Lukáš Melecký Michaela Staníčková...Lukáš Melecký1, Michaela Staníčková2 1...

13th International Scientific Conference

“Economic Policy in the European Union Member Countries”

September 2-4, 2015, Karolinka, CZECH REPUBLIC

ISBN 978-80-248-3796-3

Conference Proceedings

© Faculty of Economics

VSB - Technical University Ostrava, 2015

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ASSESSMENT OF EU REGIONAL RESILIENCE USING COMPOSITE INDEX

Lukáš Melecký1, Michaela Staníčková

2

1 VŠB-Technical University of Ostrava, Faculty of Economics, Department of European Integration

Sokolská třída 33, 701 21 Ostrava 1, Czech Republic

Email: [email protected]

2 VŠB-Technical University of Ostrava, Faculty of Economics, Department of European Integration

Sokolská třída 33, 701 21 Ostrava 1, Czech Republic

Email: [email protected]

Abstract:

Composite indices have received substantial attention in recent years and various methodologies have been

developed to handle different aspects of economic and business issues which also includes the issue of EU

resilience largely determined by strength and flexibility between regions and countries in the frame of enlarged

European Union. The aim of the paper is to introduce methodology for assessing the resilience of EU28 NUTS

2 regions based on construction of composite weighted index calculated from five factors of regional resilience

derived from EU Regional Competitiveness Index database of indicators using Factor analysis. Construction of

composite indicators includes several steps that have to be made and corresponding methods have to be chosen.

Primarily, selection of sub-indicators, normalizing methods, weighting schemes and aggregation formulas are

fundamental. When used in benchmarking framework, weights can have a significant effect on the overall

composite indicator and the evaluated units’ rankings. Therefore an objective approach of determining

weighting scheme in composite weighted index for measuring EU regional resilience is suggested. In the paper,

we use the entropy method to determine objective weighting scheme based on information available in the data

set.

Keywords: Entropy method, EU28, composite index, NUTS 2, regional resilience, weighting scheme.

Jel classification: C38, C43, O18, R11.

1. Introduction

Nowadays, European Union (EU) is facing one of the most difficult periods since its establishment,

with multiple challenges for the policy-makers mainly at 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 future outlook remains uncertain. There is widespread

agreement that the root causes of this prolonged crisis lie in the lack of competitiveness of many

countries (World Economic Forum, 2013). 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 world economy. The EU, its regions

and larger territories are increasingly affected by developments at the global level. New emerging

challenges impact on territorial development and require policy responses. Territorial imbalances on

the other hand challenge economic, social and territorial resilience within the EU.

Resilience measurement and evaluation at any level of territorial development is frequently

associated with the lack of integrated approaches and methodologies. Within this paper, the

application of integrated approach by using 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




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about the key problematic issues in region (and thus in country) on the one side and its development

potential on the other side. The main goal of the paper is to introduce 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 Regional Competitiveness Index 2013 approach. For

this purpose, the paper will determinate and compute five factors of regional resilience and propose a

construction of Composite Weighted Index of Regional Resilience (CWIRR) for EU28 NUTS 2

regions. The hypothesis of the paper is based on the general concept of regional competitiveness

presented e.g. by Gardiner et al. (2004), Bristow (2005), Meyer-Stamer (2008) or Institute for

Management and Development (2014) that 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 provide

worse conditions and assumptions for regional development potential, and vice versa. The paper, in

content of previous hypothesis, intends to establish the general presumption that in NUTS 2 regions

of (more) developed EU28 Member States, i.e. is higher level of regional resilience in comparison

with the level of regional resilience in NUTS 2 regions of less developed EU28 Member States.

2. Economic 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. We are particularly

concerned with regional economic resilience: why are some regional economies that are adversely

affected by shocks able to recover in a relatively short period of time while others are not? Economic

resilience is a concept that is 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 flexibility. Conceptually, there are two separate, though not necessarily

unrelated, concepts. The first is based on ‘equilibrium analysis’ in which resilience is the ability to

return to a pre-existing state in a single equilibrium 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, N. 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 can be 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 minimize

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 to be an equilibrium state) in the presence of some type of exogenous shock. Although only

a few studies explicitly use the term ‘resilience’, the economic literature that deals with the idea of

resilience typically is concerned 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 shock1, see e.g. Blanchard and Katz (1992), Rose and Liao (2005), Briguglio

et al. (2006) or Feyrer, Sacerdote and Stern (2007). A related concept of resilience is the extent to

which a regional economy avoids having its previous equilibrium state disrupted by an exogenous

shock. This could involve avoiding the shock altogether (e.g. by having a regional economy that is

not dependent on an industry that is likely to experience a negative demand shock) or withstanding

1 Although these macroeconomic indicators are commonly used, it is also possible to apply this and other resilience

concepts to other measures of regional economic performance, such as wage inequality or measures of environmental

sustainability.




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the shock with little or no adverse impact (e.g. by having sufficiently diversified economy that the

shock has little macroeconomic effect).

Research describing patterns and determinants of shock resistance and/or economic resilience is

sparse. As Hill (1992) have mentioned, the regional literature points to several features of regions that

may contribute to either shock-resistance or resilience. With respect to industrial structure, Feyrer,

Sacerdote and Stern (2007) find that counties that experienced auto and steel job losses in the late

1970s and early 1980s had higher post-shock population growth if they had warm, sunny climates and

were located near large metropolitan areas. Kolko and Neumark (2010), in a study of the impact of

regional and industry employment shocks on establishment-level employment, find that employment

in corporate headquarters and, to a lesser extent, in small, locally owned chains, is less likely to

decline in response to these shocks. Therefore, high concentrations of these types of businesses would

be expected to make regions more shock-resistant. Chapple and Lester (2010) find evidence that

regions in which technology and knowledge-based work are growing rapidly exhibit greater

resilience in terms of average earnings per worker. They also find that regions attracting highly

skilled workers have greater increases in average earnings per worker. The broader literature on

regional resilience, especially the literature on resilience to natural disasters, also has insights that

may be relevant to regional economic resilience. For example, a com-mon finding in that literature is

that access to economic resources promotes regional or community resilience in the face of natural

disasters, see e.g. Paton and Johnston (2001), Rozario (2005) or Norris et al. (2008). This suggests

that regions with higher average incomes or wages (independent of human capital) may recover more

quickly from economic shocks. Resilience thinking thus constitutes an alternative approach.

‘Planning for resilience’ can find a home in planning theory as an analysis of the external

dynamics that accelerate economic, social and spatial vulnerability and as an approach that helps to

link social and economic processes with ecological processes, calling for a reconsideration of the

'substance' of planning so as to enhance capacity to deal with slow and sudden changes of different

forms. This can occur within a process that focuses on ‘building a self-organization capacity’

alongside a change in the value system that can overcome the unequal power relations (Svoboda,

2014). In recent decades, regions have endured significant changes in production structures and

labour processes under the pressures of globalization, the rise of new technologies and the increasing

role of knowledge and learning processes have brought about substantial changes in the built

environment, lifestyles and patterns of consumption, resp. these changes have eroded their resilience

(Hudson, 2009). Increased incorporation into the new global economy has brought vulnerabilities

that are amplified by the structural problems of regions, thus opening the door to external pressures.

While economic and social vulnerability have been the subject of broad discussions with reference to

financial and economic crises and the domino effect among regions all over the world, democratic

deficits and vulnerability in governance have been widely disputed. Many territories in the world

have seen a significant restructuring of their economies in order to adapt and compete in the newly

emerging conditions and risks in the global economy. While the deregulation of the flow of goods,

capital and people decreased the level of protection of local economies to external affects, the

volatility of the global economy intensified the vulnerability of regional systems. Today, regions all

over the world are facing pressures that are forcing them to rethink the impacts of policies aimed at

competitiveness and integration into global economy on their socio-spatial structures, following a

period of entrepreneurial policies shaped by the notions of globalization and competition (Eraydin,

2013). Competitiveness is expected to contribute to the economic performance and welfare of cities,

firstly 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; and 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 through the use of different

assets that define to what extent a particular region is able to integrate into the global economy.




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However, the existing assets of competitiveness can quickly be 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. Moreover, 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).

At any given time, the actual or potential performance of any system can be measured as a point in

a multidimensional space of performance measures. Over time, performance can change, sometimes

gradually, sometimes abruptly. Abrupt changes in performance occur in the case of disastrous events

like a major earthquake. In these cases, a system can fail, leading to a major reduction or complete

loss in performance with respect to some or all measures. Resources are then needed to restore a

system’s performance to its normal levels. Similarly, the performance of a system over time can be

characterized as a path through the multidimensional space of performance measures. Normal

fluctuations will show as minor fluctuations in performance. Disastrous events create abrupt changes

in performance, followed by a gradual restoration to normal performance levels, depending on the

resources employed. This characterization of system performance leads to a broader

conceptualization of resilience. Resilience can be understood as the ability of the system to reduce the

chances of a shock, to absorb a shock if it occurs (abrupt reduction of performance) and to recover

quickly after a shock (re-establish normal performance), as specified Bruneau et al. (2003).

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

resilience of economies. Quantifying systems and regional resilience is a complex process, and scales

for measuring resilience, at any level, do not currently exist. Following the below research studies,

indicators and subsequently factors of regional resilience factors are designed which are considered

as crucial for purposes of the paper. We do not want to use just previously defined factors, but to find

out relevant indicators which could be part of factors which are crucial for CWIRR what is the main

aim of the paper. But what are the main characteristics for regional resilience? The first group of

factors suggests Martin (2012) and among the key factors of regional resilience ranks: dynamic

growth of region, structure of the economy, export orientation and specialization of region, human

capital, innovation rate, business and corporate culture, localization of region, and institutional

arrangement in region. The second group of factors defines Foster (2006) and among the key factors

of regional resilience suggests: regional economic capacity, socio-demographic capacity of 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 above we can be 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) and this approach will be used for purposes of

this paper. In the paper, we 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 us to

not only think about wealth generation of our territories, but also ensure the wellbeing of all citizens,

enable sustainable economic development, and how to manage economic shocks and decline into our

territorial strategies (Tamásy and Diez, 2013). The authors point out that regional resilience is has

certainly influenced by the nature state economic policy, export-orientation of regions, business and




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corporate culture, institutional arrangement of regions and other factors. However, the above

indicators are considered as the basic initial research in this area.

3. Composite Weighted Index of Regional Resilience: Methodology and Structure

Relatively independent and frequently used approach to the measurement and evaluation of

competitiveness and resilience is the construction of comprehensive integrated indicators and

composite indices. Composite indicators (CIs) which compare country or region performance are

increasingly 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. For instance Bandura (2006) cites more

than 160 composite indicators in his study. Such composite indicators provide simple comparisons of

countries or regions that can be used to illustrate complex and sometimes elusive issues in

wide-ranging fields, e.g., socio-economic environment, economy, international trade, society or

technological 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 case study based on

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 demonstrated Vahalík (2014) by using and

calculating five different indices describing various features of product and territorial export

diversification. As such, their construction owes to universally accept scientific rules for encoding.

With regard to models, the justification for a composite indicator lies in its fitness for the intended

purpose and in peer acceptance (Rosen, 1991).

Within the aim of the paper, construction of specific composite sub-indices of regional resilience

and composite weighted index of regional resilience has been proposed because these indices can

summarise complex and multi-dimensional view of regional resilience and are easier to interpret than

a battery of many separate resilience indicators. Therefore these composite indices 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 indicators of regional resilience coming from RCI approach database presents

two-phase model based on selected mathematical and multivariate statistical methods. In the first

phase, method of 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 composite weighted index of regional resilience. This

procedure has been demonstrated by Nardo, et al. (2005) and used in construction of aggregate

synthetic indices in several empirical analysis of regional development (see e.g. Kutscherauer et al.,

2010; Svatošová and Boháčková, 2012; Žižka, 2013 or Melecký, 2015).

Territorial competitiveness approach plays a key role from the perspective of appropriate database

of regional resilience indicators for the CWIRR design. To improve the understanding of territorial

competitiveness at the regional level, the European Commission has developed the Regional

Competitiveness Index (RCI) which shows the strengths and weaknesses of each of EU regions. The

RCI index focuses on EU NUTS 22 regions. NUTS 2 regions are administrative or statistical regions

which do not take into account functional economic links. RCI was first published in 2010 by Annoni

2 The NUTS (Nomenclature of territorial units for statistics) classification is a hierarchical system for dividing up the

economic territory of the EU for the purpose of collection, development and harmonisation of European regional statistics

and socio-economic analyses of the regions. The NUTS regulation defines minimum and maximum population thresholds

for the size of the NUTS regions. NUTS 2 regions present basic regions for the application of regional policies with more

or less minimum of 800.000 to maximum 3.000.000 population.




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and Kozovska (2010) as the result of a coordinated action between the Joint Research Centre and

European Commission, the Directorate-General for Regional Policy. The index development started

in 2008 and has been built on the methodology developed by the World Economic Forum (WEF) for

the Global Competitiveness Index (GCI). 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. The second edition of the RCI index (RCI 2013)

was finally performed by Annoni and Dijkstra (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.

The index proved to be a robust way to summarise many different indicators into one index. The roots

of the RCI 2013 lay in the GCI reported by the WEF. RCI 2013 is based on a set of 80 candidate

indicators of which 73 have been included in the index. Candidate indicators are mainly 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 (Annoni and Dijkstra, 2013).

RCI 2013 has basically the same framework and structure of the 2010 edition (RCI 2010) and

includes most recent data for all the indicators mainly between 2009 and 2011. As for the previous

version, the index 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) Macro-economic 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, the RCI framework is designed to capture short- as well as long-term

capabilities of the regions. RCI scores is computed for each pillar as simple average of the Z-score

standardized and/or transformed indicators. Sub-indices for the basic, efficiency and innovation

groups of pillars are computed as arithmetic means of pillar scores. The overall RCI 2013 score is

instead the result of a weighted aggregation of the three sub-indexes, based on the WEF-GCI

approach. For more details about RCI 2013 design see Annoni and Dijkstra (2013).

Unique construction of Composite Weighted Index of Regional Resilience (CWIRR) is based on

procedure in Table 1. The first step on analysis is to find out relevant indicators for regional resilience

measuring, in the second step factor analysis (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 composite weighted index of regional

resilience is provided.

Table 1. Procedure of Composite Weighted Index of Regional Resilience (CWIRR).

Input data analysis

Pre-processing phase » Collection of indicators » Groups of indicators for resilience » Bivariate correlation of normalized

variables for 273 EU28 NUTS 2 regions

Factor analysis

Correlation » Standardization (Z-score) » Principal component analysis » Varimax with Kaiser Normalization »

Resilience factors » Set of new composite sub-indices » Resilience factors description

Entropy method

Weighting scheme for factors of resilience » Set of weights for each resilience dimension (factor)

Composite weighted index calculation

Linear combination of factor analysis and entropy method results » Final calculation of CWIRR

Source: Own elaboration, 2015.




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Based on the information mentioned above, basic approach for choosing relevant indicators is the

EU Regional Competitiveness Index 2013 (RCI 2013). Used indicators in the framework of ‘flagship’

areas of resilience with link to competitiveness are as follows: (1) Institutional dimension:

Government 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), Accessibility 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 definition type of composite indicators used in this paper is adopted by the European

Commission, i.e. composite indicators are based on sub-indicators that have no common meaningful

unit of measurement and there is no obvious way of weighting these sub-indicators (Saisana and

Tarantola, 2002, p. 5). For this reason, when try to suggest a construction of CWIRR in the paper – the

main attention is dedicated to choice of 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 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 criticized by opponent of composite indices. Although various functional forms for the

underlying aggregation rules of a composite indicator have been developed in the literature, (see e.g.

Munda and 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 and Nardo, 2005,

p. 3) as shown in following equation (1):

1

n

i i

i

CI w .x ,

(1)

whereix is a scale adjusted variable and

iw is a weight attached toix , usually with

1

1N

i

i

w

and

0 1 1 2iw ,i , ,...,N. In this framework, a crucial issue presents the concept of weight. The

evaluation of the criteria weights may be subjective, objective and integrated. List of the most

common weighting methods is summarized, for instance, in Ginevičius and Podvezko (2004) or

OECD (2008). All quantitative approaches of criteria weighting are based on the matrix ijrR

1 1( i ,..., p; j ,...,k) of the criteria significances 1 kR ,...,R , characterizing the compared alternatives

1 pA ,...,A . These significances rij may be statistical data or the estimates provided by experts.

Subjective methods of weight determination are based on expert evaluation. His/her experience and

knowledge allows for providing the most valuable information about the compared objects. There are

numerous techniques for subjective determining the criteria weights (significances), including

ranking or pairwise comparison (for details see Ginevičius and 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 indicator. 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 themselves. In this paper we used the




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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 the following formula (2):

1 2

1

(p , ,..., ) . .ln ,n

n i i

i

S p p c p p

(2)

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 for given i, 1

ipn

reaches 1 2 nS(p , p ,..., p ) maximum value.

From matrix R we can determine share of the i-th variant on the sum of the j-th criteria for all criteria

pij from the formula (3):

1

1 2 1 2ij

i j p

ij

i

rp ,i , ,..., p, j , ,...,k .

r

(3)

For the j-th criterion entropy (sj) is determined by formula (4):

1

. .ln , j 1,2,..., k .p

j ij ij

í

s c p p

(4)

If suppose1

lnc

p , then 0 1js is guaranteed. Non normalized entropy weight of j-th criteria

(dj) can be found in formula (5):

1 1 2j jd s , j , ,...,k, (5)

while the respective normalized weights wi are obtained from the formula (6) where sum of weights in

each dimension is equal to one (6):

1

1 2j

j k

j

j

dw , j , ,...,k .

d

(6)

Based on general equation of CI (1) we can finally calculate the Composite Weighted Index of

Regional Resilience (CWIRR) described in procedure by Table 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:

r

5

1

.r f f

f

CWIRR zw F

(7)

where:

rCWIRR Composite Weighted Index of Regional Resilience for r-th region;

fzw normalized weights of f-th factor of regional resilience;




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rfF factor score of f-th factor of regional resilience for r-th region;

r EU28 NUTS 2 region; r = {1 = AT11, … , 273 = UKN0};

f factor of regional resilience; f = {1 = CL, 2 = HC-SDS, 3 = LM, 4 = EP, 5 = ISR}.

4. Brief Empirical Analysis of Regional Resilience in EU28 NUTS 2 Regions

Analysis of regional resilience is based on 25 selected indicators important for regional resilience

from the competitiveness point of view. These indicators has been carefully selected from dataset of

RCI 2013 disparities and are described in chapter above. Each dimension of regional resilience

presented by extracted factor of regional resilience is presented nearly by equal number of selected

indicators listed in Table 2. The reference period laying down between the years 2009 and 2011 is

determined by construction of RCI 2013 and their data availability in all 273 EU28 NUTS 2 regions.

Factor analysis 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 competiveness as

well. FA was calculated via IBM SPSS Statistics 22 and applied following features: Principal

Component Analysis as extraction method; Varimax with Kaiser Normalization as rotation method.

In the paper, five dominating factors has been extracted: Community links (CL), Human capital

and socio-demographic 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 2). Table 2 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 within each factor

are relevant for its dimension of resilience; also number of indicators is balanced across factors.

Based on entropy method results it is evident, that values of weights are also 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.

Table 2. 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 6 6 4 4 5

Weights 0,205 0,223 0,195 0,194 0,182 1,000

Source: Own calculation and elaboration, 2015.

In Fig. 1, prime 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,

higher value of CWIRR and curve has more distant from 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 Composite Weighted Index of Regional

Resilience for all 273 EU28 NUTS 2 regions are shown in Annex 1 where (based on traffic light

method) dark-grey colour means higher values of composite index, i.e. greater resilience, and

conversely (white colour means percentile 50).




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Fig. 1. CWIRR for EU28 NUTS 2 Regions


5. Conclusion

This paper 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 much broader

concept beyond the economic dimension. It is also reasonable to assume that application of similar

indices at lower territorial level will require adaptation to national conditions and specifics.

Composite index designed in the paper represent the initial concept with which is necessary

continually operate in 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 could serve for establishing the tasks required to achieve required objectives. This

framework makes it possible to assess and evaluate the contribution to resilience of various activities

implemented in regions, whether focusing on components, systems, or organizations, with

applications ranging from lifelines and building systems to the organizations that provide critical

services. Well-defined and consistently applied quantifiable measures of resilience make it possible




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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, and to assess changes in regions resilience over time. The ultimate objective of

this paper is to propose the concept of regional resilience index which is presented in the paper.

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

preliminary 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 so as to achieve no-regret conditions in the long term.

This baseline research points many future additional lines of inquiry, e.g. drop the outliers –

exceptionally advantaged regions could behave enough differently to mask the influences in other

regions; use panel data methods that would allow us to use information about connections among

observations and indicators, resp. sub-indices – potentially, we could recognize 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.

6. Acknowledgement

This research was financially supported by the SGS project (SP2015/106) of Faculty of Economics,

VŠB-Technical University of Ostrava and the Operational Programme Education for

Competitiveness – Project No. CZ.1.07/2.3.00/20.0296.

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Annex 1. Values of CWIRR for EU28 NUTS 2 Regions (in alphabetical order).

NUTS 2 CWIRR NUTS 2 CWIRR NUTS 2 CWIRR NUTS 2 CWIRR NUTS 2 CWIRR NUTS 2 CWIRR NUTS 2 CWIRR NUTS 2 CWIRR AT11 2,093 DE13 2,404 DK01 2,665 FR30 1,925 HU23 0,962 NL31 2,876 SE11 2,831 UKJ2 2,677

AT12 2,250 DE14 2,403 DK02 2,365 FR41 1,881 HU31 0,837 NL32 2,739 SE12 2,485 UKJ3 2,574

AT13 2,250 DE21 2,485 DK03 2,320 FR42 2,168 HU32 0,843 NL33 2,651 SE21 2,387 UKJ4 2,447

AT21 2,149 DE22 2,128 DK04 2,444 FR43 1,931 HU33 0,943 NL34 2,440 SE22 2,564 UKK1 2,610

AT22 2,120 DE23 2,184 DK05 2,297 FR51 1,951 IE01 1,777 NL41 2,613 SE23 2,555 UKK2 2,477

AT31 2,211 DE24 2,200 EE00 1,472 FR52 1,966 IE02 1,949 NL42 2,541 SE31 2,307 UKK3 2,156

AT32 2,285 DE25 2,349 ES11 1,677 FR53 1,873 ITC1 1,737 PL11 1,186 SE32 2,334 UKK4 2,346

AT33 2,289 DE26 2,311 ES12 1,680 FR61 1,951 ITC2 1,756 PL12 1,458 SE33 2,375 UKL1 2,094

AT34 2,307 DE27 2,253 ES13 1,700 FR62 2,033 ITC3 1,769 PL21 1,291 SI01 1,657 UKL2 2,388

BE10 2,365 DE30 2,302 ES21 2,003 FR63 1,885 ITC4 1,804 PL22 1,258 SI02 1,935 UKM2 2,311

BE21 2,435 DE41 2,302 ES22 1,892 FR71 2,108 ITD1 1,964 PL31 1,123 SK01 1,816 UKM3 2,158



BE24 2,365 DE60 2,575 ES30 2,152 FR82 1,907 ITD4 1,797 PL34 1,124 SK04 0,944 UKN0 1,950

BE25 2,362 DE71 2,571 ES41 1,652 FR83 1,755 ITD5 1,841 PL41 1,176 UKC1 2,103

BE31 2,365 DE72 2,318 ES42 1,592 FR91 1,274 ITE1 1,717 PL42 1,141 UKC2 2,174

BE32 1,825 DE73 2,253 ES43 1,485 FR92 1,321 ITE2 1,727 PL43 1,197 UKD1 2,202



BE35 2,010 DE92 2,318 ES53 1,658 GR11 0,852 ITF1 1,503 PL61 1,069 UKD4 2,329

BG31 0,396 DE93 2,183 ES61 1,441 GR12 1,038 ITF2 1,285 PL62 1,130 UKD5 2,049

BG32 0,660 DE94 2,174 ES62 1,584 GR13 0,859 ITF3 1,033 PL63 1,274 UKE1 2,117

BG33 0,732 DEA1 2,365 ES63 1,385 GR14 0,959 ITF4 1,121 PT11 1,470 UKE2 2,392



BG42 0,752 DEA4 2,273 FI13 2,161 GR23 0,995 ITG1 1,034 PT17 1,805 UKF1 2,401

CY00 1,802 DEA5 2,243 FI18 2,485 GR24 0,898 ITG2 1,341 PT18 1,531 UKF2 2,551

CZ01 1,895 DEB1 2,285 FI19 2,266 GR25 0,917 LT00 1,068 PT20 1,331 UKF3 2,360

CZ02 1,895 DEB2 2,229 FI1A 2,161 GR30 1,529 LU00 2,566 PT30 1,325 UKG1 2,472

CZ03 1,617 DEB3 2,336 FI20 2,470 GR41 1,084 LV00 1,059 RO11 0,792 UKG2 2,327

CZ04 1,315 DEC0 2,180 FR10 2,465 GR42 1,158 MT00 1,782 RO12 0,720 UKG3 2,226

CZ05 1,598 DED1 2,102 FR21 1,749 GR43 1,139 NL11 2,534 RO21 0,623 UKH1 2,442

CZ06 1,585 DED2 2,204 FR22 1,848 HR03 0,932 NL12 2,447 RO22 0,549 UKH2 2,617

CZ07 1,449 DED3 2,209 FR23 1,828 HR04 0,905 NL13 2,470 RO31 0,669 UKH3 2,617

CZ08 1,445 DEE0 2,052 FR24 1,953 HU10 1,362 NL21 2,543 RO32 1,304 UKI1 2,617

DE11 2,496 DEF0 2,222 FR25 1,821 HU21 1,129 NL22 2,602 RO41 0,717 UKI2 2,617

DE12 2,486 DEG0 2,240 FR26 1,830 HU22 1,211 NL23 2,739 RO42 0,696 UKJ1 2,773


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