XX. mezinárodní kolokvium o regionálních vědách
Concept of resilience
in the context of regional development:
literature review of theory
and realistic aspects
Michaela Staníčková – Lukáš Melecký
Department of European Integration
Faculty of Economics
VŠB-Technical University of Ostrava
Czech Republic
OUTLINE
Theoretical Part
• Introduction/Motivation
• Economic Resilience: Concept, Literature Review, Measurement
• Composite Weighted Index of Regional Resilience: Methodology
Empirical Part
• Empirical Analysis Background
• Data
• Preliminary Results
• Conclusion
June 14-16, 2017, Kurdějov, Czech Republic
ACKNOWLEDGEMENT
Impact of Economic Shocks on Efficiency and Competitiveness of
the European Union in Terms of Regional and International Levels:
Nonparametric Approach
Project registration number: SP 2015/106 (Ing. Michaela Staníčková, Ph.D.)
Period of research: 1. 1. – 31. 12. 2015
Recipient: VŠB –Technical University of Ostrava, Faculty of
Economics, Department of European Integration
Regional Specialization and Diversity of the EU 28 NUTS 2 regions:
Analysis of Regional Dynamics Determinants
Project registration number: SP 2017/111 (Ing. Michaela Staníčková, Ph.D.)
Period of research: 1. 1. – 31. 12. 2017
Recipient: VŠB –Technical University of Ostrava, Faculty of
Economics, Department of European Integration
June 14-16, 2017, Kurdějov, Czech Republic
INTRODUCTION/MOTIVATION
• The aim:
– Introduction of methodology for assessing the resilience of EU28 NUTS 2 regions
based on construction of composite weighted index calculated from factors
of regional resilience derived from EU Regional Competitiveness Index approach.
– Determination and computation of Composite Weighted Index of Regional
Resilience (CWIRR).
• Research assumptions:
− Based on the general concept of regional competitiveness (Gardiner et al. (2004);
Bristow (2005); Meyer-Stamer (2008); IMD (2014))
− 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’ achieve the lower level of resilience in the territory that provide worse
conditions and assumptions for regional development potential, and vice versa.
− NUTS 2 regions of (more) developed EU28 Member States have higher level of regional
resilience in comparison with the level of regional resilience in NUTS 2 regions of less
developed EU28 Member States.
June 14-16, 2017, Kurdějov, Czech Republic
• Frequently used but rarely well defined concept. Routinely used in research in
disciplines ranging from environmental research to materials science and
engineering, psychology, sociology, and economics.
• No uniform definition of economic resilience → concept remains one of the basic
standards of performance evaluation and reflection of area success in wider
comparison.
↓
• Conceptually, there are two separate, though not necessarily unrelated, concepts of
economic resilience:
• Equilibrium analysis in which resilience is the ability to return to a pre-existing state
in a single equilibrium system (ability to adjust to ‘normal’ or anticipated levels of stress).
• 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 (ability to adapt to sudden shocks
and extraordinary demands).
• The most natural conceptual meaning of economic resilience → the ability
of regional economy to maintain or return to pre-existing state in the presence of
some type of exogenous shock.
ECONOMIC RESILIENCE: CONCEPT AND LITERATURE REVIEW (i)
June 14-16, 2017, Kurdějov, Czech Republic
ECONOMIC RESILIENCE: CONCEPT AND LITERATURE REVIEW (ii)
June 14-16, 2017, Kurdějov, Czech Republic
Time Authors Understanding of resilience concept
2015 Martin, Sunley Resilience refers to a capacity to withstand or recover from market, competitive and environmental shocks.
2012 Martin
The capacity of regional economy to reconfigure, that is adapt, its structure (firms, industries, technologies and institutions) so as to maintain
an acceptable growth path in output, employment and wealth over time.
2010 Gunderson et al.
The resilience concept does not necessarily imply a return to the pre-existing state, but could be referred to as the capacity to respond
to opportunities which arise as a result of change.
2009 Rose
Process by which a community develops and efficiently implements its capacity to absorb an initial shock through mitigation and to respond and adapt
afterward so as to maintain function and hasten recovery, as well as to be in a better position to reduce losses from future disasters.
2008 Cutter et al.
Resilience is the ability of social system to respond and recover from disasters and includes those inherent conditions that allow the system to absorb
impacts and cope with an event, as well as post-event, adaptive processes that facilitate the ability of the social system to reorganize, change, and
learn in response to a threat.
2008 Hill et al.
The ability of regional economy to maintain a pre-existing state in the presence of some type of exogenous shock; the extent to which a regional
or national economy that has experienced an external shock is able to return to its previous level and/or growth rate of output, employment
or population.
2008 Norris et al. Process linking a set of adaptive capacities to a positive trajectory of functioning and adaptation after a disturbance.
2006 Foster The ability of region to prevent, prepare, respond and 'recover' after a disturbance so as not to stand this obstacle to its development.
2006 Perrings
The ability of system to withstand either market or environmental shocks without losing the
capacity to allocate resources efficiently
2004 Coles et al. A community’s capacities, skills, and knowledge that allow it to participate fully in recovery from disasters.
2004 Walker
Capacity of system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity,
and feedbacks.
2003 Bruneau et al.
The ability of system to reduce the chances of shock, to absorb shock if it occurs (abrupt reduction of performance) and to recover quickly after
a shock (re-establish normal performance).
2001 Carpenter et al. The adaptive capacity that allows for continuous development like a dynamic interplay between sustaining and developing with change.
1997 Reich
The structure of relationships among macroeconomic variables that persists over a long period of time and the economic, political, and social
institutions that condition this structure.
1973 Holling
The amount of disturbance that ecosystem could withstand without changing self-organized processes and structures, defined as alternative stable
states, i.e. measure of the persistence of systems and of their ability to absorb change and disturbance and still maintain the same relationships
between populations or state variables.
1958 Elton Resilience as the amplitude of changes brought about by disturbance and by dynamics of post-disturbance recovery.
• No mainstream approach for measurement and expression of resilience.
• Own meta-analyses of more than 70 approaches to conceptualizing
and measuring of resilience in different theoretical and methodological ways.
• Quantifying systems of regional resilience is a complex process, and scales for
measuring resilience, at any level, do not currently exist.
• Main characteristics (factors) for regional resilience (literature review):
• First group: 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 (Martin, 2012).
• Second group: regional economic capacity, socio-demographic capacity of region and regional
community capacity (Foster, 2006).
• Third group: main macroeconomic indicators, labour market indicators and additional ones
(Koutský et al., 2012), (Tamásy and Diez, 2013).
↓
• In the paper, we link the concepts of regional resilience with regional
competitiveness → we define (with a certain degree of generalization proceed) a set
of indicators of regional resilience which are important in terms of regional
competitiveness (based on common relation).
ECONOMIC RESILIENCE: CONCEPT AND MEASUREMENT (i)
June 14-16, 2017, Kurdějov, Czech Republic
• Several possible ways of measuring the degree and ‘shape’ of a region’s resilience to
an economic shock can be found in literature.
Method Focus Literature Review
Case study
based
Mainly narrative based, may involve
simple descriptive data and
interviews with key actors,
interrogation of policies
Munich (Evans and Karecha,
2013); Cambridge and Swansea
(Simmie and Martin, 2010);
Buffalo and Cleveland (Cowell,
2013), EU (ESPON, 2014)
Resilience
indices
Singular or composite, comparative,
measures of (relative) resistance and
recovery, using key system variables of
interest
UK regions (Martin, 2012); US
cities and counties (Kathryn A. Foster,
2011; Augustine et al, 2012; Hans and
Goetz, 2013)
Statistical
time series
models
Impulse response models; error
correction models. These estimate
how long it takes for impact of shock to
dissipate
US regions (Blanchard and Katz,
1992); UK regions (Fingleton,
Garretsen and Martin, 2012)
Causal
structural
models
Embedding resilience in regional
economic models to generate
counterfactual positions of where
system would have been in the
absence of shock
US metropolitan areas
(Doran and Fingleton, 2013)
ECONOMIC RESILIENCE: CONCEPT AND MEASUREMENT (ii)
June 14-16, 2017, Kurdějov, Czech Republic
• Composite indicators (CIs) which compare country or region performance are
increasingly recognised as a useful tool to the measurement and evaluation of
competitiveness and resilience.
• 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.):
• Bandura (2006): cites more than 160 composite indicators in his study,
• Al Shramin (2011): district-level analysis,
• Kutscherauer et al. (2010); Svatošová and Boháčková (2012); Žižka (2013); Melecký (2014,
2015): measuring regional disparities in different spatial level,
• Staníčková and Melecký (2014, 2015, 2016): describing various features of territorial
diversification,
• and many others...
COMPOSITE WEIGHTED INDEX OF REGIONAL RESILIENCE: METHODOLOGY (i)
June 14-16, 2017, Kurdějov, Czech Republic
• Own construction design of composite synthetic index is based on selected indicators of
economic, social and territorial indicators of regional resilience coming from RCI approach
database and our CI presents three-layer model based on selected mathematical and
multivariate statistical methods.
• Composite index used in the paper is based on sub-indicators that have no common
meaningful unit of measurement and there is no obvious way of weighting these subindicators
(see e.g. Saisana and Tarantola, 2002).
COMPOSITE WEIGHTED INDEX OF REGIONAL RESILIENCE: METHODOLOGY (ii)
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 » 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
June 14-16, 2017, Kurdějov, Czech Republic
COMPOSITE WEIGHTED INDEX OF REGIONAL RESILIENCE: METHODOLOGY (iii)
June 14-16, 2017, Kurdějov, Czech Republic
• Weighting and aggregation systems have a crucial effect on outcome of each
composite index.
• In standard practice, a composite indicator (CI) can be considered as weighted linear
aggregation rule applied to a set of variables (Munda and Nardo, 2005, p. 3) as shown
in following equation (1):
(1)
COMPOSITE WEIGHTED INDEX OF REGIONAL RESILIENCE: METHODOLOGY (iv)
1
 
n
i i
i
CI w .x ,
where is a scale adjusted variable and is a weight attached to , usually with
and
• Crucial issue presents the concept of weight. We used the entropy method to
determine the weight of evaluating factors → objective approach 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.
Entropy method requires knowledge of the values of all the criteria for all
variants in the matrix R.
ix iw ix
1
1
N
i
i
w


0 1 1 2iw ,i , ,...,N.  
June 14-16, 2017, Kurdějov, Czech Republic
• Entropy method (I)
• 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):
(3)
COMPOSITE WEIGHTED INDEX OF REGIONAL RESILIENCE: METHODOLOGY (v)
1 2
1
(p , ,..., ) . .ln ,

  
n
n i i
i
S p p c p p
• Suppose all pi equal, then for given i, reaches maximum value.
1
ip
n
 1 2 nS(p , p ,..., p )
• 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 2

  

ij
i j p
ij
i
r
p ,i , ,..., p, j , ,...,k .
r
• 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)
June 14-16, 2017, Kurdějov, Czech Republic
• Entropy method (II)
• If suppose , then is guaranteed. Non normalized entropy
weight of j-th criteria (dj) can be found in formula (5):
(5)
COMPOSITE WEIGHTED INDEX OF REGIONAL RESILIENCE: METHODOLOGY (vi)
1 1 2j jd s , j , ,...,k,  
while the respective normalized weights wi are obtained from the formula
(6) where sum of weights in each dimension is equal to one:
1
1 2
j
j k
j
j
d
w , j , ,...,k .
d

 

1
ln
c
p
 0 1js 
(6)
June 14-16, 2017, Kurdějov, Czech Republic
• CWIRR
• Based on general equation (1) we can calculate the Composite Weighted
Index of Regional Resilience (CWIRR).
• CWIRR is designed for five factors of regional resilience for each of 273
EU28 NUTS 2 regions by equation (7) as weighted linear aggregation:
(7)
COMPOSITE WEIGHTED INDEX OF REGIONAL RESILIENCE: METHODOLOGY (vii)
where:
Composite Weighted Index of Regional Resilience for r-th region;
normalized weights of f-th factor of regional resilience;
factor score of f-th factor of regional resilience for r-th region;
EU28 NUTS 2 region; r = {1 = AT11, … , 273 = UKN0};
factor of regional resilience; f = {1 = CL, 2 = HC-SDS, 3 = LM, 4 = EP, 5 = ISR}.
r
5
1
. ,r f f
f
CWIRR zw F

 
rCWIRR
fzw
rfF
r
f
June 14-16, 2017, Kurdějov, Czech Republic
 Territory
 273 NUTS 2 regions within
EU-28 Member States.
 NUTS 2 region: 800,000 –
3,000,000 inhabitants.
 Administrative or statistical
regions which do not take
into account functional
economics links.
 Reference period
 2007-2013
 Periodicity of data
 Annual
 Source of data
 Regional Competitiveness
Index.
 Indicators
 Cross-sectional data.
 25 selected indicators important
for regional resilience from the
competitiveness point of view.
 Methods
 Z-score transformation
 Factor analysis (Principal
Component analysis)
 Entropy method
 Weighted linear aggregation
 Statistical software
 SPSS Statistics 24, Microsoft
Excel 2016, ArcGIS 10.5
EMPIRICAL ANALYSIS BACKGROUND
June 14-16, 2017, Kurdějov, Czech Republic
DATA FRAMEWORK
June 14-16, 2017, Kurdějov, Czech Republic
OBSERVED DATA
Dimension Indicators
Institution Government effectiveness (GE), Corruption (C), Rule of law (RL)
Infrastructure Motorway potential accessibility (MPA), Railway potential accessibility (RPA)
Health
Healthy life expectancy (HLE), Cancer disease death rate (CDDR), Heart disease death
rate (HDDR)
Education
Population 25-64 with higher education (PE), Lifelong learning (LL), Accessibility
to universities (AU)
Labour market Employment rate (ER), Long-term unemployment (LTUR), Labour productivity (LP)
Market size Disposable income (DI), Gross domestic product (GDP)
Business
sophistication
Employment in sophisticated (K-N) sectors (ESS), Gross valued added of sophisticated
(K-N) sectors (GVA)
Innovation
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)
June 14-16, 2017, Kurdějov, Czech Republic
Results of Factor Analysis and Entropy Method
RESULTS (i)
• 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).
• Extracted factors explained 84.368 % of total variability of selected indicators.
• Indicators associated within each factor are relevant for its dimension
of resilience; also number of indicators is balanced across factors.
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
June 14-16, 2017, Kurdějov, Czech Republic
RESULTS (ii) - CWIRR for EU28 NUTS 2 regions
20
Note:
The higher
CWIRR score
→ the more
resilient and
resistant
region (e.g.
to crises).
RESULTS (iii)
June 14-16, 2017, Kurdějov, Czech Republic
Note:
The higher
CWIRR
score, the
greener
colour →
the more
resilient and
resistant
region (e.g.
to crises).
CONSLUSION
• Contribution of the paper:
• Framework for defining regional resilience and specifying quantitative measures
of resilience that can serve as foci for comprehensive characterization of the socioeconomic
problem to establish needs and priorities.
• 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.
• A proactive approach could considerably minimize the role of external economic
factors; therefore, cooperation of all crucial institutional actors is desirable particularly
concerning those measures aimed at safeguarding economic growth and employment,
which ultimately can contribute to increasing resilience.
• Not only proactivity can provide an adequate response to external challenges, it can
also put regions in a position where they generate and lead a change.
June 14-16, 2017, Kurdějov, Czech Republic
Conference Announcement
June 14-16, 2017, Kurdějov, Czech Republic
XX. mezinárodní kolokvium o regionálních vědách
Michaela Staníčková – Lukáš Melecký
Department of European Integration (120)
Faculty of Economics
VŠB-Technical University of Ostrava
Czech Republic
Q/A
Comments
Suggestions
Thank You for Your Attention
June 14-16, 2017, Kurdějov, Czech Republic