Sborník příspěvků XVII. mezinárodní kolokvium o regionálních vědách Hustopeče 18.–20. 6. 2014
76
DOI: 10.5817/CZ.MUNI.P210-6840-2014-8
MEASURING THE EFFICIENCY OF EU13 NUTS 2 REGIONS
BASED ON RCI APPROACH
MĚŘENÍ EFEKTIVITY REGIONŮ NUTS 2 ZEMÍ EU13 NA ZÁKLADĚ
PŘÍSTUPU RCI
ING. MICHAELA STANÍČKOVÁ
Katedra evropské integrace
Ekonomická fakulta
Vysoká škola báňská - Technická univerzita Ostrava
Department of European Integration
Faculty of Economics
VŠB – Technical University of Ostrava
* Sokolská třída 33, 701 21 Ostrava, Czech Republic
E-mail: michaela.stanickova@vsb.cz
Annotation
Paper deals with an application of Data Envelopment Analysis methods to multicriteria efficiency
evaluation of NUTS 2 regions within “new” Member States joining the EU in 2004, 2007 and 2013.
The main aim of the paper is to analyse a level of efficiency achieved in individual NUTS 2 regions of
EU13. Empirical analysis is based on the competitiveness scores (input and output dimension)
individually achieved by all evaluated regions within Regional Competitiveness Index 2013 approach.
Using of DEA method in the form of efficiency and super efficiency model seems to be convenient
because there is not only one factor evaluated, but a set of different factors that determine the level of
regional competitiveness. DEA method is based on input and output indicators and evaluates the
efficiency how regions are able to transform their inputs into outputs. Therefore, efficiency of each
region is thus perceived like a source/mirror of competitiveness.
Key words
competitiveness, DEA method, NUTS 2 region, RCI, regional efficiency
Anotace
Příspěvek se zabývá aplikací metody analýzy obalu dat za účelem vícekriteriálního hodnocení
efektivity regionů NUTS 2 v rámci skupiny „nových“ členských států EU, jež přistoupily v letech 2004,
2007 a 2013. Hlavním cílem příspěvku je analyzovat úroveň efektivity dosahované jednotlivě každým
regionem NUTS 2 v rámci skupiny států EU13. Empirická analýza je založena na hodnotách skóre
indexu konkurenceschopnosti (dimenze vstup a výstup) dosahované jednotlivými hodnocenými regiony
v rámci konceptu Indexu regionální konkurenceschopnosti 2013. Využití metody DEA ve formě
modelu efektivity a super efektivity se jeví jako vhodné, jelikož není hodnocen pouze jeden faktor, ale
skupina rozličných faktorů určujících úroveň regionální konkurenceschopnosti. Metoda DEA je
založena na indikátorech vstupu a výstupu a hodnotí efektivitu, s jakou jsou regiony schopny
transformovat vstupy na výstupy. Z tohoto důvodu je efektivita každého regionu považována za
zdroj/zrcadlo konkurenceschopnosti.
Klíčová slova
konkurenceschopnost, metoda DEA, NUTS 2 region, RCI, regionální efektivita
JEL classification: C67, R11, R13
Introduction
The European Union (EU) is an economic and political partnership representing a unique form of
cooperation among 28 Member States today. In the EU, the process of achieving an increasing level of
Sborník příspěvků XVII. mezinárodní kolokvium o regionálních vědách Hustopeče 18.–20. 6. 2014
77
competitiveness is significantly difficult by the heterogeneity of countries and regions in many areas.
The EU countries are highly heterogeneous in their sectorial specialisations and performance.
Although, the EU is one of the most developed parts of the world with high living standards, there
exist significant and huge economic, social and territorial disparities having a negative impact on the
balanced development across Member States and their regions, and thus weaken EU’s competitiveness
in a global context (Poledníková, Lelková, 2012). The European integration process is thus guided by
striving for two different objectives: to foster economic competitiveness and to reduce disparities
(which were growing after EU enlargement history) (Molle, 2007). The EU has a long viewed
enlargement process as an historic opportunity to further the integration of the continent by peaceful
means and an extraordinary opportunity to promote political stability and economic prosperity in
Europe. Since 2004, EU Membership has grown from 15 to 28 EU Member States, bringing in most
states of Central and Eastern Europe and fulfilling an historic pledge to further the integration of the
European continent by peaceful means. The carefully managed process of enlargement is one of the
EU’s most powerful policy tools, and that, over the years, it has helped to transform many European
states into functioning democracies, free market economies and more affluent countries. The EU
maintains that the enlargement door remains open to any European country be able to fulfil the EU’s
political and economic criteria for Membership. At the same time, many observers assess that EU
enlargement may soon be reaching its limits, both geographically and in terms of public enthusiasm
for further expansion.
The gradual access of new Member States into the EU was associated with increased regional
disparities (gaps) and a threat to the competitiveness and internal cohesion. Has increased integration
within the EU and the rest of the world helped the EU as a whole to become a more globally
competitive? Certainly, but different EU Member States, or groups of Member States have taken
different approaches to this integration process. From this point of view, the main aim of the paper is
to measure and evaluate the level of efficiency achieved by individual EU NUTS 2 regions within
“new” EU Member States based on competitiveness scores of these regions within Regional
Competitiveness Index (RCI) approach. Efficiency of each region is thus perceived like a mirror of
competitiveness and the differences between regions within Central and Eastern Europe and Balkan
Countries are the main orientation of this paper.
1. Relations between competitiveness and efficiency
The support of cohesion and balanced development together with increasing level of competitiveness
belong to the temporary EU’s key development objectives. In the global economy regions are
increasingly becoming the drivers of the economy and generally one of the most striking features of
regional economies is the presence of clusters, or geographic concentrations of linked industries.
Current economic fundamentals are threatened by the shifting of production activities to places with
better conditions. The regional competitiveness is also affected by the regionalization of public policy
because of the shifting of decision-making and coordination of activities at the regional level. Within
governmental circles, interest has grown in the regional foundations of national competitiveness, and
with developing new forms of regionally based policy interventions to help improve the
competitiveness of every region and major city, and hence the national economy as a whole. Regions
play an increasingly important role in the economic development of states. In relation to
competitiveness, efficiency is complementary objective, which determines the long-term development
of areas in a globalized economy. Therefore in recent years, the topics about measuring and evaluating
of competitiveness and efficiency have enjoyed economic interest. Although there is no uniform
definition and understanding of these terms, these multidimensional concepts remain ones of the basic
standards of performance evaluation and it is also seen as a reflection of success of area in a wider
comparison. Increasing competitiveness is generally considered to be the only sustainable way of
improving living standards in the long-term period (Barrell, Mason, O´Mahony, 2000).
Efficiency management is one of the major sources of sustainable national competitiveness. A
systematic understanding of the factors that affect efficiency, and subsequently also competitiveness,
is very important. Dynamic efficiency is also highly important for many economic subjects (e.g.
Sborník příspěvků XVII. mezinárodní kolokvium o regionálních vědách Hustopeče 18.–20. 6. 2014
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companies, states and regions) as a whole and for the individuals involving in it. But it necessary to
distinguish between efficiency and effectiveness. Efficiency and effectiveness analysis is based on the
relationship between inputs (entries), outputs (results) and outcomes (effects). As it can be seen in Fig.
1, the efficiency is given by the ratio of inputs to outputs, but there is difference between the technical
efficiency and the allocative efficiency. The technical efficiency implies a relation between inputs and
outputs on the frontier production curve, but not any form of technical efficiency makes sense in
economic terms, and this deficiency is captured through the allocative efficiency that requires a
cost/benefit ratio. The effectiveness implies a relationship between outputs and outcomes, thus effects
of activities to the real economy and the essential conditions for national competitiveness.
Fig. 1: Relationship between efficiency and effectiveness
Source: Melecký, 2013
2. Theoretical background of empirical analysis
Measurement and evaluation of efficiency is an important issue for at least two reasons. First, in a
group of units where only limited number of candidates can be selected, the efficiency of each must be
evaluated in a fair and consistent manner. Second, as time progresses, better efficiency is expected.
Hence, the units with declining efficiency must be identified in order to make the necessary
improvements (Greenaway, Görg, Kneller, 2008). The efficiency of areas, in this case of regions, can
be evaluated in either a cross-sectional or a time-series manner, and the Data Envelopment Analysis
(DEA) is a useful empirical method for both types of efficiency evaluation. DEA is a relatively new
”data oriented” approach for providing a relative efficiency assessment and evaluating the efficiency
of a set of peer entities called Decision Making Units (DMUs) which convert multiple inputs into
multiple outputs. DEA is thus a multi-criteria decision making method for evaluating efficiency and
productivity of a homogenous group (DMUs). The aim of DEA method is to examine DMU if they
are efficient or not efficient by the size and quantity of consumed resources by the produced outputs.
In DEA approach, DMUs usually use a set of resources as inputs and transform them into a set of
outcomes as outputs. The efficiency score of DMUs in the presence of multiple input and output
factors is defined as follows (1):
weighted sum of outputs
.
weighted sum of inputs
Efficiency of DMU = (1)
In recent years, we have seen a great variety of applications of DEA for evaluating the performances
of many different kinds of entities engaged in many different activities. Because of low assumption
requirements DEA has also opened up possibilities for use in cases which have been resistant to other
approaches because of the complex (often unknown) nature of relations between multiple inputs and
multiple outputs involved in DMUs. DEA method is a convenient method for comparing regional
efficiency as an assumption for performance of territory because DEA does not evaluate only one
factor, but a set of different factors that determine degree of economic development (Melecký, 2013).
Efficiency analysis starts from building database of indicators that are part of RCI 2013 approach. RCI
covers a wide range of issues related to territorial competitiveness including innovation, quality of
institutions, infrastructure (including digital networks) and measures of health and human capital. RCI
may serve as a tool to assist EU regions in setting the right priorities to further increase their
Sborník příspěvků XVII. mezinárodní kolokvium o regionálních vědách Hustopeče 18.–20. 6. 2014
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competitiveness. Because of this reason, eleven pillars of RCI are grouped according to the different
dimensions (input versus output aspects) of regional competitiveness they describe. The terms ‘inputs’
and ‘outputs’ are meant to classify pillars into those which describe driving forces of competitiveness,
also in terms of long-term potentiality, and those which are direct or indirect outcomes of a
competitive society and economy (Annoni, Kozovska, 2010). Methodology of RCI is thus suitable for
measuring regional efficiency by DEA method. In this paper, as input indicators to DEA are not used
the initial RCI 2013 indicators (73 indicators entered RCI 2013 having passed the statistical tests), but
competitiveness scores of RCI 2013 pillars which are available at regional level. RCI 2013 scores are
adjusted to positive values through Factor analysis, since DEA does not allow negative values of the
input variables. In Appendix 1, input pillars and output pillars are specified – these are used in the
paper.
Analysis is applied to regional territory of “new” EU Member States, i.e. 13 countries joined to the
EU in 2004, 2007 and 2013. These 13 countries cover in total 57 NUTS 2 regions1
– Bulgaria 6 (BG),
Cyprus 1 (CY), Czech Republic 7 (CZ), Estonia 1 (EE), Croatia 2 (CR), Hungary 7 (HU), Lithuania 1
(LT), Latvia 1 (LV), Malta 1 (MT), Poland 16 (PL), Romania 8 (RO), Slovenia 2 (SI) and Slovakia 4
(SK). Why was this group of regions chosen for empirical analysis? Where the impact of enlargement
is seen perhaps most clearly is in the developments in intra-EU trade and, particularly, in trade in
intermediate goods. The EU13 Member States have become important suppliers of intermediate goods
to several key EU producers (Annoni, Dijkstra, 2013). Their inputs are therefore increasingly vital to
the competitiveness of final goods exports from other EU countries. In addition, EU13 countries are
themselves expanding their sourcing of intermediate goods abroad, both within the Union and
globally. Thus on the one hand EU13 companies are becoming more important sources for industries
in other EU countries, while they themselves are becoming more globalised, taking advantage of
greater openness both within the EU and towards the rest of the world to better integrate their
production structure. On the other hand, “new” EU Member States have to scope with conditions of
Single internal market and rules of EU policies, what is in some areas problematic because of their
historical heritage of mark “Countries behind Iron Curtain”. So, the main question is, what is the
current position of individual regions within the group of Central, Eastern and Balkan European
countries? Do all of these regions have the same position and conditions for competing with “old” EU
Member States?
Empirical analysis is based on a frontier non-parametric approach and aims to study efficiency and
trend of returns to scale (RTS). This is based on model introduced by A. Charnes, W.W. Cooper and
E. Rhodes in 1978, i.e. CCR model assuming constant returns to scale (CRS). In this paper, it´s used
input orientation of this model, because the attention is paid to endogenous factors of regional
competitiveness. According to the chosen model and the relationship between number of DMU and
number of inputs and outputs, the number of efficient units can be relatively large. Because there were
many efficient regions in the classification, in the paper is also designed a model of super efficiency.
The way in which DEA program computes efficiency scores can be explained briefly using
mathematical notation in model (2) (Cook and Seiford, 2009):
min - ε( ),+ =
q +T T
qz e s e s (2)
subject to
,l
+ = qq qX s x
,+
l - = qY s y
, , 0 ,+ l
³s s
where z is the coefficient of efficiency of unit Uq; qq is radial variable indicates required rate of
decrease of intput; ε is infinitesimal constant; eT
λ is convexity condition, in the case of CRS: eT
= (1,
1
In RCI 2013, capital regions are merged with one or more of their neighbouring regions: Wien (AT), Brussels
(BE), Prague (CZ), Berlin (DE), Amsterdam (NL) and London (UK). The remaining NUTS 2 regions may
contain multiple functional urban areas, but they do break up a single functional urban area in to distinct parts.
Sborník příspěvků XVII. mezinárodní kolokvium o regionálních vědách Hustopeče 18.–20. 6. 2014
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1, …, 1); s+
, and s−
are vectors of slack variables for inputs and outputs; λ represent vector of weights
assigned to individual units; xq means vector of input of unit Uq; yq means vector of output of unit Uq;
X is input matrix; Y is output matrix. In CCR model aimed at inputs the efficiency coefficient of
efficient DMU equals 1, but the efficiency coefficient of inefficient DMU is lower than 1.
In CCR model, efficiency coefficients of efficient units equal to 1. Depending on chosen model, but
also on the relationship between number of units and number of inputs and outputs, number of
efficient units can be relatively large. Due to the possibility of efficient units' classification, it is used
Andersen-Petersen's model (APM) of super efficiency. Following constant return to scale (CRS)
model is input oriented dual version of APM (3) (Andersen and Petersen, 1993):
min ,qq (3)
subject to
1
; 1, 2, ...,-
=
¹
l + = q =å
n
ij j i q iq
j
j k
x s x i m
1
; 1, 2, ..., r+
=
¹
l - = =å
n
ij j i iq
j
j k
y s y i
, , 0, 0+ l
³ l =j i i qs s
1,2,..., .j n=
where xiq and yiq are i-th inputs and i-th outputs of DMUq; qq is efficiency index (intensity factor) of
observed DMUq; λj is dual weight which show DMUj significance in definition of input-output mix of
hypothetical composite unit, DMUq directly comparing with. The rate of efficiency of inefficient units
(qq < 1 ) is identical to model (1); for units identified as efficient in model (2), provides IO APM (2)
the rate of super efficiency higher than 1, i.e. qq ≤ 1.
For solution of DEA method software tool based on solving linear programming problems is used in
the paper – Solver in MS Excel 2010, such as the DEA Frontier 2011.
3. Application of DEA for efficiency evaluation of EU13 NUTS 2 regions
The aim of DEA method is to examine DMU if they are efficient or inefficient by the size and
quantity consumed resources by the produced outputs. The overall evaluation of efficiency of EU13
NUTS 2 regions is presented in Tab. 1, which shows levels of regional efficiency in IO CCR model
and position of each region based on IO APM model of super efficiency (because of classification of
all evaluated DMU). In Tab. 1, all evaluated NUTS 2 regions and their efficiency coefficients in IO
APM CRS model of super efficiency are coloured by shadows of grey colour. Regions belonging to
the group of the most efficient region are marked by dark grey colour and placed at front 20th
positions
– these regions achieved level of efficiency coefficients at 1.0 in IO CCR CRS model (marked by bold
font in Tab. 1). The group of the most efficient regions is followed by the group of slight efficient
regions. Some NUTS 2 regions of all EU13 countries are included in this group. These regions
achieved level of efficiency coefficients lower than 1.0 and higher than 0.9 points and are placed from
21st
to 49th
position. Last DMUs belong to the group of inefficient NUTS 2 regions – this group is
covered by Bulgarian, Czech, Hungarian and Polish NUTS 2 regions. Level of efficiency coefficients
is lower than 0.9 in the case of inefficient regions and they are placed from 50th
to 57th
position – it´s
marked by italic font.
The best results achieved Romanian and Bulgarian regions. Generally, these regions belong to regions
with average or lower/the lowest level of efficiency. In the paper are thus detected anomalies in the
final classification of some NUTS 2 regions based on values of efficiency coefficients in IO APM
CRS model of super efficiency. DEA method evaluates the volume of inputs for given outputs, which
in case of some Bulgarian, Hungarian and Romanian regions seems to be more efficient than others,
although these regions generally belong to the less or average developed regions within the whole EU.
Sborník příspěvků XVII. mezinárodní kolokvium o regionálních vědách Hustopeče 18.–20. 6. 2014
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This fact could be a prerequisite for further research on evaluation of regional efficiency by other
advanced DEA models, e.g. in 1st
phase to divide evaluated regions into groups-levels according to all
efficient frontiers via Context-Dependent DEA approach. By this stratification, into efficiency
analysis will enter more homogenous groups of regions, which will be evaluated separately according
to closer features.
In Tab. 1, trend of returns to scale (RTS) of each region is also presented – constant, increasing or
decreasing. This trend is calculated based on comparison results of efficiency within RTS orientation
– constant (CRS) and variable (VRS).
Tab. 1: DEA efficiency within EU 13 NUTS 2 regions
DMU
Efficiency Super Efficiency RTS Final Rank – IO APM CRS
IO CCR CRS IO APM CRS IO VRS IO CRS Sl IO RTS NUTS 2 Region Rank
BG31 1.00000 1.07073 1.00000 1.00000 1.00000 Constant RO32 - 2.23004 1
BG32 0.87966 0.87966 1.00000 0.87966 0.76108 Increasing BG41 - 1.25227 2
BG33 1.00000 1.01302 1.00000 1.00000 1.00000 Constant HU32 - 1.24526 3
BG34 1.00000 1.16981 1.00000 1.00000 1.00000 Constant MT00 - 1.23443 4
BG41 1.00000 1.25227 1.00000 1.00000 1.00000 Constant BG34 - 1.16981 5
BG42 0.89150 0.89150 0.95518 0.89150 0.86427 Increasing RO21 - 1.16067 6
CY00 1.00000 1.05866 1.00000 1.00000 1.00000 Constant EE00 - 1.09747 7
CZ00 0.90111 0.90111 0.91930 0.90111 1.03334 Decreasing RO42 - 1.09301 8
CZ03 0.85648 0.85648 0.87117 0.85648 0.94799 Increasing RO31 - 1.09024 9
CZ04 0.88021 0.88021 0.88098 0.88021 1.00177 Decreasing HU10 - 1.08781 10
CZ05 0.88682 0.88682 0.90605 0.88682 0.92214 Increasing HR04 - 1.08023 11
CZ06 0.88903 0.88903 0.89109 0.88903 1.01165 Decreasing RO11 - 1.07814 12
CZ07 0.95769 0.95769 0.97279 0.95769 0.94224 Increasing BG31 - 1.07073 13
CZ08 0.97881 0.97881 0.99080 0.97881 0.93720 Increasing CY00 - 1.05866 14
EE00 1.00000 1.09747 1.00000 1.00000 1.00000 Constant HU23 - 1.05087 15
HR03 0.98401 0.98401 1.00000 0.98401 0.91388 Increasing RO41 - 1.01797 16
HR04 1.00000 1.08023 1.00000 1.00000 1.00000 Constant BG33 - 1.01302 17
HU10 1.00000 1.08781 1.00000 1.00000 1.00000 Constant PL22 - 1.00993 18
HU21 0.94373 0.94373 0.97757 0.94373 1.04489 Decreasing SI02 - 1.00930 19
HU22 0.88275 0.88275 0.91720 0.88275 1.03013 Decreasing RO22 - 1.00708 20
HU23 1.00000 1.05087 1.00000 1.00000 1.00000 Constant HU31 - 0.99916 21
HU31 0.99916 0.99916 0.99923 0.99916 1.01776 Decreasing HR03 - 0.98401 22
HU32 1.00000 1.24526 1.00000 1.00000 1.00000 Constant SK01 - 0.98217 23
HU33 0.97979 0.97979 0.98969 0.97979 1.01273 Decreasing SK03 - 0.98057 24
LT00 0.93862 0.93862 0.98744 0.93862 0.90384 Increasing HU33 - 0.97979 25
LV00 0.92565 0.92565 0.98565 0.92565 0.84348 Increasing CZ08 - 0.97881 26
MT00 1.00000 1.23443 1.00000 1.00000 1.00000 Constant PL11 - 0.97807 27
PL11 0.97807 0.97807 0.98181 0.97807 0.98320 Increasing PL41 - 0.96692 28
PL12 0.95736 0.95736 0.96079 0.95736 1.00598 Decreasing PL21 - 0.96049 29
PL21 0.96049 0.96049 0.96289 0.96049 0.99119 Increasing CZ07 - 0.95769 30
PL22 1.00000 1.00993 1.00000 1.00000 1.00000 Constant PL12 - 0.95736 31
PL31 0.93838 0.93838 0.99568 0.93838 0.86238 Increasing PL34 - 0.95443 32
PL32 0.90691 0.90691 0.98839 0.90691 0.80465 Increasing PL33 - 0.95045 33
PL33 0.95045 0.95045 1.00000 0.95045 0.81898 Increasing PL52 - 0.94861 34
PL34 0.95443 0.95443 1.00000 0.95443 0.88770 Increasing PL51 - 0.94732 35
PL41 0.96692 0.96692 0.96870 0.96692 0.99219 Increasing SK04 - 0.94546 36
PL42 0.88443 0.88443 0.91650 0.88443 0.91517 Increasing HU21 - 0.94373 37
PL43 0.92325 0.92325 0.92444 0.92325 0.99473 Increasing RO12 - 0.94341 38
PL51 0.94732 0.94732 0.96734 0.94732 0.92956 Increasing LT00 - 0.93862 39
PL52 0.94861 0.94861 0.95252 0.94861 0.98436 Increasing PL31 - 0.93838 40
PL61 0.93391 0.93391 0.97649 0.93391 0.86321 Increasing PL61 - 0.93391 41
PL62 0.92978 0.92978 0.98449 0.92978 0.87324 Increasing PL62 - 0.92978 42
PL63 0.91905 0.91905 0.96617 0.91905 0.87209 Increasing LV00 - 0.92565 43
RO11 1.00000 1.07814 1.00000 1.00000 1.00000 Constant PL43 - 0.92325 44
RO12 0.94341 0.94341 0.98088 0.94341 0.88172 Increasing SI01 - 0.92109 45
RO21 1.00000 1.16067 1.00000 1.00000 1.00000 Constant PL63 - 0.91905 46
RO22 1.00000 1.00708 1.00000 1.00000 1.00000 Constant SK02 - 0.91105 47
RO31 1.00000 1.09024 1.00000 1.00000 1.00000 Constant PL32 - 0.90691 48
Sborník příspěvků XVII. mezinárodní kolokvium o regionálních vědách Hustopeče 18.–20. 6. 2014
82
DMU
Efficiency Super Efficiency RTS Final Rank – IO APM CRS
IO CCR CRS IO APM CRS IO VRS IO CRS Sl IO RTS NUTS 2 Region Rank
RO32 1.00000 2.23004 1.00000 1.00000 1.00000 Constant CZ00 - 0.90111 49
RO41 1.00000 1.01797 1.00000 1.00000 1.00000 Constant BG42 - 0.89150 50
RO42 1.00000 1.09301 1.00000 1.00000 1.00000 Constant CZ06 - 0.88903 51
SI01 0.92109 0.92109 0.94875 0.92109 0.92251 Increasing CZ05 - 0.88682 52
SI02 1.00000 1.00930 1.00000 1.00000 1.00000 Constant PL42 - 0.88443 53
SK01 0.98217 0.98217 1.00000 0.98217 1.19833 Decreasing HU22 - 0.88275 54
SK02 0.91105 0.91105 0.91217 0.91105 0.99297 Increasing CZ04 - 0.88021 55
SK03 0.98057 0.98057 1.00000 0.98057 0.87488 Increasing BG32 - 0.87966 56
SK04 0.94546 0.94546 0.99651 0.94546 0.86778 Increasing CZ03 - 0.85648 57
Source: own calculation and elaboration, 2014
In following Fig. 1 it´s possible to see evidential differences of efficiency IO CCR CRS model among
57 NUTS 2 regions. The line at 1.0 represents the efficient frontier – at this level, DMU ratio of
consumed inputs and produced outputs is in optimum. Distance of all evaluated regions from the
efficient frontier is presented at Fig. 1, the most efficient regions are ranged at efficient line 1.0,
inefficient regions are ranged below the efficient frontier; greater distance means lower efficiency.
Fig. 1: Efficient frontier of EU 13 NUTS 2 regions based on IO CCR CRS model
Source: own calculation and elaboration, 2014
Conclusion
The EU’s enlargement has helped the EU to maintain a strong performance, in spite of increased
global competition. Challenges certainly remain, but its recent performance gives reason to believe
that the EU can leverage its strengths even as the economic environment toughens. Regions have
indeed to pick priorities for their development strategies. The economic crisis made this even more
difficult as public funding becomes scarcer. This approach, evaluation competitive
advantages/disadvantages by DEA efficiency analysis can provide a guide to what each region should
focus on taking into account its specific situation, its overall level of development and level of
efficiency of using inputs (internal factor endowment) to produced outputs (direct/indirect outcomes)
which are able to withstand competition.
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[2] ANNONI, P., DIJKSTRA, L., (2013). EU Regional Competitiveness Index 2013. Luxembourg: Publication
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0.75
0.80
0.85
0.90
0.95
1.00
1.05
CZ03
BG32
CZ04
HU22
PL42
CZ05
CZ06
BG42
CZ00
PL32
SK02
PL63
SI01
PL43
LV00
PL62
PL61
PL31
LT00
RO12
HU21
SK04
PL51
PL52
PL33
PL34
PL12
CZ07
PL21
PL41
PL11
CZ08
HU33
SK03
SK01
HR03
HU31
BG31
BG33
BG34
BG41
CY00
EE00
HR04
HU10
HU23
HU32
MT00
PL22
RO11
RO21
RO22
RO31
RO32
RO41
RO42
SI02
Sborník příspěvků XVII. mezinárodní kolokvium o regionálních vědách Hustopeče 18.–20. 6. 2014
83
[6] GREENAWAY, D., GÖRG, H., KNELLER, R., (2008). Globalization and Productivity. Cheltenham:
Edward Elgar Publishing Limited. ISBN 978-1845423803.
[7] MELECKÝ, L., (2013). Comparing of EU15 and EU12 Countries Efficiency by Application of DEA
Approach. In 31st International Conference Mathematical Methods in Economics. Jihlava: College of
Polytechnics Jihlava. p. 618-623. ISBN 978-80-87035-76-4.
[8] MOLLE, W., (2007). European Cohesion Policy. London: Routledge. ISBN 978-0415438124.
[9] POLEDNÍKOVÁ, E., LELKOVÁ, P., (2012). Evaluation of Regional Disparities in Visegrad Four
Countries. Germany and Austria using the Cluster Analysis. In 15th International Colloquium on Regional
Sciences. Conference Proceedings. Brno: Masarykova univerzita. pp. 36-47. ISBN 978-80-210-5875-0.
This paper was created under SGS project (SP2014/111) of Faculty of Economics. VŠB-Technical
University of Ostrava and Operational Programme Education for Competitiveness – Project
CZ.1.07/2.3.00/20.0296.
Appendix 1: Input and output indicators for DEA modelling – RCI 2013 scores
DMU
NUTS 2
regions
INPUTS OUTPUTS
I1
I2
I3
I4
I5
O1
O2
O3
O4
BG31 1.65 2.80 2.16 2.14 2.04 2.96 2.45 2.67 2.63
BG32 1.91 2.79 2.24 2.92 2.23 3.07 2.58 2.60 2.82
BG33 2.89 2.85 1.90 2.88 2.02 2.97 2.45 3.10 2.88
BG34 1.95 2.81 1.42 2.59 2.11 3.24 2.43 2.64 2.54
BG41 2.18 3.03 2.87 3.40 2.62 4.10 2.84 4.66 3.69
BG42 2.84 2.91 2.36 2.63 2.19 3.10 2.52 2.49 2.64
CY00 3.91 2.99 4.42 3.71 3.34 4.35 3.13 3.94 3.69
CZ00 3.49 3.82 3.72 4.63 4.09 4.47 3.74 4.39 4.51
CZ03 3.92 3.51 3.33 3.89 3.92 4.10 3.18 2.77 3.39
CZ04 3.13 3.77 2.90 3.84 3.87 3.47 3.48 2.72 3.22
CZ05 3.90 3.43 3.59 3.95 4.06 3.98 3.33 2.70 3.43
CZ06 3.59 3.54 3.78 3.59 3.99 3.85 3.30 3.21 3.58
CZ07 3.57 3.20 3.44 3.55 3.82 3.85 3.33 2.71 3.25
CZ08 3.67 3.26 3.38 3.85 3.97 3.56 3.55 2.72 2.92
EE00 3.88 2.79 3.16 3.98 3.94 3.70 2.46 3.64 4.20
HR03 1.95 2.90 2.89 2.88 3.22 3.36 2.74 3.74 3.05
HR04 2.21 3.07 2.91 2.89 3.09 3.29 3.22 3.79 3.24
HU10 3.05 3.61 2.61 3.78 3.81 3.95 3.59 4.72 4.40
HU21 3.65 3.45 2.07 3.54 3.61 3.72 3.24 2.74 3.38
HU22 3.65 3.53 2.35 3.61 3.60 3.97 3.08 2.75 3.09
HU23 3.65 2.85 1.86 3.30 3.45 3.38 2.74 2.95 3.60
HU31 3.59 3.10 1.84 3.31 3.40 3.18 3.04 2.51 3.55
HU32 3.59 3.00 1.36 3.13 3.31 3.30 2.86 2.71 3.12
HU33 3.59 3.07 1.78 3.02 3.42 3.67 2.84 2.75 3.24
LT00 3.10 2.88 2.09 3.52 3.63 3.39 2.69 2.89 3.45
LV00 3.17 2.94 2.19 3.34 3.11 3.24 2.45 3.53 3.22
MT00 4.33 2.84 4.51 2.53 4.43 3.60 2.68 4.01 3.67
PL11 3.18 3.21 2.57 3.24 3.24 3.82 3.27 2.88 3.16
PL12 3.02 3.46 2.84 4.01 3.24 4.23 3.49 4.16 4.00
PL21 3.12 3.28 3.21 3.41 3.21 3.69 3.38 2.84 3.70
PL22 2.96 3.46 2.91 3.80 3.21 3.59 3.78 3.06 3.17
PL31 3.16 2.85 2.86 3.42 3.09 3.72 2.80 2.46 3.14
PL32 3.12 2.89 3.20 3.22 3.09 3.31 2.85 2.22 3.19
PL33 3.17 2.95 2.70 3.40 3.09 3.25 3.10 2.24 2.71
PL34 3.06 2.76 2.99 3.22 3.09 3.77 2.66 2.45 2.91
PL41 2.96 3.14 2.82 2.98 3.30 3.48 3.14 2.53 3.04
PL42 3.11 3.16 2.67 3.12 3.30 3.56 2.86 2.95 3.09
PL43 3.05 3.22 2.55 2.94 3.30 3.77 2.93 2.62 3.01
PL51 2.90 3.19 2.64 3.45 3.32 3.81 3.23 3.04 3.22
PL52 3.32 3.25 3.02 3.27 3.32 3.56 3.27 2.56 3.06
PL61 3.07 3.01 2.65 3.19 3.20 3.24 3.03 2.59 3.10
PL62 3.28 2.84 2.70 2.75 3.20 3.32 2.69 2.62 2.89
PL63 3.21 3.03 2.97 3.37 3.20 3.78 2.96 3.03 3.42
Sborník příspěvků XVII. mezinárodní kolokvium o regionálních vědách Hustopeče 18.–20. 6. 2014
84
DMU
NUTS 2
regions
INPUTS OUTPUTS
I1
I2
I3
I4
I5
O1
O2
O3
O4
RO11 2.81 2.80 2.10 2.43 2.12 4.07 2.59 2.39 3.03
RO12 2.51 2.85 2.20 2.57 2.07 3.18 2.62 2.45 2.67
RO21 1.97 2.79 2.16 2.42 1.85 3.94 2.55 2.35 2.59
RO22 2.03 2.78 1.93 2.29 1.99 3.14 2.58 2.40 2.51
RO31 2.21 3.10 2.00 2.42 2.13 3.31 2.95 2.16 2.58
RO32 1.21 3.39 2.92 4.08 2.74 4.46 3.79 4.13 4.65
RO41 2.44 2.75 2.33 2.35 2.07 3.44 2.61 2.18 2.90
RO42 1.83 2.87 1.93 2.62 2.18 3.84 2.57 2.41 3.26
SI01 3.80 3.22 3.64 4.22 3.64 4.07 3.23 3.49 3.51
SI02 3.80 3.33 3.71 4.64 3.64 4.45 3.48 4.53 4.43
SK01 3.44 3.98 3.52 4.69 3.79 4.51 4.09 5.23 5.03
SK02 3.13 3.52 2.98 3.51 3.78 3.44 3.38 2.63 3.27
SK03 3.26 2.97 2.87 3.19 3.61 3.16 3.16 2.86 3.03
SK04 3.26 2.92 2.66 2.93 3.52 2.97 2.94 2.97 2.93
I1
Institutions. I2
Infrastructure. I3
Health. I4
Higher Education and Lifelong Learning. I5
Technological
Readiness. O1
Labour Market Efficiency. O2
Market Size. O3
Business Sophistication. O4
Innovation
Source: own calculation and elaboration, 2014