How resilient are the European regions? : evidence from the societal response to the 2008 financial crisis.
Date: 2020-09-10
Institutions: Joint Research Centre
Subjects: European Region, economic recession, economic recovery, monetary crisis, regional economy, regional policy, research report, social well-being

How resilient are the European regions? 

Evidence from the societal 
response to the 2008 
financial crisis 

Benczúr , Péter 

Joossens, Elisabeth 

Manca, Anna Rita 

Menyhért, Bálint 

Zec, Slavica 

2020 

EUR 30352 EN

This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It 
aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a 
policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is 
responsible for the use that might be made of this publication. For information on the methodology and quality underlying the data used 
in this publication for which the source is neither Eurostat nor other Commission services, users should contact the referenced source. The 
designations employed and the presentation of material on the maps do not imply the expression of any opinion whatsoever on the part 
of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation 
of its frontiers or boundaries. 

Contact information 
Name: Péter Benczúr  
Address: Via E. Fermi 2749. 21027 Ispra 
Email: peter.benczur@ec.europa.eu 
Tel.: +39 0332 78 5772 

EU Science Hub 
https://ec.europa.eu/jrc 

JRC121554 

EUR 30352 EN

PDF 

ISBN 978-92-76-21452-6 

ISSN 1831-9424 

doi:10.2760/383460 

Luxembourg: Publications Office of the European Union, 2020 

© European Union, 2020

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How to cite this report: Benczur, P., Joossens, E., Manca, A.R., Menyhert, B. and Zec, S., How resilient are the European regions: Evidence 
from the societal response to the 2008 financial crisis, EUR 30352 EN, Publications Office of the European Union, Luxembourg, 2020, 
ISBN 978-92-76-21452-6, doi:10.2760/383460, JRC121554. 

Abstract 

This report proposes a new approach for measuring regional resilience that goes 
beyond the assessment of traditional economic dimensions. It defines resilience as the 
societal ability to preserve and generate well-being in the presence of shocks and 
persistent structural changes in a sustainable manner, without hindering the well-
being of future generations. The empirical exercise concentrates on the 2008 financial 
and economic crisis and the subsequent overall response of EU regions to the economic 
shock. We implement a three-step methodology: (i) select an extensive list of economic 
and non-economic variables that span the entire production process of societal well-
being; (ii) compute regional resilience indicators based on the joint dynamic response 
of these variables to the crisis; (iii) identify those pre-crisis characteristics that 
differentiate resilient regions from the non-resilient ones. Our analysis reveals 
substantial heterogeneity in resilience across the European regions. It confirms the 
importance of expanding the measurement strategy to a broader list of subjective and 
objective well-being measures (like social inclusion, social capital, and quality of life). 
We show that observed resilience performance is highly dependent on the time horizon: 
resilience rankings of European regions are markedly different in the short and long 
run. The analysis of the recovery time provides additional information on the strength 
and weaknesses of regions, and it is largely dependent on the specific dimensions 
(variables) considered. Finally, our results highlight that certain country-level and 
regional  characteristics,  such  as  private  sector  credit  flows  and  the  gender 
employment gap, are strong predictors of resilient regional behaviour after the crisis. 

Keywords: regional resilience, societal well-being, impact, recovery, medium run, bounce 
forward, financial and economic crisis, absorption, adaptation, transformation. 

JEL Classification: C50, I31, R11. 

3 

Acknowledgments 

The authors would like to express their gratitude to JRC colleagues for their helpful and 
constructive comments during internal discussions and seminars. They are grateful to 
colleagues in DG EMPL for the comments received during the Triple A Talk (June 2019, Brussels) 
and in DG ECFIN for the suggestions received during the Midday seminar (October 2019, 
Brussels).  

Furthermore, this paper was presented in several national and international conferences, 
among which the 59th European Regional Science Association Congress (August 2019, Lyon), 
the 17th International Society for Quality-of-Life Studies Conference (September 2019, 
Granada), the Regional Studies Association Conference (June 2019, Santiago de Compostela), 
the Annual Meeting of the Hungarian Association of Economics (December 2019, Budapest). The 
authors are grateful for the comments and feedback received during these events.   

Inaccuracies, misinterpretations or mistakes are strictly under the authors’ responsibilities. 

4 

 
 
 
 
Contents 

Abstract ................................................................................................................ 3 

1  Introduction ...................................................................................................... 7 

2  Theoretical framework ...................................................................................... 10 

2.1  Conceptual framework ................................................................................ 10 

2.2  The regional response to the 2008 financial crisis:   the measurement framework

11 

3  Empirical methodology: a three-step approach .................................................... 14 

3.1  Variable selection: implementing the system view ......................................... 14 

3.2  Metrics and Indicators ................................................................................ 17 

3.2.1  Magnitude-based resilience metrics ...................................................... 18 

3.2.2  Duration-based resilience metrics ........................................................ 19 

3.3  Characteristics .......................................................................................... 20 

4  Results ........................................................................................................... 22 

4.1  Magnitude based metrics, components and indicators .................................... 22 

4.1.1  The metrics ....................................................................................... 22 

4.1.2  Resilience by its three main components............................................... 24 

4.2  Beyond the economic aspect of resilience ..................................................... 24 

4.2.1  Social and economic dimension ........................................................... 25 

4.2.2  Household income and subjective measures .......................................... 25 

4.2.3  Overall resilience ............................................................................... 26 

4.3  Time to recover from the crisis .................................................................... 28 

4.3.1  How long does each variable recover from the crisis? ............................. 30 

4.3.2  Regional speed of recovery ................................................................. 31 

4.4  Characteristics .......................................................................................... 32 

5  Conclusions .................................................................................................... 36 

6  References ...................................................................................................... 38 

Annex 1: List of selected system variables ....................................................................... 41 

Annex 2: Correlation matrix of the system sub-components ................................................. 42 

Annex 3: Correlation of categories ............................................................................... 43 

Annex 4: Comparison of different measures .................................................................... 44 

Annex 5: Impact, medium run and bounce forward over the EU .............................................. 46 

Annex 6: Time to the maximum impact of the shock ............................................................ 48 

Annex 7: List of selected characteristics from the univariate regressions ................................. 49 

5 

 
 
 
 
 
List of Tables 

Table 1: List of resilience metrics ...................................................................................18 

Table 2: Time to recover by variable ...............................................................................31 

Table 3:Multivariate regressions for the three resilience indicators .........................................33 

List of Figures 

Figure 1: The JRC system view ......................................................................................11 

Figure 2: List of final system variables ............................................................................16 

Figure 3: Data system ................................................................................................17 

Figure 4: Schematic overview of the resilience metrics ........................................................20 

Figure 5: Impact ......................................................................................................22 

Figure 6: Medium run ................................................................................................23 

Figure 7: Bounce forward ...........................................................................................23 

Figure 8:  Resilience indicator by Asset, Engine and Outcomes ................................................24 

Figure 9: Impact and Medium run: comparison Social vs Economic measures across the Macro regions 
of the EU ...............................................................................................................25 

Figure 10: Resilience indicators: Average household income vs average measure of positive perceptions 
of society. ..............................................................................................................26 

Figure 11: Maps of the three resilient indicators for all regions ................................................27 

Figure 12: Impact, medium run and bounce forward indicator for all regions ................................28 

Figure 13: Relation between the size of impact for the GDP and the time to recover ........................29 

Figure 14: Rank in impact vs rank in time to recover .............................................................30 

Figure 15: Rank of regions in the overall score of time to recover .............................................32 

6 

 
 
1 

Introduction 

In the last decades, EU society has been exposed to many challenges. Demographic imbalances, 
climate  change,  migration  pressure,  the  financial  and  economic  crisis,  technological 
transformation, and changes in the geopolitical landscape are just some of the examples. These 
trends drive economic, technological, social, democratic, and geopolitical revolutions and might 
reshape global and European society (ESPAS, 2015; Joint Research Centre, 2020). Even though 
change and transformation have been at the core of human and societal development from the 
earliest  of  times,  with  distress  often  being  a  catalyst  for  future  improvements,  recent 
developments seem to result in a growing sense of discontent and uncertainty. 

These tendencies have contributed to a growing interest in understanding the capacity to thrive 
despite adversity. From 2000 onwards, resilience has become a popular topic among scientists 
and  policy-makers  in  various  fields.  The  concept  of  resilience  had  already  been  extensively 
studied  in  engineering  (as  in  Holling,  1973  or  Pimm,  1984),  psychology  (Garmezy,  1973),  and 
ecology (Batabyal, 1998). Recently, it has also gained a strong interest in economics and regional 
sciences (as in Bristow, 2010; Martin, 2012; Modica and Reggiani, 2015; Sensier et al., 2016).  

As of late, international organisations have also made different attempts to promote resilience 
through political declarations1 and scientific analysis (Duval and Vogel, 2008; Caldera Sánchez 
et  al.,  2015;  OECD,  2014;  Sondermann,  2018).  One  example  concerns  the  three-way  resilience 
concept (structured around vulnerability, shock absorption, and recovery) put forward by the 
Commission's  Directorate‑General  for  Economic  and  Financial  Affairs  for  the  Economic  and 
Monetary Union, which provides a detailed taxonomy of economic resilience factors by policy 
area (European Commission, 2017). Furthermore, this line of work emphasises the importance 
of completing the single market, the banking union, and the capital markets union in order to 
strengthen  resilience  in  especially  in  case  of  reduced  traditional  adjustment  capabilities  in 
monetary and fiscal areas (European Commission, 2018). In the Quarterly Report on the Euro 
Area of November 2019, a full chapter is dedicated to the discussion on how structural reforms 
have  contributed  to  the  functioning  of  the  Euro  area  over  the  past  20  years  by  stimulating 
growth, convergence,  and resilience  (Canton et al., 2019).  Other  recent  Relevant  EC research 
includes Pontarollo and Serpieri (2018) that assesses regional renewal capacity (i.e. the extent 
to  which  regional  economies  ‘renew’  their  growth  paths  after  experiencing  a  shock)  of  the 
European regions in the aftermath of the 2008 financial and economic crisis. Another study can 
be found in Annoni et al. (2019) that explores the regional resilience to the 2008 economic and 
financial crisis by assessing the initial level of economic development of European regions. Di 
Pietro  et  al.  (2020)  show  that  a  general  equilibrium  model  can  be  used  to  analyse  the 
vulnerability,  recovery  and  resistance  of  European  regions  with  respect  to  a  demand,  total 
factor  productivity,  and  risk  premium.  The  aforementioned  approaches  have  all  focused  on 
  employment,  productivity)  of  resilience.  Earlier 
macroeconomic  aspects  (e.g.  GDP, 
Commission work, that can be found in Canova and Kontolemis (2012), examined the adjustment 
capacity of industrial sectors of the EU to shocks and analysed the role that institutional factors 
and product market regulations play in this adjustment process. 

Despite these efforts, the concept of resilience has not yet become an entirely operational policy 
target  for  at  least  three  reasons.  First,  there  still  exists  neither  a  common  definition  nor  a 
common  measurement  approach  to  resilience,  as  explained  in  Martin  and  Sunley  (2015)  or  
Martin et al. (2016). Second,  resilience is shock-specific and  observable only  retrospectively, 
which is often in contrast with the requirements of policy-makers for timely data and forward-

1Consider, for example, the joint Rome Declaration by the EU institutions (https://www.consilium.europa.eu/en/press/press-
releases/2017/03/25/rome-declaration/), the European Commission’s Reflection Paper on Globalisation 
(https://ec.europa.eu/commission/sites/beta-political/files/reflection-paper-globalisation_en.pdf), or the Note on Resilience 
Principles by the G20 (http://www.bundesfinanzministerium.de/Content/DE/Standardartikel/Themen/Schlaglichter/G20-
2016/Note-Resilience-Principles-in-Economie.pdf?__blob=publicationFile). 

7 

 
                                           
looking information. Third, most relevant empirical work has focussed on economic resilience 
only,  despite  an  increasing  need  to  go  beyond  merely  economic  indicators  (such  as  GDP  per 
capita or  GDP  growth) and to develop “indicators  that  are as  clear and  appealing  as GDP, but 
more inclusive of environmental and social aspects of progress” 2 (also discussed in Folke et al., 
2010; Sensier et al., 2016; Manca et al., 2017). 

To contribute to filling this gap, the Joint Research Centre (JRC) of the European Commission, in 
co-operation with the European Political Strategy Centre, started a joint reflection on resilience 
(Joint Research Centre, 2015) and set up a dedicated EU-wide policy network.3 The first results 
of these efforts were the development of a conceptual framework of resilience in Manca et al. 
(2017)  and  the  construction  of  an  empirical  measurement  strategy,  described  in  Alessi  et  al. 
(2019).  Within  this  approach,  a  society  is  defined  as  resilient  if  it  retains  the  ability  to  deliver 
societal well-being in a sustainable way even in the face of shocks and/or persistent structural 
changes.4  

Resilience is a complex and multidimensional phenomenon which can be studied at the level of 
countries  (Alessi  et al.,  2019),  regions  (Modica  and Reggiani, 2015;  Martin, 2012; Sensier  et al., 
2016;  Pontarollo and Serpieri,  2018),  as  well  as  individual, households  or  firms  (Masten,  1994;  
Martin et al., 2015; Ambulkar et al., 2015). It can be expected that there is substantial interaction 
between  the  different  societal  layers.  For  instance,  resilient  communities  require  resilient 
individuals, but it is also true that individuals become resilient in societies that better cope with 
distress, as claimed in Joossens et al. (2020).  

Why study resilience at the regional level? It is a shared opinion that regional economies  are 
characterized  by  complex,  non-linear,  and  non–equilibrium  dynamics  (Simmie  and  Martin, 
2010). For this reason, studying resilience in a regional context can yield many essential insights 
from a policy perspective. First, it can uncover robust spatial patterns and substantial within-
country  heterogeneity  in  resilience. Second,  it can  reveal  a lot  about  the nature  of  resilience 
itself, by indicating the role of countries and country- versus regional-level characteristics in 
driving  regional  performance.  Third,  zooming  in  from  the  country  level  to  subnational  level 
enables a more reliable empirical analysis: a large number of regions as statistical units can 
yield more robust conclusions. 

There are many studies focused on the issue of regional resilience in the aftermath of the 2008 
financial  crisis  from  an  economic  standpoint,  see  for  example  Christopherson  et  al.  (2010), 
Hudson (2010),  Martin et al. (2016), Simmie and Martin (2010), Faggian et al. (2018). Resilience 
performance  of  regions  seems  to  depend  on  their  socio-economic  characteristics  and  the 
diversity of economic structures and development strategies (Dabson et al., 2012). JRC studies, 
such as Pontarollo and Serpieri (2018), Di Pietro et al. (2020), Benczur et al. (2020), investigate 
regional  patterns  of  economic  resilience  to  identify  geographical  and  thematic  areas  of 
strategic policy intervention.   

Overall, the insights gained in these areas are very important from a policy point of view. They 
help  to  make  sense  of  the  growing  regional  inequality  and  divergence  observed  since  the 
beginning of the millennium (Dijkstra et al., 2015; Iammarino et al., 2018). Together with country-
level  findings,  they  help  to identify  the  available and feasible policy  tools  that can be  used by 
local, regional, and national governments to promote resilience. Moreover, they demonstrate 
that  resilience  can  be  increased  by  sound  intervention,  policy  planning,  and  strategic 
monitoring. Finding the right balance and adequate targeting of such interventions is one of the 
critical challenges facing policy-makers. Our contribution is threefold: first, it takes a holistic 

2 Source: the Beyond GDP homepage, http://ec.europa.eu/environment/beyond_gdp/index_en.html 
3  

The Commission-wide Research Network on Measuring Resilience, Resil.net, is composed by representatives of each 
Directorate General of the European Commission. 
Though resilience can be shock dependent while sustainability is a general feature, the link between this definition of resilience 
and the concept of sustainability is very close. In a sense, sustainability is the goal to be reached, while resilience is the means 
to remain on, or return to, a sustainable development path in the presence of distress. 

4  

8 

 
                                           
perspective  by  extending  the  current  strictly  economic  view  to  broader  socio-economic 
dimensions;  second,  it  expands  the  concept  of  resilience  to  societal  wellbeing  and  links  it  to 
sustainability;  and,  finally,  it  assigns  prominent  importance  to  the  time  dimension  for  the 
resilience assessment. 

The measurement strategy of the current study builds upon Alessi et al. (2019) and complements 
the  analysis  presented  in  Benczur  et  al.  (2020),  with  the  aim  to  assess  the  response  of  EU 
regions to the 2008 global financial and economic crisis. In particular, this paper highlights the 
regions that have shown resilient behaviour during and after the crisis, with particular attention 
not  only  to  the  amplitude  of  resilience  along  different  time  horizons  but  also  to  the  speed  of 
recovery  European  regions  have  experienced.  Furthermore,  it  discusses  the  empirical 
implications of complementing the selection of economic variables with indicators  related to 
the  social  aspects  of  our  society.  Finally,  it  highlights  the  importance  of  regional  or  country-
level characteristics that are associated with revealed regional resilience. The final goal of this 
exercise  is  to  provide  policymakers  with  tools  and  analytical  methods  to  better  understand 
resilience and enhance the regional capacity to intervene (prevent, prepare, protect, promote 
and transform) to better face potential future shocks of similar nature. 

The structure of this report is as follows. Section 2 outlines the theoretical and measurement 
framework. Section 3 presents relevant details of the empirical analysis, from selecting system 
variables to creating resilience indicators and resilience characteristics. Section 4 presents the 
result  of  the  analysis  in  terms  of  regional  resilience  rankings  and  patterns,  while  also 
identifying  those  regional  and  country-level  features  that  are  most  closely  associated  with 
resilience performance from a statistical standpoint. Section 5 puts this into a policy context and 
concludes.  

9 

 
2  Theoretical framework 

Resilience is a complex and multidimensional concept, representing the general ability to thrive 
despite adversities. The traditionally dominant focus on the ability to restore a system’s original 
function and stability has been further developed to include adaptability, the ability to bounce 
forward a pre-crisis situation, and the speed of recovery. , in this section, we recap briefly the 
motivation  and  theoretical  background  which  guide  us  in  the  choices  for  our  empirical 
approach. We build upon  Manca  et al.  (2017)  and  extend the  analysis in,  where the  concept of 
resilience  has  been  broadened  to  include  societal  resilience.  We  highlight  the  main 
assumptions for overcoming the complexity of societal resilience and the relevant features for 
building various resilience indicators. 

2.1  Conceptual framework 

As  mention  before,  our  starting  point  is  the  framework  developed  in  Manca  et  al.  (2017)  and 
extended in  Alessi at al., (2019), which aims to provide a common ground to define and measure 
societal resilience. This framework places societal well-being at its core and defines a system 
as  resilient  if  it  can  face  challenges  without  losing  its  ability  to  deliver  well-being  in  a 
sustainable way.  

To  be  able  to  assess  the  dynamics  of  societal  well-being,  one  needs  to  look  at  different 
components  of  its  generation  process.  Societal  well-being  is  not  a  simple  sum  of  individual 
well-being  levels:  it  is  strongly  linked  to  the  structure  and  fabric  of  the  society,  such  as 
community values and social capital. It is thus important to take a broad and disaggregated view, 
which is able to capture both monetary and non-monetary aspects of society.  

In particular, looking exclusively at the economic behaviour of a region is not sufficient to fully 
understand its reaction to a shock. The economic reaction is just a part of the picture: the social, 
institutional,  and political infrastructure  should  be  taken  into account  to fully understand  the 
overall resilience. Moreover, shocks can affect differently the segments of the societal system, 
implying  the  need  to  adopt  a  comprehensive  analysis  of  the  sub-components  of  the  system 
itself.  

To  this  end,  building  on  the  holistic  model proposed  by  Costanza  et  al.  (1997),  the  framework 
breaks  down  the  socio-economic-political  system  into  three  main  pillars.  The  first  pillar 
concerns the ‘assets’, represented by input levels of human, social, natural, and built capital. The 
second  deals  with  the  target  ‘outcomes’  in  terms  of  economic  welfare  (i.e.  investment  and 
consumption) and social well-being dimensions (i.e. health, employment, life satisfaction, and 
happiness). The third is the ‘engine’, which encapsulates the interaction of main stakeholders 
within  socio-economic,  political,  and  socio  and  eco-system  services,5  whose  function  is  to 
utilise  the  assets  and  transform  them  into  outcomes  (Figure  1).  Overall,  this  simplified 
representation of society is what we refer to as “the system view” later on.6 

Societal resilience is about the way society responds to shocks over time. Sensier et al. (2016) 
argue that different shocks have different outcomes, and shocks differ in magnitude, type, and 
length. Depending on the interaction between the time of exposure and the intensity of distress, 
identified:  absorption,  adaptation,  and 
three  different  resilience  capacities  can  be 

5

6  

Eco-system services are the contributions of ecosystem structure and function (in combination with other inputs) to human 
well-being. Socio-system services are linked to the social relations among individuals. For further information see, Manca et 
al. (2017). 
It also highlights the importance of the relationship between resilience and sustainability through the use of capitals, along the 
lines of Stiglitz et al. (2010), More importantly, as argued in OECD (2018), it allows a more flexible approach to measure and 
monitor resilience than the usual ‘four capitals’ approach. 

10 

transformation  (Manca  et  al.,  2017).  They  represent  structurally  different  behavioural 
adjustments needed to ensure the optimal functioning of the system after being hit by a shock.7  

Figure 1: The system view of Manca et. al. (2017)

2.2  The regional response to the 2008 financial crisis: the measurement framework 

The  intensity  and  duration  of  the  economic  downturn  were  diverse  across  EU  regions  (ECB, 
2010). Not all regions experienced an economic decline. The territorial impact of the crisis has 
significantly  varied  even within  the  same  country  (Martin,  2011;  European  Commission,  2013). 
While some regions experienced a swift return to pre-crisis levels of employment and output, 
the process of recovery was much longer for many regions (Pontarollo and Serpieri, 2018).  

To  assess  the  resilience  capacity,  we  build  from  the  methodology  developed  in  Alessi  et  al. 
(2019).  This  methodology  has  then  been  adapted  to  regional  level  analysis  in  Benczur  et  al. 
(2020). To the best of our knowledge, this was a first effort to operationalize a multidimensional 
approach  to  regional  societal  resilience.  This  study  provides  a  complementary  analysis  that 
focuses  more  on  the  time  dimension  of  resilience,  on  the  interdependencies  between  the 
different pillars of the system (asset, engine and outcome) and pays more attention to the social 
response to the economic downturn. 

7  

It is important to note that these capacities are by no means mutually exclusive. In fact, the boundaries between absorptive, 
adaptive  and  transformative  capacities  are  rather  blurred,  and  the  way  a  certain  response  to  a  shock  can  be  interpreted 
naturally depends on the way the system is defined, as well as on the time horizon, risk aversion, performance metrics etc. 
considered. This implies that both the resilience strategies themselves, as well as their ranking and desirability need to be 
assessed on a case-by-case basis.   

11 

 
 
 
 
                                           
Following the two aforementioned studies, our basic assumption is that the crisis was a single 
common event, hitting all European regions in the same way. This is a simplifying assumption: 
though the global financial crisis originated in the US and hit Europe as an exogenous shock, the 
degree to which a given region was exposed and vulnerable to this common shock varied to a 
considerable degree. Since it would be virtually  impossible to pin down  the magnitude of the 
shock in each particular case, our chosen approach acknowledges that vulnerability to a shock 
and the lack of resilience are not separable in the early phase of the crisis. As long as reducing 
an entity’s vulnerability is tantamount to increasing its shock absorption capacity, this seems a 
reasonable assumption. 

We  represent  the  regional  society  in  a  simplified  way,  as  stylized  in  Figure  1.  This  so-called 
“system  view”  is  a starting  point for building  a  regional  resilience indicator.  The  system  view 
moves away from the narrow economic analysis and expands it with social aspects including 
subjective indicators which provide an indication of individual perceptions. In this perspective, 
even when studying resilience at the regional level, individuals become important players as 
perceptions drive behaviour, which adds complexity to the functioning of the society. Indeed, the 
system view  allows tackling complexity  and  interconnections  since it  sheds light on  how the 
shocks spread among the different segments of the system, how they interact with each other, 
and  what  are  the  possible  policy  entry  points  within  a  regional  context.  However,  in  this 
exercise,  we  do  not  keep  track  of  the  interconnectedness  between  the  country  (or  even 
European) system and the regional one, which themselves can too be perturbed by the crisis. 
We  only  take  into  account  the  country-level  factors  for  regional  resilience  as  explained  in 
section 4.4. 

Given  that  resilience  is  characterised  by  the  observed  dynamic  response  of  a  system  to 
disturbances,  the  direct  and  continuous  monitoring  of  such  an  empirically  based  indicator 
would require a continuous re-assessment8. For example, should the unemployment rate be 
considered as the basis for such an indicator, one would regularly need to re-estimate how it 
responds to each new disturbance. This may not only be inconvenient but also impossible given 
the  absence  of  identifiable  new  shocks  most  of  the  time.  Moreover,  studying  the  dynamic 
responses of system variables in an ongoing manner would not provide direct guidance on what 
policies may foster resilience, or how a system could deal with unknown future disturbances. 

Our proposed measurement framework is based on a three-step approach. First, using a unique 
historical episode marked by a single common shock (as assumed), we select an extensive list 
of economic and non-economic indicators that span the entire process of societal well-being 
production. Second, we focus on the joint dynamic response of all system variables to the crisis 
to derive resilience indicators associated with different time horizons and coping capacities (i.e. 
the impact of the crisis, the medium run, and the bounce forward). Learning from the experience 
in Alessi et al. (2019) and further elaborating the research in Benczur et al. (2020), we develop an 
alternative indicator of resilience which accounts for the speed (time) of recovery. As a third and 
last step, we search for some pre-determined, pre-crisis systemic features that differentiate 
resilient regions from non-resilient ones. These resilience characteristics intend to capture a 
region’s general ability to respond to disturbances, which can then also drive the effectiveness 
of  specific  interventions  during  the  crisis.  The  characteristics  can  be  monitored  regularly  to 
assess  resilience  in  a  forward-looking  manner  and  can  identify  potential  entry  points  for 
policies. 9 

The motivation for studying societal resilience at the regional level includes the need to increase 
the robustness of the empirical analysis and capture the regional and sub-national aspects of 
societal  resilience.  Nevertheless,  shifting  the  analysis  from  countries  to  the  regional  level 

8  

9  

There  are  expert-opinion-based  alternatives,  like  the  Resilient  City  Index  of  the  Rockefeller  Foundation  and  Arup 
(https://www.cityresilienceindex.org/#/). 
For  instance,  as  illustrated  in  Alessi  et  al.  (2019),  the  pre-crisis  expenditure  on  social  protection  has  been  revealed  as  a 
significant predictor of country resilience in the absorption capacity. 

12 

 
                                           
involves various challenges. Data availability influences variable selection at the regional level, 
given the inadequacy of specific indicators due to their national character (e.g. stock price index) 
or  lack of data granularity.    There  is  a  trade-off between  selecting  a large  number  of diverse 
resilience  indicators  of mixed  regional-national character  or  resorting  to  a  lower  number  of 
indicators with sufficient regional granularity and character.   

13 

 
3  Empirical methodology: a three-step approach 

In this section, we describe the operational choices associated with the measurement strategy. 
Three  main  considerations  were  followed.  The  first  is  to  go  beyond  the  narrow  economic 
perspective and account simultaneously for the social dimensions when assessing resilience, 
as already proposed in Manca et al. (2017), Alessi et al. (2019), and Benczur et al. (2020). To this 
purpose, we follow the approach of representing the society in a systematic way and finding the 
empirical  information  that  matches  the  system  view.  This  means  that  each  segment  of  the 
theoretical societal model is mapped to the available regional information. The selected system 
variables, therefore, span all observable aspects of the regional socio-economic system. They 
represent the empirical counterpart  of  the  regional  system  view (see section 4.1 for details). 
These selected regional system variables are measured in terms of their response to the shock 
across  different  time  horizons.  Further,  these  responses  have  been  accordingly  aggregated 
into various resilience indicators.  

The second is to acknowledge the existence of different resilience capacities related to the time 
horizon.  Given the highly time-dependent  character of most system’s  reactive  capacities,  we 
propose  employing  different  time  horizons  when  assessing  resilience  in  the  aftermath  of  a 
shock. This enables us to obtain a more complete picture of the response path and to analyse in-
depth what it takes to get back to normal. 

The third idea rises from the need to obtain timely continuous monitoring of resilience. This is 
not  feasible  with  the  revealed  resilience  since  we  would  need  to  continuously  evaluate  the 
longer-term  behaviour  after  a  specific  shock.  Hence,  learning  from  past  episodes  becomes 
crucial. Analysing past episodes provides an opportunity to identify resilience characteristics. 
These can be defined as those regional features, which are pre-determined to the shock and 
represent strong predictors of resilient behaviour during the crisis. Resilience characteristics 
can  represent  potential  policy  entry  points  which  can  enhance  better  response  to  future 
distress. 

Taking  these  three  ideas  into  account,  we  have  operationalized  the  high  dimensionality  of 
resilience  where  its  measurement  reflects  upon  absorption,  adaptation,  and  transformation 
capacities.  The  results  are  three  different  magnitude  indicators:  impact,  medium  run,  and  a 
bounce  forward  indicator  that  jointly  describe  the  behaviour  of  regions  in  coping  with  the 
economic  and  financial  crisis,  complemented  with  a  measure  of  the  duration  of  recovery. 
Furthermore,  as the magnitude-based  resilience metrics capture  only  one  (albeit  important) 
aspect of resilience, following Sensier et al. (2016), we propose an alternative perspective by 
focusing  on  the  time  dimension  of  resilience.  Specifically,  the  magnitude-based  medium-
horizon resilience metrics as in Alessi et al. (2019) and Benczur et al. (2020), is tailored to assess 
the combined (net) effect of impact and recovery at a fixed point in time, without any regard for 
the speed or extent of recovery. While comparing the impact and medium-run score of a region 
can certainly reveal some information on the extent of recovery, its duration and speed can only 
be adequately captured from a different perspective. Furthermore, one could assess the extent 
to which a particular geographical area might be characterised by no recovery at all after five or 
ten  years  from  the  crisis.  Therefore,  complementing  the  impact,  medium-run  and  bounce-
forward indicators with some insights about the duration of recovery promises to be helpful in 
better understanding regional resilience. 

3.1  Variable selection: implementing the system view  

There are different authors, such as Sherrieb et al. (2010), De Carvalho (2011), Stiglitz et al. (2018), 
who argue about the importance of having a multidimensional approach for the representation 
of  the  society.  There  are  existing  indices  and  measurement  frameworks  which  managed  to 

14 

 
include multiple aspects of the society and well-being (see the European Social Scoreboard,10 
Social Progress Index by Porter and Stern (2017) or Human Development Index by Anand and 
Sen (1994)). 

When multiple variables are considered, there are many ways to aggregate single dimensions 
into the sub-components and final indices. The choice of the variables, standardisation methods 
or the weights to be used are inevitably subject to a certain degree of subjectivity (Nardo et al., 
2008).  This  is  a  common  criticism  of  many  authors  who  prefer  relying  on  a  single  resilience 
measure based on a small set of variables (see Faggian et al., 2018). However, given our aim to 
provide a wider view of the regional society and its reaction to the shock, it becomes of crucial 
importance to capture different aspects of such society and their joint reaction to a shock. 

Starting from the system view, our society is represented by three main building blocks: assets, 
engine,  and  outcomes.  The  first  main  block, assets,  includes  natural,  built,  human  and  social 
capital. The world's stock of natural assets includes geology, soil, air, water, and all living things. 
Built  capital  is  the  collection  of  physical,  material  and  technological  objects  that  have  been 
created by the society: it includes material and technological infrastructures Human capital is 
defined  as  the  stock  of  knowledge,  habits,  social  and  personality  attributes.  It  also  includes 
creativity, embodied in the ability to perform labour to produce economic value. Social capital 
refers  to  interpersonal  relationships,  shared  norms  and  values,  trust,  cooperation,  and 
reciprocity.  Overall,  the  assets  should  contain  variables  related  to  education,  skills,  labour 
force, and trust and fairness, social networks, etc.11 

The second main block represents the fabric and structure of the society, the so-called engine. 
It is a functional mechanism that transforms societal assets into the overall level of well-being. 
It  includes  structural  elements  of  our  entire  socio-economic,  political  and  environmental 
institutions. It can be represented with measures of productivity and quality of institutions. From 
the measurement point of view, this block is the most difficult to cover with actual variables at 
the  regional  level,  also  given  its  high  degree  of  interconnectedness  with  the  country  level 
‘engine’. 

The third main block refers to outcomes that directly relate to the level of societal well-being, 
such  as  income,  consumption,  wealth,  inequality,  poverty,  or  the  level  of  crime.  This  pillar 
represents  a  set  of  social  progress  targets  towards  which  society  aims.  It  also  contains 
investment,  the  purpose  of  which  is  to  maintain  and  increase  the  stock  of  the  four  types  of 
capital. 

Given our standpoint about the multidimensional nature of resilience, we find it necessary to 
measure its level in each pillar. This choice is grounded on the observations from Alessi et al. 
(2019)  who argue  that resilience  in the  outcomes cannot be realised without the resilience in 
other parts of the system. 

Given  the  system  view  and  being  constrained  by  the  data  availability,  we have  developed  the 
empirical  counterpart  of  the  regional  model  for  the  representation  of  society,  illustrated  in 
Figure 2 (details on coverage and variability are included in Annex 1). 

10  

11  

https://ec.europa.eu/social/main.jsp?langId=en&catId=1196&newsId=9163&furtherNews=yes 

Given that in this exercise we consider only an economic shock with limited time frame for evaluation, we assume that there 
was no direct effect on natural and built capital, but only on human and social.   

15 

 
 
 
                                           
 
Figure 2: List of final system variables 

The final list of selected system variables is the result of an iterative process. First, an initial pool 
of about fifty ‘candidates’ was considered and classified for inclusion into the system view based 
on expert judgment. The assignment of variables to the respective parts of the system was often 
not  possible  on  fully  objective  grounds  given  the  multifaceted  character  of  most  available 
indicators.  In  the second  step,  data  availability  and  coverage  along  various  dimensions  were 
verified. Specifically, we applied the dual selection criterion of minimum time coverage (i.e. data 
availability for at least three years preceding, one year during and one year after the crisis) and 
geographical coverage (i.e. data availability for at least 60 per cent of relevant regions). The third 
step  concerns  the  assessment  of  each  remaining  candidate  variable’s  dynamic  behaviour 
following  the  crisis.  Only  those  were  retained  that  exhibited  ‘sufficient’  variation  for  most 
regions in the post-crisis period, as measured by the comparison of observed post-crisis and 
pre-crisis volatilities.12  

There is a trade-off in the choice of the number of variables to include. The higher the number of 
variables,  the  better  the chance  of  being  able  to  take  into  account the  various  aspects  of  the 
society,  but,  at  the  same  time,  the  higher  the  chance  the  analysis  would  suffer  from  data 
coverage. 

Given the regional depth of this exercise, data is gathered at different levels: the country level 
(NUTS0),  intermediate  NUTS1  (95  regions  included),  and  more  detailed  NUTS2  (269  regions 
included)13 following the 2013 NUTS classification. When taking into consideration the financial 
and economic crisis, we define three important reference points: the pre-crisis period is defined 

12   Specifically, when post-crisis observations of a variable do not deviate from the extrapolated pre-crisis trend, we conclude 
that there was no visible impact of the crisis. The deviation is defined as falling out from the 95 per cent prediction area of the 
linear regression based on the pre-crisis data. For a variable to be included, a minimum of 50 per cent of the regions is required 
to show sufficient variation due to the crisis. Some variables do not exhibit sufficient variation with the 50 per cent threshold. 
However, they are potentially acceptable in case they cover important factors of the system which are not or insufficiently 
covered by the other variables. This was the case of subjective variables such as happiness, life satisfaction or trust. In these 
cases, the threshold is lowered to 40 per cent of regions exhibiting sufficient variation. 
Some small overseas regions (PT2, PT3, FRA*) have been removed for this analysis. 

13  

16 

 
 
 
                                           
as  the time window 2000-2007, the crisis  is considered  to have happened between 2007  and 
2012, while for post-crisis all years after 2012 are included. We have taken 2016 as the final year 
as many variables did not have more recent data available. We obtain a data matrix as can be 
visualized by Figure 3.  

Figure 3: Data system 

There is a trade-off between data availability and precision of the final results, at NUTS2 level 
data is less aggregated and therefore more precise in representing the phenomenon of interest, 
but fewer variables are available. The sparsity of data at a more granular level can be due to (i) 
budget constraints allocated  to the collection  of  the  data  so  they are not collected or are not 
sufficiently  reliable,  or  to  (ii)  conceptual  reasons  (i.e.  the  stock  market  index  exists  only  at 
country level).  Moreover,  some variables  are  collected  in  some selected countries  but not in 
others (frequently this is the case for subjective well-being variables). The number of missing 
regions increases as we go from NUTS0 to NUTS2. 

The selection of variables, based on the coverage and variability is performed at the NUTS1 level. 
A final list is then obtained by removing the variables that measure conceptually similar parts 
of the system, and are highly correlated with other variables (i.e.  risk of social exclusion and 
poverty  risk).  The selected  variables  are  then  used  at  the  NUTS2  level  as  well.  In  the  case  of 
missing  regions  at  this  granularity  level,  we  have  chosen  to  apply  the  following  imputation 
methodology. If for a given variable a NUTS2 region is not sufficiently populated over time but 
belongs to a NUTS1 region that is sufficiently populated, we imputed the NUTS2 data points for 
that variable using the data from the corresponding NUTS1 regions.  

The initial list of 50 variables has been reduced to 24, the ones illustrated in Figure 1. Although 
the final list of variables does not manage to cover all parts of the system, we find that most parts 
(expected to be hit by the financial crisis) are sufficiently covered. 

3.2 

   Metrics and Indicators 

As for resilience indicators, two different types are used. The first is based on the magnitude of 
change in system variables at various time horizons in the aftermath of the crisis. The second 
type is related to the duration of the wave period, and the number of years it takes for system 
variables to fully recover from the crisis-induced shock.   

17 

 
 
 
3.2.1  Magnitude-based resilience metrics 

Similar  to  Alessi  et  al.  (2019),  the  raw  time  series  of  each  variable  is  used  to  compute  three 
magnitude-based resilience metrics: impact, medium-run, and bounce-forward. (See Figure 4 
and Table 1 for more details.) The first two measure the absorption capacity of a region through 
the  observed  (negative)  effect  of  the  crisis-related  initial  shock  at  short-  and  medium-term 
horizons. The bounce-forward metric, on the other hand, accounts for regions’ adaptation and 
transformation capacity, based on a discrete score related to the extent and completeness of 
their recovery from the crisis over a 10-year period.    

Table 1: List of resilience metrics14 

The  aforementioned  three  metrics  are  calculated  for  each  region  and  system  variable 
individually. Contrary to Sensier et al. (2016), no distinction is made between the cyclical and the 
trend behaviour, due to the difficulty of reliably assessing long-term variable trends based on 
the  relatively  short  observation  window  at  hand.15  To  mitigate  potential  distortions  resulting 
from this choice, we concentrate on stationary variables (wherever it is possible) and pin down 
pre-crisis reference value based on multi-year averages (rather than a single-year value). 

To  combine  each  of  the  above  metrics  across  24  system  variables  for  a  single  composite 
indicator,  we  use  the  normalised  z-scores  associated  with  each  system  variable,  as 
standardised  over  the  entire  cross-section  of  regions.  This  means  focusing  on  the  relative 
position of each region with respect to each system variable – with the advantage of creating 
comparable  regional  rankings  that  account  for  both  the  mean  and  dispersion  of  resilience 
performance  across  European  regions.16  Once  standardised,  the  24  resilience  metrics  are 
aggregated  by  weighted  (arithmetic)  averaging  into  the  respective  synthetic  indicators  for 
impact,  medium-run,  and  bounce-forward  resilience.  The  variable-specific  aggregation 
weights are calculated in a way to ensure that  each part of the system (i.e. asset, engine and 
outcome) is equally important.   

14   The column min/max refers to the sign. The calculations of the metric is slightly different whether a variable is supposed to be 
in the positive sign, i.e., the higher the better or crisis at the minimum which are indicated with min. While those with label max 
are the opposite or have a negative sign (the less the better or crisis at a maximum level). 
As far as for the employment and  GDP series we could rely on a  long time series, this was not possible for others type of 
variables which imply individual perceptions, such as health, trust or life satisfaction, or related to different aspects of  the 
society such as inequality, share of permanent contracts or education level. 

15  

16   For each of the variables, the value of the medium run metric corresponding to the zero level is reportedError! Reference 

source not found.. Values for the other two metrics are available in Annex 5. 

18 

Metricmin/maxDefinition used for variablesInterpretationminMinimum of the period 2008-2016 minus the 2007 datamax2007 data minus the maximum of the period 2008-2016minDifference between most recent and 2007maxDifference between 2007 and most recentmin1 in case medium run is larger than pre-crisis volatility, 0 in case medium run in absolute value is smaller than volatility and -1  otherwisemax1 in case medium run is smaller than pre-crisis volatility, 0 in case medium run in absolute value is smaller than volatility and -1  otherwiseBounce forwardIs the medium term effect significant? Did the situation improve significantly positively or negatively with respect before the crisis (significance of medium run)ImpactHow much has the crisis effected the relevant region in the short term? High in case crisis did not hit , the lower the worse.Medium-runWhat is the medium term effect? Change between the beginning of the crisis and now. In case of good development this will be high 
 
 
                                           
The resulting impact and medium run indicators can be interpreted in relative terms: they are 
close to zero if the performance of a region is close to the average performance of EU regions. 
High positive values indicate more resilient behaviour, while low negative values indicate lower 
resilience than the EU regional average. The interpretation of the bounce forward indicator is 
somewhat  different.  It  depends  on  the  share  of  system  variables  in  which  a  region  has 
outperformed  (stayed  steady  or  underperformed)  its  pre-crisis  levels.  It  varies  from  one  to 
minus one: if it is close to one, it means that it has performed better in almost all dimensions, 
while if it is close to minus one, this means that the post-crisis levels are worse off than the pre-
crisis levels. A zero score may indicate that the region simply returned to the pre-crisis levels 
in  all  the  dimensions,  but  it  can  also  mean  that  the  region  has  performed  very  well  in  some 
dimensions and very poorly in others. 

This  type  of  composite  indicator  can  be  surely  criticized  for  the  level  of  subjectivity  in  its 
construction. Namely, when two regions have the same resilience score, they might have very 
different  underlying  values  for  the  different  components,  concealing  potentially  significant 
heterogeneity.  Despite the  potential  criticism  of using aggregate indices, we believe that it is 
necessary  to  include  more  than  just  economic  growth  measures.  This  is  mostly  due  to  the 
complexity of regional reaction to the crisis which does not remain within the economic domain 
but also in social, political, and environmental.  

3.2.2  Duration-based resilience metrics  

A schematic overview of the duration-based resilience metrics is presented in  Figure 4. Two 
things are taken into account here. First, the year of the worst level of the variable is recorded. 
Then it is checked if the pre-crisis 2007 level has again been reached after the downturn and if 
yes, the year in which this happened has been recorded. The shortest time span between these 
two  recorded  events  is  then  defined  as  the  overall  time  to  recover  for  a  region  for  a  specific 
system variable. Using the number of years, it is easy to compare between variables or between 
regions.  However,  if  one  wants  to  assess  an  overall  ability  to  recover  fast,  this  becomes  a 
challenging task. One possible way is to look into variables one by one and derive some measure 
based on a share of regions that recovered within a given time frame. While this provides for 
each variable an overall capacity of recovery, we lose the ability to compare the regions. It is not 
reasonable to assess the average time to recover across the system variables since there are 
dimensions in which some regions never recovered. A viable solution that allows comparing 
regional  performance  in  the  time  dimension  of  resilience  which  overcomes  the  two 
aforementioned problems is the Copland pairwise aggregation method (Saari and Merlin, 1996). 
It provides a regional score which is relative by nature since it reflects where a region stands 
with  respect  to  the  other  regions  in  terms  of  necessary  time  to  recover.  Copland  score  is 
obtained  by  employing  a  tournament  (a  pairwise  contest)  of  regions  for  each  of  the  system 
variables. The final score is an aggregation of all the regional “tournament” results across the 
system variables17.  

17   For  each  variable,  regions  are  evaluated  according  to  whether  they  recovered  faster  or  slower  with  respect  to  the  other 
regions. They are assigned a score: this score reflects how many times a given region was better (or worse) in a “tournament” 
with other regions. For regions that have not yet recovered are always considered as equal at the bottom, i.e. when competing 
with a region that already recovered they are considered worse while when competing with an another region that has not yet 
recovered they are considered equal. The intermediate result is that for each variable, we have a performance score of each 
region, and the regions can be ranked according to that score. The final aggregate score of a region is the simple sum of all the 
region’s scores across all the 24 variables. 

19 

 
 
 
 
                                           
Figure 4: Schematic overview of the resilience metrics  

3.3  Characteristics 

We refer to resilience characteristics as those pre-determined, pre-crisis features of countries 
or regions that are found to be statistically related to the resilience performance. In particular, 
we focus on identifying those policy-relevant features which span across the different aspects 
of the society, have the potential to contribute to resilient behaviour and which can be monitored 
over time.  

To  pin  down  the  most  important  characteristics  of  resilient  behaviour,  the  following 
methodological  approach  has  been  applied.  Starting  from  a  slightly  further  extended  list  of 
characteristics  of  Alessi  et  al.  (2019),18  we  first  assess  their  relevance  through  a  univariate 
regression analysis, considering the characteristics at all geographical levels for each of the 
three  magnitude  metrics.  This  allows  selecting  the  variables  that  express  the  highest 
explanatory  power  for  each  of  the  three  indicators  (adjusted  R2  .0.15).  Then,  we  address  the 
multicollinearity  issue  of  the  remaining  characteristics,  removing  collinear  variables  with 
similar  information  content,  using  variance  inflation  factor,  and  Pearson  correlation  criteria 
(drop the variables that correlate higher than 0.9). 

In  the  second  stage,  we  perform  an  in-depth  multivariate  regression  analysis  to  identify  the 
most robust characteristics. To this end, we have used a stepwise leap and bound algorithm on 
Ordinary Least Squares (OLS) models19  to select the best set  of characteristics  according to 
their Akaike Information Criteria. This final set of characteristics has been used across three 

18  

19  

The more than 250 series range from institutional features to government finances to measures of subjective 
well-being. Their source is the Global Competitiveness Index of the World Economic Forum, the World Bank, the 
OECD, the Gallup World Poll Survey, the Macroeconomic Imbalance Procedure Scoreboard, the Eurostat, the EC 
digital scoreboard, as well as the European Institute for Gender Equality. 
This  methodology  performs  variable  selection  for  linear  regression  in  situation  with  a  large  number  of 
predictors.  It  uses  the  the  Furnival-Wilson  leaps-and-bounds  algorithm,  which  organizes  all  the  possible 
models  into  tree  structures  and  scans  through  them,  leaping  over  those  that  are  definitely  not  optimal.  The 
optimal model is the one with the smallest value of AIC, AICc, and BIC; the largest value of R2 ADJ; and a value of 
Mallows’s Cp that is close to the number of predictors in the models +1 or the smallest among the other Mallows’s 
Cp values. 

20 

 
 
                                           
different models, which reflect the nested nature of our data of regions within countries: OLS 
with country clustered standard errors, random intercept model, and fixed effect model. 20 

It is to be noticed that for this exercise, the analysis of the characteristics has been performed 
only  for  the  impact/medium  run/bounce-forward indicators,  and  not  the duration  based  one. 
This choice is motivated by the nature of the final indicators, where the duration  based one is 
entirely based on rankings and hence less suitable for the regression analysis. 

Given the need to take into account the different granularity of the characteristics to see at which 
geographical level they effectively play a role, certain characteristics have been examined both 
at country and regional level (i.e. GDP). Also, the variables were set to be at their pre-crisis level, 
typically using the 2000‒2007 multi-year average. 

20      The full regression methodology is presented in Benczur et al. (2020). 

21 

 
 
                                           
4  Results 

4.1  Magnitude based metrics, components and indicators 

4.1.1 

The metrics 

For the comparison of the resilience performance of European regions in all parts of the socio-
economic system,  the  heatmaps  below provide  schematic  but  ample descriptive  evidence  by 
resilience metric. Figure 5, 6 and 7 indicate regional resilience scores and rankings through the 
tertiary  red-yellow-green  colour  palette,  with  darker  green  (red)  tones  representing  more 
(less) resilient behaviour. Missing regions are left white. Each row corresponds to an individual 
system  variable  (grouped  by  system  parts),  with  the  last  row  representing  the  aggregate 
indicator  score  itself.  As  for  columns,  they  represent  each  of  the  NUTS2  region  sorted  by 
nomenclature code and country affiliation.  

Let us focus on the medium run metric (Figure 6): the mean level refers to the raw metric and 
shows the average regional resilience performance in the medium run. In particular, nine out of 
24 dimensions show that on  average  the  EU  regions  have not fully  returned to  the pre-crisis 
level.  Among  these,  most  refer  to  the  social  burden  of  the  crisis:  income  inequality,  the 
frequency of social interactions, trust in the European Parliament. The situation is critical also 
in  the  average  regional  performance  in  NEET  and  unemployment  (long-term  unemployment 
included). A modest improvement is shown by the GDP level, while an overall positive medium-
run  performance  can  be  observed  for  the  level  of  education,  female  employment  and  life 
satisfaction. 

The different behaviour of the different dimensions provides additional evidence of the need to 
go beyond the purely economic perspective. For example, we can observe the behaviour of GDP 
and it has a very different regional dispersion if compared to NEET, education level, or wages. 
One  variable alone is  not sufficient to be  able  to tell  the full story:  indeed, all the 24 together 
contribute to the final performance of each region.  

Figure 5: Impact 

note:  The mean level refers to the mean level of the metric before the transformation. It provides an idea of the EU average impact for each variable.  

22 

Mean levelLTLULVMTeducated_labour-4.937education_level-0.369fairness-4.318migration_rate-4.917trust_people-4.679female_employment-1.674lprod-0.033permanent-1.431rd_gdp-0.044social_weekly-8.466trust_ep-7.049trust_legal-3.012wages-0.071employment-3.109gdp-0.066health-2.218hh_income-0.033inequality_s80s20-0.101lifesat-3.277neet-3.930stress_disease-26.144suicide-2.460unemployed_12m-2.823unemployment-4.744ImpactSICYEEHRIELT|LU|LV|MTOutcomeATBEBGAssetEngineNLCZDKDEELESFIFRHUITUKPLPTROSESK 
 
 
 
Figure 6: Medium run 

note:   The mean level refers to the mean level of the metric before the transformation. It provides an idea of the EU  average medium run  for each 
variable. 

Figure 7: Bounce forward 

note:  The mean level refers to the mean level of the metric before the transformation. It provides an idea of the EU average bounce forward capacity 
for each variable. 

How  justified  is  the  inclusion  of  each  variable  into  different sub-components?  In  Annex  2  we 
present  the  correlation  coefficients  between  system  variables,  components  and  the  final 
indicator. Most variables are indeed included in the component with which the variables exhibit 
the highest correlation. However, there are some exceptions (such as female employment rate 
or share of the labour force with secondary education) which appear more correlated with the 
component to which they do not belong. However, to obtain balance and conceptual consensus, 
we have decided to keep them as originally assigned using the JRC expert judgement. 

23 

Mean levelLTLULVMTeducated_labour-0.892education_level8.647fairness2.895migration_rate-1.238trust_people2.572female_employment3.229lprod0.140permanent0.180rd_gdp0.233social_weekly-2.706trust_ep-2.242trust_legal6.805wages0.409employment1.105gdp0.047health0.138hh_income0.116inequality_s80s20-0.003lifesat4.281neet-0.597stress_disease0.429suicide-0.158unemployed_12m-1.078unemployment-1.459MediumrunLT|LU|LV|MTSIOutcomeSESKBEBGCZDEDKEEIEHRCYUKAssetEngineITNLPLPTROELESFIFRHUATMean levelLTLULVMTeducated_labour-0.1718education_level0.7318fairness0.3532migration_rate-0.0720trust_people0.3085female_employment0.5802lprod0.5375permanent0.0723rd_gdp0.6522social_weekly-0.2338trust_ep-0.0398trust_legal0.4776wages0.0655employment0.2595gdp0.4275health-0.1243hh_income0.6880inequality_s80s20-0.0108lifesat0.3582neet-0.0958stress_disease0.2231suicide-0.0885unemployed_12m-0.2000unemployment-0.1303BounceOutcomeAssetEngineCYEEHRIELT|LU|LV|MTSIATBEBGCZDEDKELESFIFRHUITNLSKUKPLPTROSE 
 
 
 
 
 
 
4.1.2  Resilience by its three main components 

Let  us  first  concentrate  on  the  resilience  performance  across  various  parts  of  the  system: 
assets, engine and outcomes. Given the chosen construction of the indicator, regions need to be 
resilient in each of these parts to achieve a high overall resilient score (as in Alessi et al., 2019). 
Pairwise  correlation  coefficients  are  positive  between  the  respective  components  (asset, 
engine  and  outcomes)  for  all  three  resilience  indicators.  No  matter  which  indicator  is 
considered, outcome and engine variables are the most highly correlated, while correlation is 
weaker  with  respect  to  those  belonging  classified  as  assets.  Nevertheless,  as  shown  in  the 
medium run there are few exceptions. Greek Aegean islands and Crete show high resilience in 
the assets, associated with a low performance both in the engine and outcomes. Some other 
regions, such as Northern central of Bulgaria, the region of Brussels and Bratislava stand out 
for a very good resilience performance in the outcomes associated with very low resilience in 
the assets. 

Figure 8:  Resilience indicator by Asset, Engine and Outcomes 

Impact 

Medium run 

Bounce forward 

4.2  Beyond the economic aspects of resilience 

One  of  the  main  contributions  of  our  analysis  is  to  enlarge  the  list  of  variables  by  including 
broader economic and social aspects of the regional structural tissue. We have classified our 
24  variables  along  with  the  two  different  criteria.  The  first  is  to  distinguish  between  the 

24 

 
 
 
 
 
 
 
economic  and  social  aspects  of  the  system.  The  economic  aspects  include  labour  market 
variables (employment, unemployment and their variants, and skilled labour) and indicators of 
wealth and productivity (such as  GDP,  household income  and  wages,  investment in research 
and  development).  Social  variables  include  education  (level  and  NEET),  health  (perceived, 
stress-related and suicide rate), inequality and perceptions about own life and society (fairness 
and different levels of trust, life satisfaction), migration rate and social activity. 

4.2.1  Social and economic dimension 

To compare the social and economic dimensions, the scatterplots below present the relevant 
one-sided resilience indicator scores for each region, for income and medium-run indicators 
as well as by broad geopolitical area. These reveal that the statistical relationship between a 
region’s  resilience  score  in  the  economic  and  social  dimension  is  rather  weak  (Pearson 
correlation  coefficient  equal  to  0.28).  The  second  thing  we  observe  is  that  impact  resilience 
presents  more  clusterization  of  the  regions,  while  in  the  medium  run  the  situation  is  more 
blurred.  Southern  regions  are  associated  with  low  resilience  both  in  social  and  economic 
dimensions,  while  Northern  regions  are  more  around  average  values  of  resilience  in  both 
measures. Western European regions are mainly concentrated around high values of resilience 
in social and economic measures while Eastern regions show high resilience within economic 
performance but low in the social dimensions. Results for the bounce forward are analogous to 
those of medium run (details can be found in Annex 4). 

Figure 9: Impact and Medium run: comparison Social vs Economic measures across the Macro regions of the EU 

Impact 

Medium run 

4.2.2  Household income and subjective measures 

Similar  to  separating  economic  aspects  from  social  ones,  alternative  decompositions  of  the 
system are also possible. One of these concerns differentiating between variables of subjective 
and objective character, respectively. There are some purely subjective measures, such as the 
perception of fairness, trust in people and institutions, perceived health and life satisfaction. All 
the other variables can be classified as a more “objective” since they are not directly derived 
from people opinions and perceptions, but focus on a more tangible and observable condition 
(such as average household income). Since one of the very innovative aspects of this approach 
is precisely the inclusion of subjective measures and their reaction to a shock, we wanted to 
distil and understand the behaviour of such measures in relation to the more objective ones. 

If  we  take  into  consideration  resilience  in  household  income,  and  try  to  assess  what  is  the 
association with resilience in perceived subjective measures (averaged). We can observe from 
Figure 10 that there is no significant association between the two. We can also notice that while 

25 

 
 
 
regions do exhibit some grouping along with the household income measure according to their 
belonging to the EU  macro area,  this  is  not  the  case for the  perceptions  measures. Southern 
regions are mainly concentrated around the low level of resilience in household income but split 
between the low and high level of resilience in perception measures. Regions from the Eastern 
block, on the contrary, are positioned at the very high level of household income resilience but 
have very mixed results when considering perception measures. This suggests that lacking the 
subjective  measures  from  the  big  picture  could  lead  to  fragmentary  understanding  overall 
societal resilience of the regions since perceived dimensions truly do behave rather differently 
from the ones related to economic prosperity. Detailed graphs of the pairwise comparison for 
the bounce forward and other “objective” (such as GDP) measures are presented in Annex 4. 

Figure 10: Resilience indicators: Average household income vs average measure of positive perceptions of society. 

Impact 

Medium run 

4.2.3  Overall resilience 

To visualize the resilience of all regions, we present in Figure 11 the maps for all three indicators, 
an alternative view is presented in Annex 5. These highlight the country and regional variation 
of resilience. If we look at the map of resilience  in  impact, it becomes  evident that Greek and 
Spanish regions suffered the most within the short run. The situation is similar but somewhat 
less dramatic for Latvia and most Irish regions, while on the other hand many German and some 
Polish regions exhibited an excellent absorption capacity.  

The regional behaviour mostly follows the country patterns. However, some regions stand out. 
This is the case of the Greek North Aegean islands that regardless of their low resilience at the 
moment of the crisis, managed to be highly resilient in the medium run.  

What  concerns  the  bouncing  forward,  there  are  some  clear  country  differences  in  regional 
inequalities in resilience. For instance, the UK and Romania are the countries where there are 
the  most  substantial  differences  between  the  best  and  the  worst-performing  regions.  Very 
unequal are also Finland and Denmark. It is also true that some regions have strikingly different 
behaviour from the overall country: Spain and Italy did not perform well in the overall bounce 
forward, yet Catalonia and Bolzano did extremely well.  

26 

 
 
 
 
Figure 11: Maps of the three resilient indicators for all regionsi 

i The designations employed and the presentation of material on the map do not imply the expression of any opinion whatsoever on the part of the European Union concerning the legal 
status of any country, territory or area or of its authorities, or concerning the delimitation of its frontiers or boundaries  

27 

Moreover,  the  multidimensionality  of  resilience  is  also  related  to  the  timeframe  of  its 
measurement. This is supported by the observation that the three indicators (impact, medium 
run and bounce forward) exhibit a different performance. Even though the correlation between 
them is high, they have very diverse patterns of regions in their ranking. In particular, Figure 12 
shows interesting cases where the time dependence of resilience to the crisis matters: Greek 
islands (North Aegean region, EL41) managed to outperform in the medium run, while in bounce 
forward as well as in impact showed a low score. At the other end, the region Gelderland (NL22) 
performs well in impact and bounce forward but only managed to perform like the EU average 
in the medium run. Bratislava region (SK01) suffered in impact and the medium run but managed 
to achieve a high score in bounce forward. 

Figure 12: Impact, medium run and bounce forward indicator for all regions 

4.3  Time to recover from the crisis 

We  have  developed  an  additional  indicator  to  measure  resilience  based  on  the  duration  of 
recovery following the crisis. Given the indicator measures the number of years it took to reach 
the 2007 pre-crisis level again from its worst level, it is easy to compare between variables or 
between regions. It is more difficult to create a meaningful average for the duration of overall 
variables as not all regions succeeded to recover within the considered timeframe.  

The statistical relationship between impact score and recovery time can be shown for each of 
the  individual  variables,  Figure  13  shows  this  in  relation  to  GDP.  The  robust  negative 
relationship indicates that resilient regions in terms of shock absorption posted much faster 
recovery times. It is worth noting, however, that conditional on full recovery (i.e. when regions 
that  have  not  yet  recovered  are  excluded),  recovery  time  is  largely  independent  of  impact 
resilience  –  as  illustrated  in  Figure  13  where  the  similar  impact  resilience  scores  are 
associated with 3 to 9 years of recovery time.  

28 

 
  
 
 
 
 
 
Figure 13: Relation between the size of impact for the GDP and the time to recover21 

For an overall view, Figure 14 below presents the absolute ranking of regions in both impact 
resilience  and  recovery  time.  It  shows  that  regions  of  the  same  country  are  often  grouped 
together,  further  supporting  the  notion  of  even  regional  resilience  performance  within 
countries. On the other hand, the statistical similarities between the two rankings are rather 
limited: good relative absorption capacity does not necessarily go together with quick recovery. 
This partly suggests that the relatively close alignment seen in relation to GDP in Figure 13 is not 
present in relation to other system variables. In some countries (such as France or the United 
Kingdom),  within-country  heterogeneity  in  resilience  performance  is  relatively  large,  while 
other countries (such as Germany or Spain) are characterized by a much more concentrated 
outcome.  Furthermore, there are different  Italian  regions  which  were  relatively high in their 
resilience in impact, but rank much lower in terms of their speed of recovery. The opposite is 
true for some Polish, Slovakian and Swedish regions that were not very resilient in the short 
run, but rank very high in terms of speed of recovery.  

21   Note that the size of impact is the normalized impact measure (zscore) where a positive sign stands of impact 

which is less severe than the average 

29 

 
 
                                           
Figure 14: Rank in impact vs rank in time to recover 

4.3.1  How long does ittake to recover from the crisis in each dimension?  

Results  show  that  each  variable  presents  a  different  recovery  time.  Table  2  presents  the 
cumulative  distribution of recovery times across NUTS2  regions  by system  variables.  It  also 
shows, for each system variable, the relative share of regions for which no lower value than the 
pre-crisis  reference  point  was  recorded  in  the  post-crisis  period  –  and  needed  no  recovery 
whatsoever.  In  this  regard,  the  education  level  and  R&D  expenditures  merit  particular 
attention, as these remained unaffected by the crisis for around half of all European regions. 
Subjective  perception  indicators  (fairness,  trust,  weekly  socially  meet,  life  satisfaction)  and 
real wages, on the other hand, have been hit hard across all regions unequivocally. In general, 
objective  and  subjective  indicators  follow  different  patterns  of  recovery  where  the  former 
records  a  faster  speed  of  recovery  as  compared  to  the  latter.  If  we  consider  regions  and 
variables with non-zero impact, recovery has been partial in every single case: 10% of affected 
regions did not recover fully in education, 18% in household disposable income, 58% in real wage 
and  71%  in  weekly  social  meetings.  Annex  6  presents  a  detailed  table  of  the  speed  at  which 
regions have been reached out by the crisis. It emerges that already within two years 72% of 
regions  were  hit  in  labour  productivity,  58%  of  regions  registered  a  significant  drop  in 
household income while 53% of regions in education level. 

30 

 
 
 
 
 
 
 
 
 
 
Table 2: Time to recover by variable22 

Note: Variables with a * are only available every two years 

4.3.2  Regional speed of recovery 

Obtaining  such  an  overall  measure  presents  some  difficulty  as  some  regions  might  have 
recovered quickly in a given dimension, slowly  in others, and never recovered in yet another 
one. Aggregating across the variables becomes rather challenging and difficult to interpret. To 
tackle this, we have employed the Copeland method.23 This method implements tournaments 
between  regions  for  each  variable.24  All  regions  start  with  an  initial  score  of  zero.  Regions 
compete in  pairwise comparisons  where the winner,  which is the  region  that  has the lowest 
time to recover, gains a score of 1, a score of 0 in case of equal time to recover for both regions 
and finally a score of -1 to the region with the highest time (or no recovery). We then sum up the 
region's comparison scores.25 When all pairwise comparisons are completed, we sum up the 
score across all variables to obtain an overall score. The final ranking provides a measure of 
how well regions managed to recover from the crisis. The final ranking is shown in Figure 15, 
where we can highlight some unusual cases. In particular, there is considerable heterogeneity 
in the time to recover from the crisis in France and the United Kingdom. In Italy, which overall is 
placed  among  the  medium-low  performers,  only  one  region,  Trentino  Alto  Adige,  shows  an 
outperforming ranking compared to the rest of the regions. Germany, positioned among the top 
performers, shows a high concentration of regions among the top national ranking while the 
region of Bremen lagged behind the rest of the country, but still ranks as top performers within 
the overall regional rankings. 

22   Specifically, the column ‘recovered within 2 years’ provides the share of regions that hit a level below 2007 and 
managed to recover within 2 years. While the next column ‘recovered within 3 years’ includes those ‘recovered 
within 2 years’ plus the share of regions that that hit a level below 2007 recovered after 3 years. Etc.  

23   See R package votesyst (april 2018), Saari and Merlin (1996) 
24   Missing regions remain at a score 0.  
25  

The regional score for one variable varies between ± 268, which is obtained from the number of regions minus 1. 

31 

Never reached a value below 2007Recovered within  2 yearsRecovered within 3 or less than 3 yearsRecovered within 4 or less than 4 yearsRecovered within 5 or less than 5 yearsRecovered within less than 6 yearsRecovered within less than 7 yearsRecovered within less than 8 yearsRecovered within less than 9 yearsNot yet recoveredLabour force with secondary eduction15%0%4%6%7%10%16%21%26%59%Education level51%10%20%27%30%33%34%36%39%10%Self-perceived fairness0%0%7%7%27%27%50%50%58%42%Net migration9%6%15%19%23%28%37%43%47%43%Self-percieved trust in people*0%0%11%11%27%27%45%45%55%45%Female employment rate31%1%5%11%19%27%36%44%50%20%Labour productivity20%6%25%38%42%47%50%57%57%23%Share of permanent contract26%5%7%10%16%25%34%38%38%37%Total expenditure in R&D48%9%11%16%21%25%27%30%30%23%Weekly socially meet*0%0%10%10%16%16%22%22%29%71%Real wages*0%0%8%8%24%24%31%31%42%58%Trust in the Europan Parliament*0%0%15%15%39%39%50%50%59%41%Trust in the legal system7%2%9%10%14%16%22%40%40%53%Employment rate22%1%4%5%10%17%25%33%42%36%GDP12%0%13%32%35%42%47%54%59%29%Self-perceived health28%0%7%10%13%16%18%20%20%51%Household disposable income26%0%16%26%41%44%47%55%56%18%Income inequality21%15%22%24%27%31%41%49%49%30%Life satisfaction*0%0%24%24%40%40%48%48%61%39%NEET6%2%11%16%20%27%39%47%55%38%Death to stress  related diseases43%11%18%22%22%27%31%31%31%26%Death rate due to homicide10%6%15%23%31%40%49%56%56%34%Long term unemployment rate22%0%0%1%2%3%7%15%23%55%Unemployment rate12%0%5%9%9%12%21%30%38%51%AssetEngineOutcomes 
                                           
Figure 15: Rank of regions in the overall score of time to recover 

4.4  Characteristics 

Following the methodology described in Section 3.3, we have searched through a wide set of 
potential  candidate  characteristics  to  identify  the  best  possible  predictors  of  resilient 
behaviour.extending those of also reported elsewhere (Benczur et al., (2020). 

For  each  of  the  magnitude-based  resilience  metrics,    the  selected  set  of  280  potential 
characteristics has been narrowed down to 48 characteristics for impact, 31 for the medium run 
and 43 for bounce forward (see Annex 7). Each of these sets has then been further reduced to 
the top 10 characteristics set, separately for each indicator. 

Results  of  the  leap  and  bound  stepwise  algorithm  to  obtain  the  best  subset  for  the  OLS 
regression, using the Akaike criteria. are presented in Error! Reference source not found.Error! 
Reference source not found.. 

32 

 
 
 
Table 3: Multivariate regressions for the three resilience indicators 

IMPACT 

MEDIUM RUN 

BOUNCE FORWARD 

(1) 

(2) 

(3) 

(4) 

(5) 

(6) 

(7) 

(8) 

(9) 

OLS 

Random 
Effects 

Fixed 
effects 

OLS 

Random 
Effects 

Fixed 
Effects 

OLS 

Random 
Effects 

Fixed 
Effects 

VARIABLES 

 Country level  

Private sector credit flow 

-0.049** 

-0.044*** 

-0.029*** 

-0.026** 

-0.023*** 

-0.022** 

(0.014) 

(0.012) 

(0.007) 

(0.010) 

(0.004) 

(0.007) 

Innovation capacity 

Net Intl Investment Position 

0.005* 

0.006** 

(0.002) 

(0.002) 

Non-routine manual workers 

4.164 

Private spending in R&D 

(2.978) 

-0.123 

2.769 

(2.017) 

-0.115 

-0.173 

-0.100 

(0.078) 

(0.094) 

(0.106) 

(0.088) 

-0.117* 

-0.073 

(0.050) 

(0.075) 

Current account balance 

0.014 

0.007 

(0.015) 

(0.016) 

0.022 

0.043 

(0.030) 

(0.025) 

Social expenditure (pensions 
excluded) 

Export market shares 

Women in the labour force 

Pay to productivity 

Regional level 

Gender employment gap 

-0.018** 

-0.008** 

-0.003 

(0.005) 

(0.003) 

(0.003) 

GVA: construction share 

0.040 

(0.026) 

3.418* 

(1.587) 

0.175 

(0.092) 

0.038 

(0.031) 

2.474* 

(1.243) 

0.235* 

(0.119) 

0.035* 

0.035 

(0.013) 

(0.026) 

1.910** 

1.277 

(0.567) 

(1.003) 

0.147* 

0.220* 

(0.059) 

(0.104) 

-1.703 

(1.161) 

-1.915 

-1.848 

(1.007) 

(1.028) 

GVA: industry share 

1.513*** 

0.768*** 

0.553* 

0.875* 

-0.119 

-0.250 

0.525* 

-0.179 

-0.285 

(0.381) 

(0.231) 

(0.226) 

(0.340) 

(0.199) 

(0.196) 

(0.192) 

(0.168) 

(0.168) 

GVA:  Professional/scientific 
sector share 

2.027 

1.779* 

1.649* 

(1.278) 

(0.803) 

(0.779) 

Accessibility 

0.001 

0.000 

0.000 

0.003** 

0.001 

0.000 

0.002** 

0.001 

0.000 

(0.001) 

(0.001) 

(0.001) 

(0.001) 

(0.001) 

(0.001) 

(0.001) 

(0.001) 

(0.001) 

Observations 

R-squared 

253 

0.725 

253 

253 

0.057 

253 

0.658 

253 

253 

0.011 

253 

0.676 

253 

253 

0.042 

33 

 
 
 
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Country clustered SE 

Adj-R2 

Number of countries 

Within R2 

Between R2 

YES 

0.714 

YES 

0.648 

NO 

NO 

20 

0.057 

20 

0.052 

0.742 

NO 

NO 

20 

0.008 

0.534 

20 

0.011 

YES 

0.665 

NO 

NO 

20 

0.042 

20 

0.04 

0.620 

Overall R2 

 *** p<0.001, ** p<0.01, * p<0.05, Robust standard errors in parentheses 
The number of observations is 253, clustered in those 20 countries which are not a single region. 
 Adjusted R2 for OLS regression using only country level characteristics is as follows: Impact =  0.49, Medium run =  0.60, Bounce forward =  0.62. 

0.696 

0.611 

0.640 

The  first  salient  finding  is  that  most  regional  heterogeneity  is  explained  by  country-level 
factors. This is not a surprise given the high between-country variability for all three resilience 
indicators (81 per cent for the impact, 85 per cent for the medium run and 79 per cent for the 
bounce forward).  

The pre-crisis average of private sector credit flows26 appears to be the most robust predictor 
of resilient behaviour in all three indicators. The effect is negative, meaning that the higher the 
amount  of  county-level  liabilities  that  households  and  firms  have  incurred,  the  lower  is  the 
overall regional resilience. The effect appears to be somewhat stronger for the impact than for 
the medium run and bounce forward, but the overall finding is consistent across the indicators 
and different specifications. 

The net international investment position27 is another prime country-level characteristic that 
explains  regional  resilience  in  impact.  It  has  a  positive  correlation  with  impact  resilience, 
suggesting that a better standpoint of a country in terms of its balance of assets vs liabilities 
with  respect  to  the  rest  of  the  world  yields  better  absorption  capacity.  Other  country-level 
characteristics,  which  show  relatively  high  correlation  with  the  impact  in  univariate 
regressions, but lose their significance in multivariate specifications (OLS or random effects) 
are  the  share  of  non-routine  manual  workers,28  private  spending  on  research  and 
development,  current  account  balance  and  social  expenditures.29  This  latter  variable  is 
marginally significant in the random-effects model though. 

For  resilience  in  the  medium  run,  country-level  characteristics  such  as  private  spending  in 
research and development,30 export market share, the share of women in the labour force and 
the  perception  of  whether  wages  are  linked  to  productivity  (pay  to  productivity)  have  been 
included.  Although  the  proportion  of  variance  explained  in  univariate  regressions  has  been 
higher than 15 per cent, there is not enough evidence in our data to confirm their significance in 
multivariate OLS or random effects specification. 

26  

27  

28  

29  

The  private  sector  credit  flow  represents  the  net  amount  of  liabilities  which  the  sectors  Non-Financial  Corporations  and 
Households and Non-Profit Institutions Serving Households have incurred along the year. Data are in consolidated terms, and 
expressed in percentage of GDP (source: Eurostat). 
The international investment position (IIP) is a statistical statement that shows at a point in time the value and composition of (i) 
financial  assets  of  residents  of  an  economy  that  are  claims  on  non-residents  and  gold  bullion  held  as  reserve  assets,  and  (ii) 
liabilities  of  residents  of  an  economy  to  non-residents.  The  difference  between  an  economy’s  external  financial  assets  and 
liabilities  is  the  economy’s  net  IIP,  which  may  be  positive  or  negative.  The  net  international  investment  position  provides  an 
aggregate view of the net financial position (assets minus liabilities) of a country vis-à-vis the rest of the world. The indicator is 
expressed in percent of GDP (source: Eurostat). 
Employment  rate  by  type  of  contract  occupation:  non-routine  manual  tasks  (service  and  sales  workers  and  elementary 
occupations –isco08: 5, 9). 
Government expenditures on social protection as percentage of GDP (pensions excluded). 

30   Company spending on Research and Development. It is built upon the Executive Opinion Survey, where experts have been asked to 

evaluate the extent to which companies invest in R&D. (Source: World Economic Forum) 

34 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
Pay to productivity31 is positively correlated with the bounce forward capacity. Differently from 
the medium run indicator, it is significant and robust across model specifications. It suggests 
that  the  regions  in  those  countries  characterized  by  a  labour  market  where  employees  are 
better incentivized with their salaries are fitter in terms of adaptation and transformation. 

Average annual export market shares and the share of women in the labour force yield mixed 
results  across  the  specifications.  Both  are  consistent  in  sign  and  size,  however,  while  the 
coefficients stay significant in OLS, this is not the case for the random-effects model. Innovation 
capacity,32  although  being  a  positive  and  significant  predictor  in  the  univariate  analysis  of 
bounce  forward,  in  the  multivariate  specification  it  turns  surprisingly  negative  and  loses  its 
significance in the random effects specification. 

The  scope  of  this  exercise  was  also  to  assess  why  certain  regions  within  a  country  are 
performing  better.  Therefore,  we  attempted  to  capture  the  within-country  variation  with 
selected  regional  characteristics.  Again,  we  started  with  the  ones  that  show  the  most 
explanatory power in univariate regressions. 

Starting  from  backward,  once  we  eliminate  the  between-country  variation  (the  fixed  effects 
models of column (3), (6) and (9)), regional variation in characteristics is not sufficiently strong 
to contribute to explaining regional resilience. Once we look at random effects or OLS, some 
regional characteristics do prove to be informative. 

The  gender  employment gap  exhibits  a  negative  correlation  with  the  resilience  in  impact,  as 
shown in both OLS and random effects regressions. The high leverage for this effect is that the 
majority of Greek and Spanish regions have the highest gender employment gap and the lowest 
impact on resilience. 

The  Gross  Value  Added  share  of  Industry33  is  another  robust  predictor  for  the  absorption 
capacity. This result suggests that the regions with more intensive industrial activities are the 
ones who have performed better in the aftermath of the crisis. Similar results are found for the 
intensity  of  the  professional,  scientific  and  technical  activities,  administrative  and  support 
service,  especially  when  we  were  explicitly  modelling  only  within-country  variation,  using 
random and fixed effects models. On the contrary, a different specification of the intensity of the 
construction sector has always shown a negative correlation with the indicators of resilience, 
suggesting the vulnerability of regions with a higher economic weight of construction activities. 
In the multivariate specification, it remains consistent in sign, but not significant. 

Regional  potential  accessibility34  is  a  characteristic  that  appears  to  be  less  relevant  for  the 
impact,  but  more  relevant  (both  in  terms  of  size  and  significance)  for  the  medium  run  and 
bounce forward. However, although it does suggest that regions with better accessibility are 
the ones who were “fitter” to catch up and even bounce forward when modelling strictly within-
country variation, it loses its significance. 

31  

Pay to productivity is an indicator of labor market efficiency. It is built upon the Executive Opinion Survey, where experts have been 
asked to evaluate the extent the pay is related to employee productivity. (Source: World Economic Forum) 
32  

Perception about the country companies’ capacity to innovate, where higher score represent greater extent of innovation 
perceived. Based on Executive Opinion Survey (Source: World Economic Forum)  

33   Here Industry comprises the following NACE sectors: B – Mining and quarrying, C - Electricity, gas, steam and air conditioning 

34  

supply D - Water supply; sewerage, waste management and remediation activities 
The accessibility model measures the minimum travel time between all NUTS3 regions for rail, road and air separately. For 
multimodal  accessibility  the  accessibility  by  road,  rail  and  air  are  integrated  into  one  indicator  expressing  the  combined 
effects of these modes for each NUTS3 region. (Source: ESPON) 

35 

 
                                           
5  Conclusions  

This report has shed light on how regions have responded to the economic and financial crisis. 
We have seen that regions exhibit substantial heterogeneity in resilient behaviour during and 
after the crisis. Greek and Spanish regions suffered a lot in the short run, followed by Latvia and 
most Irish regions, showing the lack of absorption capacity. On the contrary, German regions 
not only were able to absorb the shock but also to bounce forward in many dimensions.  

Results show that resilience is highly dependent on the time horizon, whether it includes the 
immediate aftermath of the crisis or it takes a longer window of analysis. Striking is the example 
of  the  Greek  islands,  who  outperform  in  the  medium  run,  although  they  suffered  a  lot  in  the 
immediate  aftermath  of  the  crisis.  The  ability  to  react  to  a  shock  has  been  also  measured 
through the duration of recovery which has shown great heterogeneity across regions and by 
variables.  

Overall,  the  EU  member  states  appear  to  be  substantially  heterogeneous  while  the  within-
country variation of regions is smaller. However, while country data might be able to capture 
overall  behaviour,  regional  data  is  important  to  capture  the  within-country  regional 
differences. There are some clear regional disparities within Denmark, Finland, France, and the 
UK, especially in the bounce forward capacity.  

Our broader approach may contribute to providing insights in multi-national contexts, and we 
have  further  shown  how  to  operationalize  a  “beyond  GDP”  approach 
in  resilience 
measurement. As the ultimate objective is the maintenance of overall societal well-being, one 
should not stop at assessing small and selected sectors of well-being production but should 
aim to capture the broader behaviour of the system. Regions are entities made of economic, 
institutional and individual actors, and the social dynamics in their behaviour and interactions 
cannot  be  ignored.  This  analysis  highlights  the  importance  of  expanding  the  measurement 
strategy to a broader list of variables which takes into account the time dimension: the impact 
on core economic variables is not sufficient to tell the full story. Medium run resilience for GDP 
is  not high for the same regions  as  the  resilience  of,  for  example,  NEET, wages  or education 
level.  Furthermore,  results  showed  different  patterns  of  recovery  between  objective  and 
subjective indicators where on average the former is faster and the latter much slower.  

As resilience is property of the dynamic response to a shock or distress, its monitoring would 
need continuous shocks and their continuous reassessment over time. This would turn to be 
impossible given the difficulty of identifying new shocks most of the time. To identify directions 
for policies to foster resilience, we need to look for resilience characteristics. They serve as 
important  measurement  tools  for  gauging  and  assessing  the  resilience  of  countries  and 
regions  in  a  forward-looking  manner,  with  reference  to  a  hypothetical  future  scenario  of  a 
similar kind. This report contributes by identifying candidate characteristics that influence the 
resilience of regions, potentially differing across resilience capacities. 

Results  show  that  there  is  a  common  pattern  across  the  impact,  medium  run  and  bounce 
forward indicators, where lower pre-crisis levels of private sector credit flows appear to be the 
most  robust  country-level  predictor  of  resilient  behaviour.  Taking  into  account  the  within-
country  regional  performance,  results  have  shown  that  regions  with  a  lower  gender 
employment gap are associated with a higher resilience in impact. Also, the relative importance 
of the industrial sector in the overall regional economy is another very robust predictor of the 
absorption capacity.  

Unlike  elsewhere  in  the  literature  (see  Sensier  et  al.  (2016)),  we  do  not  find  substantial 
differences between the performance of employment and GDP. The correlation is roughly 0.65 
for both their impact and medium run metrics. The employment bounce forward metric is -1, 
meaning there was no bounce forward, for 28 per cent of the regions while it is 26 per cent for 
GDP. However, we also find that the employment score reflects overall resilience better than 

36 

 
GDP itself. Taking it from another perspective, pre-crisis level of GDP and employment, in our 
regressions exercise, do not have the power to distinguish between the more and less resilient 
regions (see Section 4.4 and Annex 2). 

It  is  important  to  keep  in  mind  that  these  characteristics  are  meant  to  capture  some  of  the 
ingredients of the general ability of a country or region to withstand a similar crisis. However, 
this still does not allow them to provide complete guidance about available or preferred policy 
actions to prepare  for  future  difficulties.  To  this  end,  it would be important to analyse a wide 
number of economic shocks and eventually find common characteristics. Another important 
element would be to explore in detail the underlying mechanisms and transmission channels 
through which resilience characteristics may translate into resilient behaviour in general, and 
successful  crisis  management  policies  in  particular.  This  is  clearly  beyond  the  scope  of  the 
statistical analysis presented in this report but is of great relevance for future research.  

To further emphasize this point, it may be useful to speculate briefly about some of the ways 
resilience  characteristics  may  improve  the  ability  to  cope  with  a  crisis.  First,  resilience 
characteristics  may  correspond  to  buffers  that  one  can  draw  on  and/or  deplete  in  times  of 
distress.  For  example,  a  favourable  net  international  investment  position  or  private  sector 
credit  flows  may  enable  a  country  to  boost  economic  performance  or  promote  social 
protection. On the other hand, at the regional level, increasing equal opportunities in the labour 
market for women and men may lead to a better utilisation of human capital and a better overall 
performance. 

The ultimate use of resilience characteristics would be to monitor them continuously. A similar 
tool already in use is the macroeconomic imbalance procedure scoreboard, which serves to 
identify potentially harmful macroeconomic imbalances that could adversely affect economic 
performance.  The  annual  (or  multi-year  moving  average)  behaviour  of  resilience 
characteristics would signal whether the resilience of a country or a region is improving or not, 
so whether it would be able to face future economic shocks better or worse than previously.  

Our approach proposes to focus on bounce forward instead of bounce back, acknowledging the 
importance  of  the  transformative  ability.  Bounce  back  does  not  always  represent  true 
resilience, as regions should not necessarily return to the pre-crisis level if it had led them to a 
non-sustainable  path.  This  report  proposed  a  simple  measure  of  bounce  forward,  but  it 
requires further efforts to fully capture the transformative ability of regions necessary to face 
the uncertainties in future. Our analysis is a first attempt of this kind and sets the building blocks 
for further investigations on how to transform the crisis into windows of opportunities. 

37 

 
 
 
 
 
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Annex 1: List of selected system variables 

Note: system variables indicated in italic” A3, A5, E5, E6., E6, O3, O5, O6) have a high number of NUTS1 imputed values 
at NUT2 level. Within the type column, S stands for social, E for economic, P for perceptions and O for objective. 

ESS data have been retrieved from European Social Survey Cumulative File, ESS 1-7, (2016), SILC/GSOEP/BHPS  for 
respectively Germany and Great Britain uses Socio-Economic Panel (SOEP), (2017) and the Institute for Social and 
Economic Research, (2017). Moreover, system variables indicated in italic (A3, A5, E5, E6, E7, O3, O5, O6) have a high 
number of NUTS1 imputed values at NUTS2 level. 

41 

VariabletypecodeFull namesourceSource code of tabledefinitionSystem partunittrans-formationsigneducated_labourE/OA1Labour force with at least secondary educationEurostatlfst_r_lfe2emprc(_edu)Labour force with at least secondary education as percentage of the total labour force (upper secondary and post-secondary non-tertiary education (levels 3 and 4)) population from 15-64yASSET%mineducation_levelS/OA2Education levelEurostatedat_lfse_12the proportion of 30–34 year-olds with tertiary educational attainment ASSET%minfairnessS/PA3Self-perceived fairnessESSpplfairSurvey based –subjective measure. The survey question "Most people try to take advantage of you if they got the chance, or try to be fair".  The variable is the share of those answering 7 to 10 (agree with fairness) out of a scale of 10.ASSET%minmigration_rateS/OA4Net migrationEurostatdemo_r_gind3Population change - Demographic balance and crude rates at regional level (NET MIGRATION) (Crude rate of net migration plus statistical adjustment) as share of populationASSET%mintrust_peopleS/PA5Trust in peopleESSppltrstThe survey question "Most people can be trusted or you can't be too careful".  The variable is the share of those answering 7 to 10 (agree with trust).ASSET%minfemale_employmentE/OE1Female employment rateEurostatlfst_r_lfe2emprtfemale employment rate (age: 15-64)ENGINE%minlprodE/OE2Labour productivityeurostat elaborationnama_10r_2gdp// nama_10r_2emhrwReal GDP volume  (own elaboration) per hours worked ENGINElogminpermanentE/OE3Share of permanent contractLFStempshare of the employees that have permanent contract over all employed, where employment is defined as number of persons aged 20 to 64 who are working.ENGINE%minrd_gdpE/OE4Total expenditure in R&DEurostatrd_e_gerdregPublic Expenditure in R&D, Percentage of gross domestic product (GDP), all sectors (other secotrs are: Business enterprise sector, Government sector,Higher education sector, Private non-profit sector)ENGINE%logminsocial_weeklyS/OE5Weekly socially meet ESSsclmeetSurvey based- subjective measure. The share of respondents answering they weekly socially meet with friends, relatives or colleaguesENGINE%mintrust_epS/PE6Trust in the europan parliamentESSTRSTEP Survey based- subjective measure. The share of respondents answering 7-10  on a scale from 0 (not trust at all) to 10 (complete trust)ENGINE%mintrust_legalS/PE7Trust in the legal systemESSTRSTLGLSurvey based- subjective measure. The share of respondents answering 7-10  on a scale from 0 (not trust at all) to 10 (complete trust)ENGINE%minwagesE/OE8Real wageEurostatnama_10r_2coe //nama_10r_2emhrwCompensation of employees per hour worked in Purchasing Power Standard (PPS) deflated (own elaboration) (in '000)ENGINElogminemploymentE/OO1Employment RateEurostatlfst_r_lfe2emprtNumber of persons aged 20 to 64 in employment by the total population of the same age groupOUTCOME%mingdpE/OO2Gross Domestic ProductEurostatnama_10r_2gdp// nama_10_gdpReal GDP volume, index 2010=100 (own elaboration )OUTCOME%logminhealthS/PO3Self-percieved healthSILC/GSOEP(for DE)/ BHPS (for UK)healthSurvey based –subjective measure. Share of respondents indicating a level of "good" or "very good" (the top two out of 5)OUTCOME%minhh_incomeE/OO4Household disposable incomeEurostatnama_10r_2hhincHousehold net disposable income, PPS per capitaOUTCOME PPS /capitalogmininequality_s80s20E/OO5Income inequalitySILC/GSOEP(for DE)/ BHPS (for UK)The S80/S20 ratio. S80/S20 is the ratio of the average income share of the 20% richest to the 20% poorest.OUTCOME%logmaxlifesatS/PO6life satisfactionESSstflifeSurvey based –subjective measure. The survey question: “How satisfied would you say you are with your life these days?” The variable is the share of those answering 7 to 10 on a scale from 1  (very dissatisfied) to 10 (very satisfied)OUTCOME%minneetS/OO7Not in employment nor in education and training Eurostatedat_lfse_22Young people (15-24 years) neither in employment nor in education and training,  percentage of the total population in the same age groupOUTCOME%maxstress_diseaseS/OO8Death rate due to stress related diseasesEurostathlth_cd_acdr2// hlth_cd_acdrcrude death rate per 100 000 inhabitants - annual datadue to diseases of the circulatory system(I00-I99)OUTCOME%maxsuicideS/OO9Death rate due to suicideEurostathlth_cd_acdr2// hlth_cd_acdrcrude death rate per 100 000 inhabitants for Intentional self harming (http://ec.europa.eu/eurostat/statistics-explained/index.php/Causes_of_death_statistics)OUTCOME%maxunemployed_12mE/OO10Long term unemployment rateEurostatlfst_r_lfu2ltuShare of long term unemployed (more than 12 months) over the work force. The labour force is the total number of people employed and unemployed. OUTCOME%maxunemploymentE/OO11UnemploymentEurostatlfst_r_lfu3rtUnemployed persons as a percentage of the labour force (15-74 years) . The labour force is the total number of people employed and unemployed. OUTCOME%maxnote: system variables indicated in italic (A3, A5, E5, E6, E7, O3, O5, O6) have a high number of NUTS1 imputed values at NUTS2 level 
 
 
Annex 2: Correlation matrix of the system sub-components 

The  table  presents  correlation  coefficients  between  the  variables,  their  system  part  and  the  overall 
resilience indicators. 

The darker the colour, the higher the correlation. Red colour marks negative correlations. * For the reference year 
(2007), Polish regions report a low level with respect to the rest of the indicated time series, while this is not true for 
the country-level data.  This exacerbates the overall negative correlation in the impact and medium run. 

42 

 
 
 
Annex 3: Correlation of categories 

Correlation between different categories within the Impact, Medium run and Bounce forward 

Impact 

Medium run 

Bounce forward 

43 

ASSETENGINEOUTCOMESOCIALECONOMICPERCEPTIONOBJECTIVEIMPACTASSET10.480.650.770.620.670.720.84ENGINE0.4810.700.590.790.390.850.84OUTCOME0.650.7010.690.880.420.940.90SOCIAL0.770.590.6910.440.710.720.80ECONOMIC0.620.790.880.4410.310.920.88PERCEPTION0.670.390.420.710.3110.390.58OBJECTIVE0.720.850.940.720.920.3910.97IMPACT0.840.840.900.800.880.580.971ASSETENGINEOUTCOMESOCIALECONOMICPERCEPTIONOBJECTIVEMEDIUM RUNASSET10.400.510.730.500.590.620.79ENGINE0.4010.720.490.780.350.840.83OUTCOME0.510.7210.570.870.310.950.88SOCIAL0.730.490.5710.280.620.600.72ECONOMIC0.500.780.870.2810.230.910.85PERCEPTION0.590.350.310.620.2310.280.51OBJECTIVE0.620.840.950.600.910.2810.95MEDIUM RUN0.790.830.880.720.850.510.951ASSETENGINEOUTCOMESOCIALECONOMICPERCEPTIONOBJECTIVEBOUNCE FORWARDASSET10.460.550.710.590.570.670.83ENGINE0.4610.580.600.680.480.770.80OUTCOME0.550.5810.610.880.330.930.85SOCIAL0.710.600.6110.400.610.680.78ECONOMIC0.590.680.880.4010.290.920.86PERCEPTION0.570.480.330.610.2910.320.55OBJECTIVE0.670.770.930.680.920.3210.95BOUNCE FORWARD0.830.800.850.780.860.550.951 
 
 
 
 
Annex 4: Comparison of different measures 

Impact: comparison Social vs Economic measures and Household income and GDP vs Perceptions measures 

Panel A 

 Panel B 

Panel C 

44 

 
 
 
 
 
 
 
 
 
 
 
Medium run: comparison Social vs Economic measures and Household income and GDP vs Perceptions measures 

Panel A 

 Panel B 

Panel C 

Bounce Forward: comparison Social vs Economic measures and Household income and GDP vs Perceptions measures 

Panel A 

 Panel B 

Panel C 

45 

 
 
 
 
 
 
 
 
 
 
 
Annex 5: Impact, medium run and bounce forward over the EU 

46 

47 

Annex 6: Time to the maximum impact of the shock 

How fast is the minimum obtained due to shock? 

48 

Share of regions that did not decreaseWithin 1 yearWithin 2 yearsWithin 3 yearsWithin 4 yearsWithin 5 yearsWithin 6 yearsWithin 7 yearsWithin 8 yearsWithin 9 yearsWithin 10 yearsLabour force with secondary eduction15%1%11%24%35%45%70%84%95%100%100%Education level51%32%53%63%71%76%81%83%89%100%100%Self-perceived fairness0%24%24%52%52%83%83%96%96%100%100%Net migration9%17%26%34%44%60%77%82%92%100%100%Self-percieved trust in people*0%24%24%51%51%77%77%93%93%100%100%Female employment rate31%4%17%32%47%63%84%93%97%100%100%Labour productivity20%19%72%78%82%88%93%96%99%100%100%Share of permanent contract26%10%16%28%45%56%65%77%100%100%100%Total expenditure in R&D48%22%31%37%61%66%74%83%100%100%100%Weekly socially meet*0%20%20%34%34%59%59%87%87%100%100%Real wages*0%17%17%42%42%70%70%87%87%100%100%Trust in the Europan Parliament*0%24%24%56%56%82%82%96%96%100%100%Trust in the legal system7%17%36%41%57%65%83%99%100%100%100%Employment rate22%1%13%28%43%55%84%95%98%100%100%GDP12%1%56%64%67%71%88%95%96%100%100%Self-perceived health28%6%23%31%39%45%61%81%100%100%100%Household disposable income26%2%58%64%69%80%92%100%100%100%100%Income inequality21%24%29%39%47%54%72%87%100%100%100%Life satisfaction*0%36%36%60%60%79%79%96%96%100%100%NEET6%3%18%28%39%57%79%90%96%100%100%Deathto stress  related diseases43%23%36%41%42%52%57%58%100%100%100%Death rate due to homicide10%10%27%35%45%59%73%86%100%100%100%Long term unemployment rate22%0%0%9%15%29%45%74%87%100%100%Unemployment rate12%0%8%22%32%43%69%85%95%100%100%Of those that reached a minimum, when did it occurAssetEngineOutcomesAnnex 7: List of selected characteristics from the univariate regressions 

Impact 

Medium run 

Bounce forward 

COUNTRY LEVEL CHARACTERISTICS 

Adr2 

Coeff. 
Sign 

Source 

COUNTRY LEVEL CHARACTERISTICS 

Adjr2 

Coeff. 
Sign 

Source 

COUNTRY LEVEL 
CHARACTERISTICS 

Adjr2 

Coeff. Sign 

Source 

Private sector credit flow, consolidated - 
% GDP 

Net  international  investment  position  - 
annual data 

Capacity for Innovation 

0.33 

0.32 

0.30 

Current account balance annual data 

0.28 

Social expenditure - no pensions 

0.28 

Non-routine manual work 

Private spending on R&D 

Availability  of  research  and  training 
services 

0.27 

0.26 

0.25 

Income automatic stabilizer -EUROMOD 

0.25 

Women share in the labour force 

State of cluster development 

0.22 

0.21 

University-industry collaboration in R&D 

0.21 

Extent of market dominance 

Quality of the education system 

0.20 

0.20 

Expenditure on social protection 

0.19 

- 

+ 

+ 

+ 

+ 

- 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

MIP

Private sector credit flow, consolidated - % GDP 

0.35 

MIP

Income automatic stabilizer q1 -EUROMOD 

0.28 

WEF

Share of women in the labour force 

E 

Company spending on R&D 

MIP

Youth  unemployment  rate  -  %  of  active  population 
aged 15-24 

JRC

Non-routine manual work 

WEF

University-industry collaboration in R&D 

WEF

Firm-level technology absorption 

JRC

Tertiary education enrolment, gross % 

E 

WEF

WEF

WEF

WEF

Share of people who trust EP 

Quality of scientific research institutions 

Pay and productivity 

Capacity for Innovation 

Social expenditure - no pensions 

E 

Intensity of local competition 

0.27 

0.25 

0.25 

0.25 

0.22 

0.21 

0.20 

0.20 

0.19 

0.18 

0.18 

0.17 

0.16 

- 

+ 

+ 

+ 

+ 

- 

+ 

+ 

- 

- 

+ 

+ 

+ 

+ 

+ 

MIP

JRC

E 

WEF

MIP

JRC

WEF

WEF

E 

E 

WEF

WEF

WEF

E 

WEF

Private  sector  credit 
consolidated - % GDP 

flow, 

0.32 

Private spending on R&D 

Share of women in the labour 
force 

Income  automatic  stabilizer  - 
EUROMOD 

University-industry 
collaboration in R&D 

0.28 

0.27 

0.26 

0.26 

Firm-level 
absorption 

technology 

0.24 

Quality  of  scientific  research 
institutions 

Youth  unemployment  rate  -  % 
of  active  population  aged  15-
24 

0.22 

0.22 

Pay and productivity 

0.22 

Non-routine manual work 

0.21 

Intensity of local competition 

0.20 

Extent of market dominance, 

0.19 

Social  expenditure 
pensions 

-  no 

0.19 

Tertiary  education  enrolment, 
gross % 

0.19 

-

+

+

+

+

+

+

+

+

-

+

+

+

-

MIP

WEF

E 

JRC

WEF

WEF

WEF

MIP

WEF

JRC

WEF

WEF

E 

E 

49 

Tertiary education enrolment, gross % 

0.19 

Share  of  people  who  trust  the  EU 
Parliament 

0.19 

Quality of scientific research institutions 

0.17 

Business sophistication 

Local supplier quality 

Value chain breadth 

Non-routine cognitive work 

Global Competitiveness Index 

Export  market  shares  -  %  of  the  world 
total 

Foreign market size 

0.17 

0.17 

0.17 

0.17 

0.16 

0.16 

0.15 

- 

- 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

+ 

MIP

WEF

WEF

MIP

MIP

E 

Net international investment position - annual data 

0.16 

ESS

Extent of market dominance, 

0.16 

WEF

Availability of research and training services 

0.16 

Export market shares - % of the world total 

Long-term  unemployment  rate,  %  of  active 
population aged 15-74 

0.15 

0.15 

WEF

WEF

WEF

REGIONAL CHARACTERISTICS 

Adjr2 

Coeff 

Source 

JRC

GVA construction 

Employment to services ratio in employment 

Gender employment gap 

WEF

MIP

WEF

0.27 

0.27 

0.22 

- 

- 

- 

E 

E 

E 

REGIONAL CHARACTERISTICS 

Adjr2 

Coeff 

Source 

GVA in construction 

Gender employment gap 

Social exclusion 

Accessibility 

Inequality 

Gross 
fixed 
manufacturing 

0.34 

0.26 

0.23 

0.22 

0.21 

- 

- 

- 

+ 

- 

+ 

E 

E 

E 

Espon

E 

E 

capital 

formation 

in 

0.16 

Capacity for Innovation 

Share of people who trust the 
EU Parliament 

Export  market  shares  -  %  of 
the world total 

Foreign competition 

Domestic competition 

0.19 

0.18 

0.18 

0.18 

0.17 

Prevalence 
ownership 

of 

foreign 

0.17 

Net  international  investment 
position - annual data 

0.16 

Judicial independence 

0.16 

Trustworthiness 
confidence 

and 

0.16 

Global Competitiveness Index 

0.16 

Efficacy of corporate boards 

0.15 

+

-

+

+

+

+

+

+

+

+

+

WEF

E 

MIP

WEF

WEF

WEF

MIP

WEF

WEF

WEF

WEF

REGIONAL 
CHARACTERISTICS 

Adjr2 

Coeff 

Source 

Construction 
to 
employment ratio 

services 

0.30 

GVA in construction 

Gender employment gap 

Accessibility 

Gross fixed capital formation - 
professional/scientific sector 

0.28 

0.21 

0.17 

0.15 

-

-

-

+

+

E 

E 

E 

E 

E 

50 

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doi:10.2760/383460 

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