging of markets and parallel to
this, the centre-periphery organization of company processes. Summing up the previous
conceptual definitions, the sector value chains coming into being within the industrial
sector are internationally active, while the activities connected to the individual territories are characterized by globalization.
On the side of the industrial sector markets, besides the new possibilities of emerging
markets, the significance of the (national) bases remained dominant for the “traditional”
car industrial districts (EU, USA) and individual car manufacturers (Figure 1). Regional
embedding has a very strong effect, regarding production sales ratios, which still reflect
the dominance of “basis regions”. The smaller rearrangements in production and sales
observable in the recent years mainly reflect the organization of interregional production
forms and the increasing freedom of world trade.4
FIGURE 1
Global trade flows in automobiles in 2004
Note: Trade flows in billion USD
Source: Dicken (2007, 304).
169
On the production side we can also find strongly regional systems with global
characteristics, which tendency has been strengthening since the 1980s. Car manufacturers and their multinational supplier partners operate in a global context consisting of
multi-regional systems. In the operative working of the production chain the component
producing and supplier system installed into a particular local region supplies regional
manufacturing markets. Outsourcing and FDI activity, which is regional or aimed at the
peripheries between regions, can also be considered a general trend, utilizing cheap
production and operational costs (the relationship of USA–Mexico in North-America;
Spain and Eastern Europe in Europe, Southeast-Asia and China in Asia).
Regional organizations are strengthened by political and commercial pursuits both
within and outside regions. In many cases automotive industry is iconic in the eye of the
public (e.g. organized trade union and industrial lobby). The import products and the
sharpening, crowding competition may trigger strong political counter-reactions against
a car manufacturer wishing to enter the market, which may hamstring the taking of new
markets for the new importer manufacturer. This effect is especially significant if
domestic car production represents a traditional national brand, a significant industrial
potential bearing national characteristics and an employment base. On the other hand,
the economic-political regime and the public (trade unions) welcome manufacturers
entering the market as investors and creators of workplaces more warmly. In spite of the
expansion and integration of trade zones (e.g. WTO), in line with these considerations
car manufacturers establish local production units in the regions, instead of exportoriented distribution, “crossing” thereby the obstacles of the political and economic
environment.5
The expansion and borders of the socio-technological space can be defined as a
regional integration force, which constitutes an important element in the operative
working of the corporations’ production and manufacturing strategies. The forms of
cooperation norms and coordination play a basic role in the building of company connections, and the efficient operation of production chains, (JIT) production process
systems. The role of incoming and outgoing logistics in the operation of production
systems based on modern JIT principles constitutes an additionally important factor. In
this kind of operation of supplier and logistics connections expected by car manufacturers the suppliers localize their activity near the manufacturers, ensuring thereby the
expected flexibility in the production chain. On the whole it may be said that component
part production takes place in global dimensions, while the modular production processes operate in the regional network of manufacturers installed in the target markets
and suppliers.
The localizations of automotive industry in the economic space
Automotive industry dominated by a few companies, similar to many other sectors, is
characterized by increasing and dynamically changing competition, which is (was)
doubtless catalyzed by globalization. For companies the conscious definition and
development of competitive advantages constitute key factors. The geographical locali-
170
zation of competitive advantages (in the Porterian sense) and company activities shows
a close connection (Freyssenet–Lung 2004b; Porter 1990), which reinforces the significance of the concept and investigation of regional competitiveness in the evaluation of
the automotive industry of the Visegrád Countries, in the situational analysis of car
industrial districts.
In their survey, in the course of the typification of the localization of automotive industry Florida–Sturgeon (2000) differentiated between four types according to the motivation of the companies’ activities, and the qualitative evaluation of localization. The
typification demonstrated by them can be analysed on the basis of localization viewpoints (Bernek 2000) (Table 2).
“Nowadays the most important territorial level is the global economy itself. The
most important elements and tendencies of world economy globalization are organized
and prevail at this territorial level: a remarkably quick technological, especially communication technological development; transnational companies and international production organized by them; and a never before experienced acceleration of the role and
importance of money” (Bernek 2000, 89–90). Starting from the previously defined
qualitative meaning of globalization we can say that the spatial lines of economic
organization are drawn by the spatial flows taking place between internationally active
economic actors (in our case the companies of automotive industry), while the principles of spatial organization are characterized by the organizational principles of the
flows. These flows include both physical (the physical content of products and services), and virtual (services and information) flows. Following this line of thought in the
investigation of the spatial organization of car manufacturing, the value chain modelling
the flows of the industrial sector and the actors (companies) defined behind the flows
and the characteristics of their connections may serve as a pivot for the evaluation of the
organization of position, localization in the heterogeneous (global) economic space. In
other words, why certain company activities land at particular points of determined
characteristics in the economic space, along what principles certain activities (companies) make their localization decisions.
In this chapter, we are going to look at the company strategies spanning the automotive industry and their impact on the industrial sector as a whole following this line
of thought. The sector value chain described by Humphrey–Memedovic (2003, 22) characterizes the companies of this industrial sector and their strategies in a comprehensive
way. These correspondences of economic space and automotive industry provide a
practical viewpoint and frame of interpretation to the county level regional competitiveness analysis of the Visegrád Countries. Our aim is to outline a comprehensive evaluation approach on the regions’ competitiveness situation from the viewpoint of automotive industry as well.
The production and business structures of automotive industry have undergone fundamental changes in the mirror of the processes consummating from the 1980s, global
and regional processes equally characterize the changes of the production structures of
automotive industry. The globalization processes and newly established production
systems, company outsourcing and specializations rearranged and reinterpreted the
171
place and role of the actors of the automotive industry value chain.6 The linear perception of production processes was more and more replaced by an integrated value chain,
and the adaptive cooperation of the actors in it (Figures 3–4) (Grosche–Schmid 2008).
TABLE 2
Typification of OEM’s locations
Florida-Sturgeon typification
Localization
viewpoint
Strategic intent
Capacity level
Wages
Development
Level of
integration
Level of background industry
Export
Type 1
Type 2
Closeness of marCloseness of
kets, company
markets, company
competitive
competitive
advantages
advantages
(capabilities)
(capabilities)
High
High
High
High
Yes
In some cases
High
High
Type 3
Type 4
Cost cutting,
rationalization,
efficiency
Covering of
markets
High
Low
No
Medium
Low
Low
No
Low
High
Medium-to-high
Medium
Low
Low
(except Japan)
Low
High
Low
(General) Typification of Dunning’s eclectic theory
Type of interna- Vindicating stratetional investgic advantages
ment
Ownershipspecific
advantage
Market-oriented Increasing efficiency Exploiting local
resources
Long-term strategic Increasing market
aims, sustaining
(global, regional,
international
local) success,
controlling local
competitiveness
market
Localization
The above factors Cost differences,
and the competimarket size and
advantage
tiveness of the
type, government
given territorial
politics
level
Internalization
Competitive and Decreasing transacstrategic advan- tion costs, adjusting
advantage
tages, risk
to local demands
decreasing, controlling of markets
Rationalization of Increasing competitiveness
existing investments
Production speciali- Differences in the
zation and concen- costs of production
tration of national
factors
economies
Vertical company Price regulation,
integration –
direction, controlcompany value
ling of markets
chain
Source: Own compilation based on Bernek (2000, 95) and Florida–Sturgeon (2000, 13).
172
The value chain of automotive industry is characterized by global supplier connections and production and marketing processes localized in the regions.9 The schematic
diagram of regional structure embedded in a global context that is being built in automotive industry is illustrated on Figure 5.
As several essays analysing the localization and spatial differentiation of the industrial sector point out (Bieserbroek–Sturgeon 2010; Chanaron–MacNeill 2005;
Freyssenet–Lung 2004a; Freyssenet–Lung 2004b; Haiss–Mahlberg–Molling 2009;
Heribert 2007; Humphrey–Memedovic 2003; Haiss–Mahlberg–Molling 2009), the spatial localization of automotive industry is decisively determined by the installation decisions of car manufacturers (see before Florida–Sturgeon (2000) and Dunning’s typification).
FIGURE 3
Value chain and organizational structure of the automotive industry
Product-Design planning
and development
Material
production
3
3
3
Design
and
engineering
companies
CAR MANUFACTURER
Financial and investment partners
2
3
2
3
2
1st tier supplier
and
technological
partner
Agencies
Distributors and
traders
Service
providers
Source: Own compilation based on Bieserboeck et al. (2009, 16).
component
production
Production
of modules,
subassemblies
Assembly
Marketing
Distributionn
After-sales
services
FIGURE 4
Restructuring of the car manufacturing pyramid
Source: Grosz (2000, 128).
FIGURE 5
Regional structures of car manufacturing
Basis
(carindustrial district)
Technological, design and
devolpment centers
(carindustrial district)
Production
Regionally localized units of
compnents-modules assembly
(carindustrial districtcts)
Global outsourced
processes
(componenets, etc.)
Regional market and
production strutures
173
174
In the industrial sector, the 1st tier supplier and strategic partners, as global companies at the same time, localize their own functional units near the car manufacturers’
units (plants) in order to make use of the advantages of spatial closeness. For the 1st tier
supplier the car manufacturer is the client, so the values of the advantages of spatial
closeness rise in the efficient service of the car manufacturer, the support of the
processes. This advantage has a special significance in the process management of
modular production built on JIT principles. This tendency is referred to as the phenomenon of co-localization in literature. This tendency is unequivocally described by
Florida–Sturgeon (2000) when examining the temporal closeness of car manufacturers
and the plant establishment of the 1st tier supplier integrating the supply base (Florida–
Sturgeon 2000, 64). This effect is especially strong in case of the automotive industry’s
new localization built on global principles, which in the course of the past 20–30 years
meant the industry’s outsourcing of production and supplier capacities into peripheral
regions. The supplier integrator can either lean locally (at a territorial level) on the
established supplier network (tier 2, tier 3 supplier), or on his globally organized own
channels (global link).
Car manufacturing localization should not be interpreted as the static places of production processes connected to the industrial sector, but rather in the networks defined
by Bernek (2000), the position of the value chain’s functional subsystems (as a subnetwork built from factors) in the differentiated economic space. In this definition of the
localization of car manufacturing the concept of place is rather replaced by geographical
expansion and the concentration of a network (car manufacturing district). In the value
chain of automotive industry (see before) the individual functions are supplied by a
network of subsystems built at certain points of the economic points. The strategic directors of this global production system are the car manufacturers (OEM) and its integrator organizers are the 1st tier suppliers. The other actors of the network and the subnetwork located in space (tier 2, tier 3 supplier) take part in the car manufacturing value
chain connected to them.
The competitive advantages taken in the Porterian sense and the geographical localization of company activities show a close connection (Freyssenet–Lung 2004b; Porter
1994). A company’s competitive advantages defined in Porter’s diamond model can be
seen well at the different levels of the value chain, which also explains the locality of
activities (company localization), the industrial sector map reflecting firm strategies.
The company activities exploiting high level competitive advantages (technology and
innovation, financial connections) centralize in developed areas capable of establishing
specialized factors. In a region the activities requiring the closeness of the market and
the coordination connected to the company are located on the peripheries, which are
mainly built on companies capable of fulfilling the appropriate functions of the value
chain, and workforce that has the necessary qualifications, but costs less. The localization of these company activities are motivated by the basic factors, and the availability
of a few special factors, which are important from the viewpoint of the company’s
activity. In car manufacturing, besides the appropriately built basic infrastructures the
special factor is constituted by the area with technological-industrial culture/past and the
175
workforce of the appropriate basic qualification. Standardizable and mass production
activity are globally outsourced.7
In the interpretation of a region it shall be emphasized that in the localization of the
activities, the competitive advantages available in the different territories of the individual regions and the localization possibilities of the companies capable of exploiting
them were expanded by the development of traffic infrastructures and coordination
techniques (process organization, computer and data transfer technologies, etc.). This
way the regional production structures which used to characterize car manufacturing in
earlier times constitute systems spanning whole continents with the appropriate level of
coordination.
Positions of the Visegrád Countries
in the car manufacturing of Europe
European car manufacturing is characterized by both global processes discussed earlier
and regional structures. The biggest change of the region’s car manufacturing industry
was the integration of the Central and Eastern European economy, which simultaneously
brought changes of global origin into the industrial sector and dislocation in the region’s
car manufacturing localization, which are emphasized by the industrial sector analyses and
the theoretical literature everywhere (EC 2002, Freyssenet–Lung 2004a, Freyssenet–Lung
2004b, Haiss–Mahlberg–Molling 2009, Heribert 2007, Radosevic–Rozeik 2005).
With respect to globalization the integration of these countries into the European
economy created a bridge, a gate of entry into the Western-European markets for Asian
car manufacturers (Toyota, Kia, Hyundai, Suzuki), who, as FDI investors, built
production capacities in the region’s countries. The cars manufactured and assembled
here were not burdened by import obstacles, and the properly qualified workforce
available in these countries, which is much cheaper than in the west, and makes
production possible directly for the European markets, provided a serious market
possibility, which the Asian manufacturers did exploit. On the other hand, as a result of
the economic convergence processes these countries are potential and growing markets
for car manufacturers.
The production and supplier networks of the European car manufacturing industry
were substantially restructured in the last twenty years by the economic integration of the
Central and Eastern European countries, which process is still going on nowadays (Haiss–
Mahlberg–Molling 2009). The European car manufacturers built new component and
production capacities to exploit the competitive advantages available in the area: based
mainly on a low-income, but appropriately qualified workforce, and utilizing the industrial
structures capable of adapting production technologies. (First in Poland and the Czech
Republic, as well as in Hungary, and later in Romania, and we also have to mention
Turkey, which became one of the greatest car manufacturers of the world.) In these
economies governments and economic policies sought to accept significant direct and
176
indirect assistance and attract these investors in order to promote economic close up
(Haiss–Mahlberg–Molling 2009; Radosevic–Rozeik 2005).
The appearance of Asian manufacturers in Europe altered the structure of European
car manufacturing structures. The European supplier networks, built on the chain of
industrially independent companies for the utilization of the Japanese modular production technologies, developed systems supporting modular production technologies,
localized their operative units in the vicinity of the production and assembly units
established in Eastern-Europe. These newly built systems had an effect on the transformation of both Asian and European car manufacturing systems.8 Today the European
production structure is characterized by those innovative supplier integrator-partners,
who support the establishment of systems between manufacturers and suppliers. The
modular and component supplying companies worked out special competences, taking
over this activity from car manufacturers. Since the end of the 1990s the systems of
these specialized, independent companies have served as the supplier bases of car
manufacturing value chains (Heribert 2007).
In Europe the presence of the specialized small and medium supply companies
further strengthened the outsourcing processes. With the progress of integration these
enterprises formed networks, giving rise to syndicated product development and joint
production capacities. The building of these systems is enhanced by cluster-based
policies, and the institutional support of network innovations (Heribert 2007).
Considering the whole region, the localization of car manufacturing industry is
determined by the interwoven processes of specialization and clustering (EC 2002):
− Specialization appears in the marketing and production strategies of car
manufacturers, to exploit the expansion of markets relevantly available for the
whole industrial sector, and entering these markets.
− The economic and territorial process of car manufacturing’s industrial clustering
is decisive in Europe, especially in the integrated economic space of the EU.
Notwithstanding, the cluster-based economic policies replacing the modern
industrial sector approach and the EU policies encourage the establishment of the
dynamic competitive advantages of the industrial sector concentration and networking connected to the economic space, emphatically supporting the development of clusters.
The integration process of the regional car industrial structure was assisted by the
expansion of international and interregional trade within Europe, which strengthened
the specialization of production processes and potentials. This process is especially well
reflected by the localization of the assembly plants of car manufacturers, which is
motivated by platform-based model production and marketing strategies, and the
installation decisions building on the given region’s capabilities (Figure 6).
The economic structure of the industrial sector reveals a rearrangement due to the
explosive development of the Central and Eastern European component and modular
subassembly production, although the centre-periphery relationships are still persistent,
the differences are significant. The leading developed Western car industrial areas in
177
Europe clustered by the establishment and exploitation of competitive advantages
preserved their position in the industrial sector, sustaining their technological and
market advantages through established and operating manufacturer-supplier connections
and efficient innovation networks (EC 2002). As we have seen earlier, considering the
nationality of basis markets and car manufacturers, the base country has a decisive role
in the capacities of individual car manufacturers, with respect to either the number of
manufactured cars, or the number of employees.
FIGURE 6
Localization of the automotive industry in Europe
Key: 1 – ACEA-tagok (BMW, DAF, Daimler-Chreyler, Fiat, Ford, GM, MAN, Porsche, PSA
Peugeot-Citroen, Renalult, Scania, Volvo, Volkswagen Ag.); 2 – Nem ACEA-tagok (főként
kelet-európai és néhány nyugati kisebb gyártó); 3 – Japán gyártók (Honda, Isuzu, Mitsubishi,
Nissan, Suzuki, Toyota); 4 – Koreai gyártók (Daewoo, Hyndai).
Source: ACEA (2008).
178
The 2002 analysis of the EC states that the production units and regions, which
came into being in Southern-Europe, especially in Spain as a result of the companies’
outsourcing strategies in the 1980s, still have a significant industrial sector role in European car manufacturing. When the European economic integration started, these areas
represented the outsourcing destinations of labour-intensive or standardizable (mass
production) activities, and assembly activities. Governments actively supported working
capital investments, and announced economic development and regional development
programs based on the presence of automotive industry. As a result of the investments,
industrial capacities related to car manufacturing and connected potentials came into being
in individual regions, car industrial districts and areas were formed. The assembly and
production networks established here, besides the car industrial proliferation of the areas
newly joining the European integration, have the edge owing to the already established
and working regional car manufacturing and supply networks. Cohesion resources and
community economic policies encouraging clustering have also promoted this process,
which means the countries of Central and Eastern Europe have had for less than ten years.
From the almost one decade perspective of the report we can say that the competitiveness
of this area has worsened as a consequence of disposing government policies, which
increased the standard of wages by raising internal consumption in a proportion that is
bigger than the increase of productivity.9
The automotive industry of Central and Eastern Europe displays a continuous
development, which continues further in the period after the crisis (Haiss–Mahlberg–
Molling 2009). The institutionalization of clustering can already be seen, although its
functional operation approaches the level of development of car industrial regions
operating in the west only in one or two regions (mainly in the Czech Republic).
In case of the Visegrád Countries, and especially the regions of Western Transdanubia,
Central Transdanubia and Central Hungary, the creation of the local embedding of car
manufacturers is a key question from the viewpoint of the area’s automotive industry and
regional economic development. It is important to make steps in this direction. The former
appeal of the area, the lowly paid, but appropriately qualified workforce providing a
competitive advantage for car manufacturers, and the vicinity and good accessibility of
Western-Europe constitutes less of a competitive advantage nowadays.10
With the lack of an established local supply base, a major part of the added value of
the assembly car manufacturing depending on import supply chains comes from outside
of the region. Its economic impact, excluding the numbers of the export macroeconomic GDP, does not really go beyond the factory gates. The small and medium
enterprises of the region incorporating as suppliers into the value chain of car manufacturing can literally connect the car manufacturer’s activity to the region. The added
value connected to the area’s economy, the creation of workplaces and wages make the
multiplicator influences prevail. The region’s knowledge and innovation potential has
an important role in drawing car industrial activities of high added value into the region,
and the conscious establishment of industrial sector connections, the formation of networks, and the dynamization of the regional innovation systems are also decisive
(Blöcker–Jürgens–Heinz 2009; Dimitrova–Stratmann 2008).
179
Placing the Visegrád Countries in the system of European automotive industry and
economic space, we have to expand the concept of the macro-region of automotive
industry even beyond the borders of the widened European Union. The process of economic integration, the business connections and production chains cover a much larger
territory in the EFTA system than the institutional borders of the Union.
Consequently, from the viewpoint of automotive industry, the competitiveness indicators of the counties of the four Visegrád Countries have to be placed and evaluated in
a much larger context. The analysis of competitiveness based on the pyramid model gives
a relative picture about the countries’ counties. Interpreting the competitiveness of the
territorial units of the macro-region that is Central and Eastern Europe, important consequences can be drawn with respect to automotive industry as well. The comparative
analysis points out the situation of our country’s counties and their positions in the
competition of the Central and Eastern European regions, showing what level of company
advantages the economies of the individual territorial units are capable of creating. The
analysis extending to the counties of the Visegrád Countries provides a comparative
picture about the relative competitiveness of the Central and Eastern European regions.
Utilizing the earlier described industrial sector strategies and structures as a theoreticallogical frame, the analysis of the region’s competitiveness can provide important lessons about the positions and the direction of development of particular local regions and
the car industrial districts localizing in them.
Empirical analysis
The purpose of the analysis is to investigate the competitiveness of the Visegrád
Countries at NUTS3 level, and to rank the competitiveness of these levels. To do this,
first the indicators and the model on which the investigation is based shall be
introduced, followed by the methods applied.
Frames of investigation
The uniform definition of competitiveness and the pyramid model elaborating it serve as
the basis of our investigation. The applicability of the model requires the availability of
the appropriate indicators. This means that each category of the model should be
characterized by a commeasurable index-number. This proved to be difficult. In the
course of empirical investigations only those indicators can be applied which in their
content mean the same for each territorial unit, i.e. the index number expresses in
content the same in the different countries. Consequently, in the course of the analysis
only the EUROSTAT data could be used. Furthermore, the circle of these indicators is
fairly narrow, so in case of certain categories of the pyramid model there are 4–10,
while in case of other categories there are absolutely no data available at NUT3 level.
Consequently, only a part of the pyramid model may be applied for analysis, in such a
way that the pyramid should not tilt. In the first approach this means two things. On the
180
one hand, all categories shall be applied at the given level; on the other hand, the
number of indicators describing the individual categories shall be in balance. We
reached the conclusion that in the first approach only the topmost level of the pyramid
will be used for analysis, i.e. the basic categories (wages, labour productivity,
employment), in a way that each category will be described by maximum 2 indicators.
Then we will compare the results with the index-numbers describing other, not applied
categories of the pyramid model (Table 2).
Since more indicators are applied collaterally, the multivariable statistical procedures can be used as basic methods. Firstly, we would like to set a competitive ranking
for the investigated territories on the basis of the indicators describing the basic
categories. For this one-dimensional scaling a special case of multidimensional scaling
will be used. And finally the result of the ranking shall be used with the other indicators.
Secondly, we will group the investigated NUTS3 units with the help of cluster analysis.
Then the established clusters will be typified, characterized.
TABLE 2
Indicators used in the analysis
Indicators
describing basic
categories
Indicators used for
further
characterization,
typification
GDP per capita at market price in the average percentage of the EU, 2008
Gross added value per employed person, million Euro/capita, 2008
Unemployment rate,%, 2008
Employment rate,%, 2008
Population growth in comparison to the previous year,%, 2008
Migration change in comparison to the previous year,%, 2008
Life expectancy at birth, year, 2008
The share of agriculture, fishing from the territory’s gross added value,%,
2008
The share of industry, excluding building industry from the territory’s
gross added value,%, 2008
The share of building industry from the territory’s gross added value,%,
2008
The share of services from the territory’s gross added value,%, 2008
The share of wholesale and retail, hotels and restaurants, traffic from the
territory’s gross added value,%, 2008
The share of financial mediation, real estate services from the territory’s
The share of administration, community service from the territory’s gross
added value,%, 2008
The number of enterprises per 1000 inhabitants
Activity rate,%, 2008
Gross added value per capita, million Euro/capita, 2008
181
Multidimensional scaling (MDS)
Multidimensional scaling provides the geometrical representation of objects (Füstös–
Kovács 1989). MDS, as data reduction system starts from a distance matrix, and by
significantly reducing the dimension number, reaches its output, a diagram displaying
correspondences, from which in a fortunate case the incidental clusters can be delineated
(Lengyel 1999). In effect we are expecting a point diagram drawn in a reduced, two
dimensional space, which displays the investigated units’ position in comparison to each
other from a complex competitiveness aspect (Lukovics 2008).
The reduction of dimensions has to occur in a way that the order of the elements’
distance should not change. S-stress is one of the most common control-indicators used
for this, the value of which falls between 0 and 1. The geometrical representation
formed as a result of multidimensional scaling is the more perfect the less its S-stress
value is (Székelyi–Barna 2003; Kovács–Petres–Tóth 2006).
The meaning of the established artificial dimensions can be analysed by the correlation
relationship of the dimensions and the variables shaping the dimensions. Since in case of
the coordinates resulted by MDS the order and not the exact numerical value is essential,
the correlation can be characterized by the Spearman’s rank correlation coefficient. The
artificial dimensions can be named on the basis of the significant relationships.
If we try to represent the objects in one dimension, we are talking about onedimensional scaling. On the basis of this it is theoretically possible to determine the
objects’ ranking besides the aggregation of the original variables, which can provide the
opportunity to establish a complex competitive ranking. For this two conditions have to
be met. On the one hand, the S-stress value cannot exceed 0,1, and on the other hand,
the established artificial variable can be considered as competitive ranking. This can be
ascertained on the basis of the indicators providing the basis of MDS, and the direction
and strength of the correlation of the established artificial dimension.
It shall be noted that the advantage of the methodology applied for the establishment
of the ranking is that it does not attempt to examine competitiveness on the basis of one
indicator. However, this complexity can cause several problems and limitations in the
course of the analyses. On the one hand, the basic model serving as the basis of the
investigation and the set of indicators describing it can greatly influence the established
rankings. This means that either the changing of the set of indicators or an investigation
conducted in a frame system other than the pyramid model may result in another ranking.
Applying the one-dimensional scaling to the indicators characterizing the pyramid
model’s basic category, on the basis of the 0,053 S-stress value, the procedure can be
considered to be good.
On the basis of studying the rank correlation coefficients (Table 3) it can be stated
about the established artificial dimension that the value of the coordinate established in
the artificial dimension is in a strong positive relation with the value of GDP per capita,
the gross added value per one employed person and the employment rate, and is in a
strong negative relation with the unemployment rate. On the basis of this we can estab–
182
lish the complex competitive ranking of the NUTS3 units of the Visegrád Countries on
the basis of 2008 data (Table 4).
According to our expectations, the ranking is lead by the four capitals: Prague,
Warsaw, Bratislava and Budapest. If the MDS coordinates of the analysed territories are
illustrated on a point diagram depending on ranking, together with the marking of the
country (Figure 5), then on the one hand it can be seen that the four capitals and Poznan
stand out at the top of the list, and on the other hand, the Czech territories can be found
rather in the first half of the ranking, while the Hungarian territories are positioned
rather in the second half of the ranking. The Polish territories can be found everywhere
in the ranking, the Slovakian territories are also rather in the first half of the ranking, but
here we can find elements near both end of the ranking, too.
To examine whether there is a significant difference between the positions of the
Visegrád Countries in the NUTS3 ranking a Kruskal-Wallis test was applied. According
to the null hypothesis of the test there is no significant difference between the positions
of the NUTS3 territories of the countries, which however we reject besides a five per
cent significance level (Test function=20,3). In the SPSS 18.0 software it is possible to
investigate this more deeply (Figure 6), on the basis of the Post Hoc test supplied by
correction based on the comparison number.
In the ranking the average rank number of the Hungarian counties is 66., while that
of the Czech territories is 20,4, that of the Polish territories is 58,3, and that of the Slovakian territories is 54. These discrepancies are significant only in the Czech-Hungarian
and Czech-Polish correlation, i.e. it can be established that the Czech NUTS3 territories
occupy a significantly better place in the ranking than the Hungarian and Polish ones
(Table 5).
If we look at the position of the Hungarian counties, we can see that Budapest is
among the firsts in the ranking. The Counties of Komárom-Esztergom and GyőrMoson-Sopron also belong to the first third of the list, at identical places (21,5.) The
first half of the list also includes (at places 34–48) Pest, Fejér, Vas and Zala. These
territories perform poorly along maximum one parameter. Nógrád, Borsod and Szabolcs
can be found at the end of the list. These territories perform poorly along each
dimension (Table 6).
TABLE 3
Relation of the MDS dimension and the indicators of the base categories
Variable
GDP per capita at market price in the
average percentage of the EU, 2008
Gross added value per one person
employed, million Euro/capita, 2008
Unemployment rate,%, 2008
Employment rate,%, 2008
Correlation coefficient
value
Significance level
N
,886
,000
108
–,824
,000
108
,644
,690
,000
,000
108
108
183
TABLE 4
Competitive ranking of NUTS3 units
Rank
number
Territory
Rank
number
Territory
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
22
22
23
24
25
26
27
Hlavní mesto Praha
Miasto Warszawa
Bratislavský kraj
Budapest
Miasto Poznan
Legnicko-Glogowski
Miasto Kraków
Trnavský kraj
Stredoceský kraj
Jihomoravský kraj
Miasto Wroclaw
Tyski
Trojmiejski
Jihocecký kraj
Plzenský kraj
Miasto Lódz
Katowicki
Pardubický kraj
Zlínský kraj
Královéhradecký kraj
Komárom-Esztergom
Gyor-Moson-Sopron
Vysocina
Miasto Szczecin
Warszawski-zachodni
Moravskoslezský kraj
Poznanski
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
Zilinský kraj
Opolski
Rybnicki
Vas
Karlovarský kraj
Nitriansky kraj
Sosnowiecki
Ciechanowsko-plocki
Skierniewicki
Warszawski-wschodni
Leszczynski
Zala
Piotrkowski
Bialostocki
Sieradzki
Gorzowski
Rzeszowski
Czestochowski
Zielonogórski
Lódzki
Wroclawski
Veszprém
Olsztynski
Csongrád
Ostrolecko-siedlecki
Oswiecimski
Lubelski
28
29
Trenciansky kraj
Liberecký kraj
64
65
30
31
32
33
34
35
Olomoucký kraj
Ústecký kraj
Bydgosko-Torunski
Bielski
Pest
Gliwicki
66
67
68
69
70
71
Szczecinski
Sandomierskojedrzejowski
Kaliski
Pilski
Starogardzki
Kielecki
Elblaski
Krakowski
36
Fejér
72
Tarnowski
Rank
number
Territory
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
Gdanski
Suwalski
Hajdú-Bihar
Bács-Kiskun
Jász-Nagykun-Szolno k
Koninski
Slupski
Bytomski
Lomzynski
Nowosadecki
Tolna
Tarnobrzeski
Krosnienski
Elcki
Baranya
Radomski
Chelmsko-zamojski
Bialski
Przemyski
Somogy
Nyski
Békés
Heves
Grudziadzki
Jeleniogórski
Wloclawski
Pulawski
100 Walbrzyski
101 Koszalinski
102
103
104
105
106
107
Presovský kraj
Stargardzki
Kosický kraj
Nógrád
Borsod-Abaúj-Zemplén
Szabolcs-SzatmárBereg
108 Banskobystrický kraj
184
FIGURE 5
Competitive ranking of countries
FIGURE 6
Average location of NUTS3 areas
185
TABLE 5
Locational comparison of NUTS3 areas
Position (relative location)
Country of comparison
Czech Republic
Czech Republic
Slovakia
–33,57
Slovakia
+33,57
+37,87
(sig<0,05)
+45,57
(sig<0,05)
Poland
Hungary
Poland
Hungary
–37,87
(sig<0,05)
–4,30
–45,57
(sig<0,05)
–12,0
+4,30
+12,00
–7,7
+7,70
TABLE 6
Hungarian counties in the given dimensions
Collectively On the basis On the basis On the basis of On the basis
of unemployof GDP
gross added value of employper employed
ment rate
per capita
ment rate
person
Budapest
Komárom-Esztergom
Győr-Moson-Sopron
Pest
Fejér
Vas
Zala
Veszprém
Csongrád
Hajdú-Bihar
Bács-Kiskun
Jász-NagykunSzolnok
Tolna
Baranya
Somogy
Békés
Heves
Nógrád
Borsod-AbaújZemplén
Szabolcs-SzatmárBereg
4
21,5
21,5
34
36
40
48
58
60
75
76
77
15,5
27
8
27
31,5
31,5
48,5
53,5
67
83,5
79
76,5
4
26,5
18,5
43
38,5
43
47,5
61
61
68
80
86,5
4
72
22
23
45
59
74
71
79
70
85
93
4
7
37
88
44
39
32
63
47
71
54
49
83
87
92
94
95
105
106
92
96
95
93,5
97
103
106
68
68
91,5
94,5
74,5
108
86,5
62
61
86
91
65
99
68
77
80
65
74
79
105
91
107
107
106
84
104
186
Analysing the connection of the established ranking (MDS coordinate) (Table 7)
with the previous variables, we can see that the ranking does not show any significant
relation with the growth of the population, the share of building industry, services
(general), wholesale and retail, financial services from the gross added value.
While at the same time, the competitive ranking shows a significant positive relation
of medium strength with migration change and life expectancy at birth.
The competitive ranking shows a significant weak relation of positive direction with
the number of enterprises per one thousand inhabitants and the activity rate, as well as
the share of industry (excluding building industry) from the gross added value.
TABLE 7
Relation of the competitive ranking and other indicators
Variable
Population growth in comparison to the
previous year,%, 2008
Migration change in comparison to the
previous year,%, 2008
Life expectancy at birth, year, 2008
The share of agriculture, fishing from the
area’s gross added value,%, 2008
The share of industry (excluding building
industry) from the area’s gross added
value,%, 2008
The share of building industry from the
area’s gross added value,%, 2008
The share of services from the area’s
The share of wholesale and retail, hotels
and restaurants, traffic from the area’s
The share of financial mediation, real
estate services from the area’s gross
added value,%, 2008
The share of administration, community
service from the area’s gross added
value,%, 2008
Number of enterprises per 1000
inhabitants
Activity rate,%, 2008
Gross added value per capita, million
Euro/capita, 2008
Correlation coefficient
value
Significance
level
N
,077
,427
108
,494
,000
108
,526
,000
108
–,591
,000
108
,259
,007
108
–,061
,529
108
–,141
,146
108
,086
,374
108
,069
,481
108
–,611
,000
108
,278
,004
108
,368
,000
108
,883
,000
108
187
At the same time the competitive ranking shows a significant negative relation that
is stronger than medium with the share of fishing, agriculture, and community service
from the gross added value, i.e. the more agriculture and community service dominate
in the gross added value in an area, the worst position does the area have in the ranking.
Cluster analysis
In the course of cluster analysis we can attempt to create groups the elements of which
are as closely related to each other as possible, and relatively differ more from the
elements of the other clusters (Falus–Ollé 2000). The objects are assigned to a precise
class on the basis of their similarity or difference. The distance of the objects per pair
constitutes their degree of similarity (Hajdu 2003). Since the units of the variables may
differ greatly from each other, we are working with standardized data. In practice
several clustering procedures are known, which differ from each other mainly in the
applied metrics and the applied clustering method. In the course of our analyses twostep clustering was used. This technique was chosen for more reasons.
On the one hand, this procedure automatically offers a cluster number, and on the
other hand, we get the average Silhouette coefficient as a result of the procedure, which
practically serves to decide whether the established clusters can be statistically
interpreted, i.e. whether the grouping is appropriate. The value of this can fall between -1
and +1. An indicator value below 0,2 cannot be interpreted, an indicator value above 0,5
refers to excellent, while a value between 0,2 and 0,5 refers to acceptable classification
(Kaufman–Rousseeuw 1990).
Thirdly, the problem with the k-center clustering prevalent in practice is that the
software chose the initial cluster centers pseudo randomly, i.e. they regard the data of a
given record as cluster center. The hazard of this is that by rearranging the data table
(rearranging the small areas) we get different results. In order to eliminate this error
source we decided not to apply this method.
Two-step clustering is practically an entropy-based clustering procedure, which
alloys dynamic and hierarchical clustering techniques. Clusters are formed in two steps.
First all objects will be classified into an existing cluster or a new cluster will be
opened. Secondly, the clusters established as a result of the previous step are classified
by the procedure using a hierarchical procedure – in our case – on the basis of Akaike’s
information criterion.
In the course of our analysis the clustering is done on the basis of the competitive
ranking (on the basis of MDS coordinates) comprising the basic categories of the
pyramid model.
Since clustering is sensitive to the outlier values, these have to be filtered first. A
possible way to do this is to do a hierarchical clustering on the basis of the principle of
the closest neighbour.
The outlier observations can be then detected on the basis of the dendrogram
established in the course of the procedure (Sajtos–Mitev 2007). The result of the
188
procedure is the same as what we have seen on the graphic diagram of the competitive
ranking: we can find two outlier groups. The first group is composed of the capitals and
Poznan, while the other group consists of the counties occupying the last two places of
the ranking: Szabolcs-Szatmár-Bereg County and Banskobystrický kraj. Filtering these
two groups, we performed the two-step clustering on the remaining NUTS3 areas.
The applied clustering procedure suggested the application of five clusters (Table 8).
The value of the average Silhouette coefficient is 0,7, which can be considered expressly
good.
TABLE 8
Relation of the competitive ranking and other indicators
Cluster
Frequency
Distribution
%
Averages
MDS value
18
24
32
21
6
101
17,8
23,8
31,7
20,8
5,9
100,0
0,67
0,22
–0,21
–0,57
–0,95
–
Area of relatively strong competitiveness
Area of stronger than average competitiveness
Area of weaker than average competitiveness
Area of weaker competitiveness
Area of relatively weak competitiveness
Total
In the relatively strong cluster there are 8 Czech and 7 Polish NUTS3 areas, and
only 1 Slovakian and two Hungarian areas, the Counties of Komárom-Esztergom and
Győr-Moson-Sopron which also have car industries (Table 9).
TABLE 9
NUTS3 areas according to clusters and countries
Cluster
Country
Czech
Republic
Area of relatively strong
competitiveness
Area of stronger than average
competitiveness
Area of weaker than average
competitiveness
Area of weaker
competitiveness
Area of relatively weak
competitiveness
Total
Hungary
Total
Poland
Slovakia
8
2
7
1
18
5
3
13
3
24
0
6
26
0
32
0
5
16
0
21
0
2
2
2
6
13
18
64
6
101
189
54,2 per cent of the stronger than medium cluster is Polish. The proportion of
Hungarian, Czech and Slovakian areas may be regarded balanced in case of this cluster.
81,3 per cent of the weaker than average cluster is Polish, while 18,8 per cent is
comprised of Hungarian areas. Here there are absolutely no Czech and Slovakian areas.
The cluster of weaker competitiveness, similarly to the former one, consists of only
Polish (76,2%) and Hungarian (23,8%) areas.
The cluster of relatively weak competitiveness is made up of 2-2-2 Slovakian, Polish
and Hungarian NUTS3 units.
From the point of view of countries we can say that the Czech areas belong only to
the relatively strong (61,5%) group and that of stronger than average competitiveness
(38,5%).
The Polish NUTS3 areas belong most frequently to the areas of weaker than average
competitiveness (40,6%).
The Hungarian counties belong to the following clusters (Table 10).
TABLE 10
Hungarian counties and clusters
Area
Budapest
Komárom-Esztergom
Győr-Moson-Sopron
Pest
Fejér
Vas
Zala
Veszprém
Csongrad
Hajdú-Bihar
Bács-Kiskun
Jász-Nagykun-Szolnok
Tolna
Baranya
Somogy
Békés
Heves
Nógrád
Szabolcs-Szatmár-Bereg
Cluster
Outstandingly good
Falling behind
190
By determining the average value of the indicators describing the basic categories of
the pyramid model in the individual clusters (Figure 8) we can say that the worse competitiveness cluster someone belongs to, the worse the employment and unemployment
rates will be. With respect to the GDP per capita and the gross added value per person
employed we can see a similar picture, except for the fact that the parameters of the two
weakest clusters display a reverse order.
FIGURE 8
Average rate of indicators of base categories in clusters
All the counties belonging to the cluster of relatively strong competitiveness have
reached a prestigious place in the indicator shaping the competitive ranking. The
counties belonging to the cluster of relatively weak competitiveness are very weak
especially in their employment and unemployment data, while they are rather weak
according to the two other dimensions. The weaker a cluster is, the worse is the picture
shown by the average rank number of the individual dimensions’ rankings (Table 11).
191
If we examine the average share of agriculture, industry (building industry exclu–
ded), building industry, services within a cluster (Figure 9), it can be stated that the
weaker competitive cluster someone belongs to, the less is the share of industry
(building industry) from the gross value added.
TABLE 11
Hungarian counties and clusters
On the basis of
On the basis of
unemployment rate GDP per capita
Cluster
Area of relatively strong
competitiveness
Area of stronger than
average competitiveness
Area of weaker than
average competitiveness
Area of weaker
competitiveness
Area of relatively weak
competitiveness
On the basis of
gross added value
per person
employed
On the basis of
employment rate
22,8
15,7
27,7
22,2
40,7
37,3
36,0
54,2
57,6
69,0
74,4
59,5
84,3
88,7
80,8
70,8
103,2
78,6
52,2
97,8
FIGURE 9
‘Average’ rate of the gross added value in clusters
Area of rela tively weak competitiveness
Area of wea ker competitiveness
Area of wea ker tha n a vera ge…
Area of stronger tha n a vera ge…
0%
20%
40%
60%
80%
100%
The sha re of a griculture, fishing from the a rea ’s gross va lue added, %, 2008
The sha re of industry, excluding building industry, from the area’s gross value a dded, %, 2008
The sha re of building industry from the area’s gross value a dded, %, 2008
The sha re of services from the a rea ’s gross va lue added, %, 2008
192
Summary
The automotive industry of the Visegrád Countries, with Hungary among them, doubtlessly constitutes part of the European automotive industry nowadays. At a global level
automotive industry used to be the car manufacturing of individual nations in the form
of quasi territorial subsystems independent of each other. In the value chain the
production processes are no longer connected to the car manufacturers in one person,
they rather constitute an international network and flow processes coordinated by the
strategic participants of the industrial sectors, the car manufacturers and great suppliers.
National level industrial sector units covering the whole verticum have been
transformed, today automotive industry represents an industrial sector production and
marketing system embracing the macro-regions of economic integrations (ASEAN and
MERCOSUR, EFTA, NAFTA).
With respect to automotive industry we can practically speak about national car
industries integrated into a “global” system. Within this the individual national industrial
sector networks operate as functional sub-network units in the industrial sector structure of
a macro-region. “Global” here is a qualitative attribute, i.e. the companies at the certain
levels of the car manufacturing value chain function in a horizontally coordinated network
as elements of a vertically organized industrial sector structure.
As the spatial reflection of these processes, the geographical dimensions of
automotive industry have also changed. The “economic geography” of the globally
active automotive industry is determined by the macro-regions of the individual
industrial sectors and the networks interpreted within this. The spatial dimension of
global processes is provided by the territorial networks located in the vertical system of
the industrial sector. The strategic directors of the industrial sector’s organization are
the car manufacturers and the first tier suppliers strategically cooperating with them.
The other companies of the value chain, the other participants of the local networks are
connected to the industrial sector through the integrator first tier suppliers. The
localization decisions of car manufacturers and global suppliers have an essential role in
the spatial development of automotive industry, in the establishment and development
of local industrial sector networks.
Placing the Visegrád Countries in the system and economic space of European
automotive industry we have to expand the concept of the macro-region of automotive
industry, business relations and production chains cover a much larger area in the EFTA
system than the industrial borders of the European Union. Interpreting the
competitiveness of territorial units of the macro-region comprising Central and Eastern
Europe important consequences can be drawn with respect to automotive industry as
well. The comparative analysis demonstrating relative positions illuminates the situation
of the Hungarian counties and their position in the competition of Central and Eastern
European regions, and reveals what level of company competitive advantage the
economies of the individual territorial units are able to establish. The empirical study
extending to the counties of the Visegrád Countries provides a comparative picture of
the relative competitiveness of the Central and Eastern European territories. The
193
competitiveness analysis of the regions offers important lessons about the positions of
the individual local spaces, and car industrial districts localized in them, about the
direction of courses of development in the policies of the territories building greatly on
automotive industry, as well as for the participants, companies of already existing
industrial sector networks.
In our essay we examined the Visegrád Countries on the basis of GDP per capita,
gross value added per employee, unemployment rate and employment rate at NUTS3
level. With the help of multidimensional scaling a complex ranking of NUTS3 units
was established. The ranking is lead by the four capitals: Prague, Warsaw, Bratislava
and Budapest. We pointed out that the Czech NUTS3 areas are significantly better
placed in the ranking than the Hungarian and Polish NUTS3 areas. It was demonstrated
that if in a territory agriculture and community services dominate in the gross value
added, the territory occupies a worse place in the ranking.
Territorial units were typified with the help of cluster analysis. Besides an
outstandingly good (capitals and Poznan) and an outstandingly weak cluster, 5 clusters
were identified. In the relatively strong cluster there are 8 Czech and 7 Polish NUTS3
areas, and altogether 1 Slovakian and two Hungarian areas, Komárom-Esztergom and
Győr-Moson-Sopron Counties which have car industries. It was established that the
weaker competitive cluster an area belongs to, the less the industry’s (building
industry’s) share will be from the gross value added.
Note
1
Eleven car manufacturing companies give almost 85% of the car industrial production even
besides the expansion of car manufacturing in the dynamically developing countries. The
global expansion of the leading car manufacturers and the biggest car industrial suppliers was
strengthened in the 1990s by purchases, fusions and associations formed by cross proprietary
shares (Bieserbroeck et al. 2009; Dicken 2007). After the financial crisis part of these
associations are still standing (pl. Nissan-Renault), while in some cases consolidation pressure
entailed the detachment of earlier purchased companies (e.g. GM-Saab).
2.
The institutional system of the EFTA has expanded much earlier, more quickly and much
further than the institutional borders of the European Union along economic relation systems
and interests.
3
E.g. the production of right hand drive and left hand drive cars, stronger suspension and bigger
fuel tanks in the developing countries with sparse infrastructure etc.
4
The markets of emerging and developing countries play a great role in this tendency, where
milliards are becoming potential customers with the raising of wages. The data of the table
reflect the changes of ten years, which comparing the relative and absolute magnitude of the
numbers show a very dynamic tendency.
5
American car manufacturers outsourced a great proportion of their component, module and car
manufacturing processes to Mexico, which process was greatly promoted by the establishment
of NAFTA. The political lobby of American manufacturers had a great role in this. The
establishment of the NAFTA made it possible for Japanese and European car manufacturers
supplying the American market to employ cheap South-American workforce and apply
outsourced production and marketing strategies building on the re-export to North-American
markets. In spite of this they built their production potentials and supplier network on the
North-American target markets (Biesrbroeck et al. 2009, Bieserbroeck–Sturgeon 2010).
194
6
7
8
9
10
Almost 60% of the value of a manufactured new car is produced by the suppliers (Chanaron–
MacNeill 2005).
We furthermore mention the statements of Porter’s global-local paradox each of which is true
in automotive industry (Lengyel 2010, 92).
The systems of Japanese car manufacturers represent a different approach of modular
production. Toyota and Honda strongly control and organize production systems, and
concentrate their development activities parallel to this. The developing Asian regions, which
serve as target areas for outsourcing for Japanese car manufacturers, often have very limited
infrastructural and technological production systems and no background of developed small
and medium enterprises which characterizes Europe. Wide-scale process management and
control thus become key factors in the outsourced building of supplier and production systems
(Freyssenet–Lung 2004a, Heribert 2007).
The financing difficulties following the crisis of 2008 have unequivocally brought these
problems to the surface at a macroeconomic level. In the “recovery period” following the
outbreak of the crisis the tendency was that these economies showed continuous stagnation or
even weakening, while western economies, which had had developed bases even before the
crisis, found their way back to growth. Macroeconomic performance, wage standard raised by
governmental disposing politics utilizing EU sources, and the abyss formed between increased
internal consumption and usage brought the deformity of economic structures to the surface,
which manifests in culminating unemployment and state debt rates that affected Portugal,
Spain, and not to mention Greece. The opening of the scissors between wage standard and the
productivity of economic structure worsened the general competitiveness in these economies.
The previous tendencies of this process have presumably deteriorated the car industrial
competitiveness of these countries and their attractiveness from the viewpoint of capital
investments in the long run.
The economic integration of Eastern-Europe and the economic development of these countries
made previously existing differences more balanced. The wage standard of the workforce is
lower even in comparison to the wage standard of the Visegrád Countries, while the
development of infrastructures created the relevant availability of Europe’s periphery regions
for the production and marketing networks of car manufacturers.
References
ACEA (2008) Automotive production & assembly sites in Europe. http://www.acea.be/images/
uploads/st/20080121_list_of_production_sites. pdf [8 September 2011].
Bernek Á. (2000) A globális világ „új gazdaságfölrajza”. Tér és társadalom. 4. pp. 87–107.
Bieserbroeck, J. – Gereffi, G. – Memedovic, O. – Sturegeon, T. J. (2009) Globalisation of the
automotive industry: main features and trends. Technological Learning, Innovation and
Development. 2. pp. 7–24.
Biesebroeck, J. – Sturgeon, T. J. (2010) Effects of the Crisis on the Automotive Industry in
Developing Countries. A Global Value Chain Perspective. World Bank.
Blöcker, A. – Jürgens, U. – Heinz, R. M. – Paris, J. J. (2009) Anticipation of Change in the
Automotive Industry. Study 3. Analysis of Automotive Regions.
http://www.anticipationofchange.eu [29 August 2011].
Chanaron, J. J. – MacNeill, S. (2005) Trends and drivers of change in the European automotive
industry: (I) mapping the current situation. International Journal Automotive Technologie and
Management. 1. pp. 83–105.
Dicken, P. (2007) Global shift. Mapping the changing contours of the world economy. 5th edition.
SAGE Publications, London.
Dimitrova, G. – Stratmann, G. (eds) (2008) Automitve clustering in Europe. Case studies on
cluster management. Europäischer Wirtschafts Verlag, Darmstadt.
195
Eurofound (2003) Vehicle plants in Europe. Distribution of car assembly plants in European
countries. http://www.eurofound.europa.eu/emcc/automotivemap/#car [8 September 2011].
European Commission (2002) EU research on social sciences and humantites. Coordinating Competencies and Knowledge in the European Automobile System. Eurpean Commission,
Brussels.
Florida, R. – Sturgeon, T. J. (2000) Globalization and jobs in the automotive industry. Industrial
Performance Centre, Massachusetts Institute of Technology, Cambridge.
http://web.mit.edu/ipc/publications/pdf/00-012.pdf [9 September 2011].
Freyssenet, M. – Lung, Y. (2004a) Multinational Carmakers’ Regional Strategies. – Carrillo, J. –
Lung, Y. – Tulder, R. van (eds) Cars, carriers of regionalism? Palgrave Macmillan,
Basingstoke. pp. 42–56.
Freyssenet, M. – Lung Y. (2004b) Car Firms’ Strategies and Practices in Europe. – Faust, M. –
Voskamp, U. – Wittke, V. (eds) European Industrial Restructuring in a Global Economy:
Fragmentation and Relocation of Value Chains. SOFI Berichte, Göttingen, pp. 85–103.
Grosche, P. – Schmid, S. (2008) Management internationaler Wertschöpfung in der Automobil–
industrie. Strategie, Struktur und Kultur. Bertelsmann Stiftung, Gütersloh.
Grosz A. (2000) A gépjárműipari klaszterek külföldi tapasztalatai és a Pannon Autóklaszter
lehetőségei. Tér és Társadalom. 4. pp. 125–145.
Haiss, P. – Mahlberg, B. – Molling, M. (2009) The Automotive Industry in Central and Eastern
Europe. Engine of Grow or Free Rider? Oxford Business and Economics Conference
(OBEC), Oxford University, U.K. 24–26. June.
Heribert, D. (2007) Transnational Production Networks in the Automobile Industry. Notre
Europe. http://www.notre-europe.eu/uploads/tx_publication/Etud58_01.pdf [7 August 2011].
Humphrey, J. – Memedovic, O. (2003) The Global Automotive Industry Value Chain: What
prospects for upgrading by developing countries. UNIDO; Vienna.
Kaufman, L. – P. J. Rousseeuw. (1990) Finding groups in data: An introduction to cluster
analysis. New York: John Wiley and Sons.
Kovács P. – Petres T. – Tóth L. (2006) Válogatott fejezetek statisztikából. Többváltozós
statisztikai módszerek. JATEPress, Szeged.
Lengyel I. (2010) Regionális gazdaságfejlesztés. Versenyképesség, klaszterek és alulról szerveződő stratégiák. Akadémiai Kiadó, Budapest.
Lorentzen, J. (2011) Multinational strategies, local human capital and global innovation
networks in the automotive industry. EMAEE. Evolutionary perspectives on technical change
and industrial dynamics. LEM – Scuola Superiore Sant’Anna, Pisa, 14–16. February.
Lukovics M. (2008) Térségek versenyképességének mérése. JATEPress, Szeged.
Porter, M. E. (1990) The competitve advantage of nations. The Free Press, New York.
Porter, M. E. (1994) The role of location in competition. Journal of the Economics of Business. 1.
pp. 35–39.
Radosevic, S. – Rozeik, A. (2005) Foreign direct ivestment and restructuring int he automotive
industry in Central and East Europe. University College London, Centre for the Study of
Economic and Social Change in Europe – School of Slavonic and East European Studies,
London.
Sajtos L. – Mitev A. (2007) SPSS kutatási és adatelemzési kézikönyv. Alinea Kiadó, Budapest.
Székelyi M. – Barna I. (2003) Túlélőkészlet az SPSS-hez. Többváltozós elemzési technikákról
társadalomkutatók számára. Typotex Kiadó, Budapest.
U.S. International Trade Commission (2010) ASEAN: Regional Trends in Economic Integration,
Export Competitiveness, and Inbound Investment for Selected Industries. U.S. International
Trade Commission, Washington DC.
COMPETITIVENESS OF AUTOMOTIVE CENTRES IN
CENTRAL AND EASTERN EUROPE
TAMÁS DUSEK
Keywords:
competitiveness car industry Central and Eastern Europe complex indicators
The aim of the investigation is to analyse the competitiveness of automotive centres in Central and
Eastern Europe compared to each other and their environment. Due to practical and
methodological reasons discussed in the analysis the investigation covers four countries: Hungary,
the Czech Republic, Slovakia and Poland. There are many examples of the analyses of
competitiveness between countries and regions within countries, investigations below national
level, referring to more countries is however more rare and presents a greater challenge
methodologically due to spatial division which differs from country to country, and due to the
international limitations of comparison of national regional statistics. In our essay we first
present the investigated territory itself and its spatial division, together with the theoretical and
practical reasons which play a part in the delimitation of the territory. Afterwards we will look at
the availability of data and the compilation of the database. We are of the opinion that this review
is necessary in order to ensure the better understanding of the practical analysis. In the course of
the practical analysis all territorial units will be examined together first, then we will analyse the
situation of territories with car factories separately, where we will demonstrate that on the
average these territories can be regarded more competitive than the territories without car
factories.
The methodological background of the analysis
Choosing the territory to be examined and the territorial level
Due to conceptual and practical reasons we included the car factories of four countries
in the analysis: those of Hungary, Slovakia, the Czech Republic and Poland. The circle
of countries could have been extended primarily westwards and south-eastwards. It is
well-known that these four countries are characterized by a significantly different level
of development at a national level than Austria and Germany. In addition to such
significant differences, in the course of any spatial division below national level the
territorial units are mainly distinguished by which country group they are found in (in the
more developed Austria and Germany, or the less developed four countries). Therefore the
number of groups and categories used in the analysis should be significantly increased for
appropriate differentiation, i.e. a great part of Hungary for example should not be
considered homogenous compared to Austria and Germany. The same problem, but with
the opposite sign, would present itself if Romania and Bulgaria would be included in the
investigation.
Competitiveness of Automotive Centres in Central and Eastern Europe
197
Apart from these conceptual reasons, the circle of the examined countries is also
narrowed by the practical possibilities related to the compilation of the database. From
this viewpoint the difficulty is that each additional country decreases the number of
usable indicators, since the scope of spatial statistical indicators of the individual countries differ. We only wished to use those indicators which are available in all countries.
In the course of certain analyses, especially when examining more countries, incomplete
indicators are also used, complementing the missing values in an artificial way. Because
of the small number of countries in the present analysis this disputable practice was not
deemed feasible. It is also necessary to examine whether the content, operationalization,
localization, temporality of indicators which can be considered identical on the basis of
their names is really identical or at least does not contain such a degree of differences
which exclude analysability. Independent of this, in case of financial indicators the
problem of converting the different currencies of the countries to a common currency
still presents itself.
The spatial level of the analysis is determined partly by conceptual, partly by practical reasons. The use of settlement level data and the conducting of settlement level
analyses were rejected due to conceptual reasons. Although automotive factories are
point-like and well localizable, they are generally situated on the border of a settlement,
further away from the inhabited area, while their workforce-attraction range extends
much further than the given settlement; depending on traffic possibilities, it can be up to
100 km, or a two-hour journey. The purchase of service-type inputs (cleaning, maintenance, safeguarding, printing etc.) exerts its influence in a similar spatial range, i.e. not
at settlement level. Therefore an investigation conducted at settlement level would not
be reasonable even in case of cities of the similar order. A few automotive factories
however are situated in small cities or villages. Altogether six factories are situated in
settlements with a population of less than 10 thousand inhabitants, from which the
population of two settlements does not even reach two thousand inhabitants. For
instance administratively the Czech Hyundai factory belongs to Nosovice, the area of
which is 6,45 square kilometre (from which two square kilometres belong to the factory), which had a total of 994 inhabitants in 2010. The number of people employed in
the Hyundai factory was 2000 in 2008, 2500 in January 2011, and the planned final
number of employees is 3400. The settlement is situated five kilometres from the
Frýdek-Mistek of sixty thousand inhabitants, and thirty kilometres from Ostrava, along
road E462.
The comparison of settlements with car factories is not possible methodologically
because the order of differences and the administrative borders are inadequate from the
point of view of analysis. Theoretically the comparison of the labour market attraction
range of car factories would be an appropriate solution, there are however no statistical
data to draw such arbitrary borders. On the other hand, the attraction districts do not
cover the space without gaps, and the attraction districts of certain centres have parts
overlapping each other. Therefore it is the NUTS3 territorial level where the analysis
can be conducted in an adequate way and where there are data available. Lower level
territorial data comparable among countries do not exist due to the different territorial
198
Tamás Dusek
administrative systems and the basic territorial statistics of the individual countries.
While in Hungary an imposingly wide scale of settlement level indicators are available
for research, in the other countries there are less available data even in case of cities,
and at the level of units which correspond to villages, though they are different from
those of Hungary in terms of administration and average size, it is even more difficult, if
not impossible to compile a comparable database.
The basic characteristics of spatial division and territorial units
The four examined countries, Hungary, Slovakia, the Czech Republic and Poland are
divided into a total of 108 territorial units at NUTS3 level (Table 1). The circle of
statistical indicators available in the individual countries is largely determined by
whether the territorial units of the given level are administrative units at the same time,
or exist only in a statistical sense for territorial statistical analyses. Hungary, Slovakia
and the Czech Republic are identical with each other in the sense that in all the three
countries the NUTS3 level consists of units which beside statistical functions fulfil
administrative functions as well (counties and krajs). This is fortunate from the
viewpoint of the availability of data, since the official territorial statistics come into
being within the frames of the administrative units. In Poland however the situation is
different, in this country the NUTS3 level consisting of units called sub-regions,
NUTS3 only serves the purpose of statistical information, has no administrative content,
so the available data are much more limited compared to the Hungarian data. The
NUTS2 level however has administrative functions in Poland (the 16 counties) while in
the other three countries it primarily serves purposes of territorial development and
statistics and has less administrative content. In Hungary for example there is only a
minimal difference between the NUTS2 and NUTS3 levels in terms of available data,
and the NUTS2 data are almost exclusively the aggregates of NUTS3 data. Only the
results of a few regional surveys (e.g. household statistical data) cannot be disaggregated to county level.
TABLE 1
Number of NUTS 3 units per country
Country
Czech Republic
Hungary
Poland
Slovakia
Source: Eurostat.
Number of territorial units
14
20
66
8
199
For the sake of simplicity in the analysis territorial units shall be referred to as subregions when we look at all territorial units together, because the county-kraj-sub-region
triple name would be clumsy to use, so the traditional name of “county” will be used
only in case of the Hungarian counties. The expression of “region” is usually applied to
NUTS2 level. The territorial arrangement is very favourable from the viewpoint of
comparability, there are no outstandingly big or small units whether on the basis of
territorial size, or the number of the population (Table 2). The units comprising
metropolises differ slightly from this, which are double or triple of the average in terms
of the number of population, and one tenth of the average in terms of their size. This
significance however is inevitable due to the cohesiveness of the settlement network and
the indivisibility of the settlement. In the Czech Republic, Poland and Hungary the
capital is an independent sub-region, while in Slovakia the almost 50 km district of
Bratislava constitutes a mixed urban-rural sub-region. In addition to this, we can find
further seven metropolises in Poland (Lodz, Krakow, Wroclaw, Poznan, Katowice,
Szczecin, Gdansk–Gdynia–Sopot) which constitute independent sub-regions, separated
from their environments. This spatial division is favourable owing to the greater
information content of data, while at the same time the artificial statistical division of
the cities from their environment entails certain disadvantages as well, because this way
the regional-territorial differences and the differences along the hierarchy of settlements
appear simultaneously in the given spatial division, therefore this has to be taken into
account separately in the course of the analyses.
TABLE 2
Main characteristics of NUTS 3 units per country
Territory size (km2)
Country
Czech Republic
Hungary
Poland
Slovakia
Population (person)
average
minimum
maximum
dispersion
average
5,633
496
11,015
2,659
747,682
308,403 1,250,255 316,593
4,651
525
8,445
1,790
501,549
207,637 1,712,210 350,313
4,738
262
12,091
2,727
577,816
276,767 1,709,781 196,070
6,034
2,053
9,454
2,209
655,848
490,378
minimum
maximum
803,955
dispersion
95,283
Source: Own calculation on the basis of Eurostat data.
The outstanding characteristics of the independent metropolitan sub-regions appear in
the territorial units’ population density as well (Figure 1). The sub-region of Bratislava
does not belong to the units of extremely high population density; its population density
is only a tenth of that of Budapest. There are three sub-regions in Upper Silesia, in
South-Poland with higher population density than Bratislava. There are no extremely
lowly populated territories (as in Scandinavia or the Baltic Countries).
200
Tamás Dusek
FIGURE 1
Population density of NUTS3 units
Population density
Inhabitant/square km
1351-3310
250-568
100-250
80-100
40-80
Map: Tamás Hardi.
The generation of such simple indices however poses two major problems in case of
small territorial units, which should either be integrated into the indicator itself or
should be taken into consideration when interpreting the results. One of the problems is
related to the commuting of the workforce among regions, and the other is connected to
the demographical composition of the population (age structure, health indicators). As a
result of the commuting of the workforce among regions it can happen that more people
work in a region than those who have permanent residence there (this is true to miniregions such as the previously mentioned village of Nosovice, or Kékkút in Hungary),
201
but commuting perceivably influences the development of specific indicators even at
NUTS3 level, also in relation to wages and production (GDP) (Dusek–Kiss 2008).
Commuting exerts an influence on the specific level of employment as well: the
workforce can be taken into account also on the basis of the place of work (this is in the
numerator of the specific indicator), and population (denominator) according to
residence. This leads to the fact that 904 thousand people work in Prague, which is a
proportion of 74,6% compared to the number of the population. In Warsaw this same
proportion is 68,4%. These high proportions are obviously a result of the excess
workforce coming from commuting. The appropriate indicator of employment is based
on workforce surveying, since there the population is counted on the basis of residence,
so the inhabitants of Central Czech Republic working in Prague appear in Central Czech
Republic.
Age structure and health composition also makes the comparison of small territorial
units more difficult. It can occur that certain territories attract people of high income in
pension age, which worsens the specific indicators of production and employment
compared to the number of the population, but otherwise would not constitute a real
competitive disadvantage. It is difficult to filter these types of factors because they
would be investigable by the connecting of various independent databases, which for
the most part cannot be done.
The basic characteristics referring to economic output are however not sufficient to
examine competitiveness, since as it was previously mentioned, competitiveness is a
multidimensional phenomenon which can be characterized by a group of indicators. The
compilation of a database containing wide-scale economic-social data covering four
countries is not easy even at NUTS3 level, because the circle of data available at
Eurostat is considerably restricted; it is mainly limited to GDP and its composition.
Even the considerably general demographic data are incomplete. So the indicator list
had to be complemented in addition to the Eurostat data by the territorial data of
national statistical offices. In selecting the indicators the previously mentioned
condition that the data had to be available for all the four countries appeared as a
limiting factor. For this reason mostly Poland became the bottleneck with the least
number of NUTS3 level indicators. Unfortunately it happened in case of several
potential indicators (e.g. the average wages of industrial employees, the average of
pensions, R&D expenses, R&D employees, the income of self-governments) that they
were missing only in one of the four countries, and therefore could not be used. At the
same time not all indicators would have been automatically included in the analysis
even if significantly more indicators had been available. E.g. the usability of R&D data
at this territorial level is questionable, because the NUTS3 units are too small compared
to the research conducted in today’s global networks. The lack of data on export is
similarly not considered to be a disadvantage, because the majority of this also comes
from the transactions between the units of multinational companies situated in different
countries, which moreover is an indicator containing an aggregation (the purchased
material inputs) and can be therefore very deceptive.
202
Tamás Dusek
The indicators used in the analysis are shown in Table 3 according to the basic
categories of competitiveness. These are in line with the indicators used in the relevant
literature and earlier competitiveness analyses, and except the development of average
wages, which was unavailable at the given territorial level, no important index number
is missing. We had to leave out of the analysis three potential indicators despite the fact
that their inclusion, as secondarily important indicators, would have been considered
reasonable, and they were available for all the four countries: number of enterprises
(enterprise activity), number/proportion of university students, and the number/proportion of libraries.
TABLE 3
Indicators used in the analysis
Basic factors of
competitiveness
Economic output
Employment
Appeal
Social capital
Health
Indicator
GDP per capita, in Euros, in purchase power
parity
Unemployment rate
Economically active population
Number of people employed in industry
Number of dwellings built
Migration balance
Number of crimes
Life expectancy at birth
Source*
Year
E
2008
E
E
E
N
E
N
N
2008–2009
2008–2009
2008
2008
2006–2008
2008
2008
* E – Eurostat; N – National statistical publications.
The recording of enterprises in Poland took place with a different content than in the
other three countries, due to the two orders of magnitude smaller Polish data, this data
proved to be inadequate for comparison between countries. The situation is the same in
connection with the number of university students, in 23 sub-regions of Poland there
were absolutely no university students, and there were places with only 11 students.
Therefore this indicator had to be left out, too. The use of the number of libraries was
also rejected on the basis of the analysis of data, this indicator rather reflects settlement
network characteristics (the number of settlements), and cannot be used to describe
human infrastructure.
The indicators refer to the most recent year available, which is in most cases the
year 2008. Regional GDP data of 2009 are for example still unavailable in August 2011.
The majority of Eurostat data are available in a timeline (the earliest starting year is
1999, but it is a later year in case of the majority of the indicators), which also opens the
possibility for a temporal comparison in a narrower circle. The 2009 data of
unemployment rate and economic activity are known; these are used in the analysis
based on individual indicators. When generating complex indicators however, we
calculated with 2008 data in case of these indicators as well.
203
General review of the whole territory’s competitiveness according
to individual indicators
First from among the available index numbers three key indicators will be examined
separately: the regional gross domestic product per capita, which is the best substitute
for civil income from all possibilities. The second indicator is the unemployment rate,
and the third is economic activity. The second and the third are more closely related to
each other, than the first. Later we will generate from these indicators a synthetic index
number which describes competitiveness with a number; the purpose of the present
analysis is the investigation of the most important individual aspects of competitiveness.
Besides the three basic indicators we will also look at five additional indicators which
make the description more complete.
The 2008 picture generated on the basis of GDP per capita can be seen in Figure 2.
For proper differentiation we determined eight departments which are more than the
traditional. The departments consist of an identical number of sub-regions, one eighth of
the sub-regions in each. On the map on the left hand side of the figure the sub-regions
above the median (sub-regions of favourable position), while on the right hand side map
the sub-regions under the median are differentiated. All sub-regions of the Czech
Republic are above the median, and in Slovakia only the sub-region of Eperjes landed
below the median. In Hungary, as it is well-known, the capital and the north-western
quarter of the country are above the average, the other counties are below the average.
Nógrád County is the last territorial unit, Szabolcs-Szatmár-Bereg County is the last but
one, followed by eight East-Polish sub-regions. A west-east division can also be
observed in Poland, but in a much smaller degree than in Hungary, since there are
regions deeply below the average even in the west, and slightly above the average in the
east. In the Czech Republic two western regions are the last ones, but the differences are
not as big as in the other three countries.
Urban territorial units occupy the first two places, which is not surprising, and is in
line with every previous experience and expectation. From among these the sub-regions
of the capitals are at the first four places. This on the one hand means a “real” difference
of development, greater productivity and employment, a greater proportion of higher
income professions and higher income received for identical work. On the other hand,
however, the difference is excessive in comparison to the actual differences of
development due to the influences of commuting. Among non-urban sub-regions the
first four are the Lower-Silesian Legnicko-Glogowski, Nagyszombat (Trnava) and the
Upper-Silesian Tyski and Lower-Moravia. Győr-Moson-Sopron County, which is the
first among the Hungarian counties, is at place 15 of the whole ranking, and place 8
among non-urban sub-regions.
204
Tamás Dusek
FIGURE 2
GDP per capita in PPP, in the average of the four countries, 2008
GDP/capita
115-277
101-115
89-101
76-89
46-89
GDP/capita
46-59
59-66
66-71
71-76
76-277
Map: Tamás Hardi.
The temporal development of competitiveness connected to economic output can
also be analysed by GDP per capita. In the course of temporal comparison one has to be
careful, because the smaller territorial units we analyse, the greater random fluctuations
can be from one year to another, which reflect the outstandingly good or bad
performance of a dominant enterprise or industrial sector for the given year, but do not
necessarily indicate permanent change. Therefore the isolated analysis of changes from
one year to another are not recommended. When analysing a longer time period this has
to be taken into consideration in such a way that the basis year should possibly not
contain outstanding data, but the longer a time period is, the less significant it is. The
year 2002 was not an outstanding one and six years constitute a sufficiently long time
period, therefore it was selected to be the basis year. There is no structural change,
significant regional rearrangement between the two time periods. The only significant
change was the change of Hungary’s and Slovakia’s average positions, which can also
be seen in the static map, but it is even more prominent in the figure showing the
difference between the two years (Figures 3–4). During this period the national average
of Hungary decreased from 113% of the four countries’ average to 102%, while
Slovakia increased from 99% to 115%. The changes of the averages of Poland (89%)
and the Czech Republic (129%) are within one percentage point, meaning that their
GDP change was more or less identical to the average of the four countries in that
period.
At Sub-regional level we can naturally find much greater differences. The first
placed Bratislava increased with 41,9 percentage points above the average of the four
countries, the second is Tmava with 32,8 percentage points. Among the first seven
FIGURE 3
GDP per capita in PPP, in the average of the four countries, 2002
GDP/capita
GDP/capita
116-269
51-62
103-116
88-103
62-69
81-88
52-81
69-73
73-81
81-269
Map: Tamás Hardi.
FIGURE 4
The change of GDP per capita between 2002 and 2008, in the average
of the four countries
GDP/capita increase
above the average, %
10-42
5-10
2-5
0-2
under the average
Map: Tamás Hardi.
GDP/capita increase
under the average, %
10-22
5-10
2-5
0-2
above the average
205
206
Tamás Dusek
subregions there are five Slovakian and two Polish ones (Legnicko-Glogowski and the
Central-Polish Ciechanowsko-plocki). The least growing Slovakian sub-region is
Banská Bystrica, where there was only a 0.5 percentage point growth. This is little in
comparison to the Slovakian average, and reflects significant national regional
inequalities, but it is significantly more favourable for instance than the 16% decrease of
the neighbouring Nógrád County. Although the Czech Republic did not change in terms
of national average, the Liberecký and Karlovy Vary sub-regions decreased significantly (20% and 16%), while Lower-Moravia increased significantly (10%). In Poland
only the performance of the sub-region of Szczecin was outstandingly bad (18%
decrease). In Hungary only the relative situation of Komárom-Esztergom County
improved (four percent increase), all the other countries decreased to an extent of more
than five percentage points. The decrease naturally refers to the relative position, in an
absolute sense there was economic growth everywhere during the six years, it was only
well below that of the average of the four countries.
Unemployment rate can also be considered one of the basic indicators of
competitiveness. There are strong territorial differences on the basis of this as well in
the sub-regions of the four countries, which do not totally correspond to what we have
seen in case of the indicator of GDP per capita (Figures 5–6). E.g. although the capital
sub-regions are in the most favourable position, the majority of the Polish metropolitan
sub-regions can be found only in the middle field, worse placed than on the basis of
GDP. The principal territorial difference is given by the outstandingly high
unemployment rate of Central- and Eastern-Slovakia, the rate here exceeds 13% in four
sub-regions. Excluding this, the indicator is similar to the GDP in the sense that
generally the Czech sub-regions are in the most favourable position, and within
Hungary and Poland the ranking of sub-regions is fairly similar to the ranking according
to GDP.
At the same time, compared to 2002, the situation was significantly rearranged by
2009. In 2002 unemployment was extremely high in Poland, it was 12% even in
Poznan, the sub-region of the most favourable position, and it exceeded 30% in six
West-Polish sub-regions. We can hardly find an example of so high unemployment at a
national level. On the other hand, the first 29 places were occupied by only Hungarian
and Czech sub-regions, and the thirtieth place was given to Bratislava, the best
positioned sub-region in Slovakia.
It is necessary to look at the development of unemployment together with the
development of the activity rate, because low activity coupled with low unemployment
rate can be more unfavourable from many aspects than a higher economic activity
coupled with higher unemployment. In the first case low unemployment can be
explained by low activity, the great number of passive unemployed people withdrawn
from the labour market. At a national level the data differentiate well into the very low
activity Hungary (42% in 2009), the equally low activity Poland (45,3%), and the high
activity Czech Republic (50,5%) and Slovakia (49,7%) (Figure 7). The interesting point
of regional differentiation however is that the Polish sub-regions can be found roughly
evenly dispersed between the minimum and maximum values.
FIGURE 5
Unemployment rate in 2009,%
unemployment rate, %
3,1-5,7
11,7-19,0
5,7-7,0
7,0-7,6
10,4-11,7
7,6-8,5
8,5-19,1
9,3-10,4
8,5-9,3
3,1-8,5
Map: Tamás Hardi.
FIGURE 6
Unemployment rate in 2002,%
3,6-5,5
24,5-34,5
5,5-8,3
8,3-13,4
21,6-24,5
13,4-16,1
16,1-19,4
3,6-16,1
16,1-34,5
Map: Tamás Hardi.
19,4-21,6
207
208
Tamás Dusek
FIGURE 7
Economic activity (Economically active population/total population), 2009
economic activity, %
economic activity, %
52,3-56,8
34,3-38,0
49,4-52,3
47,8-49,4
38,0-40,1
45,6-47,8
43,2-45,6
34,3-45,6
45,6-56,8
40,1-43,2
Map: Tamás Hardi.
The first nine sub-regions of the lowest activity are Polish, followed by Polish and
Hungarian sub-regions up to place 62, where the Kassa sub-region, the Slovakian subregion of the lowest activity, can be found. The Hungarian territorial unit of the greatest
activity is Budapest, at place 59, i.e. 49 sub-regions overtake it from the other three
countries, its activity is even lower than that of the weakest Slovakian sub-region and
significantly lower than the weakest Czech sub-region. So on the average the activity of
Hungary and Poland only moderately differ from each other, the Polish activity rate is
diffused in a much wider range, since there are 11 Polish sub-regions among the most
active 14 sub-regions. The economic activity of the Czech Republic and Slovakia is
significantly higher than that of the two other countries’, and the regional differences
within the country are significantly smaller.
It is characteristic of Hungary and Slovakia that where unemployment is lower, the
economic activity is higher (the correlation between the two indicators is stronger than
medium, 0,7). This applies to the Czech Republic, too, but the West-Czech sub-region
Karlovy Vary has an outstanding value, where besides higher unemployment there is
higher activity, and this significantly decreases the closeness of the relation. On the
other hand in Poland there is no relation between unemployment and activity rates, there
are several examples for all four types (lower unemployment–higher activity, lower
unemployment-lower activity, higher unemployment-higher activity, higher unemployment-lower activity).
Next we are going to examine five index numbers, which describe certain aspects of
competitiveness indirectly. The correlation coefficients of the new indicators with the
earlier indicators are shown in Table 4. From the relations of the already analysed
209
210
Tamás Dusek
indicators it is worth to note how little the relation between the unemployment rates of
2002 and 2009 is, especially compared to the relation between GDP/capita in 2002 and
2008. This is interesting also because analysing this separately in case of the various
countries we can find much closer relations everywhere, the drastic decrease of
unemployment in Poland (from a very high basis) however resulted in a lower
coefficient when the four countries were analysed together. The table also affirms that
higher economic activity entails lower unemployment, but the lowness of the correlation
indicates that there may be many exceptions to this. The other parts of the table shall be
referred to in the course of the introduction of the five new index numbers.
Migration balance is important because a positive balance characterizes the subregions offering attractive possibilities for work and making a living, while a rather
negative balance characterizes those sub-regions which have less favourable work
opportunities to offer. This indicator however is less suitable for the comparison of
countries, because international migration belongs to those areas which are statistically
more difficult to follow. Figure 8 shows the balance of internal and international
migrations, for greater reliability in the average of three years. In Hungary, Slovakia and
the Czech Republic this indicator correlates well with economic output and unemployment rate. In Poland the picture is more complex again, it is apparent here as well that
the balance of urban agglomerations is rather the positive one, and there is no close
relation between economic output and migration in other aspects either.
FIGURE 8
Internal and international migration per 10,000 inhabitants, average of 2006–2008
migration per 10000 inhabitants
migration per 10000 inhabitants
48-190
-81- -31
12-48
-5-12
-31- -22
-12- -5
-16- -12
-12-190
-81- -12
Map: Tamás Hardi.
-22- -16
211
Life expectancy at birth is dispersed in a 9.2-year range, which would be of a very
significant degree in a comparison between countries. Borsod-Abaúj-Zemplén County
is the last, falling much behind the second worst Upper-Moravia (Figure 9). It is
interesting to note that two metropolitan sub-regions, Lódz and Katowice which count
as industrial centres are also among the worst positioned sub-regions, while life
expectancy at birth is 4.5 years longer in Warsaw and Cracow (the first two Polish subregions). The Czech Republic is responsible for the majority of the total dispersion,
because here the dispersion range itself is 8.8 years. The first six places are occupied
exclusively by Czech regions, but Prague is not among them, it is only at place 27
among all sub-regions, and is in the middle field within the Czech Republic. In the other
three countries however the capital sub-regions are at the top within the particular
countries, but even so Budapest is only the 57th in the total ranking, i.e. is below the
median. Within Poland this indicator does not correlate with economic output either,
because the south-eastern and eastern sub-regions which are placed at the back in the
economic ranking are early in the ranking here.
FIGURE 9
Life expectancy at birth, 2008
Life expectancy
Life expectancy
77,0-81,1
71,9-73,5
76,0-77,0
75,5-76,0
73,5-74,0
75,0-75,5
71,9-75,0
74,5-75,0
75,0-81,1
74,0-74,5
Map: Tamás Hardi.
The building of new dwellings reflects the development of the economic situation
quite well, but demographic conditions also exert a significant influence on its extent.
From all the analysed secondary indicators this is the most closely connected to GDP
per capita and unemployment rate; it has a positive relation with the former, and a
negative with the latter. The agglomeration sub-regions surrounding metropolises
212
Tamás Dusek
occupy the first places; these indicate the positive deviations from the values influenced
by the economic situation (Figure 10). In an absolute sense this indicator is the most
closely related to unemployment, and secondly to migration balance, which is not at all
surprising.
FIGURE 10
Newly built dwellings per 10,000 inhabitants, 2008
Dwellings/10000 inhabitants
56-111
12-19
42-56
35-42
19-22
30-35
12-30
27-30
30-111
22-27
Map: Tamás Hardi.
The proportion of industrial employment per population is indirectly also connected
to competitiveness, because a greater proportion indicates either the greater economic
activity of the population, or greater productive capacity, or both. The indicator itself is
similar in its extent to that of building dwellings, it is slightly more loosely connected to
GDP and unemployment rate, while its connection to economic activity is closer
(Figure 11). Komárom-Esztergom County ranks first, outstandingly overtaking the
second Polish Tyski sub-region by five percentage points. These first two are followed
by a number of Czech sub-regions, where only the service providing centre, Prague is
not included among the leading sub-regions. The lowest level of industrial employment
can be found primarily in the Eastern-Polish sub-regions.
The last indicator analysed is the number of crimes per inhabitant (Figure 12). It is
well-known that these kind of statistics are to be handled with extreme care, their
temporal and spatial comparison is made more difficult by the arbitrariness of
discriminating between offence and crime, their national differences and temporal
change, the difference in the reconnaissance ratio, the statistical contraction of more
crimes tried simultaneously and the lack of discrimination between crimes of extremely
different weight and kind (from murder to parking ticket counterfeit). In the knowledge
of these limitations we still present crime statistics, because even if it is less suitable for
FIGURE 11
Industrial employment per 100 inhabitants (2008)
industrial employment
per 100 inhabitants
130-195
195-229
229-245
industrial employment
per 100 inhabitants
385-673
337-385
310-337
245-276
276-310
276-673
130-276
Map: Tamás Hardi.
FIGURE 12
Crimes per 10,000 inhabitants, 2008
crimes
per 10000 inhabitants
130-195
195-229
229-245
crimes
per 100 inhabitants
385-673
337-385
310-337
245-276
276-310
276-673
130-276
Map: Tamás Hardi.
213
214
Tamás Dusek
comparison between countries, it does show regional characteristics. On the basis of
these it can be first of all ascertained that the metropolitan sub-regions are in the front
line everywhere. Territorial differences within countries are observable only in the
Czech Republic and Poland; more crimes are committed in the western sub-regions. In
case of the eight, with the two plus years altogether ten analysed indicators, it is worth
to see to what extent it is possible to ascribe territorial differences to differences
between countries and differences within countries.
This question, which can be analysed by the variance-quotient in the simplest way,
was touched on in case of certain indicators. The results are displayed in Table 5. On the
basis of this, it was in case of the 2002 unemployment rate that the value of a particular
sub-region was the most influenced by which country it is located in, because by this
time due to the high Polish data the differences between the countries were responsible
for 63.8% of the variable-quotient between regions, and only the remaining 36.2% was
ascribable to the differences within countries. Industrial employment ranks second,
which was principally influenced by the high ratio of the Czech Republic.
TABLE 5
Factors explaining spatial differences
Indicator
1
2
3
4
5
6
7
8
9
10
GDP/capita, 2008
GDP/capita, 2002
Economic activity, 2009
Unemployment, 2009
Unemployment, 2002
Migration, average of 2006–2008
Industrial employment
Crime/1000 inhabitants
By means of differences
between countries
By means of differences
between urban sub-regions
and non-urban sub-regions
part explained from regional differences,%
12,3
53,1
12,7
55,0
24,5
2,5
20,9
12,3
63,8
1,7
19,5
0,2
24,3
1,9
4,4
21,7
33,6
0,0
20,6
34,1
Source: Own calculation.
Apart from the differences between countries, the other generally analysable
characteristic feature is the effect the difference between urban regions and non-urban
regions has on the overall difference. Here the four capital regions and further seven
Polish urban regions were separated from all the other sub-regions, i.e. Bratislava was
also considered as an urban region despite the fact that it really constitutes a sub-region
together with its agglomeration and wider surroundings. This distinction has an effect
mostly on GDP dispersion, explaining more than half of the variance, in case of this
indicator the difference between countries played a negligible part. The difference of
industrial employment is not influenced by this distinction because the urban industrial
average and the industrial average of the other sub-regions roughly correspond to each
215
other, significant differences in industrial employment occur within the circle of nonurban sub-regions.
The complex indicators of competitiveness
The former analyses examined the components of competitiveness only individually.
This has its own raison d'ętre, but the individual analysis of the different indicators
cannot replace the expression of competitiveness by a synthetic index-number. First the
three basic indicators are expressed by one indicator. The original index-numbers are
transformed with the help of range in the following way:
xi' =
xi − x min
xmax − xmin
With this transformation the indicators become unit-independent, the minimum value
will be zero, and the maximum value will be one. The basic indicators of the Human
Development Index are transformed in the same way. The thus transformed indicators
of GDP per capita, unemployment rate and activity rate are averaged and then
multiplied by a thousand so that it is more easily dealt with. This way the indicator will
have a value in the range of zero and thousand. In case of unemployment rate the
indicator has to be reversed, because the smallest value is the most favourable there, and
the greatest value is the worst. The first 10 and last 10 territorial units of the resulting
competitive ranking are displayed in Table 6; in the appendix the whole list is shown.
The three capital regions stand out from the field, the fourth, Budapest significantly
falls behind the other three capitals. There are only Hungarian and Polish sub-regions
among the worst ones. Figure 13 shows a spatial distribution, not really in a
differentiated way, as in case of the former indicators, but the territorial differences are
beautifully delineated in it.
As the second method of performing a joint, complex analysis of the indicators, we
separated the sub-regions of different types from each other with the help of cluster
analysis. This method may be regarded richer in information than the first one in the
sense that it separates those sub-regions from each other which are altogether of similar
competitiveness, but which are different from the viewpoint of competitiveness factors.
For instance, a region with high GDP and high unemployment can be of an averagely
similar competitiveness to a sub-region with lower GDP and lower unemployment,
while belonging to two different types at the same time. These distinctions cannot be
ascertained from the former ranking.
Those six indicators were included into the analysis which are connected to
competitiveness and are suitable for international comparisons between countries. So in
addition to GDP per capita, activity rate, and unemployment rate, we have now the
specific building of dwellings, life-expectancy at birth and migration rate. Industrial
216
Tamás Dusek
employment was not included, because it is a structural indicator; and the number of
crimes was also excluded from the analysis due to its incomparability between
countries. Before performing the cluster analysis, the data sheets were divided by their
dispersion to make them unit-independent.
Five clusters were created, because this enables an appropriate degree of differen–
tiality. The cluster centers can be seen in Table 7, the spatial distribution of clusters in
shown in Figure 14. The cluster centers are given in their original units, because they
would be less graphic when standardized. Capitals are again the best positioned subregions, which are well separated from all the other clusters. The sub-regions of the
worst positions are also well separated, which are in the worst situation on the basis of
all the six indicators. On the basis of their spatial distribution they form an integrated
territory in Eastern-Hungary, Southern Transdanubia, East-Slovakia and NorthwestPoland, and to a lesser degree in Southwest-Poland. Among the other three clusters
there are no such great differences. Almost all of the six sub-regions of the second
cluster (except Plzen) are on the periphery of metropolises with favourable unemp–
loyment rate.
TABLE 6
Complex indicator of the competitiveness of sub-regions, 2008
Sub-region
Hlavní mesto Praha
Bratislavský kraj
Miasto Warszawa
Budapest
Stredoceský kraj
Jihocecký kraj
Skierniewicki
Trnavský kraj
Plzenský kraj
Miasto Lódz
Jihomoravský kraj
Baranya
Somogy
Békés
Stargardzki
Nógrád
Walbrzyski
Grudziadzki
Koszalinski
Szabolcs-Szatmár-Bereg
Competitiveness
GDP/capita
(Euro, PPP)
Activity
Unemployment
940
922
908
717
660
641
641
636
633
629
608
275
272
265
209
195
191
189
176
166
94
43 624
42 002
41 671
35 919
18 819
17 031
10 389
20 856
17 292
17 390
19 672
11 525
10 039
9 343
9 018
7 298
10 861
9 683
11 465
10 122
8 348
54,3
56,5
57,6
45,9
51,2
51,0
59,2
53,1
51,8
56,1
49,2
38,1
38,8
38,6
36,6
38,5
34,3
32,1
32,4
37,3
37,4
1,9
3,4
4,6
4,3
2,6
2,6
4,6
5,9
3,6
6,5
4,4
10,4
10,3
10,2
11,6
12,7
11,9
10,2
11,8
14,7
17,5
217
FIGURE 13
Complex indicator of competitiveness, 2008
complex competitiveness
555-940
482-555
408-482
336-408
94-336
Map: Tamás Hardi.
TABLE 7
Cluster center values (2008 data)
Indicator
GDP/capita
Activity
Unemployment
Building of dwellings (10000
inhabitants)
Migration (10000 inhabitants)
Number of sub-regions belonging to
the cluster
1
2
3
4
5
40 804
53,6
3,6
14 919
44,1
4,0
14 331
43,3
5,7
14 004
49,2
6,8
10 742
40,1
10,5
77,1
69,8
76,2
77,0
118,2
75,8
44,7
–2,0
77,0
30,9
3,6
74,4
23,3
–28,5
74,3
4
7
28
37
32
218
Tamás Dusek
FIGURE 14
Spatial distribution of the types of competitiveness, 2008
Clusters
1
2
3
4
5
Map: Tamás Hardi.
The difference between the third and fourth clusters is minimal, with perceivable
difference only in the number of dwellings built and life expectancy at birth, the third is
in a better position than the fourth with respect to these, however, activity is bigger in
the fourth. As a whole the picture did not really change in comparison to the former
situation of three indicators, which is caused by correlation between the indicators, i.e.
if a sub-region is more favourable from the viewpoint of an indicator, it is more
favourable in the others as well.
219
The situation of sub-regions with automotive factories
The list of the four countries’ automotive and motor factories can be seen in Table 8.
The suppliers of the automotive factories are not included in the analysis, because it
would be impossible to separate the supplies for automotive factories and other
industrial sectors. The altogether 33 factories are in 28 sub-regions, since there are two
factories in five sub-regions. The table includes only currently operating enterprises; the
ones under planning or implementation, such as Mercedes-Benz in Kecskemét are
excluded, since the effect of these would be perceivable only later. Some factories
manufacture both motors and vehicles. In Hungary there are only greenfield automotive
factories, while in the Czech Republic only two out of the 11 factories, and half of the
16 factories in Poland were established as greenfield investments. Part of the greenfield
investments were established in cities with former engineering traditions (e.g. Győr,
Szentgotthárd), and the Volkswagen factory of Bratislava is the heir of the former small
factory unit of Skoda.
TABLE 8
Automotive factories in the four countries
Settlement
Settlement’s,
population*
Factors
Beginning
of production**
Production
Tedom Divize Motory (Tedom
Engines Division)
Toyota Peugeot Citroën
Automobile Czech
Tatra
Škoda
Avia Ashok Leyland Motors
SOR Libchavy
Škoda
Hyundai Motor Manufacturing
Tedom Divize Bus
Iveco Czech Republic
Škoda
1990
2005
engine
production
cars
1990
1990
1990
1990
1990
2008
1990
1990
1990
cars, trucks
cars
trucks
buses
cars
cars
buses
buses
cars
175,183
FIAT-GM Powertrain
1990
1,250
Solaris Bus and Coach
1996
engine
production
buses
General Motors Manufacturing
Poland/Opel Polska
Toyota Motor Industries
Poland
1998
cars
2005
engine
production
1
Czech Republic
Jablonec nad Nisou
45,328
2
Kolin
30,935
3
4
5
6
7
8
9
10
11
Koprivnice
Kvasiny
Letnany (Prague)
Libchavy
Mlada Boleslav
Nosovice
Trebic
Vysoké Myto
Vrchlabi
Poland
Bielsko-Biala
1
2
3
Bolechowo
(Poznan)
Gliwice
4
Jelcz-Laskowice
23,044
1,381
1,248,026
1,708
44,750
994
38,156
12,669
12,710
195,181
15,496
220
Tamás Dusek
Count. Table 8
Settlement
Settlement’s,
population*
Factors
Beginning
of production**
Production
engine
production,
pick-up trucks
trucks
5
Lublin
348,961
Andoria MOT
1990
6
7
8
9
10
11
Niepolomice
Polkowice
Poznan
Poznan
Slupsk
Starachowice
9,263
22,087
552,735
552,735
96,871
51,766
MAN Nutzfahrzeuge
Volkswagen Motor Polska
MAN Nutzfahrzeuge
Volkswagen Poznań.
Scania Production Slupsk
MAN Nutzfahrzeuge (MAN
Star Trucks and Buses)
Fiat Auto Poland
2007
1999
1998
1990
1990
1990
2002
1990
cars
632,561
632,561
Toyota Motor Manufacturing
Poland
FSO (Fabryka Samochodów
Osobowych)
Volvo Polska
Jelcz Polskie Autobusy
cars, engine
production
cars
1995
buses, trucks
buses
30,914
130,478
Magyar Suzuki Zrt
Audi Hungaria Motor Kft
1991
1993
General Motors Powertrain –
Magyarország Autóipari Kft.
1991
auto
engine
production,
cars
engine
production
Volkswagen Slovakia
PCA Slovakia (PSA Peugeot
Citroën)
Kia Motors Slovakia (HyundaiKia)
1991
2006
cars
cars
2004
cars, engine
production
12 Tychy
129,438
13 Walbrzych
120,724
1,716,855
14 Warsaw
15 Wroclaw
16 Wroclaw
Hungary
1 Esztergom
2 Győr
3
Szentgotthárd
1
2
Slovakia
Bratislava
Trnava
3
Zilina
8,881
431,061
67,605
85,252
1990
trucks
cars
buses
trucks, buses
*2009 or 2010; **In case of the year 1990 production started before 1991.
Source: Collected by Melinda Pató.
The number of employees directly employed in factories was an estimated 95
thousand in 2010 (Table 9). The proportion of employees in automotive industry is high
especially in the Czech Republic, while at the same time, the value of Hungary can be
considered significant because Suzuki, Audi and General Motors all started their
production after 1990. Automotive industry has the greatest tradition in the Czech
Republic, principally due to Skoda founded in the 19th century, which manufactured 193
thousand cars in 1989, from which 45 thousand were exported to Western-Europe
221
(Jakubiak et al. 2008). 55% of people employed in the Czech Republic work in the Skoda
factories. Although the proportion of greenfield investments is small in the Czech
Republic, these happened after 2000 and are very significant in their absolute size
(Toyota-Peugeot-Citroën in Kolin and Hyundai in Nosovice). Hungary counted as a great
power in bus production until 1990, but following a slow cut-back the sector disappeared
by 2007. In Slovakia there were two greenfield and one brown field investments. In
Bratislava Volkswagen used Skoda’s licence manufacturing Bratislavské automobilové
závody (BAZ, Bratislava Automotive Factors) facilities, and introducing a totally new
technology (Jakubiak et al. 2008). This way Volkswagen’s settlement here may as well be
regarded as a greenfield investment. There used to be a factory manufacturing pick-up
trucks in Trnava, but the Peugeot-Citroën factory settling here can be considered a
greenfield investment, similarly to KIA in Zilina. Owing to these three great new
automotive industrial investors, Slovakia became the first in the world with respect to the
number of cars per inhabitant in the course of the 2000s.
TABLE 9
Number of automotive factories and their employees
Country
Czech Republic
Poland
Hungary
Slovakia
Altogether
Number of factories
Employees
(2010, thousand people)
11
16
3
3
33
40,0
29,3
12,2
14,0
95,5
Source: On the basis of the collection of Melinda Pató.
The average competitiveness of sub-regions with automotive factories significantly
exceeds the competitiveness of sub-regions without automotive factories (Tables 10–
11). This is true both of the complex and all other individual indicators, even if the urban and
non-urban regions are analysed separately. The cause and effect relationship is probably not
one-way, but there is a mutually strengthening back and forth relationship. I.e. higher
competitiveness creates more advantageous circumstances for the establishment of
automotive factories, and the establishment and operation of automotive factories improves
the region’s competitiveness. It is worth to note that the GDP per capita increased by 6
percentage points faster in the automotive industrial regions between 2002 and 2008. This
was not only due to the previously mentioned outstandingly growing sub-region of
Bratislava, because even if we consider the only non-urban regions, the difference is
still 3,8 percentage points. In Slovakia the greatest competitiveness difference is
between the averages of the three sub-regions with automotive industry and the five
sub-regions without automotive industry. The merging of cause and effect can be well
observed within Slovakia: the three western regions with automotive industry were
222
Tamás Dusek
originally more developed than the Central- and Eastern-Slovakian regions, but the
coming of automotive factories has further increased the already existing differences.
TABLE 10
Competitiveness of sub-regions with and without automotive factories by countries
Country
Average of subregions with
automotive factories
640
474
520
684
548
Czech Republic
Poland
Hungary
Slovakia
Altogether
Average of subregions without
automotive factories
577
412
338
404
410
Average of all subregions
609
412
365
509
444
Source: Own calculations.
TABLE 11
Competitiveness of sub-regions with and without automotive factories
Indicator
Sub-regions with automotive factories
total
Complex indicator
GDP/capita, 2008
Activity
Unemployment
Life expectancy at
birth
Dwellings
Migration
GDP/capita, 2002
GDP changes, 2002–
2008
cities only non-urban
sub-regions
only
Sub-regions without automotive
factories
total
cities only non-urban
sub-regions
only
548
123,6
46,5
5,6
771
224,8
49,8
3,8
495
99,5
45,8
6,0
410
79,0
44,3
7,9
561
135,9
46,3
5,7
398
74,5
44,1
8,1
75,6
76,5
75,4
75,1
75,0
75,1
477
2626
120,1
793
2887
212,6
401
2563
98,1
332
–496
81,5
501
–733
139,7
319
–477
76,9
3,5
12,2
1,4
–2,5
–3,8
–2,4
It is worth to look at the automotive industrial sub-regions of weaker competitiveness separately. These are found in Poland: the complex indicator value of the subregions of Walbrzych, Slupsk, Gliwick, Bielsk is below the average of the sub-regions
without automotive factories. The Toyota motor factory in Walbrzych was established
with a middle-size greenfield investment in 1999, the number of employees was two
thousand in 2010. The Scania factory in Slupsk was originally the joint company of
Scania and Kapena, until all shares were bought by Scania in 2003. The number of
employees amounts to 700 people. The General Motors factory in Gliwice employs
223
three thousand people. There is a Fiat motor factory in Bielsko-Biala, with 1200
employees. All factories belong to the smaller, small-medium sized factories at the
most, at least considering the average size of automotive factories (in the four countries
jointly 2900 employees constitute the average according to places of production). The
smallest factory however can be found in Prague; 283 employees worked in the Avia in
2008. The small weight of this obviously does not perceptibly influence the situation of
Prague, while Volkswagen in Bratislava with its eight thousand (more than ten thousand
back in 2006) employees counts as a dominant enterprise even on Bratislava’s scale.
The biggest factory is Skoda’s central plant in Mlada Boleslav, and owing to its
successful operation Mlada Boleslav with forty-five thousand inhabitants is one of the
richest Czech cities.
Summary
The site of the analysed four countries’ automotive factories are situated in settlements
of remarkably different sizes, the difference between the smallest and the largest
settlement is four orders of magnitude, more than a thousand-fold. Therefore the
methodological conditions of settlement level comparison are not given, an average size
automotive factory plays a totally different part in the labour market, economy of a city
of million inhabitants, as in a thousand-inhabitant village and its surroundings. The
territorial effect of automotive factories were therefore analysed at NUTS3 level that can
be filled with statistical data. In the four countries the competitiveness of automotive
industrial regions, sub-regions on the average significantly exceeds the competitiveness
of regions without automotive industry, with higher income producing capacity, lower
unemployment and higher economic activity. This also demonstrates that automotive
industry is a key actor of modern processing industry, which affects the production of
many other industrial sectors due to its size and input- and output connections, and
generally influences economy through the income generated in the industrial sector.
Automotive industry has the greatest traditions in the Czech Republic, but two
significant new greenfield investments took place here as well. In Poland about half of
the automotive industry has a history before 1990, while in Slovakia and Hungary the
currently working factories of the industrial sector were established as a result of new
greenfield investments.
References
Castillo-Manzano, J. I. – Castro-Nuno, M. – Laxe, F. G. – López-Valpuesta, L. – ArévaloQuijada, M. T. (2009) Low-cost port competitiveness index: Implementation in the Spanish
port system. – Marine Policy. 33. pp. 591–598.
Deas, I. – Giordani, B. (2001) Conceptualising and measuring urban competitiveness in major
English cities: an exploratory approach. – Environment and Planning A. 33. pp. 1411–1429.
224
Tamás Dusek
Devolving Decision Making: Meeting the Regional Economic Challenge – Increasing Regional
and Local Flexibility. (2004) H. M. Treasury, London.
Dusek T. – Kiss J. P. (2008) A regionális GDP értelmezésének és használatának problémái. –
Területi Statisztika. 3. pp. 264–280.
Jakubiak, M. – Kolesar, P. – Izvorski, I. – Kurekova, L. (2008) The Automotive Industry in the
Slovak Republik: Recent Developments and Impact on Growth. – Commission on Growth
and Development Working Paper. No. 29.
Kitson, M. – Martin, R. – Tyler, P. (2004) Regional Competitiveness: An elusive yet Key
Concept? – Regional Studies. 38. pp. 991–999.
Krugman, P. (1996) Making sense of the competitiveness debate. – Oxford Review of Economic
Policy. 12. pp. 483–499.
Lengyel I. (2000) A regionális versenyképesség tényezői, különös tekintettel a Dél-Alföldre.
Versenyképesség. – Regionális versenyképesség. – Szerk.: Farkas Beáta, Lengyel Imre,
JATEPress, Szeged.
Lukovics M. (2008) Térségek versenyképességének mérése. JATEPress, Szeged.
ODPM (2003) Cities, Regions and Competitiveness. Office of the Deputy Prime Minsiter,
London.
ODPM (2004) Competitive European Cities: Where Do the Core Cities Stand? – Office of the
Deputy Prime Minister Urban Research Paper 13.
Oral, M. – Chabchoub, H. (1996) On the methodology of the World Competitiveness Report. –
European Journal of Operational Research. 90. pp. 514–535.
Porter, M. (1990) Competitive Advantage of Nations. Macmillan, London.
Productivity in the UK: 4 – The Local Dimension. (2003) H. M. Treasury, London.
Regional Competitiveness and the State of the Regions Department of Trade and Industry. (2004)
DTI (Department of Trade and Industry).
Solleiro, J. L. – Castanón, R. (2005) Competitiveness and innovation systems: the challenges for
Mexico’s insertion in the global context. – Technovation. 25. pp. 1059–1070.
Teng, J. Y. – Huang, W. C. – Huang, M. J. (2004) Multiciriteria evaluation port competitiveness
of eight east Asian container ports. – Journal of Marine Science and Technology. 12. pp. 256–
264.
Yeo, G. T. – Roe, M. – Dinwoodie, J. (2008) Evaluating the competitiveness of container ports in
Korea and China. – Transportation Research Part A. 42. pp. 910–921.
Eurostat: http://epp.eurostat.ec.europa.eu/portal/page/portal/eurostat/home.
225
APPENDIX 1
Complex indicator of the competitiveness of sub-regions, 2008
Code and name of sub-region
Competitiveness
CZ010 – Hlavní mesto Praha
940
CZ020 – Stredoceský kraj
660
CZ031 – Jihocecký kraj
641
CZ032 – Plzenský kraj
633
CZ041 – Karlovarský kraj
526
CZ042 – Ústecký kraj
498
CZ051 – Liberecký kraj
546
CZ052 – Královéhradecký kraj
601
CZ053 – Pardubický kraj
604
CZ063 – Vysocina
602
CZ064 – Jihomoravský kraj
608
CZ071 – Olomoucký kraj
533
CZ072 – Zlínský kraj
604
CZ080 – Moravskoslezský kraj
525
HU101 – Budapest
717
HU102 – Pest
479
HU211 – Fejér
467
HU212 – Komárom–Esztergom
530
HU213 – Veszprém
416
HU221 – Gyor–Moson–Sopron
555
HU222 – Vas
476
HU223 – Zala
457
HU231 – Baranya
275
HU232 – Somogy
272
HU233 – Tolna
310
166
HU311 – Borsod–Abaúj–Zemplén
HU312 – Heves
287
HU313 – Nógrád
195
HU321 – Hajdú–Bihar
302
HU322 – Jász–Nagykun–Szolnok
337
HU323 – Szabolcs–Szatmár–Bereg
94
HU331 – Bács–Kiskun
336
HU332 – Békés
265
HU333 – Csongrád
370
PL113 – Miasto Lódz
629
PL114 – Lódzki
482
PL115 – Piotrkowski
587
PL116 – Sieradzki
533
GDP/capita
(Euro, PPP)
43 624
18 819
17 031
17 292
13 957
16 249
14 753
16 907
16 848
15 868
19 672
15 187
17 091
17 420
35 919
14 150
15 057
16 933
11 797
18 281
14 068
13 309
11 525
10 039
11 525
10 122
11 137
7298
11 423
10 438
8348
10 794
9343
12 010
17 390
12 037
12 258
10 173
Activity
Unemployment
54,3
51,2
51,0
51,8
52,1
48,6
48,3
50,0
49,7
49,9
49,2
49,0
49,9
49,1
45,9
44,0
43,1
46,2
43,6
44,5
44,5
45,4
38,1
38,8
40,5
37,3
40,3
38,5
37,9
40,9
37,4
40,7
38,6
41,1
56,1
50,9
58,5
53,2
1,9
2,6
2,6
3,6
7,6
7,9
4,6
3,9
3,6
3,3
4,4
5,9
3,8
7,4
4,3
5,1
5,5
5,1
6,9
3,5
5,5
6,6
10,4
10,3
10,1
14,7
11
12,7
8,9
8,5
17,5
8,6
10,2
7,7
6,5
8,2
7,7
6,2
226
Tamás Dusek
Count. Appendix 1
PL117 – Skierniewicki
PL121 – Ciechanowsko–plocki
PL122 – Ostrolecko–siedlecki
PL127 – Miasto Warszawa
PL128 – Radomski
PL129 – Warszawski–wschodni
PL12A – Warszawski–zachodni
PL213 – Miasto Kraków
PL214 – Krakowski
PL215 – Nowosadecki
PL216 – Oswiecimski
PL217 – Tarnowski
PL224 – Czestochowski
PL225 – Bielski
PL227 – Rybnicki
PL228 – Bytomski
PL229 – Gliwicki
PL22A – Katowicki
PL22B – Sosnowiecki
PL22C – Tyski
PL311 – Bialski
PL312 – Chelmsko–zamojski
PL314 – Lubelski
PL315 – Pulawski
PL323 – Krosnienski
PL324 – Przemyski
PL325 – Rzeszowski
PL326 – Tarnobrzeski
PL331 – Kielecki
PL332 – Sandomiersko–
jedrzejowski
PL343 – Bialostocki
PL344 – Lomzynski
PL345 – Suwalski
PL411 – Pilski
PL414 – Koninski
PL415 – Miasto Poznan
PL416 – Kaliski
PL417 – Leszczynski
Competitiveness
GDP/capita
(Euro, PPP)
Activity
Unemployment
641
505
521
908
352
523
460
587
475
287
408
336
441
410
344
365
392
539
437
528
393
509
431
369
362
352
467
367
386
10 389
14 870
10 400
41 671
10 106
11 516
16 773
21 855
9484
8390
10 505
8735
11 911
13 851
13 719
11 199
15 267
20 219
14 575
20 286
8252
8386
12 436
8671
8684
8140
11 000
10 316
12 099
59,2
52,8
52,6
57,6
44,7
48,3
39,5
47,6
49,1
36,4
43,3
36,7
45,1
37,5
35,9
42,9
38,6
47,0
44,6
42,4
46,1
54,6
49,1
47,2
44,2
43,8
47,1
45,5
45,7
4,6
9,5
6,5
4,6
10
4,3
4,4
5,4
6,3
7,3
6,5
5,2
6,6
4,4
6,6
8,7
6,6
6,7
7,7
4,4
7,9
7,4
9,8
10
8,5
8,5
6,1
9,8
9,8
569
512
355
355
309
408
569
430
374
9937
12 202
8559
9368
10 980
10 616
28 166
10 948
11 817
58,6
49,5
40,6
41,2
35,9
46,4
38,0
46,2
36,8
7,6
5,9
6,6
7,3
7,1
8,4
3,3
7,4
4,8
227
Count. Appendix 1
Competitiveness
PL418 – Poznanski
PL422 – Koszalinski
PL423 – Stargardzki
PL424 – Miasto Szczecin
PL425 – Szczecinski
PL431 – Gorzowski
PL432 – Zielonogórski
PL514 – Miasto Wroclaw
PL515 – Jeleniogórski
PL516 – Legnicko–Glogowski
PL517 – Walbrzyski
PL518 – Wroclawski
PL521 – Nyski
PL522 – Opolski
PL613 – Bydgosko–Torunski
PL614 – Grudziadzki
PL615 – Wloclawski
PL621 – Elblaski
PL622 – Olsztynski
PL623 – Elcki
PL631 – Slupski
PL633 – Trojmiejski
PL634 – Gdanski
PL635 – Starogardzki
SK010 – Bratislavský kraj
SK021 – Trnavský kraj
SK022 – Trenciansky kraj
SK023 – Nitriansky kraj
SK031 – Zilinský kraj
SK032 – Banskobystrický kraj
SK041 – Presovský kraj
SK042 – Kosický kraj
445
176
209
442
382
409
448
518
348
495
191
513
288
434
431
189
296
417
387
284
290
491
360
367
922
636
582
506
495
279
321
334
GDP/capita
(Euro, PPP)
16 115
11 465
9018
18 136
12 834
12 246
12 002
21 649
11 074
21 200
10 861
12 272
9011
13 830
15 771
9683
10 386
9867
11 653
8962
10 982
19 024
9768
10 898
42 002
20 856
16 456
15 366
15 796
13 657
10 659
14 922
Activity
Unemployment
38,2
32,4
36,6
39,8
42,3
41,7
45,6
42,8
46,2
47,3
34,3
52,1
38,2
40,6
40,3
32,1
42,5
45,1
41,9
38,2
37,3
38,5
35,4
40,9
56,5
53,1
49,8
50,9
48,5
50,1
46,7
45,2
4,1
11,8
11,6
6,1
8,2
6,3
6,6
5,8
11,5
9,5
11,9
7,5
8,7
5,1
5,9
10,2
11,5
6,9
7,2
8,8
8,9
3,3
3,7
7,2
3,4
5,9
4,5
8,5
7,8
18,5
13,0
13,4
LOCAL ECONOMIC DEVELOPMENT AND THE
AUTOMOTIVE INDUSTRY IN GYŐR
MIHÁLY LADOS – KATALIN KOLLÁR
Keywords:
local economic development strategy automotive industry
The essay introduces the brief theoretical background of local economic development. The
development path of Győr is analysed, followed by the examination of how the theoretical
frameworks of local economic development can be seen in this development path, paying special
attention to automotive industry, the actors of local economic development, and also the practice
of strategic planning of the city and its economic development strategy. Furthermore the reader is
given a brief introduction to the automotive industry of Győr-Moson-Sopron county, including
Győr.
A theoretical background of local economic development
It is not easy to give a definition for local economic development. The difficulty is
caused by the fact that there are overlaps in some characteristics with other development
concepts such as settlement development, spatial development, rural development and
regional development. In order to get closer to an independent definition of economic
development, it is reasonable to look at the concepts of the above-mentioned fields of
development.
Settlement development entails planning and implementation activities that aim at
the influencing of the processes of the settlement. As a part of spatial development it is
actually the implementation of spatial development at settlement level (Farkas 2006).
Spatial development is the conscious, development oriented intervention of municipal
self-governments and national governments into the spatial processes (Faragó 2001).
Rural development is a field within spatial development, during which conscious
interventions are made in those rural areas whose population density is usually low and
where agriculture is dominant among the branches of the economy (G. Fekete 2005).
Regional economic development, putting economic processes in the centre, is focused
on nodal regions (Lengyel 2002). Although local and regional economic development
are similar to each other, there are some differences too: in the case of local economic
development, the focus of development is a specific dominant settlement, while this is
not always the case in regional economic development (Bajmócy 2011).
Having briefly discussed the above development definitions, in our study we use the
following definition for local economic development: local economic development is a
conscious intervention into the life and processes of local economy that may utilise both
external and internal resources, its initiator may be an external actor like the central
Local Economic Development and the Automotive Industry in Győr
229
government or foreign capital, still the most important is the cooperation of the local
actors who may act as initiators, supporters, managers or acceptors of the development
ideas (Mezei 2006).
The range of actors in local economic development is extremely wide. Different
authors apply different methods for grouping them. Lengyel, e.g., differentiates among
four “legs” of local economic development; in his opinion the four main actors are as
follows (Lengyel 2010):
−
−
−
−
Local governments (Subnational governments);
Business sphere;
Institutions of knowledge transfer;
Development agencies.
The thoughts in the paragraphs above reveal that local economic development is by
far not the exclusive responsibility of the local governmental organs and is not a process
implemented according to the regulations of the central government, either. The system
standing “on four legs”, on the other hand, is not complete, because none of the
categories include local inhabitants who may also be active participants in local
economic development, either by individual actions or group activities. These groups
e.g. may become non-governmental organisations, which is again a category that is
missing in the above list of four types of actors. Nevertheless we can say that the first
two “legs” are the ones that play the most important role in the process in the majority
of cases.
In the definitions we have to highlight the importance of the local actors. External
actors often participate too in the planning process wither with their financial, or
intellectual capital and even trust, but the participation of local actors is indispensable,
in fact, it is best if they are the initiators of the developments. Trust is also important
because it is an irreplaceable tool to “start up” the community – it is an engine, actually
(Czene–Ricz 2010).
The role of the central government in the realisation of local economic development
is evident, in fact, it is of crucial importance in the implementation of some development objectives, as they generate the creation of the institutional system necessary for
the development or e.g. the provision of the regulatory background (Horváth 1998). The
next level is the territorial level, whose role in local economic development is extremely
different across the various countries; it is usually the regulatory background and the
administrative system that are the dominant factors. The role of this tier was appreciated
in the Western part of Europe in the early 1990s (Pálné Kovács 1999). This circle contains all actors of the subnational levels, including regional self-governments, local selfgovernments, municipal associations, development agencies, businesses, institutions of
knowledge transfer, non-governmental organisations and the inhabitants.
The role of local government is crucial, in some countries exlusive in the process of
local economic development. This is usually influenced by three factors: the prevalent
political direction, in the first place; the legal environment and finally the municipal
organisation. The political direction has a significant impact on objectives and the
230
Mihály Lados – Katalin Kollár
methods for the realisation of them. Although municipal self-governments do not have
obligatory local economic development tasks, the legal environment does play a crucial
role in the obligations and freedom of action of the self-governments. The organisation
of the municipal self-government, finally, also influences the success of the tasks to be
implemented during development, because the methods of the realisation will be
adjusted to the characteristics of the existing structure (Bajmócy 2011).
The reason for the creation of local and regional development agencies is also the
implementation of economic development objectives. Coming from their operation,
they are closely linked to the governmental sector. Agencies may be e.g. innovation
agencies, business development agencies or even local economic development agencies.
Local businesses participate in local economic development in two functions: on the
one hand, they are active developers, and they are also the target group, on the other
hand. As a target group, however, businesses are far from being a single group; the
character and the tools of development are not the same for each business and group of
businesses, either. Local economic development itself targets a group of the businesses
and not the whole of the business sector. The size of the company is a dominant factor
in how they can join the process of economic development; larger companies are able to
put more capital, time and resources into the process, but it may happen that coming
from their power and role they become absolute leaders, leaving no space for the
consideration and implementation of objectives other than theirs. It is also important to
see that it is not always the demand of the existing local businesses that should be taken
into consideration: we must not neglect the possibility of the birth or strengthening of
new industries, either (Bajmócy 2011).
It is a dominant feature of businesses that these days they participate in market
process not on their own but in cooperation with one another in different forms. Such
cooperations are e.g. networks or clusters (Angyal 2003).
The fourth group of actors includes the knowledge transfer institutions: these
institutions have a very important role on the development of the competitiveness of the
respective area. Their main task is the creation of new knowledge, the promotion of its
flow and the training of human resources.
As we have already mentioned, non-governmental/non-for-profit organisations can
also play a significant role in the process of local economic development, either if they
take place in the elaboration of development documents, the work of the municipal selfgovernments or even in the management of investment as their main activity, or if they do
so indirectly, e.g. by information transfer or opinion shaping (Reisinger 2010).
In the next section we are focusing on the „first leg” of local economic development,
the role of local governments. We will analyse that how the theoretical framework of
local economic development turned into practice in a Hungarian big city, Győr.
231
Development path of the city of Győr
Győr is the sixth biggest city in Hungary by population (131,267 people), the total
number of inhabitants in the city and its hinterland exceeds 200,000. In the European
urban hierarchy system Győr and its agglomeration is thus taken as a Functional Urban
Area (FUA) with international/national importance. These are the centres that are
featured in the maps of European urban centres (HCSO).
While the number of population of the city has by and large been stagnating in the
recent two decades, as a combined effect or natural decrease and positive migration
balance, the number of inhabitants of the settlements in the direct attraction zone of
Győr – along the main transport routes – is continuously growing, induced by the
impacts of suburbanisation and agglomeration processes. The engine of this process has
been the successful transformation and the implemented development path of the
economy of Győr has over the last two decades. This process resulted that Győr has
become by now the second strongest economic pole in Hungary after Budapest and its
agglomeration. The leading factor in this economic development is automotive industry.
This development is not without roots: industry, including machinary and
automotive industry settled down in Győr 120 years ago. The emblematic representative
of this industry, the Rába Magyar Vagon- és Gépgyár (Rába Hungarian Wagon and
Machinery Works) manufactured a wide range of vehicles until the end of World War
II: railway carriages, cars, lorries, city vans ands military vehicles. In fact, during the
war the company even produced aircraft (e.g. Messerscmidt Me2010 bomber).
After the establishment of the socialist planned economy and the birth of the internal
market of the eastern block, the COMECON, the activity of the company became
specialised. Apart from the military vehicles, the manufacturing of complete vehicles
was stopped. On the other hand, the manufacturing of parts of different vehicles became
dominant, like bus engines, undercarriages of trucks and agricultural machinery
(tractors). After the systemic change, the elimination of the COMECON and the decline
of state orders resulted in a significant loss of markets, which forced several times the
restructuring and the narrowing of the activity of the factory (Dusza 2003).
In the present product range of the company, the manufacturing of undercarriages is
dominant. Looking at the hundred-year history of the factory we can say that the Rába
factory accumulated a substantial knowledge base of automotive industry in the city of
Győr, from development through manufacturing of parts right to assembly. This basis
was very much necessary after the second third of the 1990s for the automotive industry
“boom” in the city, including the settling down of Audi Hungaria Motor Kft. in Győr.
The city of Győr implemented the economic restructuring concomitant with the
systemic change more successfully than the average Hungarian and Central and Eastern
European cities did. The reasons for this are manifold. The most important factor is the
accessibility. Győr was relatively rapidly accessible both on rail and road, and on a
network of better quality than the Hungarian average of transportation. So the city
became geographically more easily accessible for foreign direct investment.
232
This in itself, however, would be too little for a successful change. A factor almost
as important as accessibility is the almost century-old industrial culture of the city,
offering job opportunities for men and women alike, and the trained labour force
necessary for this. In addition, coming from the outstanding industrial role that Győr
had before the systemic change too, the infrastructure (both quality and quantity) was
much better built out in the city than in the majority of the Hungarian and in many other
Central and Eastern European cities. This gave Győr a competitive advantage in the
attraction of production plants. This was supplemented by the receptiveness and
openness of the (municipal self-government of the) city toward investors. These factors
together made Győr a really popular destination for the foreign direct investments, both
as regard greenfield and brownfield investments, often realised through privatisation.
In the two decades following the systemic change, the economic structure of the city
basically transformed. Induced by the changes of the market conditions and the cost
factors influencing them (costs of labour and transportation), within the formerly
diversified industry of Győr the centres of light industry (textile and food processing
industry) declined or even ceased to exist in several waves. Parallel to this, other sectors
like commerce or financial services were reinforced by the rapid building out of the
commercial and banking and insurance networks. The most intensive growth, however,
was induced by machinery sector including automotive industry, as a result of the
settling down and the attraction of Audi company (Mónus 2007).
Of course this process cannot be linked to a single company, because Győr is not
located in a vacuum. The automotive industry investments realised in the environment
of the city in a 200–300 kilometre radius in the last two decades, the relative proximity
of spare parts manufacturing and car assembly in the hinterland of Győr is leading to the
birth of an automotive industry cluster in the Central and Eastern European space in the
Marshallian sense of the word. The investment plans made for this decade make the city
of Győr, together with its region, one of the real automotive industry poles of the
Central European space (Grosz 2005b).
Actors and tools of local economic development in Győr
Starting from the broader frameworks, in the time of the systemic change the external
actors influencing the operation of the local economy can be divided into two groups.
One of the actors is the Hungarian state that promoted economic transformation, the
realisation of the shift to the market economy by the creation of the necessary regulatory
environment. The most important of these is the Corporation Act, the Transformation
Act and the Privatisation Act, and on the side of the resources, the operation of the
Investment Promotion Fund.
In Győr the acts listed above resulted in an entrepreneurial activity above the
national average, which could be seen in the growth above the countryside average in
the number of enterprises per thousand inhabitants, the smaller number of factory
closedowns than in other parts of Hungary and parallel to this the unemployment rate
233
far below the national average. In the first third of the 1990s, the majority of the Győr
centred state owned companies were transformed into economic corporations, and in
many cases they were already privately owned after the privatisation. Several of the
traditional light industry companies of long traditions, however, did not survive this
transition (e.g. Richards, Vegetable Oil Factory, Dairy Company).
The other dominant external actors in this period were the member states of the
European Union and the OECD with their advanced economies, who, as a part of their
national economic policy, promoted working capital investments in the countries of the
disintegrating east block. This had a major contribution to the transformation of the
market structure of the respective countries, on the one hand, and, on the other hand, the
local employment of the labour force could decrease immigration from these countries
into the more advanced states after the borders were less strictly guarded.
In these years Győr became a popular destination of foreign direct investments, in
fact, the city was often referred to as a part of the investment golden triangle of Central
Europe, together with Vienna and Bratislava. Foreign direct investments were active not
only in green-field and brownfield developments but also in the creation of the hard and
soft business infrastructure like Győr International Industrial Park and the Business
Assistance Enterprise Development Foundation.
In the second half of this decade and in the beginning of the new millennium the
state promoted local economic development through the system of economic and spatial
development. In the first period, an example is the launch of the industrial park
programme in 1996. In the framework of this programme, in the first step businesses
and organisations that wanted to develop concentrated industrial locations could gain the
industrial park title. The parks were founded by city governments in many cases. In the
second step, parks that had been awarded the title and businesses located in these parks
could apply to the Earmarked Provision for Economic Development for location
development; in 1997 a total of 74 projects were given no less than 5.1 billion HUF
support. As a result of the supports, investments worth six and a half times the resources
were realised (http://cegvezetes.hu/1998/04/penzhez-lehet-jutni/).
International actors now include the European Union as a result of the accession
process to this integration. In Győr and its neighbourhood several economic infrastructure development projects were implemented in the framework of the Austria –Hungary
Interreg IIA Phare CBC programme, e.g. the development of port, airport, innovation
centre and chamber trade centre.
As regards the fourfold division of the local actors, it was definitely the municipal selfgovernment that had a leading role in the initial period of the transformation. The selfgovernment of the City of Győr was a partner in the foundation of the Győr International
Industrial Park Ltd., the Győr-Gönyű Port Inc. and the Győr-Pér Airport Ltd.
234
Tools of investment promotion
Why should companies choose Győr? As we have already mentioned, the geographical
location of the city is excellent: on the one hand, it is on the Vienna–Bratislava–Budapest
innovation axis, on the other hand, Győr is halfway between Vienna and Budapest.
The transport endowments of the city are excellent; it has good east-west railway,
road and waterway connections, although its north-south relations are in need of
development. At the north end of the park there is the Vienna-Budapest international
railway line, to which the park has two own rails. The river port of Gönyü located 20
kilometres away allows access to the city via a port linked to sea waterway. As regards
international airports, the one at Budapest is 130 kilometres, the Vienna-Schwechat
Airport is 90 kilometres, the Bratislava Airport is 60 kilometres away from Győr. In
Pér, only 15 kilometres from Győr, there is a small airport suitable for the traffic of
aircrafts up to 75 passengers.
The development of the airport started around the millennium, with the construction
of a paved runway, the reception building and the related establishments. The significant part of resources of this development was provided by the European Union and
also by Audi Hungária Motor Kft. The investments of the Győr-Pér Airport Ltd. –
founded in 1994 – include, among other things, the fencing of the airport and the provision of the lighting system. The municipality considers the continuous development of
the airport indispensable, as one of the most important aspects for investors in their
location decisions is the presence of a nearby working airport. The traffic figures of the
last year (Table 1) show that airport had an increasing operation until the world wide
financial and economic crisis started in autumn 2008. Novadays the growth of traffic
flow is in increase again, however the most important user of the airport is still Audi.
TABLE 1
Traffic flow of Győr-Pér Airport between 2003 and 2010
Specification
Number of international
operations
International passengers
Number of all operations
2003
2004
2005
2006
2007
2008
2009
2010
358
867
1 252
1 258
1 727
1729
2 127
2 738
2 387
4 376
6 620
3 450
9 761 12 893 13 395 13 289
3 117 3 208 3 719 3 287
8 137 10 329
4 369 4 981
Source: By the authors, based on data of the Győr-Pér Airport.
Although the owners (the Municipality of Győr and Pér, and self-government of
Győr-Moson-Sopron county) decided to sell the airport in 2008, the primary objective is
the development of the facility, and the new owner is expected to continue the operation
of the airport (Győr Megyei Jogú Város Gazdasági… 2011).
Location decisions of companies may also be influenced by the rate of local
business tax. In the city of Győr the tax is 2% of the net income, which is the maximum
rate by the Law on Local Taxation. The municipality offers tax allowances of the local
235
business tax in different forms in order to attract businesses to the city. These tax
allowances are included in a municipality decree on local taxes. Formerly an allowance
of this kind was the exemption from taxation that new businesses locating in the Győr
industrial park were offered for the first two years of their existance. Exemptions of
enterprises from local taxation had to be cancelled by the city as of 1 January 2008, in
harmony with the Competition Act of the European Union.
The municipality introduced local business tax, property tax and tourism tax from
among the local taxes. A central tax above which the city disposes is motor vehicle tax.
As regards the volume of income, local business tax is the most significant (Table 2).
The revenues of the city from this source almost doubled from 2007 to 2008. This is
primarily due to the elimination of the tax exemptions of the businesses – Audi Hungária
Motor Kft. in the first place and other businesses located in the industrial park to a lesser
extent. The decline in the amount of local business tax in the two years afterwards,
however, reflects the financial and economic world crisis. An opposite tendency can be
seen in property tax, due to the enlargement of the tax base provided by the new
commercial facilities inaugurated in the respective period. The municipality expects its tax
revenues to increase in the middle run, but this is hindered by central regulations in the
field of local business tax: the central budget takes away a part of this revenue (Győr
Megyei Jogú Város Önkormányzatának Gazdasági Programja 2011–2014).
TABLE 2
Local taxes and motor vehicle tax revenues in Győr between 2007 and 2010,
million HUF
Local business tax
Property tax
Tourism tax
Motor vehicle tax
2007
2008
2009
2010
8,098.4
1,610.4
57.8
1,047.2
15,725.2
1,539.4
67.5
1,044.6
13,893.3
1,690.6
56.9
1,014.1
13,601.3
1,910.4
63.1
1,070.4
Source: by the authors, based on the Economic Programme of the Municipality of Győr 2011–2014.
Institutional frameworks
One element of the institutional framework of local economic development is the Győr
International Industrial Park Ltd. established in 1991. This company operates the industrial park. The ownership of Győr municipality has represenred 40% since the foundationof the company. Although the initial concept of the park was to offer a location for
small and medium-sized enterprises, from the first half of the 1990s it became more
attractive for foreign companies, because the financial situation of the small and mediumsized enterprises did not allow the implementation of new green-field investments. The
more intensive moving in of foreign companies started after 1995. The first time that a
Hungarian owned company bought a site in the park was in 1996, by now the Hungarian
businesses have outnumbered their international counterparts. The industrial park – which
236
was the first industrial park not only in Hungary but in the whole of Central Europe – was
awarded the title of “Industrial Park” in 1997. At that time it boasted of 11 companies and
1,000 employees (Deák 2002). Novadays the park is situated on a land of 175 hectares,
of which approximately 90% is utilised, according to data of 31 August 2011. Presently
a total of 101 businesses from no less than 14 countries operate here, the number of
employees reaches 5,000.
The majority of the businesses working in the industrial park are in the manufacturing
industry sector (Figure 1). The main economic branches include machinery, automotive
industry, electronics, plastic industry, commerce and logistics.
The investment worth 455 million € produces approximately 200 billion HUF
revenue annually (www.ipgyor.hu). Looking at the revenues of the businesses operating
in the park we can see that the incomes of the biggest ones exceed 3 or 4 billion HUF,
and there are many companies that have somewhat less income which is still above
1 billion HUF. The crisis had an impact on the industrial park, although even in the year
most affected by the crisis, 2009, the total revenues of the companies exceeded
122 billion HUF, and the number of employees was above 5,500. This number has
decreased by approximately 1,000 as an effect of the crisis, but is expected to rise to the
starting figure of 2009 again by the end of this year (Győr Megyei Jogú Város
Gazdasági… 2011).
FIGURE 1
Enterprises in the Győr Industrial Park on the grounds of sectors
Financial
Transportation, services; 1,2%
storage; 1,2%
Other public
and personal
services; 2,4%
Real estate and
producer
services;
19,5%
Manufacturing;
41,5%
Trade, repair;
26,8%
Construction;
4,9%
Energy-, gas-,
heating-, water
and waste
management;
2,4%
Source: Lakatos (2011, 39).
The presence of Audi Hungária Motor Kft. has an outstanding significance in both
the city and the industrial park. There are several companies among the businesses of
237
the industrial park that are suppliers to Audi. The new investment recently launched by
Audi will certainly have a significant impact on the Industrial Park in the future as well,
and also on the city of Győr, increasing the recognition and economic role of Győr both
in the region and Hungary. Although there is still 13.2 hectares of free land in the park,
the plans of the Győr International Industrial Park Ltd. include the 15–30 hectare
enlargement of the park, as a response to the development activity by Audi.
In the park there is a company called INNONET Innovation and Technology Centre,
established in 1997 as a result of the collaboration of several institutions and businesses,
supported by the Phare CBC programme of the European Union. The establishment of
this centre did not only increase the prestige of the park but also resulted in significant
cooperations among the international businesses of the park and the local small and
medium-sized enterprises. The centre operates as a non-for-profit company, its owners
are the Municipality of Győr, the Győr-Moson-Sopron County Chamber of Commerce
and Industry, the Hungarian Association for Innovation and the Universitas-Győr
Foundation. The Centre actually works as an incubator, its objective is the creation of
favourable conditions for innovative small and medium-sized enterprises. For the time
being the capacities of the centre are utilised in almost 100%, so its present capacities
do not allow the Centre to offer services for new businesses (http://www.ipgyor.hu/#).
The TECHNONET Automotive Industry Technology Competence Centre was opened
in September 2011 as an enlargement of the INNONET. In the implementation of the
project, the INNONET owners had a significant role, of special important among them
was the Municipality of Győr and the Győr-Moson-Sopron County Chamber of
Commerce and Industry that raised capital in order to create the TECHNONET. For the
implementation of the first phase of the project INNONET was given a 400 million
HUF non-refundable support which covered 80% of the total costs. In the first phase
offices, meeting rooms and joint service facilities were constructed, the second phase
was about the construction of four workshops for small enterprises. The objective of
TECHNONET is the promotion and support of the research and development activity of
businesses. The significance of the newly created institution lies in the provision of
advanced technology services besides a high level management support, as opposed to
INNONET that offers basic services (Ingatlan.net).
In cooperation with the British company United Biscuit that privatised the Győri
Kekszgyár (Győr Biscuit Factory), the city of Győr established the Business Assistance
Foundation, an organisation dealing with assistance for the creation of start-up
businesses and the development of small and medium-sized enterprises. The Foundation
started to operate in 1992, its objectives include the decrease of unemployment, the
improvement and enlargement of the skills, entrepreneurial capacities and knowledge of
the economic actors (Business Assistance Alapítvány bemutatkozás).
238
Innovation potential of Győr and knowledge transfer
Knowledge can be accumulated (Dőry 2005). The foundation of the development of
knowledge-based regions is given by the creation of knowledge, as well as its utilisation
in the economy. The scenes for the creation of knowledge can be research places,
research institutes, and higher education institutions (Dőry–Mészáros–Rechnitzer 1998).
The spread of knowledge is a formal and informal process during which the research
findings become known to the public. The spread of knowledge means the transfer of
knowledge mediated by the educational system, and the sales of knowledge products as
well. One way of the creation of knowledge is research and development, whose
objective may be enlargement of the knowledge or its utilisation for the development of
new applications. Knowledge becomes a socially useful thing by innovation, i.e. its
application in society and economy (Smahó 2008).
The production and utilisation of knowledge is the basis of the development and
renewal ability of settlements. A survey conducted in 2004 and 2005 analysed the
knowledge-based renewal capacity of 251 Hungarian towns and cities on the basis of
five groups of indicators: innovation, human resources, social activity, economic
development level, and schooling and management. The settlements were categorised
into 12 clusters, of which seven contains towns and cities with high renewal ability.
Győr, together with Székesfehérvár and Kecskemét, is in the third cluster called
’strong economic centres with emerging innovation potential’. According to the
findings of the research, the economic development level of these cities is high, their
innovation parameters are adequate, but their human resources potential is limited
(Csizmadia 2005; Rechnitzer–Csizmadia–Grosz 2004).
Research and development, and innovation can be seen as the primary determinants
of competitiveness. Both factors are extremely important in automotive industry, as this is
a sector with a very strong competition. As regards the region of Western Transdanubia,
the overwhelming majority of the research and development activity done by automotive
industry companies is realised in Győr-Moson-Sopron county (Table 3). Of the seven
automotive industry companies of the region that operated a research place in 2009, the
headquarters of six are in this county. Also, 90% of the staff doing academic activity
worked for a Győr-Moson-Sopron organisation in the respective year. Looking at the
regional level we can say that more than half of the expenditure of academic activities
was spent on automotive industry related purposes between 2005 and 2009. The total of
the budget of the businesses in this sector on research and development investments was
19 billion HUF, three-quarters of which can be linked to companies located in GyőrMoson-Sopron county.
239
TABLE 3
R&D expenditures in automotive industry between 2005 and 2009, million HUF
Specification
R&D costs
R&D investments
R&D expenditure
R&D in Western Transdanubia
From which: automotive industry
Within that:
Győr-Moson-Sopron county
Vas county
Zala county
Hungary (automotive industry)
31,064.7
16,997.9
3,542.3
1,968.3
34,607.0
18,966.2
12,872.0
4,125.9
–
52,229.9
1,673.6
294.7
–
8,476.4
14,545.6
4,420.6
–
60,706.2
Source: HCSO (2011, 47).
The increase of productivity can be promoted by research and development, but also
by innovation. Innovation activity is especially important in automotive industry,
because this is a market where there is a definitely high demand for products featuring
advanced technology. At regional level, the total expenditure of automotive industry
companies related to innovation exceeded 96 billion HUF in 2008 (HCSO 2011).
If we look at the innovation potential of Győr, we can see that in the physical plan of
the city the dynamism visible in the economy – growing output and productivity, high
level investments – are not harmonised by the innovation performance of the region. As
regards the city’s research and development and base and its role in the Hungarian
higher education, Győr lags behind and can only be taken as a second rank innovation
centre, although the actors and institutions that have innovation potential are present in
the city (Physical Plan of the City of Győr 2005).
We do believe that such institutions are the already mentioned INNONET,
TECHNONET Automotive Industry Technology Competence Centre, and also the
PANAC, the Széchenyi István University, the university’s Regional University
Knowledge Centre of Automotive Industry and Research Centre of Automotive Industry,
Electronics and Logistics Cooperations.
The decision on the foundation of the Pannon Automotive Cluster (Pannon
Autóipari Klaszter, PANAC) was established in December 2000. The concentration of
automotive industry in Northern Transdanubia offers an excellent opportunity for the
clustering of automotive industry. Active participants in the creation of the cluster were,
among others, the Western Transdanubian Regional Development Council and the automotive industry businesses of the area, such as Suzuki, Audi, Opel or Rába, but Széchenyi
István University also joined as a founding member. By 2008, the number of organisations
joining the cluster reached 95. The primary objective of this cluster, similarly to the other
clusters, is the preparation of the members, their enabling to become successfully
operating suppliers. The cost efficiency of the suppliers is also an interest of the
transnational companies of the region, because they can increase their cost advantages
thereby. The biggest obstacle of the creation of the supplier network with the participation
of Hungarian companies is that the majority of these businesses do not meet the require-
240
ments of the customers, so the primary suppliers of the transnational corporations are
usually foreign businesses.
The most important concrete objective of the cluster then is the promotion of the
modernisation of the capacities, and the technical development of the Hungarian
businesses, and also the improvement of the financial stability. Further goals include the
support of the internationalisation of the activity of the members and the creation of the
national automotive industry strategy. On the foundation of the cluster a survey was made
that tried to explore in which areas companies expected assistance. The responses revealed
that it is mostly the access to supports, preferential operational credits, partner search, and
the access to information in which members require most help from the cluster. The major
obstacle for the cluster in carrying out its activity is the lack of financial resources (Grosz
2005a).
The most prominent representatives of research and development in both the region
and Győr are higher education institutions. Győr accommodates the Apáczai Csere
János Faculty of the West Hungarian University, the Theological College of Győr and
the Széchenyi István University.
In the three faculties of the latter university (Kautz Gyula Faculty of Economics,
Faculty of Technical Sciences, and Deák Ferenc Faculty of State Sciences and Law) and
its Music Institute approximately 12 thousand students learn in 43 BA, five-year and
master’s courses and also in 13 higher level vocational trainings and 11 postgraduate
specialist training courses. The main focus of technical training is on automotive
industry, logistics and informatics. There is an opportunity for participating in PhD
training in three doctoral schools of the institute, in the field of regional and economic
sciences, technical sciences and law (SZE).
At the Széchenyi István University, R&D activity is done in organised frameworks,
with extended cooperations within the institute among the faculties, and also between
the academic and the business sector, contributing thereby to the improvement of the
competitiveness of the latter.
Between 2004 and 2006 a number of regional university knowledge centres and
cooperation centres were established in Hungary, with the aim of stimulating the
connections among the economic organisations, higher education institutions and
research institutes, on the one hand, and for the promotion of innovation and R & D, on
the other hand (www.nkth.gov.hu). At Széchenyi István University too these institutions
were founded, named Regional Knowledge Centre of Automotive Industry, and
Cooperation Research Centre of Automotive Industry, Electronics and Logistics.
The Regional University Knowledge Centre of Automotive Industry, created with
the support of the Pázmány Péter Programme in 2005, deals with the research of up-todate materials and technologies related to automotive industry, and the Centre is also
active in the featuring of new possibilities in mechanical constructions (JRET). The
foundation of the Centre took place with the participation, in addition to the University,
of Rába Futómű Kft., Borsodi Műhely Kft. and SAPU Bt. (now SMR Automotive
Mirror Technology Hungary Bt.). The research activity is done in joint business and
university research groups, alleviating thereby bilateral knowledge flow and researchers’
241
mobility. The knowledge centre sees as its mission the promotion of the innovation and
research and development activity of the economic organisations operating in Győr and
its technology region (Szilasi 2007).
The Cooperation Research Centre of Automotive Industry, Electronics and Logistics
started its operation in 2008 with the support of a tender of the Economic Development
Operational Programme. The businesses and departments participating in the project
actively cooperate in the area of automotive industry, informatics, electronics and
logistics in order to contribute by the high level implementation of joint R & D activity
to the increased efficiency of companies (http://jelkkk.sze.hu). The most important partners of the Centre include Audi Hungária Motor Kft., GM Powertrain Magyarország
Kft. and Magyar Suzuki Zrt. (Hungarian Suzuki Inc.), but of course there are many
other first and second level automotive industry suppliers such as Nemak Győr Alumíniumöntöde Kft., for example (Szilasi 2007).
The Audi Hungária Group of Automotive Engineering Departments operating at the
university was established in early 2012 for the coordination and promotion of the joint
research activity of Audi and the university.
In the joint financing of the university, Audi Hungária Motor Kft. and the Municipality of Győr a modern combustion engine laboratory was created, and the Research
Centre of Automotive Industry also started its operation in March 2011. On 1 January
2012 the Audi Hungária Group of Automotive Engineering Departments was founded,
consisting of three departments. The objectives of the creation of the group included the
further strengthening of the intensive presence of Audi in research activity, the further
development of practice-oriented training of engineers, and the strengthening of the
research and development potential related to automotive industry. The group of
departments deals with the development of materials and technologies that are applied
in the engines as well.
Last but not least we have to mention the secondary level institutions that also participate in the process of knowledge transfer. There are 30 secondary schools in Győr
for the time being, of which 23 are maintained by the municipal self-government. Audi
has regular cooperations with e.g. the Lukács Sándor vocational school of Mechatronics
and Mechanical Engineering.
Strategic planning and local economic development
strategy in Győr
The dominant or perhaps the most important factor in the success of local economic
development is the creation of the development strategy. Researches have pointed out
that the objective of such strategies is usually the strengthening and enlargement of the
local economy and capacities. Its point is in the harmonisation and reconciliation of the
different aspects and goals of the local actors. In this process it is not the so-called
allocative planning method that is applied; the ground is a kind of future vision. The
242
comparison of this vision with the reality, the present situation of the respective
territorial unit confronts us with the special problems of the area.
During the 1990s the municipal government of Győr has had a relatively moderate
strategic planning activity. The preparation of the new physical plan of the city started
at the time of the transition to the local governmental system. Part of the approximately
four-year planning process was the definition of a development strategy, in which a
special emphasis was on the support of developments promoting the development of the
university. As regard the economy, the main goal was the assistance of the development
of small and medium-sized enterprises, in which a key element was the development of
the Győr International Industrial Park. No economic development concept or strategy as
such was made in this period, with the exception of the tourism development strategy
approved by the general assembly in 1996.
The new decade, with the approach of the accession to the European Union and then the
actual accession in 2004 reinforced the strategic planning activity of the city. This first plan
was the creation or renewal physical plan in the first third of the decade. Of course it is an
internal need to supervise the physical plans at certain intervals, but in this case there was an
external determination as well. The process of the accession to the EU appreciated the role
of planning at national, territorial (regional, county and micro-regional) and local level. A
precondition for the acquisition of EU development resources is the presence of adequate
plans. At local level this required fresh and valid physical plans for the development that
required space. Accordingly, all municipalities were obliged to renew their physical plans
until the end of 2002. This deadline was put one year later by the Parliament, on the initiative
of a representative from Győr.
The regulation of the content requirements of the regional development and physical
plans (1998) ordered urban development concepts to be the first chapters in the physical
plans of the settlements, so the preparation of this document was the first of the strategic
plans in the first third of the decade. A characteristic feature of this strategy was the
direct inclusion of the local society into the strategy making process, in the framework
of “Future workshop” meetings organised in the 23 districts of Győr. Simultaneously
the city also sensed that its development track should be planned together with its
hinterland, the agglomeration of Győr.
Thinking about the future in the local community was also promoted by the fact that
it became known that a Hungarian city would be the European Capital of Culture
(ECC) in 2010. This opportunity activated the majority of the big cities in Hungary.
Győr also made its ECC concept, formulated during intensive professional consultations
with the potential stakeholders. In this dialogue not only the cultural actors of the city
(institutions and artists) were involved but also the representatives of the economy, the
university and the civic sector. The concept was not concentrated on the possible cultural
events of the year 2010 exclusively but on the linking of culture with the hundred year old
industrial traditions and modernisation of the city. In this a key role was played by
innovation and knowledge represented by the automotive industry, as generator, sponsor
and consumer of culture.
243
The national planning process founding the 2007–2013 Union programming period
started in 2004–2005. The New Hungary Development Plan had a strong focus on the
growth pole theory of regional economics and its French practice. The government defined
the circle of those cities that, as selected development poles of the programme, would pull
their regions with themselves as a result of the innovation and economic development
implemented in them. Győr was in this circle too, so the city had to work out its own pole
strategy. Each city had to concentrate on their own defined pull sectors, which in Győr was
evidently automotive industry, so the pole of Győr was named “Autopolis”.
This concept is of special importance because its final version is a definite local
development strategy. The city was given government subsidy for the planning process
that was done in a cooperation of the city, the university and the major actors of the
local economy (chamber, Győr International Industrial Park, Innonet, PANAC). The
strategy was focused on the development of economic infrastructure, with a special
emphasis on automotive industry. From the resources provided by the government not
only a general strategy was made; in the framework of the strategy the respective actors
defined their key projects and prepared the necessary planning documentation for them
(Table 4).
TABLE 4
Planned resource and cost structure of the projects of the Pole Programme
between 2007 and 2013 (indicative list)
Name of key project
Resources total
(in million HUF)
Planned date of
implementation
Actual date of
implementation
Széchenyi István University:
“New Knowledge-Space” Building
3000
2007–2009
2011
Széchenyi István University: “INNOSHARE Regional Information
Transfer Centre”
2500
2007–2009
2011
TECHNONET Automotive Industry
Technology Competence Centre I.
1000
2007–2009
2011
TECHNONET Automotive Industry
Technology Competence Centre II.
300
2009–2011
2011
Source: by the authors on the basis of the Economic Programme of the City of Győr 2006–2010
(2006).
For the coordination of implementation, a pole management organisation had to be
set up, which was founded as a 100% municipality-owned company. The managing
body of the programme is the Győr Development Policy Coordination Task Force that
looks at the future vision of the city and the development ideas of the Széchenyi István
University, and defines those factors on which the future of the pole can be built. These
are as follows:
244
− vehicle manufacturing;
− enlargement of the suppliers and logistics capacities;
− increased use of renewable energies.
The objective then is the increase of international competitiveness by the development
of vehicle manufacturing, the development of the suppliers and logistics capacities and the
utilisation of renewable energies. The future vision of Győr is to become the regional
centre of innovation, on the basis of knowledge and technical inovations as a development
pole of Western Transdanubia. The strategy stated that the long-term development of the
city is jeopardised by the inadequate quality of human resources, and quantitative
problems can also be seen in some segments. As regards higher education, a definite effort
has to be made for the support of the launch and development of trainings that promote the
dynamic operation of the economy. Those research institutes and laboratories must be
established and supported that promote economic growth.
After these innovation-oriented developments, Győr may become the city with the
strongest economy in the region and may also be suitable for joining researches and
developments of European significance. The industrial and service activities having
higher added value induce the appearance of spillover effects in other settlements of the
region as well. The development of vocational training – by which we mean both
secondary school and university and adult education – is indispensable for Győr to
satisfy the needs of the economy and for modern industries to settle down in the city.
Also, closer ties should be built with the businesses and the organisations dominant on
the labour market (Győr Megyei Jogú Város Fejlesztési… 2006).
The pole programme as a selected governmental economic development programme
of the New Hungary Development Plan “faded away” after the inner reshuffling of the
government. However, the plans have not been made in vain. By now, all projects
specified in the Autopolis strategy have been implemented by the support of the
Economic Development Operational Programme and the Western Transdanubian
Operational Programme (see Table 4).
This process was assisted by the fact that the region of Western Transdanubia, as the
only region, linked the planning of the regional operational programme for the 2007–
2013 period with a regional plan package made on partnership basis. In the framework
of this the comprehensive development programme of the region was made not only for
the planning period but also long term development concepts (until 2020) for the region
and the constituent three counties and the cities of the region were made, together with a
middle-term programme for the Structural Funds period. In the economic chapters of
these documents one finds the development ideas specified in the Autopolis strategy.
After the launch of the programming period, big cities were obliged to make
integrated urban development strategies (IUDS) for the implementation of projects
renewing the (inner) cities with the support of the regional operational programme. The
government provided the cities with single planning methodology and a list of the
necessary content for the making of the IUDS. The planning of the IUDS in Győr took
245
place with the cooperation of professional groups, and the plans that had been made for
the city in the previous years were also taken into consideration.
The making of the most recent initiative called Local Agenda 21 of Győr was also
connected to the implementation of an urban project. The respective document redefined
the development of the city alongside the principles of sustainable development. In the
framework of this, as the first step, professional groups by sectoral breakdown looked at in
the autumn of 2010 all comprehensive and sectoral concepts and programmes that had
been made in Győr in the previous ten years. The screening of these approximately fifty
documents in a single methodology – also taking sustainability requirements into consideration – was the foundation of a community based urban strategy that would have
served as a guideline for the 2014–2020 programming period or the development of the
city and the local economy. Unfortunately the planning process stopped after the
approval of the recommendations made on the basis of the assessment of the plans by
the general assembly in December 2010.
Győr was one of the “smart cities” assessed by the staff of the West Hungarian
Research Institute of the Centre for Regional Studies, Hungarian Academy of Sciences in
2011. Smart of liveable cities are settlements able to use available technological
possibilities in an innovative way, contributing thereby to the creation of a more
liveable urban environment. During the survey, the performance of nine Hungarian cities
– Debrecen, Győr, Kőszeg, Miskolc, Pécs, Szeged, Székesfehérvár, Tatabánya and
Veszprém – was analysed, by seven dimensions, sub-systems. Győr had an outstanding
performance in the business and communication subsystem (Figure 2).
Figure 2
Performance of Győr compared to the best practice
Source: Horváthné Barsi–Lados (2011, 9).
Within the subsystem ‘business life’ those indices and indicators were analysed that
determine the business environment of a city; these relate to the quantity of businesses,
246
their innovation capacity and their performance shown in the application of information
and communication technologies. In the subsystem ‘communication’ the presence or
lack of information and communication technology and its quality is looked at.
On the whole we can say that Győr had a very good performance in all subsystems,
although it only ranked fifth as regards the indices measuring innovation performance.
The research findings revealed that Győr has excellent economic and business
environment and circumstances but its innovation performance is weaker, it lags behind
the other cities in R & D capacity. Nevertheless the economic competitiveness and the
industrial potential of Győr make it the second big city of Hungary, after the capital city
(Horváthné Barsi–Lados 2011).
The strategic objectives defined by the strategic programme of the city, the middleterm integrated urban development strategy and the Local Agenda 21 programme, by
which the future of Győr must be secured, are as follows: Győr should
− allow inhabitants to have outstandingly high conditions of living;
− continuously renew its economy in order to increase its competitiveness;
− enhance its role in the region.
The respective development directions are the following:
−
−
−
−
Priority 1: human resources development;
Priority 2: working and developing economy;
Priority 3: development of urban services;
Priority 4: protection of the environment.
On the basis of the strategic plan of the city, the ultimate goal is to make Győr –
winner in 2010 of the title Hungarian City of Culture, the Most Sporting City, Senior
Friendly Municipality, in 2011 the title Bicycle Friendly City – a modern city with
modern economy, the centre of the region (Győr Megyei Jogú Város Stratégiai... 2003,
HHP Contact Tanácsadó KFt. – Győri Építész Műhely et al. 2008).
This is also expressed by the following slogan: Health, culture, innovation! The
future is being built in Győr!
Summary
Győr – as a result of the development path the city has trailed in the two decades following the systemic change – is one of the strongest economic poles in Hungary now.
The dominant factor in this economic development is automotive industry. Among the
automotive industry centres of Hungary, Győr – the second big city in Hungary after
Budapest in economic competitiveness and industrial potential – has number one position. Its geographical location and transportation endowments are excellent, infrastructure
is adequately built out, the city is open to receive investors and promote their settling
down; it continuously develops and influences those location factors that may play a significant role in the locations decisions of the businesses.
247
Győr, in our opinion, has implemented in the last decade an extremely intensive and
conscious urban development in which automotive industry was taken as the flagship
industry of the development of the city. In the framework of this activity, an economic
development strategy was made as well. It is an example to be followed that during the
respective planning works the municipality tried to address the communities and the
dominant stakeholders. It is good that there are overlaps of the actors in several
planning works, as this way there were no repeated breaks in the major development
directions of the various documents. In these documents, the development of the local
economy was always a priority, as this is the sector that is capable of producing the
incomes that the city can spend on welfare measures and the improvement of the quality
of life.
References
Angyal Á. (2003) A hálózatok, mint többközpontú szervezetek. Vezetéstudomány. 3–4. pp. 76–
87.
Bajmócy Z. (2011) Bevezetés a helyi gazdaságfejlesztésbe. Jate Press, Szeged.
Business Assistance Alapítvány bemutatkozás. http://www.kva.hu/index.php?tid=277703132180
6266&qs_tr=f104&menuitem=0 [20 November 2011].
Cégvezetés: http://cegvezetes.hu/1998/04/penzhez-lehet-jutni/ [20 November 2011].
Czene Zs. – Ritz J. (2010) Helyi gazdaságfejlesztés. Ötletadó megoldások, jó gyakorlatok. NFM,
NGM, VÁTI Nonprofit Kft., Budapest.
Csizmadia Z. (2005) A magyar városhálózat innovációs potenciálja. In: Grosz A. – Rechnitzer J.
(szerk.) Régiók és nagyvárosok innovációs potenciálja Magyarországon. MTA Regionális
Kutatások Központja, Pécs-Győr.
Deák Sz. (2002) A hazai ipari parkok és a betelepült vállalatok jellemzői. – Buzás N. – Lengyel I.
(szerk.) Ipari parkok fejlődési lehetőségei: regionális gazdaságfejlesztés, innovációs folyamatok és klaszterek. SZTE GTK, JATEPress, Szeged. 175–200. o.
Dőry T. – Mészáros R. – Rechnitzer J. (1998) Tudomány és regionalitás Magyarországon a 90-es
években. Tér és Társadalom. 3. pp. 105–127.
Dőry T. (2005) Regionális innováció-politika. Kihívások az Európai Unióban és Magyarorszá–
gon. Dialóg Campus Kiadó, Budapest–Pécs.
Dusza A. (2003) A birodalom végnapjai. X. Meditor Kft., Győr.
Faragó L. (2001) SWOT-elemzés a területi stratégiák kialakításának folyamatában. Falu-VárosRégió. 6. pp. 3–5.
Farkas P. (2005) Egymásba kapaszkodva: település- és közösségfejlesztés a globalizáció korában.
ĽHarmattan Kiadó, Budapest.
G. Fekete É. (2005) Vidékpolitika. Miskolci Egyetem Világ- és Regionális Gazdaságtan Intézet,
Miskolc.
Grosz A. (2005a) Klaszteresedési folyamatok Magyarországon – különös tekintettel az
autóiparra. Pécsi Tudományegyetem Közgazdaságtudományi Kara, Regionális Politika és
Gazdaságtan Doktori Iskola. Évkönyv 2004–2005.
Grosz A. (2005b) A közép-európai térség autóipari beruházásainak telephely választási
motivációi és piaci kapcsolatai. Kézirat, Győr.
Győr Ipari Park: http://www.ipgyor.hu/# [22 November 2011].
Győr Megyei Jogú Város Fejlesztési Pólus Stratégiája. Győr Megyei Jogú Város Polgármesteri
Hivatal Városépítési Iroda Stratégiai Tervezési Csoport (2006. január 31.) Győr.
Győr Megyei Jogú Város Gazdasági Programja 2006–2010. (2006) Győr.
248
Győr Megyei Jogú Város Gazdasági Programja 2011–2014. (2011) Győr.
Győr Megyei Jogú Város Középtávú Integrált Városfejlesztési Stratégiája. (2008) HHP Contact
Tanácsadó KFt. – Győri Építész Műhely – Győri Mérnök Műhely – MTA RKK Nyugatmagyarországi Tudományos Intézete, Győr.
Győr Megyei Jogú Város Stratégiai Programja. (2003) Győr.
Győr Város Rendezési Terve. (2005) Győr.
Győr-Pér Airport: http://www.gyor-perairport.hu/passenger/ [20 November 2011].
Horváth Gy. (1998) Európai regionális politika. Dialóg Campus, Budapest-Pécs.
Horváthné Barsi B. – Lados M. (2011) „Smart Cities” tanulmány. MTA RKK NYUTI, Győr.
Ingatlan.net: http://ingatlan.net/magazin/2011/09/atadtak-a-technonet-kozpontjat-a-gyori-ipariparkban/ [22 November 2011].
A járműipar helyzete és szerepe a Nyugat-Dunántúlon. (2011) Központi Statisztikai Hivatal, Budapest.
Járműipari Regionális Egyetemi Tudásközpont (JRET): http://jret.sze.hu/index.php?option=com_
content&task=view&id=35&Itemid=67 [25 November 2011].
JEL-KKK 2008–2011: http://jelkkk.sze.hu/ [25 November 2011].
Lakatos L. (2011) Egy létesíteni tervezett Üzleti Innovációs Központ potenciális helyszíneinek
elemzése a Nyugat-dunántúli régióban. Első megvalósíthatósági tanulmány. Pannon Projekt
Kft.
Lengyel I. (2002) A regionális gazdaság- és vállalkozásfejlesztés alapvető szempontjai. – Buzás
N. – Lengyel I. (szerk.) Ipari parkok fejlődési lehetőségei. Regionális gazdaságfejlesztés,
innovációs folyamatok és klaszterek. Jate Press, Szeged. pp. 24–54.
Lengyel I. (2010) Regionális gazdaságfejlesztés. Versenyképesség, klaszterek és alulról szerveződő stratégiák. Akadémiai Kiadó, Budapest.
Mezei C. (2006) Helyi gazdaságfejlesztés Közép-Kelet-Európában. – Tér és Társadalom. 3. pp.
95–108.
Mónus Á. (ed.) (2007) Zöldmezős sikertörténet – 1992–2007. Győri Nemzetközi Ipari Park Kft.,
Győr.
Pálné Kovács I. (1999) Regionális politika és közigazgatás. Dialóg Campus Kiadó, Pécs.
Rechnitzer J. – Csizmadia Z. – Grosz A. (2004) A magyar városhálózat tudás alapú megújító képessége az ezredfordulón. – Tér és Társadalom. 2. pp.117–156.
Reisinger A. (2010) Társadalmi részvétel a helyi fejlesztéspolitikában – különös tekintettel a
civil/nonprofit szervezetek szerepére. Doktori értekezés. Győr.
Smahó M. (2008) A tudás és a regionális fejlődés összefüggései. Doktori értekezés. Győr.
Széchenyi István Egyetem (SZE): http://felveteli.sze.hu/catview/index/id/3230/m/636 [2012.
január 10.].
Szilasi T. (2007) Széchenyi István Egyetem és a vállalati szféra kapcsolata. In: Lados M. –
Rechnitzer J. (szerk.) Egyetem a régióért. MTA Regyionális Kutatások Központja, Pécs–
Győr.
PART III.
CHARACTERISTICS OF THE SUPPLIER
NETWORK
FEATURES AND SPATIAL DIFFERENTIATION OF
SUPPLIER NETWORKS IN AUTOMOTIVE INDUSTRY
ZOLTÁN CSIZMADIA
Keywords:
supply chain supplier networks intercompany co-operations
The objective of this study is to examine the fundamental features and spatial differentiation of
automotive industrial supplier networks. Relying on former researches conducted in Hungary, the
goal is to give an overall presentation of the fundamental peculiarity which suggests that the
supplier networks are not deep, not complex, the forms of cooperation beyond the supply and
ordering relations are missing, the chance to achieve the direct Tier 1 supplier position is not
much and all in all a still limited functionality characterises these inter-organisational
cooperation channels in Hungary. Our findings confirm the former experiences, however, at certain
points some changes took place. The characteristics of cooperation are summarised along three
issues: 1) fundamental features of supplier and customer relations (the profile of the system of
relationships), 2) special differentiation potentials, 3) and the effects of economic-organisational
facilities shaping the relationships in the network.
Introduction
The comprehensive objective of this research is to map the embedding of undertakings –
that are situated in the two examined regions, and that are predominantly related to
automobile manufacturing – into the economic field of the area concerned and into the
broadly defined region; their cooperation with the available economic capacities; the
networking processes that have taken place hitherto and the potentials through the
empiric analysis thereof based on the national survey of intercompany supplier and
customer relations.
The sub-analysis presented here primarily aims to give the possibly most complex
description of the cooperation between suppliers and customers (another study deals
with the external relations of other direction), the identification of the influencing
factors and collect the peculiarities of spatial, temporal, and content/functional
characteristics. Considering the fact that the sample composed of 118 elements includes
companies 94.5% of which take the positions of both the supplier and customer, this
survey provides appropriate grounds for the descriptive analysis of the properties
characterising both relationship types.
According to our research methodological model, an undertaking may have relations
oriented to at least three directions in organisational space: to the potential suppliers
and customers, to the players of development, support, and innovation institutional
system in the form of other external relations (Figure 1). The analysis of the relation-
252
Zoltán Csizmadia
ships extends, in addition to occurrence and quantitative parameters, to essential
properties, such as the factors influencing the establishment of the relationship, the
territorial location of partners, the period of time, the firmness of the relationship, the
exclusiveness of relationship, the occurrence of assisting/supporting and the research/
development aspects.
Besides the mapping of network parameters, as a second very important target, we
also pay attention to examining along what peculiarities do supplier-networks show any
special regional differentiation, i.e. in what sense are supplier and customer networks of
undertakings – performing business activities in the Northern Transdanubian region –
different and in what respect do the networks show individual features. Finally, at the
level of correlations, the roles of other, non-territorial based (economic-organisational)
factors influencing the networks are reviewed.
FIGURE 1
Directions of the analysis – network types and potential influencing factors
Relationship with the suppliers
− Does it have suppliers?
− Number of suppliers
− Reasons
− Volume of purchases
− Spatial location
− Term
− Type of relationship
− Assistance
− R&D cooperation
− Other types of cooperation
Suppliers
Other external relationships
− Importance of horizontal
relationships
− Direction of relationships
− Types
− Intensity
− Purpose of relationships
− Cluster membership
Company
Relationship with the customers
− Who they supply to?
− Complexity of the sphere of
customers
− Spatial location
− Reasons
− Exclusiveness
− Term
− Type of relationship
− Forms of cooperation
− Assistance
Customers
Factors influencing relationships
Year of incorporation – Plants – Ownership structure – Number of employees – Sales revenues – Since
when it is a supplier? – Main product supplied – Proportion of supply activities in sales revenues – Number of
product lines – Proportion of sales revenues deriving from the largest customers – Exclusiveness – R&D –
Knowledge flow motivation – Future prospects – Strategy – Export – Market position
Source: Own draft.
Features and Spatial Differentiation of Supplier Networks in Automotive Industry
253
Some fundamental concepts
Positioning of automotive industrial companies often takes place presuming a complex
relationship network, sometimes even seeming confusingly complicated. Dual situation
is presumed, that is, the companies may concurrently take supplier and customer
positions, integrating at different levels into the comprehensive structure of supplier
systems/pyramids/networks. It is worth clarifying briefly the major points in the
theoretical/ conceptual issues of supplier networks, before kicking out to present what was
measured and how in the empiric part of the sub-research. The comprehensive study
prepared by Andrea Gelei can be a good starting point to this task, which provides a
detailed overview of the key concepts basing upon the international literature (Gelei
2008).
In the majority of cases suppliers are members of complex business networks.
Business network is a structure, in which a number of nodes (these are the individual
business units, e.g. manufacturing companies, customers, logistical or financial service
providers, research centres, etc.) are interlinked through several connecting ties having
special contents (these are the actual inter-organisational relations). Thus the networks
are not merely conceptual units, but also the organised patterns actually observable in
the interactions between the cooperating partners (Gelei 2008, 4). Due to their
complexity business networks can be disintegrated into certain components. The most
commonly spread distinguishing of the components is internal and external business
network. Internal business network comprises on the one hand the internal structural
units that are predominant, play central roles, and typically owned by the parent
company in a multinational corporation, on the other hand the system of relationships
existing among such units. External business network is the conglomeration of
suppliers, salespersons, and other independent organisations as well as their relations
forming around a company or corporate group. In this sense, in the framework of this
research we attempt to reveal, classify and compare the links or bonds constituting the
structures of external business network.
In another approach, the specific patterns of any such economic interactions may be
considered as supply chains, supply networks, which are deemed as a sequence of value
creating processes required for the creation of a product or service package and
connecting several cooperating organisations that eventually produce a product or
service suitable for satisfying the customer’s needs and requirements. In this case a
function and process oriented approach is discussed, and the focus is on the operating
and direct functions of the network instead of the organisation thereof.
The business, supply networks defined above may also be considered as peculiar
organisational model. According to Gelei, the vertically integrated company operation
model and network operation model can be distinguished (Gelei 2008, 10). The essence
of the vertically integrated model is that organisations endeavour to keep the activities
fundamentally defining competitiveness in-house, the importance of partnership
relations is minor, dependence on cooperating partners is low, cooperation based
relations are for a short term only and partners can be relatively easily replaced. On the
254
Zoltán Csizmadia
contrary, in the network operation model the emphasis is placed on the fact that the
entire supply chain, also the activities being dominant in terms of the central company’s
competitiveness are carried out outside the boundaries of the company, thus partnership
relations become critical factors influencing success, those involved in such cooperations highly depend on one another, and typically powerful, stable and long term
relationships are formed.
Major results of former Hungarian research
In the recent years the researches on supplier relationships and complex supplier
networks have been more and more upgraded, and the issues regarding these often come
up also in economy development and innovation policy related discourses and
documents. On the basis of some lately published research and development support
materials a relatively comprehensive picture may be drawn about the current status of
domestic supplier networks and their development trends.
It is by and large commonly agreed in the Hungarian literature that the construction,
operating and extension of the supplier networks are essential issues regarding the
modernisation and long term development of economy (Gém–Mikesy–Szabó 2011). In the
past two decades Central and Eastern European region has become the “foreign
multinational machine and automobile manufacturers’ systematic target area in terms of
relocation and it has built in the supplier pyramid. The competitive advantage of this
region primarily derives from its cheap, flexibly employable, motivated labour force and
fast access to the major markets” (A magyar kis és középvállalatok supplieri szerepének...2011, 4). The study emphasises that the development of the supplier network in
Hungary is in excess of the regional average, but compared to West-European standard it
is lagging, the multinational companies that settled down in the country, as well as the
Tier 1 suppliers serving them have formed a supplier pyramid composed of at least three
levels. The main problem is that the majority of primary suppliers are affiliated firms of
foreign companies, thus for domestic undertakings realistically only the secondary or even
lower level supplier statuses are available.
The characteristics of supplier networks cannot be comprehended without knowing
the motivational background. What advantages may derive from supplier activities, or
from the integration into the supplier networks? The most important advantages are the
following:
− direct impact on financial results,
− customers placing orders for high volumes, paying correctly, being ready to
provide also technical assistance,
− better performance on the market,
− improvement of effectiveness,
− conquering new markets,
− launching or extending international activities,
255
− making the business activities more predictable,
− access to new knowledge, competence, relationships,
− flow of manufacturing cultures and network effects in local economy.
Gelei and Nagy (2004), relying on the international specialised literature (Walter et al
2001) seized the issue of motivation along the direct and indirect value dimensions of
supplier networks. Direct value dimensions include profit (direct profitability attributable to
a particular consumer), the quantitative (scale of volume generated by a particular consumer)
and the security (orders guaranteed for considerably long term) dimensions. Parallel with
these, four types of indirect value dimensions can also be distinguished: innovational
(innovation is resulted from the cooperation with the customer), market (cooperation and
references may bring forth new market opportunities, new orders), exploratory
(informational proceeds of cooperation) and access (access to other important role players
owing to the cooperation). All these suggest that supplier networks may represent significant
values and potentials for each company, although the probability of the development of any
such relationships depends on the interplay of many a factor.
According to the latest research results first a summary is given on what we know
about domestic industrial supplier networks (Gém–Mikesy–Szabó 2011), then it is
examined what peculiarities such co-operation have in relation to automotive industry
(A magyar kis- és középvállaltok supplieri szerepének... 2011).
The “profoundness” (complexity) of domestic industrial supplier networks is low,
and those involved focus fundamentally on the supply of only one or only few products.
A further problem is that the rate of occurrence of cooperation forms beyond supply is
very low: only 11 per cent of domestic industrial companies provide, or contract for
additional services, and only in the case of 7% can other common activities also be
observed beyond the supplies (e.g. research-development, collective tenders, etc.), and
only in the case of 3% cross supplier networks even the state borders. In conventional,
vertically integrated supplier networks relationships are much more tied, and are
restricted in general on the supply of a sole product. SMEs having horizontal sectoral
relationships perform more complex supplier activities with higher chances. The
customer large companies endeavour to establish close relationships with their suppliers
working in the same sector, however, their relationship is restricted only to product
supply, while with their SME partners working in different sectors the create strategic
relationships. Supporting the suppliers is not even, but quite a commonly accepted
practice (only 24% of the industrial customers did not provided any support for their
suppliers), and the larger a company was the more chance it had that the company
granted support to the suppliers. The three most common supplier support type is
quality assurance, logistics and technological support. Foreign large corporations use
more “sophisticated” means of supporting which have long term influence on the
relationships (advising on financial support, management and organisation).
Among the factors influencing the forming of supplier networks, according to the
surveys, the first three places were taken by personal acquaintance, former business
relations and references, as well as recommendations by business partners. Supplier
256
Zoltán Csizmadia
networks are extending constantly (78% of the companies in the industrial sector plan
future extensions) with different interests in the background. The suppliers target the
reinforcement of their market positions and the acquisition of new markets, while the
customers wish to use the extending company relationships to achieve cost reduction
primarily. The most essential inhibiting factor impeding the extension of supplier
networks is the low capability to conform to quality requirements, and it is also a
problem that there are only few Hungarian suppliers providing product related services.
The suppliers declared the too low prices and rigidness of the existing supplier networks
as the most serious problems that they face. The latest survey carried out among
companies also highlights that the companies share the opinion, that “market incentives
(such as tax allowances) and not regulatory means (e.g. the specification of supplier
quotas) shall be applied to promote deepening and establishment of company relations”
(Gém–Mikesy–Szabó 2011, 19).
As regards the Western Transdanubian region there is another essential finding of
the survey. In accordance with the assessment of market achievements and macro–
economic processes, the schemes for investments and the investment of resources, as
well as the supplier’s proportional share the performance of Hungarian SMEs engaged
in supply activities was classified by cluster analysis. Four groups were set up: passive
winners (14.7%), the successful ambitious (28.7%), zealous strugglers (33.3%), the
hopeless (23.3%). In the first two groups comprising the most competitive companies
the firms engaged in machine engineering (46%), the middle sized enterprises and the
“West-Hungarian companies being able to exploit any external economic boom or
upturn due to their favourable geographical situation” (32%) are all over-represented.
Furthermore, among the successful suppliers the cross-sectoral, more profound, and
more complex supplier networks, more significant assistance provided by the customer,
less impeding or inhibiting factors are more typical, and merely the strengthening of
industrial sectoral clusters can further enforce the supplier networks (Gém–Mikesy–
Szabó 2011, 61–62).
The survey referred to above (A magyar kis- és középvállalatok supplieri szerepé–
nek... 2011) and addressing the strategy based development of machine and automotive
industrial sectors, at several points refines and tinges the overall view depicted in the
foregoing. Estimated according to the West-European standard the automobile industrial
supplier network in this region may be considered to be lagging, moreover some 90% of
the products manufactured by automotive industrial suppliers – according to estimations
made in this industrial sector – goes to export. Low is the number and proportion of
supplier companies and in the views of those excluded the suppliers compose a “closed
elite club”. It is only in extraordinary cases, that at present, a company manages to
become Tier 1 automotive industrial supplier, moreover, Hungarian companies are rare
among them, and in the near future, Hungarian companies can not achieve integrator
positions either. They have the potential only to get Tier 2 or lower supplier positions.
Those highly liquid middle sized enterprises having free capacities have the best
chances, which have already proven “their profess–sionnal and organisational merits
257
and values” and their scope of activities also conforms to actual customer demands (A
magyar kis- és középvállalatok supplieri szerepének... 2011, 8–14).
By today, a “vertical supplier network comprising at least three hierarchically const–
ructed levels have developed in machine and automotive industry”, in which the second
level (Tier 2) suppliers mean the “load bearing backbone” of the whole supplier
pyramid. Long term partnerships here depend primarily on the supplier’s technological
and R&D capabilities, the personnel conditions, and the distance from the customer.
Keeping pace with the ever changing and increasingly higher requirements of the
customers is a key factor in sustaining the supplier networks. The following list
represents with appropriate accuracy the group of factors that may have influence
efficient supplying:
−
−
−
−
−
−
−
−
−
−
−
−
−
−
ability to manufacture technically more complex products,
adaptation of the customer’s production documents,
production related development,
improve the effectiveness of production technology,
product development upon the customer’s request,
mutual product design,
management of the supply chain,
ability to sell the engineering knowledge, the development results, intellectual
work,
strategy construction, improvement strategy,
qualified staff, team of engineers, the standard of management,
technical and economic management communication, data provision, language
command, accessibility,
trust and social capital of the firm
membership in professional associations, networks and clusters,
geographical or at least temporal closeness, favourable transport infrastructure.
In relation to the problems in co-operation between the supplier systems the study
underlines two main possible reasons. For domestic companies the most serious obstacle in
achieving the supplier position is the difficulty to fulfil the quantities expected by the
customers and to create the capacities necessary for this, as well as to satisfy the constant
development demands. The authors of the study made an obvious recommendation to
resolve the foregoing problems, namely that the actors of the split supplier market should
cooperate more, or through mergers or acquisition they should establish larger companies.
The cluster should be the special form of the co-operation built upon this loose collaboration
(A magyar kis- és középvállalatok supplieri szerepének... 2011, 25).
The above mentioned very critical assessing analyses also cast light on several
problems and breakthrough points, and specified the reference system of our own
assessment, in which the results revealed in the special pattern of supplier companies
focused on the research sector become comparable and may be interpreted in a broader
circle.
258
Zoltán Csizmadia
Fundamental nature of supplier and customer networks
The majority of the undertakings involved in the survey (94%) take both supplier and
customer positions in certain supply chains and networks. Consequently the fundamental
nature of their supplier and customer relations is practical to be discusses next to one another
but still distinguished. In the following two summarising tables (Table 1 and Table 2)
essentially the major features of inter-organisational relations with such functions are
included and the substantial descriptive networking parameters adhering to the supplier and
customer positions are set in focus. In the first step, the profile of the two relationship types
is depicted on the basis of some distribution figures.
The majority, 94%, of the supplier companies, that we could contact, are also
present in the network as customers. This means basically different network strategic
position and function for them.
The rate of suppliers supplying the car factories directly (Tier 1 suppliers) is surprisingly high (56%), and also the proportion of those supplying to tier 1 suppliers (tier
2 suppliers) is also high (68%). The majority of companies involved in the survey are
really ‘close to the fire’, and take prominently important positions in the deep structure
of the supply chains due to the shortness of the distance from the manufacturing company in the network.
For the majority of suppliers the automotive industrial supply activities is the most
important channel of economic transactions (with a sales revenue ration of 75%), and a
remarkable proportion of their sales (on the average 50%) derives from the business
deals with a sole (their largest) customer, which suggests significant dependence on the
network and system of relations (Table 1). Supplier networks are not territorially, or
spatially concentrated, and the proportion of EU and domestic customers is also
remarkable. By and large one-third of them have only domestic relations and the other
one-third has only international relations. One-third of them are in supplier positions
exclusively. It is impossible to attribute any typical feature to contracts behind supplier
networks on the basis of their term, but it is obvious that, in general, the co-operation
contracts are concluded for a term at least or even exceeding three years.
We may not speak about a complex motivation and success factor system.
According to the majority of the companies behind the orders good quality (80%),
reasonable price levels (74%) and the suppliers’ capabilities and capacities (60%) are
guaranteed. Entrance to the network, remaining and movements in it depend basically
and primarily on these three factors.
The majority of companies consider the supplier networks established with their
customers as a relationship with long term prospects built upon framework orders,
which is reinforced by the peculiarities of the formerly experienced concentration of
financial resources assigned to the channels of interaction (proportion within the sales
revenues, weight in sales), and the features of the consequential and potential
dependence and interdependence. At the majority of companies supporting activities are
also associated with the formal, relatively long term, often exclusive or prominent
supplier positions. Customers/Clients are, in general, the most willing to provide
259
assistance in the field of quality assurance and technical support. On the average a
typical supplier may count on any support or assistance provided by the customer in two
fields. According to expectations, the frequency of activities going beyond the supplier
relations is low in the networks (although it is higher than shown by national surveys
performed earlier). Merely 36% of the undertakings mentioned any co-operation with
the customers also in other fields. With the highest probability co-operative research
and development (20%), or collective purchasing (13%) occurs.
TABLE 1
Characteristics of supplier networks
Characteristics
Proportion of automotive industrial supplier activities in sales revenues
is over 75%
Average proportion of the largest customer’s share in the total sales
N
%
62
53
48
Supplies EU companies
Supplies domestic companies
Supplies only domestic companies
Supplies only EU companies
82
76
35
41
70
65
30
35
Supplies automobile factories directly
Supplies Tier 1 suppliers
Supplies Tier 2 suppliers
Supplies only one tier
Surely exclusive supplier
Surely not exclusive supplier
65
79
46
61
39
46
56
68
40
53
34
40
Typical term of supply contracts with customers – longer than 3 years
One year
Less than 1 year
Assessment of the relationship – long term, perspective relationship built upon
framework contracts
Relationship built upon strategic alliances
39
27
23
35
25
21
61
53
34
29
Support to suppliers – received assistance from the customer, yes
Quality assistance related support
Technical assistance
On the average how many types of assistance could it expect from the
customers? (Max 6)
83
52
52
71
44
44
Cooperation with the customer – yes
Collective research-development
Collective purchasing
43
24
15
36
20
13
Reason for the order – good quality
Favourable price level
Supply capacity, capability
94
87
70
80
74
59
Source: Questionnaire survey 2011. N=118.
2
260
Zoltán Csizmadia
TABLE 2
Characteristics of customer relationships
Characteristics
N
%
It has suppliers – customer and supplier positions
Typical number of suppliers – between 11–50
Over 100
111
45
26
94
41
24
Average proportion of purchases from suppliers in sales revenues,%
Varies between 34–66%
Varies between max 33 or 67–100%
44
26/26
48
46
27/27
It has only domestic suppliers
It has only foreign suppliers
It has domestic and foreign suppliers too
30
26
55
27
23
50
Supplier selection – purchasing division
Parent company
56
38
51
34
36
23/24
34
22/22/22
61
55
Typical term of purchasing contracts concluded with the suppliers– 1 year
Less than 1 year, 1–2 years, over 3 years
Assessment of the relationship – long term, perspective relationship built
upon framework contracts
Loose relationship, built upon orders
28
25
Support to suppliers – granted assistance to suppliers, yes
Quality assistance related support
Technical assistance
On the average how many types of assistance was granted to suppliers?
(Max 7)
68
47
44
61
43
40
Cooperation with the customer – yes
Collective purchasing
collective research-development
collective training and coaching
36
19
13
12
33
17
12
11
Reason for the order – good quality
Favourable price level
Flexible supply capacity, capability
97
91
72
88
83
66
Suppliers’ individual R&D activities are deemed to be important – yes
Collective R&D activities are performed with the suppliers – yes
14
21
13
19
2
The majority of companies, as referred to above, are not only automotive industrial
suppliers, but also customers at the same time. That is why it is possible to summarise
the basic characteristics of customer relations as well, becoming familiar with the
expectations towards the companies being on the supplier side of the system, so to say,
from the customer’s point of view. With highest probability the scale of an average
sphere of suppliers varies between 11 and 50 companies. One-fourth of the companies,
however, have over 100 suppliers. Purchases from suppliers represent a very significant
proportion of sales revenues (48% average proportions) at most of the companies,
261
moreover, at one-fourth of the companies this proportion even exceeds a rate of 67%,
which shall be considered as a remarkable volume concentration. The spatial location of
their suppliers shows a heterogeneous picture: at most of the companies the customer
relations are built upon the miscellaneous sphere of international and domestic partners.
Examining such automotive industrial economic transactions and relations from the
customer’s side, the main factors of motivation, selection and success are also quality
(88%), favourable price (83%) and willingness to flexible supplies (66%). Approaching
the relationships from the customer’s side, the time interval or term becomes shorter,
and there is no unequivocally preferred response category, although the occurrence of
the one-year interval is higher than the average. All in all, there is no specified time
interval or term in customer relations. As customers, the companies prefer the strategic
alliances less, besides framework orders a more prominent role is attributed to loose
relations (25%). As customers, 61% of the companies support their suppliers, primarily
in the field of quality assurance and technology.
As customers, one-third of the companies’ relations with suppliers extend to other
levels as well. In principal, inter-organizational cooperation can be observed in the field
of purchasing (17%), R&D (12%) and in minor rate in the field of training (11%). On
the other hand, individual research-development does not seem to be an important
criterion when suppliers are selected. Merely 13% of the companies appearing also as
customers deem this an essential criterion, and only every fifth company carries out
R&D activities collectively with its supplier.
Spatial differences
After summarising the fundamental basic features of the two network forms in this
section the spatial differentiation of the significant elements are examined. The answer
is sought for the question whether the supplier networks have certain special peculiarities in the Northern Transdanubian region (34 questionnaires, 28.8% of the samples).
Altogether over 25 variables were used to describe the basic features of supplier and
customer relations. In the two secondary spatial samples cross-table analysis was
applied to examine the spatial differentiation of these relationship network parameters.
In the case of seven sets of questions can significant differences be observed as regards
the basic characteristics of the networks of automotive industrial supplier companies
performing their business activities in the Northern Transdanubian region and in other
parts of the country. Accordingly, two groups were compared, and the summary is
given in the following table (Table 3) with the indication of significant deviations,
demonstrating the special peculiarities of the companies belonging to the economic field
of the northern part of Transdanubia. Surprisingly, there are no regional differences at
all in terms of closeness to car factories and Tier 1 suppliers and within the proportion
within the sales revenues deriving from supply shows such a concentration rate (that is a
typical tendency), behind which no spatial differentiation can be observed.
262
Zoltán Csizmadia
TABLE 3
Marks of the spatial differentiation, regional specific features
Features of Supplier networks
Special features of Northern Transdanubia
Commencement of automobile industrial
activities
They have foreign customers
Also before the 90’s, early entries,
Companies from the years preceding 2000 are
overrepresented
higher probability, 79%
Average term of supply contracts
Longer than 3 years (55%)
Strength of relationship with customers
Strategic (38%) or framework type (53%) occur
with higher probability
Number of suppliers
high (between 51–100: 29%, over 100:32%)
Proportion of purchases from suppliers in sales
revenues
higher (at least a proportion of two-third in the
case of 37% of them)
Frequency of foreign suppliers
higher (91%)
One of the most obvious difference appears in the fact that the automobile industrial
activities of suppliers performing their activities in Northern Transdanubia has longer traditions, and their proportion is higher at companies, which are engaged in this sector for over
10 or 15 years. The other region specific element is the probability of occurrence of foreign
suppliers and/or customers, which is 12–14 percentage point higher in Northern Transdanubia. As suppliers, the companies in the Northern Transdanubian region conclude their
contracts for longer terms and the relationships established with the customers are closer,
stronger and more profound (strategic and perspective framework agreements occur in
higher proportion).
On the other side of supplier networks or chains, as customers or clients the companies of the region also show some peculiarities. On the one hand the scale of the supplier set is bigger than in the case of other companies of the region; the undertakings in
this region with 50 or even 100 supplier sets are over represented (their rate collectively
is 60%, while in other regions only 32%). Relationship networks established with the
suppliers show special differences also in the case of international interactions, or in the
rate of sales revenues deriving from purchases from them. In the region the occurrence
and higher numerical ratio of international suppliers is more typical, and purchases from
them are more concentrated and the volume of sales revenues is more intensive.
Economic-organisational parameters influencing the networking
The correlations between the (pendant) network variables included in the researchmethodological model described at the beginning of this study, and the independent
variables measuring the organisational background were tested through cross-table analyses.
This procedure is suitable for the analysis of paired correlations, and it is expedient due to
263
the usage of low sample element number and only few elements of categorical variables.
The mapping of paired correlations of nearly 20 different supplier and customer network
indicators and 8 to 10 organisational-economic background parameters means by scale 200
cross tables. The results are summarised in Tables 4 and 5. Only significant relationships are
discussed, the indices were arranged in accordance with the scale of the number of
explanatory variables influencing certain aspects of the inter-company cooperation, while the
internal ranking was set up in accordance with the value of the test statistics (Cramer’s V)
measuring the correlation for the sake of the possibly fastest transparency and easiest
orientation. Potential background factors influencing the relationships established with the
customers (as suppliers) and suppliers (as customers) were separately analysed. Our
objective was to identify the main determining factors and depict a certain big picture. At
this point deeper and multi-variable correlations are not discussed.
The most apparent phenomenon was the low number of variables influencing the
relationships, at least in the sphere of independent variables applied by us. Basically in
most of the cases only the staff number and the sales revenues being closely correlated
may be considered as a considerable differentiating factor. In certain cases even the
foreign ownership interest, the number of product lines manufactured, the proportion of
blue collar workers, and the weight or concentration of the supply or purchasing within
the sales revenues got roles. Other independent variables of our initial model were in
nearly each case without any effect.
The summarising tables include the directions of correlations, or data from which
these may me read and defined, but some more important results were highlighted. All
in all it may be stated that the fundamental peculiarities that we could become familiar
with during former domestic researches return in the data of the latest empiric survey,
thus we may not speak about any surprising, schematic or new tendencies.
As suppliers (Table 4) primarily in the foreign (or in minor rate and following a
reverse logic the domestic) customer frequency can remarkable differences be observed
in accordance with the economic-organisational profile of the companies. The bigger
the company, the higher the sales revenues, the proportion of supplies within the sales
revenues, the number of product lines manufactured, and the proportion of blue collar
workers or the foreign ownership interest, the more probable is the occurrence of
foreign companies in the sphere of customers. Besides the occurrence of supplier
networks the term or time interval thereof is also influenced by several factors. A
positive relationship can be observed between the term of the contract (2 to 3 or even
more years) and the site, the sales revenues, the foreign ownership interest and the
weight of the supply within the sales revenues as well. Actually, in other cases only the
size or scale of the company can be considered as determining factor, and the
commonly known correlation is confirmed at us too, that direct automobile industrial
suppliers, the strong supplier networks, the complex cooperation are more predominant
at large suppliers. In addition to the mainly conventional relationships that may be
considered linear (the more, the larger, the more complex, all the more...) other
correlation pattern can be detected in only one case: in the case of assistance received as
a supplier the middle sized companies are in more favourable situation.
264
Zoltán Csizmadia
TABLE 4
Differentiation of relationships with customers
Frequency of EU customer/client (sample average 70%)
– Size of the undertaking; Cramer’s V: 0.525
large company (90%) – middle–sized undertaking (87%) – small–sized undertaking (46%)
– Sales revenues; Cramer’s V: 0,482 – with the increase of sales revenues the probability grows
– Proportion of automotive industrial supplies within sales revenues; Cramer’s V: 0.422
Over 75% (85%) – under 25% (37%)
– Foreign undertaking; Cramer’s V: 0.327
higher probability – 100% foreign ownership (94%)
– Number of product lines manufactured; Cramer’s V: 0.293; 2–5 types (54%) – 11–25 types
(92%)
– Proportion of blue collar workers; Cramer’s V: 0.242
Between 50 and 75% (78%) – over 75% (71%) – under 50% (33%)
Frequency of domestic customer/client (sample average 65%)
– Sales revenues Cramer’s; V: 0.451; with the increase in sales revenues its probability decreases
small–sized undertaking (84%) – middle–sized undertaking (61%) – large company (39%)
maximum 50% (87%) – 75% over (51%)
– Foreign undertaking; Cramer’s V: 0.236; minor probability – 100% foreign ownership (47%)
V. Term of Supplier contracts
– Sales revenues; Cramer’s V: 0.345
With the growth of sales revenues (over 2 billion HUF) the probability of long term
contracts also increases
Positive relationship – the larger a company is, the longer term the contracts are concluded for
Long term contracts occur with higher probability – 100% foreign ownership (64%)
the larger a company is, the larger the probability of occurrence in the case of long term
contracts is
Direct supplies to automobile factories (sample average 43%)
– Sales revenues; Cramer’s V: 0.320; with the increase in sales revenues its probability grows
75% over (54%) – 25% under (23%)
Strength of supplier relationship (perspective or alliance based)
Strategic alliances occur mainly over 2 billion HUF
With the growth of size the probability of close relationships also increases
Assisting the supplier’s activities by the customer (sample average 70%)
Support or assistance granted to companies with low or high sales revenues is rare (60%), it is the
most frequent between 0.5 and 10 billion HUF (87%)
265
Cooperation with the customer (sample average 35%)
Lower probability – 100% foreign ownership (18%)
TABLE 5
Differentiation of relationships with the suppliers
Frequency of foreign suppliers (sample average 73%)
with the increase in sales revenues its probability grows
The higher this proportion is the more probable the frequency of foreign suppliers is: with a
proportion over 75% (88%) – with a proportion under 25% (39%)
higher probability – 100% foreign ownership (91%), not foreign ownership (64%)
Proportion of purchases from the suppliers within the sales revenues in 2010
The larger the size the higher the proportion in sales revenues is
67–100% proportion: large company (44%) – middle–sized undertaking (34%) – small–
sized undertaking (9%)
higher probability of concentration – 67–100% proportion in the case of companies with
100% foreign ownership (48%)
Frequency of cooperation with the supplier (sample average 32%)
over 75% (43%) – under 25% (6%)
Foreign undertaking; Cramer’s V: 0.232
higher probability – 100% foreign ownership (48%), not foreign ownership (25%)
Number of suppliers
With the growth of sales revenues the number of suppliers also increases
Positive relationship: large company over 100 suppliers (55%) – majority of SMEs with 50
(52%), but mainly with de under 10 suppliers only
Frequency of domestic supplier (sample average 75%)
266
Zoltán Csizmadia
with the increase in sales revenues its probability decreases
Lower probability – 100% foreign ownership (59%)
Assistance, supporting of suppliers (sample average 61%)
When focusing on the customer roles of companies under review and the relationships established by them in these roles with other suppliers (Table 5), no significant
new differentiating factors were revealed to step in from the research model either. In
this case, also the frequency of foreign suppliers, the volume or concentration of purchases from foreign suppliers, and as a new element, the occurrence of co-operation can
be considered to be the most differentiated.
All in all the direction of the revealed relationships followed the same logic: besides
the higher sales revenues, the number of staff, the foreign ownership interest, and in
certain cases, the customer’s position, the concentration of supplier activities, its weight
within the sales revenues also contribute to the more favourable tendency in the occurrence of international orders, cooperation with the suppliers, the scale of the supplier
network (number of suppliers) and support granted to the suppliers.
Conclusions
Owing to the size of the corporate data base forming basis for this survey it is not suit–
able for the usage of more sophisticated research models of explanatory character.
Nevertheless, the screening of correlations, that seem to be reliable in statistical sense,
from the loads of possibilities of relations provided a base for interpretation which we
could securely build upon to state that the general features of supplier networks and the
systems thereof that we could get familiar with during less targeted surveys that applied
more comprehensive and more heterogeneous samples than automotive industry also
“work” in and apply to automotive industry. The really individual feature besides the
descriptive, profiling raw data it is more rather the spatial differentiation where we
managed to detect certain individual features of the suppliers and customers (secondary
sample) carrying out their business activities in the Northern Transdanubian region.
The interviewed companies are surprisingly close to the central players (automobile
manufacturers and Tier 1 suppliers) of supplier networks and chains, or they, themsel–
ves, can be considered as Tier 1 suppliers, or being integrated into the system at a lower
level, nearly each of them had a relatively widespread own set of suppliers too. Behind
the relations built upon supplier and customer cooperation there is a significant
267
concentration of resources, and the weight of important partners within the proportion
of sales revenues and sales is really prominent. In line with the findings of international
and domestic researches, the quality, price and flexibly supply capacities mean the
triple, fundamental basis for entering and remaining in the networks successfully. Relationships, in general, are not multiplex, in the majority of cases they are focused on a
sole function. Nearly one third of the automotive industrial suppliers interviewed had a
multiplex relationship network, in which collective R&D activities, trainings and
coaching, or synchronized purchases were also included. Half or two-third of the companies participated both in international and domestic supplier networks or systems, and
only a minor proportion (25%) has relationship networks that were reduced to the domestic economic field.
We managed to reveal also spatial differences among the automobile industrial
suppliers. The companies in the Northern Transdanubian region joined to supplier net–
works earlier, and established relationships with foreign customers with higher proba–
bility, which are based upon close relationships of long term framework contracts or
strategic alliances, and as customers they had more widespread supplier networks with
higher volume concentration within the sales revenues.
References
A magyar kis és középvállaltok beszállítói szerepének erősítéséről szoló stratégia kidolgozása a
gép- és járműipari ágazatban: a jelenlegi helyzet tanulságai és a lehetőségek kihasználásának
eszközei 2011. Kopint Konjunktúra Kutatási Alapítvány – Commerzbank – Noerr és Társai
Iroda, 2011. május. Munkacsoport-vezető: Klauber Mátyás.
Gelei Andrea – Nagy Judit 2004. Partnerkapcsolatok értéke a hazai ellátási láncban – fókuszban a
beszállítói vállalatok. Budapesti Közgazdaságtudományi és Államigazgatási Egyetem,
Vállalatgazdaságtan Tanszék, 51. sz. Műhelytanulmány, HU ISSN 1786–3031.
Gelei Andrea – Nagy Judit 2005. Versenyképesség az autóipari ellátási láncban – a vevői érték és
dimenziói az egyes beszállítótípusok esetében. – Vezetéstudomány. 3. pp. 10–20.
Gém Erzsébet – Mikesy Álmos – Szabó Zsolt (2011) Beszállítói kapcsolatok: a méret a lényeg? A
beszállítói kapcsolatok természete és hatása a vállalati működésre az ipari szektorban.
Magyar Fejlesztési Bank Zrt., Budapest.
Sass Magdolna – Szanyi Miklós 2009. Klaszterek és a multinacionális vállalatok helyi beszállítói
hálózatának fejlődése. – Európai Tükör. 9. pp.21–46.
Walter, A. – Ritter, T. – Gemünden, H. G. 2001. Value creation in buyer-seller relationships:
theoretical considerations and empirical results from a supplier's perspective. – Industrial
Marketing Management. 30. pp. 365–377.
INNOVATION ACTIVITIES OF COMPANIES
RELATED TO AUTOMOTIVE INDUSTRY
Internal and external factors influencing innovation activity
in the supplier network
MÁRTA NÁRAI
Keywords:
automotive industry innovation activity
In my essay I explored the innovation activity of companies working (also) in automotive industry
taking part in the research, and attempted to identify the factors assisting/encouraging innovation
activity together with the judgement of their effect, power to influence. In line with the relevant
literature, in the course of our research innovation was interpreted in the fields of product,
process, organizational-organizing and marketing innovation, and analysed accordingly. Our
results demonstrate that in the circle of companies working (also) in automotive industry
innovation activity is significantly higher than among companies, enterprises operating in other
sectors. A significant part of the companies implemented innovations related to production,
manufacturing, and the range of products.
The factors promoting innovation were analysed with the help of a five-grade scale; where the
presence of solvent demand proved to be the most decisive factor, which was followed by the
presence of properly qualified workforce, the appropriate capital supply, and the presence of the
appropriate suppliers and subcontractors. The last places of the ranking are occupied by the cooperation with educational and scientific institutes, research capacities, and the availability of
consulting services; the automotive companies do not regard the effect, influencing power of
these factors on innovation activity too favourably.
Introduction
Automotive industry is one of the most innovative industrial sectors worldwide, from
where the majority of the most modern technologies, solutions, concepts proceed
(Demeter–Gellei–Jenei 2004). At the same time, in this industrial sector the effectiveness
of the supply chain is very decisive from the point of view of competitiveness, it is of
crucial importance how efficiently the companies, company groups can operate and cooperate with each other (Demeter–Gellei–Jenei 2004). Automotive industry is basically
built on the pyramid principle, the great automotive companies stand at the top of the
hierarchy, followed by the subsidiaries, then the integrators (first tier suppliers), and the
second-third tier suppliers. The maximum collaboration of the members of the supplier
chain is indispensable for efficiency.
Innovation Activities of Companies Related to Automotive Industry
269
Among the companies, firms operating in the country, performing automotive
industrial activities, mainly fulfilling supplier roles, one of the important segments of the
research conducted by the István Széchenyi University’s JÁTÉK research group is on the
one hand to get to know these companies’ innovation activity, see their innovation
activity, and on the other hand to explore those determining factors which in case of
enterprises connected to car manufacturing, automotive industry influence their innovation
activity. The emphasis does not only fall on the exploration of those factors which
determine the innovation and development activity of a company within the organization,
but we are also seeking those external conditions, circumstances which shape
development possibilities, situation in this segment. Our purpose is to identify, define both
those factors which support, assist innovation, and those factors which obstruct, hinder it.
Innovation activities
In the recent years several researches were conducted in the individual regions (e.g.
Western Transdanubia, Central Transdanubia, Southern Transdanubia), the purpose of
which was to explore the companies’ innovation activity and the factors determining it
(Grosz–Rechnitzer 2005; Csizmadia–Grosz–Tilinger 2007; Csizmadia–Grosz 2009;
Szépvölgyi 2009). These surveys however were not focused on a particular industrial
sector, as our present research; the innovation activity, inclination for innovation of the
interviewed enterprises proved to be considerably low: 60–70% of the companies did not
make any steps which would qualify as innovative in any area. In general the processing
industrial companies proved to be the most innovative. The previous researches which
were not conducted at industrial sector level serve as good possibilities for comparison for
our present analysis.
According to the approaches dealing with innovation, an enterprise realises an innovative activity if it implements an improvement, development or further development in
one of the following areas:
− product-innovation (the development of a new product or service, the further
development of an already existing product/service)
− process-innovation (manufacturing, production methods, activities, technology,
logistics, transport or distribution methods)
− organizational-organizing innovation (methods related to business practice, work
supervision, management system, the appearance of new methods in a workplace
organization, organizational structure, decision-making procedure, external
contact management), and recently
− marketing innovation (product design, packaging, product advertising, market
launch, pricing).
In the course of the research conducted in the circle of automotive industrial
suppliers we also interpreted innovation activity, in line with the relevant literature, in
the field of product, process, organizational-organizing, and marketing innovation, and
analysed it accordingly. In our questionnaire we listed 17 innovation activities, and
270
Márta Nárai
asked the answering companies to indicate which characterized the given company,
enterprise in the past three years.
The great majority (91.5%) of the 118 companies working (also) in automotive
industry, car manufacturing that filled in the questionnaire are characterized by
innovation; there were only 10 enterprises in the sample (8,5%) that did not implement
any innovation or development connected to automotive industry in the past three years.
Innovation activity is much higher in the circle analysed by us, than in the circle of
companies, enterprises working in other sectors – i.e. not in automotive industry (see
Grosz–Rechnitzer 2005; Csizmadia–Grosz–Tilinger 2007; Csizmadia–Grosz 2009;
Szépvölgyi 2009).
The majority of non-innovative companies comprising less than a tenth of our
sample (seven out of ten) are micro and small enterprises, while there is no example of
this among big companies with more than 250 employees. The correspondence between
company size (on the basis of the number of employees) and innovation activity is
significant, but not too strong (the Cramer V coefficient value is 0,3191). There is an
interesting correspondence to note between regional, territorial location and innovation
activity: there is a significant, very strong (Cramer V 0,667) connection between which
county the questioned company operates in and whether it has any innovation activity.
On the basis of the established results the counties of Baranya, Borsod-Abaúj-Zemplén,
Szabolcs-Szatmár-Bereg, and Békés can be highlighted, the automotive industrial
suppliers working in these counties are the least characterized by any kind of innovation
activity, the proportion of innovative companies in their circle is substantially lower
than the sample average (0; 40; 50; and 67%). The reliability of this correspondence’s
validity is however strongly influenced, decreased by the low number of elements – the
number of elements is very much dispersed between 1–17 per county.
No significant correspondence was found between the year of funding and innova–
tion activity.
The surveyed companies implemented improvements connected to automotive
industry in an average of 6.1 areas in the past three years (considering all companies this
is an average of 5.6). The minimum value with respect to innovative organizations was
1, while the maximum value was 15 (Figure 1), i.e. there are companies (2) in the
sample, who indicated almost all innovation activities listed by us. The majority
implemented four kinds of innovations (20 companies), but many companies indicated
three (13), two, five, or eight (10–10) areas.
271
FIGURE 1
Number of innovation activity types implemented in the last three years*
* Calculated on the basis of the indicated innovation activities.
Source: Questionnaire survey (2011).
The companies striving for innovation implemented mainly process-innovations and
product innovations (Table 1). The proportion of those companies was outstanding
(57,6%–57,6%), which developed further already existing products and/or improved an
applied technology, or changed technology. On the other hand, making developments in
informatics, improving manufacturing, production methods; or inventing, developing,
establishing some new product used in automotive industry characterized at least half of
the companies.
A relatively small proportion (11–21%) of the companies implemented marketing
innovation developments, improvements. Among organizational-organizing innovations
most companies (a bit more than half of the companies) mentioned developments in
informatics, and introduced new methods in work supervision, organization. Changes,
innovations were implemented least of all in contact management, decision-making
procedure, distribution method and management (Table 1).
We attempted to create innovation groups with the help of cluster analysis, but did
not get meaningful results, we distinguished two main groups, the groups of innovative
and non-innovative companies, this however has not provided any plus information in
comparison to our findings so far.
272
Márta Nárai
TABLE 1
Frequency of innovation activities implemented in the past three years
Innovation type
Product-innovation
development of a new product
development of a new service
further development of an already existing product
further development of an already existing service
Process-innovation
change of manufacturing, production method
introduction of a new activity
technology change, development of an applied technology
Organizational-organizing innovation
change, development of logistics, transport method
change of distribution method
introduction of a new method in work supervision,
organization
introduction of a new method affecting the management
system
change in workplace organization, structure
introduction of a new method in the decision-making
procedure
appearance of new methods in the external contact
management
development in informatics
Marketing innovation
developments affecting marketing
developments in sales
Innovative companies
number
proportion (%)
60
28
68
28
50,8
23,7
57,6
23,7
62
47
68
52,5
39,8
57,6
34
13
28,8
11,0
51
43,2
26
22,0
36
30,5
16
13,6
13
11,0
61
51,7
23
25
19,7
21,2
We tried to explore the latent structure of the complexity of innovation activities
with factor analysis. In case of our present sample the Maximum-likelihood method was
deemed the most appropriate. From the 17 innovation types listed in the questionnaire
six factors were created by 47% information preservation. The KMO-value – which is
one of the most important index-number to judge how appropriate the variables are for
factor analysis (Sajtos–Mitev 2007) – is 0,77 in the completed survey, which means that
our variables are appropriate. The element number of the sample warns us that the
factor weights have to reach at least 0.5 so that they can be considered significant.
The innovation activities falling into one factor generally go together, i.e. if one of
them was present in a company, then generally the innovation constituting the other
elements of the factor took place with a greater probability. The factors are shaped in an
interesting way, the composition of which is shown in Table 2. The last three factors
273
consist of only one innovation activity, the development of a new product, the develop–
ment affecting the logistic, transport method, and the introduction of a new activity
form individual factors respectively. At the same time, there are three activities, which
do not fit into the model on the basis of factor values, these are the following: the deve–
lopment of a new service; the appearance of new methods in external contact manage–
ment; the further development of an already existing product.
The factor analysis was repeated without the non-fitting three variables. This
resulted in four factors (KMO 0,757), the explained variance is still not better than
before, it is 45%, i.e. a significant part of the information is lost. The composition of the
factors created this way is shown in Table 3. The composition of the factors was
changed to the extent that from the variables previously composing the three indepen–
dent factors two variables were moved into other factors, while the introduction of a
new activity still constitutes an independent factor.
The high degree of innovation activity and the most common forms of innovation
well demonstrate that the automotive industry is an innovative sector, and the majority
of the national companies active in automotive industry implements innovations affecting production, manufacturing, and product range.
The dimension of innovation plays an important role in automotive industry among
the value functions offered by the suppliers to the customer (Gelei–Nagy 2004). On the
basis of the approach of the cited authors, Möller–Törönnen (2003) innovation is interpreted as an efficiency function, which greatly contributes to competitiveness, since
innovation ability refers to the possibility that as a result of the cooperation with a customer (client) a product or process innovation may ensue, i.e. the supplier is capable of
development, innovation, is able to realize the customer’s (client’s) idea, and adapt
itself to the – even continuous – changes of customer demand. Gelei–Nagy (2004) re–
gards this form of innovation an indirect (incremental) innovation, which is an expectation towards every automotive industrial supplier who wish to remain in the competition
in the long run. There is however a ‘higher’ level of innovation called strategic innovation, which strongly increases the competitiveness of first tier suppliers (integrators); at
this level the supplier has to respond not only to the innovation induced by the cus–
tomer, but in some cases the supplier itself becomes the true innovator, i.e. the supplier
TABLE 2.
274
Márta Nárai
275
TABLE 3.
The composition of factors without the three non-fitting variables, factor weight matrix
Maximum-likelihood method – Rotated Factor Matrix a
Factors
1
Sales –
marketing
Developments in sales
Developments affecting marketing
Change of distribution method
Development of a new product
Developments in informatics
Change of manufacturing, production
method
Introduction of a new method in work
supervision, organization
Change of technology, development
of applied technology
Change, development of logistic,
transport method
Introduction of a new method in the
decision-making procedure
Introduction of a new method affecting
the management system
Change in workplace organization,
structure
Introduction of new activity
2
3
Technology –
Work
methodology organization
.802
.618
.569
.469
4
New
activity
.281
.250
.712
.575
.553
–.267
.439
.392
.765
.630
.251
.595
.268
.617
formulates suggestions – developments –, which make the customer (client) change,
taking steps to adapt.
In spite of the fact that among the surveyed companies innovation is strongly present, only one third (31.4%) of the companies filling in the questionnaire evaluated the
capacity for innovation as the most important element of the company’s competitiveness. One fifth of them emphasized R&D activity. From the mentioned factors the
capacity for innovation proved to be only the fifth most decisive factor of competitiveness, much behind good price (72,9%), good contact with customers (69,5%), cheap but
qualified workforce (50,0%), and standing on more legs (40,7%).
Compared to judging it a competitiveness factor, significantly less companies, only
one tenth (9,3%) considered innovation the main driving force of their company’s
operation, and a similarly small proportion of answering companies (8,5%) set this goal
276
Márta Nárai
for the next three years. They lay emphasis primarily on the expansion of clientele
(59,3%), the decrease of expenses (29,7%), and the expansion of their product range
(28,0%).
Innovation activity/capacity appears in the judgement of competitiveness and the
plans of the future as a less stressed element. For that matter, a significant connection
was found between the (subjective) judgement of the companies’ – both national and
international – competitiveness and their innovation activity with an error limit of 5%,
although the strength of the connection is weak, its Cramer V value does not reach 0.3
(in case of the judgment of national competitiveness it is 0,261, and in case of the
judgement of international competitiveness it is 0,249). A significant proportion of
companies implementing innovations, developments consider themselves very competitive (41%) and of average competitiveness (44% and 55%) both in a national and international comparison. At the same time, among non-innovative companies there was no
company that would consider itself very competitive either in national, or in international comparison; in a national regard they all evaluated their companies as average,
while in an international regard half of them considered their companies to be hardly
competitive.
We examined whether we can find a correspondence between the applied innovations, innovation activities and the judgement of competitiveness. On the basis of the
established results, however, even with an error limit 5% (significance level), no significant connection can be demonstrated between innovation activities belonging to the
circle of product, process, organizational-organizing, marketing innovations and the
judgement of national and international competitiveness. The judgement of competitiveness is therefore independent of the type of innovations a company implemented in
the past three years.
The factors determining innovation
The relevant literature generally classifies the factors influencing innovation negatively
into four groups:
− the group of expense factors (e.g. lack of capital, lack of potential resources
available outside the enterprise, high innovation costs)
− problems connected to knowledge (lack of qualified workforce and information)
− market factors (e.g. the market is dominated by already established enterprises,
the demand for innovation is uncertain)
− reasons against innovation (e.g. it is not needed due to previous innovations, lack
of demand for innovations) (e.g. Csizmadia–Grosz–Tilinger 2007; Szépvölgyi
2009).
Among the assisting factors generally the following ones are the most common to
appear (Szépvölgyi 2009):
− capital resources
−
−
−
−
−
−
−
−
277
availability of risk capital
presence of solvent demand
properly qualified workforce
presence of proper suppliers and subcontractors
willingness to cooperate
innovation and economic support
research capacities and supply
consulting services.
From the viewpoint of innovativity not only a company’s size and capital resources
and the presence of personal conditions prove to be important factors, but also the way
of company management, contact characteristics, production and sales co-operation, and
collaboration, especially with universities, scientific centres, other innovative enterprises and service providers. The presence of innovation incentives, supports, services is
also not of negligible significance, nor their degree of accessibility, and at the same time
more long-term economic development strategies would also be absolutely needed (Pitti
2008). Getting to know all these factors and explore their power to influence is indispensable in the course of our present research.
In our automotive industrial research we analysed the factors which promote
innovation with the help of a five-grade scale, we listed 12 factors, the role of which
from the viewpoint of a given company had to be judged with the help of a five-grade
scale (where 1: absolutely not, and 5: to a significant extent) on the basis of how much
they do or do not assist, encourage the innovation activity of a company.
Among the surveyed 118 companies (suppliers) with automotive industrial interests
the presence of solvent demand proved to be the most decisive factor promoting innovation activity with average values above 4 (Figure 3). This factor influenced, assisted
the innovation activity of half of the companies to a significant degree, and to a decisive
degree (value four) in case of almost third of the companies (Figure 4). No other factor
reached the average value of 4 or above, and it did not happen in case of any other factor that at least half of the companies, enterprises considered its influencing power significant. After solvent demand, the presence of properly qualified workforce, appropriate capital resources, and the presence of appropriate suppliers and subcontractors
proved to be the factors assisting, encouraging innovation activity the most. The roles of
production and sales co-operations, innovation and economic supports, or the general
business climate were valued much lower. The co-operation with educational and
scientific institutes, like for example the co-operation with universities, research capacities, and the availability of consulting services are at the last places of the ranking with
average values of approximately 2.5; the impact, influential power of these factors on
innovation activity are not too favourably evaluated by automotive companies. In the
opinion of a great proportion of them (around 50%) these factors absolutely not assist,
encourage innovation activity or do so only to a very minimal degree. At the same time
it is important to note that there is a relatively decisive proportion of companies (about
278
Márta Nárai
30%), for which even the latter factors – except the availability of consulting services –
prove to be decisive or significant assisting/encouraging factors (Figure 4).
Those companies that are in contact with universities and/or research institutes,
evaluate the innovation encouraging influence of educational and research institutes
much more positively, only a very small proportion of them are of the opinion that these
institutes do not at all promote the innovation activity of market actors (6,3% in case of
universities, 2,7% in case of research institutes), contrary to those who do not cooperate
with such actors (34,6%; and 26,6%). In case of both universities and research institutes
the contact is significant and of medium strength (Cramer V 0,412; and 0,379).
Our results also demonstrate that the presence of contact with the different
professional and/or scientific institutes in itself does not have an innovation supporting
influence, which is true both for the co-operation with research institutes and the
chamber of commerce and industry or incubator houses. In case of the co-operation with
universities, however, we found – although with an error limit of 5% – a significant
(although of a very weak strength) connection between the presence of the contact and
the implementation of innovation activity. A great majority (80%) of the non-innovative
companies is not in contact with universities, while among innovative companies this
proportion is much smaller (41,7%, and 45% when considering the whole sample).
Among the companies which do not co-operate with universities the proportion of
companies that implement no innovations whatsoever is five times higher than among
those who have such co-operations (15% as opposed to 3%; sample average: 8,5%).
FIGURE 3
Factors promoting innovation, average values
4,23
The presence of solvent demand
The presence of properly qualified workforce
3,92
Proper capital supply
3,8
The presence of appropriate suppliers and subcontractors
3,65
General business climate
3,23
Production and sales co-operations
3,23
Innovation and economic supports
3,1
The presence, coherence of economic development…
2,85
Co-operation with educational and scientific institutes
2,76
The availability of risk capital
2,74
2,64
Research capacities and demand
The availability of consulting services
2,5
0
0,5
1
1,5
2
2,5
3
3,5
4
4,5
279
FIGURE 4
Frequency of judgements related to the influence of factors
promoting innovation activity,%
2,6
3,4 12,9
2,6
4,3
23,3
The presence of solvent dema nd
The presence of properly qua lified workforce
7,8
Proper ca pita l supply
8,6
11,6
Production a nd sa les co-opera tions
7,8
Genera l business clima te
12,5
35,7
10,3
18,4
21,4
21,1
19
25
The a va ila bility of risk ca pita l
23,4
18,7
Resea rch ca pa cities a nd dema nd
22,1
The a va ila bility of consulting services
23,7
25,9
26,2
27,4
28,9
20
3
40
4
6
22,8
32,5
23,9
18,8
37,1
31,6
Co-opera tion with educa tiona l a nd scientific institutes
2
24,3
38,8
15,8
0
38,8
38,3
The presence, coherence of economic development stra tegies
not at all
33,6
26,7
23,5
12,3
Innova tion a nd economic supports
51,7
36,2
18,1
7,8 6,1
The presence of a ppropria te suppliers a nd subcontra ctors
29,3
14,9
23,7
7
21,6
8,6
24,3
7,5
21,2
28,9
60
5,3
10,5 7,9
80
100
to a significant degree
More than half of the companies (55%) are in contact with universities, a third of
them are in contact with research institutes, two third with the chamber of commerce
and industry, 40,7% of them with schools, and 9,3% of them (also) with incubator
houses.
When discussing innovation it is necessary to look at what characterizes the
companies’ R&D activity, since the factors of research-development and innovation
capacity cannot be separated from each other. Let us suppose that the two activities are
not independent of each other. Let us see the results of our research! Less than half of
the questioned companies (46,2%) have R&D activity, only a quarter of these
companies have an independent R&D department, and in case of a further eighth of
them the engineering constitutes the basis of the R&D activity. On the other hand, more
than half of the questioned companies, enterprises at least partly regard it important that
the automotive suppliers have R&D activity.
There were companies in the sample that laid great emphasis on research-development,
that continuously observe the market and the other competitors, and many times develop
further what a competitor ’comes up’ with. They are of the opinion however that for an
efficient development activity not only the work of the engineers but also that of the
marketing professionals is important, since they are the ones who find out things to develop
and the directions for development.
280
Márta Nárai
„It is not the task of the development engineers to think about what to develop,
that is the task of the marketing professionals…” (excerpt from an interview)
A quarter of all the companies and more than half of the companies having R&D
activity (57%) are capable of product development, not only production development or
smaller modifications related to the products. In their case, about a great majority of
them (77%) it can be stated that they are not only capable of product development, but
in the past three years have actually developed some new products. Then again this is
also true for more than the third of the companies who do no research-development.
Although we can find a significant relation between R&D activity and this – and only
this – form of product-innovation, this relation is of a slightly weaker than average
strength (Cramer V 0,361). At the same time, no significant connection can be
demonstrated between R&D activity and the further development of an existing product,
the development of a new service, or the further development of an existing one. The
same can be said about the developments affecting the field of procedures, organizationorganizing, and marketing, i.e. whether a company innovates in these fields is
independent of whether it does research-development or not. Our previous assumption
was not justified. In addition to product development, a correspondence was found in
only one instance: in case of the change of the manufacturing-production method a
weak connection could be demonstrated, with an error limit of 5%, with the presence of
R&D activity (Cramer V 0,26).
A fifth of the companies co-operate with the company that orders the supply of a
product connected to automotive industry in the field of joint research-development,
which can doubtlessly have some part in the relatively high research-development and
high innovation activity characterizing the sample. This form of co-operation is
’practiced’ by the majority, joint acquisitions and sales were indicated by much less
companies (12,7%; and 6,8%). It is also important to emphasize that a significant part of
the organizations received support/assistance for their becoming automotive suppliers
from the company ordering the supply, which (can) help, and (can) promote
development, improvement, further development, i.e. innovation, and which could be
the generator of the company’s further development. These assistances include e.g. the
sharing of know-how; the training, teaching of colleagues; the transfer of machines,
technologies or any help related to quality insurance. The latter was mentioned by more
than two fifth of the questioned companies, a fourth of them mentioned the transfer of
technology and the training of colleagues, while a sixth of them mentioned the sharing
of know-how (Figure 5). A significant connection however cannot be demonstrated
between any of these factors and innovation activity.
281
FIGURE 5
Proportion of companies who received support from the company ordering the supply
of products which has (could have) a positive influence on innovation according to the
type of assistance (%)
50
44,4
45
40
35
30
20
26,7
24,8
25
16,4
15
10
5
0
The sharing of
know-how
The traning,
teaching of
colleagues
The transfer of
machines,
technologies
The help related to
quality insurance
Conclusion
In our essay we explored the innovation activity of companies working (also) in
automotive industry taking part in the research, and attempted to identify the factors
assisting/encouraging innovation activity together with the judgement of their effect,
power to influence. In line with the relevant literature, in the course of our research
innovation was interpreted in the fields of product, process, organizational-organizing
and marketing innovation, and analysed accordingly. Our results demonstrate that in the
circle of companies working (also) in automotive industry innovation activity is
significantly higher (91,5% of them mentioned some innovation activity) than among
companies, enterprises operating in other sectors. The high degree of innovation activity
and the most frequent forms of innovation – a significant part of the companies
implemented innovations related to production, manufacturing, and the range of
products – well demonstrate that automotive industry is really an innovative sector. In
spite of the high innovation activity, the role of innovation capacity was not evaluated
by companies as very significant from the viewpoint of competitiveness.
The factors promoting innovation were analysed with the help of a five-grade scale;
where the presence of solvent demand proved to be the most decisive factor, which was
followed by the presence of properly qualified workforce, the appropriate capital
supply, and the presence of the appropriate suppliers and subcontractors. The last places
of the ranking, with average values of approximately 2.5, are occupied by the co-
282
Márta Nárai
operation with educational and scientific institutes, research capacities, and the
availability of consulting services; the automotive companies do not regard the effect,
influencing power of these factors on innovation activity too favourably.
Note
1
The Cramer V coefficient is a symmetrical indicator, it is connected to Chi-square statistics, and
indicates the strength of the connection between two variables. Its value is between 0 and 1, the
closer it is to 1, the stronger connection it indicates between the two given variables. Cramer V
can be considered one of the most reliable indicators (Sajtos–Mitev 2007).
ANALYSIS AND DEVELOPMENT STRATEGIES
– SUMMARIZED EXPLORATORY RESEARCH
OF COMPANY PERFORMANCE
LÁSZLÓ JÓZSA
Keywords:
automotive suppliers market environment economic crisis R+D+I+O activities
Automotive industry is deemed to be a prominent sector in Hungary and can be mentioned as one
of the propulsive industries in Hungarian economy despite the hardships entailed by the economic
crisis. In terms of strategic aspects its job creating ability, contribution to the GDP and its power
to form the reputation of the country in business respect are essential. This study makes an
analysis on the market environment of domestic suppliers and it examines the companies’
performance and development directions, focusing in particular on the automotive industry of the
Central and Western Transdanubian regions.
Introduction
From the beginning of the 1990’s an intense upsurge was experienced on the car market
in Hungary. The status of infrastructure in the country also played a positive role in the
extension of automotive industrial market. Favourable endowments or facilities of
premises, the professional knowledge and experience of experts, and the relatively
cheap but still qualified labour force all contributed to attracting several foreign
investors into the country and to encouraging them to commence intensive development
projects, utilising Hungary’s favourable geopolitical situation. Additionally, diverse
macro-economic factors, such as the government’s tax policy, influenced the processes
of economy favourably.
Suppliers have remarkable automotive industrial traditions and owing to this wellconstructed supplier chains were established during the past 20 years. Hungarian
automotive and parts sectors have been integrated properly into the European and global
division of labour. The two decades of experiences of investors’ programs helped in
organising the specialised domestic industrial capacities into a network, where the
suppliers are clustered in significant organisations of interest representation (Havas,
2010). The clusters that can be managed as organisational innovations are functioning
for the time being only formally until the end of the state subsidy period, however, they
are either incapable of sustaining individually, or can sustain individually but only to a
limited extent. The distrust being a typical feature of Hungarian business life does not
favour to the evolution of supplier networks either.
284
László Józsa
Outlook to Europe – Processes on the automotive industrial market
in the recent past
Role players of automotive industry – Sales, regional competition
In our days it is still a typical tendency that the Central and Eastern European region has
gradually become the centre of European automobile production and from the viewpoint
of Asian car manufacturers it has, step by step, become the centre of automobile
manufacturing, since it is anticipated that the demand for – primarily small and
environment friendly – vehicles produced in this region will probably increase in the
future.
In accordance with the report made by the European Automobile Manufacturers’
Association (Association des Constructeurs Européens d’ Automobiles – ACEA) in
2010 nearly 13 million vehicles were produced in the European Union, which was 15%
higher than the volume manufactured in the same period of the previous year, but it still
had a 14% lag compared to the quantities produced in 2008 before the recession.
According to the forecasts of the Business Monitor International (BMI) the
automotive industry of Central and Eastern Europe may count with an increase by
nearly 7% in the forthcoming 5 years. Consequently, the annual production of this
region is in excess of 4.22 million units, highly the level of 3.4 million units achieved in
2008. Hungary, Slovakia and Romania – owing to their export oriented approach –
enhanced their positions on the automotive market of the region with an annual increase
of 25.9%, 20.7% and 18.4% in 2010 respectively. In accordance with forecasts
automobile manufacturing will reach the pre-recession level of production by 2012, and
afterwards further increase is expectable (Figure 1).
The importance of this region is evidenced by the fact that 10 world leading
automobile manufacturers have production facilities in Hungary. The sales potentials of
Central and Eastern European markets are determined either by the domestic demand,
such as in Poland, or they depend on the West European demands (such as in the Czech
Republic, in Hungary, Slovakia and Romania) (BMI, 2010).
Besides the significance of emerging markets the automobile manufacturers should
not disregard the developed markets, which have got long term priority among the
foreign automobile manufacturers. One of the main attractive factors of the WestEuropean region for the manufacturers is that it possesses all the advantages that are
typical of developed markets (infrastructure, qualified labour force, technology, etc.).
Additionally, due to the higher life standard on demand-side it is assumed that demand
will not sink very low and the market will remain stable.
The fact, that the policies of most West-European governments treat the supporting
of automotive industry as an essential objective, plays an important role in the
sustaining of the leading positions of developed markets. It should be instanced here
that these countries not only introduced the wreck premium faster at the peak of the
recession, but they also attempted to resolve doubts throughout the European Union
about the emission norms to be put in force in 2015. But it should also be mentioned
Analysis and Development Strategies – Summarized Exploratory Research…
285
that different European programs have been launched to popularise environment
friendly vehicles. German government has separated 500 million Euro for the purpose
of supporting the realisation of the plan that by 2012 one million electric cars shall run
on roads; the French government intends to devote 1.5 billion Euros, until 2020, to
reach the aim that 2 million electric and hybrid cars shall be in traffic on roads. In the
meantime the United Kingdom has granted 25% premium or discount for the
purchasing of electric cars the price of which is less than 5000 Pounds (ACEA, 2010).
FIGURE 1
Automobile manufacturing in Central and Eastern Europe, 2007–2014
Source: Lepsényi, 2010.
West-European countries can hardly compete with the Central and Eastern-European
countries in respect of low production and wage costs. For restoring profitability the
automobile manufacturers have been focusing on restructuring, thus they relocate
certain part of their production to Central and Eastern Europe.
Development of sales figures in Europe in the period between 2009 and 2010
In Europe compared to 2009 in 2010 the number of cars sold decreased by 4.7%: in the
continent altogether 13.7 million cars were sold. The Greek market suffered the largest
decline (–36.1%; 140 691 cars), followed by the Hungarian car market (–27.9%; 43 815
cars, which is less by 5 thousand than that of Luxembourg), and the German (–23.4%; 2
916 260 cars) car market. From among the largest car markets the sales dropped in
France (0.7%) and in Italy (9.2%) (Jato Dynamics, 2010).On the contrary, there were
286
László Józsa
markets where the number of cars sold increased: for example in Ireland (53.9%; 88 423
cars), in Iceland (45.7%; 3106 cars) and in Sweden (35.7%; 289 683 cars) the car pur–
chases showed an upswing. The British and Spanish markets extended slightly by 1.8,
and 3.1 per cent. Volkswagen kept leading the list of brands even in 2010, with VW
Golf which was the most popular car in Europe. Table 1 demonstrates the change in the
aggregate sales in Europe of the 10 most popular passenger car brands and models in
the period 2009–2010. In 2011 in Europe, none of the 10 most popular models of the
year 2010 could improve the number of registrations realised in the first quarter of
2010. The most popular model was still Volkswagen Golf, while the second one was the
Ford Fiesta, which posted 27.7% decrease in turnover compared to the previous year
(Table 2).
TABLE 1
Development of sales of the Top 10 brands and models in Europe, 2009–2010 (units)
Top 10 brands
1.
Volkswagen
2.
Renault
3.
Ford
4.
Peugeot
5.
Opel/Vauxhall
6.
Citroen
7.
Fiat
8.
Audi
9.
BMW
10.
Mercedes
Top 10 Model
1.
Volkswagen Golf
2.
Ford Fiesta
3.
Volkswagen Polo
4.
Renault Clio
5.
Opel/Vauxhall Corsa
6.
Peugeot 207
7.
Opel/Vauxhall Astra
8.
Renault Megane
9.
Fiat Punto
10.
Citroen C3
2010
2009
Change (%)
1,536,473
1,138,180
1,118,089
1,002,956
998,692
835,114
823,097
623,510
608,502
590,412
1,642,114
1,088,736
1,289,599
990,276
1,057,579
866,483
1,010,696
612,378
571,688
588,100
–6,4
+4,5
–13,3
+1,3
–5,6
–3,6
–18,6
+1,8
+6,4
+0,4
492,556
402,207
354,068
338,245
317,950
305,468
291,219
260,542
257,645
224,953
571,075
472,158
283,069
313,102
351,858
367,474
275,906
231,015
324,125
167,400
–13,7
–14,8
+25,1
+8,0
–9,6
–16,9
+5,6
+12,8
–20,5
+34,4
Source: Jato Dynamics, 2011. http://hvg.hu/cegauto/20101119_auto_toplista#utm_ ource =hvg_
daily&utm_medium=email&utm_campaign=newsletter2010_11_19&utm_content=normal,
(Date of downloading: 22.1.2011).
287
TABLE 2
Development of sales of the Top 10 brands and models in Europe, 2011
(January to September) (units)
Top 10 Brand
March
2011
March
2010
Change
(%)
Q1 2011
Q1 2010
Change
(%9
Volkswagen Golf
Ford Fiesta
Volkswagen Polo
Opel/Vauxhall Corsa
Opel/Vauxhall Astra
Ford Focus
Renault Clio
Peugeot 207
Renault Megane
Citroen C3
53,055
50,534
39,311
39,189
37,125
36,339
36,048
31,021
26,534
26,021
59,267
69,085
37,726
42,244
39,313
40,332
43,830
41,540
29,848
29,653
–10.5
–26.9
+4.2
–7.2
–5.6
–9.9
–17.8
–25.3
–11.1
–12.2
123,480
101,859
93,740
83,383
79,404
73,222
89,157
73,280
65,092
56,737
135,745
140,932
95,154
86,421
79,390
79,758
103,359
91,808
70,942
64,606
–9.0
–27.7
–1.5
–3.5
+0.0
–8.2
–13.7
–20.2
–8.2
–12.2
Source: Jato Dynamics, 2011 (URL:http://hvg.hu/cegauto/20110421_nepszeru_autok (Date of
downloading: 20.10.2011).
TABLE 3
Top 10 brands and models in Hungary, period January–October, 2010
Top 10 brands
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Volkswagen
Ford
Skoda
Opel
Renault
Suzuki
Toyota
Nissan
Peugeot
Fiat
Sold units
3658
4303
3681
3649
2973
2049
2140
1170
1196
1397
Top 10 types
Skoda Octavia
Suzuki Swift
Ford Focus
Volkswagen Golf
Opel Astra
Renault Mégane
Nissan Qashqai
Volkswagen Polo
Renault Fluence
Opel Corsa
Sold units
2250
753
2361
1424
1839
851
693
719
758
553
Forrás: Jato Dynamics (2010). http://hvg.hu/cegauto/20101119_auto_toplista #utm_source=hvg_
daily&utm_medium=email&utm_campaign=newsletter2010_11_19&utm_content=normal
(Date of downloading: 20.11.2010).
In Hungary, in 2010, the most popular car brand was Ford, the second one was
Skoda, and the third one was Volkswagen. The most popular model was still the Ford
Focus, followed closely by Skoda Octavia (Table3). In the period between January and
September in 2011 nearly 34 thousand passenger cars were registered or put in service
in Hungary. The domestic sales figures related to the 10 most popular car models are
illustrated in Table 4.
288
László Józsa
TABLE 4
Top 10 brands and models in Hungary, period January-September, 2011
Sold units
Top 10 brand
1.
Skoda Octavia
2.
Opel Astra
3.
Ford Focus
4.
Volkswagen Golf
5.
Volkswagen Passat
6.
Renault Fluence
7.
Renault Mégane
8.
Dacia Duster
9.
Volkswagen Polo
10.
Opel Corsa
2339
2135
1925
917
828
766
720
704
672
664
Forrás: Jato Dynamics (2011). http://hvg.hu/cegauto/20110421_nepszeru_
autok (Date of downloading: 20.10.2011).
Analysis of the domestic suppliers’ market environment
Following the political transformation in Hungary the car market and automotive
industry soared. The favourable situation of infrastructure in Hungary also contributed
to the expansion of the industry and market. Analysing the supply-side it can be stated
that due to the favourable endowments of plants, the experts’ competence as well as
their experiences gained from former projects, and last but not least the wages of labour
force being lower than those paid on West-European markets the foreign investors saw
excellent potentials in the Hungarian supply market.
However the demand-side also changed concurrently. Better living standards of
consumers, the higher real incomes of people, and perhaps their desire and openness to
novelties contributed to the fast development of car market. But the recession has
changed the situation to worse.
In Figure 2 the regional distribution of passenger cars in 2010 is indicated.
In terms of industrial production, in Hungary, the largest setback, by 17.2% was
faced in 2009, and the export sales dropped by 18.8%. By 2010 the sector figures
improved, with the exception of domestic sales, but they still did not reach the level
recorded prior to the recession.
Apart from the automobile and automobile component manufacturers several other
suppliers can be categorised among leather, rubber, plastic, paint, glass, metal
processing, or electronic industrial sectors from among the role players of domestic
automotive industry. The market of the part manufacturing companies also differ a lot
from one another, considering the fact that there are companies who produce only for
automobile factories, while others supply only commercial vehicle assembling plants,
289
another group supplies both types of vehicle manufacturers, and others supply also
consumers outside the automotive industry. At the turn of millennium Hungarian
vehicle manufacturing was an industrial sector generating the third highest production
value, and in the period between 2005 and 2007, it was the second one in this ranking:
during this period of two or three years its weight increased from 12.2% (2000) to
17.3% (2007), i.e. by 41.8%. The weight of companies engaged in automotive industrial
activities was even higher, but it did not exceed the rate achieved by the largest domestic processing industrial subsector, the “production of electric machinery and instruments”, with 25 to 26% (KSH, 2008).
FIGURE 2
Regional distribution of cars in 2010
Western
Transdanubia
11%
Central
Transdanubia
11%
Southern Great
Plain
13%
Northern Great
Plain
13%
Northern Hungary
10%
Central Hungary
33%
Source: Own editing based on the figures of KSH, 2011.
Southern
Transdanubia
9%
290
László Józsa
FIGURE 3
Regional distribution of buses in 2010
Western
Transdanubia
9%
Central
Transdanubia
15%
Southern Great
Plain
10%
Northern Great
Plain
12%
Northern Hungary
13%
Central Hungary
31%
Southern Hungary
10%
FIGURE 4
Regional distribution of trucks in 2010
Western
Transdanubia
10%
Central
Transdanubia
11%
Southern Great
Plain
14%
Northern Great
Plain
12%
Northern Hungary
10%
Central Hungary
34%
Southern
Transdanubia
9%
The share of automotive industry in the aggregate sales revenues of the 500
domestic companies’ with highest sales revenues was 9.8%, and within the sales
revenues the rate of export was 90% (HVG, 2010). This rate was generated by
altogether 40 companies, namely the following:
Audi Hungaria Motor Kft.
Magyar Suzuki Zrt.
Robert Bosch Elektronika Kft.
Lear Corporation Hungary Kft.
Denso Gyártó Magyarország Kft.
LuK Savaria Kuplunggyártó Kft.
Visteon Hungary Kft.
BorgWarner Turbo Systems Kft.
Continental Teves Magyarország Kft.
BPW-Hungária Kft.
Rába Járműipari Holding Nyrt.
Hammerstein Bt.
ZF Hungária Kft.
SMR Automotive Mirror Technology
Hungary Bt.
Robert Bosch Energy and Body
Systems Kft.
ZF Lenksysteme Hungária Kft.
Ibiden Hungary Kft.
MÁV-Gépészet Zrt.
LKH Leoni Kábelgyár Kft.
291
Rába Futómű Kft.
Knorr-Bremse Fékrendszerek Kft.
Delphi Thermal Hungary Kft.
Knorr-Bremse Vasúti Jármű Kft.
BOS Automotive Products Bt.
Linamar Hungary Nyrt.
Magyar Toyo Seat Kft.
Emcon Technologies Kft.
Dana Hungary Kft.
General Motors Powertrain Autóipari Kf t.
Suoftec Kft.
W.E.T. Automotive Systems Kft.
Schwarzmüller Járműgyártó Kft.
Modine Hungária Kft.
Videoton Autóelektronika Kft.
Benteler Autótechnika Kft.
AGC Autóipari Magyarország Kft.
Summit D&V Autóipari Kft.
Rába Járműipari Alkatrészgyártó Kft.
Wescast Hungary Zrt.
Bombardier MÁV Kft.
Hungarian automotive industry’s internal resources and features are taken into
consideration, and the external, directly or indirectly influencing factors, such as
economic policy, political, legal and other features are summarised in the framework of
a SWOT analysis in Table 5.
Development trends at supplier companies
The companies of this sector pay much attention to R&D&I activities, which, in long
terms, is an indispensable precondition to competitiveness. The intensity of domestic
automotive industry’s R&D activities, however, shows significant lag compared to
other EU member states, and more precisely, to Central European countries. The R&D
activities in the domestic automotive industry are less intensive only in Portugal,
Romania and Slovakia, and this fact – showing continuous improvement – means a
considerable challenge for the role players of domestic automotive industry. There are
several powerful, large and middle sized companies also carrying out R&D activities,
and manufacturing for the international market present in Hungary, but R&D&I
activities need to be enhanced in order that the companies shall be able to keep up with
other role players of the global automotive industry. To this end it is indispensable to
armour the technical education with innovative approach, to let it give competent,
proactive and excellently qualified experts to the labour market.
292
László Józsa
Description of automotive industrial Research&Development
Significant academic and corporate technical research and development traditions may
be deemed as strengths as well as the fact that at Tier1supplier level at several large
companies remarkable R & D activities are carried out for the international market.
Among the weaknesses it should be mentioned that R & D is highly concentrated, that
is only few companies perform effectively R & D activities; the weakness of the R & D
activities performed at automotive industrial SMEs is attributable to the lack of sources
and motivation; apart from the select universities there are few higher educational
institutes giving well-qualified development engineers to the market. A tool to keep the
multinational large and middle sized enterprises in Hungary and to attract new ones is
relevant motivation of R & D activities through economic political means; in line with
R & D activities ordered by the industry and market knowledge centres and national
laboratories should be set up. It may be identified as a certain risk that there is a lack of
researchers in West Europe nevertheless the salaries paid to Hungarian research and
development engineers are nearly half of those paid in West-Europe. Local R&D
activities related to production represent specific attractiveness, which assists to retain
the automotive industrial capacities once settled in Hungary and to which foreign
interests are associated, along with a supplier sector of appropriate scale and quality.
Additionally, the fact should be mentioned, that as an effect of the financial crisis
certain part of the R&D experts of automotive industry will leave his or her profession.
TABLE 5
Description of the automotive industrial suppliers’ environment
Strengths
− Remarkable traditions in automotive industry and the related supplier chains;
− Hungarian
automotive
and
part
manufacturing sector have integrated
properly into the European and global
division of labour;
− The majority of the large Tier 1 international automotive suppliers are present in
Hungary;
− Several very strong large and middle sized
companies performing also R&D activities
and manufacturing to the international market are present in the country;
− Two decades’ experience gained in investment programs assisted in the organisation
of domestic specialised industrial capacities
into a network;
Weaknesses
− The clusters are functioning, for the time
being, only formally and only until the end
of the state subsidy period; they are unable
to sustain independently;
− Tier 2–3 suppliers’ prices are so much
depressed by customers that they hardly
generate any profit, thus they do not have
any source for development;
− Supplier companies “ripen” relatively
slowly: on the average a company becomes
a full supplier for the customer in two years;
− Hungarian supplier network is not adaptive,
either they are incapable or they can only
slowly follow the increase of production
volume at the automobile factory;
− The rate of Hungarian supplies – in the case
of both passenger car assembling plants in
Strengths
− Suppliers have significant professional interest representation organisations.
Weaknesses
Hungary – is low and often they are not
directed to products or parts conveying high
technical value, but a high rate of supply is
represented by services not integrated in the
finished product and by subsectors being
not knowledge intensive;
− In the region of existing car factories there
are no proper building sites available for the
suppliers.
Opportunities
− Large automotive industrial companies may
act as cluster organisers;
− Due to the high volume orders the car
factories and their tier 1 suppliers are
classed-up by suppliers;
− New suppliers might be attracted to
Hungary due to the settlement of Daimler
factory, which provides opportunities for
the whole sector;
293
Threats
− As a consequence of the financial recession
loan-lending to the SMEs is more
expensive, or it might also be terminated,
which may entail the bankruptcy of the
masses of SMEs who are not liquid enough;
− Distrust being a typical characteristic in
Hungarian business life is not favourable
for the development of supplier networks;
− Circular debts may reach the suppliers, thus
even the already existing supplier network
may also get damaged.
Source: Own editing (2011) based on BMI, 2010 and Havas, 2010.
In a short term, the substitution of this intellectual capacity is unrealistic. Large companies do not let the R&D activities of key fields out of the parent company’s hands, thus
in Hungary there is no chance to carry out R&D activities of higher relevance, or if yes,
then just in very few exceptional cases.
Some factors influencing competitiveness and being important in respect of strategy:
− Improvement of labour force-supply,
− Development of labour force demand/supply, number of vacant positions,
− Number of employees bearing higher educational degrees or PhD scientific
degrees in research sector,
− Retaining labour force, number of jobs saved,
− tendering system adjusted to the needs in automotive industry,
− displacement of supplies in the direction of products to be integrated and
representing higher technical standard categories (based on the priorities of the
supplier program),
− ration of component supply within overall supply,
− development of transport and network infrastructures,
− acceleration of logistical developments being relevant in respect of automotive
industry,
− number and value of logistical investments,
294
−
−
−
−
−
−
−
−
László Józsa
preservation of R&D workplaces,
introduction of close-to-production R&D at Tier–3 suppliers,
R&D expenses at Tier 3 suppliers,
Pursuance and resuming of investment promoting, economy development and
cluster-building programs with higher sources,
Number of winning automotive industrial clusters,
Acceleration of investment promoting programs,
Reliefs for administration and taxation and reliefs provided by industrial parks,
Investors’ opinion of the business environment.
According to Havas (2010) Hungary takes the last-but-one position in the EU–27
ranking set up in accordance with the ratio of innovative undertakings, and is also
lagging significantly behind the EU–27 average: with its ratio of 20.1% compared to the
average 38.9%. However the ratio of innovative – i.e. introducing a new product or
procedure – Hungarian automotive industrial undertakings is much over the processing
industrial average, and the rate of sales revenues from new products is also higher in
automotive industry.
From among the types of innovation being typical to the Hungarian automotive
industry we may mention product innovation; process innovation: just-in-time, TQM,
lean manufacturing cells, production planning, heat treatment; organisation management
innovation: set up of new functions (divisions) in the rural plants becoming more and
more independent as well as at Hungarian affiliate companies receiving more rights in
decision making; introduction of new financial and accounting methods (e.g. Suzuki
suppliers); marketing innovation. But it is also important to mention that knowledge
sharing networks: supplier programs (Suzuki, Opel, ZF), clusters appeared.
Heat treatment shall be mentioned among the non-research and development activity
based innovation; modernisation of the manufacturing equipment from maintenance
costs (in the lack of sources granted for development projects, for the sake of a good
achievement in the competition between the plants of the international parent company,
for gaining new contracts (Havas, 2010).
Presentation of the supplier companies’ performance – Findings of the
exploratory research
This sub-section is based upon the data of the primary research accomplished in the first
quarter of 2011. One stage of the survey is composed of the in-depth interviews made
with automotive industrial suppliers functioning in Central and Western Transdanubia.
The information comprised in the in-depth interviews pertaining to company
performance and the competition on the market is discussed below.
The majority of the companies involved in the survey have no competitors on the
domestic markets and they participate in international competition through their parent
companies, where, as they declared, they play important roles. The following strengths
can be underlined in the case of automotive suppliers based on the survey:
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
295
Professional/vocational knowledge,
Social resources,
System of relations,
flexibility,
possibility for small series production,
provision of extra services,
proactivity,
conforming quality,
geographical location (infrastructural background),
wide product portfolio,
wide market,
stable base of employees,
experience,
specialisation,
product development,
innovation,
success orientation,
business environment,
integrated corporate management system,
family owned,
reliability,
market share.
The interviewees mentioned the following factors as weaknesses:
− problems attributable to the small size of companies (lack of capacity, fast
increase, manufacturing large series products),
− burdens,
− bureaucracy,
− statutes,
− change of generations,
− local lack of labour force, shortage of experts especially young specialists,
− economic background is not predictable, the market is uncertain,
− lack of foreign language command,
− brain drain by other companies,
− geographical location (closeness of the state border, drain of labour force),
− insufficiently developed office culture (workers’ low discipline).
Two major trends are observable as regards the customer relations. At companies
with foreign parent companies the headquarters concludes contracts, salespersons are
employed i.e. the company headquarters liaise with one another in the seller-costumer
relationships. The significance of the other trend rests in the fact that customer relations
are established and maintained with the assistance of the personal and internal system of
296
László Józsa
relations built at the former workplaces, taking benefit from former references and good
reputation. In the case of existing customer relations at management level trust,
transparency, reliability, quality and consistence are inevitable.
Among the weaknesses of automotive suppliers the secure raw material supply is the
fundamental one to be mentioned. Unfavourable purchasing conditions, rising raw
material prices, due to the recession reduced capacities, quality defects detected among
the Hungarian suppliers and inflexibility may also be listed as reasons for weaknesses.
The lack of experts is also a problem, whereas the generation of under–40 to 50years of age is missing in several professions and trades. “Soil-bound” character of
Hungarian employees should also be mentioned as a weakness. Proper business culture,
business norms, business strategies are missing, and in many cases the fundamental goal
is still to gain the most profit in the possibly shortest time.
The majority of the suppliers involved in the survey do not participate in any
cooperation networks for manufacturing, sales or business.
Regarding automotive industrial development the companies examined in this
survey availed themselves of several technology and other development related tender
facilities in the past two years.
− ÚMFT (procurement of assets)
− GOP (capacity expansion, procurement of assets, technology development,
production management system, job creation, product development),
− NKTH Nemzeti Technológiai Platform (National Technology Platform) (R&D
co-operation with universities within the Integrated Automotive Industrial
Product and Technology Development System (IJTTR) and the procurement of
assets),
− INNOREG (special precision-type face grinding and form grinding technology).
− NYDOP (plant development, education)
− Baross Gábor Program (product development, production of new tools,
procurement of assets)
− INNOCCSEKK (motor development, prototype production)
− KDOP (enlargement of the production hall, technology development)
The companies determined also development targets to be achieved by 2014. Fundamentally, they intend to stabilize their operation, and subsequently they wish to
enhance their market shares – by way of portfolio extension (both in Europe and on the
global markets). Extension is accompanied on the one hand by organisation development and staff increase, on the other hand by the education and development training
provided to the existing blue collar and white collar staff. Some of the companies will
accomplish these by means of their own, in-house training and coaching materials. As a
result of the expansion instead of the rented premises or facilities they intend to have
their own, independent premises. Once they have their own premises they target to
expand it (production hall, office building). Besides increasing the production capacities
they also intend to enhance effectiveness of the existing machinery, and they plan to
297
carry out technology developments especially for the purpose of quality product manufacturing.
In relation to the development of supplier system the reduction of the shipping
expenses is set as a target, to this end, steps are taken to establish the Hungarian supplier sphere. Their future strategies include on the one hand to reinforce relationships
with the current customers (proactivity, common developments), on the other hand to
extend the clientele in order to obviate dependence in the relationships.
In terms of finance, the most essential is to sustain liquidity.
The problems of the organisations are associated with the lack of professionally or
vocationally qualified labour force, with the economic and legal environment, and with
the size of the company.
In the regions under review the highly qualified and experienced labour force is
missing, in principal, the lack of resource in competent engineers with foreign language
command causes problems. It is even more difficult to keep employees with competitive
abilities. In the field of high labour costs minor companies cannot be competitive, and
this results in high fluctuation. As regards qualified labour force the change of generations causes dilemmas, namely due to the fact that the career correction or the possibility of shifting between qualifications in the case of elderly labour force has yet to be
solved on the Hungarian education market. The younger generation has no sufficient
professional knowledge, foreign language command and professional experience.
During the rapid growth following the first cycle of recession difficulties were faced
with the provision of appropriate number of labour force and in many cases its unpredictable character also entailed problems. Not even after the rapid growth did the
development of organisational structure take place, considering the fact that the structure supporting the decision making mechanism is still not in hand.
One of the major problems in the economic environment is the fact that material and
energy costs are growing, the profit is declining, there is a dependence on material
suppliers, the exchange rate varies, and the capacity of the supplier network is still
instable. Even troubles with financing emerge (due to delayed payment of customers);
capital lacks and borrowing loans is also troublesome. Fast changing customer demands
even amplify economic uncertainty.
Many companies are inflexible due to its small size. In several cases, however, there
is no way to extend the business.
Results of the questionnaire survey
This subsection is built upon the data collected in the course of the questionnaire survey
performed in the framework of this research. Table 6 demonstrates the per-country
distribution of automotive suppliers involved in the survey.
298
László Józsa
TABLE 6
Per-county distribution of suppliers in Western Transdanubia and Central Hungary
Frequency
%
Valid%
Cumulative%
Budapest
Győr-Moson-Sopron County
Komárom-Esztergom County
Pest County
Vas County
Zala County
12
7
6
15
5
4
24.5
14.3
12.2
30.6
10.2
8.2
24.5
14.3
12.2
30.6
10.2
8.2
24.5
38.8
51.0
81.6
91.8
100.0
Total
49
100.0
100.0
Source: Own survey, 2011.
From the Western Transdanubian and Central Hungary 49 automotive suppliers participated in the questionnaire survey. The sales revenues in 2010 were under 500 million
HUF in the case of nearly one-third of the respondents.
Over half of the undertakings involved declared that the activities performed as
automotive suppliers dominate in respect of their sales revenues.
In respect of product lines with the highest frequency the manufacturing of 2–5, and
6–10 product lines takes place, which suggests strong diversification of the portfolio.
The following position is typical to most of the Hungarian automotive suppliers as
regards pricing and market tendencies.
In the past three years the major impetus for the undertakings was represented by
market expansion, the growth of existing customers and the extension of the product
range. Willingness for co-operation is unfortunately still to be considered very low.
As for market position, over half of the respondents experienced slight improvement
during the past three years.
Stableness and the existing relations predominate when partnership relations are
established.
Conclusions, recommendations
Several factors influence automotive industrial decision making. Customers’ requirements are determining as regards the style, reliability and output of the cars.
Commercial, security and environmental regulations set incentives to encourage
modernisation, technology improvement, and alterations in the fields of design and
manufacturing. Both the competition between companies and company strategies give
essential impetus to research, innovation and the development of manufacturing processes. In addition to consumer demands exerting constant pressure on car factories,
national peculiarities and the definition of new market segments are also prominent.
Their roles taken in the industrial segment is determined by how fast they are able to
react to any such demands. One of the most essential factors is the demand for brand
299
new cars, which on the average increased by less than 1% during the past decade, and
according to forecasts this tendency will continue, but its focus will be set in the world
outside Europe. The most rapidly developing region is currently South America, with an
average growth of 10%. Development of the energy prices is also an influencing factor,
whereas it affects the consumers’ decisions, accordingly, the tendency – that low fuel
consumption as a requirement towards a car overrules the comfort level and size of a car
– was especially observable in the period between 2009 and 2011.
Furthermore, clearly distinguished layers of consumers are also identifiable, for
whom environment awareness is of crucial importance, and besides they possess the
income ratio and purchasing potential to become interested in the purchasing of cars
produced with the application of the latest, environment friendly technologies.
Changes are also significant on supply side as well compared to the period preceding the recession. From among the great many economizing measures triggering cost
reduction the factor essentially affecting the labour market shall be highlighted,
according to which in many cases the companies prefer employing wage workers and
workers with definite term contracts to their own employees or workers at the production lines. All in all it may be stated that Hungarian automotive industry is to be cope
with global challenges. Table 9 below gives a summary of such challenges.
Table 10 gives a summary of the trends in automotive industry.
TABLE 9
Challenges in the automotive industry
External factors
− Legal background (environment,
security/safety),
− Material and energy price,
− Exchange rate and interest rate,
Customers
− Stagnating demand, depressed prices,
− Segmentation and polarization (low price
vs. premium),
− Declining loyalty,
Competition
− All the segments are reached fast,
− Everyone is optimising, regrouping,
− global – aggressive Asian companies,
Industry
− complex alliances,
− consolidation, “ecosystem” (suppliers, sales
persons).
Source: BMI, 2010 and Havas, 2010, and own editing based on own studies, 2011.
TABLE 10
Trends in the automotive industry
Supplier’s side
−
−
−
−
differentiated outsourcing
outsourcing to cheap countries
risk management
transparency
Demand side
−
−
−
−
unbalanced growth
fragmentation
increasingly volatile
importance of remanufacturing
Source: BMI, 2010 and Havas, 2010, and own editing based on own studies, 2011.
300
László Józsa
Limits of this Research
It is essential to underline the following factors that represent the limits of this research:
− This research is of revealing character.
− Research regions rely on primary figures (Western Transdanubia and Central
Hungary).
− The survey was performed in the first half of 2011.
− The method of this research does not enable generalisation, considering the fact
that it is not supported by research results received from representative samples.
Practical application of the results, management implications
–Trends of future research work
One of the possible future trends of research to be underlined might extend to the
examination of sales figures relevant to the complete Hungarian automotive industry,
based on which middle term and long term forecasts may be provided for the companies
of this sector.
Relevant to the manufacturers and suppliers’ R&D&I activities there were no
figures with sufficient information-content available and accessible, thus this field is
recommended to be studied. The research is worth to be extended in the future
territorially and respondents should be involved in the survey from each region of
Hungary and the international comparison is also possible.
Furthermore in respect of the competitiveness of this industrial sector and Hungary
it shall also be surveyed in which direction the manufacturers and suppliers perform
research-development-innovation activities; and what position they take compared to
other role players of the European and global markets.
LIST OF CONTRIBUTORS
Györgyi Barta, DSc, university professor, Széchenyi István University, bartagy@sze.hu
scientific advisor, Hungarian Academy of Sciences Research Centre for Economic
and Regional Studies Institute of Regional Studies, barta@rkk.hu
Zoltán Csizmadia, PhD, associate professor, Széchenyi István University
csizmadia@sze.hu
research fellow, Hungarian Academy of Sciences Research Centre for Economic
and Regional Studies Institute of Regional Studies, cszoltan@rkk.hu
Tamás Dusek, PhD, associate professor, Széchenyi István University, dusekt@sze.hu
Anita Füzi, PhD student, Széchenyi István University Doctoral School for Regional
Science and Economics, anita.fuzi@sze.hu
Szandra Gombos, PhD student, Széchenyi István University Doctoral School for
Regional Science and Economics, gombossz@sze.hu
László Józsa, CSc, university professor, Széchenyi István University, jozsal@sze.hu
Katalin Kollár, project administrator, Audi Akademie Hungaria Kft.,
akademie.Kollar@audi.hu
PhD student, Széchenyi István University Doctoral School for Regional Science and
Economics, kollarkatalin86@gmail.com
Mihály Lados, CSc, associate professor, Széchenyi István University, lados@sze.hu
senior research fellow, head of department, Hungarian Academy of Sciences
Research Centre for Economic and Regional Studies Institute of Regional Studies,
ladosm@rkk.hu
Imre Lengyel, DSc, university professor, University of Szeged,
ilengyel@eco.u-szeged.hu
Miklós Lukovics, PhD, associate professor, University of Szeged,
miki@eco.u-szeged.hu
Márta Nárai, PhD, associate professor, Széchenyi István University, naraim@sze.hu
research fellow, Hungarian Academy of Sciences Research Centre for Economic
and Regional Studies Institute of Regional Studies, naraim@rkk.hu
János Rechnitzer, DSc, university professor, Széchenyi István University,
rechnj@sze.hu
scientific advisor, Hungarian Academy of Sciences Research Centre for Economic
and Regional Studies Institute of Regional Studies, rechnj@rkk.hu
Péter Savanya, PhD student, University of Szeged, savanya.peter@eco.u-szeged.hu
Melinda Smahó, PhD, assistant professor, Széchenyi István University,
smahom@sze.hu
Tamás Tóth, PhD student, Széchenyi István University Doctoral School for Regional
Science and Economics, tamas.toth@sze.hu
Research supporting staff
Károlyné Pálvölgyi, administrative assistant, Széchenyi István University,
palvolgy@sze.hu
Melinda Pató, executive-expert, Széchenyi István University, patom@sze.hu
Eszter Szabados, executive-expert, Széchenyi István University, szabados@sze.hu

Vehicle Industry and Competitiveness of Regions in Central and

Transcription

Similar documents

RHS AND CRYROP - Redlands High School

Mobiletron - moocreations

GNYADA Sales Academy - Greater New York Automobile Dealers

Presented to WRIC TV 8 Richmond, VA

Yahoo! Yellow Pages - Halifax Community College

1674-OREG- O`Regan`s Apprentice Academy.indd

Youth football 5 - Louetta Automotive

To be where the customers are

Dilawri Corporate Fleet Program

automotive upholstery handbook - California Bill`s Automotive