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Scope The sudden appearance and exponential development of severe forms of COVID-19 and the pressure induced on health care systems, have led almost all European governments to put in place – more or less strictly – measures to limit economic and social activities. For the first time in recent history, voluntary measures have generated an economic and social crisis on an unprecedented scale. While most of the consequences of this crisis are still ahead of us, the first real effects are already being felt after the lock-down of entire sectors of the economy such as rising unemployment, social issues and explosion in public spending. As a matter of emergency, all Member and Partner States have taken measures to mitigate the immediately identified consequences. These have been accompanied at European level by the European Commission and implemented in particular with the support of the European Investment Bank. At regional and local levels, public authorities have been called upon to provide emergency services to the population and mitigate as far as possible the impact on economies and societies. The objective of this ESPON activity is to produce pan-European territorial evidence to contribute to the efforts undertaken at EU, national and regional levels: to help understand better the territorial patterns of the epidemic and to support the definition of renewed place-based policies to tackle the upcoming socio-economic crisis. Policy questions How did the circulation of the virus affect health care systems during the first months of the outbreak? What has been the kinetics of the epidemic across European regions? What can be said about the geography of the outbreak? About the persistency in the spatial concentration of severe cases? About the spatial spread of the epidemics after the appearance of the first uncontrolled clusters (severe cases and deaths)? How can the modes of diffusion be described (between the topological logic and the network logic)? How can regional variations be explained? Is it possible to identify links between the spread of the disease and variables likely to influence it such as density, types of territories, the structure of the population, socio-economical characteristics? Can differentiated approaches to lockdowns explain some of these variations? What were the main tendencies among emergency policy-measures taken at regional and local levels (cities, metropolitan areas…)? How can they be categorized by domain, territorial levels, time perspective (short-, mid- or long term)? What were the most significant initiatives in terms of cross-border cooperation? Which priorities for further research on territorial resilience and recovery can be identified in relation to the geography of the outbreak and the typologies of first policy answers?
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ESPON STUDY //
Geography of COVID-19 outbreak
and first policy answers in
European regions and cities
Policy Brief // December 2020
This ESPON STUDY is conducted within the framework of the ESPON 2020 Cooperation
Programme, partly financed by the European Regional Development Fund.
The ESPON EGTC is the Single Beneficiary of the ESPON 2020 Cooperation Programme. The
Single Operation within the programme is implemented by the ESPON EGTC and co-financed by
the European Regional Development Fund, the EU Member States and the Partner States,
Iceland, Liechtenstein, Norway and Switzerland.
This delivery does not necessarily reflect the opinions of the members of the ESPON 2020
Monitoring Committee.
Coordination:
Sebastien BOURDIN, EM Normandie Business School
Nicolas ROSSIGNOL, ESPON EGTC
Authors
Mounir AMDAOUD, EconomiX, CNRS, University Paris Nanterre
Giuseppe ARCURI, EconomiX, CNRS, University Paris Nanterre
Sebastien BOURDIN, EM Normandie Business School
Damiana COSTANZO, University of Calabria
Mihail EVA, University of Iași
Corneliu IATU, University of Iași
Bogdan IBANESCU, University of Iași
Ludovic JEANNE, EM Normandie Business School
Nadine LEVRATTO, EconomiX, CNRS, University Paris Nanterre
Fabien NADOU, EM Normandie Business School
Gabriel NOIRET, EM Normandie Business School
Marianna SUCCURRO, University of Calabria
Acknowledgements
EUROCITIES, Council of European Municipalities and Regions (CEMR), European
Confederation of Local Intermediate Authorities (CEPLI), ESPON contact points across Europe,
European Centre for Disease Prevention and Control (ECDPC)
Information on ESPON and its projects can be found at www.espon.eu.
The website provides the possibility to download and examine the most recent documents
produced by finalised and ongoing ESPON projects.
ISBN: 978-2-919795-71-0
© ESPON, 2020
Published in December 2020
Graphic design by BGRAPHIC, Denmark
Printing, reproduction or quotation is authorised provided the source is acknowledged and a copy
is forwarded to the ESPON EGTC in Luxembourg.
Contact: info@espon.eu
ESPON STUDY //
Geography of COVID-19
outbreak and first policy
answers in European regions
and cities
Policy Brief // December 2020
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
ESPON // espon.eu 5
Table of contents
1 Introduction ........................................................................................................................ 8
2 Understanding the spread of the virus and its determinants ........................................ 9
2.1 Geography of the outbreak: a European perspective.................................................................. 9
2.1.1 From Wuhan to Europe: between topographical and topological diffusion ................................. 9
2.1.2 Mapping the circulation/diffusion of the virus .............................................................................. 9
2.2 Understanding the kinetics of Covid-19 .................................................................................... 13
2.2.1 A spatial analysis of the outbreak ............................................................................................. 13
2.2.2 The determinants of regional disparities in terms of mortality ................................................... 17
2.2.3 A spatial heterogeneity of the influence of the different factors ................................................. 22
2.2.4 Has the lockdown been effective? ............................................................................................ 24
2.2.5 Advantages and limitations of spatial techniques...................................................................... 25
3 Overview of policy answers to the Covid-19 on a regional/local scale ...................... 27
3.1 Typology of the measures ......................................................................................................... 27
3.1.1 General context......................................................................................................................... 27
3.1.2 Public Health Security - overview of the local and regional policy measures ............................ 30
3.1.3 Daily way of life and work - overview of the local and regional policy measures....................... 31
3.1.4 Support to vulnerable populations............................................................................................. 34
3.1.5 Support to the economic actors and recovery ........................................................................... 35
3.2 Understanding commonalities and differences in tackling consequence of the pandemic ....................... 36
3.2.1 Incidence of first policy responses for each regional GDPPC ................................................... 37
3.3 Incidence of first policy responses depending on the COVID-19 mortality levels ..................... 38
3.4 Incidence of first policy responses for each type of territory ..................................................... 39
4 Conclusion........................................................................................................................ 41
Appendix………………. ..................................................................................................................... 43
References ......................................................................................................................................... 53
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List of maps, figures, charts and tables
List of maps
Map 1: Incidence of deaths (end of March)
Map 2: Incidence of deaths (end of May)
Map 3: Incidence of deaths (end of July) .................................................................................................... 11
Map 4: Evolution of deaths (February-March)
Map 5: Evolution of deaths (April-May)
Map 6: Evolution of deaths (June-July) ....................................................................................................... 15
Map 7: GWR-Hospital beds
Map 8: GWR-Life expectancy ..................................................................................................................... 23
Map 9: GWR-Poverty
Map 10: GWR-Governance Index ............................................................................................................... 24
Map 11: Geographical location of selected case studies ............................................................................ 45
List of figures
Figure 1: Frequency of local and regional policy measures classified according to their nature and
sector (N=477) ...................................................................................................................... 29
Figure 2: Measures classified according to their nature and their temporal perspective ............................. 30
Figure 3: Examples of measures taken by European cities and NUTS-3 regions during the first wave
of the pandemic in the Health security area ......................................................................... 31
Figure 4: Total number of policy measures for each territory ...................................................................... 37
Figure 5: Frequency of policy measures depending to the GDPPC level ................................................... 37
Figure 6: Most resilient measures ............................................................................................................... 38
Figure 8: Frequency and nature of local and regional policy measures according to the mortality level ..... 39
Figure 9: Average number of measures for each type of territory ............................................................... 40
Figure 11: Proposed grid for analysing policy responses on the local scale ............................................... 50
List of tables
Table 1: Definition and source of the variables ........................................................................................... 19
Table 2: Empirical results for Covid-19 deaths rate determinants ............................................................... 20
Table 3: The effect of the lockdown and government quality on the mortality rate due to Covid19 ............ 25
Table 4: Frequency of measures according to their nature ......................................................................... 28
Table 5: Examples of policy measures taken in the daily way of life and work” area, classified
according to their assumed temporality ...................................................................................................... 32
Table 6: Examples of measures in the daily way of life and work” area classified according to their
nature .......................................................................................................................................................... 33
Table 7: Examples of measures to support vulnerable populations classified according to their temporal
perspective ................................................................................................................................................. 34
Table 8: Examples of measures to support vulnerable populations classified according to their nature ..... 35
Table 9: Examples of measures to support economic actors and recovery classified according to their
nature .......................................................................................................................................................... 36
Table 10: List of case studies...................................................................................................................... 46
Table 11: Main sources used for collecting data on first local and regional policy measures ..................... 47
Table 12: Proposed typology of policy measures and corresponding conceptual categories ..................... 48
Table 13: Proposed criteria for classifying measures .................................................................................. 51
Table 14: No of policy measures according to their nature and level of wealth in the corresponding
NUTS-3 regions .......................................................................................................................................... 52
Table 15: Frequency of policy measures according to their nature and the GDPC level (EU27=100) in
the corresponding NUTS-3 regions (average no of policy measures per region/city). ................................ 52
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Table 16: Share of the number of circumventing and exploding measures depending on the GDPPC
level in the corresponding NUTS-3 region .................................................................................................. 52
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1 Introduction
General context
Since the appearance of the virus in China at the end of last year, the European way of considering the
coronavirus has changed radically. While the first cases of Covid-19 occurred in Europe at the end of Janu-
ary, the European Centre for Disease Prevention and Control, an EU agency, reported only 275 cases a
month later. On the 1st of June, Europe totalled 170,000 deaths and on the 1st of November, it was 223,000
deaths that was registered, figures that give an idea of the pandemic scale on the continent.
In response to the pandemic, the leaders of the 27 Member States took more or less drastic measures. Italy,
the first country to be massively affected by Covid-19, confined its population on the evening of the 9th of
March. Most of the other 26 Member States gradually followed suit, adopting policies of social distancing,
closure of non-essential businesses and border closures to limit the circulation of the virus. Spain has im-
plemented the most radical containment on the continent, banning children from going out until the 26th of
April. In Central and Eastern European countries, the virus propagated later and lockdown measures have
been implemented in the very early stages of the virus propagation. For example, Romania instituted a
national lockdown on the 24th of March, at a moment when there were only 726 confirmed cases across the
country, and similar decision patterns can be found in most CEE countries. Conversely, Sweden has adopted
the least restrictive policy in Europe. The government has advocated the strategy of herd immunity, accord-
ing to which the circulation of the virus allows the immunisation of a majority of the population and would
therefore make the disease harmless.
Since early May, many countries have entered a phase of deconfinement. This is particularly the case in
Europe, where most of the measures taken in March to combat the spread of the coronavirus have been
made more flexible. But the “second wave” that Europe is undergoing has, once again, led to containment
measures in several countries.
Understanding the spread of the virus and its determinants
More than ever, the epidemic of coronavirus infection makes the social facts that geography questions visible
through space and at all scales: worldwide, European, national, local and even the finest one. The epidemic
acts here as a powerful indicator of the organization of geographical space. It highlights the multiple interac-
tions between territories at different scales.
In Germany as in France, there are significant geographical contrasts in the density of confirmed cases and
deaths. However, these inequalities correspond to very different patterns of spatial organization in the two
countries. This example reinforces the usefulness and interest of conducting a comparative and multiscale
study to understand the drivers of the spread of the disease and its impacts fully. By compiling the data
produced by the national statistical institutes in Europe at a fine scale, we retraced the Covid19 geography
from the beginning of the pandemic until the end of August.
By cross-referencing data on mortality with socio-economic, demographic, and institutional data, we were
able to understand why some territories were more affected than others.
Overview of policy answers to the Covid-19 on the regional/local scale
The EU's response to COVID19 is based on four priorities: (i) limiting the spread of the virus; (ii) ensuring
the supply of medical equipment; (iii) promoting research on treatments and vaccines; (iv) supporting jobs,
businesses and the economy. At the same time, local authorities are/were in the front line of the pandemic.
Consequently, various territorial responses have been implemented with the aim of limiting the spread of the
Covid-19. These measures include health/social policy, economic policy and fiscal policy. From that point,
we identified, on the local scale, the policies adopted and the tools implemented to mitigate the effects of
the epidemic.
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2 Understanding the spread of the virus
and its determinants
2.1 Geography of the outbreak: a European perspective
2.1.1 From Wuhan to Europe: between topographical and topological diffusion
The unprecedented crisis we are currently experiencing with the coronavirus pandemic, like any global epi-
demic, is part of an evolving spatiotemporal process: the hierarchy of the territories affected by high mortality
linked to Covid-19 evolves over time; this is what we call "the kinetics of the pandemic" in medical terms.
After its outbreak in the megalopolis of Wuhan (whose yearly airport traffic represents 25 million people), the
capital of the Hubei province in central China, the spread initially occurred neighbourhood by neighbourhood
(topographical spread) in nearby countries having intense relations with China. On the 20th of January 2020,
South Korea reported its first confirmed case: a Chinese woman. On the 21st of January 2020, Taiwan de-
tected a first case with a Taiwanese businesswoman established in Wuhan who came back to the island. At
the same time, Asia, such as Japan, Hong Kong and Singapore, were affected, as the city-state was the first
air destination from Wuhan.
The virus could not remain Asian since Asia, and particularly China, has many relations with other continents.
From this point of view, the epidemic clearly took advantage of the forces of globalisation to spread to the
most economically integrated metropolitan areas. This is what we call a topological diffusion, i.e. reticular
diffusion, in the networks of the most globalised cities. The virus has thus exploited the mobility networks, at
the heart of our economic development and tourism, and has flourished in the densest and most productive
and sociably intense spaces. The fact that Northern Italy was the first European region to be particularly
affected (Map 1) was logical, given the importance of the Chinese diasporas and mobility between Milan and
China. It should be remembered that the Milan-Malpensa airport, the second Italian airport after that of
Fiumicino in Rome, has above all become the FedEx hub in Southern Europe and an important freight
airport, well connected to China, the world's leading exporter. Milan is also directly connected to China
through the rail freight, with the inauguration of the first direct link between Chengdu and Milan on the 12th
of February 2019, thus contributing to the structuring of the new Silk Roads. This could explain why Lom-
bardy was hit earlier than other regions. Southern Italy, much further away from globalisation, found itself
somewhat protected.
2.1.2 Mapping the circulation/diffusion of the virus
In order to map the circulation/diffusion of the virus, we created a geodatabase on the regional scale (NUTS
2 or 3) with data on daily deaths. We collected data from the beginning of February to the end of August.
Maps 1, 2 and 3 show the cumulative death rate per 100,000 inhabitants across the European regions. As
it can be observed, during the first months of the pandemic, the first cases were strictly limited to some
regions in Italy (5.9 per 100,000 people in Lombardy), France (4.25 per 100,000 people in Haut-Rhin) and
Spain (4.15 per 100,000 people in Madrid Community). Over the following weeks, the Covid-19 epidemic
spread out throughout the continent, and, at the end of May, high levels of death ratio were also recorded in
other European countries as the United Kingdom (Northern Ireland and North East England), Belgium (Brus-
sels region) and Sweden (Stockholm). A similar picture is shown by Map 3 that presents the spatial diffusion
of the Covid-19 epidemic at the end of the first wave (end of July 2020). All in all, remote rural regions were
less affected. Except some Romanian and Polish regions, most of the regions located in the Baltic countries
and in Eastern and South Eastern Europe were spared by this first wave, the necessary measures having
been put in place before the virus entered these territories.
Nevertheless, it should be noted that the places where Covid-19 emerged in Europe are not solely based
on metropolitan logics (Map 1). Indeed, in addition to these globalised circuits, the virus has found favourable
development conditions in other places and this unexpected second phase arose as a game-changer and
shaped the peculiar regional geography of this first wave. In addition to global networks, Covid-19 found
favourable circulation conditions through super-spreading events which had an accelerating effect: a reli-
gious conference in Eastern France, a football game in northern Italy, carnival festivities in Western Ger-
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
10 ESPON // espon.eu
many, a night event in a ski resort in the Austrian Alps. Some entailed major regional outbreaks that ex-
plained most of the uneven distribution of fatalities between regions in some countries. For instance, a reli-
gious event gathered 2,500 people on a weekend in February 2020 was the cause of the first massive
outbreak of Covid-19 in France. On the 1st of April 2020, one-third of the deaths due to Covid-19 in the
country originated this region. 4 months later, they still represented 22% (Map 3).
All of these super-spreading events contributed to widespread relocation diffusion through interregional mo-
bility. The pandemic indeed spread to many different regions, with an intensity depending on the flows of
returnees from touristic and business trips abroad. Italy as a source of infections was important since the
north of the country faced a drastic COVID-19 outbreak beginning in mid-February, and this country is both
one of the most connected regional economy of Europe and a trendy touristic destination in winter. Jumping
in space over large distances, Covid-19 diffusion via mobile network societies caused extreme scaling of the
outbreak process in many European regions, strongly affecting the biggest metropolitan areas. From then
on, several regions in Scandinavia, the Benelux countries and the UK started being strongly affected (Map
2). These examples of relocation diffusion demonstrate the importance of network logics in the regional
development of the outbreak during this first phase.
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Map 1: Incidence of deaths (end of March) Map 2: Incidence of deaths (end of May) Map 3: Incidence of deaths (end of July)
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2.2 Understanding the kinetics of Covid-19
2.2.1 A spatial analysis of the outbreak
After the first cases were reported in Europe, some studies pointed out that European regions were not
equally hit but that strong differences existed between the peripheral ones where the infection rate remained
limited and the core ones where the rates reached high levels (Amdaoud et al. 2020, Bourdin et al. 2020).
The regional and local impact of the COVID-19 crisis is highly heterogeneous, with a strong territorial dimen-
sion that has significant consequences for crisis management and policy responses. Governments, on a
subnational scale, are responsible for crucial issues of containment measures, health care, social services,
economic development, and public investment, putting them in the front line of the crisis management
(OECD, 2020).
The analysis conducted in this report is based, on the one hand, on an explanatory analysis of spatial auto-
correlation which makes it possible to account for the level of dependence of the death rate linked to Covid-
19 in different places. On the other hand, we use spatial regression models to capture the diffusion effect of
the epidemic between neighbouring regions and the role exerted by territorial determinants in the spread of
the epidemic.
To test the existence of a spatial data clustering phenomenon, we apply the Exploratory Spatial Data Anal-
ysis (Box 1) which can provide useful summary information about the spatial arrangement of mortality rate
related to the Covid-19 epidemic.
Maps 4, 5 and 6 display the LISA of the Covid-19’s evolution between different dates and reveal distinctive
geographic patterning of the epidemic’s spreading. Between February and March (Map 1), we can easily
identify where the “hot points” of the epidemic were: Italy, Spain, Belgium and the West part of Germany
have high-high clustering, meaning that it was in these red areas that the progression of the epidemic was
the highest. The high-high clustering recorded in France had as starting point in the eastern region but rapidly
spread out from the East to the Ile de France region (the political and economic centre of the country) and
formed a big cluster. On the contrary, Central and Eastern Europe seemed to be spared (low-low clustering).
Through a visual analysis of map 1 compared to maps 2 and 3, some distinctive geographical patterns
appear. First of all, the lockdown seems to have had the expected effects since, for the April-May and June-
July periods, the evolution of the number of new deaths/100,000 inhab. is low in a large part of the areas
that were the most hit in February-March. It should be noted that over the April-May period, the United
Kingdom and Ireland experienced an explosion in the number of deaths (high-high clustering). This can be
explained by the late decision to implement a containment. Sweden, which did not implement any contain-
ment, recorded a significant increase in the number of deaths for the same period.
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BOX 1
Exploratory Spatial Data Analysis (ESDA)
Spatial autocorrelation is defined as the correlation of a variable with itself due to the spatial
location of the observations. It is said to be positive when similar values of the variable to be
studied are grouped together geographically: close geographical units are more alike than
distant units following Tobler's first law of geography (Tobler, 1970). Conversely, it is negative
when variables dissimilar to the variable to be studied are grouped together geographically:
close geographic units are different from distant units. Finally, the spatial autocorrelation is
equal to zero when the observations of the variable are randomly distributed in space (see
figure below).
Forms of Spatial Autocorrelation
Particularly, the LISA (Local Indicators of Spatial Association; Anselin, 1995) maps provide the iden-
tification of clusters or collections of similar geographical units, based on the indicator used. They
are used to identify hot spots or cold spots across space. Positive spatial autocorrelation is observed
in areas considered as high-high (i.e. high death rates in a region surrounded by high values of the
weighted average rate in the neighbouring regions), and low-low (low rate in a region surrounded
by low values of the weighted average rate in the neighbouring regions). There are also two forms
of negative spatial associations (i.e. association between dissimilar values); high-low (high rate in a
region surrounded by low values of the weighted average rate in the neighbouring regions), and
low-high (low rate in a region surrounded by high values of the weighted average rate in the neigh-
bouring regions).
The LISA indicator is expressed as follows:
=

  
where is the difference of the variable y in region i from the global mean (), is the differ-
ence of the variable y in region j from the global mean (), and  is an element of the Spatial
Weight matrix x which expresses for each observation (row) those locations (columns) that
belong to its neighbourhood set as nonzero elements. In this study, the specification of these ele-
ments are nonzero relies on the inverse of distance weight function such as  =1
where the
effect of observation j on i is a declining function of the distance between them.
Positive Spatial
Autocorrelation
Negative Spatial
Autocorrelation
No Spatial
Autocorrelation
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Map 4: Evolution of deaths (February-March) Map 5: Evolution of deaths (April-May) Map 6: Evolution of deaths (June-July)
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2.2.2 The determinants of regional disparities in terms of mortality
In the following section, we apply spatial regression models to our data, to capture the diffusion effect of the
epidemic between neighbouring regions and the role exerted by territorial determinants in the spread of
Covid-19. Spatial and geostatistical techniques have been used widely in several contributions dedicated to
viruses, such as Hepatitis C infection, MERS-CoV, H1N1 influenza, HIV, dengue, and recently Covid-19
(Bourdin et al., 2020; Amdaoud et al., 2020).
Clinical data and surveillance reported, at an early stage, two important aspects of the epidemic: (i) there is
a higher risk for the elderly male population (over the age of 65) and for patients suffering from co-morbidities
such as diabetes, hypertension, chronic respiratory diseases, cancer, and cardiovascular disorders to die
from Covid-19 (Du et al., 2020), and (ii) a strong territorial dimension in SARS-CoV-2 pandemic spread
(OECD, 2020). In this train of thought, a multitude of local features (social, demographic and economic ones)
have been considered as potential determinants for the observed variation in the Covid-19 outcome.
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BOX 2
Spatial econometrics models
The econometric specification considered in this research takes the Ordinary Least Squares (OLS)
linear regression model as its starting point:
Y = X+ (1)
Y is the dependent variable (Covid-19 death rate). X stands for the explanatory variables used, is
the vector of parameters to assess, and is the error term. When a spatial autocorrelation is ignored
in the model specification but is present in the data generating process, the OLS estimators are
biased and non-convergent.
The spatial autoregressive model (SAR) consists in correcting this bias by integrating an "endoge-
nous shifted variable" WY in the model (1) and taking into account the spatial autocorrelation related
to the variable Y. The model is written as follows:
Y = Y + X+ (2)
WY is the shifted endogenous variable for the inverse distance matrix W, is the autoregressive
parameter indicating the intensity of the interaction between the observations of Y. In this model,
the observation of Y is partly explained by the values of Y in the neighbouring regions.
A second way of incorporating spatial autocorrelation in econometric models is the Spatial Error
Model (SEM) which consists in specifying a process of spatial dependency of errors in a regression
model. The SEM model is defined as follows:
Y = X+ with = W+ u (3)
The λ parameter reflects the intensity of the interdependence between the residuals of the regres-
sion and u is the error term. Omitting a spatial autocorrelation of errors produces unbiased but
inefficient estimators, so that the OLS-based statistical inference will be biased.
These two models can be combined to produce a general model called Spatial Autoregressive Con-
fused (SAC). It includes a lagged endogenous variable and a spatial autocorrelation of errors. The
model is written as follows:
Y = Y + X+
= W+ u (4)
There are different approaches that can be used to choose models. We have adopted the so-called
bottom-up approach, which consists in starting with the non-spatial model. Tests of the Lagrange
multiplier (Anselin et al., 1996) then make it possible to decide between the SAR, SEM, SAC or
non-spatial models.
The importance of local parameters in explaining the health of populations and mortality rates is widely
demonstrated in the literature (Cambois and Jusot, 2007). This dimension is also found in the declaration of
the Millennium Development Goals signed in September 2000, which underlines the importance of the fight
against poverty and the improvement of the care conditions on the reduction of mortality especially those of
children. In addition, these factors have been associated with other epidemics in the past, and there is no
reason why this should not be the case for this new disease. For instance, Linard et al. (2007) found that
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environmental and socio-economic factors play a crucial role in determining the spatial variation of Puumala
and Lyme borreliosis infection in Belgium. Stanturf et al. (2015) come to the same observation in their study
on the Ebola epidemic in 2014 in three West African countries (Liberia, Sierra Leone, and Guinea). Results
shows that taking these contextual elements into account is essential in the study of health-related questions
and that their omission would lead to a partial understanding of the phenomena studied, as underlined by
Geronimus et al. (1999) or, more recently and regarding Covid-19 in Italy, Bayer and Kuhn (2020). The latter
thus assume that family structures and the presence of several generations within the same household differ
according to the regions and would thus explain the geographic differences observed.
We venture the hypothesis that spatial dependence between the regions across different channels explains
the variety in the spread of Covid-19. To analyse the unequal spatial diffusion of the epidemic across Europe,
we use for that an original dataset covering 377 European regions in 28 countries. For each region, we
calculate an indicator to describe the prevalence of the pandemic in the territory. It is defined as the ratio
between the number of Covid-19-related deaths over the number of inhabitants. Data on Covid-19-related
deaths were collected at three moments of the pandemic’s first wave (end of March, end of May and end of
July).
Table 1: Definition and source of the variables
Variable Definition Year Source
Covid death rate 10.000*(cumulative deaths due to Covid-19/Popula-
tion)
2020 WHO and National
Health Ministers
Population density Total population per km² (log) 2019 Eurostat
Share of the population
aged 65 and over
Number of people aged 65 and older over total
population
2019 Eurostat
Life expectancy Life expectancy at birth rate 2019 Eurostat
GDP per capita Gross domestic product (GDP) per capita at current
market prices
2016 Eurostat
Poverty rate Percentage of people at risk of poverty 2019 Eurostat
Hospital beds 100 000*(number of hospital beds/Population) 2017 Eurostat & NHS
Governance Index of Good Governance derived from the Euro-
pean Government Quality Index (University of
Gothenburg)
2009 ESPON
Education Part of population aged 25-64 with tertiary educa-
tion level (levels 5-8). The variable equals one if the
value is greater or equal to the mean
2019 Eurostat
Region typology Urban/rural typology: Variable that classifies re-
gions as predominantly urban, intermediate, or pre-
dominantly rural regions.
2020 OECD
We distinguish different groups of indicators that may explain the spatial heterogeneity in mortality due to
the Covid-19 pandemic: demographic & concentration determinants (Population Density, Share of the pop-
ulation aged 65 and over, Life expectancy), income & wealth determinants (GDP per capita, Poverty Index),
health care determinants (hospital beds), an index that proxies the governance quality in the regions and an
indicator describing the region typology (Urban, Intermediate, Rural). Table 1 shows the definition and
source of the variables.
This econometric analysis provides spatial dynamic information about the spread of Covid-19 in the Euro-
pean regions and identifies socioeconomic factors associated with mortality (Table 2).
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Table 2: Empirical results for Covid-19 deaths rate determinants
N.S. = Not Significant
Demography is a factor of spatial differentiation in health and, as such, is the subject of systematic analyses
to explain the international (Hu and Goldman, 1990) and regional (Frohlich and Mustard, 1996) differences.
Among the demographic variables (Population density, Share of the population and Life expectancy), only
one is significant. We observe that a higher proportion of people with high expectancy life causes a higher
prevalence of Covid-19. This result confirms the risk factor of age frequently mentioned in the literature
(Wilson et al. 2020) and the critical role of demography, particularly, how the age structure of a population
may help explain differences in fatality rates across regions and how transmission spreads. According to
WHO Regional Office for Europe (2020), 95% of the people who died from Covid-19 in the European regions
are over 60, and more than 50% of all the deaths were people aged 80 years or older.
Wealth and income dimensions play a major part in driving the pattern of Covid-19 cases and deaths around
the world. An important literature points out the responsibility of poverty in the prevalence of epidemics. Low-
income are linked to living and housing conditions. For example, accommodation that is too small or over-
populated has been related to a high risk of infection from several pathogens, such as tuberculosis or Ep-
steinBarr virus (Sannigrahi et al., 2020). GDP per capita is also another aspect used in modelling health
outcomes, health system performance and mortality trends (Markowitz et al., 2019).
Regarding our results, we do not find sound evidence on the effect of wealth and income determinants (GDP
per capita and Poverty index) over the period considered in the analysis. Thus, our results do not bring
additional evidence about the aggravating role of inequalities and social exclusion in the spreading and
intensity of the epidemic. This can be partly explained by the level of aggregation of our study (NUTS2 or
NUTS3 levels). If we had focused on the metropolis scale (comparison between urban districts), maybe we
would have found some results confirming the importance of economic variables.
The quality of the health care system may also explain the differences between regions. For many countries
in Europe, the local scale is the relevant level in public health organization. This reason clearly argues in
favour of a geographic approach of the Covid-19. On this subject too, empirical studies report firstly that
well-structured healthcare resources positively affect a government’s capacity to deal with Public health
emergencies as major epidemics (Gizelis et al., 2017). Secondly, the healthcare infrastructures also have a
considerable impact on the government's ability to rapidly detect, diagnose, and report the new infections
(Hogan, et al., 2018). Health care determinants reflect the Government's and regional health spending prox-
ies by the number of hospital beds. The variable exerts a negative influence on Covid-19 prevalence (after
the month of March) displaying greater susceptibility to the virus infection, confirming that regions in which
the quality of health system is low are more likely to have a more significant mortality associated with Covid-
March 31st May 31st July 31st
Population density N.S. N.S. N.S.
Share of the population aged 65 and over N.S. N.S. N.S.
Life expectancy Positive Positive Positive
GDP per capita N.S. N.S. Negative
Poverty N.S. N.S. N.S.
Hospital beds N.S. Negative Negative
Governance index Negative Negative Negative
Education Negative Negative N.S.
Intermediate region N.S. Positive Positive
Urban region N.S. Positive Positive
Neighbourhood effects +++ +++ +++
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19. With a need for hospitalization in intensive care units for >15% of infected patients (Qiu et al. 2020), the
number of available beds has been a critical issue in the management of the Covid-19 emergency and the
death rate among areas (Vinci et al. 2020). Adjunctive pharmacologic therapies are, of course, a critical
resource to face the pandemic but on the short term and as long as no vaccine is available, supportive care
services determine the treatment of infected people. Over the studied period, the capacity of the medical
system to treat patients with Covid-19 obviously depends on critical care beds. At the country level, re-
sources shortening has been shown to be a critical issue when the number of COVID-19 severe cases are
higher than the available resources (McCabe et al., 2020). It is also an issue on the regional scale as demon-
strated by Guzzi et al. (2020) according to the availability of hospital structure resources which should be
managed to limit the spreading of the disease.
Regarding different studies (Putnam, 1998; Alesina and Ferrara, 2000), education is one of the most im-
portant determinants of social capital. Education reflects an orientation towards the future by strengthening
human capital and social capital for economic and social development. Schooling spreads knowledge the
basic component of human capital and cultivates social norms, the core of social capital. Through civil
education from schooling, students learn the basic standards and responsibilities in society and practice in
a peer culture that shapes values such as reciprocity, respect and trust. It is for this reason that we introduce,
in this study, the Education variable (Part of population aged 25-64 with tertiary education level) as a proxy
of social capital. Our findings show that death rates due to Covid-19 are negatively correlated with social
trust. It is reasonable to consider that social trust functions similarly to institutional trust in that a crisis inten-
sifies the primary trust culture. People with low trust thus tend to identify negative aspects of ambiguous
situations (to consider that others do not respect the rules and therefore try to bypass them, amplifying the
severity of the epidemic). On the contrary, in places where a vast majority of citizens exhibits a high level of
confidence in others, rules are expected to be more respected by others. They are indeed more respected
by everyone, inducing a lower mortality rate. This sort of self-fulfilling process can explain the increasing
coefficients over the period under study.
With this finding, we confirm that an essential aspect of epidemic spreading is citizentocitizen trust, an
intangible asset capable of shaping the consequences of the Covid-19 phenomenon. Several sorts of trust
are at stake in such a process. Top-down trust between citizens and authority figures are commonly evo-
cated in the literature. This point has been enhanced by OECD which worries about the decline in confidence
in governments and member states and underlines that during all stages of the COVID-19 pandemic trust
in public institutions is vital for governments’ ability to respond rapidly and to secure citizen support. But,
beyond trust in institutions which are mostly national, horizontal, and local, trust also matters. Social rela-
tions, more influenced by local culture and habits, also shape the spreading of the disease, as mentioned
by Edelman (2020). Indeed, people willing to engage in protective behaviours and respect lockdown rules
depend on the belief that others act in the same way and, broadly speaking, on social capital, as shown by
Chuang et al. (2015). In a paper examining whether each of the social capital dimensions contributes to the
intention to adopt any of the health-protective behaviours during an influenza pandemic (wearing a mask or
washing hands), those authors show that relational trust (relationships between the trusting person and
another) is a more powerful predictor of compliance with recommended behaviours than calculative trust
(the other’s past behaviour), particularly in an unknown situation. The spreading of the virus could thus be
less effective in places where interpersonal relations and social trust are high than in low social trust regions,
as already mentioned by Habibov and al. (2017). Our results are consistent with research concluding that
the pandemic tends to shape trust and solidarity between citizens and, by the way, the degree of compliance
to the rules enacted by the government such as wearing a mask, maintaining social distance. This is espe-
cially true when it comes to wearing a mask, since it may cause various reactions, including mistrust, even
if it is widely agreed that wearing masks is a sensible thing to do (Sunstein, 2020). Knowing that this protec-
tion against Covid-19 has been shown to be a critical factor in the control of the epidemic, one can under-
stand that wearing it was more readily accepted in regions where people trust each other more, thus leading
to a lower contagion rate and, consequently, to a lower mortality rate. Our finding confirms those of Putnam
et al. (1993) who conclude that information and political decisions are not enough to ensure the success of
sanitary policies. Instead, they recommend mobilizingsocial capital' in the community as an informal mean
of action against the epidemic.This background help to adapt the measures to the context and to increase
their effectiveness.
The global spread of Coronavirus (COVID-19) has been accompanied by a wave of disinformation that is
undermining policy responses and amplifying distrust and concern among citizens. Around the world, gov-
ernments are leveraging public communication to counteract disinformation and support policy. The efficacy
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of these actions will depend on the way they will be integrated into open government principles, chiefly
transparency, to create trust in public institutions. It is in this context that we consider the governance quality
on the subnational scale as a fundamental indicator that could deeply influence the prevalence of the pan-
demic. Our findings confirm the hypothesis that a higher level of governance on the subnational scale re-
duced the death rate registered in the European regions significantly.
In the context of the Covid-19 pandemic, this type of intervention offers the dual advantage of supporting
the effective implementation of emergency measures and satisfying the need for clear and definitive infor-
mation. Public communication can also be deployed rapidly since, virtually, all governments have press
offices and digital channels in place. These structures are especially important in contexts where pre-exist-
ing mechanisms or regulations against disinformation are inexistent or weak. In order to be effective and to
foster public trust in governments, any activities conducted in this respect must be guided by the principles
of transparency, integrity, accountability, and stakeholder participation set out in the OECD Recommenda-
tion of the Council on Open Government (OECD, 2017). These considerations are extremely relevant in the
context of the lockdown imposed by several European countries. In response to the pandemic, several
national governments, during the first wave, implemented a lockdown related to some specific territories or
to the entire country. The lockdown corresponded to a set of measures that implies travel restrictions (na-
tional and international traffic) and social distancing requirements, such as closing schools, public spaces,
shops, shopping malls and restaurants. Italy was the first to lockdown the entire country on the 11th of March,
followed by Spain on the 14th of March, France on the 17th of March, the United-Kingdom on the 24th of
March, and many other European countries. These measures are enforced to minimise the spread of the
coronavirus disease transmission and reduce the peak healthcare demand, especially respiratory support
with the objective to flatten the infection curve.
2.2.3 A spatial heterogeneity of the influence of the different factors
Despite the global results obtained with the previous models, it’s reasonable to assume that some variable
may have a positive effect in some regions, while negative effects are observable in others. The Geograph-
ically Weighted Regression (GWR) is a local estimation technique capable of coping with the spatial heter-
ogeneity issue (Box 4). The GWR relaxes the supposed spatial stationarity in global regression (OLS, SAR,
SEM, etc.) in the relationships between explanatory variables and dependent variable. Thus, it allows for
the parameters to vary over space.
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BOX 3
Geographically Weighted Regression (GWR)
GWR is based on locally linear regressions in order to obtain estimators at each point in space. The
estimation procedure is based on a Gaussian principle in which the observations which are the clos-
est to the regression point have greater weights than the other observations.The GWR model is
formalised as follows:
= 
(,)+
where (,) is the location in a geographic space of the observation. When calibrating the GWR
model, it is assumed that the observed data which are close to an "i" point have a greater influence
in the estimation of the values of (,) than the data located far from . The equation therefore
measures the relations inherent to the model around each point . In GWR, an observation is
weighted with respect to its proximity to point . The choice of weighting scheme is an important
step in model specification since it implies that the observations which are closest to the location
(,) have a greater influence over the estimated parameters of this location than the observations
that are the furthest away. So, weight (,) can be considered as a continuous, ever-decreas-
ing distance function (,). This is the Gaussian function which is used the most:
=1(/)
where > 0 and is defined as being the bandwidth of the function or, in other words, the radius
of the sphere of influence for point .
According to the results, four determinants (Life expectancy, Poverty, Hospital Beds, Governance) offer a
heterogeneous effect on the Covid-19 Death Rate registered in the European regions. The results are sum-
marised in the following figures. Generally speaking, we observe an East/West differentiation logic.
Map 7: GWR-Hospital beds Map 8: GWR-Life expectancy
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Map 9: GWR-Poverty Map 10: GWR-Governance Index
The visualisation of the GWR model’s coefficients made possible by this method highlights the spatial vari-
ations of the parameters. GWR enables us to evaluate where and how the relation between selected ex-
planatory variables and the dependent variable are the highest. From the figures below, we can observe
that the geographic distribution of the estimated coefficients is not random.
The map showing the effects of the number of beds per 100,000 people suggests a negative association
with mortality due to the Covid-19 pandemic. The magnitude of the coefficient varies considerably over
space. It is strongly high in Portugal, Spain, the south and the east of France. This relationship is not sur-
prising since the number of hospital beds is lower compared to the German regions, for example. This
suggests the importance of accessibility to care beds in the fight against the pandemic. This finding is con-
sistent with that of Bauer et al. (2020).
Life expectancy appears to have a positive influence over the severity of the coronavirus disease particularly
in the Spanish and Portuguese regions (see Figure 8) and in a slightly lesser extent in Germany, Austria,
the Netherlands and Sweden. It can be assumed that in the regions where life expectancy is higher, the
elderly population is more present.
As illustrated in figure 9, poverty is a substantial factor in the description of the geographic distribution of the
COVID-19 incidence rates in several regions of France, Italy, Germany, Ireland and the UK. Poor people
with a low level of educationare more likely to have a low level of health literacy and are therefore likely
to increase the transmission of Covid-19. In effect, understanding the responsibility of complying with the
recommended measures such as social distancing is crucial to prevent the spread of the virus across the
regions. This aspect is documented in Singu et al. (2020)
The governance quality is an influential factor in the explanation of the disease’s incidence rates across the
regions in Europe (i.e., France, Germany, Belgium, Netherlands, the UK). Local government quality as a
proxy of capacity to deal with Public health emergencies such as the Covid-19 outbreak contributes to re-
duce the potential mortality due to Coronavirus. Restrictive measures such as lockdown are a manifestation
of the measures taken by regional governments to minimise the transmission spreading of the new corona-
virus and to contain the peak healthcare demand.
2.2.4 Has the lockdown been effective?
We conducted an additional analysis with Ordinary Least Square Regression (OLS) to determine whether
the lockdown measures effectively minimise the spread of the Covid-19 transmission or not. Based on the
results obtained with the spatial model, we ventured the hypothesis that the effectiveness of the restriction
measures depends on the governance quality on the local scale (government principles, chiefly transpar-
ency, to create trust in public institutions).
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BOX 4
Ordinary Least Square (OLS) Model
The OLS model we consider is detailed in the following equation:
  =+++ +  +
The dependent variable indicates the Cumulative Covid19 death rate in region i during week t over-
population, multiplied by 10000, with i=1, …380 regions and three dates for our analysis.
Lockdown is a dummy variable equal to 1 for European regions where a full lockdown has been
voted by the government, 0 otherwise.
Govi is the measure of institutional quality on a regional scale.
We add the interaction term Lockdowni*Govi to the model since we assume that the effect of the
lockdown on the mortality rate due to Covid19 is different concerning the regional government qual-
ity.
The regression controls for all the other variables considered in the spatial regression (Table 3).
The results of this regression are shown in table 3. The empirical evidence shows that the lockdown
measures adopted by several European countries have been significant since they reduced cumulated
deaths. Indeed, both the coefficient and the significance level of the variable “Lockdown” increase over time
since the adoption of the measures, exhibiting its real effectiveness in the medium-long term.
It is interesting to note that the interaction term enters with a negative sign and a high significance level in
all the regressions, with a relatively high coefficient. The econometric findings would indicate that the effect
of the lockdown measures on deaths is different depending on different values of “government quality”. More
specifically, for regions with very weak government quality.
Table 3: The effect of the Lockdown and government quality on the mortality rate due
to Covid19
March 31st May 31st July 31st
Lockdown Negative Negative
Negative
Governance Negative Negative
Negative
Lockdown Governance Negative
Negative
Negative
2.2.5 Advantages and limitations of spatial techniques
The relations between values observed in neighbouring territories have long been a focus for geographers.
Waldo Tobler summed up the problematic in a statement often referred to as the first law of geography:
“Everything interacts with everything, but two nearby objects are more likely to do so than two distant ob-
jects”. The availability of localised data, combined with the spatial statistics procedures now pre-pro-
grammed into multiple statistical software tools, raises the question of the way this proximity can be modelled
into economic studies. The use of spatial econometrics tools has become particularly popular in studies in
recent years (Arbia and Paelinck, 2003; Le Gallo et al., 2003).
The motivation for using spatial econometrics tools is obvious: taking regional units as “isolated islands”
(e.g. by using non-spatial estimation techniques) may lead to the wrong results, and in the presence of
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spatial effects in regression analysis, the OLS estimations may be biased or inaccurate1. Spatial autocorre-
lations of residuals with spatial data, i.e. dependency between nearby observations, are quite common. This
dependency in the observations may either impair the OLS method (the estimators will be unbiased but less
precise, and the tests will no longer have the usual statistical properties), or produce biased estimators. If
the model omits an explanatory variable spatially correlated to the variable of interest, then omitted variable
bias is said to occur. In addition, comparing multiple spatial econometric models leads to question the un-
certainty of the data-generating process, which is never known, and verify the robustness of the results.
As spatial autocorrelation is measured based on a comparison of the value of an individual variable with
that of its neighbours, the definition of the neighbourhood will have a significant impact on the measurement
of spatial autocorrelation. Codifying the neighbourhood structure, the larger the planned neighbourhood is,
the greater the number of neighbours is considered, and the greater the probability that their average will
be closer to the population’s average is, which may lead to a relatively low value for spatial autocorrelation.
A change in scale can also have implications when measuring spatial autocorrelation. The term MAUP
(Modifiable Areal Unit Problem) introduced by Openshaw et al. (1979) is used to describe the influence of
spatial breakdown over the results of statistical processing or modelling. Arbia et al. (1996) speak of a “sec-
ond law of geography”.
More precisely, the irregular forms and limits of the administrative levels that do not necessarily reflect the
reality of the spatial distributions studied are an obstacle to the comparability of the irregularly distributing
spatial units2. According to Openshaw (1984), MAUP is a combination of two distinct but similar problems:
The scale problem stems from a change in the information generated when a set of spatial units is
aggregated to form smaller and larger units for the needs of an analysis or due to data availability issues;
The aggregation problem or zoning stems from a change in the diversity of information generated by
the various aggregation schemes which are possible on the same scale. This effect is a characteristic
of administrative partitioning and increases the scale effect.
The choice of the aggregation level is thus of paramount importance in any spatial econometric analysis.
Some geographers recommend adopting a multi-scale approach to study the diversity of spatial aspects
within a single phenomenon. However, in general, there is no solution to the MAUP problem. This aspect,
in some way, can limit the analysis.
1 For example, in the case of the European regions, we can expect that the infection rate of a region will be influenced by
those of the neighbouring regions. Similarly, a COVID-19 outbreak in a given region may be correlated with outbreaks in
neighbouring regions if unobserved variables display spatial dependence.
2 For instance, it is perfectly possible, when analysing the economic convergence process of a set of regions, to observe
convergence on the European NUTS-2 scale, and, conversely, divergence on another scale (e. g. the NUTS-3 scale).
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3 Overview of policy answers to Covid-19
on the regional/local scale
3.1 Typology of the measures
3.1.1 General context
In the first months of 2020, COVID-19 has affected the lives of millions of people around the world. The
pandemic has led national and local governments to operate in a context of uncertainty, and have to deal
with difficult trade-offs given the health, economic and social challenges.
Beyond the health emergency and the human tragedy it represents, the disease triggered a social, human
and economic crisis. The severe lockdown and social distancing policies put a stop on global economic
growth. This led to a declining economic activity, a contraction of consumption and massive job losses.
In the second quarter of the year 2020, still marked by COVID-19 containment measures in most Member
States, seasonally adjusted GDP decreased by 12.1% in the eurozone and by 11.9% in the EU, compared
with the previous quarter, according to a preliminary flash estimate published by Eurostat, the statistical
office of the European Union. Among the Member States, whose data are available for the second quarter
of the year 2020, Spain (-18.5%) recorded the highest decline compared to the previous quarter, followed
by Portugal (-14.1%) and France (-13.8%). Lithuania (-5.1%) recorded the lowest drop (Eurostat, 2020).
Regions and cities had to face food insecurity among vulnerable populations, overall declining consumption,
increasing numbers of small and medium companies at risk of bankruptcy, high unemployment rates, and
declining public revenues. Consequently, regional and local authorities found themselves in the front line of
the fight against the pandemic. They had to take measures to cope with the pandemic and the subsequent
crisis without having a pre-defined plan to tackle such situations. Furthermore, each city and region found
itself in a particular and unique situation that required tailored responses to face the variegated conse-
quences of the pandemic. Hence, a myriad of first policy responses have been taken by cities and regions
throughout Europe during the first wave of the pandemic, and the most inspiring measures have already
caught the eye of the media, policymakers and researchers.
However, previous studies dealing with public policy against COVID-19 mostly focused on analysing and
comparing policy measures on a national scale (e.g. OECD, 20203; European Centre for Disease Prevention
and Control, 20204; Eurofund, 20205 etc.), whilst those taken on the regional and the local scale were rather
overlooked. A few exceptions include reports from OECD and UN, which have greatly advanced the
knowledge on first policy answers on the regional and the local scale. These reports paid a special attention
to measures taken by urban areas, considering that cities were and still are in the front lines of the Covid-
19 crisis (OECD on City Policy responses6; UNESCO on learning from cities’ responses to Covid-197; UN
3 OECD, “The Territorial Impact of COVID-19: Managing the Crisis across Levels of Government” (OECD, June 16, 2020),
https://www.oecd.org/coronavirus/policy-responses/the-territorial-impact-of-covid-19-managing-the-crisis-across-levels-
of-government-d3e314e1/.
4 European Centre for Disease Prevention and Control, “Data on Country Response Measures to COVID-19,” March 11,
2020, https://www.ecdc.europa.eu/en/publications-data/download-data-response-measures-covid-19.
5 Eurofund, “COVID-19: Policy Responses across Europe” (Luxembourg: Publications Office of the European Union, June
24, 2020), https://www.eurofound.europa.eu/publications/report/2020/covid-19-policy-responses-across-europe.
6 OECD, “Cities Policy Responses” (OECD, 2020), https://www.oecd.org/coronavirus/policy-responses/cities-policy-re-
sponses-fd1053ff/.
7 UNESCO, “Urban Solutions: Learning from Cities’ Responses to COVID-19,” 2020, https://en.unesco.org/urban-solu-
tions-Learning-from-cities-responses-to-COVID19.
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on a Policy Brief on COVID-19 in an Urban World8), whilst regional policies have mostly been ignored.
Overall, great emphasis has been put by various organisations on the collection of city responses to the
ongoing crisis. However, systematic comparative approaches that facilitate cross-regional and cross-city
policy learning have not been conducted yet. Consequently, there is still a need for having an overview on
the nature of local and regional policy answers across the EU, as well as a need for a proper understanding
of territorial commonalities and differences in tackling the consequences of the pandemic.
Consequently, the aim of this second part of this ESPON project is to provide quality evidence of policies
implemented by EU’s cities and NUTS3 regions during the first wave of the pandemic. This research work
aims at assessing whether EU regions have taken steps to reinvent themselves and accelerate the transition
to a green, sustainable, digital and inclusive Europe or not. To what extent did European regions and cities
take policy measures that could be considered as resilient? Being resilient in such circumstances is partic-
ularly important as the current crisis brought back to the surface, not only significant challenges but also
new opportunities. Furthermore, this research work addresses two distinct objectives: a) delivering an over-
view of first policy responses on the regional and the local scale across the EU by developing a new typology
of policy measures based on the latter’s nature (resiliency) and temporality; b) unveiling the differences and
commonalities (from one type of territory to another) in tackling the consequences of the pandemic. It is
possible by exploring three dimensions regarding the causes of variation in incidences and the nature of the
measures taken i) the regional GDPPC level, ii) the Covid-19 mortality rates, and iii) the type of territory
(capital city, regional city, predominantly urban region, intermediate region, predominantly rural region).
This systematic approach is further justified by the fact that there are lessons to be learned from comparing
the approaches taken by various types of regions and cities across the European Union. Furthermore, it is
worth mentioning that, overall, the focus of this research work was not set on the basis of restriction
measures, but rather on actions designed to ease the impact of restrictions. Hence, the results could be
translated into a potential guideline for future regional and urban policy strategies during the second wave
of the pandemic, as well as during the recovery phase of the crisis. Methodological aspects of this second
part of the study are reported in the Appendix section.
A total of 477 local and regional policy measures have been collected and analysed following the 35 case
studies. Results show that, overall, during the first wave of the pandemic, the policy measures taken by local
and regional authorities are overwhelmingly defensive, with mitigating and compensating measures ac-
counting for 73.6% of all the measures taken (Table 4).
Table 4: Frequency of the measures according to their nature
Type of measure
Number of measures
Share (%)
Defensive
Mitigate
351
73.58
Compensate
50
10.48
Offensive
Circumvent
65
13.63
Exploit
11
2.31
Total
477
100.00
Source: first 35 factsheets
However, significant differences can be noticed between public action areas (Figure 1). Highest shares of
adaptive and transformative strategies concern the ‘daily way of life and work’ area, including culture, leisure,
education, mobility and public administration. Measures taken in this area display the highest share of cir-
cumventing initiatives, suggesting that regions and cities have mostly aimed at continuing daily activities via
alternative ways that circumvent the negative effects of the pandemic and lockdowns. A large majority of
8 UN, “Policy Brief: COVID-19 in an Urban World,” 2020, https://unsdg.un.org/resources/policy-brief-covid-19-urban-
world.
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these measures involve turning to online and digital solutions. A still significant, but lower share of offensive
and proactive measures was found in the ‘support to economic actors and recovery’ area, in which the
highest share of exploiting measures is to be found (5%).
On the contrary, the offensive measures display the lowest shares in the ‘support to vulnerable populations’
area, suggesting that, despite significant mobilisation to help the poor, the homeless, vulnerable women,
migrants and minorities, few of these policy measures are fundamentally transformative and, hence, few of
them are expected to last.
The temporal perspective of local and regional policy measures further suggests that ‘economy’ and ‘daily
life and work’ are the areas that are most likely to undergo significant changes in the long run (Figure 2).
They display the highest shares of policy measures that are aimed to be held in places in the post-pandemic
era (though only 11%). On the contrary, measures to support the vulnerable populations are overwhelmingly
short and medium term-oriented.
Figure 1: Frequency of local and regional policy measures classified according to
their nature and sector (N=477)
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Figure 2: Measures classified according to their nature and their temporal
perspective
3.1.2 Public Health Security – overview of the local and regional policy
measures
Overall, the measures aiming at increasing the health security during the COVID-19 pandemic underscore
the general answer, which consisted in easing the first effects of the shock. Two major outcomes emerge at
first glance: a) the vast majority of the measures in this category, regardless of the territorial classification
used for this study, aims at mitigating the impact of the pandemic, with very few actions addressing more
ambitious strategies (compensating or circumventing); b) most of these measures are short-term orientated,
designed to cover the period of the pandemic exclusively, with limited repercussions afterwards.
Therefore, these actions were taken for the most part during the initial weeks, in March, April or May, and
covered specific strategies for reducing the spread of the virus and the protection of the inhabitants (cleaning
and disinfection of public spaces and public transport vehicles, facemasks becoming mandatory in public
space). Health systems were of great importance during the first weeks of the pandemic; they benefited
from several dedicated measures like the purchase of supplementary medical equipment (Calabria, Kaliski,
Braila), or various reorganisations of health care services (Bordeaux, Cluj, Skane). Most of the compensat-
ing measures focus on the reduction of the devastating impact of the pandemic on healthcare systems.
Therefore, the most common actions undertook by policy-makers call for recruitment of additional medical
staff (Skane, Havelland, Dalarnas), new or temporary hospitals (Bordeaux, Cluj, Mantova, Cuenca). These
measures cover the whole European Union, regardless of geographical position, type of region or initial
impact of COVID-19, being instead an instantaneous, responsive, strategy that particular territorial units felt
compelled to adopt, most likely based on an evaluation or prognosis of the virus spreading.
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
ESPON // espon.eu 31
Figure 3: Examples of measures taken by European cities and NUTS-3 regions
during the first wave of the pandemic in the Health security area
Mitigating
Compensating
Circumventing
Exploiting
Cleaning/Disinfection of
public spaces
Mandatory use of masks
Increasing testing capacity
(Facilitating the) purchase
of protective equipment
(Stimulating) local produc-
tion of protective equip-
ment
Creation of a crisis man-
agement unit
Tensile structures / new
temporary hospitals/ new
clinics / facility extension
for COVID-patients
(Support for) recruitment
of medical staff/ volunteers
in the medical sector
Development by Region
Skåne (SE) of a self-as-
sessment online test to be
used by the population to
test for cold symptoms.
Remote follow up of pa-
tients using new digital
services (the patients
themselves send their val-
ues via a web application
that is followed up by
healthcare professionals)
(Skåne, SE).
Introducing medical tele-
consultation and/or e-pre-
scription (Warszaw, PL;
Bordeaux, FR)
Financing the University of
La Mancha (30,000 EUR)
to study the properties of
local purple garlic as an
anti-inflammatory that
could help in the treatment
of Covid-19 (Cuenca, ES).
Source: first 35 factsheets
It is worth noticing that several regions adopted slightly innovating measures, which denote a more proactive
approach, e.g. teleconsultations and online follow-up of patients (Bordeaux, Skane), self-testing for non-
essential consultations (Skane). These measures are rather circumventing, trying to continue the provision
of medical services while ensuring a proper responsive strategy to the COVID-19 pandemic.
The existence of a sole measure classified as ‘exploiting’ is particularly interesting. Given the nature of
health security measures, this type of action denotes not only an innovating approach and a proactive strat-
egy but also an enterprising and capitalising method of local potential. The measure was undertaken by
Cuenca authorities who awarded a 30,000 EUR grant to the University of La Mancha in order to study the
proprieties of local purple garlic as an anti-inflammatory that could help in the treatment of COVID-19. While
the measure does not compete with the massive investments in medical treatments and the vaccine indus-
try, which were deployed last year, it shows an ingenious strategy to take advantage of local resources. The
investment is far from being indispensable to Cuenca authorities and could prove to be extremely profitable
if successful. In the worst-case scenario, this measure would represent an investment in local HEI. Its rep-
licability, its high profitability in case of success, and its proactive strategy make this action very susceptible
for replication.
3.1.3 Daily way of life and work overview of the local and regional policy
measures
The local and regional policy measures taken during the first wave of the pandemic suggest that the daily
way of life and workarea will undergo significant transformative changes in the future. Some of these
changes are most probably going to last and significantly impact culture, leisure, education, public admin-
istrations and mobility during the post-pandemic period (Table 5). First policy answers also suggest that
some cities and regions have taken the opportunity to enhance and stimulate positive transformations, by
adopting offensive and bold strategies aimed at sustainable and smart societal transformation, whilst others
did not.
The compensating and circumventing measures in the area of culture and leisure were very diverse. Some
cities and NUTS3 regions have circumvented the closure of cultural events by enhancing online cultural
services, such as online virtual tours of zoos (Warsaw-PL), museums (Leipzig-DE, Cuenca-ES), online
streamed cultural programs (Bologna-IT, Cuenca-ES) and performances of plays and classical concerts
(Warsaw-PL). Other local authorities physically brought small cultural events in the neighbourhood to allow
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
32 ESPON // espon.eu
the vulnerable populations to enjoy culture from home (Goteborg-SE), or help all the residents overcome
isolation difficulties where lockdowns have been implemented (Bologna-IT).
Direct financial support (including grants) for cultural associations, institutions and individual artists, along
with financial compensation for the organisers of cultural events have also been widely reported: some
public authorities delivered direct financial support to relief financial difficulties (Ghent-BE), whilst others
launched calls with new regulations enhancing innovative forms (including digital) which enable everyone
to enjoy culture (Goteborg-SE, Warsaw-PL).
Table 5: Examples of policy measures taken in the “daily way of life and workarea,
classified according to their assumed temporality
Short term Medium term Long term
Adaptation of urban public
transport frequency and
other services.
Contribution to companies
that incentivise employees
to go to work by bike.
Transforming neighbour-
hoods into culture venues
that host small theatre per-
formances, mobile storytell-
ers, monologues, thus bring-
ing culture to the vulnerable
populations’ home.
Offering online educational re-
sources to families with children,
to keep them entertained, edu-
cated and physically exercised
without leaving the house.
Using car lanes to create tempo-
rary bike lanes & more space for
walking in the most frequented
streets.
Municipal grants temporarily
changing their regulations to en-
courage (cultural) projects
opened up to digital and other
new forms of expression.
Long-term plan to digitalise the in-
stitutions (e.g. smart working, vide-
oconferencing, new functionalities
of the IT system for human re-
sources management, adopting
management software based on
Open Source Database architec-
ture, etc.).
New definitive bike lanes and in-
creased walkability.
Creation of opportunities for experi-
encing online culture (museum or-
ganising digital resources, libraries
borrowing digital books).
Investments in IT functionality for
schools (fibre optic data connec-
tion, Wi-Fi connection inside
schools, internal data security for
schools).
Source: authors based on Factsheets
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ESPON // espon.eu 33
Table 6: Examples of measures in the daily way of life and work area classified
according to their nature
Mitigating Compensating Circumventing Exploiting
Financial support to cul-
tural workers and cul-
tural organisations dur-
ing the pandemic;
Exemption from rent for
artists;
Exceptional advance
payments for cultural
agents;
Almost free advertising
on LCD screens in
trams for foundations,
associations and those
organising/conducting
educational or cultural
activities (Warszaw,
PL).
Financial support for lo-
cal arts and the culture
sector.
Financial support for
cultural events that were
cancelled.
Transforming neighbour-
hoods into culture venues
that host small theatre per-
formances, mobile storytell-
ers, monologues, etc. (Bolo-
gna, IT);
Aid package for actors in the
social, cultural and economic
life intended to cushion the
consequences of the corona
crisis and to gradually reacti-
vate city life;
Call for constituent munici-
palities aimed at promoting
art, exhibitions, and muse-
ums;
Initiation of consultations for
building a provincial agree-
ment for culture during the
pandemic time;
Light construction interven-
tions in schools (rearranging
the interior spaces) and buy-
ing new furniture to meet
anti-COVID measures.
Investment in new anti-
Covid-19 furniture and digital
technological equipment for
schools;
Providing all schools with
support through the Ghent
Education Centre;
Renting additional facilities
for schools to meet distanc-
ing requirements.
Creation of opportunities for expe-
riencing online culture (museums
organising digital resources, li-
braries borrowing digital books)
(Göteborg, Leipzig, Cuenca etc.);
Online guided tours for natural at-
tractions, parks, gardens, green
routes, zoos (Lisbon, Warsaw);
Performances of all cultural
groups to be recorded and up-
loaded online (Cuenca, ES);
Online cultural programme pro-
posed by theatres, museums, and
other cultural centres (Warszaw);
Traditional programmes aiming at
the promotion of local artists
online (Cuenca, ES);
Investments in IT functionality for
schools (Amsterdam, Mantova);
Offering online educational re-
sources to families with small chil-
dren to help and keep them enter-
tained, educated and physically
exercised without leaving the
house (Bologna, IT);
Make
digital educational platforms
more accessible;
Training courses for teachers re-
lated to the use
of digital tools and
remote teaching methods (War-
saw);
Investing in a
greater number of
projects related to
the film industry
(Goteborg);
Municipal grants
temporarily chang-
ing their regulations
to encourage (cul-
tural) projects
opened up to digital
and other new forms
of expression (Gote-
borg);
Support for cultural
digital actions
(Rhone);
Fast track funding
for art projects
reaching out citizens
digitally (Amster-
dam);
Extra, shortened
procedure for apply-
ing for a play street.
Source: authors based on Factsheets
Interestingly, in terms of urban mobility, the pandemic brought to the surface old differences between tradi-
tional transport planning and sustainable urban mobility planning. Some cities and NUTS3 regions (e.g.
Warsazw-PL, Iasi-RO) only focused on traffic issues by taking regular disinfection of public transport vehi-
cles, limiting the maximum number of passengers in public transport vehicles, closing the buses’ front door
to passengers until further notice to protect the drivers, etc. These measures have made public transport
less attractive and could have negative consequences on the modal share of public transport in the long
run. Others instead (e.g. Skåne-SE) chose a different approach and focused more on people by accompa-
nying traffic reduction measures with customer-oriented measures in organising public transport: adaptation
of pricing schemes to the pandemic, and rapid delivery of innovative services such as online applications
that show in real time the number of free seats on buses and trains.
Furthermore, some cities took the opportunity to accelerate the transition towards active and clean mobility
(Bologna-IT, Zaragoza-ES, Gent-BE), while others did not (Iasi-RO). Those who did focused on one or more
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
34 ESPON // espon.eu
of the following measures: (1) providing new definitive or temporary infrastructure for bicycles and increasing
walkability (Ghent-BE, Zaragoza-ES); (2) conducting marketing campaign to encourage biking (Bologna-
IT), (3) offering financial incentives to companies which encourage their employees to go to work by bicycle
(Bologna-IT).
Overall, measures targeting the daily way of life and workarea display the highest share of medium and
long-term impact measures (45%) as well as the highest share of offensive measures (38%), among the
four sectors of public action (Figure 1 and Figure 2). This suggests that most of the long-lasting changes
are to be expected in the daily life and work” area.
3.1.4 Support to vulnerable populations
Just like health security measures, the actions undertook in this category are overwhelmingly considered as
being mitigating (91%) and having a short-term perspective (69%). The most common measures involve
actions to support the homeless, seniors, unemployed people, vulnerable families and disabled people.
While the actions fall under the same category, each vulnerable group has specific preventive or institutive
procedures.
The measures in favour of the homeless deal almost exclusively with food as a support and, very commonly,
shelter (Bologna, Zaragoza, Iasi, Leipzing), they focus on a short-term or mid-term perspective, the main
strategy being to mitigate the initial impact of the virus and its immediate spread. The measures dedicated
to the older and disabled people are quite the same, encompassing actions such as home delivery services
(Zaragoza, Bologna, Guadalajara, Bordeaux, Gorj), the creation of dedicated helplines (Bologna, Bordeaux,
Goteborg, Poznanski, Zaragoza), or dedicated public transport vehicles (Iasi). The unemployed were
granted financial aids (Cuenca, Calvados, Hannover) or online training courses (Skane).
Table 7: Examples of measures to support vulnerable populations classified
according to their temporal perspective
Short term Medium term Long term
Accommodation and hot meals for the
homeless
Vouchers for those who are in serious
economic difficulty to buy food, per-
sonal and home hygiene products.
Protective masks for people in need
Activation of the “heatwave” plan to
ensure the follow-up of isolated peo-
ple.
Corona helpline for people feeling
lonely or under psychological stress
due to the excpetional situation
Phone line offering digital support
for people aged 65 and over
Building a bridge between people
who want to offer their help and peo-
ple in need.
Computers for vulnerable fami-
lies with young children
Providing tablets to disabled
people
Launch of online solidarity
platforms.
Source: authors based on Factsheets
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ESPON // espon.eu 35
Table 8: Examples of measures to support vulnerable populations classified
according to their nature
Mitigating Compensating Circumventing
Launch of a solidarity network of
volunteers collecting donations
and goods for the most vulnera-
ble citizens.
Financial aid for vulnerable peo-
ple
Protective equipment for vulner-
able people.
Initiative called "Culture to the
neighbourhood" aimed at reduc-
ing loneliness among the elderly
by allowing them to “enjoy live
music from their window or bal-
cony” (in 50 residential areas)
(Goteborg, SE).
Supplementary employees for
services aimed at vulnerable
populations.
Launch of online solidarity platforms
(Calvados, Amsterdam).
Launch of a social TV programme to
“promote solidarity fundraising in fa-
vour of the most fragile people in Bo-
logna and of three city public hospi-
tals” (Bologna).
Creation of a Labor Pact steering
group to support people who have lost
their job and need help to find a new
job as quickly as possible (Gent).
Organising “Meeting points on Fb” in
different areas of Goteborg (aimed at
seniors) (Göteborg).
Collecting and sharing free online
training opportunities for the unem-
ployed (Skane).
Source: authors based on Factsheets
While almost all actions are generally defensive, there are several measures and types of measures which
stand up for their mid-term or long-term approach. These measures represent positive examples of proactive
strategy regarding the support to vulnerable populations in case of pandemic:
Digital-based measures; the digitalisation of several services, functions and utilities proved to be highly
efficient for the vulnerable populations, regardless of the type of measure. Most examples cover tablets,
computers or various hardware improvement for digital access (Rhone, Calvados, Bordeaux), online
training courses (Emsland), online platform for solidarity solutions (Nièvre, Calvados). Their impact is
immediate; their replicability is relatively smooth, and represent long-lasting tools, which make them
imperious during a pandemic-like shock. Furthermore, the measures, while designed for fighting the
virus, they also contribute to the reduction of digitalisation drawbacks noticeable among vulnerable pop-
ulations throughout the European Union.
Socialising measures; one of the most encountered psychological distress among the citizens during
the lockdown was social alienation. It had severe repercussions upon the vulnerable populations, who
were forced to overcome a supplementary hurdle. Therefore, a series of measures designed for fighting
social alienation among the vulnerable populations stand up for their extreme appropriateness. They
both originate from Goteborg and represent solutions which are easy to replicate: communication sup-
port to vulnerable people and online meeting points.
3.1.5 Support to economic actors and recovery
The area measures in support of the economic actors and recovery have been dominated by measures
aimed at helping small and medium companies to overcome the crisis.
Measures taken by the local and regional authorities have been defensive in most cases (85%). Mitigating
measures are commonly dominated by fiscal measures aimed at relieving small and medium companies of
financial stress. Most of them aim at postponing, reducing or cancelling various local taxes and municipal
rents and have been taken by all the cities included in this study. On the other hand, the public authorities
which contemplated a compensating strategy wanted to regain the pre-crisis balance by taking measures
such as planning massive investments in public works to relaunch the economy, support to financial institu-
tions to raise the number of loans granted to companies, or give bars and restaurants the right to extend
their outdoor areas without any charge.
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
36 ESPON // espon.eu
Around 11% of the measures can be considered as long-term measures aiming at being effective right away
and beyond the pandemic period (Figure 2). Also, around 15% of these measures could be considered as
circumventing or exploiting, suggesting that some of the local authorities saw new opportunities and took
steps to adapt actively and even to exploit them sometimes (Figure 1). Some local authorities have mobilised
local staff and provided companies and entrepreneurs with consultancy so that they could find the right
support and explore every existing opportunity (e.g. Goteborg-SE), whilst others offered them financial sup-
port for the purchase of external consultancy so they can adjust operations and ensure long-term survival
on global markets, through digitalization, export subsidies for new markets, and implement a green transition
(e.g. Skåne-SE). Interestingly, some of the exploiting measures originate from rural, peripheral regions that
launched tourism marketing campaigns to attract tourists from neighbouring metropolises. Following such a
campaign, the Cuenca Province from Spain managed to increase the number of tourism arrivals by 23% in
July 2020 compared to the same period the previous year.
Table 9: Examples of measures to support economic actors and recovery classified
according to their nature
Mitigating Compensating Circumventing Exploit
Compensation for the
loss of income of self-
employed people or free-
lancers
Financial aid for constitu-
ent municipalities
Fiscal reliefs (on rents,
taxes, fees)
Absence of legal actions
against companies to re-
gain various rights
Providing consultancy to
companies regarding ur-
gent issues
Reducing the average
period of payment to
suppliers so that compa-
nies can have more li-
quidity.
Logistical support for
opening new distribu-
tion points
Planned investments
in public works to re-
launch economy
Support to financial in-
stitutions to rise the
number of loans
granted to companies
Extension of the out-
door areas in bars and
restaurant (for tables
and chairs) without
any charge.
Providing training
courses to enhance the
companies’ digitalization
Organising virtual B2B
(business-to-business) or
B2C (business-to-con-
sumer) events for local
businesses
Grants/Subsidies to help
businesses install hard-
ware and software re-
quired for teleworking
Launch of an online, tax
free platform (B2C) for
restaurants, to connect
them with clients
Support for the conver-
sion of local accommoda-
tion units into long-term
rentals
Using the Living Lab con-
cept to co-create solu-
tions for the gastronomy
sector.
Financial support to com-
panies for the purchase of
external consultancy so
that they can reinvent their
products / ensure long-
term survival / find new
markets
Promoting specific eco-
nomic sectors to exploit
the new opportunities cre-
ated by the pandemic (e.g.
proximity tourism in rural
areas)
Call for a study to find out
which areas of economy
will be most dynamic dur-
ing the next two decades
so that the city/region can
best position itself in the
global market.
Creation of a regional vehi-
cle to boost investment in
sustainable economic de-
velopment (of Invest-MRA,
Amsterdam).
Programme dedicated to
the recovery phase: smart
mobility, circular economy
and digital city (Amster-
dam).
Source: authors based on Factsheets
3.2 Understanding commonalities and differences in tackling
consequence of the pandemic
The 477 measures taken by the 35 territorial entities included in this study have finally been analysed to
determine whether their incidence and nature are significantly associated or not with the 1) GDP per capita,
2) Covid-19 mortality levels and 3) type of territory.
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
ESPON // espon.eu 37
0,0
5,0
10,0
15,0
20,0
25,0
High (>110) Average (80-
110)
Low (<80)
Mitigate Compensate Circumvent Exploit
Frequency of policy measures depending on
the GDPPC level (EU27=100) of correspond-
ing NUTS3 regions (average no of policy
measures per region/city). See also the Ap-
pendi section.
3.2.1 Incidence of first policy responses for each regional GDPPC
Results issued from our exploratory analysis show that both the incidence and the nature of the measures
taken by the local/regional authorities are significantly associated with the GDP per capita of the correspond-
ing NUTS3 region. NUTS3 regions and cities from NUTS3 regions with a GDP per capita higher than 110%
of the EU 27 average took, on average, two times more measures than their counterparts whose GDP per
capita is lower than 80% of the EU average (Figure 4 and Figure 5). Further research is needed to determine
whether the pattern is due to the fact that the rich have been hit by the pandemic harder, and hence more
measures were needed to help them cope with the shock, or whether the pattern is rather explained by their
greater financial capacities and creativity.
Figure 4: Total number of policy measures for each territory
Figure 5: Frequency of policy measures depending to the GDPPC level
Rich regions and cities (GDPPC>110% of the EU27 average) account for almost half of the circumventing
measures (49%) and almost 2/3 of the exploiting measures (63%) (Figure 6). However, they only represent
1/3 of the 35 territorial entities included in this study. Interestingly, we do not identify any exploiting measure
in region/cities where incomes are low (GDPPC less than 80% of the EU average). This suggests that,
although rich regions might have been most severally impacted by the crisis, they are also those which are
the most capable of adapting and of reinventing themselves for the post-pandemic world. Hence, in the
absence of efficient place-based policies, the current crisis is most probably going to increase territorial
inequalities.
0
50
100
150
200
250
High (>110) Average (80-
110)
Low (<80)
Mitigate Compensate Circumvent Exploit
Total number of policy measures taken by cit-
ies/NUTS3 regions classified according to the
level of GDPPC (EU27=100)
of the
corresponding NUTS3 region. Data computed
for a number of 35 regions/cities (11 with high
GDPPC, 13 with average GDPPC, 11 with low
GDPPC)
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
38 ESPON // espon.eu
Figure 6: Most resilient measures
Most resilient measures (circumventing and exploiting) occur in regions with higher levels of GDP per
capita
* Source: fact sheets and EUROSTAT (nama_10r_3gdp). * Data computed for 11 high GDPPC regions, 13 Average
GDPPC regions and 11 low GDPPC regions.
3.3 Incidence of first policy responses depending on the COVID-19
mortality levels
Regions that experienced high mortality levels during the first wave (> 12 deaths/100,000 inhabitants) were
significantly (almost two times) more active in taking on local policy answers. Furthermore, they add up 77%
of the most resilient measures (circumventing and exploiting ones). The explanation for the low incidence
of offensive/pro-active measures in regions where mortality is low, compared to those where it is high, is
subject to further research. However, one may hypothesise that the areas where the mortality rate is high
mostly correspond to those having a higher GDPPC, or that profound shocks enhance creativity and the
emergence of more adaptive and transformative initiatives.
Figure 7: Number and nature of local and regional policy measures according to the
mortality level
0
50
100
150
200
250
High mortality Low mortality
Mitigate Compensate Circumvent Exploit
Computed for 15 NUTS3 regions and cities
where mortality is high, and 13 NUTS3
where
mortality is low.
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
ESPON // espon.eu 39
Average no of measures per NUTS3
region/city.
Figure 7: Frequency and nature of local and regional policy measures according to
the mortality level
3.4 Incidence of first policy responses for each type of territory
Finally, the incidence and nature of the policy measures have been analysed in respect of each type of
territory. As expected, the highest incidence is encountered in capital and regional cities. This confirms the
commonly stated affirmation that cities have been in the front line of the pandemic, at least during the first
wave. Interestingly, the share of innovative and policy measures is not necessarily lower in intermediate and
predominantly rural regions, as one may expect. In fact, some of the predominantly rural regions proved to
be resilient by implementing circumventing and exploiting measures, such as Cuenca (ES).
0,0
2,0
4,0
6,0
8,0
10,0
12,0
14,0
16,0
High mortality Low mortality
Mitigate Compensate Circumvent Exploit
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
40 ESPON // espon.eu
Figure 8: Average number of measures for each type of territory
Data computed for 5 capital cities, 7 regional cities, 7 predominantly urban regions, 7 intermediate regions,
and 7 predominantly rural regions.
The low incidence of measures in predominantly urban regions is subject to further research, as it might be
due to either the small sample used or the fact that they host important metropolises that took the initiative
of implementing the largest number of measures.
0,0
5,0
10,0
15,0
20,0
25,0
Capital cities Regional cities Predominantly urban
regions
Intermediate region Predominantly rural
regions
Mitigate Compensate Circumvent Exploit
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
ESPON // espon.eu 41
4 Conclusion
Between February and July 2020, the uneven circulation of the virus across the European regions raised
immediate geographic questions regarding the socio-economic, environmental, financial and demographic
dimensions of the pandemic. Why were some areas hit harder than others? How could regional variations
be explained? Is it possible to identify links between the spread of the disease and territorial characteristics
likely to influence it? Another questioning whose aim is to understand what local policy solutions had been
implemented by cities and regions to counteract the socio-economic effects of the containment. The ambi-
tion of the ESPON study about the “Geography of COVID-19 outbreak and first policy answers in European
regions and cities” was to provide this first regional reading of the pandemic on the European scale.
As for any disease, mortality due to Covid-19 originates from individual characteristics. However, the local
economic and social context also matters, as it is recalled by the abundant literature (McCoy,2020) which,
considering the regional characteristics as determinants of the regional mortality, may help understand the
regional discrepancies observed from the beginning of the epidemic better. This paper sheds light on the
spatial heterogeneity between the European region and its persistence during the expansion, peak, and
beginning of decrease of the epidemic. It points out that, whereas some regions were severely hit by Covid-
19 forming clusters where the mortality rates were significantly higher than on average, other regions have
been spared, forming a belt of places where the mortality rate was low, mainly located near the eastern and
southern borders of Europe.
Our first conclusion is thus that Covid-19 is a global pandemic taking the form of intense local epidemics. In
addition to this peculiar spatial distribution of the mortality rates, our results lead us to conclude that if some
peculiar events (football matches, church gathering, arrival of infected people coming from already affected
non-European countries, etc.) the epidemic spread can be explained by a mix of factors describing the socio-
economic context. In addition to the classical demographic indicators, we found that the degree of urbani-
sation, on the one hand, and both governance quality and public health policies, on the other hand, were
tangible elements enabling us to explain the local differences observed. In addition to these aspects, the
introduction of an intangible asset as education (in some extent proxy of social trust) made it possible for us
to enrich the analysis by considering culture and interpersonal relationship. They are showed to influence
the mortality rate of Covid-19 and that their role increased over the period. According to our findings, com-
pliance to sanitary rules imposed to control the epidemic and to flatten the peak of infections to limit con-
gestion in hospitals depends on trust. This cultural aspect should thus be considered when deciding on the
implementation of sanitary rules because, beyond their expected theoretical effects, their real effect depends
on their actual use resulting from social trust.
Our research underlines the importance of regional differences in the mortality rates and their origin along
with the epidemic. This contribution can be of interest to policymakers and health agencies. The regional
dimension of public health policies, even in centralized countries such as France, requires efforts to disen-
tangle spatial aspects of epidemics to design policies adapted to the context in which they occur. Strength-
ening this local dimension is all the more essential for two main reasons. First, Covid-19, unlike other epi-
demics such as the flu, does not spread uniformly across regions but tends to remain clustered. Secondly,
the high contagion rate of this disease requires a rapid detection of zero patients to adopt almost immediately
the necessary sanitary rules that help to prevent the spreading of the cases. Moreover, the proximity be-
tween policymakers and citizens allows the former know the culture, social norms, and trust better. Conse-
quently, measures adopted to reduce the severity of epidemics could be more effective when defined as
closely as possible to the field.
The geography of COVID-19 has also highlighted inequalities in access to care services. Therefore, a pol-
ycentric organisation of access to health services seems to be essential. Public authorities must deploy
several digital solutions and support platforms for professionals, users, private and public research, and
innovation stakeholders.
The second part of the study, which focuses on the analysis of local political responses, shows several
structuring trends in the way local public authorities have reacted to the CoViD-19 crisis: (i) there is a dom-
inance of emergency measures, designed for the short term, corresponding to the first identified effects of
the pandemic ; this mainly refers to measures to support the local economic fabric, from which immediate
effects are expected; (ii) only a few measures can be described as "long-term" ones, (iii) some measures
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
42 ESPON // espon.eu
are part of the medium term but risk not being sustainable, whereas the pandemic has sometimes led to
innovation and the effects of the latter could remain beneficial beyond the closing horizon of the pandemic.
It is a challenge of anticipation that local authorities will have to face, especially the anticipation of other
crises. Therefore, we think that Territorial Economic Intelligence can help us to negotiate the management
and the crisis aftermath. If we need immediate reactions (attenuate, compensate), the strategic dimension,
which is a matter of anticipation, requires hindsight and reflection.
Most periods of crisis accentuate emerging or underlying trends. Digital change was already accelerating in
recent years, and the pandemic has precipitated this trend with an increase in teleworking, deployment of
distance learning, digitalisation of services. By putting an abrupt end to physical exchanges, the Covid-19
pandemic, led to unprecedented digital use peaks. Covid-19 has greatly accelerated the pace: transfor-
mations that were planned for several years ahead were achieved in a few weeks, both in terms of uses
and transformation. At all levels, we can see that the mastery of technology and the ability to use it gives a
real advantage in this crisis.
We have entered the fourth industrial revolution, characterised by digital acceleration. The coronavirus crisis
will mark the delay accumulated by certain companies and organisations in digitalisation. For companies, it
will require substantial actions to avoid a drastic loss of customers or even the cessation of their respective
activities. The crisis will also have revealed regional inequalities in terms of digital access. Taking these
issues into account is fundamental to avoid places being left behind.
As a result, several recommendations can be made: (i) accelerate the digital coverage of the European
territory, (ii) strengthen the digital inclusion programme, so that no European is left on the edge of the digital
path, (iii) provide financial incentives for businesses - from local shops to industrial SMEs - to embark on the
digital transition, (iv) continue efforts to promote the emergence of European digital champion businesses,
by supporting entrepreneurs and investors.
Furthermore, economic recovery from the coronavirus crisis cannot result in a return to previous practices.
It must be an opportunity to make real progress towards sustainable development. A structural transfor-
mation of the transport sector will be necessary if green and environmentally sustainable economies are to
become a reality. According to the authors of a new study published entitled Jobs in green and healthy
transport: Making the green shift9, this could lead to the creation of millions of new jobs. Therefore, local
authorities should use this opportunity to speed up public transport as an alternative to car use. Giving more
space to soft mobility should, therefore be a priority.
9 https://www.ilo.org/global/publications/books/WCMS_745151/lang--fr/index.htm
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
ESPON // espon.eu 43
Appendix
1. Descriptive statistics
Variable
Obs
Mean
Std.Dev.
Min
Max
Covid death rate week 9
377
0,00
0,00
0
0,01
Covid death rate week14
377
0,34
0,79
0
9,26
Covid death rate week18
377
1,46
2,13
0
13,19
Covid death rate week22
377
2,09
2,82
0
19,20
Covid death rate week27
377
2,32
3,07
0
20,52
Covid death rate week31
377
2,37
3,13
0
22,41
Covid death rate week36
377
2,41
3,16
0
22,86
Population density
377
4,82
1,22
0,96
9,95
Share of the population aged 65 and over
377
20,78
3,32
10,72
30,32
Life expectancy
377
81,22
2,76
74,10
85,50
GDP per capita
377
10,14
0,67
8,29
12,05
Poverty
377
20,34
7,69
8,5
53,60
Hospital beds
377
552,82
202,38
138,12
1286,28
Governance
377
1,17
0,21
0,5
1,50
Education
377
0,48
0,50
0
1,00
Intermediate region
377
0,42
0,49
0
1,00
Rural region
377
0,37
0,48
0
1,00
Urban region
377
0,21
0,41
0
1,00
2. Correlation matrix
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(1) Population den-
sity
1.000
(2) % of pop aged
65 and over
-0.475***
1.000
(3) Life expectancy
0.120**
0.238***
1.000
(4) GDP per capita
0.325***
0.027
0.796***
1.000
(5) Poverty rate
-0.028
-0.105**
-0.496***
-0.547***
1.000
(6) Hospital beds
-0.005
0.080
-0.321***
-0.208***
-0.098*
1.000
(7) Governance in-
dex
0.012
0.205***
0.651***
0.735***
-0.591***
-0.155***
1.000
(8) Education
0.173***
-0.081
0.433***
0.455***
-0.278***
-0.320***
0.367***
1.000
(9) Intermediate re-
gion
-0.011
-0.041
0.054
0.079
-0.047
-0.179***
0.004
0.024
1.000
(10) Urban region
0.584***
-0.289***
0.126**
0.296***
-0.007
-0.053
0.103**
0.207***
-0.435***
1.000
3. Methodological aspects of part 3 “Overview of the policy answers to
Covid-19 on the regional/local scale
3.1. Types of territories and geographical analysis level
The present study complements previous policy notes and reports provided by the UN, OECD or UNESCO
dealing with the COVID-19 impacts and responses; however, in order to boost the potential utility of findings,
ESPON STUDY // Geography of COVID-19 outbreak and first policy answers in European regions and cities
44 ESPON // espon.eu
we deepened our territorial focus in order to get more accurate results. Previous infra-national policy docu-
ments mostly focused on urban policy measures10,11,12. Indeed, due to their connectivity, high concentra-
tion, and high density of inhabitants, cities represent, beyond the shadow of a doubt, the most affected type
of territory. Therefore, case studies dedicated to urban areas, metropolises and mostly urban NUTS 3 units
are included in this study. Nonetheless, we considered that an excessive spotlight on urban areas failed to
fully understand the territorial realities, by omitting rural and semi-rural areas. As a result, the current study
incorporated case studies dedicated to rural and intermediate NUTS-3 units, which, although less populated,
less financially potent and mostly peripheral, had to face specific challenges during the first pandemic wave.
Hence, our analysis aimed, to a greater extent, at the NUTS-3 administrative units, a decisional level which
provides a complementary vision of the territorial impacts induced by the current pandemic. This approach
allows a more thorough observation and interpretation of the policy responses, given the asymmetrical na-
ture of the way the shock rippled throughout the European Union.
3.2. Overview of the methodological approach
The approach relies on qualitative research design. Its main pillars and steps are defined as follows:
1. Selection of 35 case studies across the European Union (21 NUTS-3 regions and 14 cities);
2. Collection of data on the policy measures taken by the 35 territorial entities (and creation of 35
subsequent factsheets;
3. Development of a theoretical typology of policy responses based on their nature (intend outcome),
area of public action, and temporal perspective;
4. Categorisation of measures according to the typology created;
5. Analysis of first policy responses on the regional and local scale based on their nature, temporal
perspective, and area of public policy action;
6. Computing descriptive statistics to see whether there are significant differences in incidence and
nature of policy responses between i) territories with different economic performance before the
crisis (GDPPC), ii) territories with high/low mortality levels due to the pandemic, and iii) different
types of territory (predominantly rural regions, intermediate regions, predominantly urban regions,
regional cities and capital cities).
3.3. Case study selection
A number of 35 territorial entities have been selected such as:
An almost equal number of regions and cities with low/high covid-19 mortality levels;
The same number of territorial entities for each type of territory: 7 predominantly rural regions, 7
intermediate regions, 7 predominantly urban regions (according to the OECD rural/urban typology),
7 regional cities and 7 capital cities;
An equal number of regional cities and NUTS-3 regions from each selected country. Capital cities do
not follow this rule as they have been added later to the study.
10 OECD, “Cities Policy Responses” (OECD, 2020), https://www.oecd.org/coronavirus/policy-responses/cities-policy-re-
sponses-fd1053ff/.
11 UNESCO, “Urban Solutions: Learning from Cities’ Responses to COVID-19,” 2020, https://en.unesco.org/urban-solu-
tions-Learning-from-cities-responses-to-COVID19.
12 UN, “Policy Brief: COVID-19 in an Urban World,” 2020, https://unsdg.un.org/resources/policy-brief-covid-19-urban-
world.
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ESPON // espon.eu 45
Map 11: Geographical location of selected case studies
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46 ESPON // espon.eu
Table 10: List of case studies
Urban-rural typology NUTS3 Region or City Low level of COVID-19 mor-
tality1
GDPPC2
Predominantly urban
region
Brăila (RO)
Low
Low
Rhône (FR)
High
High
Valencia (ES)
High
Average
Lodzki (PL)
Low
Low
Taranto (IT)
High
Low
Uppsala Ian (SE)
High
High
Hannover (DE)
Low
High
Intermediate region
Cluj (RO)
Low
Average
Calvados (FR)
Low
Average
Guadalajara (ES)
High
Low
Poznanski (PL)
Low
Average
Reggio di Calabria (IT)
High
Low
Skane (SE)
High
Average
Havelland (DE)
Low
Low
Predominantly rural
region
Gorj (RO)
Low
Low
Nièvre (FR)
High
Low
Cuenca (ES)
High
Average
Kaliski (PL)
Low
Low
Mantova (IT)
High
Average
Dalarnas (SE)
High
Average
Emsland (DE)
Low
High
Regional cities
Iași (RO)
Low
Low
Bordeaux (FR)
High
Average
Zaragoza (ES)
High
Average
Katowice (PL)
Low
Average
Bologna (IT)
High
High
Göteborg (SE)
High
High
Leipzig (DE)
Low
High
Capital cities
Amsterdam (NL)
High
Warsaw (PL)
High
Oslo (NO)
High
Lisbon (PT)
Average
Riga (LV)
Average
Ghent (to be replaced with Vienna)
High
Porto (to be replaced with Athens)
Low
Source: authors based on Factsheets
Notes: (1) threshold: 12 death/100.000 inhabitants; (2) Low = less than 80% of the EU27 average, Average
= between 80 and 110 of the EU27 average, high = more than 110 of the Eu27 average. GDP expressed
in purchasing power standard (PPS) per inhabitant. Data source: EUROSTAT (nama_10r_3gdp)
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ESPON // espon.eu 47
3.4. Data collection
Data on first local and regional policy measures has been retrieved from publicly-available websites and
platforms, such as those maintained by pan-European organisations and institutions which collected local
and regional policy measures from their networks. Evidence and qualitative data on policy measures have
also been collected from the local media and, above all, from official websites of corresponding local author-
ities.
Table 11: Main sources used for collecting data on first local and regional policy
measures
Region/City
Main sources used
NUTS3 regions
Brăila (RO)
Local media (online news articles)
Rhône (FR)
https://eurocities.eu/; https://www.rhone.fr/
Valencia (ES)
https://www.dival.es/
Lodzki (PL)
Local media (online news articles)
Taranto (IT)
https://www.provincia.taranto.it/
Uppsala Ian (SE)
https://www.lansstyrelsen.se/uppsala/
Hannover (DE)
https://www.hannover.de/
Cluj (RO)
Local media (online news articles), and https://www.cjcluj.ro/
Calvados (FR)
www.calvados.fr
Guadalajara (ES)
http://www.dguadalajara.es/) and local online media.
Poznanski (PL)
Local media (online news articles)
Reggio di Calabria
(IT)
https://www.cittametropolitana.rc.it
Skane (SE)
https://www.skane.se/; https://www.skanetrafiken.se/, and two of the Regions’ official Face
pages
Havelland (DE)
https://www.havelland.de/
Gorj (RO)
https://gj.prefectura.mai.gov.ro/ ; https://www.cjgorj.ro/ ;
Nièvre (FR)
https://nievre.fr/
Cuenca (ES)
https://www.dipucuenca.es/
Kaliski (PL)
Official website of Kaliski Powiat Council: www.powiat.kalisz.pl
Mantova (IT)
https://www.provincia.mantova.it/
Dalarnas (SE)
https://www.lansstyrelsen.se/dalarna.html
Emsland (DE)
https://www.emsland.de/
Cities
Iași (RO)
Local media (online news articles)
Bordeaux (FR)
http://www.bordeaux.fr and https://rue89bordeaux.com/ and https://www.bordeaux-
metropole.fr
Zaragoza (ES)
https://www.zaragoza.es/sede/portal/coronavirus
Katowice (PL)
https://koronawirus.katowice.eu/ ; https://eurocities.eu/;
Bologna (IT)
http://www.comune.bologna.it and https://covidnews.eurocities.eu/
Göteborg (SE)
https://goteborg.se/
Leipzig (DE)
https://www.leipzig.de/
Amsterdam (NL)
Eurocities ; https://www.amsterdam.nl/ ; www.metropoolregioamsterdam.nl ;
Warsaw (PL)
https://warszawa19115.pl/; https://covidnews.eurocities.eu/; www.themayor.eu; http://ngo.um.wa
szawa.pl/
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48 ESPON // espon.eu
Region/City
Main sources used
Oslo (NO)
https://www.oslo.kommune.no/
Lisbon (PT)
https://eurocities.eu/; https://lisboagreencapital2020.com/en/
Riga (LV)
https://www.riga.lv/
Source: authors
3.5. Data analysis and typology of local and regional policy responses
Data collection has resulted in a database of 477 local and regional policy measures. These measures have
been categorised afterwards, according to three criteria, as described.
Table 12: A proposed typology of policy measures and corresponding conceptual
categories13
Area of public action
Temporal perspective
1
Nature of intention
2
Conceptual
categories
Public health security
Daily way of life and work
Support to vulnerable populations
Support to economic actors and recovery
Short term
Medium term
Long term
Mitigate
Compensate
Circumvent
Exploit
Source: Authors
Notes: Short termrefers to measures having effects during the first 6 months, Medium termrefers to
measures having effects during the pandemic, Long termrefers to measures having effects during the
post-pandemic time).
Local and regional policy measures have been previously classified in thepublic action and temporal per-
spective” category (e.g. OECD, 2020)14. On the other hand, categorising measures according to their inten-
tional nature and resiliency constitutes a new approach. This implies the classification of the measures into
four categories, as follows. ‘Mitigating’ measures show the capacity of local authorities to resist the crisis,
and to take measures that limit the negative effects, whilst ‘Compensating’, ‘Circumventing’ and ‘Exploiting’
measures show the local authoritiesintention to recover, either towards the pre-crisis balance (Compen-
sating and Circumventing) or a newly created one (Exploit).
‘Mitigating’ measures range from typical social distancing measures (similar to the initial Czech quaran-
tine) to more sophisticated and smart approaches (that rival the ‘smart quarantine’ from South Korea
and Singapore)15.
‘Compensating’ measures aim at regaining the pre-crisis balance, but also going back to business and
the usual way of doing things.
13 Other frameworks conceived for the analysis of policy responses to COVID-19 include the four-stage RISE model
developed by Craig et al. (2020) and the five-stage ‘5R’ model developed by the McKinsey Global Institute (2020). These
two frameworks are conceived as a set of 4/5 conceptual categories of actions that follow one another in time. The RISE
framework has been conceived for the particular case of local governments’ fiscal responses to the crisis, and stresses
the importance of distinguishing Resilience (understood as resistance, i.e. the financial capacity of local governments to
maintain current operations, thus including no particular actions from their part), Intention (“immediate actions taken to
minimise financial impacts”), Sustain (“short term actions in the next budget cycles towards stabilisation”) and Endurance
(“adapt to the ‘new’ normal, reform and rethink, and strategic planning”). Quite similarly, the 5R framework conceptualises
the reaction of the private and public stakeholders as raging from Resolving to Resilience, Returning, Re-imagining, and
finally Reforming.
14 OECD, “Cities Policy Responses.”
15 For a blanked quarantine’ and ‘smart quarantine’ overview, see Kouřil & Ferenčuhová (2020) -
https://www.tandfonline.com/doi/full/10.1080/15387216.2020.1783338
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ESPON // espon.eu 49
‘Circumventing’ measures show a higher resilience from the local authorities as they are aimed at re-
gaining the pre-crisis balance in an adaptive way, following the logic of doing the same things differently.
‘Exploiting’ measures suggest the intention to follow an original recovery path, by creating a new bal-
ance and by following a transformative process, usually by taking advantage of the effects of the crisis
and by doing new things compared to the pre-crisis period.
The aim of this new framework is twofold: 1) employ it as a conceptual instrument to produce knowledge
and 2) assist the local public stakeholders and practitioners in the understanding of the range of existing
options as well as their pros and cons.
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50 ESPON // espon.eu
Figure 9: Proposed grid for analysing policy responses on the local scale
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ESPON // espon.eu 51
Table 13: Proposed criteria for classifying measures
Question
Criteria
Mitigate
Compensate
Circumvent
Exploit
Q1: In response to
what? (response to
negative effects vs. to
opportunities created
by the pandemic)
The measure is a response to negative
effects or associated risks
The measure is a response to opportunities created by the
pandemic
Q2. To what pur-
pose? (Aiming or not
at restoring pre-crisis
balance)
The measure is a response to urgent needs (it does not
aim at restoring the balance)
The measure aims at restoring pre-crisis balance (the
“business as usual” way)
The measure aims at restoring pre-crisis balance (doing
things with the same purpose but in a different way*)
The measure aims at creating a new balance (the purpose
and function of the system are changed)
Q3. What and how?
(same things vs. new
things; same way vs.
new ways of doing
things)*
The measure supposes doing the same things in the
same way
The measure supposes doing the same things in a differ-
ent way
The measure supposes doing new things
Source: authors
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52 ESPON // espon.eu
Table 14: No of policy measures according to their nature and level of wealth of
corresponding NUTS-3 regions
Level of GDPPC Mitigate Compensate Circumvent Exploit Total
High GDPPC (>110) 152 19 32 7 210
Average GDPPC (80-110) 128 20 26 4 178
Low GDPPC (<80) 101 11 7 119
Total 381 50 65 11 507
Note: Data computed for a number of 35 regions/cities (11 with high GDPPC, 13 with average GDPPC, 11 with low GDPPC).
Data sources: first 35 facheets for policy measures (see Annex 1); EUROSTAT (nama_10r_3gdp) for GDPPC (Purchasing power
standard per inhabitant in percentage of the EU average).
Table 15: Frequency of policy measures according to their nature and GDPC level
(EU27=100) of corresponding NUTS-3 regions (average no of policy measures per
region/city).
Level of GDPPC Mitigate Compensate Circumvent Exploit
High GDPPC (>110) 13.8 1.7 2.9 0.6
Average GDPPC (80-110) 9.8 1.5 2.0 0.3
Low GDPPC (<80) 9.2 1.0 0.6 0.0
Note: Data computed for 35 regions/cities (11 with high GDPPC, 13 with average GDPPC, 11 with low GDPPC).
Data sources: first 35 facheets for policy measures (see Annex 1); EUROSTAT (nama_10r_3gdp) for GDPPC (Purchasing power
standard per inhabitant in percentage of the EU average).
Table 16: Share of the number of circumventing and exploding measures depending on the
GDPPC level of the corresponding NUTS-3 region
Level of GDPPC (EU27=100) Mitigate Compensate Circumvent Exploit
High (>110) 40 38 49 64
Average (80-110) 34 40 40 36
Low (<80) 27 22 11 0
100 100 100 100
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ESPON // espon.eu 53
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