Influence of Science and Research on Regional
Competitiveness-Czech Republic Case Study
Simona Pichova and Jan Stejskal
Faculty of Economics and Administration, University of Pardubice, Pardubice, Czech Republic
Email: {simona.pichova, jan.stejskal}@upce.cz
Abstract—Concept of the knowledge economy and
innovation development is an important aspect of regional
competitiveness. Rating competitiveness of regions and
identifying disparities between them is an important tool
that can be used both for exploring the extent of the effects
of individual determinants of the competitiveness of the
region, but also for the government in case of a decision on
granting aid for the purpose of promoting economic
development and increased competitiveness. This paper
presents the application of the Index of regional
competitiveness in the NUTS3 regions in the Czech Republic.
The aim of the present paper is the analysis of selected
determinants of the knowledge economy and their impact
on regional competitiveness. This is done with the help of
elasticity calculations. It is a new concept in measuring the
level of competitiveness and measuring the impact of
individual determinants.
Index Terms—region, regional competitiveness,
development, innovation
I. INTRODUCTION
Economic development is influenced by uneven
distribution of manufacturing resources, economic and
extra-economic factors which influence the total
effectiveness of economic development. In consequence
of this fact, there is space differentiation and therefore
regional disparities origin. This is the source of regional
policy which tries to reduce negative economic as well as
social impacts of uneven development and at the same
time to support regional potential and its utilization for
the increase of inhabitants´well-being [1].
Regional policy increases its importance also by the
fact that regions are considered in the current globalized
economy as driving engines of their economic
development and growth and therefore also as driving
engines of the development and growth of national
economies. There is utilization of available
manufacturing resources and other components of
potential (mainly knowledge and ability to learn). It is
possible to realize the concept of industrial clusters and
other forms based on triple-helix principles and
cooperation better at the regional level than at the
national one [2]. The regional policy supporting creation
of innovations or creation of innovative systems

Manuscript received July 7, 2014; revised September 15, 2014.
contributes significantly to consolidation of regional
importance in the economic development.
II. REGIONAL COMPETITIVENESS
Competitiveness is considered as a basic determiner of
long-term success not only of regions but also states, as
regards economic development. Generally,
competitiveness expresses country’s ability to penetrate
into the foreign market and so being able to compete with
other states [3]. The assessment of competitiveness in a
region has its specifics. In reference [4] is defined the
regional competitiveness as the ability of local economy
to attract companies with stable or growing shares in the
market and also the ability of this regional economy to
ensure stable or growing living standard of the
participating parties [4]. According to [5], the regional
competitiveness isn’t defined only by the sum of success
of individual companies but it also reflects the impact of
non-profit organizations and companies´cooperation. A
suitable tool for meeting this target seems to be
innovations as results of knowledge use in the production
process. References to [6] they perceive the ability of
creating innovations as a determiner of business
competition and considers the space level of regions as an
ideal for innovations´enforcement. He states also that
innovations are driving force for companies and their
influence sets ambitious targets for companies. These
targets lead to the renewal of industrial structures and
contribute also to the origin of new branches in economic
activities. According to [7], investments in creation,
propagation and utilization of new knowledge started
being applied more and more in contrast to traditional
factors (material, working, capital ones). This knowledge
becomes so one of the main sources of wealth of people,
companies, regions and countries. In reference [8]
suppose that the economic growth depends on the
accumulation of knowledge and its spread via business
activities. This collective developed the concept of
knowledge filtration system which would work as a
barrier limiting the total change of knowledge into new
products, procedures and organizations. They argue that
the change of knowledge in regional economies proceeds
only via common actions (=cooperation) between
corporate subjects and creators or carriers of knowledge
(=scientific research organizations and universities). The
carrier of costs for this cooperation can be the public
sector. [9] This meets triple-helix principles. Innovations
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incurred from this cooperation represent transformed, socalled
relevant economic and uneconomic knowledge.
The economic knowledge represents results of an applied
research which supports companies in creating new
economic opportunities and introducing new ideas in the
market. On the other hand, uneconomic knowledge is the
result of a basic research. During its transformation, this
knowledge doesn’t have any economic value
commercialized in the market. However, in the long term
this knowledge is necessary for the applied research and
gaining of important competitive advantage of involved
regional economic subjects.
When searching for the sources of regional
competitiveness, more and more economists (often called
as so-called Marshall’s followers) call attention to the
importance of basic determiners influencing the
economic growth of a region and also its competitiveness.
These determiners are relative geographical proximity
(sometimes also agglomeration) and spillover effects
incurred from cooperation ([10]-[12]). Thanks to the
geographical proximity of cooperating subjects and via
natural contacts, there are interactions causing positive
effects, often synergistic ones. If there is utilization and
development of knowledge based on science and research
within this cooperation, spillover effects of knowledge
are concerned. Corporate subjects involved in such
cooperation gain benefits which reflect in their economic
results ([1], [13]). They have an opportunity to gain a
competitive advantage in the market as compared to
isolated companies. However, natural increase of
knowledge and skill potential of employees is typical for
them without spending any additional costs for this
increase. Another effect is consolidation of cooperative
linkages and development of science and research as the
platform for creation of innovations [14]. The above
mentioned text implies to the fact that the basic
production factor which has the creation potential of
global competitive advantage is knowledge. Knowledge
is the main driving force of technological progress as well
as economic growth [15]. Other researches show however
that the excess of geographical proximity, respectively
too dense cooperative linkages among subjects in a small
region, can work also negatively. Too large
agglomeration and too dense knowledge transactions
disperse subsequently the advantages of knowledge
spillover effects because losses, caused by the decrease of
appropriability, incur [16]. Appropriability is company’s
ability to keep the added value which the company
creates for its own benefit. Nevertheless, who has benefit
from this added value depends on company’s decision,
structure of the market where it works and sources of the
added value [17].
Many authors (e.g. [10], [18]-[20] etc.) investigated the
nature of processes which proceed in regions when
realizing cooperation based on knowledge (and of course
processes where knowledge spillover effects incur). Two
main processes were described. The first one describes
only so-called Marshallian externalities which origin
mainly within one certain branch within the agglomerated
territory with a dense internal structure of cooperative
linkages. The second type of processes was originally
designed by [18] and it focuses on various externalities
which support creativity and origin of new ideas across
branches. Diversity of local activities plays an important
role in the innovative process because a completely
natural request for the increase of production capacity
results from this diversity. This request leads to
production of more and various goods and services.
Interesting expansion is provided also by [19]. They
argue that specialization and diversity of externalities can
appear also within additional (service) industrial branches
which use the results of science and research.
The importance of knowledge spillover effects, as the
determiner of new knowledge creation, is supported by
most authors of theoretical models of endogenous growth,
e.g. [21]-[23].
They agree that cooperative linkages in a certain
relatively small territory (region) and between creators
(science and research or universities) and recipients of
knowledge (companies) are necessary for the origin of
spillover effects. Thus, knowledge transformation into a
commercialized innovation proceeds faster, is cheaper
and contributes to the effective allocation of private as
well as public means. However, the mentioned statements
about the influence of “university determiner of economic
growth and development” don’t apply in all cases, for
sure. There are many researches of the influence of
science and research on the economic development of
regions ([5]-[6]). However, all of them wrestle with
heterogeneous methodology or methods of
competitiveness evaluation or hard measurable
conception of science and research or with their results
[24].
Therefore the ambition of this article is to consider the
influence of science and research on regional
competitiveness in the Czech Republic and evaluate its
development in the period 2004-2012. The indicator of
regional economic efficiency and its sensitivity reaction
to selected determiners will be used for the analysis. The
indicator will help us find out if there isn’t so-called
innovative paradox or if the support of science and
research in Czech regions abets to increase the
competitiveness.
III. COMPETITIVENESS AND ITS EVALUATION
To define the concept of competitiveness it is possible
to view from a microeconomic or macroeconomic
perspective. Competitiveness is generally defined by
OECD: Competitiveness is the rate of ability with which
a country is able to produce goods and services in open
market conditions. This goods and services have to
compete in the international competition test and also
keep and increase the real domestic income [25].
In reference [24] considers competitiveness as a
combination of positive business efficiency and extras.
References to [26] they consider a subject, which can best
adapt itself to the changing environment or will create
this environment with its activity, as the most competitive
subject.
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It is quite an abstract concept which is however one of
the most quoted characteristics of economic subjects
(mainly companies) as well as regions and states in
globalized economies. All the mentioned entities deal
with the maximization of their prosperity which however
builds always on economic or social development, as
regards regional viewpoint. Even the ability of regional
development to contribute to the increase of economic
subjects´prosperity in the given region is often expressed
even by regional competitiveness.
There are several sources of regional competitiveness.
It is mainly export specialization which concentrates on
location factors influencing production costs. It is mainly
utilization of present production factors, savings thanks to
the market proximity, various advantages or savings and
also the influence of localized direct foreign investments.
The second source of regional competitive advantage is
the region itself as a source of growing revenues.
Therefore subjects focus on cultivation of direct human
potential of manpower, utilization of available
technologies, results of science and research, huge
structural investments and concentrate on the origin of
innovations. The last source of competitive advantage can
be knowledge, ability to learn and its concentration in a
region. To gain this advantage, suitable innovative
environment have to be created in regions, there have to
be industrial clusters, business networks, eventually
regional innovative systems. All the sources of
competitive advantage can mingle in a region. This
causes synergistic effects.
There are many factors which influence
competitiveness and this implies to the difficulty of its
measurement. Nowadays, there isn’t any unified
procedure of measuring but many authors have already
published their own procedures, e.g. [4], [16], [27]-[29],
etc.
Other national approaches of member states of the EU
evaluate the competitiveness according to various types
of efficiency-economic and innovative one-and a part of
the evaluation is also the quality of life. Another
approach to the measurement of competitiveness was
provided by [27] who constructed the index of
competitiveness in Great Britain. It was the first attempt
to affect competitiveness with one number. Till then,
competitiveness was reviewed only by means of
individual indicators. In reference [27] tried to include
indicators evaluating inputs, outputs and results of
economy into the index. The index of regional
competitiveness was elaborated further by the European
Union. The Regional competitiveness index (RCI), which
compares regions of the European Union at the NUTS2
level from the viewpoint of their competitiveness, was
created. Many other similar indicators of regional
competitiveness were created later.
In the sixth periodic report of social and economic
situation and development of regions in the European
Union, the European Commission considers productivity
and employment and close interconnection of these
factors with other indicators as the main indicators of
competitiveness. Productivity is the result of other
factors´ influence. The number of investments in a
country, expansion of research and development, direct
foreign investments and capital level in the country
contribute to the productivity growth. A high productivity
level causes consequently higher wages, then higher
living standard and employment. A close linkage of
competitiveness and regional productivity is confirmed
also by [3].
IV. EVALUATION OF COMPETITIVENESS-CZECH
REPUBLIC CASE STUDY
A. Methodology of Evaluation
Based on the above proven relation between
productivity and employment, such an indicator was
chosen which affects this relation and is also able to
analyze the regional competitiveness or efficiency of the
utilization of individual production factors.
For the evaluation of regional competitiveness-in the
Czech Republic case study-the CER coefficient was
chosen. This index can be used for various levels of
regions and most often for analyses at the levels of
NUTS2 and NUTS3 regions. The results of CER
coefficient analysis record development of
competitiveness of individual regions during monitored
periods from the viewpoint of the relation between
productivity and employment.
Based on Hanclova´s work [30], it is possible to write
the form of CER coefficient as follows:
CER = , where (1)
CP = , where (2)
GDP region-gross domestic product in the given region,
GDP-gross domestic product in the Czech Republic
and
CZ = , where (3)
Z region-the number of employed people in the NUTS3
region,
Z-the number of employed people in economy,
S region-the number of inhabitants in the NUTS3 region,
S-the total number of inhabitants.
After some mathematical adjustments, the pattern (1)
can be written in the following form:
. (4)
The CER values indicate the competitiveness rate
which was reached in the given region with certain
employment. Efficiency expresses the ratio between
inputs and outputs. The CER coefficient has such values
which amount indicates the following facts:
 If CER = 1, it means that pace of regional
development (in the case study of NUTS3) and
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competitiveness are equal to the pace of entire
economy development;
 If CER < 1, the region reaches lower development
pace of the competitiveness rate than in case of the
national pace;
 If CER > 1, the region reaches higher pace of the
competitiveness rate, it is also more competitive
than the entire country.
B. Results of the Analysis and Discussion
Based on the data of the Czech Statistical Office for
the period 2004-2012, the CER coefficient was calculated
for individual Czech regions (NUTS3 size). The results
are mentioned in Table I.
TABLE I. VALUES OF THE COEFFICIENT CER IN THE NUTS3 REGIONS CZECH IN 2004-2012
Region 2004 2005 2006 2007 2008 2009 2010 2011 2012
Prague 1.80348* 1.86099* 1.86879* 1.87046* 1.96190* 1.95185* 1.91617* 1.91020* 1.86302*
Central Bohemia .89777 .90349 .87679 .91771 .89471 .88974 .87212 .85863 .85353
South Bohemia .86557 .86475 .88016 .84755 .84070 .84040 .84423 .82993 .85340
Plzen .89070 .91063* .90530 .92197 .87035 .84822 .82925 .85911 .85502
Karlovy Vary .78355 .76312 .76400 .70030 .71828 .68033 .71568 .70011 .71718
Usti .89296 .86766 .86019 .87798 .86532 .85767 .89493 .84440 .87673
Liberec .78618 .79112 .86098 .84569 .77480 .72574 .75773 .78373 .81634
Hradec Kralove .90627 .88672 .85527 .84372 .83246 .86275 .87320 .88399 .87109
Pardubice .86238 .82503 .81924 .85895 .83904 .84319 .80716 .81771 .80252
Vysocina .86719 .84498 .84436 .82840 .83265 .78767 .81863 .83509 .86086
South Moravia .95483* .94351* .93727* .94862* .94312* .97539* .94236* .95515* .95439*
Olomouc .85181 .83718 .78496 .78647 .80957 .79701 .82224 .82763 .82265
Zlin .83842 .83867 .81866 .82520 .80932 .85606 .87235 .86754 .89684
Moravia-Silesia .91162* .88931 .94032* .90212 .88820 .85889 .88064 .91668 .91841
Average .93662 .93051 .92973 .92680 .92003 .91249 .91762 .92071 .92586
Deviation +5 % .98346 .97704 .97622 .97314 .96603 .95812 .96350 .96674 .97215
Deviation -5 % .88979 .88398 .88325 .88046 .87403 .86687 .87174 .87467 .87956
Source: own processing
Note: * The value of the CER in the region is higher than average rate of modified competitiveness of the country for the year
Table I shows that only the capital city, Prague,
reaches higher regional competitiveness rate than 1. The
CER values in other regions are below 1. This result can
lead to distorted conclusions because it is necessary to
modify the data by means of creating an average CER
value for the entire republic, then set deviations from the
average and based on these values, we can better consider
the real competitiveness and economic situation of the
given NUTS3 regions. The deviation rate was set for +/-
5 %. According to [31], the average of values with the
same initial base provides the following results:
 CER values above + 5 % deviation from the
average indicate regions with a high
competitiveness rate;
 CER values oscillating around ±5 % average from
the average indicate regions with an average
competitiveness rate;
 CER values below-5 % deviation from the average
indicate regions with a low competitiveness rate.
Next research should investigate belonging of the
given regions to categories which were defined for a new
EU programming period 2014-2020 (depending on GDPless
advanced regions, intermediate regions and more
advanced regions). This way can increase the predicative
ability of the CER coefficient.
The CER coefficient enables interregional comparison
and on its base it is possible to monitor the economic
development in a time line. The capital city, Prague, and
the South Moravian Region record above-average values
for the whole monitored period 2004-2012 (the highest
CER value was noted in 2008 in Prague-1.96190).
Therefore we can say that these regions are the most
advanced ones with stable economic development and the
highest competitiveness rate. Except for 2006, the second
most advanced competitive region is the South Moravian
Region. The South Moravian Innovation Centre is
situated there. It was founded in 2003 and its main target
is support of origin and development of innovations for
enterprisers as well as students and private persons. The
annual report of the South Moravian Innovation Centre
for the year 2013 states that companies of the South
Moravian Region are disposed to invest more financial
means in research and development each year and that the
share of investments in innovations is higher than in other
regions. Innovative companies are more stable, can better
gain the foreign capital and invest in the utilization of
new technologies and creation of innovations. This
contributes significantly to the increase of own
competitiveness. We can consider the Moravian-Silesian
Region as the next most advanced region which exceeded
the average values in 2004, 2006, 2011 and 2012. The
survey of development of regional CER results helps find
out if and how the rate of regional competitiveness
changes in individual regions (none of the best evaluated
regions has constantly growing pace in 2012).
On the other hand, the last position is occupied by the
Karlovy Vary Region which showed the lowest CER
values (maximal value 0.8) for the whole monitored
period. Foundation of low CER values can be the absence
of universities and consecutive absence of highly-skilled
manpower and experts who are necessary for research
and creation of innovations. Further, there is a missing
infrastructure which would enable easier transport
connections with other regions. The Liberec Region
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showed significantly below-average values too. In 2006,
2007 and 2009 the Olomouc Region dropped below the
limit of 0.8 CER and the Vysocina Region dropped also
below this limit in 2009. The lowest value was measured
in 2009 in the Karlovy Vary Region (.68033).
The CER coefficient offers the possibility of
investigating competitiveness also during a time period.
In 2008 there was an economic crisis which had an
influence on deterioration of macroeconomic indicators
in the following years. The mentioned CER values imply
also to the fact that CER values were growing in most
regions until 2008. Regions were developed and
economically growing.
The development in 2009 is interesting. Only three
regions were above-average or oscillating around the
average (within the range of +/- 5 % deviation from the
average). In 2010 the situation was much better and seven
regions were included in the above-averagely or
averagely competitive regions. In 2011 and 2012 there
was a drop of CER values and only four regions out of
fourteen belonged to the group with a high or average
competitiveness rate.
In spite of the economic crisis, there was an increase of
the CER value in four regions in 2009: Hradec Kralove,
Pardubice, South Moravia and Zlin Region. We can
sustain the situation by the geographical proximity of the
Hradec Kralove and Pardubice Region. The same
situation is in the South Moravian Region which
neighbours on the Zlin Region. Another reason can be
mutual cooperation between the neighbouring regions or
spillover of knowledge, innovations and other
competitive tools. This conclusion is supported also by
the study of Srholec, Žížalová [32].
Source: own processing
Figure 1. The regions in which there was an increased rate of CER IN
2009
C. Analysis of CER Determiners
To have a higher predicative ability of regional
competitiveness rate, which is measured by the CER
coefficient, it is necessary to analyse in more detail the
influence of determiners influencing the competitiveness,
e.g. by the sensitivity analysis. It enables to monitor how
individual determiners (variables) influence the
competitiveness rate of each region. The principle of
sensitivity analysis is the calculation of elasticity between
determiners and the CER coefficient-Edet.
Regarding the fact that determiners of the knowledge
economy have the highest proven influence on
competitiveness of regions, the following determiners
were selected in compliance with study results ([4], [16],
[27]-[29]):
 Share of university students, studying in a region,
in regional inhabitants aged 16-64 years (STU);
 Number of scientific and research employees
(EMP);
 Expenses for science and research in a region
(FIN);
 Scientific and research outputs in a region (sum of
issued patents and utility designs; OUT);
 Number of subjects dealing with science and
research in a region (ENT).
Considering the range of calculations, analyses will be
executed only in selected NUTS3 regions of the Czech
Republic. The regions which CER coefficient indicated a
low or high competitiveness rate were chosen for the
presentation of differences between the influences of
individual competitiveness determiners:
 South Moravian Region and Moravian-Silesian
Region (CER2012 > .91841),
 Karlovy Vary Region and Liberec Region
(CER2012 < .81634).
We can find out the sensitivity or reaction when
calculating according to the following pattern:
Edet = (5)
where t1-value of a determiner during the monitored year;
t0-value of a determiner during the previous year.
The most negative reaction was noted in the Karlovy
Vary Region in 2006. The sensitivity reaction (with the
value-476.68572) to the change of CER coefficient was
noted when changing the output of science and research.
The most positive reaction with the value of 186.08430
was noted in the South Moravian Region in 2013.
According to the selected determiners, the following
biggest sensitivity reactions to the change of CER
coefficient were noted:
 Share of students in inhabitants aged 15-64 years:
the smallest reaction was in the Moravian-Silesian
Region in 2012 (-35.78440); the biggest one was
in the South Moravian Region in 2012 (84.79613);
 Number of scientific and research employees; the
smallest reaction was in the Liberec Region in
2009 (-8.61085); the biggest one was in the South
Moravian Region in 2012 (128.27158);
 Expenses for science and research; the smallest
reaction was in the Karlovy Vary Region in 2006
(-125.88256); the biggest one was in the South
Moravian Region in 2012 (186.08430);
 Scientific and research outputs; the smallest
reaction was in the Karlovy Vary Region in 2006
(-476.68572); the biggest one was in the
Moravian-Silesian Region in 2012 (107.93727);
 Subjects working in science and research; the
smallest reaction was in the Karlovy Vary Region
in 2006 (-78.83554); the biggest one was in the
South Moravian Region in 2008 (10.87047).
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TABLE II. ELASTICITY OF DETERMINANTS ON THE DEVELOPMENT INDICATORS CER IN SELECTED REGIONS IN 2004-2012
Year
Region CERy STU ESTU EMP EEMP FIN EFIN OUT EOUT ENT EENT
2004
Moravia-Silasia .91162 .04267 n/a 6 575 n/a 4 578 n/a 177 n/a 265 n/a
South Moravia .95483 .08657 n/a 16 347 n/a 14 654 n/a 145 n/a 466 n/a
Karlovy Vary .78356 .00639 n/a 200 n/a 204 n/a 12 n/a 22 n/a
Liberec .78618 .02775 n/a 2 286 n/a 2 850 n/a 55 n/a 90 n/a
2005
Moravia-Silasia .88931 .04413 -1.39872 6 313 1.63032 4 941 -3.23976 155 5.20330 259 .92510
South Moravia .94352 .08880 -2.17499 14 456 9.75587 11 170 20.05731 141 1.84878 445 3.80175
Karlovy Vary .76313 .00733 .17016 159 .14556 124 15.03894 32 -67.49477 22 n/a
Liberec .79112 .02899 .14081 2 147 - .10315 1 861 -55.22295 60 14.59962 93 5.30453
2006
Moravia-Silasia .94033 .04503 .35456 5 716 -1.64950 3 114 -6.44573 151 -0.38488 228 -2.08646
South Moravia .93728 .08962 -1.39945 14 017 4.60168 8 411 37.36686 126 16.05590 420 8.49901
Karlovy Vary .76401 .00829 .22980 154 .19272 106 -125.88256 15 -476.68572 20 -78.83554
Liberec .86099 .03062 1.57218 1 872 - .69112 1 449 -2.50674 68 1.55413 91 - .24350
2007
Moravia-Silasia .90213 .04499 .02147 5 356 1.54862 3 030 .66401 142 1.45310 201 2.91505
South Moravia .94863 .08852 -1.01726 13 529 -2.87367 8 127 -2.78867 149 14.76373 365 -10.81532
Karlovy Vary .70030 .00888 .27877 183 .23164 92 1.58398 9 4.89370 23 -1.79895
Liberec .84569 .03090 -1.94068 1 702 .19482 1 329 4.66151 68 - .41592 87 2.47419
2008
Moravia-Silasia .88821 .04194 4.39581 5 395 - .46707 2 661 7.89068 131 5.41462 192 2.90120
South Moravia .94313 .08535 6.17362 12 277 15.95788 6 047 44.15159 145 4.92333 342 10.87047
Karlovy Vary .71829 .00881 .24214 160 .19715 98 2.53913 10 7.55987 21 -3.38551
Liberec .77481 .02906 1.41250 1 727 -5.65336 1 517 -1.68769 45 4.01962 81 .82279
2009
Moravia-Silasia .85890 .03893 2.17416 5 336 .33103 2 765 -1.18436 143 -2.94134 189 .47349
South Moravia .97539 .08154 -1.30504 11 442 -1.98959 5 726 -1.55165 164 3.95683 321 -1.79483
Karlovy Vary .68034 .00859 0.26677 217 0.21669 78 3.86266 8 3.53920 22 - .90129
Liberec .72574 .02623 .65043 1 740 -8.61085 1 312 2.13399 74 -10.03276 74 1.36470
2010
Moravia-Silasia .88065 .03629 -2.67581 4 496 -6.21359 5 535 39.55403 135 -2.21850 181 -1.67122
South Moravia .94237 .07647 1.83578 10 963 1.23631 5 057 3.45074 191 -4.79213 315 .55206
Karlovy Vary .71569 .00778 .23845 330 .20216 71 -1.72730 8 n/a 19 -2.62460
Liberec .75774 .02345 - .41567 1 892 .50308 1 483 2.95636 48 -8.01097 74 n/a
2011
Moravia-Silasia .91668 .03339 -1.95494 3 886 -3.31455 2 182 -14.80495 183 8.65961 176 - .67512
South Moravia .95516 .07117 -5.11349 11 391 2.88072 4 654 -5.87180 242 19.78095 292 -5.37993
Karlovy Vary .70012 .00749 .30463 151 .24577 76 -3.23753 12 -21.60727 16 7.25888
Liberec .78373 .02165 - .44614 1 604 - .22567 1 110 -7.33203 105 34.90963 73 - .39394
2012
Moravia-Silasia .91841 .03113 -35.78440 3 831 -7.56048 2 212 7.29059 220 107.93727 160 -48.20627
South Moravia .95440 .06636 84.79613 10 227 128.27158 3 964 186.08430 246 -18.57333 292 n/a
Karlovy Vary .71718 .00638 .27561 268 .23560 96 10.79625 11 -5.86082 20 10.25644
Liberec .81634 .02058 - .83920 1 656 1.29355 868 -5.23971 114 1.94814 74 .32922
Source: own processing
The above mentioned statement implies to the fact that
the highest reaction values were found out in four out of
five determiners in 2012 when changing them to the CER
values. A more detailed situation in 2012 is described in
diagram no. 1. The lowest reaction values of determiners´
change to the CER coefficient were noted in the Karlovy
Vary Region which was already sooner marked as the
least advanced region of the Czech Republic.
The highest sensitivity reactions were noted in the
South Moravian Region which was mentioned already
above as the most competitive region in the Czech
Republic following the capital city, Prague.
The biggest reactions to determiners´changes were
noted in the South Moravian Region in 2012. The region
reacted very positively when changing the development
of number of university students, scientific and research
employees and expenses for science and research. In case
of the reaction to scientific and research outputs, negative
relation was noted. Because there was no change in the
number of subjects which are active in science and
research, the value of reaction is zero. The MoravianSilesian
Region shows a very positive reaction when
changing OUT where the value of reaction reaches
107.93727. This region reacts positively also to the
change of FIN but the value is much higher (7.29059).
For other determiners, the elasticity rates are negative.
The Karlovy Vary Region doesn’t react to changes of
STU and EMP indicators. We noted positive reactions to
changes of FIN and ENT, negative ones were noted for
OUT.
The Liberec Region reacts only little to changes of
individual determiners; sensitivity reactions are negative
or oscillating around 0.
V. CONCLUSION
The presented way of evaluating regional
competitiveness represents an easily applicable tool
which doesn’t require much various data which gaining is
often problematic in practice. The CER coefficient
provides basic orientation information which can serve to
policy markers when creating public policies and
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©2016 Engineering and Technology Publishing
strategies supporting the competitiveness increase of the
given region. The elasticity analysis of individual
indicators has the potential to provide sufficiently
accurate data about the influence of individual
determiners on economic productivity and employment
or the competitiveness rate. The disadvantage of this
approach is incapability to consider time delay or
spillover effects or cross-border effects and sensitivity to
extreme fluctuations of values. These fluctuations can’t
be removed by the method. The advantage is simplicity
of the method, availability of necessary data, performance
speed. Other advantages can be also variability of
individual determiners which we can group and analyse
the influence of entire determiner groups on the
competitiveness rate.
Results of the primary analysis can be used also in
other ways. They are useful for the regression model
which enables to quantify the influence rate of individual
determiners on the CER coefficient. However, the choice
of determiners for the regression analysis can be
complicated by multicollinearity because of the small
number of surveys or small changes of individual
determiners. Another complication can be mutual
correlativeness of individual determiners. Suitable
variables can be chosen by means of the factor analysis
which can provide rates of individual variables whereas
the data isn’t encumbered with multicollinearity.
Therefore it is possible to apply the method of regression
analysis to resultant groups of indicators. Such results
could provide more accurate results for interregional
comparison.
The analysis revealed that there are significant
disparities in the monitored sample of regions, as regards
the regional competitiveness rate. These disparities
influence the economic development of regions and their
future development. The executed analysis helped divide
individual Czech regions in weakly, averagely and
strongly competitive ones, according to the
competitiveness rate. In addition to that, important
positive effects of knowledge determiners on regional
competitiveness were found out. Even the partial analyses
of individual determiners enable to improve the approach
of public policies to creation of positive corporate
environment or the environment which will positively
influence the competitiveness of economic subjects
which are located in the given region.
ACKNOWLEDGEMENTS
We gratefully acknowledge the help provided by
constructive comments of the anonymous referees. This
work was supported by a grant provided by the scientific
research project of the Czech Sciences Foundation Grant
No: 14-02836S.
REFERENCES
[1] M. Viturka, P. Halámek, V. Klímová, P. Tonev, and V. Žítek,
The Quality of the Business Environment, Regional
Competitiveness and Regional Development Strategies, Czech
Republic, Prague: Grada Publishing, 2010.
[2] K. Matatkova and J. Stejskal, “Assessment of shift-share analysis
suitable for identification of industrial cluster establishing in
regions,” Economic Journal, vol. 60, no. 9, pp. 935-948. 2012.
[3] M. E. Porter, On Competition, Boston: Harvard Business School
Press, 1998.
[4] M. Storper, Keys to the City: How Economics, Institutions Social
Interaction, and Politics Shape Development, Oxford. Princeton
University Press, 2013, pp. 92-166.
[5] K. Skokan, “Clusters and their impact on competitiveness,” in
Clusters and Their Impact on Company Performance, Praha:
Grada Publishing a.s., 2009, pp. 27-44.
[6] O. Hudec, Regional Innovation Systems, Kosice: C-press.sk,
2007.
[7] J. Gibarti, Innovation Environment of Czech Regions, Prague:
Publishing of ČVUT, 2009.
[8] Z. Acs, P. Braunerhjelm, D. Audretsch, and B. Carlsson, “The
missing link: Knowledge diffusion and entrepreneurship in
endogenous growth,” Small Business Economics, no. 34, 2010.
[9] K. Matatkova and J. Stejskal, “Descriptive analysis of the
regional innovation system-novel method for public
administration authorities,” Transylvanian Review of
Administrative Sciences, no. 39, pp. 91-107, 2013.
[10] D. Audretsch, M. Hulsbeck, and E. Lehmann, “Regional
competitiveness, university spillovers, and entrepreneurial
activity,” Small Business Economics, vol. 39, no. 3, pp. 587-601,
2012.
[11] A. B. Jaffe, M. Trajtenberg, and R. Henderson, “Geographic
localization of knowledge spillovers as evidenced by patent
citations,” Quarterly Journal of Economics, no. 108, 1993.
[12] G. Dumais, G. Ellison, and E. L. Glaeser, “Geographic
concentration as a dynamic process,” Review of Economics and
Statistics, no. 84. 2002.
[13] Kaderabkova and Collective, Yearbook of Czech Republic
Competitiveness in 2006-2007, Prague: Linde Publishing, 2007.
[14] J. Stejskal and P. Hajek, “Competitive advantage analysis: A
novel method for industrial clusters identification,” Journal of
Business Economics and Management, vol. 13, no. 2, pp. 344-
365, 2012.
[15] P. Thompson, “Patent citations and the geography of knowledge
spill-overs: Evidence from inventor and examiner added
citations,” Review of Economics and Statistics, no. 82, 2006.
[16] C. Antonelli, P. Paptrucco, and F. Quatraro, “Productivity growth
and pecuniary knowledge externalities: An empirical analysis of
agglomeration economies in European regions,” Economic
Geography, no. 87, 2010.
[17] R. Sanchez, “Knowledge management and organizational
competence,” Oxford Scholarship Online, 2010.
[18] J. Jacobs, The Economy of Cities, London: Vintage Books, 1969.
[19] E. L. Glaeser, H. Kallal, J. Scheinkam, and A. Shleifer, “Growth
in cities,” Journal of Political Economy, no. 100, 1992.
[20] E. Brouwer, H. B. Nadvornikova, and A. Kleinknecht, “Are
urban agglomerations a better breeding place for product
announcements?” Regional Studies, no. 33, 1999.
[21] P. M. Romer, “Endogenous technological change,” Political
Economy, vol. 98, no. 2, pp. 71-102, 1990.
[22] Aghion and Howitt, “A model of growth through creative
destruction,” Econometrica, no. 60, 1992.
[23] Grossman and Helpman, Innovation and Growth in the Global
Economy, London: MIT Press, 1993.
[24] P. Krugman, “Competitiveness, a dangerous obsession,” Foreign
Affairs, pp. 28-44, March/April, 1994.
[25] OECD, Technology and the Economy: The Key Relationships,
Paris, OECD, 1992.
[26] M. E. Porter, “Building the microeconomic foundations of
prosperity: Findings from the business competitiveness index,” in:
Global Competitiveness Report 2002-2003, World Economic
Forum, Harvard University, 2003.
[27] Huggins, “Creating a UK competitiveness index: Regional and
local benchmarking,” Regional Studies, vol. 37, pp. 89-96, 2003.
[28] M. Kitson, R. Martin, and P. Tyler, “Regional competitiveness:
An elusive yet key concept?” Regional Studies, vol. 38, no. 9,
2004.
[29] R. Camagni and R. Capello, “Regional competitiveness and
territorial capital: A conceptual approach and empirical evidence
from the European Union,” Regional Studies, no. 47.9, 2013.
28
Journal of Advanced Management Science Vol. 4, No. 1, January 2016
©2016 Engineering and Technology Publishing
[30] J. Hanclova, I. Kubicová, M. Macháček, A. Melecký, L. Melecký,
M. Melecký, J. Nevima, and J. Ramík, Macroeconomic
Modelling of Czech Economy and Selected Economies of EU
Countries, Ostrava: VŠB-TU Ostrava, 2010.
[31] J. Nevima, “Competitiveness evaluation using selected
quantitative methods to the case of the Czech republic,” Ph.D.
thesis, Ostrava: VŠB-TU Ostrava, 2008.
[32] M. Srholec and P. Zizalova, “European planning studies:
Mapping the geography of R&D: What can we learn for regional
innovation policy in the Czech Republic and beyond?” European
Planning Studies, 2013.
Simona Pichova was born on 30th May 1988 in Šumperk, Czech
Republic. She is currently a PhD student at the University of Pardubice,
Czech Republic. She received Bachelor degree in “Public Economics
and Administration” from the University of Pardubice, Pardubice,
Czech Republic in 2011. She then finished master degree in
“Economics of the Public Sector” at the same university in 2013. At
present, she is studying PhD studies in “Regional and Public Economics”
at the same university where she also teaches Microeconomics and
Public Economics. She works presently in project team concerning the
modelling of knowledge spillover effects in the context of regional and
local development.
Ing. Pichova has different research interests including innovation and
development, knowledge economy and valuation of public services.
Jan Stejskal is Assoc. Prof. in public and regional economics, Ph.D.
obtained at University of Pardubice, Faculty of Economics and
Administration in Pardubice, Czech Republic. His domain is connection
of the public economy in the regional scope and view. In the papers he
describes and analyses specially the regional policy, tool of the local
and regional economic development, public policies after care of the
investors, systems of support in national economy in EU countries.
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Journal of Advanced Management Science Vol. 4, No. 1, January 2016
©2016 Engineering and Technology Publishing