Boosting the EU Competitiveness as Response to Economic Shocks: Composite Weighted Index of Regional Resilience

Abstract

Since 2008, the world has faced the economic crisis that has had devastating effects on many regions to various degrees. How regions respond to economic shock depends on regional economic structure and performance, administrative capacity, resources, human capital, social capital, and other factors, were perceived as resilience. Resilience has recently risen to prominence in several disciplines, has also entered policy discourse, and is one of the future strategic goals for the European Union. The aim of the chapter is to introduce a methodology for assessing the resilience of EU28 NUTS 2 regions based on a construction of composite weighted index derived from EU Regional Competitiveness Index database of indicators using Factor analysis and their classification by Cluster analysis. Construction of composite indicators includes several steps that have to be made and corresponding methods have to be chosen to handle different aspects of economic issues including features of EU resilience.

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Chapter 11
DOI: 10.4018/978-1-5225-3856-1.ch011
ABSTRACT
Since 2008, the world has faced the economic crisis that has had devastating effects on many regions
to various degrees. How regions respond to economic shock depends on regional economic structure
and performance, administrative capacity, resources, human capital, social capital, and other factors,
were perceived as resilience. Resilience has recently risen to prominence in several disciplines, has also
entered policy discourse, and is one of the future strategic goals for the European Union. The aim of the
chapter is to introduce a methodology for assessing the resilience of EU28 NUTS 2 regions based on a
construction of composite weighted index derived from EU Regional Competitiveness Index database of
indicators using Factor analysis and their classification by Cluster analysis. Construction of composite
indicators includes several steps that have to be made and corresponding methods have to be chosen to
handle different aspects of economic issues including features of EU resilience.
INTRODUCTION
It is generally accepted that the level of economic development is not uniform across territories. On
the contrary, it substantially differs. This plays an important role in many research studies that made to
assign an appropriate evaluation of economic and social development in European area (Balcerowicz et
Boosting the EU
Competitiveness as Response
to Economic Shocks:
Composite Weighted Index
of Regional Resilience
Michaela Staníčková
VŠB – Technical University of Ostrava, Czech Republic
Lukáš Melecký
VŠB – Technical University of Ostrava, Czech Republic

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Boosting the EU Competitiveness as Response to Economic Shocks
al., 2013, Easterly & Levine, 2012, Watt & Botsch, 2010, Ghosh et al., 2009). As human activities are
related to economic development and affected by territorial development, the way of measurement of the
conditions of national development is really essential and important in the determination of a country’s
socio-economic policies (Halkos & Tzeremes, 2005). The issue of socio-economic advancement but also
disparities of territories closely link to the setting and evaluation of competitiveness (Gardiner, Martin
& Tyler, 2004, Ocubo, 2012). The dynamics of economic, social, political and cultural change in the
contemporary world increasingly shape by the pursuit and promotion of competitiveness. The economy
may be competitive but if the society and the environment suffer too much the country will face major
difficulties, e.g. in the form of economic crisis, and its competitive and comparative advantages and
disadvantages are subject to evolution and adaptation with respect to these processes (Fojtíková, 2016).
Starting in 2008, an economic crisis with no comparable precedent after WWII has affected most of
the World, and Europe in particular. Yet, despite the pervasiveness of the crisis, it has affected differ-
ently different European Union (EU) countries, with some countries losing a very large number of jobs,
and others being able to maintain employment. At the same time, for some countries, the burden on
public finances due to increasing interest rates has become un-tractable, while others have been able to
maintain public finances under control, also thanks to a lower starting debt. Global economic changes
have caused problems for both individuals and businesses, affecting entire economic sectors, regions,
and their socioeconomic structures. Therefore, also EU is facing one of the most difficult periods since
its establishment, with multiple challenges for the policy-makers mainly at the regional level. Recent
years have seen a myriad of economic and social difficulties, i.e. stagnating economic growth, rising
unemployment leading to social tensions, continuing financial troubles and sovereign debt crises in
several European countries, exacerbated by the fact that the outlook remains uncertain. There is wide-
spread agreement that the root causes of this prolonged crisis lie in the lack of competitiveness of many
countries. The EU faces increased competition from other continents, their nations, regions and cities.
Territorial potentials of European regions and their diversity are thus becoming increasingly important
for the resilience and flexibility of the European economy, especially now in times of globalisation
processes in the world economy. The EU, its regions and larger territories are increasingly affected by
developments at the global level. New emerging challenges influence the territorial development and
require policy responses. Territorial imbalances on the other hand challenge economic, social and ter-
ritorial resilience in the EU (Staníčková, 2016).
Resilience measurement and evaluation at any level of territorial development is frequently associ-
ated with the lack of integrated approaches and methodologies. Within this chapter, the application of
integrated approach by using the construction of five composite non-weighted indices and one composite
weighted index of regional resilience are introduced in the frame of Regional Competitiveness Index
(RCI) approach in 273 EU28 NUTS 2 regions. Resilience in the frame of regional competitiveness is
a major obstacle to the balanced and harmonious development of the regions, but also of the whole
territory. Analysis of resilience may bring the important information about the key problematic issues
in the region (and thus in the country) on the one side and its development potential on the other side.
The main goal of the chapter is to introduce the construction of specific composite weighted index and
verify this approach through brief empirical analysis and evaluation of selected economic, social and
territorial indicators that reflect the level of regional resilience in EU28 NUTS 2 regions using most
recent data from RCI approach. For this purpose, the chapter determinates and computes five factors of
regional resilience and proposes a construction of Composite Weighted Index of Regional Resilience
(CWIRR) for EU28 NUTS 2 regions. The chapter hypothesis is based on the general concept of regional

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Boosting the EU Competitiveness as Response to Economic Shocks
competitiveness presented e.g. by Gardiner, Martin & Tyler (2004) or Bristow (2005). Regions with
the lower level of productivity and ability to create and maintain an environment that sustains more
value creation for its enterprises and more prosperity for its people’ prosperity, achieve the lower level
of resilience in the territory that provides worse conditions and assumptions for regional development
potential, and vice versa. The chapter, in the content of the previous hypothesis, intends to establish
the general presumption that in NUTS 2 regions of (more) developed EU28 Member States is a higher
level of regional resilience in comparison with the level of regional resilience in NUTS 2 regions of less
developed EU28 Member States.
RESILIENCE AND CRISIS
Macroeconomic country-level effects are very important, but and also within countries the impact on the
various regions has been far from uniform, with some regions, often the urbanist, able to resist the crisis
better than others. Economic crisis has presented many challenges to policy planners, who are trying to
protect countries from negative effects and are seeking solutions for more resilient development in the
future. The structure of countries is hence an important determinant of how they can afford periods of
distress and contemporary regional development issue is enriched with the concept of regional resilience.
Resilience: Concept and Literature Review
Economic shocks occur periodically to economies, though the effect that these shocks have varies from
region to region as does the region’s adjustment and recovery to them. Authors are particularly con-
cerned with regional economic resilience: why are some regional economies that are adversely affected
by shocks able to recover in a relatively short period while others are not? Economic resilience is a
concept frequently used, but rarely well defined. The concept of resilience is routinely used in research
in disciplines ranging from environmental research to materials science and engineering, psychology,
sociology, and economics. The notion of resilience is commonly used to denote both strength and flex-
ibility. Conceptually, there are two separate but not necessarily unrelated, concepts. The first bases on
‘equilibrium analysis’ in which resilience is the ability to return to a pre-existing state in a single equi-
librium system. The second defines resilience in terms of ‘complex adaptive systems’ and relates to the
ability of a system to adapt and change in response to stresses and strains (Pindus et al., 2012). The term
implies both the ability to adjust to ‘normal’ or anticipated levels of stress and to adapt to sudden shocks
and extraordinary demands. In the context of hazards, the concept thought of as spanning both prevent
measures that seek to prevent hazard-related damage and losses and post-event strategies designed to
cope with and minimise disaster impacts.
For regional economic analysis, perhaps the most natural conceptual meaning of economic resilience
is the ability of a regional economy to maintain or return to a pre-existing state (typically assumed an
equilibrium state) in the presence of some type of exogenous shock. Although only a few studies use
the term ‘resilience’ explicitly. The economic literature dealing with the idea of resilience typically
concerns with the extent to which a regional or national economy is able to return to its previous level
and/or growth rate of output, employment, or population after experiencing an external shock, see e.g.
Blanchard & Katz (1992), Rose & Liao (2005), Briguglio et al. (2006) or Feyrer, Sacerdote & Stern
(2007). Competitiveness expects to contribute to the economic performance and welfare of cities, firstly

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Boosting the EU Competitiveness as Response to Economic Shocks
by enhancing attractivity for international capital; secondly, to enable local agents to export their products
and services all over the world and join global value chains. Thirdly, to acquire global functions that will
allow them to benefit from the spill over effects of the global circulation of knowledge, information and
technology. Competitiveness can be attained with different assets that define to what extent a particular
region is able to integrate into the global economy. However, the existing assets of competitiveness can
be quickly eroded, since their effects may differ from place to place. More importantly, the reliance on
global conditions and the dominance of deregulatory measures make regions vulnerable in economic
terms. The financial crisis of the recent past has led to deep economic problems in many countries,
which is just one example of how problems in local economies can easily disseminate within the global
economy and can cause complications even in countries with relatively stable economies. The dependence
on global markets and the conditions imposed by global capital has also very important implications on
performance and resilience (Eraydin, 2013, pp. 20-21).
Opinions vary to the definition of resilience, and there is no mainstream approach for measurement and
expression of resilience and therefore no uniform strategies for strengthening the resilience of economies.
Quantifying systems and regional resilience is a complex process, and scales for measuring resilience,
at any level, do not currently exist. What are the main characteristics of regional resilience? The first
group of factors suggests Martin (2012) and among the key factors of regional resilience ranks: dynamic
growth of the region, the structure of the economy, export orientation and specialisation of the region,
human capital, innovation rate, business and corporate culture, localisation of region, and institutional
arrangement in the region. The second group of factors defines Foster (2006) and among the key factors
of regional resilience suggests regional economic capacity, the socio-demographic capacity of the region
and regional community capacity. In the Czech Republic, Koutský et al. (2012) engage issues of regional
resilience determinants and define following factors: the main macroeconomic indicators, labour market
indicators and additional ones. Based on these three sets of factors of regional resilience, the authors can
define (with a certain degree of generalization proceed) a set of indicators of regional resilience which
are also important in terms of competitiveness (based on common relation), see (Melecký, Staníčková,
2015 a, b) and this approach will be used for purposes of this chapter. In the chapter, the authors link the
concepts of resilience with competitiveness. It is very important to understand the extent to which areas
(territories/localities or regions) compete with each other, where this competition comes from, and what
factors determine a territorial economic attractiveness. Taking the competitiveness concept a step further,
understanding territorial resilience challenges allows not only thinking about wealth generation of our
territories, but also ensuring the wellbeing of all citizens, enabling sustainable economic development,
and how to manage economic shocks and decline into our territorial strategies (Tamásy & Diez, 2013).
The authors point out that regional resilience has certainly influenced by the nature state economic
policy, export-orientation of regions, business and corporate culture, institutional arrangement of regions
and other factors. However, the above indicators are considered as the basic initial research in this area.
Crisis and Territorial Consequences
Despite the growing importance of socioeconomic resilience during the current period of economic
crisis, this concept has not been carefully defined or satisfactory measured within the more general issue
of socioeconomic resilience. This concept has received increasing interest in Europe, particularly in the
time of several times mentioned the issue, i.e. economic crisis and external shocks. In this meaning, a

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Boosting the EU Competitiveness as Response to Economic Shocks
long-term, holistic perspective, in contrast, would emphasise the structure of relationships among mac-
roeconomic variables that persists over a long period and the economic, political and social institutions
that condition this structure (Reich, 1997). As an example, a social structure is not static; although it
persists for a long time, it evolves in ways that ultimately threaten firms’ profitability and long-term mac-
roeconomic growth. Economic systems that experience negative shocks may exhibit three different kinds
of responses. Some of these may have returned to or exceeded their previous growth within a relatively
short period; these regions might be called economically resilient. Some may not has been thrown off
their growth path at all; these regions might be called shock-resistant. Finally, some regions may have
been unable to rebound and return to or exceed their previous path; these can be called non-resilient
(Mancini et al., 2012). To implement a socioeconomic resilience measure is necessary to address a series
of measurement issues (as will be discussed below), and especially consequences of economic shocks.
Economic shocks occur periodically to economies, though the effect that these shocks have varies
from region to region as does the region’s adjustment and recovery to them. The chapter is particularly
concerned with economic resilience: why are some economies that are adversely affected by shocks
able to recover in a relatively short time-period while others are not? There is widespread agreement
that effective and efficient way to respond the economic shocks is to improve the economic resilience
of the territories, also in the case of economic policies as the reaction on economic crisis starting in
2008 (Staníčková, 2016). A number of scholars agree in considering the recent financial crisis one of
the most severe economic crises in post-war economic history (Arestis, Sobreira & Oreiro, 2011). The
2008 global economic crisis has been the most severe economic recession since the Great Depression.
Far from being limited to the instabilities of some of the world’s largest private financial institutions, as
it appeared to be at the early stages, the financial crisis gradually turned into a global economic crisis,
resp. economic recession based on the theory of economic cycle and its phases. The pervasiveness and
geographical heterogeneity of its impacts have attracted increasing interest in understanding how and
why territories, local and regional economies react to economic shocks.
In the EU, the crisis interrupted constant average GDP growth and employment growth, opening the
doors of several countries to the economic recession. The recession technically started in the first quarter
of 2008 and lasted until the last quarter of 2009. Between the second half of 2010 and 2011, the EU
recorded a second wave of negative economic growth. Whereas the recession has influenced the majority
of European countries, its depth has been highly unequal across the Continent and its long-term impacts
are likely to be similarly uneven (Crescenzi, Luca & Milio, 2016). As argued by earlier policy reports
and academic papers, the proper understanding of the recession impacts upon which to modulate future
regional policies calls for a perspective able to take into account the different geographies and intensities
of the social, economic and territorial dynamics triggered by the downturn. The recession is, in most EU
Member States, a private debt crisis that turned into a sovereign debt crisis (Milio et al., 2014). These
two different, yet intertwined, phases of the crisis have followed successive paths, with the outbreak
of the private debt crisis in 2008 and the subsequent uprising of the sovereign debt crisis in 2010. The
Economic recession in a severe downturn, leading to a slump in demand, a fall in economic output and
increasing unemployment. Europe was no longer clearly moving towards economic and social cohesion
(Melecký, 2015). The territorial impacts are asymmetrical. The impacts of the recession vary greatly
throughout Europe and not all countries and regions experienced economic decline. The important point
is the fact that crisis developed at different times and ‘Resilience’ has become an increasingly significant
concept in European policymaking.

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Boosting the EU Competitiveness as Response to Economic Shocks
CWIRR METHODOLOGY
Relatively independent and frequently used approach to the measurement and evaluation of competitive-
ness and resilience is the construction of comprehensive integrated indicators and composite indices.
Composite indicators (CIs) which compare country or region performance are recognised as a useful tool
in policy analysis and public communication. The number of CIs in existence around the world is growing
year after year. Bandura (2006) cites more than 160 composite indicators in his study. Such CIs provide
simple comparisons of countries or regions used to illustrate complex and sometimes elusive issues in
wide-ranging fields, e.g. socio-economic environment, economy, international trade, society or techno-
logical development. CIs can be much ‘better’ to describe (instead of ten values for each region we have
only one) than to examine several independent indicators separately. Different types of CIs can be used
for univariate, bivariate or multivariate analyses of data in any territorial level (country, region, district,
municipality, etc.) as Al Sharmin (2011) illustrates in a case study based on the district-level analysis.
On the other hand, CIs can send misleading messages to policy makers if they are poorly constructed
or interpreted as evidenced by Nardo et al. (2005). Composite indicators are much like mathematical
or computational issues. As such, their construction owes to universally accepted scientific rules for
encoding. With regard to models, the justification for a CI lies in its fitness for the intended purpose and
in peer acceptance (Rosen, 1991).
Within the aim of the chapter, construction of specific composite sub-indices of regional resilience
and composite weighted index of regional resilience has been proposed because these indices can sum-
marise complex and multi-dimensional view of regional resilience and are easier to interpret than a bat-
tery of many separate resilience indicators. Therefore, these CI reduce the visible size of a selected set
of resilience indicators without dropping the underlying information base. Own construction design of
composite synthetic sub-indices based on selected indicators of economic, social and territorial indica-
tors of regional resilience coming from the Regional Competitiveness Index (RCI) approach database
presents a two-phase model based on selected mathematical and multivariate statistical methods. In the
first phase, a method of a standardized variable (Z-score) is used. In the second phase, factor analysis
(FA) for partial calculation of resilience factor scores is used. Factor scores obtained from the FA play
the key role in the second phase of construction of the Composite Weighted Index of Regional Resilience
(CWIRR). This procedure demonstrated by Nardo, et al. (2005) has been used in the construction of
aggregate synthetic indices in several empirical analysis of regional development; see e.g. Žižka (2013)
or Melecký (2015).
Territorial competitiveness approach plays a key role from the perspective of the appropriate data-
base of regional resilience indicators for CWIRR design. To improve the understanding of territorial
competitiveness at the regional level, the European Commission has developed RCI, which shows the
strengths and weaknesses of each of EU regions. RCI index focuses on EU NUTS 2 regions. NUTS 2
regions are administrative or statistical regions, which do not take into account functional economic
links. Annoni & Kozovska (2010) have published first edition of RCI (RCI 2010) in 2010. It 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. Annoni & Dijkstra
(2013) finally performed the second edition of RCI (RCI 2013). RCI 2013 can provide a guide to what
each region should focus on, taking into account its specific situation and its overall level of development.
RCI proved to be a robust way to summarise many different indicators into one index. RCI is based on
a set of 80 candidate indicators of which 73 have been included in RCI. Candidate indicators are mainly

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Boosting the EU Competitiveness as Response to Economic Shocks
selected from Eurostat with some additional official sources, such as the WEF, a novelty of this release,
OECD-PISA and Regpat, the European Cluster Observatory, the World Bank Ease of Doing Business
Index and Governance Indicators. RCI is based on eleven pillars describing both inputs and outputs of
territorial competitiveness, grouped into three sets describing basic, efficiency and innovative factors
of competitiveness. The basic pillars represent the basic drivers of all economies. They include (1)
Quality of Institutions, (2) Macroeconomic Stability, (3) Infrastructure, (4) Health and the (5) Quality
of Primary and Secondary Education. These pillars are most important for less developed regions. The
efficiency pillars are (6) Higher Education and Lifelong Learning (7) Labour Market Efficiency and (8)
Market Size. The innovation pillars, which are particularly important for the most advanced regional
economies, include (9) Technological Readiness, (10) Business Sophistication and (11) Innovation. This
group plays a more important role for intermediate and especially for highly developed regions. Overall,
RCI framework is designed to capture short as well as long-term capabilities of regions.
The unique construction of CWIRR is based on few procedures. The first step of analysis is to find
out relevant indicators for regional resilience measuring, in the second step FA is applied for factors
of resilience defining, in the third step entropy method is applied for using different weighting scheme
for each resilience dimension (factor), and in the last step – final calculation of CWIRR is provided. In
the final step, cluster analysis is applied for categorising the EU28 Member States based on CWIRR
values. Multivariate statistical methods take into account the multidimensionality of the data and they
are able to examine relationships and differences in data. The simple multivariate statistical methods of
regional evaluation include point method, traffic light method (scaling), method of average (standard)
deviation, the method of the standardized variable, the method of distance from the imaginary point.
More sophisticated multivariate statistical methods represent cluster analysis and factor analysis Besides
traditional multivariate methods, some other alternative methods are used for regional disparities or
competitiveness measurement, e.g. the Herfindahl index, the Geographic concentration index, the Theil
index, Geographical information system, Method of convergence, etc. These methods mainly bases on
spatial and geographic data analysis. Finally, another also broadly extended approach to regional dis-
parities measurement represents easily used the methodology for modelling operational processes for
disparities evaluations, e.g. Melecký (2015).
The basic approach for choosing relevant indicators is RCI 2013. The used indicators presented the
framework of resilience with link to competitiveness are as follows: (1) Institutional dimension: Gov-
ernment effectiveness (GE), Corruption (C), Rule of law (RL); (2) Infrastructure dimension: Motorway
potential accessibility (MPA), Railway potential accessibility (RPA); (3) Health dimension: Healthy life
expectancy (HLE), Cancer disease death rate (CDDR), Heart disease death rate (HDDR); (4) Education
dimension: Population 25-64 with higher education (PE), Lifelong learning (LL), Access to universities
(AU); (5) Labour market dimension: Employment rate (ER), Long-term unemployment (LTUR), Labour
productivity (LP); (6) Market size dimension: Disposable income (DI), Gross domestic product (GDP);
(7) Business sophistication: Employment in sophisticated (K-N) sectors (ESS), Gross valued added of
sophisticated (K-N) sectors (GVA); (8) Innovation dimension: Total patent applications (TPA), Core
creative class employment (CCCE), Gross expenditure on research and development (GERD), Human
resources in science and technology (HRST), High-tech patents (HTP), ICT patents (ICT).
The European Commission adopts the definition type of composite indicators used in this chapter.
Composite indicators are based on sub-indicators that have no common meaningful unit of measure-
ment and there is no obvious way of weighting these sub-indicators (Saisana & Tarantola, 2002, p. 5).

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Boosting the EU Competitiveness as Response to Economic Shocks
For this reason, when try to suggest a construction of CWIRR – the main attention is dedicated to the
choice of the relevant way hot to set the weights for each dimension of resilience, i.e. entropy method.
Weighting and aggregation systems have a crucial effect on the outcome of each composite index.
There is not only one proper method. That is why this part of constructing composite index is the most
discussed and criticised by an opponent of composite indices. Although various functional forms for the
underlying aggregation rules of a composite indicator have been developed in the literature, (see Munda
& Nardo, 2005 or OECD, 2008), in the standard practice, a composite indicator (CI) can be considered
a weighted linear aggregation rule applied to a set of variables (Munda & Nardo, 2005, p. 3) as shown
in following formula (1):
CI w x
i i
i
n
=
=
∑. ,
1
(1)
wherexi is a scale adjusted variable andwi is a weight attached toxi, usually with wi
i
N
=
=
∑1
1
and
0 1 1 2≤ ≤ =w i N
i, , ,..., . In this framework, a crucial issue presents the concept of weight. The evalu-
ation of the criteria weights may be subjective, objective and integrated. List of the most common
weighting methods is summarised, for instance, in Ginevičius and Podvezko (2004) or OECD (2008).
All quantitative approaches of criteria weighting are based on the matrix R=rij ( , ..., p; , ..., k)i j= =1 1
of the criteria significancesR Rk1,..., , characterising the compared alternativesA Ap1, ..., . These sig-
nificances rij may be statistical data or the estimates provided by experts. Subjective methods of weight
determination are based on the expert evaluation. His/her experience and knowledge allows for provid-
ing the most valuable information about the compared objects. There are numerous techniques for
subjective determining the criteria weights (significances), including ranking or pairwise comparison
(Ginevičius & Podvezko, 2004). This type of criteria weighting includes common practice in attaching
weights where greater weight should be given to criteria (components) which are considered to be more
significant in the context of multiple-criteria decision-making process or the particular composite indi-
cator. The objective approaches to calculating the criteria weights evaluate the structure of matrix R
representing the values rij, while the values of the weights may change together with the values them-
selves. In this chapter, the authors used the entropy method to determine the weight of evaluating factors
and applied it in resilience evaluation in the case of EU28 NUTS 2 regions.
The entropy method based on information on alternatives can be used only in case of a finite number
of alternatives. This method requires knowledge of the values of all the criteria for all variants in the
matrix R. In the theory of information, the entropy is the criterion of uncertainty posed by a discrete
probability distribution pi. This degree of uncertainty is expressed e.g. by Karmeshu (2003) in follow-
ing formula (2):
S p p p p
n i
i
n
i
(p , , ..., ) . . ln ,
1 2
1
= −
=
∑
c (2)

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Boosting the EU Competitiveness as Response to Economic Shocks
where c is a positive constant. Equation (2) express entropy in statistical concept, therefore entropy can
be found as probability distribution pi and terms of entropy and probability are considered as synonyms.
Suppose all pi equal, then forgiven i, p
n
i=1 reaches S p pn
(p , , ..., )
1 2 the maximum value. From matrix
R we can determine the share of the i-th variant on the sum of the j-th criteria for all criteria pij from
formula (3):
pr
r
i
ij
ij
i
p
j, i , , ..., p, j , ,..., k .= = =
=
∑
1
1 2 1 2 (3)
For the j-th criterion, entropy (sj) determines formula (4):
s c p
j ij
í
p
ij
= − =
=
∑
. . ln , j , , ..., k.
1
1 2p (4)
If supposecp
=1
ln , then 0 1≤ ≤sjis guaranteed. Non-normalized entropy weight of j-th criteria
(dj) can be found in formula (5):
d s
j j
= − =1 1 2, j , , ..., k, (5)
while the respective normalised weights wi are obtained from the formula (6) where the sum of weights
in each dimension is equal to one (6):
wd
d
j
j
j
j
k
= =
=
∑
1
1 2, j , , ..., k. (6)
Based on the general equation of CI (1), it is possible finally calculate the Composite Weighted Index
of Regional Resilience (CWIRR) described in the procedure by Figure 1. From a statistical point of view,
CWIRR is designed for each of 273 EU28 NUTS 2 regions by equation (7) as weighted linear aggregation:
CWIRR zw F
r f f
f
=
=
∑.r
1
5 (7)
where CWIRRris Composite Weighted Index of Regional Resilience for r-th region, zw f is normalized
weights of f-th factor of regional resilience; Ffr
is factor score of f-th factor of regional resilience for r-th

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Boosting the EU Competitiveness as Response to Economic Shocks
region; r is EU28 NUTS 2 region; r = {1 = AT11, …, 273 = UKN0}; f is factor of regional resilience;
f = {1 = CL, 2 = HC-SDS, 3 = LM, 4 = EP, 5 = ISR}.
CWIRR RESULTS
Analysis of regional resilience is based on 25 selected indicators important for regional resilience from
the competitiveness point of view. These indicators have been carefully selected from the dataset of RCI
2013 disparities and are described above. Each dimension of regional resilience presented by extracted
factor of regional resilience is presented nearly by an equal number of selected indicators listed in Table
1. The reference period laying down between the years 2009 and 2011 is determined by the construction
of RCI 2013 and their data availability in all 273 EU28 NUTS 2 regions.
In the following step, Factor analysis (FA) is applied as one of the most common quantitative methods;
resp. multivariate statistical methods convenient for a high number of multivariate measured variables.
Multivariate analysis is an ever-expanding set of techniques for data analysis that encompasses a wide
range of possible research situation. FA is the statistical approach that can be used to analyse interre-
lationships among a large number of variables and to explain these variables in terms of their common
underlying dimensions, i.e. factors. The main applications of FA techniques are thus – to reduce the
number of variables and to detect structure in the relationships between variables, which is to classify
variables. The objective of FA is to reduce the number of variables by grouping them into a smaller
set of factors – for this purpose is FA applied in the chapter. Why carry out factor analyses? If we can
summarise a multitude of measurements with a smaller number of factors without losing too much
information, we have achieved some economy of description, which is one of the goals of scientific
investigation. It is also possible that FA will allow us to test theories involving variables, which are hard
to measure directly. Finally, at a more prosaic level, FA can help us establish that sets of questionnaire
items (observed variables) are in fact all measuring the same underlying factor (perhaps with varying
reliability) and so can be combined to form a more reliable measure of that factor. There are a number
of different varieties of FA (Stevens, 1986).
In this chapter, FA has been applied for finding relevant factors of resilience based on the used data
set – indicators were divided into factors that are crucial for EU regional resilience and competitiveness
as well. FA calculated via IBM SPSS Statistics 24 applied following features: Principal Component
Analysis as extraction method; Varimax with Kaiser Normalization as rotation method. In this chapter,
five dominating factors have been extracted: Community links (CL), Human capital and socio-demo-
graphic structure (HC-SDS), Labour market (LM), Economic performance (EP), Innovation, science and
research (ISR). These factors explained 84,368% of total variability of indicators (see Table 1). Table
1 also shows indicators and their belonging to relevant factors, which are also classified with respect to
their importance to resilience, i.e. weights for each dimension are mentioned. Based on FA preliminary
results it is clear, that indicators associated with each factor are relevant for its dimension of resilience; a
number of indicators is balanced across factors. Based on entropy method results it is evident, that values
of weights are lso balanced across factors. The greatest impact on the overall regional resilience has
human capital and socio-demographic structure dimension, what is logical considering the importance
of human capital and its manifestations in all economic areas.

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Boosting the EU Competitiveness as Response to Economic Shocks
In Figure 1, results of CWIRR for 273 EU28 NUTS 2 regions are illustrated. CWIRR curve closer to
value 0 presents NUTS 2 regions less resilient and resistant e.g. to crises, and conversely, the higher value
of CWIRR and curve has more distant from the centre, NUTS 2 regions are more resilient and resistant
to crises and thus more competitive. There are obvious differences between traditionally developed and
known less developed NUTS 2 regions what means that results of CWIRR are conclusive and relevant
in this regional level. Exact values of CWIRR for all 273 EU28 NUTS 2 regions are shown in Table 2
where (based on traffic light method) dark-grey colour means higher values of the composite index, i.e.
greater resilience, and conversely (white colour means percentile 50).
In Figure 2, results of the composite index for national level of analysis, i.e. for 28 EU Member States,
are illustrated. Calculation of the composite index for countries presents the median of composite index
values for regions within each country. There is the same logic in results interpretation as in the case of
Table 1. Results of factor analysis and entropy method
Factors CL HC-SDS LM EP ISR
Sum of
Normalized
Weights
Indicators GE, C, RL,
MPA, RPA
HLE, CDDR,
HDDR,
PE, LL, AU
ER, LTUR,
ESS, CCCE
LP, GVA, DI,
GDP
TPA, GERD,
HRST, HTP,
ICT
Number of indicators 66445
Weights 0.205 0.223 0.195 0.194 0.182 1.000
(Source: own calculation and elaboration, 2017)
Figure 1. CWIRR for EU28 NUTS 2 Regions
(Source: own calculation and elaboration, 2017)

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Boosting the EU Competitiveness as Response to Economic Shocks
regional level in Figure 2 and confirmed thus differences not only among regions but also at the national
level between the group of ‘old’ and ‘new’ EU countries. Exact values of the composite index for all EU
NUTS 2 regions NUTS 0 countries are shown in Table 2 where (based on traffic light method) dark-grey
colour means higher values of the composite index, i.e. greater resilience, and conversely (white colour
means percentile 50).
From the main descriptive statistics of CWIRR for national (NUTS 0) and regional (NUTS 2) level
is possible to see the differences between both territorial levels (see Table 3). A higher level of differ-
entiation is evident at the regional level, which is evident with regard to the heterogeneity of the EU
NUTS 2 regions. This fact also confirmed that national level is aggregate value of regional indicators.
In the final step, Cluster analysis (CA) is applied. CA is used in a wide range of human activity
from the natural sciences to the area of economic analysis. Methodical procedures within the CA offer
the option of choosing the most useful methodology for data processing, display outputs and results
interpretation to the problem. The advantage of the method is its simplicity, the gothic graphic chart
output of the results (called Dendrogram) and the ability to use statistical software (in this case IBM
SPPS version 24), which typically offer this method in the context of multivariate data analysis. Given
the objective of the chapter, CA is used for segmentation of various economic entities – in this case,
EU28 Member States (based on regional average) that are very similar to others in the cluster based
on a set of selected characteristics, i.e. CWIRR values. CA is a way of grouping cases of data based on
the similarity of responses to several variables. CA is a strong tool of the multivariate exploratory data
analysis. It involves a great amount of techniques, methods and algorithms, which can be applied in
Table 2. Composite weighted index of Regional Resilience for EU28 NUTS 2 regions
Note: CI = Composite Index; NUTS 0 = Country/National Level; NUTS 2 = Regional Level
(Source: own calculation and elaboration, 2017)

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various fields, including the economy. The aim of CA is to identify groups of similar objects according
to selected variables. CA classifies objects that are very similar to others in the cluster based on a set of
selected characteristics, e.g. CWIRR values. The resulting cluster of objects should exhibit high internal
(within-cluster) homogeneity and high external (between-cluster) heterogeneity. The object is sorted into
groups, or clusters so that the degree of association is strong between members of the same cluster and
weak between members of different clusters. The task of clustering is then to divide the set of objects
into the disjunctive clusters. There are many types of CA techniques. Probably the most applied method
in the economy is agglomerative hierarchical CA. It is based on a proximity matrix, which includes the
similarity evaluation for all pairs of objects. It means that various similarity or dissimilarity measures
for different types of variables can be used. Moreover, different approaches for evaluation of the cluster
similarity can also be applied. Apart from giving a possibility to analyse data files with qualitative vari-
Figure 2. CWIRR for EU28 Countries
Source: own calculation and elaboration, 2017
Table 3. Descriptive statistics of CWIRR
N Min Max Sum Mean Std. Deviation Variance
Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Statistic
CWIRR NUTS 2 273 .396 2.876 504.852 1.84927 .034614 .571923 .327
CWIRR NUTS 0 27 .696 2.572 44.895 1.66278 .113729 .590952 .349
(Source: own calculation and elaboration, 2017)

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Boosting the EU Competitiveness as Response to Economic Shocks
ables, the main advantage of this type of analysis is a graphical output in the form of a dendrogram. To
determine the optimum solution, in the chapter is used the most common approach – method of hierarchi-
cal cluster analysis and the clustering algorithm is Ward’s method applying Squared Euclidean Distance
as the distance or similarity measure, which is most suitable for territorial analysis. The aim in Ward’s
method is to join cases into clusters such that the variance within a cluster is minimised. To do this, each
case begins as its own cluster. Clusters are then merged in such a way as to reduce the variability within
a cluster. To be more precise, two clusters are merged if this merger results in the minimum increase in
the error sum of squares. This means that at each stage the average similarity of the cluster is measured.
The difference between each case within a cluster and that average similarity is calculated and squared
(just like calculating a standard deviation). The sum of squared deviations is used as a measure of er-
ror within a cluster. A case is selected to enter the cluster if it is the case whose inclusion in the cluster
produces the least increase in the error (as measured by the sum of squared deviations). This approach
helps to obtain the optimum number of clusters researchers should work with. The next step is to rerun
the hierarchical cluster analysis with this selected number of clusters, which enables us to allocate every
case in our sample to a particular cluster.
Based on results of FA and CWIRR calculation, it is possible to create cluster profile of EU28 Member
States. CA is used for defining country cluster profile based on CWIRR values. In this chapter, the best
interpretation of data ensures the three-cluster solution, as shown Figure 3 (Dendrogram), which clearly
observed the gradual grouping of the EU28 Member States into individual clusters.
Figure 3. Dendrogram of CA for CWIRR-EU28 (Source: own calculation and elaboration, 2017)

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Boosting the EU Competitiveness as Response to Economic Shocks
FUTURE RESEARCH DIRECTIONS
The composite index designed in this paper represents the initial concept needs development in further
research. Because it takes a long time to change the regional characteristics that affect resilience-related
outcomes, policies and strategies that are put in place after a region has experienced an economic shock
are challenging activities, what is our future research orientation. Future research presents many additional
lines of inquiry as dropping the outliers – exceptionally advantaged regions could behave enough differ-
ently to mask the influences in other regions; use panel data methods that would allow using information
about connections among observations and indicators, resp. sub-indices – potentially, it could recognise
that some observations pertain to the same regions and some to the same years; differentiate between
downturns before and after 2008/2009, and update the database.
CONCLUSION
Bringing together different development factors, which illustrate single aspects of competitiveness, gives
a first impression of the overall competitiveness of EU Member States and shows the diversity that exists
within the EU territory. Among the important driving forces influencing future territorial development
are demographic development (including migration), economic integration, transport, energy, agricul-
ture and rural development, climate change, further EU enlargements and territorial governance. Very
important role-play exogenous factors having the impact on regional competitiveness. Current theories of
regional competitiveness emphasise the significance of soft factors such as human, cultural (knowledge
and creativity) and socio-institutional capital, environmental quality, etc. A wide range of soft location
factors is thus of increasing importance. Soft factors like governance, culture and natural environment are
part of territorial potentials and offer synergies for the jobs and growth agenda. The potentials for these
soft factors differ widely between areas. Quality living environments and access to environmental and
cultural amenities are among factors that attract investment and people to a location what is very important
for competitiveness for each country and its competitive advantage and factor endowment. Currently,
hazards (also in the form of economic crisis) do not undermine the competitiveness of a region. Only a
few places have very low exposure to the main natural and technological hazards in Europe, and climate
change is expected to increase the risk of hazards in the future. To gaze into the future it is necessary
to understand the driving forces that shape territorial development and various possible future develop-
ments and interrelations with the territory each driving force might bring. Bringing them together into
integrated prospective scenarios is then the final challenge, which should help to be resistant to crisis.
This chapter presented a framework for defining regional resilience and specifying quantitative
measures of resilience that can serve as foci for comprehensive characterization of the socio-economic
problem to establish needs and priorities. Regional resilience is a much broader concept beyond the
economic dimension. It is also reasonable to assume that application of similar indices at the lower ter-
ritorial level will require adaptation to national conditions and specifics.
The composite index designed in the chapter represent the initial concept with which is necessary
continually operate in the following research. The framework integrates measures into five dimension
of regional resilience: Community links (CL), Human capital and Socio-demographic structure (HC-
SDS), Labour market (LM), Economic performance (EP) and Innovation, science and research (ISR),
all of which can be used to quantify measures of resilience for various types of regional systems what

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Boosting the EU Competitiveness as Response to Economic Shocks
could serve for establishing the tasks required to achieve required objectives. Regional resilience is a
much broader concept beyond the economic dimension. This framework makes it possible to assess and
evaluate the contribution to the resilience of various activities implemented in regions, whether focusing
on components, systems, or organisations, with applications ranging from lifelines and building systems
to the organisations that provide critical services. Well-defined and consistently applied quantifiable
measures of resilience make it possible to carry out various kinds of comparative studies (e.g., to assess
the effectiveness of various loss-reduction measures, such as structural problems), to determine why
some regions are more resilient than others are, and to assess changes in regions resilience over time.
The ultimate objective is to propose the concept of regional resilience index, which is presented in
the chapter. Because it takes a long time to change the regional characteristics that affect resilience-
related outcomes, policies and strategies that are put in place after a region has experienced an economic
shock are challenging activities, what is our future research orientation. In the framework of prelimi-
nary results, while a planning process that follows communicative rationality is to be used in shaping
the planning process, the methods defined within the context of decision-making can be used to define
background or remove red tape to achieve no-regret conditions in the long term. With respect to the
results, some countries and their regions are more resilient when confronted with economic shocks
than others are. These regions are either less affected by such shocks on impact and/or they recover
more quickly. Consequently, countries and regions cannot be understood as the only decisive factors in
providing resilience because they strongly depend on the macroeconomic framework conditions of the
entire country. Therefore, the first step towards more resilient economies should be made at the national
level, providing attractive socioeconomic conditions such as an innovation-friendly tax system, openness
to foreign investment, a competitive business environment, a flexible employment system, and, above
all, a trustworthy and stable political system that plays an important role in confidence building while
interacting at international level.
ACKNOWLEDGMENT
The chapter is supported by the SGS project (SP2017/111) of Faculty of Economics, VŠB-TUO, Czech
Science Agency junior project (17-23411Y) and the Operational Programme Education for Competitive-
ness - Project No. CZ.1.07/2.3.00/20.0296.
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KEY TERMS AND DEFINITIONS
Competitiveness: Ability of a firm or a nation to offer products and services that meet the quality
standards of the local and world markets at prices that are competitive and provide adequate returns on
the resources employed or consumed in producing them.
Economic Crisis: A situation in which the economy of a country experiences a sudden downturn
brought on by a financial crisis, it can take the form of a recession or a depression.
Entropy Weight Method: One of the objective method for weight setting, which determines the
entropy values of the indicators according to the amount of information each index provides, based on
this determine the index’s weight.
Factor Analysis: Multivariate statistical method used to describe variability among observed, cor-
related variables in terms of a potentially lower number of unobserved variables called factors.
NUTS Classification: Nomenclature of territorial units for statistics is a hierarchical system for
dividing the economic territory of the EU for the purpose of (1) collection, development and harmonisa-
tion of European regional statistics, (2) socio-economic analyses of the regions and (3) framing of EU
regional policies.
NUTS 2 Regions: Basic regions for the application of regional policies in the EU.
Resilience: The ability of a regional economy to maintain or return to a pre-existing state (typically
assumed an equilibrium state) in the presence of some type of exogenous shock.