Regional and
Urban Policy
Seventh report on
economic, social and territorial cohesion
My Region, My Europe,
Our Future
Seventh report on economic, social and territorial cohesion
My Region, My Europe,
Our Future
Manuscript completed in September 2017
Editor: Lewis Dijkstra, European Commission, Directorate-General for Regional and Urban Policy
This publication was produced with the technical assistance of Applica (Belgium).
This publication can be consulted on line at:
http://ec.europa.eu/regional_policy/en/information/cohesion-report/
Comments on the review would be gratefully received and should be sent to:
European Commission
Directorate-General for Regional and Urban Policy
Unit B1 - Policy Development and Economic Analysis
B-1049 Brussels
E-mail: regio-B1-head-of-unit@ec.europa.eu
Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use
that might be made of the following information.
Print	 ISBN 978-92-79-71840-3	 ISSN 2529-6736	 doi:10.2776/5244	 KN-AM-17-001-EN-C
PDF	 ISBN 978-92-79-71834-2	 ISSN 2529-6612	 doi:10. 2776/176864	 KN-AM-17-001-EN-N
Luxembourg: Publications Office of the European Union, 2017
© European Union, 2017
Reuse is authorised provided the source is acknowledged.
The reuse policy of European Commission documents is regulated by Decision 2011/833/EU (OJ L 330,
14.12.2011, p. 39).
For any use or reproduction of photos or other material that is not under the EU copyright, permission must be
sought directly from the copyright holders.
Cover illustration: © Getty Images
Printed by Bietlot in Belgium
iii
Foreword
This report comes at a crucial time, following the European Commission’s White
paper on the future of Europe and the five related reflection papers, and ahead of
next year’s Commission’s proposal for the next multi-annual financial framework.
Now is the time to decide where cohesion policy should invest, what its investment
priorities should be and how they can be implemented in a more flexible and
efficient manner.
The Seventh Cohesion Report brings the necessary data and facts to check how
cohesive, or divided, Europe is from an economic, social and territorial point of
view; and by doing so, it helps us see with more clarity and objectivity what has
been achieved and what needs to be done in the post-2020 financial period. In
short, it sets the scene for shaping tomorrow’s cohesion policy.
Though on average economic recovery seems to have taken root, GDP and employment
have reached all-time highs and regional disparities are shrinking, all is
not well. Looking at the situation more closely, we see that unemployment rates
remain above the pre-crisis level in a number of areas while too many small and
medium-sized enterprises are struggling to adapt to globalisation, digitalisation,
green growth and technology change. Even in wealthier regions, poverty and social
exclusion are still too high. At the same time, public investment remains low, especially
in those countries and regions worst hit by the recent economic and financial
crisis, to the point that cohesion funds are a lifeline for many of them.
The report also highlights that improving public administration can strengthen
competitiveness, boost economic growth and increase the impact of investments,
including those co-financed by cohesion policy. This is why it is important to continue
to modernise public institutions and implement the necessary structural reforms
to make them more efficient. Here again, cohesion policy, with its set of exante
conditions to fulfil before receiving grants and its focus on sound governance,
helps improve public administration.
The report shows that to remain competitive, we need to anticipate market changes
and our people have to have the skills required. The current economic recovery
will not be sustainable unless there is investment in both physical and human
capital to support long-term growth. This is also essential to achieve our social
goals of fairness and equal opportunity, as set out in the European Pillar of Social
Rights, which serves as a guide towards better working and living conditions
throughout the EU.
The report shows, in addition and without any ambiguity, that cohesion policy
provided much needed help to Member States and regional and local authorities
in the midst of the worst economic crisis thanks to its long-term, stable and predictable
investment. For the current financial period, cohesion policy will support
1.1 million SMEs, help more than 7.4 million unemployed people to find a job and
8.9 million people to gain new qualifications. It will invest €16 billion in the digital
economy, expanding government online services and connecting close to 15 million
households to broadband Internet. It will also invest in energy efficiency, protecting
the environment, reducing social exclusion and improving public transport
as well as the trans-European road and rail network.
S e v e n t h R e p o r t o n e c o n o m i c , s o c i a l a n d t e r r i t o r i a l c o h e s i o n
iv
In sum, the 2017 Cohesion Report shows how much cohesion policy is vital to
Europe, its citizens, its economy and its cities and regions and that reconciling
sustainable economic growth with social progress, as cohesion policy is helping to
do, is as essential as ever.
Corina Crețu
European Commissioner
for Regional Policy
Marianne Thyssen
European Commissioner
for Employment, Social Affairs
and Inclusion
.
v
Table of contents
Foreword..............................................................................................................................................................................................iii
Lexicon.................................................................................................................................................................................................vii
Highlights.............................................................................................................................................................................................xi
Introduction.....................................................................................................................................................................................................xi
Regional disparities are narrowing again.......................................................................................................................................xi
Employment has recovered, but unemployment is still above its pre-crisis level..................................................xii
Some regions have rapid population growth while others depopulate .......................................................................xv
Cities combine opportunities with challenges.............................................................................................................................xv
Investments in innovation, skills and infrastructure are insufficient........................................................................... xvii
More investments needed in energy efficiency, renewables and low-carbon transport to reduce
greenhouse gas emissions.................................................................................................................................................................. xvii
Cooperating and overcoming obstacles across EU borders...............................................................................................xix
Improving the quality of government and implementing structural reforms would boost growth.............xxi
National public investment has not yet fully recovered......................................................................................................xxi
Cohesion policy’s key role in public investment reduced the impact of crisis........................................................xxii
Cohesion policy and the future of Europe.................................................................................................................................. xxv
Chapter 1 Economic cohesion.....................................................................................................................................................1
Introduction......................................................................................................................................................................................................2
Economic trends among EU regions and Member States......................................................................................................2
Productivity in less developed Member States is catching up..........................................................................................12
The economic development clubs of European regions and the middle-income trap........................................21
Competitiveness of EU regions..........................................................................................................................................................25
Capital and metro regions are the main drivers of regional competitiveness in Europe..................................46
Chapter 2 Social cohesion.........................................................................................................................................................53
Population change is increasingly determined by migration.............................................................................................54
Employment rates are higher for those born in another EU country than for the native-born ....................60
Asylum seekers and refugees.............................................................................................................................................................62
The employment rate has surpassed its pre-crisis level, but unemployment rates are still too high.......63
Education and training............................................................................................................................................................................68
Adult proficiency in literacy and numeracy needs to be raised in several EU Member States......................71
Poverty and social exclusion is declining in the EU‑13 but growing in cities in the EU‑15.............................73
Moving at different speeds to the Europe 2020 targets......................................................................................................79
More women are studying, working and being elected to regional assemblies.....................................................83
Life in the EU is among the longest in the world but regional disparities persist ...............................................88
Measuring social progress at the regional level .....................................................................................................................91
S e v e n t h R e p o r t o n e c o n o m i c , s o c i a l a n d t e r r i t o r i a l c o h e s i o n
vi
Chapter 3 Territorial cohesion.................................................................................................................................................95
Introduction...................................................................................................................................................................................................96
Energy Union and climate change ...................................................................................................................................................96
State of environment............................................................................................................................................................................105
Sustainable cities....................................................................................................................................................................................110
Cross-border cooperation and territorial dimension of cohesion policy .................................................................124
Chapter 4 Improving institutions......................................................................................................................................... 135
Good governance affects economic growth and the quality of life............................................................................136
Quality of governance varies substantially in Europe........................................................................................................137
Institutional capacity affects policy performance and capacity to conduct reforms .......................................145
Suitable institutions increase the effects of EU support on entrepreneurship ....................................................161
Conclusions.................................................................................................................................................................................................161
Chapter 5 National policies and cohesion....................................................................................................................... 163
Public investment is still at very low levels despite the recent pick-up of the EU economy........................164
Sub-national governments play a key role in public expenditure and investment ...........................................168
Reviewing how the ESI Funds are linked to new country-specific recommendations and to sound
economic governance ..........................................................................................................................................................................171
Conclusions.................................................................................................................................................................................................174
Chapter 6 The impact of cohesion policy........................................................................................................................ 175
The policy.....................................................................................................................................................................................................176
Improving the effectiveness of the policy.................................................................................................................................179
Macroeconomic impact of the policy...........................................................................................................................................182
Innovation and competitiveness.....................................................................................................................................................187
Employment, social inclusion and education...........................................................................................................................192
Environment, transport and energy networks.........................................................................................................................195
Reinforced cooperation and addressing territorial challenges......................................................................................200
References..................................................................................................................................................................................... 203
List of figures, maps and tables.......................................................................................................................................... 211
vii
Lexicon
Cohesion policy:	 Covers all the programmes supported by the following Funds: the European Social Fund
(ESF), the European Regional Development Fund (ERDF) and the Cohesion Fund (CF).1
It is
also known as regional policy.
Structural Funds:	 The European Social Fund (ESF) and the European Regional Development Fund (ERDF).
Abbreviations
COH:	
EAFRD: 	
EMFF:	
EFSI:	
ERDF:	
ESF:	
ESIF:	
EU:	
NSI:	
OECD:	
PPS:	
Cohesion countries (EU-13 plus Greece and Portugal)
European Agricultural Fund for Rural Development
European Maritime and Fisheries Fund
European Fund for Strategic Investment
European Regional Development Fund
European Social Fund
European Structural and Investment Funds. Covers all programmes supported by ESF, ERDF,
CF, EAFRD and EMFF.
European Union, formerly known as European Coal and Steel Community (ECSC), the European
Economic Community (EEC) and the European Community (EC)
National Statistical Institute
Organisation for Economic Co-operation and Development
Purchasing Power Standards
For ease of reading, funds are consistently referred to by their current name even if some of these funds have
changed name over time.
Member States and their abbreviation
BE	 Belgium
BG	 Bulgaria
CZ	 Czech Republic
DK	 Denmark
DE	 Germany
EE	 Estonia
IE	 Ireland
EL	 Greece
ES	 Spain
FR	 France
HR	 Croatia
IT	 Italy
CY	 Cyprus
LV	 Latvia
LT	 Lithuania
LU	 Luxembourg
HU	 Hungary
MT	 Malta
NL	 Netherlands
1	 EAFRD and the Fisheries Fund have been considered part of Structural or Cohesion Policy during certain periods. But they will be treated
separately in this report.
Seventh Report on economic, social and territorial cohesion
viii
AT	Austria
PL	Poland
PT	Portugal
RO	Romania
SI	Slovenia
SK	Slovakia
FI	Finland
SE	Sweden
UK	 United Kingdom
Geographical groupings
Member State groupings
By enlargement
For ease of reading, this report refers to the European Economic Community (EEC), the European Community
(EC) as the European Union (EU).
EU-6:	 The six initial Member States: BE, DE, FR, IT, LU and NL
EU-9:	 EU-6 plus DK, IE and UK
EU-10:	 EU-9 plus EL
EU-15: 	 EU-10 plus ES, AT, PT, SE, FI
EU-12:	 All Member States that joined in 2004 and 2007: BG, CZ, EE, CY, LV, LT, HU, MT, PL, RO, SI, SK
EU-13:	 EU-12 plus HR
EU-25: 	 EU-15 plus CZ, EE, CY, LV, LT, HU, MT, PL, SI, SK
EU-27: 	 EU-25 plus RO and BG
EU-28: 	 EU-27 plus HR
By geography
Eastern Member States: EE, LV, LT, PL, SK, CZ, SI, HU, RO, BG, HR
Southern Member States: PT, ES, IT, EL, MT, CY
Western Member States: EU-15
Nordic Member States: SE, DK, FI
Baltic States: EE, LV, LT
Benelux: BE, NL, LU
By level of development
Less developed Member States: (BG, EL, EE, HR, LT, LV, HU, PL, RO) (GDP per head below 75% of EU average
in 2015)
Moderately developed Member States: (CZ, CY, PT, SI, SK) (GDP per head between 75% and 90%)
Highly developed Member States: (BE, DK, DE, IE, ES, FR, IT, LU, MT, NL, AT, SE, FI, UK) (GDP per head above
90% of the EU average)
Types of NUTS 2 regions
Cohesion policy in the period 2014–2020 uses three categories of regions based on the GDP per head for the
years 2007, 2008 and 2009 (see map).
Less-developed regions: GDP per head (PPS) below 75% of the EU-27 average
Lexicon
ix
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Structural Funds (ERDF and ESF) eligibility 2014–2020
Less developed regions (GDP/head < 75% of EU-27 average)
Transition regions (GDP/head between >= 75% and < 90% of EU-27 average)
More developed regions (GDP/head >= 90% of EU-27 average)
Category
Source: DG REGIO
©
0 500km
REGIOgis
EuroGeographics Association for the administrative boundaries
Seventh Report on economic, social and territorial cohesion
x
Transition regions: GDP per head (PPS) between 75% to 90% of the EU-27 average
More-developed regions: GDP per head (PPS) above 90% of the EU-27 average
Capital city regions: these regions consist of one or more NUTS 2 regions and approximate the functional
urban area of the national capital. In most cases, it consists of only one NUTS 2 region. The exceptions are:
Berlin, Brussels, London, Prague and Vienna. Combining these regions ensures that the distortion in economic
indicators caused by commuting is substantially reduced. These regions in most cases are different from the
capital metropolitan regions.
Types of NUTS 3 regions
Metropolitan regions
This classification was developed in cooperation with the OECD and consists of NUTS 3 approximation of all
functional urban areas of more than 250 000 as defined by the EU-OECD. Two types of metropolitan regions
are identified: capital and other. The capital metropolitan regions contain the national capital.
Predominantly urban, intermediate, predominantly rural regions
This classification is based on the OECD classification, but revised by the Commission. A detailed methodology
is included in the Eurostat Regional Yearbook 2010.
Border regions
Border regions are NUTS 3 regions which are eligible for cross-border cooperation programmes under the
European Regional Development Fund regulation.
Types of municipalities
Degree of urbanisation
Cities: Local administrative units with more than 50% of their population in an urban centre;
Towns and suburbs: Local administrative units with more than 50% of their population in urban clusters but
less than 50 % live in an urban centre;
Rural area: Local administrative units with more than 50% of their population in rural grid cells
For more information see:
http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Degree_of_urbanisation_classifica-
tion_-_2011_revision
http://ec.europa.eu/regional_policy/sources/docgener/work/2014_01_new_urban.pdf
Cities and commuting zones
Cities: Same definition as above
Commuting zones: Contiguous local administrative units with at least 15% of their working population commuting
to a city.
For more information see:
http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/European_cities_%E2%80%93_the_EU-
OECD_functional_urban_area_definition
http://ec.europa.eu/regional_policy/sources/docgener/focus/2012_01_city.pdf
xi
Highlights
Introduction
This report fulfils two requirements:
1	 It reports on how cohesion has evolved in EU regions over the recent
past and assesses the impact on this of national policies, cohesion policy
and other EU policies as required by the Treaty on the Functioning
of the European Union1
. The accompanying Staff Working Document
(SWD) consists of 6 chapters: on economic development, social inclusion,
sustainable development, improving institutions, national policies
and cohesion, and the impact of cohesion policy. The impact of other EU
policies is considered in the first four chapters.
2	 It reviews the measures linking the effectiveness of the European
Structural and Investment (ESI) Funds to sound economic governance,
as required by the Regulation on Common Provisions with regard to the
Structural Funds2
. This review is summarised in section 9 below and set
out in full in section 5.3 of the SWD.
1. Regional disparities are narrowing again
After the double dip recession in 2008 and 2011, the EU economy is now
growing again. The crisis seriously affected almost all Member States. It halted
the long-term reduction in disparities in GDP per head between Member
States. With the beginning of the recovery, however, these disparities have
started to shrink again with growth everywhere, and higher rates in countries
with lower levels of GDP per head.
The first signs of narrowing disparities are also evident at regional level across
the EU. From 2008 onwards, regional disparities in employment and unemployment
rates widened along with those in GDP per head. In 2014, disparities
in employment started to narrow, followed by disparities in GDP per head in
2015. Nevertheless, many regions still have a GDP per head and an employment
rate below pre-crisis levels.
Between 2000 and 2015, GDP per head in many less developed regions
converged towards the EU average through faster productivity growth, but
they lost employment. The manufacturing sector in these regions has for the
most part performed well, which has helped firms to compete both inside
the Single Market and globally. To ensure that their convergence continues,
1	 Hereinafter referred to as ‘the Treaty’, see Article 175 of the consolidated version, Official Journal C.326,
26/10/2012.
2	 Regulation (EU) No 1303/2013 of the European Parliament and of the Council of 17 December 2013
laying down common provisions on the European Regional Development Fund, the European Social Fund,
the Cohesion Fund, the European Agricultural Fund for Rural Development and the European Maritime
and Fisheries Fund and laying down general provisions on the European Regional Development Fund,
the European Social Fund, the Cohesion Fund and the European Maritime and Fisheries Fund (…) (OJ L
347, 20.12.2013, p. 320), see Article 23.
Seventh Report on economic, social and territorial cohesion
xii
these regions will have to move up the value chain to activities with a higher
skill, technology and innovation content, especially because globalisation and
technological change3
could quickly undermine their economic performance.
The regions with GDP per head well above the EU average have grown faster
than the less developed ones through a combination of both productivity and
employment growth. As most of the higher GDP per head regions contain a national
capital or a large city, they benefit from agglomeration economies while
a bigger labour market makes for a better matching of skills. The concentration
of activities attracts specialised services and suppliers. Infrastructure
investment in transport and ICT generates higher returns, while the spatial
proximity of firms produces more innovation and knowledge spillovers. These
benefits can be extended by improving links between large cities and their
rural hinterland or between smaller cities, where the sharing of specialised
services can give rise to economies of scale.
Several of the regions with a GDP per head close to the EU average, however,
seem stuck in a ‘middle-income trap’. On average, GDP per head growth from
2001 to 2015 was significantly below the EU average (see Maps 1 and 2).
Their manufacturing sector is smaller and weaker than in regions with both
a lower and higher GDP per head. Their costs tend to be too high to compete
with the former and their regional innovation systems not strong enough
to compete with the latter. To improve their performance, multiple changes
need to happen at the same time: a stronger export-orientation, a shift into
new sectors and activities, a boost to research and innovation, an increase
in education and training and an improvement in the business environment.
Globalisation has caused substantial job losses in many of the regions, but
the provision of training alone to workers laid-off does not ensure new job
creation and the structural transformation needed.
2. Employment has recovered, but unemployment is
still above its pre-crisis level
In 2016, the employment rate of those aged 20–64 in the EU exceeded the
pre-crisis level for the first time. At 71%, it is 1 percentage point higher than
in 2008 but still well below the 75% target for 2020 set by the Europe 2020
strategy. The situation, however, varies markedly across the EU.
The unemployment rate across the EU has fallen from a high of 10.9% in
2013 to 8.6% in 2016 and 7.7% in 2017, still above the 7% it was in 2008. In
some countries, the rate is lower than in 2008, but in others it is still at least
5 percentage points higher. Regional disparities in unemployment rates had
not narrowed up to 2016, but they had largely ceased to widen. However, in
particular people under 25 still face problems getting a job (see Map 3).
3	 European Commission ‘Reflection Paper on Harnessing Globalisation’, COM(2017) 240 of 10 May 2017.
Highlights
xiii
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2 Change in GDP per head index, 2008–2015
< -20
-20 – -15
-15 – -10
-10 – -5
-5 – 0
0 – 5
5 – 10
10 – 15
15 – 20
20 – 25
> 25
no data
Index point difference
EU-28 = 0
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1 Change in GDP per head index, 2000–2008
< -20
-20 – -15
-15 – -10
-10 – -5
-5 – 0
0 – 5
5 – 10
10 – 15
15 – 20
20 – 25
> 25
no data
Index point difference
EU-28 = 0
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Seventh Report on economic, social and territorial cohesion
xiv
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3 Young people (15–24) not in employment, education or training (NEET), 2016
< 5
5 – 7.5
7.5 – 10
10 – 15
15 – 20
> 20
no data
% of population aged 15–24
EU-28 = 11.5
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Highlights
xv
Although there was some move towards the Europe 2020 targets between
2010 and 2015, the rate of progress is not enough to achieve them by 2020.
The more developed regions are closest to achieving them, but less developed
regions made more progress towards them up to 2015. The transition regions
(those in between) made almost no progress up to then and will be overtaken
by the less developed regions by 2020 if the trends persist. Rural areas are
furthest from meeting the EU targets, but they made more progress than the
cities, towns and suburbs up to 2015.
3. Some regions have rapid population growth while
others depopulate
For the first time, deaths outnumbered births in the EU in 2015, which strengthens
the impact of migration and mobility on regional population (Map 4). The
big differences in unemployment and income across the EU encourage people
to move to find better job opportunities and/or escape unemployment and
poverty. Movements have predominantly been from the EU‑13 to the EU‑15
and within the EU‑13 from rural regions to capital and other large cities. In
several regions, this has led to rapid changes in population, which has put
pressure on public infrastructure and services either to up or downscale them.
In the recent past, the EU has also seen a rapid increase in people applying for
asylum, reaching 1.2 million first-time applications in both 2015 and 2016.
Ensuring that all refugees or migrants legally residing in the EU are effectively
integrated is important for cohesion and future prosperity. Improving their
skills to help them find a job, helping them to set up a business, providing
them with better access to finance and tackling discrimination are all key to
achieving this.
4. Cities combine opportunities with challenges
Despite the growing concentration of jobs in cities, the share of low work
intensity households is the highest in EU‑15 cities. The risk of poverty or social
exclusion in the EU has fallen back to its pre-crisis level. In the EU‑13, it
is even lower than before the crisis, but in the EU‑15 it remains higher than
before in cities, towns and suburbs. This highlights the fact that pockets of
poverty4
exist even in relatively well-off cities.
Cities are more efficient in terms of energy and land-use5
and offer the possibility
of a low-carbon lifestyle. At the same time, air pollution with all its damaging
effects on human health remains a concern in many European cities.
4	 European Commission ‘Reflection Paper on the Social Dimension of Europe’, COM(2017) 206 of 26 April
2017.
5	 European Commission and UN-Habitat (2016).
Seventh Report on economic, social and territorial cohesion
xvi
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 4 Total population change in NUTS 3 regions, 2005–2015
< -10
-10 – -5
-5 – 0
0 – 5
5 – 10
10 – 15
> 15
no data
Total change (%)
EU-28 = 2.84
DK, DEA, DED, DEE: data before 2007 was extrapolated.
PL52: NUTS 2
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Highlights
xvii
Integrated strategies can make a big impact in cities. For example, improving
urban transport can reduce congestion, make firms more productive and
connect deprived neighbourhoods. Institutes of higher education can help to
integrate migrants, promote innovation and provide skills missing in the local
labour market. Nature-based solutions, such as urban green spaces can
improve quality of life, air quality and bio-diversity.
5. Investments in innovation, skills and infrastructure
are insufficient
Overall, innovation in the EU remains highly concentrated in a limited number
of regions (see Map 5). In north-western Member States, good interregional
connections, a highly skilled labour force and an attractive business environment
have allowed neighbouring regions to benefit from their proximity to the
regions concerned. In southern and eastern Member States, the innovation
performance is weaker and regions close to centres of innovation — mainly
the capitals — do not benefit from their proximity. This calls for policies that
connect firms, research centres and specialised business services across regions.
Investing more in skills could help to improve economic growth by narrowing
the skills gap and to reduce poverty, youth unemployment and social
exclusion.
Public investment in the EU is still below its pre-crisis level with major gaps
in some of the countries most affected by the crisis. More investment will be
needed to complete the trans-European Transport network (TEN-T) and the
connections to this. Basic broadband services are accessible to all households
in the EU, but next generation access — which is much faster — is only available
to 40% of rural residents compared to 90% of urban ones.
6. More investments needed in energy efficiency,
renewables and low-carbon transport to reduce
greenhouse gas emissions
Substantial progress has been made in limiting energy consumption and
greenhouse gas emissions. Most Member States have either reached or are
close to reaching their national 2020 targets for greenhouse gas emissions
and renewable energy. This in part has been facilitated by the crisis reducing
economic activity. The current recovery may, therefore, put these achievements
in jeopardy. Reaching the more ambitious EU targets of a 40% reduction
of greenhouse gas emissions and 27% share of renewable energy by
2030 will require greater effort. The recent climate agreement (COP21) also
commits governments to assessing every 5 years whether more ambitious
targets are needed.
Seventh Report on economic, social and territorial cohesion
xviii
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 5 Regional innovation performance, 2017
Modest -
Modest
Modest +
Moderate -
Moderate
Moderate +
Strong -
Strong
Strong +
Leading -
Leading
Leading +
Source: European Commission – Regional Innovation Scoreboard 2017
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Highlights
xix
To reach the EU target for reducing greenhouse gas emissions, there is a need
to shift towards more energy efficient and cleaner transport and to make more
efficient use of existing transport infrastructure. Roads remain the predominant
mode of transport for both passengers and freight and more needs to
be done to increase the use of rail and waterways as well as public transport6
.
Climate change will have significant effects on many EU regions. It will give
rise to changes in the environment which will often be costly to adapt to and
which will necessitate substantial investment to make regions more resilient
to the consequences.
The state of the environment in the EU has improved in recent years7
.
Nevertheless, key environmental objectives such as renewable energy, energy
efficiency, air quality and, in some Member States, wastewater treatment remain
unfulfilled.
7. Cooperating and overcoming obstacles across EU
borders
The EU has always supported territorial cooperation which has played a crucial
role both in mitigating the adverse effects of internal borders and in
providing Europeans with innovative solutions as regards research, environmental
issues, transport, education, energy, healthcare, security and training.
Territorial cooperation can also help countries and regions to identify solutions
to common problems including those linked to new global challenges.
Cooperation programmes have contributed to enlarging the knowledge-based
economy across Europe by increasing R&D capacity and transfers of knowhow
between regions, stimulating investment in SMEs and diversifying local
economies. They have improved accessibility across borders, the joint management
of natural resources and environmental protection.
However, despite the elimination of many institutional and regulatory barriers,
borders continue to represent obstacles to the movement of goods,
services, people, capital and ideas. Removal of such barriers could boost economic
growth and improve access to services in the regions concerned, but it
would also help European economies to fully reap the benefits of integration8
.
6	 European Commission: ‘Assessment of the progress made by Member States in 2014 towards the
national energy efficiency targets for 2020’, COM(2017) 56 of 1 February 2017.
7	 European Environmental Agency (2015).
8	 Politecnico di Milano (2017) Quantification of the effects of legal and administrative border obstacles
in land border regions.
Seventh Report on economic, social and territorial cohesion
xx
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 6 European Quality of Government index, 2017
< -1.75
-1.75 – -1.25
-1.25 – -0.75
-0.75 – -0.25
-0.25 – 0.25
0.25 – 0.75
0.75 – 1.25
> 1.25
no data
Standard deviation, range from poor quality (negative) to high quality (positive)
EU = 0
Source: World Bank data and a regional quality of government survey.
©
0 500 km
EuroGeographics Association for the administrative boundaries
REGIOgis
Highlights
xxi
8. Improving the quality of government and
implementing structural reforms would boost growth
Low quality of government hinders economic development and reduces the
impact of public investment, including that co-financed by cohesion policy (see
Chapter 4). Government efficiency differs between Member States. There are
also significant disparities within a number of them (see Map 6). Improving
institutions would amplify the impact of cohesion policy.
Structural reforms that improve competition, the business environment, education
and skills9
, labour markets and social protection systems can have
major benefits in terms of productivity and employment growth. This is particularly
relevant for regions and countries where productivity has barely improved
over the past decade10
. Reforms requiring mainly regulatory and administrative
changes with no investment, however, are currently not linked to
cohesion policy.
According to the Doing Business report11
, there are marked differences between
how business-friendly Member States are. The state of the business
environment can also vary within countries due to differences in the efficiency
of local authorities.
Open and transparent public procurement is essential to promote development
and reward the most efficient firms. However, the use of open procedures,
the intensity of competition and the speed of decision-making as well
as the risk of corruption varies markedly between regions.
To boost economic development and the impact of cohesion policy in EU regions,
the efficiency and transparency of public institutions as well as the
effectiveness of justice systems need to be improved. Reforms are also needed
to reduce regulatory obstacles and improve the functioning of the labour
market.
9. National public investment has not yet fully
recovered
The EU economy is gradually recovering from a protracted period of crisis
which featured a significant reduction in investment in many Member States
and regions. Total investment as a share of GDP fell and has hardly grown
since.
As the EU economy has recovered, government debt in Member States has
started to decline from a peak of 87%, but is still well above its level in 2007
of 58%. As a result of pressure on public finances, public investment in the
9	 European Commission ‘A new skills agenda for Europe’, COM(2016) 381 of 2 June 2016.
10	European Commission ‘Competitiveness in low-income and low-growth regions — The lagging regions
report’, SWD(2017) 132 final of 10 April 2017.
11	World Bank. (2017a).
Seventh Report on economic, social and territorial cohesion
xxii
EU fell from 3.4% of GDP in 2008 to 2.7% in 2016. In a number of Member
States, the reduction in growth-friendly expenditure has been substantial.
Since most of these Members States have a GDP per head below the EU average,
the reduction could put at risk disparities across the EU narrowing in the
future.
Public investment was at the core of the negotiations on the current legal
framework of the ESI Funds. One of the major objectives was to improve the
consistency between the Funds and European economic governance with the
aim of ensuring that the effectiveness of expenditure financed by them is
underpinned by sound economic policies.
For this reason, Article 23 of Regulation (EU) No 1303/2013 provides the
Commission with (i) the power to request changes in programmes to address
economic policy priorities recommended by the Council and (ii) the obligation
to suspend the funds in cases of non-effective action by the Member State
to address an excessive government deficit or excessive macroeconomic imbalance.
The SWD assesses the application of this article and explains why a
legislative proposal to modify it is at this stage not deemed necessary by the
Commission (see chapter 5).
10. Cohesion policy’s key role in public investment
reduced the impact of crisis
Cohesion policy is the EU’s main investment policy, providing funding equivalent
to 8.5% of government capital investment in the EU, a figure which
rises to 41% for the EU‑13 and to over 50% for a number of countries (see
Figure 1).
This investment adds value at the European level by contributing to:
0
10
20
30
40
50
60
70
80
90
0
10
20
30
40
50
60
70
80
90
PT HR LT PL LV HU SK BG RO EE CZ EL MT SI CY ES IT DE IE BE FR UK FI AT SE NL DK LU
% of public investment 2015-2017
Cohesion policy funding as an estimated share of public investment,
2015-2017
Figure 1
Source: Eurostat, DG REGIO
Highlights
xxiii
•• The Treaty objective of reducing disparities, notably in terms of income
per head and living standards as well as social inclusion and employment
opportunities.
•• European public goods such as innovation and digital infrastructure,
skills, addressing climate change, disaster risk reduction, energy and environmental
transition, healthcare and social investment, public and smart
transport.
•• Spill-over benefits to non-cohesion countries from the increased trade
generated and from cross-border, transnational and inter-regional
programmes.
The strong EU added-value of cohesion policy was emphasised by many of
the speakers at the Cohesion Forum in June 2017 who stressed that it helped
less developed regions to catch up and all regions to invest in EU priorities and
address new challenges.
The impact of cohesion policy on the EU economies is significant and the effects
of investments build up over the long term. For the EU‑12 countries (i.e.
excluding Croatia), the QUEST model estimates that investment for the 2007–
2013 period increased their GDP by 3% in 2015, and by a similar amount for
the 2014–2020 period12
in 2023.
This has contributed to a significant convergence of GDP per head in these
countries13
. In the EU‑12, this increased from 54% of the EU average in 2006
to 67% in 2015. Moreover, the 2007–2013 programmes led directly to the
creation of 1.2 million jobs in supported enterprises.
The non-cohesion countries also benefit from spillovers generated by investments
in cohesion countries both directly (through selling investment
goods) and indirectly (through higher income and therefore increased trade).
By 2023, 2007–2013 programmes are estimated to add 0.12% to GDP in
non-cohesion countries, a quarter of which is due to spillovers from spending
in cohesion countries. This effect is particularly pronounced for Austria and
Germany because of their close trading links.
The 2014–2020 programmes plan to support 1.1 million SMEs, leading directly
to the creation of a further 420 000 new jobs14
. The programmes plan
to help more than 7.4 million unemployed people to find a job and to help
another 2.2 million people within six months of completing training co-funded
12	This time for the EU‑13, i.e. including Croatia.
13	In purchasing power standards. 2006 was chosen as the baseline year, since it was the year preceding
the 2007–2013 programmes, as well as the year preceding the accession of Bulgaria and Romania.
2015 was the latest year for these data series at the time of publication.
14	The number of new jobs of this period is lower compared to last period because a) innovative, sustainable
and high added value jobs are targeted and b) the number at the end of the period is typically
considerably higher than the number estimated at the start of the period. See Communication
‘Strengthening Innovation in Europe’s Regions Strategies for resilient, inclusive and sustainable growth’,
COM(2017) 376 final of 18 July 2017.
Seventh Report on economic, social and territorial cohesion
xxiv
by the programmes. In addition, the programmes will help over 8.9 million
people gain new qualifications.
Significant funding is being invested in the digital economy, where €16 billion
is earmarked for the development of e-government, ICT services and applications
for SMEs, high speed broadband, smart grids and intelligent energy distribution
systems, and large scale data centres. Such investment is expected
to provide 14.5 million additional households with broadband access.
Cohesion policy is making a substantial investment in environmental protection
and energy efficiency. An extra 17 million people are planned to be connected
to wastewater treatment facilities, and 3.3 million more to smart grids,
while 870 000 households will be helped to reduce their energy consumption.
Moreover, investment in transport will remove bottlenecks, reduce travel times
and lead to more urban trams and metros. The programmes plan to renovate
more than 4 600 km of TEN-T railway lines, construct 2 000 km of new TEN-T
roads and construct or improve 750 km tram and metro lines.
Cohesion policy is also making a substantial investment in social infrastructure.
Some 6.8 million children will gain access to new or modernised schools
and childcare facilities and 42 million people to improved healthcare services.
Territorial cooperation programmes are expected to see 240 000 people participate
in cross-border mobility initiatives and 6 900 businesses and 1 400
research institutions in research projects.
Several measures to improve the quality of investments have been introduced
for the 2014–2020 period:
•• Ex ante conditionalities, which are preconditions attached to the programmes
and which tackle the major systemic bottlenecks hindering effective
public investment. These have led to the speeding up of ongoing
reforms and the initiation of additional reforms. They have also strengthened
the administrative capacity to implement EU rules relating to public
procurement, state aid, environmental legislation and anti-discrimination15
.
•• Smart specialisation, which is the most comprehensive decentralised,
innovation and industrial policy in Europe today. It brings together the
key players — the research community, business, higher education, public
authorities and civil society — to target support in line with local potential
and market opportunities. The goal is to achieve critical mass, innovation
and a move up the value chain.
•• A stronger focus on results, which means that programmes must set
specific objectives, translated into clear result indicators with targets and
benchmarks. Regular reports show whether the programmes are achieving
15	European Commission (2017e).
Highlights
xxv
their goals and key indicators can be tracked online on an open data platform
to check their progress. There is also a performance reserve which
can be released if pre-set targets are met.
The funding allocated to projects selected by the 2014–2020 programmes up
to July 2017, amounts to 39% of the total available. Though this is similar to
the previous period, implementation has been slow which suggests that simplification
and capacity concerns need to be further addressed. It is still too
early to monitor progress towards achieving targets which will only become
apparent once more projects have been completed.
11. Cohesion policy and the future of Europe
The White Paper on the Future of Europe16
launched a debate on which direction
the EU should take in the coming years. Together with its 5 reflection
papers, it covers three main linked questions relating to cohesion policy:
1	 Where should it invest?
2	 What should the investment priorities be?
3	 How should the policy be implemented?
These questions are summarised below in relation to the challenges identified
in the present report. Two important agreements which cohesion policy needs
to take account of are the COP21 agreement on climate change and the UN
Sustainable Development Goals for 2030.
The Commission’s reflection paper on the future of EU17
finances poses the
question of whether cohesion policy should invest outside less developed regions
and cross-border ones.
From its inception, cohesion policy has had a particular focus on less developed
regions and territorial cooperation. It has also invested in other areas
that are mentioned in the Treaty, such as areas undergoing industrial transition,
rural areas and the outermost regions. It has invested too in areas of
high unemployment and deprived urban areas. For the last two programming
periods, cohesion policy has covered all regions.
The present report shows that the impact of globalisation, migration, poverty
and a lack of innovation, climate change, energy transition and pollution is not
limited to less developed regions.
Future funding for cross border cooperation should continue to focus on areas
of particular EU value-added and resolve cross-border problems, such as
gaps and missing links in different policy fields, including transport. Finally, the
16	European Commission ‘White paper on the Future of Europe Reflections and scenarios for the EU‑27 by
2025’, COM(2017) 2025 of 1 March 2017.
17	European Commission ‘Reflection paper on the future of EU finances’, COM(2017) 358 of 28 June 2017.
Seventh Report on economic, social and territorial cohesion
xxvi
pooling of joint public services in neighbouring border regions and institutionbuilding
needs could also be taken into account18
.
The reflection paper on EU finances states, more generally, that all EU funding
needs to focus on areas where the highest EU value-added can be achieved.
Social inclusion, employment, skills, research and innovation, climate change,
energy and environmental transition are identified as the areas which cohesion
policy needs to focus on. In addition, the reflection paper highlights other
areas where cohesion policy has a positive impact, such as support for SMEs,
healthcare and social infrastructure, transport and digital infrastructure. Last
but not least, it underlines the need to address migration and globalisation.
Both the reflection paper and the present report argue that poor institutional
quality reduces competitiveness, the impact of investment and economic
growth. Improving the quality of government, implementing structural reforms
and strengthening administrative capacity should be further emphasised.
They stress that the link with economic governance and the European
Semester may need to be strengthened to ensure that the system is simpler,
transparent and provides positive incentives to implement concrete reforms
to foster convergence. This may require new approaches, for example through
better coordination of available instruments and closer involvement of the
Commission. The lagging regions initiative19
contained several successful elements
which could be extended. The need to improve institutions is also
demonstrated by calls to make the disbursement of EU funds conditional on
legislation and institutions adhering to common EU values.
In addition to the issues raised above about the territorial coverage and investment
priorities, the reflection paper considers a number of options to improve
the implementation of cohesion policy:
•• A single set of rules for existing funds, would ensure more coherent investment
and make it easier for beneficiaries. Coherence could also be
improved by a single rule book for cohesion policy and other funding instruments
with programmes or projects of the same type. This should
lead to stronger complementarity between cohesion policy and innovation
or infrastructure funding.
•• The system of allocation of the funds could be revised by adding criteria
linked to the challenges the EU faces, from demographics and unemployment
to social inclusion and migration, from innovation to climate change.
•• The levels of national co-financing for cohesion policy could be increased
to better align them for different countries and regions and to increase the
sense of ownership in the policy.
18	European Commission ‘Boosting growth and cohesion in EU border regions’, COM(2017) 534 of 20
September 2017.
19	European Commission (2017a).
Highlights
xxvii
•• An unallocated proportion of funding could make cohesion policy more
flexible and able to respond to new challenges more quickly.
•• Faster implementation and a smoother transition between programming
periods could be achieved by changes, such as stricter decommitment
rules, shortening procedures for closing programmes and speeding up the
processes for appointing the management authorities and for programming
and making them more flexible.
•• Complementarity between financial instruments could be enhanced.
Upstream coordination, the same rules and clearer demarcation of interventions
could ensure complementarity between the European Fund for
Strategic Investment, the new pan-European Venture Capital Fund and
the loan, guarantee and equity instruments managed by Member States
under cohesion policy.
•• Finally, the policy has become increasingly complex to manage. Therefore,
a much more radical approach to simplifying implementation is needed.
Next, cohesion policy stakeholders and the general public will be invited to
participate in the public consultation as part of the impact assessment. In
May 2018, the Commission plans to adopt the proposal for the multi-annual
financial framework, followed by the proposals for cohesion policy post 2020.
Seventh Report on economic, social and territorial cohesion
xxviii
1
Economic cohesion
•• After the double dip recession in 2008 and 2011, the EU economy is now growing
again, with growth being particularly high in low-income countries.
•• The crisis reversed the long-term trend towards a narrowing of regional disparities
in GDP per head and employment. However, the first signs of convergence resuming
are evident, though in many regions GDP per head and employment remain below
their pre-crisis levels.
•• GDP per head in the less developed regions is converging towards the EU average
through both faster productivity growth and increased employment.
•• The regions with high GDP per head have grown faster than the EU average, in part
because they have benefited from the agglomeration economies from the national
capital or a large city being located there. These benefits can be further extended
by improving links between a large city and its rural hinterland or between smaller
cities to enable specialised services to be shared and economies of scale to be
realised.
•• The regions with a GDP per head between 75% and 120% of the EU average
seem stuck in a ‘middle-income trap.’ Between 2000 and 2015, their GDP per head
growth was far below the EU average. Their manufacturing sectors are smaller and
weaker than those in regions with a lower or higher GDP per head. Their costs are
too high and their innovation systems not strong enough to be competitive at the
global level.
•• Innovation in the EU remains highly concentrated. In north-western EU countries
States, however, good regional connections, a skilled labour force and an attractive
business environment have enabled surrounding regions to benefit from proximity
to highly innovative ones. In southern and eastern EU countries, the most innovative
regions are less strong and, accordingly, other regions close to them enjoy little
benefit.
chapter1
Seventh Report on economic, social and territorial cohesion
2
the differential trends in GDP per head across the
EU and in the impact of globalisation as well as
the factors underlying regional competitiveness,
such as the extent of tertiary education, entrepreneurship,
innovation and digital and transport
networks. It also presents an aggregate indicator,
the Regional Competitiveness Index, intended to
summarise the different dimensions of competitiveness
of EU regions.
The main concern throughout is to highlight the
performance of the less developed regions and of
different types of area, cities and rural areas, in
particular.
2. Economic trends among EU
regions and Member States
2.1 Convergence back on track
In 2015, more than one in four EU residents (27%)
lived in a (NUTS 2) region with a GDP per head,
in PPS terms1
, below 75% of the EU average
(Map 1.1).
Most of them are located in central and eastern
EU Member States, Greece, Portugal, Spain, and
southern Italy. They also include most of the outermost
regions.2
In Bulgaria and Romania, GDP per
head is below 50% of the EU average in all regions,
except for the capital city regions of Yugozapaden
and Bucureşti-llfov.
Between 2000 and 2015, GDP per head increased
relative to the EU average in all the regions in the
central and eastern Member States (Map 1.2).
1	 Gross Domestic Product (GDP) per head in Purchasing Power
Standards is the total value of all goods and services produced
per inhabitant. Purchasing Power Standards (PPS) adjusts for differences
between countries in purchasing power due to differences
in price levels.
2	 The European Union includes 9 outermost regions, which are a
long way from the European continent. They are: Guadeloupe, La
Réunion, Mayotte, French Guiana, Martinique and Saint-Martin
(France); Madeira and Azores (Portugal) and Canary Islands
(Spain).
1. Introduction
Regional economic divergence has become a
threat to economic progress in the EU (Iammarino
et al., 2017) at a time when globalisation poses
new challenges to economic cohesion. While the
evidence suggests that the EU economy as a
whole has benefited and continues to benefit from
globalisation, these benefits are not automatically
and evenly transmitted to all European regions
(European Commission, 2017c).
Cohesion Policy has invested heavily in reducing
economic disparities across EU regions. It has cofinanced
investment in innovation, education and
digital and transport networks, so helping to create
a single market that boosts growth, productivity
and specialisation in areas of comparative
advantage in all regions. As such, it strengthens
the position of EU enterprises in global markets
where they have to compete with both firms from
low-cost locations and highly innovative ones.
The crisis has been highly disruptive in many parts
of the EU. It has reversed the long-term trend towards
a narrowing of regional disparities. It has
led to reductions in economic activity and employment
in many Member States. However, the first
signs of the convergence process resuming can be
detected. Nevertheless, many regions still have a
GDP per head and employment rate below their
pre-crisis level.
Cohesion Policy has made a substantial contribution
to economic cohesion. In the years between
2007 and 2014, around 400 000 SMEs received
support under cohesion policy and more than 1 million
new jobs were directly created. Nevertheless
economic disparities still remain, requiring substantial
amounts of investment beyond the present
programming period if they are to be reduced.
This chapter describes recent trends in economic
cohesion in regions and cities in the EU. It covers
Chapter 1
Economic cohesion
Chapter1:Economiccohesion
3
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.1 GDP per head (PPS), 2015
< 50
50 – 75
75 – 90
90 – 100
100 – 125
>= 125
Index, EU-28 = 100
Values for the two regions in Ireland for 2015 have been estimated
from the 2014 figures on the basis of the changes in the national figure.
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.2 Change in GDP per head, 2000–2015
< -20
-20 – -15
-15 – -10
-10 – -5
-5 – 0
0 – 5
5 – 10
10 – 15
15 – 20
20 – 25
> 25
no data
Index point difference
EU-28 = 0
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Seventh Report on economic, social and territorial cohesion
4
Growth was particularly high over the period in the
capital city regions in Romania (from 56% of the
EU average to 136%) and Bulgaria (from 38% to
76% of EU average in 2015).
In Greece, the situation deteriorated. In 2008,
three of the 13 regions had a GDP per head above
75% of the EU average, in 2015, just two — the
capital city region Attiki (95%) and Notio Aigaio,
the southern Aegean islands (75%).
In Portugal, only two regions in 2015 had a GDP
per head above the 75% threshold, Lisbon (103%)
and Algarve (79%), in both substantially lower
than in 2008 before the crisis.
There are signs that the long-run process of regional
convergence, which was interrupted by the
economic crisis, has resumed. Prior to the crisis,
disparities in GDP per head in the EU were shrinking
(the coefficient of variation falling by 12% between
2000 and 2008), mainly due to regions with
the lowest levels of GDP per head growing faster
than average (Figure 1.1). In the crisis years, between
2008 and 2014, however, regional disparities
widened slightly (the coefficient of variation
increased by 4% between 2008 and 2014, but
remained well below the level in 2000). In 2015,
disparities started to narrow again, though it is too
early to say if this will be sustained.
Regional disparities in employment rates narrowed
from 2013, though this was preceded by a significant
increase as the result of the crisis and disparities
in 2016 were much wider than in 2008.
By contrast, reflecting the increased participation
in the labour market, disparities in regional unemployment
rates continued to widen, though at a
slower pace than before 2012.
Between 2000 and 2008, all the regions in the
EU‑13 except Malta converged to the EU average
(Map 1.3), with big strides (more than 20 index
points) in the capital regions of Bulgaria, the Czech
Republic, Hungary, Romania and Slovakia as well
as in the Baltic States. Most of the Greek regions
converged, while the Italian regions and mainland
Portugal diverged.
Between 2008 and 2015, all the regions in the
EU‑13 converged except Cyprus and Praha.
(Map 1.4). The Baltic States who were hit hard
by the crisis still converged. Greek regions experienced
big reductions in their GDP per head relative
to the EU average, more than reversing the
convergence achieved between 2000 and 2008.
Almost all Portuguese and Italian regions continued
to diverge. Spain was also affected by the crisis
and diverged, but not to the same extent as
Greece.
70
75
80
85
90
95
100
105
110
115
70
75
80
85
90
95
100
105
110
115
2000 2002 2004 2006 2008 2010 2012 2014 2016
GDP per head Unemployment rate Employment rateCoefficient of variation, 2000 = 100
Coefficient of variation of GDP per head, employment rate (20-64) and
unemployment rate in EU-28 NUTS 2 regions, 2000-2016 (indices, 2000=100)
Figure 1.1
Source: Eurostat, DG REGIO calculations
The coefficient of the variation is weighted by the population of each region
Chapter1:Economiccohesion
5
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.4 Change in GDP per head index, 2008–2015
< -20
-20 – -15
-15 – -10
-10 – -5
-5 – 0
0 – 5
5 – 10
10 – 15
15 – 20
20 – 25
> 25
no data
Index point difference
EU-28 = 0
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.3 Change in GDP per head index, 2000–2008
< -20
-20 – -15
-15 – -10
-10 – -5
-5 – 0
0 – 5
5 – 10
10 – 15
15 – 20
20 – 25
> 25
no data
Index point difference
EU-28 = 0
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Seventh Report on economic, social and territorial cohesion
6
Overall, the biggest relative increases in GDP per
head between 2000 and 2015 occurred in the
EU‑13, while the biggest reductions were in Greece
and Italy, in the latter both before and after the
crisis (Map 1.2). But a few regions in Belgium, the
Netherlands, France and the UK also experienced
big falls.
Mainstream economic growth theories predict
that the lower the initial GDP per head the higher
growth will be. Indeed, growth was higher than average
in both the less developed and transition regions
(located mostly in less developed and moderately
developed Member States,3
Figure 1.2),
with GDP per head in regions in less developed and
moderately developed Member States growing at
a faster pace than the EU average.
The economic and financial crisis led to a reduction
in GDP per head between 2009 and 2015 in
around 40% of regions, located mainly in Ireland,
Italy, Spain, Portugal and Greece; in most Greek
regions, the reduction amounting to over 3% a
year. The process of convergence was halted with
several of the less developed and transition regions
growing more slowly than the EU average
(Figure 1.3).
From 2000 onwards convergence was mainly driven
by the catching up of the less developed economies.
GDP per head, therefore, grew faster in real
terms in the less developed Member States than in
others over the period 2001–2016, except in 2010
and 2011, and it is forecast to continue to do so in
2017 and 2018 (Figure 1.4).
From 2011 to 2013 the average growth rate in the
moderately developed Member States was below
that in the highly developed Member States, i.e.
diverging. Only in 2014 did it overtake the rate in
the highly developed Member States and growth in
their GDP per head is forecast to be around 2.5%
in both 2017 and 2018 (as against 3.5% in less
developed Member States).
3	 See the Lexicon section for the list of less developed and moderately
developed Member States.
EU outermost regions
The European Union includes 9 outermost regions,
which are geographically remote from the
continent and located in the Caribbean basin, the
Macaronesia area and the Indian Ocean. They
are governed by the provisions of the Treaties
and are an integral part of the Union.
Around 5 million people live in the outermost regions.
Some of them have significant population
growth due to inward migration and the average
age for most of them is much lower than in their
respective countries on the mainland.
The outermost regions have a level of GDP per
head below the EU average. In Mayotte, with
a population of around 213 000, it is barely a
quarter of the average. They also have high unemployment,
higher than on the mainland, particularly
among young people (15–24), the rate
being around 47% in Guadeloupe, 51% in the
Canary Islands, and 55% in Mayotte. They represent
an asset for the EU in many areas, in biodiversity,
climate change adaptation and mitigation,
green growth and the circular economy and
are actively involved in many areas of research,
such as renewable energy, marine science and
space. However, because of their remoteness,
their difficult topography and climate, their small
markets and the fact that 8 of them are islands,
they have a special status under the Treaty (under
Article 349 of the TFEU). This distinguishes
them from other regions in the EU and from the
Overseas Countries and Territories (OCTs) that
are associated to the Union.
In the Autumn 2017, the Commission will adopt
a new strategy for them, inspired by the work of
the 4th Forum on the Outermost Regions held
in March 2017 and by the proposals submitted
by them, the Member States concerned and the
European Parliament.
Chapter 1: Economic cohesion
7
0
1
2
3
4
5
6
7
8
9
10
11
12
13
0
1
2
3
4
5
6
7
8
9
10
11
12
13
SK LT LV RO EE BG PL HR SI CZ HU EL CY PT
Average regional growth Average EU growth Average national growthAnnual average change (%)
Growth rates of GDP per head of regions in less developed and moderately
developed Member States, 2001-2008
Figure 1.2
Source: Eurostat, DG REGIO calculations
PL: 2004-2008; capital regions are indicated in red. Regions are ranked by growth rates for the period 2001-2008.
-5
-4
-3
-2
-1
0
1
2
3
4
-5
-4
-3
-2
-1
0
1
2
3
4
SK LT LV RO EE BG PL HR SI CZ HU EL CY PT
Average regional growth Average national growth Average EU growthAnnual average change (%)
Growth rates of GDP per head of regions in less developed and moderately
developed Member States, 2009-2015
Figure 1.3
Source: Eurostat, DG REGIO calculations
Capital regions are indicated in red. Regions are ranked by growth rates for the period 2001-2008.
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Highly developed Member States
Moderately developed Member States
Less developed Member statesAnnual average change (%)
Growth of GDP per head in real terms, EU-28, 2001-2018Figure 1.4
Source: EUROSTAT, DG ECFIN, DG REGIO calculations
Forecast
Seventh Report on economic, social and territorial cohesion
8
Determinants of GDP growth across NUTS 3 regions
According to mainstream economics, initial socioeconomic
conditions are major determinants of
growth of GDP per head in a given period. This relationship
is examined below for the years 2000–
2014. For more details on this analysis see Lavalle
et al. (2017).
Determinants of GDP growth and the role of spatial
spill-overs
Spatial spill-overs are the effect of economic growth
in one region on growth in neighbouring ones. This
can be positive, so that growth in regions close to
each other is self-reinforcing, or negative, so that
a region grows at the expense of surrounding ones.
Figure 1.5 shows that regions in the EU with high
growth rates are predominantly surrounded by other
high-growth regions, in that there is a relative concentration
of such regions in the top right quadrant
(and relatively few in the bottom right quadrant). At
the same time, regions with low growth are mostly
surrounded by other low-growth ones, with most of
them in the bottom-left quadrant rather than the
top left.
The relationship between regional growth and initial
conditions is examined on the basis of a spatial
lag model, which assumes that economic growth
in a region is determined by the average growth in
surrounding ones together with a set of additional
factors which explain differences in growth between
regions. Formally, the model is defined as:
Y=ρWY+Xβ+u
where Y is the growth rate of GDP per head, X is a
set of regional-specific features and W is a matrix
describing the spatial link between regions. Specifically,
two regions are considered neighbours if
they are within 150 minutes of travel time by road
(based on the JRC-Trans Tools model).
The direct effect in the table below measures the
impact of the explanatory variables on the region
itself, the indirect effect, the impact of the explanatory
variables in neighbouring regions on the region,
which, accordingly, captures spatial spill-over
effects.
Table 1.1 Estimation results (‘+’ is a positive impact;’–’ is a negative impact)
Dependent variable: growth of GDP per head 2000–2014
Variable Direct effect Indirect effect Direct effect Indirect effect
Initial GDP per head in 2000 (log) - - - Share
of population aged 25–64 with
upper secondary education in 2000
+ + + +
Share of population aged 25–64
with tertiary education in 2000
+ + + +
Agglomeration 2000 + + + +
Share of employment in
tradable sectors in 2000
+ +
Share of employment in non-tradable
sectors in 2000
- GDP
per head growth in neighbouring regions + +
R² 0.80 0.80
All the coefficients are statistically different from zero at the 1%, 5% or 10% level. The share of employment in tradable and nontradable
sectors cannot be included in the same regression because they sum to 1.
-0.04
-0.02
0.00
0.02
0.04
0.06
0.08
-0.04
-0.02
0
0.02
0.04
0.06
0.08
-0.04 -0.02 0 0.02 0.04 0.06 0.08
GrowthofGDPperheadin
neighbouringregions,2000-2014
Spatial spill-overs of economic growth
between regions in the EU, 2000-2014
Figure 1.5
Growth of GDP per head in the region, 2000-2014
Low growth
surrounded by
high growth
High growth
surrounded by
high growth
High growth
surrounded by
low growth
Low growth
surrounded by
low growth
Source: Lavalle et al. (2017)
Chapter 1: Economic cohesion
9
According to a European Commission reflection paper
(European Commission, 2017c), globalisation
has a highly differentiated impact on EU regions.
While some are well positioned to take advantage
of the new opportunities it offers, others are hit by
job losses, stagnating wages and shrinking market
shares due to low-cost competitors moving into
more technologically advanced sectors.
The best response to globalisation is a continuous
effort to move up the value chain. This requires
innovation, entrepreneurship, knowledge transfer
and continuous upgrading of the skills of the labour
force. Regions that are innovative and have a
large share of high-skilled jobs and a highly educated
work force are less likely to be hit hard by
heavy job losses than others.
There are four important risk factors linked to globalisation
and technological change: (1) a large
share of employment in low-tech manufacturing,
(2) rapidly increasing unit labour costs in manufacturing
over the past decade which may compromise
competitiveness and reduce market share,
(3) a large share of working-age population with
low educational attainment, and (4) a decline in
employment in industry between 2000 and 2014
(Map 1.5). Some 9% of EU regions, located in 7 different
Member States, are at risk from globalisation
by being exposed to up to four of these factors.
Most are located in southern or central and
eastern Europe, though there are also high risk
regions in Denmark, France, Ireland and the UK. In
many Member States, the situation is diverse with
some regions being subject to three or four risks
and others only one or none at all. These risks may
diminish over time, though probably only slowly
since changes in innovation or education attainment
levels take time to be accomplished.
2.2 Less developed regions maintain a
strong manufacturing sector, but their
agriculture needs to modernise
In 1995, industry, excluding construction (i.e. mainly
manufacturing), accounted for around 21% of
both total employment and gross value-added
(GVA) in the EU. The rise of services, automation
in manufacturing and the relocation of parts of
it to emerging economies has led to a steady reduction
in both shares since then, to 19% in the
case of GVA and 16% in the case of employment
(Figure 1.6).
In less developed regions, the share of both GVA
and employment in industry is, on average, larger
than in the more developed and transition ones4
.
Moreover, the share of GVA increased over the period
(from 21% to 24%) while the share of employment
declined — though by less than in other
4	 See Lexicon for a definition of ‘less developed’, ‘transition’ and
‘more developed’ regions.
The main results can be summarised as follows:
Spatial spill-overs between regions are of major
importance. Around half of the growth in a region
over the period is explained by growth occurring in
neighbouring ones.
Less developed economies are catching up. GDP
per head in the initial year has a negative impact,
implying that less developed regions tended to grow
faster than more developed ones and will eventually
catch up with the more developed ones.
Upper secondary and tertiary education are
strong drivers of growth. Highly-educated people
can move or commute to neighbouring regions or
work in companies that are linked to others in these
regions, so increasing their growth.
Agglomeration economies are confirmed as a
driver of economic growth. Agglomeration means
economies of scale, higher probability of innovation
and concentration of high level services that are
fundamental for growth. In addition, agglomeration
produces a direct and an indirect effect on growth
due to greater interaction between firms as well as
people.
Tradable sectors have a positive impact on economic
growth. In this case the channels of the indirect
effect might be related to commuting or subcontracting
relationships.
Seventh Report on economic, social and territorial cohesion
10
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.5 Risk factors linked to globalisation and technological change
0
1
2
3
4
Number of risk factors out of 4 (see footnote)
A risk factor is defined as a negative value for the first indicator and
a value above the EU regions average for the next indicators:
- Employment growth in the industry between 2000 and 2014;
- Share in employment of low-technology manufacturing, 2016;
- Share of people between 25 and 64 with a low educational attainment, 2016;
- Change in manufacturing ULC between 2003 and 2014.
Source: Eurostat, DG REGIO calculations
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Chapter 1: Economic cohesion
11
regions — implying an increase in productivity in
industry relative to other sectors.
The reduction in the share of employment in agriculture
in the EU over the 20 year-period was
substantial, especially in less developed regions.
In 1995, it accounted for around 9% of total employment
and by 2012, the share had fallen to 5%
when, because of low productivity — partly reflecting
subsistence farming in EU‑13 countries — the
share of GVA was under 2% (Figure 1.7). In less
developed regions, the share fell from 22% to
14% between 1995 and 2014 and, as productivity
increases, it is likely that it will fall further.
As the number of jobs in less productive segments
of agriculture and industry declines, more jobs
may be created in services and more advanced
areas of industry and agriculture. Regions can indeed
choose not to abandon agriculture and industry.
Within global value chains, economies can
increase their productivity by upgrading to higher
value segments within the same sector (Shepherd,
2013). In addition, automation has made labour
costs less relevant and may bring back some manufacturing
firms to the EU, but the jobs they will
offer will be different from those that were moved
away in past years (European Commission, 2017c,
OECD, 2016a; and Eurofound, 2016). Training may
help workers losing their jobs to gain new ones as
0
1
2
3
4
5
6
0
1
2
3
4
5
6
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
EU-28
Less developed
More developed
Transition
% of total
GVAFigure 1.7b
Source: Cambridge Econometrics, DG REGIO calculations
0
5
10
15
20
25
0
5
10
15
20
25
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
EU-28
Less developed
More developed
Transition
% of total
EmploymentFigure 1.7
Source: Cambridge Econometrics, DG REGIO calculations
10
15
20
25
30
10
15
20
25
30
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
EU-28
Less developed
More developed
Transition
% of total
EmploymentFigure 1.6
Source: Cambridge Econometrics, DG REGIO calculations
15
20
25
15
20
25
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
EU-28
Less developed
More developed
Transition
% of total
GVAFigure 1.6b
Source: Cambridge Econometrics, DG REGIO calculations
Figure 1.6 Employment and GVA shares in industry (excluding construction) 1995–2014
Figure 1.7 Employment and GVA shares in agriculture 1995–2014
Seventh Report on economic, social and territorial cohesion
12
the structure of economic activity shifts, but there
is a limit to what it can achieve.
3. Productivity in less developed
Member States is catching up
Less developed Member States tend to have a different
economic structure than the others, with
more employment in agriculture and industry
(Table 1.2)5
. In 2016, the share of employment
in agriculture was 11 percentage points higher in
less developed Member States than in highly developed
Member States (13% versus 2%). In 2016,
the share of their employment in industry was
around 21% (i.e. the same as in less developed
regions), and 7 percentage points larger than in
highly developed Member States (14% as in more
developed regions).
5	 This section analyses data at the country level because of the
unavailability of regional data on sectoral employment for 2015
and 2016 and partly for 2014.
The EU Common Agricultural Policy and the LEADER approach
The EU Common Agricultural Policy (CAP) addresses
matters of high societal value in relation to agriculture
and rural areas. About half of the EU’s territory
is farmed and the primary agricultural sector
accounts for a 5% share in total employment, with
11 million farms providing work for roughly 22 million
people. Together with food processing, food retail
and food services, agriculture makes up a sector
providing nearly 44 million jobs. The CAP contributes
to smart, sustainable and inclusive growth in
the EU through a range of policy tools which provide
support to agriculture, food and forestry sectors
as well as other entities operating in rural areas
such as non-agricultural businesses, NGOs and local
authorities.
The CAP is aimed at improving the economic viability
and sustainability of farming and rural businesses
through support to knowledge transfer and
innovation, investment in green technologies, training,
entrepreneurship and networking as well as access
to essential services and the social inclusion
of migrants and Roma. It also ensures, a basic level
of income support to farmers and helps them run
their businesses in a sustainable way by fostering
the preservation of natural resources and environmentally
sustainable land management.
The CAP is composed of two strands, financed, by
the European Agricultural Guarantee Fund (EAGF)
and the European Agricultural Fund for Rural Development
(EAFRD), the two amounting to €408.3 billion
in the 2014–2020 period.
Rural development policy is part of the Common
Strategic Framework (CSF) for Cohesion Policy
2014–2020. Its objective is to enhance the economic
resilience of the farm sector and non-agricultural
businesses by supporting investments, knowledgebuilding
and various forms of cooperation and innovation
in the rural areas. Rural development also
provides for payments to farmers who commit
themselves to provide public goods through environment
and climate-related actions going beyond
mandatory requirements.
In the 2014–2020 programming period, rural development
plays an important role in making rural areas
a better place to live and work, and in promoting
a more inclusive society. A wide range of measures
contributes to EU cohesion objectives, including operations
facilitating diversification and creation of
new small enterprises, job creation, and enhancing
accessibility to ICT in rural areas or fostering local
development.
LEADER is a local development programme which
for 20 years has involved local communities in the
design and implementation of policies and resource
allocation for the development of rural areas. For
the 2014–2020 period almost €6.9 billion (7% of
the EAFRD) has been allocated to the programme.
LEADER operates through Local Action Groups
(LAGs) which are intended to be inclusive and outward
looking in order to involve both key stakeholders
in the area and marginalised groups. In 2014–
2020, 2 536 LAGs will be set up across the EU with
the aim of implementing local development strategies
which, among other outcomes, are expected to
create 46 000 new jobs.
Chapter1:Economiccohesion
13
Less
developed
Moderately
developed
Highly
developed
EU-28 Less
developed
Moderately
developed
Highly
developed
EU-28
Share in 2016 (%)
A: Agriculture, forestry and fishing 13.3 5.6 2.4 4.5 3.5 2.5 1.3 1.5
B-E: Industry (except construction) 20.6 23.0 13.5 15.3 23.0 24.6 18.8 19.3
F: Construction 6.8 7.0 6.2 6.3 6.0 5.3 5.0 5.1
G-J: Wholesale and retail trade; et al. 26.8 27.4 27.9 27.7 27.7 26.1 24.5 24.8
K-N: Financial and insurance activities; et al 9.6 13.2 18.2 16.4 21.8 22.0 27.9 27.2
O-U: Public administration; et al. 22.9 23.7 31.9 29.7 18.0 19.5 22.6 22.1
Total 100 100 100 100 100 100 100 100
Annual average % change 2001-2008
A: Agriculture, forestry and fishing -4.9 -2.2 -1.6 -3.6 1.2 0.9 0.6 0.7
B-E: Industry (except construction) 0.1 -0.6 -1.0 -0.7 4.8 5.1 1.2 1.5
F: Construction 5.9 0.5 1.5 2.1 4.8 1.6 1.0 1.3
G-J: Wholesale and retail trade; et al. 2.3 1.9 1.2 1.4 5.1 3.4 2.5 2.8
K-N: Financial and insurance activities; et al 3.9 2.8 2.8 2.9 4.9 3.2 2.3 2.6
O-U: Public administration; et al. 1.0 1.0 1.4 1.3 2.4 1.4 1.4 1.4
Total 0.4 0.7 1.1 0.9 4.4 3.3 1.9 2.1
Annual average % change 2009-2016
A: Agriculture, forestry and fishing -3.1 -2.4 -0.8 -2.1 0.0 1.9 -0.4 -0.1
B-E: Industry (except construction) -1.3 -0.7 -1.2 -1.2 1.6 0.9 0.3 0.4
F: Construction -2.7 -3.5 -2.1 -2.3 -1.7 -4.0 -1.9 -1.9
G-J: Wholesale and retail trade; et al. 0.2 0.2 0.3 0.3 0.5 0.8 1.0 1.0
K-N: Financial and insurance activities; et al 2.2 1.3 1.2 1.3 1.5 0.8 0.9 0.9
O-U: Public administration; et al. 0.6 0.3 0.8 0.8 -0.4 0.1 0.7 0.7
Total -0.5 -0.3 0.2 0.0 0.6 0.4 0.6 0.6
Source: EUROSTAT, DG REGIO calculations
Employment GVA
Green bars indicate positive changes, red bars indicate negative changes.
Less developed: BG, EL, EE, HR, LV, LT, HU, PL, RO; Moderately developed: CZ, CY, PT, SK, SI; Highly developed: BE, DK, IE, ES, FR, DE, IT, LU, MT, NL, AT, FI, SE, UK.
Table 1.2 Employment and GVA by NACE sector and group of Member States, shares in 2016 and changes 2001–2008 and 2009–2016
Seventh Report on economic, social and territorial cohesion
14
Both agriculture and industry lost employment
between 2001 and 2008 and between 2009 and
2016. The pattern for agriculture was the same:
the less developed Member States had the fastest
reduction in agricultural employment, followed by
the moderately developed, with the slowest reduction
in the highly developed Member States. GVA
in agriculture on the other hand grew fastest in
the less developed Members States between 2001
and 2008, but it did not grow at all between 2009
and 2016.
Industrial employment remained constant in the
less developed Member States between 2001
and 2008, while it shrank in the other groups of
Member States. Joining the EU and the single market
has created more potential for specialisation
in higher value-added sectors, so less developed
Member States may have been able to maintain
a larger share of employment in industry because
the balance between labour costs, productivity and
accessibility represented an attractive location for
manufacturers. Industrial GVA in less developed
Member States grew three times faster than in
highly developed Member States between 2001
and 2008 and four times faster between 2009
and 2016.
Employment and GVA in construction grew quickly,
especially in the less developed countries in the
run-up to the crisis and fell sharply between 2009
and 2016 in all three country groups.
Over the period 2001–2008, GVA in industry in
these countries increased by more than in other
sectors, by much the same as in the business and
financial sector (K-N). It increased even over the
crisis years, 2009 to 2013, whereas it declined in
both moderately developed and highly developed
Member States.
By contrast, shares of employment and GVA in the
business and financial sector in the less developed
Member States, which used to be small, increased
towards those in the highly developed countries.
The impact of the crisis was limited, both employThe
European Maritime and Fisheries
Fund
The European Maritime and Fisheries Fund
(EMFF), which has a budget of €6.4 billion budget
for the period 2014–2020, underpins the new
Common Fisheries Policy and supports the diversification
of local maritime economies and their
sustainable development.
Due to the specific scope of the EMFF, support is
concentrated in coastal areas and major freshwater
sites.
The ex post evaluation of the 2007–2013
programmes indicates the following main
achievements:•••  •  
• EMFF support amounted to around 20% of
EU fleet investment over the programming
pe-riod and strengthened competitiveness
by removing unprofitable vessels and by
helping to modernise the remaining fleet and
landing sites.
• Investment in the aquaculture sector was
supported during the financial crisis, so helping
to slow down (or reverse in some Member
States) a downward trend in employment in
the sector.
• EFF financing helped to maintain the competitiveness
of the fish processing industry
through around 8 000 operations across the
EU involving some 2 700 beneficiaries.
• Support led to the creation of around 17 000
new jobs (10 000 in marketing and processing)
over the period and the maintenance
of many more. It also helped to improve
the quality of jobs and working conditions
through investment in safety equipment as
well as in aquaculture, processing, and fishing
ports.
Chapter 1: Economic cohesion
15
ment and GVA continuing to grow after 2008 but
at slower rates than between 2000 and 2008.
The restructuring and modernisation of agriculture
is still ongoing in the less developed Member
States. In 2016, it accounted for 13% of employment
— as against only 2% in the highly developed
Member States — but for only 3.5% of GVA.
Both shares are tending to decline as restructuring
takes place and, along with the shares in moderately
developing countries, are converging towards
those in highly developed countries6
.
3.1 Productivity and employment
contribute to the economic recovery
in the EU
In the years before the crisis, from 2001 to 2008,
GVA per head in the EU grew by 1.7% a year in
real terms, fuelled primarily by productivity growth
of 1.2% a year, with increases in the employment
rate adding another 0.6% a year (Table 1.3).
Productivity growth was also the main source of
growth in GVA per head in less developed Member
States, though both were substantially higher than
the EU average, especially productivity growth (4%
a year).
Between 2009 and 2016, GVA per head in the EU
grew slightly (by 0.3% a year), productivity grew
faster (by 0.6% a year) and the employment rate
by less (0.2% a year), while the share of working-
6	 However, in some Member States agriculture has a social function
as it absorbs labour in times of crises. Of course, this social cushioning
muddles the real productivity figures of the sector.
age population declined (by 0.4% a year) as opposed
to it remaining unchanged as it did between
2001 and 2008. The number of Member States
with a declining share of working-age population
increased markedly between the two periods, from
8 to 27, Luxembourg being the only exception.
Over the 2009–2016 period, the less developed
Member States had the highest growth in GVA per
head (0.9% a year), mainly driven by an increase
in productivity (1.2% a year), with only a slight increase
in the employment rate (0.1% a year) but
offset by a reduction in the share of working-age
population (0.4% a year). The moderately and
highly developed Member States followed a similar
pattern, but with lower growth in GVA per head
(0.4% and 0.2% a year, respectively) and productivity
(0.7% and 0.4% a year).
Between 2009 and 2016, GVA per head grew in
all of the less developed Member States except
Greece (where it fell by 3.2% a year) and Croatia
(by 0.7% a year). Productivity growth was relatively
high (between 1.4% and 2.8%) in five of the
nine countries, but employment rates either fell
or increased only slightly, except in Lithuania and
Hungary.
Among the five moderately developed countries,
GVA per head declined in Cyprus (by 1.8% a year),
Portugal (by 0.2% a year) and Slovenia (by 0.3%
a year) mainly due to a fall in employment rates.
Among the highly developed countries, only Italy
and Finland had a decline in GVA per head (1% a
Decomposing growth in GVA per head
Growth in GVA per head can be broken down into three main components: changes in productivity (GVA per
person employed), changes in the employment rate (employment relative to population of working age,
15–64) and changes in the share of working age population in the total population. Accordingly, the following
identity holds:
GVA
=
GVA
x
Employment
x
Working age population
Total population Employment Working age population Total population
The same identity can be expressed in terms of changes: The change in GVA per head is the sum of the
changes in productivity, in the employment rate and in the share of working age population.
SeventhReportoneconomic,socialandterritorialcohesion
16
Average annual change (%)
GVA per head Productivity
Employment
rate
Share of
working-age
population
GVA per head Productivity
Employment
rate
Share of
working-age
population
EU-28 1.7 1.2 0.6 0.0 0.3 0.6 0.2 -0.4
Less Developed 4.8 4.0 0.5 0.3 0.9 1.2 0.1 -0.4
Greece 3.0 1.8 1.4 -0.2 -3.2 -1.3 -1.4 -0.4
Hungary 3.5 3.7 -0.3 0.1 1.1 -0.1 1.4 -0.3
Poland 4.1 3.1 0.5 0.5 2.9 2.8 0.7 -0.5
Croatia 4.7 2.7 1.6 0.3 -0.7 0.2 -0.9 0.0
Estonia 6.5 4.7 1.7 0.1 0.7 0.8 0.4 -0.5
Bulgaria 7.1 3.8 3.0 0.2 1.8 2.4 0.1 -0.7
Romania 7.7 8.4 -0.7 0.0 1.6 2.4 -0.5 -0.3
Latvia 8.5 5.6 2.5 0.2 1.3 2.1 -0.2 -0.6
Lithuania 8.6 7.1 1.1 0.3 2.2 1.4 1.0 -0.2
Moderately developed 3.0 2.5 0.4 0.1 0.4 0.7 0.1 -0.5
Portugal 0.8 1.1 -0.1 -0.1 -0.2 0.7 -0.6 -0.3
Cyprus 2.3 0.7 1.0 0.5 -1.8 -0.1 -1.7 -0.1
Slovenia 4.1 3.2 1.0 -0.1 -0.3 0.4 -0.3 -0.5
Czech Rep. 4.3 3.6 0.5 0.2 0.7 0.7 0.8 -0.8
Slovakia 6.3 5.0 0.8 0.5 1.7 1.4 0.6 -0.3
Highly developed 1.3 0.8 0.6 -0.1 0.2 0.4 0.1 -0.3
Italy 0.4 -0.3 1.2 -0.5 -1.0 -0.5 -0.4 -0.1
Denmark 0.8 0.3 0.7 -0.2 0.0 0.8 -0.5 -0.3
France 1.0 1.0 0.0 0.0 0.2 0.4 0.1 -0.4
Belgium 1.5 1.0 0.4 0.0 0.2 0.2 0.1 -0.2
Netherlands 1.6 1.0 0.7 -0.1 0.2 0.6 -0.1 -0.4
Spain 1.6 0.1 1.5 0.0 -0.3 1.3 -1.2 -0.4
UK 1.7 1.3 0.1 0.2 0.4 0.2 0.5 -0.4
Germany 1.7 1.3 0.6 -0.2 0.6 0.1 0.9 -0.4
Austria 1.8 1.2 0.6 0.0 -0.1 -0.2 0.3 -0.1
Malta 1.8 1.2 -0.1 0.7 2.9 0.8 2.7 -0.6
Luxembourg 1.9 -0.2 2.0 0.2 0.1 0.1 -0.2 0.2
Ireland 2.0 1.2 0.4 0.4 3.9 4.9 -0.5 -0.5
Sweden 2.1 1.8 0.0 0.3 0.8 0.8 0.5 -0.6
Finland 2.4 1.4 1.1 -0.1 -1.2 -0.5 0.0 -0.6
Green bars indicate positive changes, red bars indicate negative changes.
Source: EUROSTAT, DG REGIO calculations; for Malta, real GDP was used instead of real GVA
2001-2008 2009-2016
Table 1.3 Decomposition of annual average change in GVA per head, 2001–2008 and 2009–2016
Chapter 1: Economic cohesion
17
Regions with expanding non-tradable sectors were harder hit by the 2007–2008
crisis.   
In the years following the 2007–2008 financial crisis,
many regions experienced a continuous decline
in employment. In the Norte region of Portugal, for
example, 150 000 fewer people were employed in
2015 than in 2008 as the total number in work fell
from 1.72 million to 1.57 million. Norte is not
alone in this. Of the 349 large OECD (territorial
level 2, TL2) re-gions, 46% had lower employment
in 2015 than in 2008.
A variety of factors contribute to this lack of resilience
to the crisis. Recent analysis indicates that
the strong presence of tradable sectors supports
the catching up of regions in terms of productivity
(OECD, 2016c). But such sectors are also more
exposed to global developments and more vulnerable
to shocks. Accordingly, there is a question over
whether a strong focus on tradable sectors creates
risks that could be avoided by a focus on sectors
that only serve the local economy.
In practice, employment after 2008 declined by more
in regions in which non-tradable sectors expanded
relative to tradable ones over the years 2000–2007
than in others (Figure 1.8). This may seem surprising,
but non tradable activities are not independent
of global developments. Indeed, they are very much
dependent on what happens to the tradable sector
since much of their sales either go to this sector or
are affected by its performance. For example, estimates
for Sweden indicate that for each job created
in manufacturing between 0.4 and 0.8 jobs are created
in non-tradable services, while estimates for
the United States suggest a local job multiplier of
up to 1.6 (Moretti, 2010; Moretti and Thulin, 2013).
Moreover, whereas non-tradable sectors have to
rely on local demand to pick up after a recession,
tradable sectors have the possibility of developing
new markets where demand is expanding.
(This box is based on a contribution from OECD.)
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
Less than 2.5
percentage points
increase
2.5 to 5 percentage
points
5 to 7.5 percentage
points
More than 7.5
percentage points
increase
Annualaverageemployment
change(%),2008-2014
Annual average employment growth in 2008-14 and the
change in the share of total employment in non-tradable
sectors 2000-2007 in 19 OECD countries
Figure 1.8
Change in the share of non-tradable employment 2000-2007
Data for 203 territorial level 2 (TL2) regions in 19 OECD countries: Austria, Australia, Belgium, Bulgaria, Czech Republic, Denmark,
Finland, Greece, Ireland, Italy, the Netherlands, Portugal, Romania, Slovenia, Slovak Republic, Spain, Sweden, the UK and the US.
Source: OECD (2016c) and OECD Regional Statistics Database.
Seventh Report on economic, social and territorial cohesion
18
year) between 2009 and 2016. Both experienced a
reduction in productivity and the employment rate
fell as well in Italy.
3.2 Capital metropolitan regions more
prone to boom and bust than other
regions
In 2014, metropolitan (metro) regions accounted
for 58% of population in the EU, 61% of employment
and 67% of GDP.
Accordingly, they are major centres of employment
and business activity with higher productivity than
elsewhere.
In both the EU‑15 and EU‑13, real GDP per head
in metro regions grew faster than in other regions
in the pre-crisis years between 2001 and 2008
(Table 1.4). Growth rates in capital city regions
were especially high, mainly fuelled by higher productivity
growth in the EU‑15 and higher employment
growth in the EU‑13.
The crisis had a different effect on the metro regions
in the EU‑15 than on those in the EU‑13. In
the EU‑15, GDP per head in the capital metro regions
declined at the same rate as in other regions
between 2009 and 2014. In the EU‑13, it was
rather stable in the capital metro regions, whereas
it grew in the other regions, mainly fuelled by
increases in productivity. In both the EU‑13 and
EU‑15, there was a reduction in employment in all
types of regions.
Average annual change (%)
GDP per
head Productivity
Employment
per head
GDP per
head Productivity
Employment
per head
EU-15
Capital metropolitan regions 1.5 1.1 0.4 -0.1 0.5 -0.7
Other metropolitan regions 1.2 0.7 0.5 -0.3 0.2 -0.6
Non-metropolitan regions 1.2 0.7 0.5 -0.5 0.2 -0.6
Total 1.3 0.8 0.5 -0.3 0.3 -0.6
EU-13
Capital metropolitan regions 5.6 3.3 2.3 0.2 0.6 -0.5
Other metropolitan regions 4.7 4.1 0.6 1.9 2.2 -0.3
Non-metropolitan regions 4.8 4.5 0.2 1.1 1.7 -0.6
Total 5.1 4.3 0.8 1.1 1.6 -0.5
EU-28
Capital metropolitan regions 2.0 1.1 0.9 -0.1 0.6 -0.6
Other metropolitan regions 1.5 0.9 0.6 -0.2 0.4 -0.5
Non-metropolitan regions 1.7 1.3 0.4 -0.2 0.4 -0.6
Total 1.7 1.1 0.6 -0.1 0.5 -0.6
Green bars indicate positive changes, red bars indicate negative changes.
Source: EUROSTAT, DG REGIO calculations
2001-2008 2009-2014
Table 1.4 Changes in GDP per head, productivity and employment per head by metropolitan
region, 2001–2008, and 2009–2014
Metro regions
Metro regions are NUTS 3 regions, or groupings
of NUTS 3 regions, representing all functional
urban areas of more than 250 000 inhabitants.
The typology distinguishes three types of metro
regions: capital city regions; second-tier metro
regions and smaller metro regions.
The capital city region includes the national capital.
Second-tier metro regions are the group of
largest cities in the country excluding the capital.
As it is not possible to use a fixed population
threshold to distinguish these regions from
smaller metro ones (i.e. the remaining metro regions),
a natural break is used instead.
For more details: 	
http://ec.europa.eu/eurostat/statistics-explained/
index.php/Territorial_typologies_for_European_
cities_and_metropolitan_regions
Chapter 1: Economic cohesion
19
In the EU‑13, growth of GDP per head in non-capital
metro regions over the period 2009–2014 was,
on average, twice the EU‑13 average as a result
of high productivity growth while employment remained
unchanged. Whether this launches a period
of higher growth outside the capital regions,
so a narrowing of the gap in GDP per head with the
latter, remains to be seen.
Employment in both metro and non-metro regions
generally increased between 2000 and 2008,
though at a faster rate in capital city regions than
others and by more in other metro regions than
non-metro ones (Figure 1.9). In the next two years,
it declined markedly in all regions, but it then began
to recover in the capital city regions, continuing to
grow up to 2014 when the number employed was
much the same as before the crisis. In the other
metro regions, recovery was more hesitant and
by 2014, employment was still below the level in
2008. In the non-metro regions, employment continued
to decline up to 2013 and began to increase
only in 2014.
3.3 GDP growth in rural and
intermediate regions proved to be more
resilient during the crisis years
Between 2001 and 2008, real GDP per head in rural
regions in the EU‑28 grew by 1.9% a year, slightly
higher than in other types of region (Table 1.5). At
the same time, productivity grew faster, while employment
relative to population rose more slowly.
In the EU‑15, GDP per head grew in all types of
region, fuelled in equal parts by increases in productivity
and the employment rate, though in rural
regions more by productivity.
In the EU‑13, in the years before the crisis, economic
growth was mainly driven by increases in
productivity, especially in rural regions, where increases
were accompanied by a decline in employment.
The two may be linked, insofar as higher
productivity growth was due to catching up in the
use of technology and more efficient methods of
working, including in agriculture, which in turn led
to a reduction in employment.
The crisis had a different effect on rural regions
than others, since construction and industry were
most affected and these are less present in rural
areas. Accordingly, the reduction in GDP per head
between 2009 and 2014 was less pronounced in
rural regions than in urban ones, particularly in the
EU‑15. In the EU‑13, GDP per head grew over this
period in all types of region and at much the same
rate, but in all cases by much less than before the
crisis.
99
101
103
105
107
109
111
113
115
99
101
103
105
107
109
111
113
115
2000 2002 2004 2006 2008 2010 2012 2014
Non-metro regions
Capital metro regions
Other metro regionsIndex, 2000=100
Evolution of total employment (number employed) in metro regions,
2000-2014
Figure 1.9
Source: Lavalle et al. (2017)
SeventhReportoneconomic,socialandterritorialcohesion
20
Average annual
change (%)
GDP per
head Productivity
Employment
per head
GDP per
head Productivity
Employment
per head
EU-15 2000 2008 2014 2000 2008 2014
Urban 1.4 0.8 0.5 -0.3 0.4 -0.7 132 125 122 113 113 112
Intermediate 1.2 0.7 0.5 -0.4 0.2 -0.6 106 100 98 91 90 90
Rural 1.2 0.9 0.4 -0.1 0.3 -0.4 93 88 88 80 79 81
Total 1.3 0.8 0.5 -0.3 0.3 -0.6 117 111 109 100 100 100
EU-13
Urban 5.7 3.8 1.9 1.1 1.1 -0.1 73 102 113 164 171 170
Intermediate 4.8 3.8 0.9 1.1 1.8 -0.7 41 53 59 92 89 88
Rural 4.8 5.0 -0.2 1.0 1.6 -0.6 33 43 48 73 72 71
Total 5.1 4.3 0.8 1.1 1.6 -0.5 45 60 67 100 100 100
EU-28
Urban 1.6 0.9 0.7 -0.2 0.4 -0.6 125 123 121
Intermediate 1.7 1.1 0.6 -0.2 0.5 -0.6 88 88 88
Rural 1.9 1.7 0.2 0.1 0.6 -0.5 69 71 73
Total 1.7 1.1 0.6 -0.1 0.4 -0.6 100 100 100
Green bars indicate positive changes, red bars indicate negative changes.
Source: EUROSTAT, DG REGIO calculations
2001-2008 2009-2014
GDP per head (PPS) index
EU-28 = 100
GDP per head (PPS) index
EU-15/13 = 100
Table 1.5 Real GDP per head, productivity and employment per head growth by urban-rural typology, 2001–2008, and 2009–2014
Chapter 1: Economic cohesion
21
‑‑‑‑‑‑
Employment declined in all types of region, but
more in urban and intermediate ones in the EU-15
and in urban and rural ones in the EU-13.
Productivity continued to grow in both the EU-15
and EU-13 and, as in the pre-crisis period, by more
in the latter than the former, though the difference
in rates was much smaller.
In 2014, GDP per head in rural regions in the
EU-15 was, on average, some 72% of that
than in urban ones, while in the EU-13, the
difference was much wider, the level in urban
regions being only 42% of that in rural ones.
4. The economic development
clubs of European regions and the
middle-income trap7
Economy-wide forces together with differences
in the characteristics of economies mean that it
is possible to divide countries, regions and cities
by their level of economic development. They can
be said to belong to different ‘development clubs’,
each of them characterised not only by different
income levels but also by different structural features,
such as the education level of the population,
infrastructure endowment, innovation capacity
and institutional quality.
Clubs differ systematically across these dimensions
and for each club there are specific needs and
challenges related to its starting point. Grouping
7	 Simona Iammarino, Andrés Rodríguez-Pose and Michael Storper
contributed substantially to the content of this section.
Degree of urbanisation and urban-rural typology
Since the 5th
Cohesion Report, the European Commission
has developed two typologies of local areas
which are linked to two typologies of regions.
The new measure of the degree of urbanisation is
linked to the division of regions into predominantly
urban, intermediate and predominantly rural. Both
typologies rely on a new analytical tool, the population
grid, which is used to identify three types of cell:
1.	urban centre (alternative name: high-density
cluster): contiguous grid cells of one square km
with a population density of at least 1 500 inhabitants
per square km and a minimum population
of 50 000;
2.	urban cluster: contiguous grid cells of one
square km with a density of at least 300 inhabitants
per square km and a minimum population
of 5 000;
3.	rural grid cell: grid cells outside urban clusters.
These are then used to define three types of municipality
(local administrative unit level 2) as follows:
1.	cities: at least 50% of the population living in an
urban centre;
2.		towns and suburbs: less thn an 50% of the
population living an urban centre, but more than
50% in an urban cluster;
3.		rural areas: at least 50% of the population living
in rural grid cells.
These cells are also used to define NUTS 3 regions
as follows:
•• predominantly urban:, less than 20% of the population
living in rural grid cells;
•• intermediate: between 20% and 50% of the
population living in rural grid cells;
•• predominantly rural: at least 50% of the population
living in rural grid cells.
This creates an especially close link between rural
regions and rural areas which are defined in the exactly
same way.
For more details: 	
http://ec.europa.eu/eurostat/statistics-explained/in-
dex.php/Urban-rural_typology
Seventh Report on economic, social and territorial cohesion
22
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.6 The Economic Development Clubs of European Regions
Low
Medium
High
Very high
no data
Low = GDP per head (PPS) below 75% of EU average in 2013
Medium = GDP per head (PPS) between 75% and 120% of EU average in 2013
High = GDP per head (PPS) between 120% and 150% of EU average in 2013
Very high = GDP per head (PPS) above 150% of EU average in 2013
Source: DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Chapter 1: Economic cohesion
23
  
EU regions into income clubs is a way of generating
insights into economic development and provides
a distinctive perspective on regional policy.
It brings out the uneven path of regional development
that occurs and helps to identify means of
overcoming the barriers to development in lagging
regions. For this purposes, EU NUTS 2 regions can
be divided into four groups according to their
GDP per head in 2013 (see box).
Most of the very high and high income regions
are located in a band from London through the
Benelux and Germany down to northern Italy,
with a few capital city regions outside this area
(Map 1.6). There are two other broad areas, a large
middle income part in the west of the EU and a low
income part in the south and the east.
The very high income club is dominated by a few
very large urbanised or capital city regions, and by
a number of smaller but highly urbanised interconnected
ones (e.g. Rhine-Ruhr in Germany or
Randstad in the Netherlands), specialised in the
production of high-quality goods and services.
The high income regions share many characteristics
with the very high income ones but tend to be
less city-centred. The medium income club is vast
and consists mainly of regions in the north-west
of Europe outside the very high and high income
clubs. The low income club is concentrated in the
east and south of the EU.
Total population change varies with the club gradient,
with people moving to higher income regions
and away from low income ones. Many high income
regions experienced high rates of population
increase over the period 2001–2015, except for
those in Germany (Table 1.6). In many low income
regions in the east and south of the EU as well as
in declining industrial parts of north eastern France
and northern England, population declined. While
some low income regions experienced population
growth over the period, these tend to be those with
extensive amenities and a low cost of living.
Examining the labour market in the different clubs
provides further insights. Employment
declined in the low income regions between
2001 and 2014 while it increased in the other
regions, especially in the very high income ones
(Table 1.6). The share of employment in industry
(excluding construction) is largest in low
in-come regions. In all clubs, however,
employment in industry declined over the period,
Income clubs of EU regions
1.	Very high income group: those with GDP per
head in PPS of 150% or more of the EU average
in 2013.
2.	High income group; those with GDP per head
of 120–149% of the EU average.
3.	Medium income group; those with GDP per
head of 75–120% of the EU average.
4.	Low income group, those with GDP per head
of below 75% of the EU average.
Table 1.6 European regions, by income club: some stylised facts
Income
club
Growth
of GDP
per head,
average
annual rate
(2001–2015)
%
Population
change
(2001–2015)
%
Employment
average
annual change
(2001–2014)
%
Employment
in Industry
(2014)
%
Employment
average
annual change
in Industry
(2001–2014)
%
Unemployment
rate,
(2016)
%
Patent
applications
per million
inhabitants
(average
2010–2011)
Very high 1.4 10.7 0.8 12.3 -1.2 5.8 254
High 0.9 7.3 0.5 16.9 -0.8 5.9 232
Medium 1.0 6.2 0.3 14.4 -1.5 8.4 103
Low 1.7 -2.0 -0.6 20.3 -1.0 11.6 8
EU‑28 1.3 4.4 0.1 16.1 -1.2 8.5 113
Source: EUROSTAT, Cambridge Econometrics, DG REGIO calculations based on the latest available data.
Seventh Report on economic, social and territorial cohesion
24
 
the more so in the middle income ones. Low
income regions have the highest unemployment.
Patenting activity, which is an indicator of innovation,
is highly concentrated in very high and high
income regions.
Very high and low income regions experienced the
highest growth in GDP per head over the years
2001–2015. In the former, this is mainly due to
their level of competitiveness and specialisation in
the production of high-quality goods and services,
while low income regions are catching up, taking
advantage of their ability to mobilise low-cost
capital and labour to capture activities for which
this gives them a competitive edge. Middle-income
regions had the lowest growth and face a particular
challenge — the so-called ‘middle-income trap’
— because they are neither very low-cost nor are
they particularly innovative or productive. Their
manufacturing sector tends to be smaller and
weaker than in regions with either a higher GDP
per head or lower one (Figure 1.10) and their
costs are too high to compete with the latter,
their in-novation systems not strong enough to
compete with the former.
The main challenges for regions in each club can
be summarised as follows:
1. Very high income club: many of these regions
are attracting population, though some of them
have high unemployment rates and have underperformed
since the beginning of the economic crisis
(Dijkstra et al., 2015). The main need is to keep
pace with global competitors. They need to maintain
their specialisation in high-wage activities and
their comparative advantage by continuing to push
the boundaries of innovation and technology.
2. High income club: regions in this group share
many characteristics with the very-high income
ones. Their employment rates are high and the
challenge is to remain innovative, but they are
more vulnerable to competition from the lower income
regions. They are particularly vulnerable to
standardisation of what they produce, which can
allow firms to move to regions with lower costs
and less skilled labour. Their challenge is to innovate
in their areas of specialisation and to expand
into high value-added activities related to this.
3. Medium income club: this is a large group
consisting of two sub-groups, each with specific
challenges. One consists of regions that have
lost manufacturing jobs and in which the education
level of the work force is below that in higher
income regions. In general, they are fragile economically
because of this. The other consists of regions
experiencing population growth, but mainly
of older people who move there because of the
10
15
20
25
10
15
20
25
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
EU-28
Very high and high
Middle
Low
% of total
EmploymentFigure 1.10
Source: Cambridge Econometrics, DG REGIO calculations
15
17
19
21
23
25
15
17
19
21
23
25
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
EU-28
Very high and high
Middle
Low
% of total
GVAFigure 1.10b
Source: Cambridge Econometrics, DG REGIO calculations
Figure 1.10 Employment and GVA shares in industry (excluding construction), by regional income
club, 1995–2014
Chapter 1: Economic cohesion
25
local amenities and low cost of living. Such inward
movement mainly stimulates employment
in non-tradeable local services, which gives rise to
limited skill development, innovation capacity and
export capability. Regions, in both sub-groups, risk
falling into a ‘middle-income trap’. As productivity
and wages rise, they become less attractive for labour-intensive,
low-skilled activities. Moving up the
value chain requires higher investment per worker
than in earlier stages of development, because of
the need for a better educated labour force and
new business models. To become attractive for
higher value-added activities, regions have to improve
the quality of their institutions and business
ecosystem, become more innovative and improve
the skill sets of their labour forces through better
education and training.
4. Low income club: these regions suffer from
having low levels of technology and business organisation
and a work force with limited skills, but
they have the advantage of offering low cost land
and labour. They tend to lose talented people and
well-educated young people to higher-income regions,
while at the same time being unable to attract
firms and talent from outside, so encouraging
an outward movement of population.
5. Competitiveness of EU regions
5.1 Firms in EU capital metro regions
tend to be larger and to grow at a
faster pace
In the 2014–2020 period, Cohesion Policy is focused
heavily on supporting smart growth with
particular emphasis on innovation and high growth
firms and with programmes aimed at increasing
the innovative capacity of SMEs. In previous periods
too, a substantial share of Cohesion Policy
funding was devoted to improving the business
environment and supporting entrepreneurship. In
the 2007–2013 programming period, for example,
some €47.5 billion, 24% of the total ERDF, was allocated
to support of SMEs8
.
In 2014, the largest number of firms with at least
one employee9
relative to population was in capital
metro regions in most countries (the exceptions
are France, Italy, Austria, and Spain — Figure 1.11).
There are, however, large variations across regions
in the same Member State, particularly in
Romania, Slovakia and Hungary. Firms, especially
large firms, may locate in more urbanised areas to
benefit from agglomeration economies, the three
main sources of these being matching, sharing
and learning (Puga, 2010). Cities, therefore, tend
to have larger labour markets allowing a better
matching between labour demand and supply, a
better sharing of inputs, such as infrastructure,
in the production process and more people working
and living in close proximity, enabling them to
learn more easily from each other.
8	 European Commission (2016j).
9	 The terminology employer firms will be used throughout the chapter
to indicate firms employing at least one employee.
Business demography Statistics
Business demography indicators at regional level
are useful to show where firms are located in
the EU and their dynamics, in terms of births,
deaths and growth. In this section, a set of such
indicators are examined: firm density (expressed
as the number of firms relative to population),
employees per firm, birth rates (firms created in
a region relative to total population), death rates
(firms going out of business relative to total population),
and the proportion of high growth firms.
The source of data is the Employer Business Demography
Statistics (for firms with at least one
employee) for 2014 (or the closest year available
with non-provisional data) for the total business
economy of NACE Rev.2, except insurance
activities of holding companies (sector K642).
For more details see: http://ec.europa.eu/
eurostat/statistics-explained/index.php/
Structural_business_statistics_at_regional_level
Seventh Report on economic, social and territorial cohesion
26
At the same time, firms operating in urban areas
face more competition, since larger markets attract
more firms. This tends to result in less competitive
firms being forced out of business (Melitz
and Ottaviano, 2008; Combes et al., 2012). The
data, indeed, show that firms in metro regions,
particularly in capital city ones, are on average,
larger in terms of employment than those in nonmetro
regions, apart from in Latvia and Hungary
(Figure 1.12)10
.
10	Some care is needed in interpreting this result. Some large enterprises
may be composed of multiple local units which may be
located in different regions, but with their employment registered
in the head office, often located in the capital of a country. This
may inflate the number of employees that are counted as working
in the capital city.
The birth of enterprises is one of the main drivers
of job creation and economic development.
Young enterprises are often innovative and tend
to increase the competitiveness of a region both
directly and indirectly by pushing competitors to
become more efficient.
In 2013, (the latest year for which data are available)
newly-created enterprises were more numerous
in capital metro regions, both in more developed
and less developed Member States, except
in Spain and Italy, the highest birth rates being in
Bratislava and Budapest (Figure 1.13).
0
10
20
30
40
50
60
0
10
20
30
40
50
60
NL BE RO FR CZ DE SK IT MT LT FI DK AT LV BG ES HR PT UK SI EE HU LU CY
National average
Metro region capital
Other metro regions
Non-metro regions average
Number of firms per thousand persons
Firm density by metro region, 2014Figure 1.11
2013 data for FR, BE, EE, MT and DK
Source: Eurostat, ESPON SME data for UK and CY
< 500 000
500 000 - 1 000 000
Metro region population:
1 000 000 - 2 500 000
> 2 500 000
0
5
10
15
20
25
0
5
10
15
20
25
NL DE LU LT CZ FR BE AT MT DK UK RO LV FI SK BG EE SI PT HR IT ES CY HU
National average
Metro region capital
Other metro regions
Non-metro regions average
Average number of employees
Employees per firm by metro region, 2014Figure 1.12
< 500 000
500 000 - 1 000 000
Metro region population:
1 000 000 - 2 500 000
> 2 500 000
FR, BE, MT, DK and EE: 2013
Source: Eurostat
Chapter 1: Economic cohesion
27
0
2
4
6
8
10
0
2
4
6
8
10
BE LV DE NL FR DK MT AT RO HR CZ IT LU PT UK EE BG SK ES SI FI LT HU
National average
Metro region capital
Other metro regions
Non-metro regions average
Per 1000 people
Death rate of enterprises by metro region, 2012Figure 1.14
PT: 2013
Source: Eurostat
< 500 000
500 000 - 1 000 000
Metro region population:
1 000 000 - 2 500 000
> 2 500 000
0
2
4
6
8
10
12
0
2
4
6
8
10
12
BE DE MT NL LT IT CZ FR AT FI CY LV SI HR DK ES BG RO PT LU SK EE HU UK
National average
Metro region capital
Other metro regions
Non-metro regions average
Per 1000 people
Enterprise birth rate by metro region, 2013Figure 1.13
FR: 2012
Source: Eurostat
< 500 000
500 000 - 1 000 000
Metro region population:
1 000 000 - 2 500 000
> 2 500 000
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
LT MT BG FI DK HU AT SK PT HR CZ FR ES IT RO
National average
Metro region capital
Other metro regions
Non-metro regions average
Per 1000 people
Number of high growth firms by metro region, 2014Figure 1.15
< 500 000
500 000 - 1 000 000
Metro region population:
1 000 000 - 2 500 000
> 2500 000
FR, DK: 2013
Source: Eurostat, Business Demography,
DG REGIO calculations
Seventh Report on economic, social and territorial cohesion
28
High birth rates often go together with high death
rates (Figure 1.14), as in Bratislava and Budapest.
However, some regions have high rates of start-up
but low death rates, as in Copenhagen, hinting at
local features which nurture the birth of new enterprises
while also keeping them profitable.
High growth enterprises (those growing by 10% a
year or more)11
play an important role in the economic
growth of cities and regions through their
contribution to productivity and innovation (Acs et
al., 2008).
In 2014, high growth firms were found mainly
in metro regions, except in Portugal and Italy,
though there were marked variations in their incidence
within countries (Figure 1.15). In a number
of Member States — Slovakia, Hungary and the
Czech Republic in particular — the large variation
between regions is due mainly to the large number
of high-growth firms operating in the capital metro
region12
.
5.2 Innovation remains spatially
concentrated
As widely documented in the economic literature,
research and innovation play a crucial role in determining
the economic performance of countries
and regions. Innovation, understood in the broad
sense to include product, process, market and organisational
innovation, is identified as one of the
major engines of economic growth, employment
and environmental sustainability and, accordingly,
is of critical importance for social progress as well
as prosperity.
In particular, innovation is an important driver of
long-run productivity growth and, as such, is crucial
to maintaining the competitiveness of firms.
This is particularly true for firms in the EU which
have increasingly to compete with firms located
in emerging economies in less developed parts of
11	High growth enterprises are those in which employment increased
by 10% a year or more over a three-year period and which had at
least 10 employees at the beginning of the period.
12	As indicated above, perhaps at least partly because of employment
in local units being registered in the head office.
Entrepreneurship is crucial for
regional development, but startups
and ‘scale-ups’ face particular
financing constraints
Start-ups and ‘scale-ups’ (firms expanding) need
capital. However, EU start-ups have more difficulty
in obtaining venture capital than their
US counterparts. EU scale-ups have even more
difficulty to grow and remain independent than
US firms. An additional problem is that venture
capital is usually concentrated in few places (and
often in the capital city), though there are exceptions,
such as the UK where it is more widely
available, partly due to the support from regional
development funds.
To boost investment opportunities from venture
capital and make funding more accessible to
small and innovative enterprises, the Commission
launched a pan-European Venture Capital
Fund-of-Funds under the Start-Up and Scale-Up
Initiative (COM(2016)733). This complements
other financial instruments under the EU programme
for the Competitiveness of Enterprises
and SMEs (COSME) and Horizon 2020’s InnovFin,
to facilitate the access of SMEs to guarantees,
loans and equity capital through local financial
institutions in the Member States.
To help start-ups and scale-ups, and building on
the Single Digital Gateway and existing national
and European contact points, the Enterprise Europe
Network will make available ‘Scale-up Advisors’
in all regions to provide advice on relevant
national and European regulations, funding and
partnering opportunities and how to participate
in cross-border public procurement.
Chapter 1: Economic cohesion
29
the world. The latter are not only catching up fast
in terms of technology but they also still benefit
from cheaper labour due in part to lower labour
standards, a lack of social protection for workers
and lower income expectations, though low labour
costs tend to be offset by lower productivity. From
this perspective, innovation, including the capacity
to assimilate innovation produced elsewhere, is
an important condition for maintaining the specific
features of the European social model. In addition,
contrary to growth from restructuring, growth from
innovation is in principle without bounds, which is
why it is central to sustaining growth over the long
term.
Measuring innovation is difficult, the number of
patent applications being one of the few indicators
available and the one most commonly used.
Although it is imperfect because it covers only innovations
which are patentable13
and, in the case
of the EU, only those registered at the European
Patent Office, there is a lack of alternatives. Over
the two years 2010 and 2011 (the last data available),
an average of some 113 patent applications
per million people was made to the European
Patent Office (Map 1.7). While there are large variations
in applications across regions, there is a clear
spatial pattern, with those with most applications
13	Accordingly, they relate mainly to technological innovations in the
manufacturing sector and do not a capture innovation in services,
which are often intangible. The measure is therefore liable to be
biased (Morrar, 2014).
— i.e. the most innovative — being located mostly
in the Netherlands, Germany, Austria, Denmark
and Sweden. At the NUTS 3 level, Eindhoven, in the
Netherlands, had the highest number of applications
(1 731 per million inhabitants in the period),
followed by Heidenheim in Germany (1 049) and
Rheintal-Bodenseegebiet in Austria (832).
Metropolitan areas tend to offer an environment
which is particularly conducive to the introduction
of new ideas, products and processes. A vast body
of literature explains why urban areas are likely to
be more innovative than others, such as the presence
of a creative and skilled work force, specialised
clusters of economic activity, universities and
research institutes14
.
There are not only clear-cut differences in patenting
activity between metro regions (around 140
applications per million inhabitants) and non-metro
regions (around 86 per million) (Figure 1.16), but
there is less variation between them (as measured
by the coefficient of variation), suggesting that they
generally offer a more favourable environment15
.
One of the main indicators for assessing the capacity
to innovate is the level of expenditure on
14	European Commission and UN-HABITAT (2016).
15	The coefficient of variation calculated on the average number of
patent applications in 2010 and 2011 in metro regions is 1.1, as
against 1.4 for non-metro regions.
1
10
100
1000
1
10
100
1000
RO BG LT HR SK EL LV PL PT MT CY CZ HU EE ES SI IE IT UK FR BE LU NL AT DK FI DE SE
National average
Metro region capital
Other metro regions
Non-metro regions average
Per million people (log scale)
Patents by metro region, 2009-2011Figure 1.16
< 500 000
500 000 - 1 000 000
Metro region population:
1 000 000 - 2 500 000
> 2 500 000Source: Eurostat, DG REGIO calculations
Seventh Report on economic, social and territorial cohesion
30
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.7 Patent applications to the European Patent Office (EPO), average 2010–2011
< 50
50 – 100
100 – 150
150 – 250
> 250
no data
Applications per million inhabitants
EU-28 = 113
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 1: Economic cohesion
31
R&D which tends to be essential for technical progress
to take place16
.
Expenditure on R&D in the EU‑28 amounted to
around 2% of GDP in 2014 (the latest data available)
and increased only marginally over the previous
two decades (1.8% of GDP in 1995), not by
nearly enough to close the gap with other highly
developed economies, especially Japan (where expenditure
in 2014 amounted to 3.5% of GDP) or
the US (where it stood at 2.7% of GDP in 2013).
Regions in the EU‑15 have on average slightly
higher expenditure on R&D (2.1% of GDP in 2014)
than those in the EU‑13 (1.8% of GDP). There are,
however, wide variations across NUTS 2 regions,
from over 6% of GDP in Brabant Wallon in Belgium
and Braunschweig and Stuttgart in Germany to only
0.1% of GDP in Centru in Romania and Severen
Tsentralen in Bulgaria (Map 1.8 and Figure 1.17).
16	It should be noted, however, that R&D expenditure is likely to underestimate
innovation activities, particularly in sectors outside
manufacturing where non-technological innovation is frequent.
Expenditure on R&D in 2014 exceeded the Europe
2020 target of 3% in only 30 regions, all of them
in the EU‑15 (Table 1.7). In the more developed
regions, it was less than 1 percentage point below
the target on average — which is still a significant
amount given the generally slow rate of increase
over recent years — while in less developed regions,
it was slightly over 2 percentage points
below.
In general, therefore, regions with the highest expenditure
on R&D are the most highly developed
ones, and in most cases those where the capital is
located (Germany, France, and the UK are exceptions).
Of the 20 regions with the highest expenditure,
19 regions have a GDP per head above 100%
of the EU average and regions with low levels of
expenditure tend to be either in southern, central
and eastern Member States or are the low GDP per
head ones in western Member States.
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
0
1
2
3
4
5
6
7
8
SE FI DK AT DE SI BE FR NL CZ EE UK IE HU PT IT LU ES LT PL MT SK HR EL BG LV CY RO
National average Capital region Other NUTS 2 regions% of GDP
Total expenditure on R&D, 2014Figure 1.17
Source: Eurostat, DG REGIO calculations
EU-28
Table 1.7 Total R&D expenditure and the distance to the EU2020 target, EU‑28 regions, 2014
More
developed
Transition Less
developed
EU‑28
R&D as % of GDP, 2014* 2.3 1.3 0.9 2
Distance to EU target (% point difference) 0.7 1.7 2.1 1
% of regions** that have reached the EU target 19 2 0 11
Source: Eurostat, DG REGIO calculations
* BE, DE, EL, FR, AT, FI, SE: 2013
** includes only regions for which data are available
Seventh Report on economic, social and territorial cohesion
32
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.8 Total expenditure on R&D, 2014
< 0.5
0.5 – 1
1 – 2
2 – 3
3 – 4
>= 4
no data
% of regional GDP
EU-28 = 2.04
BE, DE, EL, FR, AT, SE = 2013
The Europe 2020 R&D target is 3%.
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 1: Economic cohesion
33
A 2017 European Commission report highlights
the key role innovation plays in the development
of regions, and not only the high tech ones17
. The
Regional Innovation Scoreboard (RIS), an extension
of the European Innovation Scoreboard, assesses
the performance of regions in this respect
on the basis of a limited number of indicators. For
2017, it covers 220 regions across 22 EU Member
States and Norway, while Cyprus, Estonia, Latvia,
Lithuania, Luxembourg, and Malta are covered at
country level.
The most innovative region in the EU by this measure
is Stockholm, followed by Hovedstaden in
Denmark and South East England (Map 1.9).
Despite regional variations within countries, the
ranking of regions largely matches that of countries.
Most of the regional Innovation Leaders are
in countries also identified as Innovation Leaders
and almost all of the regional Moderate and
Modest Innovators are located in countries categorised
in the same way. However, regional ‘pockets
of excellence’ are evident in some Moderate
Innovator countries (such as, Praha in the Czech
17	European Commission (2017c).
Republic, Bratislavsky kraj in Slovakia, and Pais
Vasco in Spain), while some regions in strong innovation
countries lag behind.
The assessment of regions in terms of innovation
has changed over time. Between 2011 and 2017,
128 regions (60% of the total) improved their performance,
while for 88, performance worsened.
Although 75% of Innovation Leaders improved
their performance, only 30% of Modest Innovators
did so, implying a widening gap between them.
Performance declined in particular in more peripheral
regions, in all regions in Romania and for more
than half of those in Denmark, Finland, Germany,
the Czech Republic, Hungary, Portugal, and Spain. It
increased in all regions in Austria, Belgium, France,
the Netherlands, Norway, Slovakia, Switzerland and
the UK and in more than half of those in Greece,
Italy, Poland and Sweden.
In general, the RIS confirms the wide diversity of
regions in terms of innovation performance, so
highlighting the fact that innovation has a strong
regional dimension. Given this wide variation,
measures for supporting innovation, including
Cohesion Policy programmes, need to take explicit
account of the regional or local context when devising
the kind of support to provide. The smart
specialisation approach is helping in this regard.
5.3 The number of people with tertiary
education keeps increasing, but large
disparities persist
A well-educated work force is the key to economic
development and prosperity. Higher education
boosts upward social mobility and improves employment
prospects. In 2016, people aged 25–29
with tertiary education had an employment rate of
81%, compared to 74% for those with an upper
secondary education and 54% for those with only
basic schooling18
.
The link between educational attainment and employment
rates is also strong for the population
18	European Commission (2016b).
Regional Innovation Scoreboard (RIS)
methodology
The 2017 edition of the Regional Innovation
Scoreboard (RIS) classifies regions into four innovation
performance groups: Innovation Leaders
(53 regions), Strong Innovators (60 regions),
Moderate Innovators (85 regions), and Modest
Innovators (22 regions).
The RIS for 2017 is based on data for 18 of the
27 indicators used in the European Innovation
Scoreboard for the same year. In the same way
as the latter, the indicators for RIS 2017 have
been refined and expanded as new regional data
have become available. In addition, whereas
previous RIS reports only divided regions into
groups, the 2017 report ranks them individually.
For more details, see: http://ec.europa.eu/growth/
industry/innovation/facts-Figures/regional_en
Seventh Report on economic, social and territorial cohesion
34
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.9 Regional innovation performance, 2017
Modest -
Modest
Modest +
Moderate -
Moderate
Moderate +
Strong -
Strong
Strong +
Leading -
Leading
Leading +
Source: European Commission (2017d)
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Chapter 1: Economic cohesion
35
The regional distribution of expenditure under the EU Research and Innovation (R&I)
Programme
The objective of the EU R&I Framework Programme Horizon 2020 is to support research excellence wherever
it takes place. It, therefore, does not differentiate between regions, group or territory.
Nevertheless, a ring-fenced budget is allocated to Part IV of Horizon 2020 ‘Spreading Excellence and Widening
Participation’ (SEWP) which includes specific support for tackling the innovation divide in the EU.
In addition, the development of synergies between Horizon 2020 and the European Structural and Investment
(ESI) Funds is intended to make an important contribution to the complementary use of the two funding
sources. The Seal of Excellence is a practical manifestation of this. It is a high-quality label awarded to projects
submitted to Horizon 2020 which were deemed to deserve funding but did not receive it because of a limited
budget, which can
be used to give credence
to projects
when approaching
other funding
sources. It also helps
funding bodies (including
national and
regional authorities
receiving ESI fund
support) to identify
promising projects
more easily.
The map below illustrates
the EU financial
contribution to
NUTS 2 regions by
the 7th
Framework
Programme for Research
and Innovation
(2007–2013).
The top 5 NUTS 2
regions are Brussels,
Vlaams-Brabant,
Inner London,
Hovedstaden and
Oberbayern, which
all received €400
per inhabitant. On
average, regions in
the EU‑15 received
more than those in
the EU‑13, with capital
city regions, in
most cases receiving
the largest amounts
in each country.
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.10 Regional level of FP7 expenditure
< 5
5 - 10
10 - 20
20 - 30
30 - 50
50 - 100
100 - 150
150 - 200
> 200
Euro per inhabitant
EU-28 = 81
Source: European Commission, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Seventh Report on economic, social and territorial cohesion
36
Tradable clusters in low density and high density economies in OECD countries.
Productivity in larger cities is higher than in smaller cities or rural areas with lower population densities.
This is, in part, due to differences in the characteristics of the local work force, which, on average, is more
educated with skills that would make the workers concerned more productive no matter where they lived or
worked (OECD, 2015).
Large metropolitan areas, like London, New York or Tokyo, are home to some of the most productive and
innovative enterprises, mostly engaged in services, especially business services, but also in ICT, healthcare
and higher education (OECD, 2014). Manufacturing firms located in large cities are typically involved in innovation
and skill-intensive production. Indeed, often only the headquarters or research centres of large firms
are situated in cities. Unsurprisingly, the wages paid by firms in tradable clusters located in urban areas tend
to be higher than those in less-densely populated areas (Figure 1.18).
Rural economies are at the other end of the spectrum to large cities. They are often concentrated in agricultural
production or the exploitation of natural resources (OECD 2016b). Manufacturing in these areas tends
to be in the more ‘mature’ parts of the production process using well-established technologies. The relatively
small work force in low population-density areas tends to mean specialisation in a few activities in contrast
to large agglomerations. This requires a careful assessment of local strengths and weaknesses and support
of activities that can give rise to growth.
(This box is based on a contribution from OECD.)
The data identify 51 tradable clusters and a residual non-tradable cluster that includes all other firms. Regions with over 70% of population
living in functional urban areas, or some of their population living in a large metropolitan area with over 1.5 million inhabitants,
are classified as mostly urban. Average wage is the total wage bill of the cluster in EUR at 2010 prices divided by the number of FTE
employees.
Source: Calculations based on OECD Regional Statistics and data used in and provided by Ketels and Protsiv (2016).
Music and Sound Recording
Commun. Equipment &
ServicesVideo Prod. & Distrib.
Biopharmaceuticals
Financial Services
Insurance Services
Marketing, Design &
Publishing
Business Services
Non-tradable clusters
Appliances
Agricultural Inputs &Services
Apparel
Fishing & Fishing Products
Leather & Related Products
Forestry
Footwear
10000
15000
20000
25000
30000
35000
40000
45000
50000
55000
60000
10000
15000
20000
25000
30000
35000
40000
45000
50000
55000
60000
20 30 40 50 60 70 80 90 100
Averagewagein2010EUR
% of FTE in mostly urban area
Average wage and share of full-time equivalent (FTE) employment in tradable
clusters located in mostly urban areas, 2014
Figure 1.18
Nonmetal Mining
Chapter 1: Economic cohesion
37
aged 25–64. Only 54% of those with only basic
schooling were employed in 2016 as against 75%
of those with upper secondary qualifications and
85% of those with tertiary education. Moreover, the
gap in employment rates between those with tertiary
education and those with only basic schooling
has widened over time (from 28 percentage points
in 2006 to 31 percentage points in 2016).
The share of people aged 25–64 with tertiary education,
however, varies markedly across regions
(Map 1.11 and Figure 1.19).
Metropolitan areas, especially larger ones, tend
to have a more highly educated population than
other areas19
. Demand for highly skilled labour attracts
those with such qualifications and makes it
easier for them to find a job matching their skills.
19	European Commission and UN-HABITAT (2016).
At the same time, firms are also more likely to find
the skills they need in such areas. In 2016, around
41% of those aged 25–64 had tertiary education
in capital metro regions and 32% in metro regions
generally, as compared with an average of 30%
in the EU as a whole. The highest figures were
in Inner London, Brabant Wallon in Belgium and
Helsinki, the lowest (below 20%) in regions in Italy
and Romania.
The Europe 2020 strategy has a target of increasing
the share of the population aged 30–34 with
tertiary education to 40% by 2020. The situation in
2016, however, varies markedly between regions,
largely according to their level of economic development.
Over half the 81 more developed regions
had already achieved the target (some before it
was set in 2010). Some 22% of transition regions
had also achieved the target (as compared with
0.0
10.0
20.0
30.0
40.0
50.0
60.0
0
10
20
30
40
50
60
FI IE LU UK CY SE LT EE DK BE NL ES FR LV AT SI EL PL DE BG PT HU HR CZ SK MT IT RO
National average Capital region Other NUTS 2 regions% of population 25-64
Population aged 25-64 with tertiary education, 2016Figure 1.19
^ŽƵƌĐĞ͗ ƵƌŽƐƚĂƚ͕ ' Z'/K ĐĂůĐƵůĂƚŝŽŶƐ
London corresponds to the NUTS 1 region
Table 1.8 Population aged 30–34 with a tertiary education, by cohesion policy groups of regions,
2016
More
developed
Transition Less
developed
EU‑28
Population aged 30–34 with a tertiary education, 2016 (%) 43.2 32.7 33 39.1
% point change 2008–2016 6.8 1 12.3 8
% point change 2000–2008 9.7 9.3 8 8.8
Distance to EU‑2020 target (% point difference) 0 7.3 7 0.9
% of regions that have reached the EU target 53 22 29 41
Source: Eurostat, DG REGIO calculations
Note: The table includes only regions for which data are available
Seventh Report on economic, social and territorial cohesion
38
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.11 Population aged 25–64 with tertiary education, 2016
< 15
15 – 20
20 – 25
25 – 30
30 – 35
35 – 40
> 40
no data
% of total population aged 25–64
EU-28 = 30.7
ISCED levels 5 and 6
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Chapter 1: Economic cohesion
39
none up to 2013), while
29% of less developed
regions had done so
(Table 1.8).
Ensuring that everyone
has the right skills for
an increasingly digital
and globalised world
is essential for an inclusive
labour market
and to spur innovation,
productivity and growth
(OECD, 2016). In 2015,
around 25% of those
aged 25–64 reported
having a low level of
digital skills and 29% a
basic level, while 28%
reported having a higher level than basic. The situation
at EU level, however, hides marked differences
between Member States, particularly between
those with different levels of economic development,
digital skills tending to increase with the latter
(Figure 1.20). Whereas 35% of those in highly
developed Member States reported their digital
skills to be above basic, in less developed Member
States, the figure was only 21%.
5.4 Improving market access does not
always generate growth
Investment in transport infrastructure is widely
used to promote economic development, but its
actual impact on the economy is complex and hard
to predict. In a number of cases across the EU,
projections of transport demand made before the
infrastructure had been built to justify the investment
concerned have proved to be too optimistic.
This is demonstrated by several severely underused
motorways, airports and high-speed railway
lines (Flyvbjerg et al., 2003, European Commission,
2014).
In principle, lowering transport costs should boost
trade and economic growth. The new economic
geography theory of regional development, however,
warns that improving transport connections
between two cities may not necessarily help both
even if it improves overall productivity. For example,
if a city with less efficient firms is connected
to one with more efficient firms, the latter might
0
10
20
30
40
0
10
20
30
40
Low digital skills Basic digital skills Above basic digital skills
EU28 Less developed Moderately developed Highly developed
% of people aged 25-64
People’s levels of digital skills, by level of economic
development, 2015
Figure 1.20
Source: Eurostat, DG REGIO calculations
Measuring digital skills across the EU
Digital skills are measured by a composite indicator
which attempts to capture the competence
of those aged 16–74 in performing selected
activities relating to internet and computer software
use. The activities concerned are finding information,
communicating, problem-solving and
using software. People are asked whether they
have performed a given activity and if they have,
it is assumed they have the skills to do so.
Two skill levels, ‘basic’ and ‘above basic’ are defined
for each of the four activities and an overall
indicator is calculated from this, people being divided
into four groups: those with ‘no skills’, ‘low
skills, ‘basic skills’ and ‘above basic skills.
For more details, visit: 	
http://ec.europa.eu/eurostat/cache/metadata/en/
tepsr_sp410_esmsip.htm
Seventh Report on economic, social and territorial cohesion
40
capture the market in the other city, leading to a
reduction in economic activity there.
Regional access to markets by road is mainly determined
by the spatial distribution of population.
A remote region will always have a small market
even with large-scale road investment. Accordingly,
transport investment, especially in areas with a
mature network, cannot radically alter market access.
Potential accessibility by road is the highest in
regions and cities in the centre of the EU (European
Commission and UN-HABITAT, 2016). Many regions
in central and eastern Member States, however,
are not yet connected by an efficient road network
and will only have better access to markets after
the completion of the Trans-European Transport
Network (TEN-T — Map 1.12)20
.
The speed and frequency of trains is also much
lower in central and eastern EU countries (Poelman
and Ackermans, 2016). While some countries, such
as the Czech Republic and Hungary, have a relatively
dense rail network, the frequency and speed
of service on many of the lines make it an unattractive
alternative to travel by car (Map 1.13).
Accessibility by rail is very high in the areas in and
around the highly urbanised parts of the UK, the
Netherlands, Belgium, northern France and the
Rhine-Ruhr region in Germany. This is due to the
combination of a high concentration of population,
a dense rail network, high-speed rail connections
and relatively high frequency of service
(Map 1.14). Accessibility is still high in and around
cities in western and eastern France, many parts
of Germany, the north of Italy and some parts of
Spain. It is relatively low in Austria and Switzerland
due to the mountainous terrain and lower still in
more peripheral western parts of the EU, in Ireland,
Portugal and Spain, and in the Nordic countries,
where there are longer distances between cities
and low population density. In most of the eastern
parts of the EU, as noted above, accessibility is
low because of low frequency of service and slow
speeds.
20	The map depicts expected changes relative to the situation in
2012.
By 2050, the EU intends to complete a European
high-speed rail network, the aim being for rail,
both high and normal speed, to account for at least
50% of all medium-distance passenger travel21
.
This will require substantial investment, especially
in countries where the network is not very dense
and the service tends to be slow and infrequent.
Access to passenger flights is highly uneven across
the EU, ranging from London and surrounding areas
where people have access to over 3 000 flights
a day to regions in eastern Poland and Romania
21	European Commission, ‘White Paper, Roadmap to a Single
European Transport Area — Towards a competitive and resource
efficient transport system’. COM (2011) 144 of 28 March 2011.
  
The Connecting Europe Facility
The main source of funding for implementing
the EU transport policy is the Connecting Europe
Facility (CEF), which complements the ESI
Funds by focusing support on cross-border connections
(including maritime ones) and interoperability
between national transport networks.
Funding for the Facility amounts to €24 billion
for 2014–2020.
The CEF calls for proposals in 2014, 2015 and
2016 provided support to 604 projects with
grants amounting to €22 billion and with ca.
€41.6 billion of investment being mobilised. With
the results of the 2016 call made public in June
2016 being added, 96.3% of the budget for
grants made available for transport under the
Connect-ing Europe Facility will have been
allocated.
The TEN-T Comprehensive network is mainly
supported by the ESI Funds which also cofinance
the TEN-T Core network, particularly
non-cross-border parts and roads.
The current investment in the TEN-T amounts to
around 50 billion EUR; however, according to
the estimates it is necessary to invest 607 bn
EUR in total by the end of 2030 to complete
the TEN-T Core Network Corridors alone.
For more details: https://ec.europa.eu/trans-
port/themes/infrastructure/ten-t-guidelines/
project-funding/cef_en
Chapter1:Economiccohesion
41
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.12 Expected change in road accessibility due to the TEN-T network completion,
by NUTS 3 region
< 5
5 – 10
10 – 15
15 – 20
20 – 25
> 25
no data
%
Medium distance-decay function.
Change relative to the accessibility level
observed in 2012.
Source: Ibañez et al. 2017
l
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.13 Average speed of direct rail connections, 2014
<= 40
40.1 – 60
60.1 – 80
80.1 – 100
100.1 – 150
> 150
km/h
Speed calculated along straight lines representing the connection
between two subsequent stops. All direct train trips between
geolocated stations, starting between 6:00 and 20:00 on
02/10/2014 (EE, IE: 2013; EL, Corsica, Nothern Ireland: 2015).
Source: UIC, national railway operators, EuroGeographics,
OpenStreetMap, TomTom, RRG, DG REGIO.
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
SeventhReportoneconomic,socialandterritorialcohesion
42
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.14 Rail accessibility during morning peak hours, by city, 2014
Accessible population
< 50 000
50 000 – 100 000
100 000 – 500 000
500 000 – 1 000 000
1 000 000 – 2 000 000
>= 2 000 000
no data
Urban center population
< 100 000
100 000 – 250 000
250 000 – 500 000
500 000 – 1 000 000
1 000 000 – 5 000 000
>= 5 000 000
Population (outside the city) that can be reached within 1h30 of
travel (including initial waiting time) from stations in the
city.
Sources: UIC, rail operators, Eurostat, JRC, DG REGIO-GIS
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.15 Access to passenger flights by NUTS 3 region, 2015
time to nearest airport > 90 min.
> 10
10 – 250
250 – 500
500 – 750
750 – 1000
1000 – 1500
1500 – 2000
2000 – 2500
2500 – 3000
> 3000
no data
Average number of flights per day
Population-weighted average number of flights per day, accessible
within 90 minutes by road.
Sources: Eurostat, EuroGeographics, TomTom, JRC, DG REGIO-GIS
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 1: Economic cohesion
43
without any flights within 90 minutes driving time
(Map 1.15).
5.5 Digital networks are spreading,
but closing the gap between urban
and rural areas represents a major
challenge
Access to high capacity telecommunication networks
is vitally important for competitiveness and
growth. The use of digital services and the capacity
to operate successfully in a global business environment
increasingly rely on fast and efficient
broadband connections. ICT infrastructure is therefore
a major determinant of the development potential
of EU regions. The most prosperous regions
are in general already well-endowed in this regard,
though there are still serious gaps in many of the
less prosperous ones and pronounced disparities
between urban and rural areas.
Over 214  million EU households (98%) had access
to at least one of the main fixed or mobile
broadband technologies (excluding satellite) in
mid-2016. If satellite coverage is included, basic
broadband services are now available to
every household in the EU, so that the European
Commission’s Digital Agenda for Europe target of
basic broadband for all has been achieved22
.
22	European Commission (2016a).
The coverage of Next Generation Access (NGA)23
is expanding fast. In 2016, around 76% of households
across the EU had access to at least one NGA
network, up from 68% at the end of 2014, though
there are wide variations in coverage between and
within Member States (Map 1.16).
Access to fast broadband services in rural areas
remains a challenge. Even though 99% of rural
households across the EU‑28 had access to at
least one broadband technology at the end of June
2016, only 39% (12 million households) had access
to NGA broadband (Figure 1.21), with almost
no households with access in rural areas in Greece
(0.3%). Substantial progress has been made since
2012. The funding provided under rural development
policy to an expected 4 400 projects to install
‘last-mile’ connections to larger broadband
projects co-financed by other EU funds is planned
to improve access to ICT infrastructure and services
for an estimated 18 million people living in
rural areas.
Coverage is almost complete in most urban areas
and cities, though there are a number of areas
where it is well below the EU average (of 82% in
23	Next Generation Access Networks are defined as wired access
networks which consist wholly or partly of optical elements and
which are capable of delivering broadband access services with
enhanced features, (such as higher throughput) as compared with
those provided over existing copper networks.
0
10
20
30
40
50
60
70
80
90
100
0
10
20
30
40
50
60
70
80
90
100
EU FR EL PL HR CZ IT RO DE SE BG FI SK EE SI CY ES UK LU HU LV AT DK NL PT BE IE MT LT
Urban area 2012 Rural area 2012 Urban area 2016 Rural area 2016% of households
Households with access to Next Generation Access (NGA) broadband by type of
area, 2012 and 2016
Figure 1.21
Data are for the end of 2012 and Jan-2016.
Source: European Commission (2016a)
Seventh Report on economic, social and territorial cohesion
44
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.16 Next generation access coverage by NUTS 3 region, 2016
0 – 35
36 – 65
65 – 95
95 – 100
100
% of households
Next Generation Access (NGA) includes VDSL, Cable Docsis 3.0 and FTTP.
EU = 76%
Source: European Commission (2017b)
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 1: Economic cohesion
45
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.17 Fixed broadband coverage by NUTS 3 region, 2016
0 – 35
35 – 65
65 – 95
95 – 100
100
no data
% of all households
EU = 98%
Source: DG CONNECT, European Digital Progress Report 2017
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Seventh Report on economic, social and territorial cohesion
46
urban areas), mostly in Greece (55%) and France
(50%).
Household take-up of broadband has increased
markedly in recent years along with coverage.
While in 2009, only around 56% of households in
the EU had a broadband subscription, the figure
was over 72% in 2012 and it had increased to 83%
in 2016. However, large differences remain between
regions (Map 1.17). In 2016, the proportion
of households with broadband was below 60% in
Kentriki Ellada in Greece and Severozapaden and
Yugoiztochen in Bulgaria, while it was over 95% in
the large majority of regions in the Netherlands
and in Helsinki-Uusimaa in Finland, South-East
England and Luxembourg.
6. Capital and metro regions
are the main drivers of regional
competitiveness in Europe
The Regional Competitiveness Index (RCI) is designed
to capture the different dimensions of competitiveness
for NUTS  2 regions and is the first
measure to provide an EU‑wide perspective on this.
The 2016 edition follows the two previous ones
published in 2010 and 2013 (Annoni and Kozovska,
2010; Dijkstra, Annoni and Kozovska, 2011; Annoni
and Dijkstra, 2017). All three of them are built on
the same approach as the Global Competitiveness
Index of the World Economic Forum (GCI-WEF). The
2016 index is based on 74 mostly regional indicators
covering the 2012–2014 period though with a
number of indicators for 2015 and 2016.
The index is based on a definition of regional competitiveness
from the perspective of both firms
and residents (Dijkstra et al., 2011):
Regional competitiveness is the ability of a region
to offer an attractive and sustainable environment
for firms and residents to live and work in.
The RCI results for 2016 are in line with those for
2013. Once again, a polycentric pattern is evident
with capital and other metro areas being the main
centres of competitiveness. Spill-over effects are
evident in most of the north-west of the EU, but
less so in the in the east and south. As in 2010 and
2013, there is substantial variation both between
The Digitising European Industry initiative
Rapid technological developments, innovation in
services, demands for sustainability and an evolving
global context are generating new kinds of goods
and services, and new types of business models for
producing them. Evidence suggests, however, that
only one in five EU firms is highly digitised (Source:
Europe’s Digital Progress Report, 2016).
One of the key pillars of the ‘Digitising European
Industry’ initiative, launched in 2016 as part of
the Digital Single Market Strategy, is the set-up of
a network of «Digital Innovation Hubs» that make
latest digital innovations available to any company
in Europe, wherever situated, of whatever size and
in whatever sector. The Hubs will create innovation
ecosystems connecting users and suppliers of digital
innovations as well as investors in innovation in
all phases of business development. The target is to
ensure the presence of hubs in all regions by 2020,
in line with smart specialisation strategies. Industry
is used in a wide sense and also includes sectors like
agriculture, fisheries etc. Specific actions are ongoing
to set up Digital Innovation Hubs in EU 13 countries.
In addition, the ‘Transforming regions and cities into
launch-pads of digital transformation and industrial
modernisation’ initiative will help build regional
and local capacity for digital transformation, in line
with smart specialisation strategies. It builds on the
role of cities and regions as leaders in the digital
transformation process. They can create the right
environment to accelerate the digital transformation
of businesses, other organisations and public
authorities and to improve the life of people. Many
‘smart cities’ projects already make use of advanced
technologies to improve public services and the
use of resources while reducing the impact on the
environment.
Chapter 1: Economic cohesion
47
countries and within them, the latter, in many cases,
due to the capital city region significantly outperforming
others in the country (Map 1.18).
The so-called ‘Blue Banana’, a highly urbanised,
industrialised corridor defined in 1989 by a group
of French geographers led by Roger Brunet, with
Greater London at one end and Lombardia at the
other and encompassing the Benelux countries
and Bavaria, is not evident on the RCI map. On the
contrary, the RCI shows strong capital and other
metro regions in many parts of Europe. In some
countries, capital city regions are surrounded by
others that are similarly competitive, indicating the
presence of spill-over effects, but in many other
countries, the regions neighbouring the capital are
far less competitive. An important question for the
future is whether the strong performance of the
capital and other metro regions concerned will
help to strengthen the performance of neighbouring
ones or whether the gap between them will
widen.
Londonanditscommutingarea,whichincludesseven
NUTS 2 regions24
, is ranked top in 2016, ahead
of Utrecht in the Netherlands — for the first time
not the most competitive region — which is ranked
joint second with Berkshire, Buckinghamshire and
24	Table A.1.1 of the Appendix in Annoni, Dijkstra and Gargano  
(2017) lists the NUTS 2 regions comprising London and its commuting
areas.
Oxfordshire in the UK25
. As in 2010 and 2013, most
of the top-ranked regions include either capital cities
or large metropolitan areas which help to boost
their competitiveness. The regions at the other end
of the scale are mainly in Greece and Romania
with one in Bulgaria.
Capital city regions tend to be the most competitive
in their countries (Figure 1.22). The only exceptions
are in Germany, Italy and the Netherlands. In
the last, the capital city region is ranked second
and in Italy, Lombardia continues to be the most
competitive one as in previous years. In Germany,
many regions are more competitive than Berlin,
which may be due to the relatively short time it
has been the capital of a reunited country.
The gap between the capital city region and others
is particularly wide in some countries, especially
in Romania, Greece, Slovakia, Bulgaria and
France. A big gap of this kind is generally a reason
for concern as it puts substantial pressure on the
capital city region while possibly leaving resources
in other regions underutilised.
The gap between the capital city region and the
second highest-ranking one is relatively small in
the UK, Austria and Belgium. However, a small gap
does not necessarily mean that the whole coun-
25	It is important to note that, due to the margins of error in the set
of indicators included in the index, the difference between some of
the scores may not be statistically significant.
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
RO BG EL HR HU SK LT LV MT CY IT PT PL ES CZ SI EE IE FR AT BE FI DE UK DK SE NL LU
National average Capital region Other NUTS 2 regionsRCI 2016 scores
Regional competitiveness index, 2016Figure 1.22
Source: Annoni et al. (2017)
Seventh Report on economic, social and territorial cohesion
48
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 1.18 Regional Competitiveness Index — RCI, 2016
< -1
-1 – -0.5
-0.5 – -0.2
-0.2 – 0
0 – 0.2
0.2 – 0.5
0.5 – 1
> 1
Index: values range from low (negative) to high (positive)
EU-28 = 0
Source: Annoni et al. (2017), DG REGIO GIS
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 1: Economic cohesion
49
try is highly ranked. For example, in Belgium and
the UK, variations between regions are relatively
wide, highlighting the limitations of a nationallevel
analysis. Such variations raise questions
over whether gaps in regional competitiveness are
harmful or not for national competitiveness and
how far they can, and should, be reduced.
The changes over time in the RCI scores, as opposed
to the rankings, are informative26
. Even
though the index is not entirely consistent between
years because of recurrent and often unavoidable
revisions of regional indicators and the NUTS classification,
the three editions of the RCI provide a
unique means of monitoring and assessing the development
of regional competitiveness across the
EU. Map 1.19 shows the regions where the scores
changed by more than 5% of the difference between
the highest and lowest scores across the
three editions (i.e. the maximum score range). The
three maps show the changes between 2013 and
2016, 2010 and 2013 and over the period as a
whole. Between 2013 and 2016, competitiveness
improved in around 10% of regions and weakened
in another 10%, while between 2010 and 2013,
it improved in many more regions (26%) than it
weakened (11%).
26	Comparing the RCI over time is complicated because each edition  
of the index incorporates improvements and slight modifications.
These do not affect the overall structure of the index, but they
limit the possibilities of measuring change over time. The reasons
for the modifications are various: new indicators become available
at the regional level, while others are not updated or no longer fit
the statistical framework of the index. In addition, methodological
improvements, especially between the first and the second editions,
and changes in the definition of NUTS regions complicate
the exercise. Nevertheless, there remains a fair degree of continuity
in the indicator list — changes between 2013 and 2016
are listed in Table A.3.1 in the Appendix in Annoni, Dijkstra
and Gargano (2017).
EU regions by development levels, as
defined for the RCI
EU regions are divided into five development levels
based on their average GDP per head in PPS
in the years 2012–2014 relative to the EU average
(i.e. with the EU average =100). The levels
are as follows:
•• Level 1: < 50;
•• Level 2: 50–75;
•• Level 3: 75–90;
•• Level 4: 90–110;
•• Level 5: > 110.
Source: Annoni et al. (2017b)
The Regional Competitiveness Index
(RCI) methodology
The 2016 edition of the RCI index is based on
a set of 74 mostly regional indicators covering
the 2012–2014 period but with a number
of indicators for 2015 and 2016. It is composed
of 11 pillars that cover the different aspects of
competitiveness, which are classified into three
groups: Basic, Efficiency and Innovation. The Basic
group includes five pillars: (1) Institutions; (2)
Macroeconomic stability; (3) Infrastructures (4)
Health and (5) Basic education, which represent
the key basic drivers for all types of economy.
As a regional economy develops and advances
in its competitiveness, factors related to a more
skilled labour force and a more efficient labour
market come into play as part of the Efficiency
group. This includes three pillars: (6) Higher education,
Training and Lifelong learning; (7) Labour
market efficiency; and (8) Market size. At
the most advanced stage of development, drivers
for improvement are part of the Innovation
group, which consists of three pillars: (9) Technological
readiness; (10) Business sophistication;
and (11) Innovation.
The RCI for 2016 covers all NUTS 2 regions,
as defined by Eurostat in the latest 2013 revision
(Eurostat, 2015). As in 2010 and 2013,
the NUTS 2 regions that are part of the same
functional urban area are combined, which is the
case for 6 capital functional urban areas.
For further details on the methodology, see: Annoni
et al. (2017).
SeventhReportoneconomic,socialandterritorialcohesion
50
Map 1.19 Changes in the Regional Competitiveness Index
© EuroGeographics Association for the administrative boundaries
2013–2016
deterioration
stability
improvement
Difference between RCI 2016 and RCI 2013
0 1 000 km
2010–2013
deterioration
stability
improvement
Difference between RCI 2013 and RCI 2010
0
2010–2016
deterioration
stability
improvement
Difference between RCI 2016 and RCI 2010
Source: DG REGIO
0
Chapter 1: Economic cohesion
51
Between 2010 and 2013, competitiveness improved
in most Belgian and German regions. While
it remained largely unchanged between 2013 and
2016 in most of the latter, it weakened in several
Belgian regions, including in the capital city
region. Competitiveness also deteriorated significantly
in Greek and Irish regions between 2010
and 2013, and failed to improve over the following
three years. In regions in many countries (Austria,
Bulgaria, Czech Republic, Denmark, Spain, Finland,
Hungary, Poland, Portugal, Romania, Sweden and
Slovakia), competitiveness as measured remained
largely unchanged over the 6 years.
In the other countries, there were quite a few
changes. In France, competitiveness improved
in 12 regions between 2013 and 2016 and four
between 2010 and 2013. Conversely in the UK, it
improved in many fewer regions between 2013
and 2016 (4) than between 2010 and 2013 (9). In
Italy, it deteriorated in four regions in the first period
and remained unchanged in all regions over the
following three years. In the Baltic countries, competitiveness
improved between 2013 and 2016 in
Latvia and Lithuania, while it remained unchanged
at a relatively high level in Estonia
0
20
40
60
80
100
1 2 3 4 5
Stage 1 Stage 2 Stage 3 Stage 4 Stage 5
Trend stage 1 Trend stage 2 Trend stage 3 Trend stage 4 Trend stage 5
RCIscores2016
Relationship between RCI and the birth rate of firms (relative to population) in
NUTS 2 regions by level of economic development
Figure 1.24
Only regions in AT, BG, CZ, DK, ES, FI, FR, HR, HU, IT, LT, PT, RO and SK included. Observations denoted in grey: correlations not statistically
significant. Other observations: correlations statistically significant at the standard 1%, 5% or 10% level
Source: Eurostat, DG REGIO calculations
Birth rate of firms per 1000 inhabitants
0
20
40
60
80
100
8 9 10 11
Stage 1 Stage 2 Stage 3 Stage 4 Stage 5
Trend stage 1 Trend stage 2 Trend stage 3 Trend stage 4 Trend stage 5
RCIscores2016
Relationship between RCI and GDP per head (in PPS) in NUTS 2 regions by level of
economic development
Figure 1.23
For observations denoted in grey, correlations are not statistically different from zero. For the other observations, the correlations are significantly
different from zero, at the standard 1%, 5%, or 10% level.
Source: Eurostat, DG REGIO calculations
GDP per head
Seventh Report on economic, social and territorial cohesion
52
As might be expected, there appears to be a positive
relationship between regional competitiveness
and GDP per head, which is evident for both regions
with high levels of the latter and those with
low levels (Figure 1.23).
There is some evidence that regions which are
more competitive have higher rates of business
start-ups, at least regions which are most highly
developed and those which are least developed
(Figure 1.24).
53
Social cohesion
•• In 2016, the employment rate of those aged 20–64 reached 71% which is above
the pre-crisis level but still well below the 75% target set by the Europe 2020
strategy. The situation varies markedly across the EU. In Spain, the rate was still
5 percentage points below the 2008 level, in Cyprus 8 percentage points lower and
in Greece, as much as 10 percentage points less.
•• Unemployment in the EU has fallen from a high of 10.9% in 2013 to 8.6% in
2016 and 7.7% in 2017, still above the 7% it was in 2008. In the Czech Republic,
Germany, Hungary, Malta, Poland and the UK, the rate is lower than in 2008,
in Greece, Spain, Italy and Cyprus, at least five percentage points higher. Youth
unemployment followed a similar pattern and remains above 40% in Greece and
Spain. Regional disparities in unemployment rates have not narrowed as yet, but
they have largely ceased to widen.
•• The risk of poverty or social exclusion in the EU has fallen back to its pre-crisis level,
but it remains higher in EU‑15 cities, while it is significantly lower in EU‑15 rural
areas, as it is in all types of areas in the EU‑13.
•• Big differences in unemployment and income between regions encourage people to
move to find better job opportunities and/or to escape poverty. In several regions,
this has led to large reductions or increases in population, putting pressure on public
infrastructure and services. A major task of regional development strategies is to
tackle the factors pushing people to move.
•• The EU has recently seen a big increase in asylum-seekers, reaching 1.2 million in
2015 and in 2016. Although this represents only 0.5% of working-age population,
their distribution across the EU is far from even. The effective integration of the
people concerned is important for cohesion and future prosperity.
chapter2
Seventh Report on economic, social and territorial cohesion
54
cerned were born outside the EU, the number of
whom rose from 6.3% of total population in 2011
to 6.9% in 2016 (Figure 2.2). The increase was 2
percentage points or more in Luxembourg, Finland
and Sweden. In contrast, the share declined by over
2 percentage points in Cyprus and Slovenia and
by around 1 percentage point in the Baltic States,
because of outward migration among the people
concerned and/or because they passed away.
The share of people born in another EU Member
State (other-EU‑born) barely increased between
2011 and 2016 (from 3.7% to 3.8%), though it
rose by over 2 percentage points in Luxembourg
and Slovenia (Figure 2.3). The only countries where
it declined were the Czech Republic (by 2 percentage
points), Germany (1 percentage point) and
Ireland (0.5 of a percentage point).
1.1 Almost two-thirds of the
EU‑13 population live in a region
of population decline
In the EU, 43% of the population live in a NUTS 3
region that lost population due to a natural reduction
between 2005 and 2015. In the EU‑13, the
share was much larger (66%). The largest reduc-
1. Population change is
increasingly determined by
migration
As natural population growth in the EU slowed
down in the early 1990s, migration overtook it
as the main source of overall population growth
(Figure 2.1). In the 1960s, natural growth added
more than 3 million people a year to the EU‑28
population, in the 2000s, it added only 350 000.
In 2015, for the first time, there was a natural reduction
in the EU population. The impact of migration
in the 1960s was small, adding only about
100 000 a year, while in the 2000s, it added over
a million a year on average. In 2015, migration
increased population in the EU by 1.8 million, a figure
which does not include all the asylum seekers
who arrived during the year as they are typically
included in the population figures only after 12
months of residence or after being granted international
protection.
In 2016, 10.7% of the EU population were born
abroad, either outside the EU or in another EU
country, an increase of 0.7 of a percentage point
compared to 2011. Two-thirds of the people conChapter
2
Social cohesion
-1
0
1
2
3
4
-1
0
1
2
3
4
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Natural change Net migrationPopulation in millions
Natural change and net-migration in the EU-28, 1960-2015Figure 2.1
Source: Eurostat
Chapter 2: Social cohesion
55
tion occurred over the period in eastern Bulgaria
(a decline of more than 10%) (Map 2.1). In many
countries, rural and intermediate regions experienced
fewer births than deaths. This was particularly
so in Romania, Hungary, the Baltic States and
Germany, where there was a natural reduction
in population in almost all regions except metropolitan
ones. The same was true in large parts of
Portugal, Spain, France, Poland and the UK.
Almost a third of the EU population, 31%, live
in a region that lost population due to net outward
migration, more people leaving the region
than people entering the region, between 2005
and 2015. In the EU‑13, however, the figure was
much higher, 66%, as compared with only 22% in
the EU‑15 (Map 2.2). Lithuania, Latvia and some
Romanian regions have been particularly affected.
Metropolitan regions in these countries were the
only ones with net inward migration, more people
entering than leaving the region over this period,
although in some cases with a shift of population
from the city centre to the surrounding region.
The highest growth in total population (7.7% on
average) occurred in regions where there was both
a natural increase in population and net inward
migration (Map 2.3). Almost half the EU‑15 pop-
0
2
4
6
8
10
12
14
16
0
2
4
6
8
10
12
14
16
EU28 PL BG LT LV RO EE CZ HR FI PT SK IT SI EL HU NL FR DK ES MT UK DE SE BE AT IE CY LU
2011 2016% of total population
Population born in another EU-28 country, 2011 and 2016Figure 2.3
Source: Eurostat
31% 38%
0
2
4
6
8
10
12
14
16
0
2
4
6
8
10
12
14
16
EU28 SK RO PL BG HU CZ LT FI IE MT PT IT CY DK DE EL UK SI ES FR BE NL AT LU HR SE LV EE
2011 2016% of total population
Population born outside the EU-28, 2011 and 2016Figure 2.2
Source: Eurostat
SeventhReportoneconomic,socialandterritorialcohesion
56
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.1 Natural population growth in NUTS 3 regions, 2005–2015
< -10
-10 – -5
-5 – 0
0 – 5
5 – 10
10 – 15
> 15
no data
Total change (%)
EU-28 = 0.66
DK, DEA, DED, DEE: data before 2007 was extrapolated.
PL52, UKD3, UKI3, UKI4, UKI5, UKI6, UKI7: NUTS 2
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.2 Net migration in NUTS 3 regions, 2005–2015
< -10
-10 – -5
-5 – 0
0 – 5
5 – 10
10 – 15
> 15
no data
Total change (%)
EU-28 = 2.18
DK, DEA, DED, DEE: data before 2007 was extrapolated.
PL52, UKD3, UKI3, UKI4, UKI5, UKI6, UKI7: NUTS 2
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 2: Social cohesion
57
Measuring population change and migration
Total population change is split into the natural
change and net migration. Natural change is the difference
between live births and deaths over the period
divided by average population over the period.
More births than deaths means natural growth, the
opposite, natural decline.
Net migration is the difference between people moving
into a region and those moving out divided by
average population over the period. Since accurate
figures on movement of people are difficult to obtain,
net migration is estimated as the difference between
the total change in population and the natural
change. This means that it includes any statistical
errors or adjustments.
•• Net migration at regional level covers both people
moving between regions in the same country
and those moving from outside.
•• Net inward migration means more inward than
outward migration (i.e. positive net migration).
•• Net outward migration means more outward
than inward migration (i.e. negative net
migration).
This report shows population change over a ten year
period. It is measured by subtracting population on
the 1st
January in 2005 from population on the 1st
January in 2015 and dividing this by average
pop-ulation over the period. Net migration and
natural change are calculated in the same way.
To capture the cumulative impact on population
of international movements the following
indicators are used:
•• native-born population: Residents born in the
country they live in;
•• foreign-born population: Residents who were
born in a different country than the country
they live in, defined in terms of present borders,
which means, for example, that in the Baltic
States it includes people born in a different part
of what was then the Soviet Union who moved
to the Baltic States prior to their independence
and remained there afterwards.
The foreign-born population is divided into two sub-
groups:
•• non-EU‑born population: Residents born in a
country outside the EU‑28;
•• other-EU born population: Residents born in a
different EU‑28 country.
% of population in regions by determinants of population change, 2005-2015
Net migration change - - + - + +
Natural population change - + - + - +
Total population change - - - + + +
EU-13 49 13 4 66 5 10 19 34 100
EU-15 11 3 8 22 11 19 48 78 100
EU-28 19 5 7 31 9 17 42 69 100
Change in population, 2005-2015 (%)
EU-13 -8.1 -2.5 -2.6 -6.7 1.2 3.9 6.6 5.1 -2.7
EU-15 -6.1 -1.7 -1.7 -4.0 3.0 3.6 7.8 6.1 3.9
EU-28 -7.2 -2.1 -1.7 -5.2 2.8 3.6 7.7 6.0 2.5
Source: Eurostat and DG REGIO calculations
The first three rows show whether the change in net migration, natural population and total population is negative or positive – e.g. the first column
shows where all three are negative, the second where net migration was negative and natural population was positive, but the overall result was
still population decline and so on.
The top section shows the share of population in regions where the determinants of population change are as indicated as % of the total
population. The bottom section shows the % change over the period in each group of regions.
Population growthPopulation decline
All
declining
regions
All
growing
regions
All
regions
Table 2.1 Population share in NUTS 3 regions by determinants of population change,
2005–2015 (%)
Seventh Report on economic, social and territorial cohesion
58
ulation live in such regions, but only 19% of the
EU‑13 population. The biggest reductions (7.2% on
average) occurred in regions where there was both
a natural population decline and net outward migration.
Only 11% of the EU‑15 population live in
such a region as against 49% of the EU‑13 population
(Table 2.1).
Migration from outside the EU and mobility1
between
and within EU Member States is affected by
differences in living conditions, unemployment and
wage levels as well as the extent of discrimination
(ESPON 2017).
Capital metropolitan (metro) regions have experienced
the highest population growth, especially
in the EU‑15 Member States, where population in-
	
1 The Treaty on European Union and Article 45 of the Treaty on the
Functioning of the European Union.
creased by 8% between 2005 and 2015, mainly
driven by a natural increase in population (5%)
(Table 2.2). In the EU‑15, population also increased
in other regions (by 4%), mostly driven by net inward
migration (which added 3% to the total). In
the EU‑13, population increased in capital metro
regions as well (by 5%), entirely as a result of net
inward migration, but both the other metro and
non-metro regions lost population, mainly due to
net outward migration.
Rural regions tend to have slower population
growth than urban ones, but faster growth than
intermediate regions in both the EU‑13 and the
EU‑15. In the EU‑13, intermediate regions have the
highest net outward migration rate, in the EU‑15,
the lowest net inward migration rate. As a result,
in the EU‑28, population in intermediate regions
population change
2005 - 2015 (%)
Predominantly urban Intermediate Predominantly rural Total
EU-13
Total change -1.2 -3.5 -2.0 -2.5
Natural change -0.5 -1.4 -1.3 -1.2
Net migration -0.7 -2.1 -0.7 -1.3
EU-15
Total change 6.5 1.5 2.9 4.2
Natural change 2.6 0.3 -0.9 1.3
Net migration 3.9 1.3 3.8 3.0
EU-28
Total change 5.7 0.2 1.1 2.8
Natural change 2.3 -0.2 -1.1 0.7
Net migration 3.4 0.4 2.2 2.1
Source: Eurostat and DG REGIO calculations
Table 2.3 Population change, natural change and net migration in urban, intermediate and rural
region, 2005–2015
population change
2005 - 2015 (%)
Capital metro region Other metro region Non-metro region Total
EU-13
Total change 5.1 -2.5 -4.9 -2.5
Natural change -0.2 -0.7 -1.8 -1.2
Net migration 5.2 -1.8 -3.1 -1.3
EU-15
Total change 7.6 2.6 4.8 4.2
Natural change 5.0 1.2 -0.2 1.3
Net migration 2.5 1.4 5.0 3.0
EU-28
Total change 7.0 1.8 2.2 2.8
Natural change 3.9 0.9 -0.6 0.7
Net migration 3.1 0.9 2.8 2.1
Source: Eurostat and DG REGIO calculations
Table 2.2 Population change, natural change and net migration in capital metro, other metro and
non-metro regions, 2005–2015
Chapter2:Socialcohesion
59
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.4 Median age in NUTS 3 regions, 2016
0 – 40
40 – 42.5
42.5 – 45
45 – 47.5
47.5 – 50
50+
Median age, in years
EU-28 = 42.6 years
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.3 Total population change in NUTS 3 regions, 2005–2015
< -10
-10 – -5
-5 – 0
0 – 5
5 – 10
10 – 15
> 15
no data
Total change (%)
EU-28 = 2.84
DK, DEA, DED, DEE: data before 2007 was extrapolated.
PL52: NUTS 2
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Seventh Report on economic, social and territorial cohesion
60
remained unchanged, while it increased by 6% in
urban regions and by 1% in rural ones (Table 2.3).
Comparison between net inward migration
(Map 2.2) and the median age of population
(Map 2.4) indicates that younger people are more
mobile than older ones. In the regions with net
outward migration, the average age of the population
living in the region tends to be higher and
vice-versa. At the NUTS 3 level, regions with a
young population were more likely to have experienced
net inward migration between 2005 and
2015. This was the case for 71% of regions with
a median age below 40 (Table 2.4), while 73% of
regions with a median age of 50 and above experienced
net outward migration. Regions of net
outward migration in Portugal, central France,
southern Italy, Greece, Bulgaria, Hungary, southern
Romania, eastern Germany, Finland and the Baltic
States tend, for the most part, to have an older
than average population. On the other hand, regions
of net inward migration in southern Spain,
northern France, London and surrounding areas,
north-eastern Scotland and southern Sweden and
Finland have a younger than average population, in
many cases, migrants being attracted by dynamic
urban centres. Accordingly, net outward migration
tends to push up the median age of population,
since it is disproportionately younger people who
move, which also tends to reduce the birth rate so
reinforcing the effect on the median age.
The largest shares of young people are in the capital
metro regions in the EU‑15 — almost 23% of the
population was below 20 in 2016 — while those of
65 and older accounted for only 16% (Table 2.5).
Many young people come to the capital to study or
to find a job. The elderly, who are mostly retired, do
not need to be close to employment opportunities
and often opt for a more peaceful and a lower cost
location outside the capital.
The tendency is the same, even if less pronounced,
in other metropolitan regions. In the EU‑15, there
are about the same number of elderly as young
people (21% of both in 2016). Those below 20 are
more numerous than those of 65 and older in all
three types of region in the EU‑13.
2. Employment rates are higher for
those born in another EU country
than for the native-born
People born in the EU have the right to live and
work wherever they choose in the Union, enabling
them to gain work experience in other Member
States for short periods as well as to move there
on a long-term basis. In the EU as a whole, the
employment rate of people aged 15–64 born in
a different EU country averaged 70% in 2016,
slightly higher than that of the native-born (67%)
and substantially higher than that of people born
outside the EU (59%) (Figure  2.4). In Portugal,
Table 2.4 Division of NUTS 3 regions by median
age in 2016 and direction of net migration,
2005–2015 (% of total)
Median age
(classes)
Net outward
migration
Net inward
migration
< 40 29% 71%
40 — 50 38% 62%
> 50 73% 27%
Total 38% 62%
Source: Eurostat and DG REGIO calculations
Table 2.5 Division of population age in capital metro, other metro and non-metro regions, 2016
% of total
Age class Capital metro
regions
Other metro
regions
Non metro
regions
Total
EU‑13
less than 20 19.6 19.8 20.5 20.1
65 or more 17.2 17.0 17.5 17.3
EU‑15
less than 20 22.6 20.9 20.7 21.1
65 or more 16.4 19.5 21.1 19.6
EU‑28
less than 20 21.9 20.8 20.6 20.9
65 or more 16.6 19.1 20.2 19.2
Source: Eurostat and DG REGIO calculations
Chapter 2: Social cohesion
61
Hungary, Luxembourg, Latvia, Croatia and the UK,
the employment rate of other EU‑born was markedly
higher than that of the native-born.
People born outside the EU, on the other hand,
face multiple challenges to find a job. In most
Member States, for which there are reasonably reliable
data, the employment rate of non-EU born
was lower than that of either the native-born or
other EU‑born, including in countries with a large
share of non-EU born such as Sweden, Belgium,
the Netherlands and France. Speaking the local
language, having the right qualifications and having
them recognised are only some of the difficulties
the people concerned face in finding a job.
In most EU countries, the rate of employment of the
native-born is higher than that of those born outside
the EU, regardless of education level, whether
basic, upper secondary or tertiary2
. In some countries
(Cyprus, Czech Republic, Spain, Greece, Italy,
Luxembourg, Malta, Slovenia) the rate of employment
of non-EU born, according to data for 2016,
is higher than that of the native-born, but only for
those with basic education.
2	 Data come from the Labour Force Survey. ‘Basic’ is lower secondary
education or less (i.e. ISCED levels 0, 1 and 2); ‘upper secondary’
includes upper secondary and post-secondary, pre-tertiary (i.e.
ISCED levels 3 and 4) and ‘tertiary’ is university and equivalent (i.e.
ISCED levels 5-8).
40
45
50
55
60
65
70
75
80
40
45
50
55
60
65
70
75
80
EU-
28
SE NL DE DK UK AT EE CZ FI LV LT SI FR HU MT BE PT IE PL BG SK LU RO CY ES IT HR EL
Born in the country they live in
Born in a different EU-28 country
Born outside the EU% of population aged 15-64
Employment rate by country of birth (15-64), 2016Figure 2.4
DE: employment rate by country of citizenship. Figures with a low reliability were not included.
Source: Eurostat
The Skills Profile Tool for non-EU
nationals
To help non-EU nationals integrate into the labour
market, the European Commission, in June
2017, launched a new Skill Profile Tool, a multilingual
means of making it easier for non-EU
nationals to have their skills, qualifications and
experience recognised, such as in reception centres
and by public employment services and other
organisations working with migrants As such,
it is intended to guide third country nationals towards
the most suitable training, education or
employment and to identify their needs in these
respects. Around a quarter of non-EU nationals
in the EU have tertiary education, but around
two-thirds of them are inactive, unemployed or
overqualified for the work they do. The new Tool
is aimed at helping those with such an education
level to find a job that matches their qualifications
as well as those with lower qualification
who need further education and training.
For more information: https://ec.europa.eu/edu-
cation/news/20170725-commission-launches-
online-tool-help-integrate-newly-arrived-non-
EU‑nationals_en
Seventh Report on economic, social and territorial cohesion
62
Gender also plays a role. Employment rates of
men are higher than for women in all countries,
irrespective of the country of birth, but especially
so for the non-EU born (Figure 2.5). In Belgium,
Greece, the UK, Bulgaria, Poland Italy and Malta,
the difference for the latter was over 20 percentage
points in 2016, reflecting in part cultural
norms, lack of opportunity and inadequate wages
in respect of the women concerned.
3. Asylum seekers and refugees
In 2015, EU Member States received 1.2 million
first-time applications for international protection
and the same number again in 2016. As a share
of the current non-EU born population, the yearly
inflow in 2015 and 2016 together amounted to
7% at EU level (18% in both Germany and Finland,
16% in Sweden) and 0.5% in terms of total population
(1.8% in Sweden and 1.5% in Austria). If
confined to the number of positive first instance
asylum decisions, it was only around 0.1% of the
population (being highest in Sweden and Germany
at 0.7% and 0.5%, respectively)3
. The increase in
asylum seekers brought with it an increased flow
of the most vulnerable group seeking asylum,
3	 Hungary has seen a large inflow (2.1% of its total population in
2015 and 2016) but mostly as a transit country, as the ratio of
asylum decisions to applications was only 2%, indicating many
people absconding and highlighting the need to consider asylum
decisions as well when measuring asylum seeker inflow.
namely unaccompanied minors4
, whose numbers
in the EU almost doubled between 2013 and 2014
(from 13 000 to 23 000) and almost quadrupled in
the following year (92 205 in 2015, 59% of whom
were hosted in Sweden and Germany). Although
it declined in 2016, it was still at a relatively high
level (63 280). By their nature, those concerned
require additional protection and integration as-
4	 Unaccompanied minors are generally defined as those under the
age of 18 who arrive without parents, other adult relatives or
guardians (UNHCR).
The Action Plan against Migrant
Smuggling
The fight against migrant smuggling has been
part of EU policies tackling irregular migration
for more than a decade. In 2002, the EU adopted
a legal framework on smuggling in the form of a
Directive defining the facilitation of unauthorised
entry, transit and residence and the strengthening
of the penalties for these offences. The crisis
in 2015 for the countries along the Eastern Mediterranean
route called for a common and coordinated
response that, in May 2015, took the
form of an Action Plan against Migrant Smuggling
designed to transform smuggling from a
‘high profit, low risk’ activity into a ‘high risk, low
profit’ one, while ensuring full respect for, and
protection of, the human rights of migrants.
0
5
10
15
20
25
30
35
40
0
5
10
15
20
25
30
35
40
EU-
28
MT IT PL BG UK EL BE CZ FR IE DE HU SI HR FI LU NL SK AT ES DK EE SE LV PT LT CY
non-EU born native-bornPercentage point difference
Employment rate gap between native-born men and women and non-EU born
(20–64), 2016
Figure 2.5
DE: employment rates by citizenship; RO: data for non-EU born not available.
Source: Eurostat
Chapter 2: Social cohesion
63
sistance to find the most sustainable solutions for
them.
The distribution of asylum seekers across the EU
is highly uneven. Germany, in particular, received
more first-time asylum applications than all other
EU countries combined in 2016. Not all these have
been, or will be, granted refugee status and not
all want to stay. Accordingly, at this stage, it is too
early to say how many will remain in the EU.
Recent asylum seekers are predominantly young
and male, a disproportionate number being men
aged 18–34 (Figure 2.6).
The rapid influx represented a challenge for the
local authorities to provide asylum seekers with
food and shelter in the areas where they arrive.
Integrating them into EU society will require language
training, education and help in finding a job
or setting up a business. The evidence from an ad
hoc LFS survey in 2014 is that refugees face considerable
problems in integrating into the labour
market, as reflected in their significantly lower employment
rates than other non-EU born residents
and the EU-born population in most Member States
(European Commission 2016f). Low participation
rates among women, a large proportion of people
without upper secondary education and low levels
of proficiency in the local language underlie this
tendency (European Commission 2016b, Dumont
et al. 2016). While the chances of refugees and
others born outside the EU being employed increases
significantly with their education level, the
increase is smaller than for the native born or other-EU
born (European Commission 2015b, 2016d).
4. The employment rate has
surpassed its pre-crisis level,
but unemployment rates are
still too high
In 2016, the EU employment rate for those aged
20–64 (Map 2.5) exceeded the pre-crisis level for
the first time. At 71%, it is higher than the previous
high in 2008 of 70%, though only slightly. The
rate has not recovered, however, in all parts of the
EU. In Greece, it is still 10 percentage points lower
than before the crisis, in Cyprus 8 points lower and
in Spain 5 (Map 2.6). On the other hand, it was 10
percentage points higher in Hungary and Malta.
Only 6 Member States (Sweden, Germany,
Denmark, UK, Estonia and Netherlands) had an
employment rate in 2016 above the Europe 2020
target of 75%. In more than half of Member States
it was below 70% and in Greece, Spain, Croatia,
France and Italy, below 65%. The impact of the
crisis on employment rates has made it unlikely
that the target will be reached by 2020.
0 100 200 300 400 500 600 700 800 900
0 100 200 300 400 500 600 700 800 900
Females
Males
From 0 to 17 years
From 18 to 34 years
From 35 to 64 years
65 years or over
Per 1000 people
First-time asylum applications in the EU-28 by gender and age, 2016Figure 2.6
Source: Eurostat
SeventhReportoneconomic,socialandterritorialcohesion
64
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.6 Change in employment rate (20-64), 2008–2016
< -5
-5 – -2.5
-2.5 – 0
0 – 2.5
2.5 – 5
5 – 7.5
> 7.5
no data
Percentage point change
EU-28 = 0.8
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.5 Employment rate (20–64), 2016
< 65
65 – 70
70 – 75
75 – 80
> 80
% of population aged 20–64
EU-28 = 71
The Europe 2020 target is 75
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 2: Social cohesion
65
The rate, however, varies markedly between types
of region. The average employment rate in more
developed regions5
was 74.2% in 2016, quite
close to the 75% target (Table 2.6). In the less developed
regions the average rate was well below
the target, at only 65%. While it increased slightly
in these regions between 2008 and 2016, in the
transition regions, it did not increase at all. The
increases in employment rates in regions where
rates are low at least means that after several
years of divergence, regional disparities in employment
have started to narrow again.
Between 2008 and 2016, unemployment increased
at the same time as employment rates went up,
which means that the rate of job creation lagged
behind the growth in the labour force. Although
the unemployment rate fell from a high of 10.9%
in 2013 to 8.6% in 2016 (Map 2.7), this was still
higher than in 2008 (7%). While in some northern
and eastern parts of the EU, rates were lower than
5	 See Lexicon for the definition of less developed, transition and
more developed regions.
before the crisis, in the southern Member States,
rates were up to 10 percentage points higher
(Map 2.8). In several regions in Greece, Italy and
Spain and in the French outermost regions, rates
were still over 20%.
The youth (15–24) unemployment rate declined
from a high of 23.7% in 2013 to 18.7% in 2016,
but it remains well above the level before the crisis
of 15.9% in 2008 (Table 2.7). The rate in 2016
was particularly high in the less developed regions
(averaging 24%) but it was even more so in the
transition ones (27%). The share of young people
neither in employment nor in education or training
(the NEET rate) has also declined, in this case from
a high of 13.2% in 2012 to 11.5% in 2016, only
slightly above the 2008 level (10.9%). The NEET
rate was also highest in the less developed and
transition regions (Map 2.9).
Table 2.7 Youth unemployment, those not in employment, education or training (15–24) and
participation in education and training (25–64) by category of region, 2008–2016
Less
developed
regions
Transition
regions
More
developed
regions
EU
Youth unemployment rate
(% of labour force 15–24)
2016 24.4 27.3 16.7 18.7
change 2008–2016 4.7 6.4 2.9 2.8
Not in employment,
education or training
(% population 15–24)
2016 15.4 13.7 9.6 11.5
change 2008–2016 2.6 0.0 0.3 0.6
Participation in education
and training
(% population 25–64)
2016 4.6 11.6 12.9 10.8
change 2008–2016 0.2 2.2 1.8 1.3
Source: Eurostat, Labour Force Survey
Table 2.6 Employment and unemployment by category of region, 2016 and change 2008–2016
Less developed
regions
Transition
regions
More developed
regions
EU
Employment rate 2016 65.0 67.7 74.2 71.1
change 2008–2016 1.1 -0.2 0.8 0.8
Unemployment rate 2016 9.5 12.3 7.4 8.5
change 2008–2016 1.7 2.0 1.4 1.5
Source: Eurostat, Labour Force Survey
SeventhReportoneconomic,socialandterritorialcohesion
66
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.8 Change in unemployment rates, 2008–2016
< -4
-4 – -2
-2 – -1
-1 – 0
0 – 1
1 – 2
2 – 4
4 – 10
> 10
no data
Percentage point change
EU-28 = 1.5
Sources: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.7 Unemployment rate, 2016
< 5
5 – 7.5
7.5 – 10
10 – 15
15 – 20
> 20
% of labour force
EU-28 = 8.6
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 2: Social cohesion
67
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.9 Young people (15–24) not in employment, education or training (NEET), 2016
< 5
5 – 7.5
7.5 – 10
10 – 15
15 – 20
> 20
no data
% of population aged 15–24
EU-28 = 11.5
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Seventh Report on economic, social and territorial cohesion
68
5. Education and training
In a fast-changing, technology-driven world, people
need to have access to opportunities continuously
to update and improve their skills as well as
to acquire new ones. This is vital not only to enable
them to remain in employment and advance
in their careers but also to boost productivity and
the competitiveness of the economy.
To this end, EU Member States set a target in 2010
that by 2020, 15% of those aged 25–64 should be
taking part in continuing training as compared with
only just over 9% at the time. Progress towards
this target, however, has been slow. By 2016, the
figure had risen to only just under 11%. The target
had been reached or exceeded in only 7 Member
States and there were pronounced disparities not
only between but also within countries, especially
in Italy, France, the Czech Republic, Hungary and
Germany (Map 2.10).
Stronger efforts are needed to encourage lowqualified
adults in particular to participate in training,
since there is a larger proportion of people
with only basic schooling in the EU than in other
industrialised economies. Because such people are
the least likely to participate in training, engaging
them is particularly challenging. The New Skills
Agenda for Europe includes recommendations to
tackle this issue (see Box).
Upskilling Pathways: new
opportunities for adults
The Recommendation, adopted by the Council in
December 2016, calls on Member States to develop
a linked series of targeted interventions,
establishing a ‘pathway’ of support for lowskilled
or low-qualified adults, of whom there are
64 million in the EU. The aim is to support them
to improve their literacy, numeracy and digital
skills and to acquire a broader set of competences
by increasing their qualifications. Each would
be offered:
•• a skills assessment, to identify existing skills
and upskilling needs;
•• an offer of education or training on the basis
of this;
•• opportunities to have the skills acquired validated
and recognised.
These three steps will be accompanied by outreach
and support measures.
Implementation by Member States can be supported
by funding from the ESF, Erasmus+ and
other sources. By mid-2018, Member States
need to outline the measures they will take to
implement the Recommendation, including the
groups of low-skilled adults they will give priority
to.
Measures to combat unemployment and social exclusion among young people
Young people are one of Europe’s greatest assets
for the future. The economic crisis hit young people
particularly hard. It has widened the gap between
those with more opportunities and those with fewer.
Some are increasingly excluded from social and civic
life and, worse still, a number are at risk of disengagement,
marginalisation and even radicalisation.
This is why the Commission and Member States
have increased their efforts since 2013 to improve
their employability, their integration into the labour
market, and their inclusion and participation in society
In the face of a growing socio-economic divide,
policy must continue tackling the deep-seated social
problems that many young people face. Sustainable
solutions need to be found to reduce youth unemployment,
strengthen social inclusion and prevent
violent radicalisation. This requires more systematic
cooperation across a range of policies at EU and
Member State level in respect of employment, education,
training and social policy as well as culture,
sport and health. The ‘cooperation framework for
youth’, EU funding under the Erasmus+ programme,
the European Social Fund (ESF) and the Youth Employment
Initiative (YEI) are all targeted at young
people to help them find quality jobs, participate in
social life and develop their full potential.
Chapter 2: Social cohesion
69
One of the Europe 2020 targets is to reduce the
share of early school leavers to 10% or less. At the
EU level, the share of those aged 18–24 with no
qualifications beyond basic schooling and no longer
in education or training in the 2014–2016 period
was 11%, close to the target, but with wide differences
between and within countries (Map 2.11).
In Spain, Portugal, Italy, Bulgaria and Romania,
for example, the share in almost all regions is far
above the target, whereas in Belgium, Germany,
the UK and Greece, there is a large variation between
regions, with some close to the target or below
and others far above. In the Bruxelles-Capital
region, for instance, 15% of 18 to 24 year-olds
were early school-leavers against a country average
of just below 10%.
High rates of early school-leaving may be linked to
pockets of socio-economic deprivation, often with
high concentrations of migrants, where schools
are of low quality and are less capable of retaining
students. This is particularly the case in larger
cities.
Education and continuing training have recently
been confirmed to be among the main drivers of
economic growth, a larger proportion of poorly educated
people being more detrimental to growth
than a smaller proportion of highly educated ones.
The results of the 2016 PISA (the OECD Programme
for International Student Assessment) survey of
15 year-olds shows, in line with previous surveys,
that competence in maths is particularly problemThe
New Skills Agenda for Europe
The New Skills Agenda for Europe1
, adopted on 10
June 2016, called on Members States, social partners,
industry and other stakeholders to work together
to raise the quality and relevance of skills
training, to make skills more visible and comparable
across countries and to improve the information on
skills to enable better career choices. It launched 10
key actions:
•• A Skills Guarantee to help low-skilled adults acquire
a minimum level of literacy, numeracy and
digital skills and progress towards an upper secondary
qualification (adopted as Council Recommendation
of 19 December 2016 on Upskilling
Pathways: New Opportunities for Adults2
).
•• A review of the Recommendation on Key Competences
to help more people acquire the core
set of skills necessary to work and live in the
21st
century, with a special focus on promoting
entrepreneurial and innovation-oriented mindsets
and skills.
•• Making Vocational Education and Training (VET)
a first choice by increasing opportunities for VET
participants to undertake work experience and
1	 European Commission ‘The New Skills Agenda for Europe’,
COM (2016) 381 of 10 June 2016.
2	 Official Journal C484 of 24 December 2016, p1.
by highlighting the favourable career prospects
open to them.
•• The ‘Digital Skills and Jobs Coalition’ to support
cooperation among education, employment and
industry stakeholders to boost the supply of
digital skills.
•• A review of the European Qualifications Framework
and the related annexes to increase understanding
of qualifications and to make better
use of available skills in the labour market.
•• A ‘Skills Profile Tool Kit for Third Country Nationals’
to support early identification and profiling
of the skills and qualifications of asylum seekers,
refugees and other migrants.
•• A revision of the Europass Framework to give
people better and easier-to-use means of presenting
their skills and of obtaining real-time
information on skill needs and trends which can
help with their career and learning choices.
•• The ‘Blueprint for Sectoral Cooperation on Skills’
to improve intelligence on skills and to tackle
skill shortages in particular sectors.
•• Further analysis and sharing of examples of
best practice to tackle the brain drain.
•• A Graduate Tracking initiative to assemble information
on their performance.
SeventhReportoneconomic,socialandterritorialcohesion
70
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.11 Early school-leavers from education or training aged 18–24, average 2014–2016
< 8
8 – 10
10 – 12
12 – 14
14 – 16
> 16
no data
% of population aged 18–24
EU-28 = 11
The Europe 2020 target is 10.
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.10 Participation of adults aged 25–64 in education and training, 2016
< 5
5 – 7
7 – 10
10 – 15
15 – 20
> 20
% of population aged 25–64
EU-28 = 10.8
The Europe 2020 target is 15%.
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 2: Social cohesion
71
atic in the EU, with over 22% of those tested having
a low proficiency (Map 2.12). Around 20% of
those tested in the EU also had insufficient understanding
of what they read and a low proficiency
in science. The largest proportions with low proficiency
(over 35% in all three disciplines) were in
Bulgaria, Romania and Cyprus, while at the other
end of the scale, Finland, Estonia and Ireland had
reached the Europe 2020 target of no more than
15% of low achievers in the three disciplines, and
Denmark and Slovenia were close to it.
Vocational education and training (VET) can improve
job-specific and transversal skills, facilitating
the transition to employment and maintaining and
updating the skills of the work force. Over 13 million
people enrol in initial VET programmes every
year in the EU. Yet labour market forecasts indicate
an upcoming shortage of people with VET qualifications
in a number of Member States. Those with
recent VET qualifications at upper secondary level
generally have a smoother transition from education
to the labour market and higher employment
rates than those with upper secondary qualifications
from general education pathways who do not
go on to complete tertiary education6
.
The evidence suggests that VET programmes lead
to better employment outcomes than non-tertiary
general oriented ones. In 2015, those who had recently
completed initial VET had an average employment
rate of 73% in the EU, as against one of
61% for those who had recently completed upper
secondary general education and had not gone on
to tertiary education. The biggest difference was
in Belgium, Germany, Estonia, and Cyprus. Only in
6 countries (the Czech Republic, Ireland, France,
Malta, Finland and UK)7
was the average employment
rate of those with VET qualifications similar
or lower than those completing general upper secondary
programmes.
6	 The indicator measures the employment rates of persons aged
20 to 34 having completed education 1–3 years before the survey
with a diploma from upper secondary education (ISCED 3) or
post-secondary non tertiary education (ISCED 4), and who are currently
not enrolled in any further formal or non-formal education
or training, out of the people in the same age group.
7	 European Commission, (2016b).
Despite this, for many young people and their
parents, VET is not seen as an attractive option,
suggesting perhaps a need to improve the labour
market relevance of VET programmes. Too few
programmes at present fully exploit the potential
of work-based training or provide opportunities
to progress to tertiary education. As a response,
Member States agreed in 20158
to further
strengthen key competences in VET curricula and
provide more effective opportunities to acquire or
develop these skills.
6. Adult proficiency in literacy and
numeracy needs to be raised in
several EU Member States
The ability to read and understand both literary
and numerical information is essential for full participation
in society and the economy. Without adequate
skills of these kinds, people are likely to
8	 ‘Riga Conclusions 2015 on a new set of medium-term deliverables
in respect of VET for the period 2015–2020’.Conclusions of the
Council of Ministers in charge of vocational education and training.
Available at: http://www.izm.gov.lv/images/RigaConclusions_2015.
pdf
Measures to support apprenticeships
The European Alliance for Apprenticeships was
launched in 2013 as a multi-stakeholder platform
at EU level to improve the quality, supply
and image of apprenticeships and to promote
international mobility among apprentices. In addition,
the European Pact for Youth was initiated
in 2015 by CSR Europe (European business network
for Corporate Social Responsibility) to bring
together business and relevant stakeholders to
create apprenticeships, traineeships, internships
and entry-level jobs for young people. The latest
2017 Commission Work Programme and the
Communication on «Investing in Europe’s Youth»1
also announced that the Commission will propose
a Council Recommendation for a Quality
Framework for Apprenticeships.
1	 European Commission ‘Investing in Europe’s youth’ COM
(2016) 940 of 7 December 2016.
SeventhReportoneconomic,socialandterritorialcohesion
72
Map 2.12 Proportion of 15-year-olds with low proficiency in mathematics, reading and science, 2016
© EuroGeographics Association for the administrative boundaries
Mathematics
< 15
15 – 20
20 – 25
25 – 30
> 30
% of 15 year old students with a proficiency below level 2 in PISA
EU-28 average = 22.11
Source: OECD PISA, 2016
0 1 000 km
Reading
< 15
15 – 20
20 – 25
25 – 30
> 30
% of 15 year old pupils with a proficiency below level 2 in PISA
EU-28 average = 19.38
Source: OECD PISA, 2016
0
Science
< 15
15 – 20
20 – 25
25 – 30
> 30
% of 15 year old students with a proficiency below level 2 in PISA
EU-28 average = 20.55
Source: OECD PISA, 2016
0
Chapter 2: Social cohesion
73
remain at the margins of society and to face significant
barriers in finding a decent job.
In practice, in most Member States, substantial
numbers of people have low levels of proficiency
in reading and maths, as indicated by the Survey
of Adult Skills (PIAAC carried out by the OECD with
support from the European Commission), which
assesses the ability of people aged 16 and over
in these respects (Figures 2.7 and 2.8). According
to the survey, the highest levels of literacy and
numeracy are in Finland, the Netherlands and
Sweden together with Japan. By contrast, levels
are relatively low in Spain, Greece and Italy. The
survey shows, moreover, that high levels of inequality
in literacy and numeracy are related to
inequality in the distribution of income.
7. Poverty and social exclusion is
declining in the EU‑13 but growing
in cities in the EU‑15
Clear signs of a general improvement in the social
situation in the EU are emerging, though divergences
among Member States remain. In 2015,
almost a quarter (23.7%) of people in the EU were
80 60 40 20 0 20 40 60 80
OECD
FI
SE
NL
DK
SK
BE
CZ
AT
DE
EE
LT
UK
SI
PL
FR
IE
CY
EL
IT
ES
Below level 1 Level 1 Level 2 Level 3 Level 4 or 5% of population 16-64
Numeracy proficiency of adults (16-64), 2016Figure 2.8
Adults who are not classified are not included. UK includes only England and N. Ireland; BE includes only Flanders.
Source: OECD (2016), Skills Matter: Further Results from the Survey of Adult Skills, OECD Skills Studies, OECD Publishing, Paris
80 70 60 50 40 30 20 10 0 10 20 30 40 50 60 70 80
OECD
FI
NL
SE
EE
SK
BE
CZ
DK
UK
DE
AT
PL
IE
FR
LT
CY
SI
ES
EL
IT
Below level 1 Level 1 Level 2 Level 3 Level 4 or 5% of population 16-64
Literacy proficiency of adults (16-64), 2016Figure 2.7
Adults who are not classified are not included. UK includes only England and N. Ireland; BE includes only Flanders.
Source: OECD (2016), Skills Matter: Further Results from the Survey of Adult Skills, OECD Skills Studies, OECD Publishing, Paris
Seventh Report on economic, social and territorial cohesion
74
recorded as being at risk of poverty or social exclusion,
the poverty indicator targeted by Europe
2020 (see Box). The proportion increased during
the crisis between 2008 and 2012 but then fell
back to the 2008 level. This reduction, which was
common to most Member States, followed increases
in incomes as a result of the recovery in
economic activity, improvements in labour markets
and reductions in those affected by severe material
deprivation and those living in low work intensity
households (two of the components of the indicator).
The proportion at risk of poverty, on the other
hand was 1 percentage point higher in 2015 than
in 20089
.
Despite positive signs, the risk of poverty or social
exclusion remains a key challenge especially
in the Baltic and southern Member States. The risk
remains high despite improvements in Bulgaria,
Croatia, Latvia, Lithuania, Romania and Greece,
and it has been rising in Cyprus and Italy. Together
with an increase in inequality in many Member
States, it is one of the main challenges to social
cohesion.
In the EU‑13, the proportion of people at risk of
poverty or social exclusion is considerably larger in
rural areas (34%) than in cities (20%) (Figure 2.9).
In the EU‑15, the pattern is the opposite, the proportion
being larger in cities (24%) than in rural areas
(21%), though the difference is much smaller.
Between 2008 and 2015, the proportion fell in all
areas in the EU‑13, the difference between cities
and rural areas narrowing. In the EU‑15, the proportion
fell only in rural areas while it increased in
cities, towns and suburbs (Figure 2.10).
There is some difference in the incidence of the
three indicators combined in the aggregate measure
across the EU, though there are also similarities
since each of them is measuring an aspect
of poverty or social exclusion. In 2015, 17.3% of
the EU population was recorded as being at risk of
poverty (Figure 2.11). As in the case of the aggregate
indicator, there was a somewhat larger proportion
of households at risk in rural areas across
9	 2015 and 2008 refer to the years of the survey. The income being
measured actually relates to the previous years, i.e. 2014 and
2007.
What it means to be at risk of poverty
or social exclusion
A set of indicators is used to measure poverty or
social exclusion in the EU. The headline indicator
for those at risk of poverty or social exclusion
(AROPE) consists of a combination of three in-
dicators:
•• At risk of poverty (or relative monetary poverty)
measures the percentage of people living
in a household with equivalised disposable
income in the previous year below the
at-risk-of-poverty threshold set at 60% of
the national median.
•• Severe material deprivation measures the
percentage of people who report to the
EU‑SILC survey that they are unable to afford
any 4 of 9 items included in the survey1
.
•• Living in a households with very low work
intensity measures the percentage of people
living in households where those aged 18–
59 worked for only 20% or less of the time
they could potentially have worked during
the past year if they had worked full-time
throughout the year.
People identified as being at risk of poverty or
social exclusion are those recorded under any
one of these three indicators.
EU Statistics on Income and Living Conditions
(EU‑SILC)
The EU Statistics on Income and Living Conditions
(EU‑SILC) is the main source of data in the
EU on poverty and social exclusion. The survey
from which the statistics are derived covers a
representative sample of households in all Member
States. The survey is carried out each year
and the data on income, and therefore the risk
of poverty, and work intensity relate to the year
preceding the survey — i.e. for 2015, the risk of
poverty and low work intensity relate to 2014
while material deprivation relates to the year of
the survey, i.e. 2015.
1	 The 9 items are a colour TV; a washing machine; a telephone;
a car; a meal of meat or fish or the equivalent
every other day; a week’s annual holiday away from
home; an ability to avoid being in arrears on mortgage
payments, rent, utility bills, hire purchase instalments or
loans; an ability to make ends meet and an ability to keep
the house adequately warm.
Chapter 2: Social cohesion
75‑‑‑ 
the EU (19.8% than in cities (16.7% or towns and
suburbs (16.0%. At the same time, rural areas
have a smaller proportion of households with very
low work intensity, which suggests that their higher
risk of poverty is not mainly due to their lower
employment but to their lower incomes, or perhaps
to their incomes needing to support larger households.
The difference in the risk of poverty between
cities and rural areas at EU level is due to the big
difference in the EU-13 (11% as against
26%), while in the EU-15, the proportion at risk is
slightly smaller in rural areas than in cities.
Moreover, the proportion fell between 2008 and
2015 in rural areas solely in the EU-15 (Figure
2.12).
In line with the pattern of change in unemployment,
the proportion of people living in households
with very low work intensity in the EU in 2015 was
higher than in 2008 (10.6% as against 9.2%) but
lower than the peak in 2014 (which in fact relates
to 2013). In contrast to the risk of poverty, the proportion
was much higher in the EU‑15 than in the
EU‑13, especially in cities (18%), whereas in the
EU‑13, it was higher in rural areas (6%) than in cities
(4%) (Figure 2.13). The situation in the EU‑15
may seem surprising as employment opportunities
tend to be greater in cities. But it is also the case
that a larger proportion of people live alone than
in other areas and if they become unemployed,
-15
-10
-5
0
5
10
15
-15
-10
-5
0
5
10
15
EU-
28
EU-
15
EU-
13
EL IE BE NL IT DK EE ES PT LT DE MT UK CY SE SI FI SK CZ HU FR AT HR LV RO LU BG PL
Cities Towns and suburbs Rural areasChange in % of population
Change in the proportion of people at risk of poverty or social exclusion by
degree of urbanisation, 2008-2015
Figure 2.10
HR: 2010 instead of 2008; IE: 2014 instead of 2015
Source: Eurostat
0
20
40
60
0
20
40
60
EU-
28
EU-
15
EU-
13
EL BG BE IT UK PT IE LV ES AT CY DE DK RO MT LT EE HU NL HR SI FR SE LU FI PL SK CZ
Cities Towns and suburbs Rural areas% of population
Share of population at risk of poverty or social exclusion by degree of urbanisation,
2015
Figure 2.9
IE: 2014
Source: Eurostat
Seventh Report on economic, social and territorial cohesion
76
-8
-6
-4
-2
0
2
4
6
8
10
-8
-6
-4
-2
0
2
4
6
8
10
EU-
28
EU-
15
EU-
13
LU LV UK AT CY BG HR PL FR FI CZ IE MT IT SK HU ES DK EE LT SE EL DE SI NL PT RO BE
Cities Towns and suburbs Rural areasChange in % of population
Change in the at-risk-of-poverty rate by degree of urbanisation, 2008-2015Figure 2.12
HR: 2010 instead of 2008; IE: 2014 instead of 2015. LU: Towns and suburbs, break in series.
Source: Eurostat
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
35
40
45
EU-
28
EU-
15
EU-
13
BE DE IT ES AT EE UK PT EL DK LV MT SE SI NL LU FR LT CY IE BG FI RO HR PL HU SK CZ
Cities Towns and suburbs Rural areas% of population
The at-risk-of-poverty rate by degree of urbanisation, 2015Figure 2.11
IE: 2014
Source: Eurostat
0
5
10
15
20
25
0
5
10
15
20
25
EU-
28
EU-
15
EU-
13
BE IE EL DK ES DE NL UK AT FI IT PT HR FR CY MT HU SI CZ LU PL SE LV LT BG RO EE SK
Cities Towns and suburbs Rural areas% of population
Proportion of population living in very low-work intensity households by degree of
urbanisation, 2015
Figure 2.13
IE: 2014
Source: Eurostat
Chapter 2: Social cohesion
77
-5
0
5
10
15
-5
0
5
10
15
EU-
28
EU-
15
EU-
13
EL ES IE BE CY PT FI NL LT DK AT LV UK SE LU IT BG MT EE DE CZ SK HU SI HR FR RO PL
Cities Towns and suburbs Rural areasChange in % of population
Change in the proportion of population living in very low-work intensity
households by degree of urbanisation, 2008-2015
Figure 2.14
HR: 2008 instead of 2010; IE: 2014 instead of 2015
Source: Eurostat
0
5
10
15
20
25
30
35
40
45
0
5
10
15
20
25
30
35
40
45
EU-
28
EU-
15
EU-
13
BG EL RO HU LV CY LT IT PT HR BE IE MT PL UK SK ES DE AT CZ FR SI EE NL DK LU FI SE
Cities Towns and suburbs Rural areas% of population
Proportion of population living in severe material deprivation by degree of
urbanisation, 2015
Figure 2.15
IE: 2014
Source: Eurostat
-18
-15
-12
-9
-6
-3
0
3
6
9
12
15
-18
-15
-12
-9
-6
-3
0
3
6
9
12
15
EU-
28
EU-
15
EU-
13
EL MT IT LT CY UK BE ES IE LU NL DK EE PT DE SE CZ FR FI HU SI HR AT LV SK BG RO PL
Cities Towns and suburbs Rural areasChange in % of population
Change in the proportion of population living in severe material deprivation by
degree of urbanisation, 2008-2015
Figure 2.16
HR: 2008 instead of 2010; IE: 2014 instead of 2015
Source: Eurostat
Seventh Report on economic, social and territorial cohesion
78
household work intensity immediately falls to
zero, whereas in households with two or more people,
the other person(s) in the household may continue
to be employed. It is also the case that the
proportion of non-EU born in EU‑15 cities is four
times that in rural areas, which, because of their
lower employment rates, also tends to increase
the number of households with low work intensity.
In addition, the crisis hit cities in the EU‑15 harder
than other areas, the proportion of people living
in low work intensity households increasing by 8
percentage points as a result, whereas it remained
unchanged in rural areas. In the EU‑13, by contrast,
the proportion declined by 3 percentage points in
both rural areas and cities and by 2 percentage
points in towns and suburbs (Figure 2.14).
Income inequality in cities has a spatial dimension
Rich and poor people often live in separate neighbourhoods in cities. The difference in average prosperity and
living conditions in different parts of a city has been the subject of debate because of the potential effect on
social mobility, since the quality of schools, access to services and decent living conditions are important for
people to prosper and fulfil their potential.
Although households in European cities tend to be less spatially segregated by income than in North America,
the pattern of segregation differs across the EU. In Denmark and the Netherlands, for example, the poorest
households show the highest level of spatial concentration, while in France, as in the US and Canada, it
is the most affluent who tend to concentrate most in specific areas of a city (Figure 2.17).
The concentration of poor households in disadvantaged neighbourhoods can give rise to less favourable
outcomes for people who live and grow up there. In the Netherlands, for example, those who lived with their
parents in poor neighbourhoods (bottom 20% of the income distribution) ended up, 12 years after leaving
the parental home, having an income 5–6% lower than those who lived in the most affluent neighbourhoods.
(This box is based on a contribution from OECD.)
Source: OECD (2016b)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
The
Netherlands
Denmark France Canada United States
Bottom income Top incomeSegregation (entropy index)
Income concentration in cities by income group, 2014 or
latest available year
Figure 2.17
Higher values indicate higher concentrations
Chapter 2: Social cohesion
79
The severe material deprivation indicator identifies
people who cannot afford any four of 9 basic
items included in the EU‑SILC. The proportion concerned
in the EU‑13 was more than twice that in
the EU‑15 in 2015 (14% as against 6%), reflecting
the much lower income levels. In the EU‑13,
in the same way as the risk of poverty, it was
larger in rural areas than cities (16% as against
12%, Figure 2.15), but the difference is narrowing.
Between 2008 and 2015, the proportion fell by 9
percentage points in rural areas and 5 percentage
points in cities (Figure 2.16).
In the EU‑15, severe material deprivation is more
common in cities than rural areas (affecting 7.4%
of the population in 2015 as against 4.4%) and
has become more so over time (increasing by 1.3
percentage points while remaining unchanged in
rural areas). Although many cities in the EU‑15
have high levels of GDP per head, they also have,
in many cases, high levels of inequality, as reflected
in at-risk-of-poverty rates, higher concentrations
of deprivation than other areas and more
households with low work intensity.
8. Moving at different speeds to
the Europe 2020 targets
The Europe 2020 strategy sets out five headline
targets to be reached by 2020, covering employment,
education, poverty, innovation and climate
change. The targets for reducing greenhouse gas
emissions and increasing renewable energy have
been translated into legally-binding national tarThe
European Pillar of Social Rights
After a wide public consultation, the European Commission
published the European Pillar of Social
Rights1
on 26 April 2017. It sets out a number of
key principles and rights to support fair and wellfunctioning
labour markets. which is also essential
for building more resilient economic structures. In
particular, the European Pillar of Social Rights sets
out 20 principles in support of fair and well-functioning
labour markets and welfare systems to serve
as a guide for a renewed process of convergence
towards better working and living conditions among
participating Member States. Although it is primarily
conceived for the euro area, it is applicable to all
Member States wishing to participate. The principles
are grouped into three broad categories:
Equal opportunities and access to the labour market,
which includes equal access to education and
training, gender equality and active support to em-
ployment.
Fair working conditions, which includes the right to
secure and adaptable employment, fair wages, information
about working conditions and protection
in cases of dismissal, consultation with social part-
1	 The Pillar was published as a Commission Recommendation
and as a proposal for an inter-institutional Proclamation with
the European Parliament and the Council.
ners, support in achieving a suitable work-life balance
and a healthy and safe working environment
Social protection and inclusion, which includes the
right to childcare and support to children education,
social protection, unemployment benefits and access
to activation measures, minimum income support,
old-age pensions, affordable healthcare, support to
people with disabilities, affordable long-term care,
housing and access to essential services.
Most of the tools for delivering on these principles
are in the hands of local, regional and national authorities,
though the social partners and civil society
also have a role. The EU — and the European
Commission in particular — can help by setting the
framework, giving direction and establishing a levelplaying
field while fully respecting differences in national
circumstances and institutions.
The Pillar reaffirms rights already present in the EU
but complements them by taking account of new
realities. As such, it does not affect principles and
rights already contained in binding provisions of EU
law. But, by putting together rights and principles set
at different times, in different ways and in different
forms, it aims to make them more visible, understandable
and explicit.
Seventh Report on economic, social and territorial cohesion
80
gets. In the other cases, there are optional national
targets.
Portugal, Spain, the south of Italy, Croatia,
Greece, Bulgaria, Romania and eastern Hungary
are furthest away from achieving the EU targets
(Map 2.13). Intra-country variation is, however,
pronounced. Apart from the traditional north-south
divide in Italy, in France, Germany, Belgium, the UK,
the Czech Republic and Denmark, there are both
regions with high values of the index and those
with low values.
Between 2010, when the targets were set, and
2015, almost all regions in central and eastern
Member States made progress towards achieving
them (Map 2.14). The score on the index for the
less developed regions increased on average from
36 to 46. The score for the transition regions, on
the other hand, rose only marginally, reflecting the
impact of the crisis. The score also increased for
the more developed regions, from 76 to 80, but at
this rate even these will not reach the targets by
2020 (Table 2.8).
In general, cities are closer to achieving the targets
(Figure 2.18) than towns and suburbs or rural areas.
In Sweden, Czech Republic and Luxembourg,
cities have reached or surpassed the employment,
education and poverty reduction targets — indeed
Europe 2020 targets for the EU
1. Employment
•• 75% of 20–64 year-olds to be employed
2. R&D
•• 3% of EU GDP to be invested in R&D
3. Climate change and energy sustainability
•• Greenhouse gas emissions 20% lower than
in 1990
•• 20% of energy from renewables
•• 20% increase in energy efficiency when
compared to the projected use of energy in
2020
4. Education
•• The rate of early school-leaving to be reduced
below 10%
•• At least 40% of 30–34-year-olds to have
completed tertiary education
5. Fighting poverty and social exclusion
•• At least 20 million fewer people to be at risk
of poverty and social exclusion (equivalent
to reducing the number to 19.5% of the
population)
Constructing the Europe 2020
achievement index
The Europe 2020 achievement index measures
progress towards meeting the targets set
at EU‑level by NUTS 2 regions and by areas
grouped by degree of urbanisation (see Dijkstra
and Athanasoglou, 2015).
A score of 100 means that a region or an area
group has reached or surpassed all the EU targets,
a score of zero means that the region or
area concerned is furthest away from reaching
them.
Each headline target is weighted equally. This
means that for the index, the employment, poverty
and R&D indicator have a weight of 25%,
while the two education indicators have one of
12.5%. For the index of areas grouped by degree
of urbanisation, the employment and poverty indicator
both have a weight of 33%, while the two
education indicators have one of 16.6%.
Climate change indicators are not available below
the national level and so could not be included
in the two indices. The R&D target had to
be excluded from the index for degree of urbanisation
groups as it is not measured at this level.
For purposes of the indices, the absolute target
for reducing poverty and social exclusion was
transformed into a reduction in the share of people
at risk of poverty or social exclusion.
As not all Member States opted to set national
targets for the employment, education and poverty
reduction indicators, the index presented
here is relative to the EU target in each case.
Chapter2:Socialcohesion
81
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.14 Change in the Europe 2020 achievement index (EU targets), 2015-2010
< 0
0 - 5
5 - 10
10 - 15
15 - 20
>20
Percentage points
EU-28 = 8.5
Source: JRC
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.13 The Europe 2020 achievement index (EU targets), 2015
0 – 20
20 – 40
40 – 60
60 – 80
80 – 100
Index
EU-28 = 80.79
0 = lowest level of achievement | 100 = all targets included in
the index reached.
This index takes into account the following indicators:
Employment, R&D spending, Education (ESL and TERT) and fighting
poverty and social exclusion (AROPE).
Source: JRC
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Seventh Report on economic, social and territorial cohesion
82
some had already done so in 2010. The difference
between cities and other areas is very wide in
some cases, in Bulgaria, Romania, Spain, Hungary
and Poland, in particular, in all of which rural areas
are lagging well behind.
In some countries, especially in the EU‑15, towns
and suburbs score better than cities. In France, the
UK, Austria, Malta and, in particular, Belgium, cities
score poorly, primarily due to low employment and
high poverty rates.
While progress was made towards the targets
in almost all countries between 2010 and 2015,
though not enough to meet them by 2020, the
situation deteriorated in all three types of area
in Greece and Cyprus (Figure 2.19). The achievement
index was also lower in 2015 than in 2010
in Danish and Belgian cities, in towns and suburbs
in France and in rural areas in Spain.
0
20
40
60
80
100
0
20
40
60
80
100
EU-
28
EL IT BE MT ES PT BG HR AT IE CY RO UK FR SI LV HU DE PL DK SK LT EE NL FI SE CZ LU
Cities Towns and suburbs Rural areasIndex (0= furthest away from target, 100=target reached)
Europe 2020 achievement index (EU targets), 2015Figure 2.18
Source: Dijkstra and Athanasoglou (2015)
-30
-20
-10
0
10
20
30
40
50
-30
-20
-10
0
10
20
30
40
50
EU-
28
EL IT BE MT ES PT BG HR AT IE CY RO UK FR SI LV HU DE PL DK SK LT EE NL FI SE CZ LU
Cities Towns and suburbs Rural areasChange in index
Change in the Europe 2020 achievement index (EU targets) between 2010
and 2015
Figure 2.19
Source: Dijkstra and Athanasoglou (2015)
Table 2.8 Europe 2020 regional achievement
index (EU targets), 2010–2015
2010 2015 Change
Less developed regions 36 46 10.0
Transition regions 55 56 0.6
More developed regions 76 80 4.5
Source: Dijkstra and Athanasoglou (2015) .
Chapter 2: Social cohesion
83
9. More women are studying,
working and being elected to
regional assemblies
Equality between women and men has been enshrined
in the EU Treaties from the very beginning
and is part of the 2009 Charter of Fundamental
Rights.
In 2016, the employment rate of men aged 20–64
in the EU was 12 percentage points higher than
that of women (Map 2.15). In 2001, the gap was
18 percentage points and has narrowed every
year since then, including over the crisis years.
Employment rates of men are higher than for
women in all EU regions except Övre Norrland in
Sweden and Corse in France. The difference is over
20 percentage points in Malta and several Greek,
Italian and Romanian regions. In Malta, Greece and
Italy, the difference narrowed between 2001 and
2016, but in Romania, it increased by 5 percentage
points.
At the EU level, unemployment rates of men and
women are much the same, the rate for women being
only 0.4 of a percentage point higher than for
men in 2016 (Map 2.16). This implies that the employment
gap is primarily due to more women not
participating in the work force. The Commission’s
Strategic engagement for gender equality has
identified a number of ways of increasing employment
rates of women:
•• make it easier to balance caring and professional
responsibilities;
•• share time spent on care and household responsibilities
more equally;
•• provide childcare for 33% of children under
3 and 90% of children between 3 and mandatory
school age (the targets set under the
Barcelona agreement in 2002);
•• provide support for care of other dependants;
•• encourage more women to become
entrepreneurs;
•• promote gender equality in research;
•• improve the integration of women migrants
into the labour market.
More of the women aged 30–34 have tertiary education
than men in the EU and this is the case
in all regions, except in several German ones and
a few others scattered across the EU (Map 2.17).
On average, 43% of women in this age group had
this level of education in 2014–2016 as opposed
to only 34% of men. In Latvia, northern Sweden,
Slovenia, some Polish regions and Molise in Italy,
the share of women with tertiary education was
20 percentage points or more larger than for men.
Policies to support gender equality
•• Gender equality is a key element of the recently
adopted European Pillar of Social
Rights which states that “equality of treatment
and opportunities between women
and men must be ensured and fostered in
all areas”.
•• The Commission’s Strategic Engagement for
gender equality 2016–2019 identifies 5 priority
areas: increasing female labour-market
participation and the equal economic independence
of women and men; reducing the
gender pay and pension gaps and so combat
poverty of women; promoting equality between
women and men in decision-making;
combating gender-based violence and protecting
and supporting victims of this and;
promoting gender equality and women’s
rights across the world.
•• The Commission recently adopted the WorkLife
Balance initiative aimed at tackling
women’s under-representation in the labour
market by modernising the current EU legal
and policy frameworks for family-related
leave, flexible working arrangements and
formal care services and reducing economic
disincentives for second-earners to work.
SeventhReportoneconomic,socialandterritorialcohesion
84
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.16 Difference between female and male unemployment rates, 2016
< -1.8
-1.8 – -1.2
-1.2 – -0.6
-0.6 – 0
0 – 0.8
0.8 – 1.8
1.8 – 4
> 4
Percentage point difference (female–male)
EU-28 = 0.3
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.15 Difference between female and male employment rates (20–64), 2016
< -20
-20 – -15
-15 – -10
-10 – -5
-5 – 0
> 0
Percentage point difference (female - male)
EU-28 = -11.5
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 2: Social cohesion
85
While more women than men have tertiary education,
their fields of study differ substantially, which
may partly be a factor underlying their lower employment
rates. In particular, far more men than
women opt for a natural science, mathematics,
ICT or engineering degree in all Member States
(Figure 2.20).
Women aged 18–24 are also less likely to have left
education and training before completing upper
secondary schooling than men. (Map 2.18). There
are many reasons why young people may decide
to leave school early. Personal or family problems,
learning difficulties, a fragile socio-economic situation
are all potential reasons but the school environment,
teacher-pupil relations and the quality
of teaching may also play an important role. The
highest rates of early school-leaving are in regions
in Spain, Portugal and Italy, mostly because of
young men leaving early. In Sardegna, for example,
around 28% of young men left education before
completing upper secondary education as against
just under 15% of young women. While more men
than women leaving education early is the norm
across the EU, there are a few regions (around
10% of the total) scattered across northern, central
and eastern parts of the EU (but in Bulgaria
especially), where the reverse is true, though only
marginally so in most cases.
In 2017, women made up half or more of members
of regional assemblies across the EU in only
17 out of 297 cases. Five regional assemblies in
Hungary, Italy and Romania have no women members
at all and in several regional assemblies in
these three countries as well as in Slovakia, less
than 10% of members were women. Women were
most represented in assemblies in Belgium, Spain,
France, Sweden and Finland, where they accounted
for 40% or more of members (Map 2.19).
The average regional assembly in the EU had only
29% of members that were women in 2017, only
slightly more than in 2007 (27%), so that at this
rate of progress, it would take 100 years to reach
50%. There is also no indication of a larger increase
in countries with a small share of women
members than in others (Map 2.20).
In some countries, the share of women has increased
without the need for a gender quota. In
Sweden, for example, most political parties ensure
that every second candidate for election is a woman.
In Belgium, France, Spain, Portugal and Ireland,
however, quotas have been used to raise the number
of women at national and/or regional level of
government (Ireland does not have any regional
assemblies and Portugal has regional assemblies
only in the Açores and Madeira).
-50
-40
-30
-20
-10
0
10
20
30
-50
-40
-30
-20
-10
0
10
20
30
FI AT DE LT SE LV EE SI HU ES FR CZ MT EL SK HR PT IE BE PL UK DK BG LU RO CY NL IT EU-
28
Generic + Education + Arts and Humanities + Social Sciences + Agriculture&Forestry + Services
Bussiness, Administration and Law
Natural Science, Mathematics and Statistics + ICT + Engineering
Health and Welfare
Difference between men and women in percentage points
Difference between shares of women graduates in different fields of education
and shares of men, 2015
Figure 2.20
Missing values for: EL (Generic); IT (Generic, Natural Science, Mathematics and Statistics, Engineering, Services); NL (Generic, Social Sciences,
Business-Administration-Law, Science, Mathematics and Statistics, ICT)
Source: Eurostat
SeventhReportoneconomic,socialandterritorialcohesion
86
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.18 Gender gap (females–males) for early school-leavers (18–24), 2014–2016
< -8
-8 – -6
-6 – -4
-4 – -2
-2 – 0
0 - 2
> 2
no data
Percentage point difference
NUTS 1 level for DE2, DE7, DED, AT2, AT3, PL1, PL2, PL3, PL5, PL6.
EU-28 = -3
Source: Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.17 Gender balance of population aged 30–34 with tertiary education, average
2014–2016
< 85
85 – 100
100 – 110
110 – 120
120 – 130
130 – 140
140 – 170
> 170
Women as % of men
EU-28 = 127
This indicator takes into account the proportion of each gender
in the population.
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter2:Socialcohesion
87
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.20 Change in the shares of women in regional assemblies, 2007–2017
< -7
-7 – -3.5
-3.5 – 0
0 – 3.5
3.5 – 7
7 – 10.5
10.5 – 14
> 14
Percentage point difference
For HR regions, changes between 2013 and 2017.
Source: European Institute of Gender Equality, JRC, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.19 Women in regional assemblies, 2017
0 – 10
11 – 20
21 – 30
31 – 40
41 – 50
> 50
% of members
Countries without regional assemblies: BG, EE, IE, CY, LT, LU,
MT, SI (women in national assembly).
Regions without assembly: ES63, ES64, PT1, England excl.
London (women in national assembly).
Source: European Institute of Gender Equality, JRC, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Seventh Report on economic, social and territorial cohesion
88        
10. Life in the EU is among the
longest in the world but regional
disparities persist
The EU has one of highest life expectancies at
birth in the world, 80.6 years in 2015. Spaniards
and Italians have the longest expectancy in the EU
(83.0 and 82.7 years at birth, respectively), while
Lithuanians have the shortest (74.6 years). Most
EU Member States have a life expectancy higher
than in the United States, which is ranked only
31st
in the world in this regard, with an expected
life span of 79.3 years in 2015 (World Health
Organisation, 2017).
Differences between regions across the EU are,
however, marked (Map 2.21). Life expectancy at
birth is below 75 in many parts of Bulgaria and
Romania and the eastern regions of Hungary as
well as in Latvia and Lithuania. In 20 NUTS 2 regions
(mainly located in France, Italy and Spain
but also including the wealthiest part of London —
Inner London West — which includes Westminster),
life expectancy is over 83. Regional disparities in
infant mortality (Map 2.22) and, to a lesser extent,
road fatalities (Map 2.23) can partly explain the
differences.
In 2015, an average of 3.6 children per 1 000 born
alive died before reaching one year of age in the
EU, a reduction from 3.8 in 2012. Infant mortality,
however, was above 6 per 1 000 in 21 NUTS 2
regions in Romania and Bulgaria — all except the
capital city ones — all the French overseas regions,
the Spanish regions of Ceuta and Melilla (on the
North coast of Africa), the most eastern region in
Slovakia and the English region of Shropshire and
Staffordshire in the West Midlands. By contrast, the
rate was 2 per 1 000 or less in 18 regions scattered
across the EU — in two or more in Austria,
Finland, the Czech Republic, Slovenia and
Spain and one each in Belgium, Germany,
Greece, the Netherlands and the UK.
Road traffic fatalities vary equally widely across
the EU. Although they declined overall by 45% between
2004 and 2014, the number still averaged
51 per one million inhabitants in 2015, though
with large differences between regions (Map 2.23).
(For comparison, the US figure was twice as high in
2015, at over 100 per million.).
The regions with the highest figures, with over 99
deaths per million, are mostly in Bulgaria, Romania,
Greece, Croatia and the north-eastern part of
Poland though also in Portugal, Corse and, above
all, the Belgian province of Luxembourg, where as
many as 210 road fatalities per million inhabitants
were recorded in 2015, 38% more than in 2010.
Road fatalities are, in many cases, less in capital
city regions than in other parts of the country. The
safest capital cities in the EU in which to drive are
Stockholm and Wien, in both of which the number
of road deaths was below 10 per million in 2015,
while in London, Copenhagen, Paris, Madrid, Berlin
and Prague, fatalities are less than in other regions
(Map 2.24). This reflects in part low traffic speeds
and good public transport, which gives people the
option of not driving.
Cities, however, do not have lower fatality rates
than other areas everywhere. In Romania, Italy,
Belgium, Lithuania, Latvia and Poland, rates are
relatively high in cities. In Bucharest and many
other Romanian cities, there were more than 90
deaths per million in 2013–2014, far above the
target of 31 deaths per million set by the European
Road Safety Action Programme for 2020. In 2015,
this target was reached in only 16% of regions.
The Road Safety Programme
One of the objectives of the European Commission
is to ensure that satisfactory standards of
safety for all modes of transport throughout the
EU are met. The Road Safety Programme adopted
in 2011 is aimed at cutting road fatalities by
half between 2010 and 2020 to 31 deaths per
million inhabitants. The programme includes a
mix of initiatives, at EU and national level, focusing
on improving vehicle safety, road user behaviour
and the roads themselves.
Chapter2:Socialcohesion
89
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.22 Infant mortality rate, 2015
< 2
2 – 3
3 – 4
4 – 5
6 – 8
> 8
Deaths under 1 year of age / 1000 live births
EU-28 = 3.6
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.21 Life expectancy, 2015
< 76.0
76.0 – 77.0
77.0 – 78.0
78.0 – 79.0
79.0 – 80.6
80.6 – 81.0
81.0 – 82.0
82.0 – 83.0
> 83.0
Life expectancy at birth in years
EU-28 = 80.6
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
SeventhReportoneconomic,socialandterritorialcohesion
90
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.24 Road traffic fatalities in cities, 2013–2014
Deaths per 100 000 inhabitants
< 2
2 – 3
3 – 4.5
4.5 – 6
6 – 9
> 9
no data
Urban centre population
< 100 000
100 000 – 250 000
250 000 – 500 000
500 000 – 1 000 000
1 000 000 – 5 000 000
>= 5 000 000
The EU objective for 2020 is a rate below 3.
AT: 2013;
BG, LV, NL: 2012–2013;
FR: 2012;
IT, PL, PT, SK, NO: 2011–2012;
IE, EL: 2011;
CZ, SE: 2010–2011;
RO: 2008.
Source: Eurostat
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.23 Road traffic fatalities, 2015
< 20
20 – 30
30 – 50
50 – 70
70 – 90
> 90
no data
Deaths per million inhabitants
EU-28 = 51
The EU target for 2020 is a reduction of 50% relative to 2010: 30
road fatalities per million inhabitants.
Source: Eurostat, DG MOVE
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 2: Social cohesion
91
Further efforts and more investment are, therefore,
needed in most regions to improve road safety.
11. Measuring social progress at
the regional level
Social progress can be defined as a society’s capacity
to meet the basic human needs of its citizens,
to establish the basis for people and communities
to improve and sustain their quality of
life and to create the conditions for people to
reach their full potential. This definition underlies
the Global Social Progress Index which measures
social progress at the national level in about 130
countries worldwide10
. In an attempt to measure
social progress at the regional level in the EU, the
European Commission recently published the EU
Regional Social Progress Index (EU‑SPI) that builds
on and adapts the Global Social Progress Index.
The EU‑SPI is based on a set of 50 social and envi-
10	For more information on the Global Social Progress Index:
https://www.socialprogressindex.com
ronmental indicators, drawn primarily, though not
only, from Eurostat data. The EU‑SPI is aimed at
providing consistent, comparable and policy-relevant
measures of the social and environmental
situation in all NUTS 2 regions11
. It covers three dimensions
of social progress — basic human needs;
the foundations of well-being and opportunity —
each of which is broken down into four underlying
components (Table 2.9).
Economic indicators are deliberately excluded
which means that the EU‑SPI measures social progress
rather than economic performance and can
be compared with economic indicators.
The index has been built to identify social and environmental
strengths and weaknesses, to inform
regional development strategies and to support
peer learning between regions. It scores the various
aspects covered on a scale from 0 to 100, where 0
represents the lowest possible level of social pro-
11	For more information on the regional EU‑SPI: http://ec.europa.eu/
regional_policy/en/information/maps/social_progress
Table 2.9 The framework of the EU‑SPI index
Basic Human Needs Foundations of Well-being Opportunity
Nutrition and basic medical care Acces to basic knowledge Personal rights
Water and sanitation Access to information and communication Personal freedom and choice
Shelter Health and wellness Tolerance and inclusion
Personal safety Environmental quality Access to advanced education
35
40
45
50
55
60
65
70
75
80
85
35
40
45
50
55
60
65
70
75
80
85
BG RO HR LV HU SK EL PL IT CY LT MT PT CZ EE ES SI FR BE DE AT UK IE LU SE NL FI DK
National average Capital region Other NUTS2 regionsEU-SPI index
Degree of within-country variability of the EU-SPIFigure 2.21
Source: DG REGIO
Seventh Report on economic, social and territorial cohesion
92
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 2.25 EU Social Progress Index, 2016
< 45
45 – 50
50 – 55
55 – 60
60 – 65
65 – 70
70 – 75
75 – 80
>= 80
Index
0 = lowest level of social development
100 = highest level of social development
Source: DG REGIO
©
0 500 km
EuroGeographics Association for the administrative boundaries
REGIOgis
Chapter2:Socialcohesion
93
Map 2.26 EU Social Progress Index — sub-indices (2016)
© EuroGeographics Association for the administrative boundaries
Basic sub-index
< 45
45 – 50
50 – 55
55 – 60
60 – 65
65 – 70
70 – 75
75 – 80
> 80
Index
Source: DG REGIO
0 1 000 km
Foundation sub-index
< 45
45 – 50
50 – 55
55 – 60
60 – 65
65 – 70
70 – 75
75 – 80
> 80
Index
Source: DG REGIO
0
Opportunity sub-index
< 45
45 – 50
50 – 55
55 – 60
60 – 65
65 – 70
70 – 75
75 – 80
> 80
Index
Source: DG REGIO
0
Seventh Report on economic, social and territorial cohesion
94
gress and 100 the highest.
Results show that
social progress in the
EU is highest in Nordic
and Dutch regions and
lowest in Romanian
and Bulgarian regions
(Map 2.25). Social progress
is also moderately
high in Austria,
Germany, Luxembourg,
Ireland and the UK.
Belgium and France
score well too, though
both show large internal
differences. The
largest regional variation
is in Italy where
central regions score
higher than the rest of the country (Figure 2.21).
According to the SPI, except for some regions in
Member States which joined the EU in 2004 or after,
basic human needs are being met in almost
all regions. The ‘Foundations of well-being’ dimension
shows greater variation with only the Nordic
Member States, the Netherlands and Ireland scoring
highly in all regions. The largest differences relate
to ‘Opportunity’, with low scores in many regions
in the southern and central eastern countries
(Map 2.26).
There is a close link between the EU‑SPI and regional
GDP per head, although the relationship
indicates that at every level of economic performance
there are opportunities for more social progress
but also risks of less (Figure 2.22). In low
GDP per head regions, every extra euro of GDP
tends to lead to more social progress, while for
high GDP per head regions, this is much less true.
Among the high GDP per head regions, some regions
such as the Nordic regions and most of the
Dutch regions score higher than would be expected
given their GDP her head.
In a small number of regions, commuting across
NUTS 2 boundaries has a distorting effect on GDP
per head of some significance since commuters
increase GDP without being counted in the population.
This is the case in Brussels and London, in particular,
where around half the people working there
live elsewhere. In these regions, GDP per head is
an especially poor proxy for income and this may
partly explain why some score poorly relative to
GDP per head. Many other issues, however, make
GDP per head a poor proxy for median disposable
household income, such as the variable share of
GDP going to wages (which on average has been
shrinking), the differing degree of inequality of
earnings and the varying extents of redistribution
through taxes and social benefits, both between
people and between regions.
35
45
55
65
75
85
95
35
45
55
65
75
85
95
0 20000 40000 60000 80000 100000
EU-SPI2016,index(0-100)
Relationship between EU-SPI and GDP per headFigure 2.22
GDP 2015 (PPS per head)
Source: DG REGIO
95
Territorial cohesion
•• Substantial progress has been made in the EU in limiting energy consumption
and greenhouse emissions. Most Member States are close to reaching the targets
set under the Europe 2020 strategy. Part of the progress, however, is due to the
slowdown in economic activity during the crisis and there is a risk that the current
recovery will make it more difficult to meet the targets.
•• In transport, there needs to be a major shift towards using less energy, cleaner
modes and more efficient use of infrastructure to reach the EU goals for greenhouse
gas emissions.
•• The impact of climate change is likely to be considerable for many EU regions,
particularly the outermost regions, regions around the Mediterranean, along
coastlines generally and mountainous ones. Further investment is needed to make
EU regions and cities more resilient to the changes concerned.
•• Despite general progress in reducing environmental pressures (notably as regards
wastewater and waste treatment), more efforts are needed to meet EU environmental
goals.
•• Pollution is often more of a problem in cities than in other areas. Air pollution is a
particular concern and nature-based solutions, such as the development of green
urban spaces, can provide an efficient means of mitigating the problem.
•• Cities can be more efficient in the use of resources than other places and can make
it possible to adopt a low-carbon life style.
•• Cross-border cooperation, a major EU policy objective, has helped to mitigate the
adverse effects of internal borders, Support for cooperation has led to improvements
in cross-border security and concrete achievements in transport, education, energy,
healthcare and other areas.
•• National borders still constitute obstacles to the movement of goods, services,
people, capital and ideas and substantial gains to the regions concerned as well
as to the EU as a whole could be obtained if remaining restrictions were removed.
chapter3
Seventh Report on economic, social and territorial cohesion
96
Cohesion policy invests heavily in the installation
of facilities to improve the quality of drinking water
and to treat wastewater and in waste management
and recycling schemes as well as in measures
to increase energy efficiency. It also helps to
develop ’green’ infrastructure across the EU and
to establish a network of protected natural areas
as part of Natura 2000, while supporting a shift
towards more environmentally-friendly modes of
transport, all with the objective of ensuring a sustainable
path of development throughout the EU.
For the 2014–2020 period, around €78 billion of
cohesion policy funding has been allocated to supporting
the shift towards a low-carbon economy
(thematic objective 4), adaption to climate change
and risk prevention (thematic objective 5) and improving
environmental protection and resource efficiency
(thematic objective 6). This amounts to almost
a third of ERDF and Cohesion Fund resources,
the two sources of financing most concerned with
environmental issues.
Territorial Cooperation is a key objective of cohesion
policy, focusing on joint action and exchange
of policy ideas and experience between national,
regional and local authorities in different EU
Member States. It helps to reduce the obstacles
to development which stem from national borders
and supports the adoption of common strategies
to solve common problems. Around €10  billion
have been allocated to such cooperation for the
2014–2020 period.
2. Energy Union and climate
change
The EU has the objective of making a transition to
a low-carbon economy and of ensuring that Europe
has access to secure, affordable and climatefriendly
energy. The Energy Union is a European
priority project in which five dimensions are closely
interlinked: energy security, solidarity and trust; a
1. Introduction
As argued in the 5th
Cohesion Report, territorial
cohesion highlights various issues which are central
to cohesion policy. Among these are the environmental
dimension of sustainable development
and the use of flexible functional geographies for
territorial development. The latter aims to adapt
the geographical level of analysis and implementation
of policy to the challenges to be addressed.
Depending on the issue at stake, this ranges
from macro regions, such as the Baltic Sea or the
Danube region, to metropolitan and cross-border
areas. This chapter, therefore, covers the major environmental
challenges affecting the development
of EU regions, on the one hand, and a number of
major issues addressed by various territorial cooperation
schemes, on the other.
Environmental challenges are increasing in number
and importance. Global warming and the associated
climate change is likely to have fundamental
consequences for the EU economies and societies,
notably with the increase in the frequency of extreme
natural events that is expected to accompany
the general rise in temperature. The extension of
human settlements, built-up areas and industrial
activities accentuates the pressure on the environment
with effects notably in the form of air pollution,
a deterioration in the quality of water bodies
and the fragmentation of natural habitat, while the
production of waste has reached levels which require
a radical change in approach.
A large share of cohesion policy resources has always
been invested in measures to improve the
quality of the environment or to tackle key environmental
challenges. The policy is geared towards
supporting the shift to a low-carbon economy
while at the same time helping Member States
and regions to improve their capacity to mitigate
the negative impact of climate change.
Chapter 3
Territorial cohesion
Chapter 3: Territorial cohesion
97
fully integrated European energy market; energy
efficiency to moderate demand; action on climate
change to decarbonise the economy; and research,
innovation and competitiveness.
As part of this, targets have been set for reducing
greenhouse gas emissions progressively up to
2050. These are included in both the 2020 climate
and energy package and the 2030 climate and energy
framework.
The 2020 climate and energy package is aimed at
achieving a 20% cut in greenhouse gas emissions,
a 20% improvement in energy efficiency (both
from 1990 levels) and a 20% share of renewables
in final energy consumption. The 2030 climate and
energy framework is more ambitious, increasing
these targets to 40% for the first and to 27% for
the other two1
.
Cohesion policy plays a central role as regards the
Energy Union. By helping Member States achieve
EU climate and energy targets, cohesion policy
investments tackle energy poverty and enhance
energy security, while furthering regional development,
competitiveness, growth and jobs. By supporting
the Energy Union, the policy also contributes
to reducing air pollution which, according to
the WHO, is one of the main environmental hazards
facing us.
For the 2014–2020 period, around 21% of the
ERDF and Cohesion Fund resources are allocated
to climate-related interventions. While the ESF is
by its nature less oriented towards this area, 1.4%
of its resources still go towards combating the effects
of climate change.
Cohesion policy supports a comprehensive range
of climate-related measures, such as improving
energy efficiency in public buildings, housing and
small and medium-sized enterprises, smart grids;
renewable energy sources; clean urban transport,
railways, cycle tracks and footpaths; research on
climate change and adaptation to it, including
1	 On 30 November 2016, the Commission proposed an update to
the Energy Efficiency Directive including a new 30% energy efficiency
target for 2030.  
resilient infrastructure and risk prevention and
management.
2.1 Increasing energy efficiency
Increasing energy efficiency is critical for reducing
the energy dependence of the EU economies and
protecting the environment. Energy efficiency can
be improved at all stages of the energy chain, from
generation to final consumption. EU measures focus
on areas where the potential for savings is
greatest, buildings, in particular. Increasing energy
efficiency is one of the main objectives of the
Energy Union and one of the primary targets of the
Europe 2020 strategy. The aim is to lower EU primary
energy consumption to less than 1483 million
tonnes of oil-equivalent (Mtoe) a year and final
energy consumption to less than 1086 Mtoe 2
.
Between 2005 and 2015, EU primary energy consumption
fell by 11% from 1713 Mtoe in 2005 to
1530 Mtoe in 20153
(Figure 3.1). Primary energy
consumption fell in all Member States over this period,
except Estonia and Poland where it increased
(by 15% and 3%, respectively). Reductions were
largest (20% or more) in Lithuania, Greece and
Malta.
In 2015, primary energy consumption in the EU
as a whole was still around 3% above the
2020 target. In Malta, France, Germany, the
Netherlands and Bulgaria, substantial reductions
in energy con-sumption are still needed to meet
the indicative national targets set in 2013. In 18
Member States, on the other hand, consumption
was already below the targets4
.
2	 Note that these energy targets are not straight-forward to interpret
in energy efficiency terms. The main determinants of energy
use are GDP growth and the share of (heavy) manufacturing in
the economy and in general, changes in energy consumption per
se say very little about energy efficiency as such.
3	 Primary energy consumption is the energy supplied to industry,
transport, households, services and agriculture, including generation/
transformation losses, consumption of the energy transformation
sector and network losses.
4	 In most cases, targets reflect the objective to reduce energy consumption
by 2020. However, for some countries the target allows
an increase in primary energy consumption.
Seventh Report on economic, social and territorial cohesion
98
Final energy consumption in the EU fell by more
than 9% between 2005 and 2015, from 1191
Mtoe to 1082 Mtoe, i.e. to slightly below the 2020
target. The largest reductions were in Greece
(22%), Spain (18%) and Portugal (16%), countries
in which GDP either declined over this period
(Greece and Portugal) or increased relatively little.
Final consumption increased only in Lithuania
(by 4%), Poland (6%) and Malta (50%). Final consumption
in 2015 was below the national 2020
targets in 16 Member States but still needed to be
reduced further in the others, especially in Malta,
Lithuania, Slovakia and Hungary.
Recent analysis shows that the reduction in energy
consumption is a result not only of improvements
in energy efficiency but also of structural changes
in electricity generation and of the downturn in the
economic activity from 20085
. The economic recovery
now underway might, therefore, give rise to
an upsurge in energy consumption across the EU if
GDP growth were to be particularly high, so putting
the achievement of targets at risk.
Heating and cooling in buildings and industry account
for half of EU energy consumption. For the
most part, the energy concerned is from fossil fuels
and only 16% comes from renewables. A sharp reduction
in both and in the use of fossil fuels would
contribute greatly to meeting the EU’s climate and
5	 See European Commission (2015c).
energy goals. This would require significant investment,
which can be supported to a major extent by
cohesion policy in the majority of Member States.
2.2 Reducing greenhouse gas emissions
The EU emissions trading system (ETS) is a major
means of cutting greenhouse gas emissions from
power and heat generation, industry and aviation,
covering around 45% of such emissions in the EU.
The 2020 target requires a reduction in emissions
in the areas concerned of 21% on the 2005 level,
while the target for 2030 requires a cut of 43%.
In the other, non-ETS sectors, namely housing, agriculture,
waste and transport (excluding aviation),
Member States have set binding targets for cutting
emissions under the Effort Sharing Decision
(ESD). These differ between countries according to
their national wealth, varying from a 20% cut relative
to the 2005 level for the wealthiest to a 20%
increase for the least developed. To achieve the
2030 target of a 40% reduction in EU greenhouse
gas emissions, the ESD areas would need a cut of
30% (relative to 2005). It is in these areas that
Cohesion policy funding can help Member States
to achieve their targets.
Some Member States have already reduced
emissions markedly in ESD sectors (Figure 3.2).
Between 2005 and 2015, they were reduced by
-30
-25
-20
-15
-10
-5
0
5
10
15
-30
-25
-20
-15
-10
-5
0
5
10
15
LT EL MT UK IT RO LU DK ES SK PT HU HR CY BE SE FR DE SI NL CZ BG IE LV FI AT PL EE EU-
28
% change 2005-2015 Target% change relative to 2005
Primary energy consumption, change 2005-2015 and distance to targetFigure 3.1
Source: Eurostat
Chapter 3: Territorial cohesion
99
22% in Portugal and 27% in Greece. In other countries
emissions increased, notably in Lithuania (by
12%) and Malta (by 34%). Variations in economic
growth explain part of these differences, but other
factors are important as well. For example, emissions
were reduced by almost 21% in Sweden yet
GDP grew on average by 1.7% a year over the
period.
In 18 Member States, the level of emissions in
2015 was lower than the target set under the ESD,
most especially in Croatia, which had committed to
limiting the increase in emissions to 25% relative
to the 2005 level but actually cut them by 16%.
Some of the other countries have gone a long way
to achieving the target and have only a little more
to do. In particular, in the UK and Austria, emissions
need to be reduced by less than 1%. In Ireland, on
the other hand, they need to be reduced by almost
10%, while in Malta, emissions rose by much more
than the increase agreed.
2.3 Increasing the share of renewable
energy
The EU objective is to increase the share of renewables
in energy consumption to 20% by 2020
(10% in the transport sector) and to 27% by 2030.
Under the Renewable Energy Directive6
, EU Member
States have set binding targets for increasing their
national shares by 2020, which vary from 10% in
Malta to 49% in Sweden, reflecting differences in
both the prevailing share and the potential for expanding
it. In some Member States, therefore, the
share is already large — almost 54% in Sweden in
2015 and 34% in Latvia — while it is well below
10% in Malta, Luxembourg and the UK (Figure 3.3).
In 2015, 11 Member States had already exceeded
their targets and in another three, the share
needed to be increased by less than 3 percentage
points to meet them. In 10 countries, however,
the required increase was more than this and in
four of them — the UK, Ireland, France and the
Netherlands — 7 percentage points or more.
The potential of countries or regions for producing
renewable energy depends on their geo-physical
characteristics. For instance, coastal regions generally
have a high potential for producing wind
energy, especially those along the shores of the
North and Baltic Seas and some Mediterranean islands.
The potential for solar energy production is
obviously higher where there are large amounts
of sunshine, while the production of hydroelectricity
also requires suitable geo-physical features.
6	 Directive 2009/28/EC of the European Parliament and of the
Council of 23 April 2009 on the promotion of the use of energy
from renewable sources.
-30
-20
-10
0
10
20
30
40
50
-30
-20
-10
0
10
20
30
40
50
DK UK LU IE FI AT BE SE NL IT FR DE ES EL PT SI CZ MT PL LV BG SK EE RO HR HU CY LT
% change 2005-2015 2020 Target% change relative to 2005
Changes in greenhouse gas emissions outside the Emissions Trading Scheme,
2005-2015 and Europe 2020 targets
Figure 3.2
Target = Reduction in emissions Target = Limit increase in emissions
Source: EEA, provisional calculation method
Seventh Report on economic, social and territorial cohesion
100
0
10
20
30
40
50
60
0
10
20
30
40
50
60
MT LU UK BE NL IE CY HU SK PL EL CZ DE IT ES FR BG SI EE DK LT RO PT HR AT FI LV SE EU28
2015 2006 Target 2020% of total gross final energy consumption
Share of renewable energy in gross final energy consumption, 2006 and 2015
and target in 2020
Figure 3.3
Source: EEA, Eurostat
Realising whatever potential exists, however, depends
on the policies implemented.
Accordingly, the production of renewable energy
varies markedly from one region to another. This
is well illustrated by electricity production. In some
regions, electricity generation is still largely dependent
on coal and lignite. This is particularly the
case in most regions in Poland but also in Germany,
the UK, Italy, Ireland, Spain, Romania and Croatia
(Maps 3.1 and 3.2). In contrast, in other regions
electricity is principally produced from renewables,
notably in Cyprus, Greece, Austrian, Sweden,
Finland and France, hydroelectricity, biogas, biomass
and wind energy being the main sources7
.
2.4 Climate change
European regions differ widely in relation to
the challenges they face from climate change.
Mediterranean regions are likely to experience significant
increases in days of extreme heat, growing
risk of droughts, declining crop yields and
7	 Note that renewable energy is not necessarily environmentfriendly:
solar, wind, biomass or hydropower projects may have
significantly adverse effects on e.g. biodiversity or water bodies
(including through intensive land use and reduced connectivity
of rivers). In consequence, strategic and integrated planning with
early stakeholder involvement, in line with relevant EU legislation
(SEA, EIA, WFD, Birds and Habitats Directives) is essential to maximize
renewable energy production while reducing environmental
impacts.
more multiple climatic hazards8
. Coastal areas
face the risk of rising sea levels, increasing sea
temperatures9
and growing numbers of ‘marine
dead’ zones10
. The Atlantic region will experience
increasing instances of heavy rainfall and more
risk of river and coastal flooding and damage from
winter storms. Mountain regions are expected to
suffer higher increases in temperature than the
European average, a shift of plant and animal species
to higher ground and a greater risk of some of
them becoming extinct, as well as more chance of
rock falls and landslides and reduced potential for
hydro-electricity generation.
At the same time, climate change might create opportunities,
such as an expected reduction in energy
demand for heating in Northern European and
Atlantic regions or new possibilities for exploiting
natural resources and sea transport in Artic re-
8	 The number of hot days (those exceeding the 90th
percentile
threshold of a baseline period) has almost doubled since 1960
across Europe. Since the beginning of the 21st
century, Europe has
experienced several extreme heat waves (in 2003, 2006, 2007,
2010, 2014, 2015 and 2017). Under a high emissions scenario,
very extreme heat waves are projected to occur as often as
every other year in the second half of the 21st
century
(European Environment Agency, 2017).
9	 An increase in sea temperature is likely to have important consequences
in term of biodiversity. Wild fish stocks are responding to
changing temperatures and food supply by changing their distribution
which can affect local communities dependent on them.
10	Dead zones are hypoxic (low-oxygen) areas caused by excessive
nutrient pollution from human activity coupled with other factors
that deplete the oxygen required to support most marine life in
bottom and near-bottom water.
Chapter3:Territorialcohesion
101
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.2 Electricity generated from renewable sources, 2015
< 15
15 – 30
30 – 45
45 – 60
60 – 80
> 80
% of total electricity generated
Source: JRC
©
0 500 km
EuroGeographics Association for the administrative boundaries
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.1 Electricity generated from hard coal and lignite, 2015
< 15
15 – 30
30 – 45
45 – 60
60 – 80
> 80
% of total electricity generated
Source: JRC
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Seventh Report on economic, social and territorial cohesion
102
gions. But, in general, climate change will have
major adverse effects on the environment which
it will be necessary, and often costly, to adapt to.
Vulnerability to climate change varies widely from
one region to another. According to meta-analysis
integrating assessments covering multiple areas
(water, agriculture, tourism, ecosystems and so    
on)11
, Italy, Spain and southern and central France
are likely to have the highest number of areas adversely
affected, along with parts of south-eastern
Europe (Map 3.3).
Climate change is also expected to increase the
occurrence of natural hazards throughout the EU
in the coming decades. Recent studies12
show that
places where the effects are likely to be particularly
severe (i.e. affected by increase in the probability
of hazard occurrences of at least 20% for
three or even four of the 7 hazards considered)
will progressively extend northwards to central
and western Europe in the coming decades, covering,
by 2050, many areas of the Netherlands, the
UK and Ireland as well Spain, France, Italy, Bulgaria
and Romania (Map 3.4).
Estimating the economic costs of climate change
is particularly challenging, but most studies indicate
that these costs could be high even for
modest changes in climate13
. The PESETA II study
estimates total damages in the EU of up to
€190 billion per year by the end of the 21st
century under a high economic growth
scenario14
, mostly from heat-related deaths and
losses in agriculture and coastal areas.
The costs are expected to be far from evenly distributed
across Europe, and much higher in southern
Europe than elsewhere (the CIRCE project estimates
that Mediterranean countries could lose an
average of just over 1% of GDP by 2050 notably
from damage to tourism and energy)15
.
11	See European Environment Agency (2017) for a meta-analysis.
12	Forzieri, G., Feyen, L., Russo, S., Vousdoukas, M., Alfieri, L., Outten,
S., Migliavacca, M., Bianchi, A., Rojas, R. and Cid, A. (2016).
13	Ciscar, J. C., Feyen, L., Soria, A., Lavalle, C., Raes, F., Perry, M., Nemry,
F., Demirel, H., Rozsai, M., Dosio, A., Donatelli, M., Srivastava, A.,
Fumagalli, D., Niemeyer, S., Shrestha, S., Ciaian, P., Himics, M., Van
Doorslaer, B., Barrios, S. (2014).
14	In 2000, the Inter-governmental Panel on Climate Change (IPCC)
published the Special Report on Emissions Scenarios (SRES) which
describes greenhouse gas emission scenarios used to make projections
of possible future climate change. The SRA1B scenario
assumes rapid economic growth, a global population that reaches
9 billion in 2050 and then gradually declines, the rapid spread of
new and efficient technologies, a convergence of world income
and ways of life and extensive worldwide social and cultural
interaction.
15	Navarra, A. and Tubiana, L. (2013).
Outermost regions and environmental
challenges
The outermost regions are particularly vulnerable
to climate change and natural disasters
as shown by dramatic impact of hurricane Irma
on Saint Martin. Most of them are tropical or
sub-tropical islands with difficult topographies
and fragile economies and ecosystems. Climate
change is also likely to impact on fauna and flora,
with probable effects on agricultural products
on which their economies rely, notably sugar
cane and bananas.
Being greatly affected, the outermost regions
realised at an early stage the need to combat
climate change. For example, the French Guiana
forest is an important source of decarbonisation
of the planet and its preservation helps to limit
the rise in global temperatures.
The regions are also increasingly reducing the
use of fossil fuels for electricity generation. The
share of renewable energy in electricity production
in French Guiana is already 64%. Martinique,
Guadeloupe and Reunion Island have ambitious
targets of 100% production from renewables
by 2030 mainly through combined use of solar,
hydro-, wind and geothermal power as well as
smart grids. Guadeloupe has imposed building
regulations specifically adapted to local condi-
tions.
The Canary Islands plan to reach total energy
and water self-sustainability of the island of El
Hierro by upgrading the capacity of the existing
hydro power plant, installing additional wind
power capacity, using only electric vehicles on
the island and making further use of locally produced
biomass.
Chapter 3: Territorial cohesion
103
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
0
1
2
2
4
5
0 500
Map 3.3 Negative climate change impacts under a 2°C warming scenario
Number of sectors
Number of sectors negatively affected by climate change.
Source: EEA, adapted from IMPACT2C project, 2015
EuroGeographics Association for the administrative boundaries©
km
REGIOgis
Seventh Report on economic, social and territorial cohesion
104
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.4 Projected increase in multi-hazard climate-related exposure, 2050
1
2
3
4
No data
Number of hazards with moderate increase
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Source: JRC, Forzieri et al., 2016
Chapter 3: Territorial cohesion
105
3. State of environment
3.1 Water
One of the objectives of the Seventh Environment
Action Programme (7th
EAP) is to ensure the good
status of transitional16
, coastal and fresh water by
2020. Surface water17
is a major component of
fresh water and improving its ecological state is
critical to achieving this objective.
The Water Framework Directive18
(WFD) and other
water-related ones have contributed to improving
water protection in the EU. In general, people
throughout the EU can safely drink tap water and
swim in many of the coastal areas, rivers and
lakes. However, reducing pollution to meet the
objectives of the WFD requires as a pre-condition
that several other Directives and regulations are
fully implemented19
.
Although progress in wastewater treatment and
reductions in agricultural inputs of nitrogen and
phosphorus have helped to improve the quality of
surface water in the EU, pollution from agriculture
(particularly nitrogen losses) as well as from urban
and industrial wastewater remains significant.
According to the EEA, in 2015, only 53% of water
bodies are estimated to have good ecological status,
making it unlikely that the objective of achieving
good status of all water will be met by 202020
.
16	Transitional waters are bodies of surface water in the vicinity of
river mouths which are partly saline as a result of their proximity
to coastal waters but which are substantially affected by freshwater
flows.
17	Surface water is water on the surface of the planet in rivers, lakes,
wetlands and oceans, in contrast to groundwater and atmospheric
water.
18	Directive 2000/60/EC of the European Parliament and of the
Council of 23 October 2000 establishing a framework for
Community action in the field of water policy.
19	This includes the Urban Waste Water Treatment Directive, the
Nitrates Directive, the Directive on Sustainable Use of Pesticides
and the Industrial Emissions Directive.
20	Note that the picture is similarly bleak for marine ecosystems.
In their initial assessments for the Marine Strategy Framework
Directive, Member States indicated that only 4% of marine species
and habitats have a ‘good environmental status’, while 80%
are categorised as ‘unknown’. The indication is that marine resources
are being used unsustainably and as a number of human
off-shore and on-shore activities depend on the health, cleanliness
and productivity of the seas, there is a need for them to be used
responsibly.
Member States differ substantially in terms of the
ecological status of their river basins (Map 3.5). In
Belgium, northern Germany and the Netherlands,
over 90% of surface water is reported to be in
a ‘less than good’ ecological state. In the Czech
Republic, southern England, northern France, southern
Germany, Hungary and Poland, 70% to 90% of
freshwater bodies (lakes and rivers) are reported
to be in a similar state. The ecological status of
coastal and transitional water is also poor in the
Black Sea and greater North Sea regions. On the
other hand, a much larger share of surface water
is in good ecological state in Northern regions of
Sweden and Finland and some regions of Northern
Italy, Northern Spain, Latvia and Greece.
To achieve good status, Member States will have
to do more to reduce the pressure on water bodies.
This will require substantial investment in ways of
reducing pollution or tackling over-abstraction of
ground water and morphological and hydrological
changes21
. Such investment can be supported
by cohesion policy (in the 2007–2013 programming
period around €17.8 billion of the ERDF and
Cohesion Fund was allocated to wastewater infrastructure
in 22 Member States22
).
Appropriate collection and treatment of wastewater
to remove organic matter, nutrients (nitrogen
and phosphorus) and other hazardous substances
it contains is essential for improving the ecological
status of water bodies (marine and freshwaters)
as well as to reduce the risk to human health and
biodiversity.
The Urban Waste Water Treatment Directive23
(UWWTD) sets minimum requirements in respect
of urban wastewater treatment, making it mandatory
for settlements with the equivalent to
21	River morphology corresponds to the shapes of river channels. It
is determined by a number of processes and environmental conditions,
including the composition and erodibility of the river bed
and banks, vegetation and the rate of plant growth, the availability,
size and composition of sediments and human interaction.
River hydrology refers to the movement, distribution and quality of
water.
22	European Commission, Eighth Report on the Implementation
Status concerning urban waste water treatment COM(2016) 105
of 4 March 2016.
23	Council Directive 91/271/EEC concerning urban waste water
treatment.
Seventh Report on economic, social and territorial cohesion
106
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.5 Rivers and lakes with less than good ecological status or potential
< 10
10 – 30
30 – 50
50 – 70
70 – 90
>= 90
No data
% of rivers and lakes
Aggregated by River Basin District
Source: EEA River Basin Management Plans
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 3: Territorial cohesion
107
2 000 inhabitants or more. Since its adoption in
1991, it has led to a considerable reduction in discharges
of major pollutants but its implementation
still needs to be improved in a number of Member
States and regions.
The level of treatment required in the UWWTD depends
on the sensitivity of the areas of discharge
and on the size of the settlements. Sensitive areas
are those where the environmental risks due to the
adverse effects from wastewater discharge are
particularly high (e.g. risk of eutrophication by excess
of nutrients) or which require specific protection,
such as drinking water abstraction areas and
waters for bathing and those where shellfish live.
Secondary (biological) treatment, which decomposes
most of the organic matter responsible for
oxygen depletion, is the minimum requirement in
‘normal’ or non-sensitive areas. Tertiary (or more
stringent) treatment, which removes nutrients and
disinfects the water, is required in large settlements
(with the equivalent of 10 000 inhabitants
or more) discharging into sensitive areas.
According to the UWWTD 9th
Reporting Exercise
(2014), high compliance rates are generally observed
in most EU‑15’ Member States, especially
in Austria, Germany and the Netherlands, which
have largely implemented the Directive. However,
there are still a number of EU‑15 countries which
have compliance gaps and have delayed the implementation
of necessary measures. This is notably
the case for Italy, Spain, Belgium, Luxembourg
and Ireland.
The picture is different for EU‑12 Member States
(i.e. excluding Croatia, for which the deadline for
compliance is 2018). This is partly a result of
their later accession and the transitional periods
for compliance which have been granted to them.
The last available results, however, show a substantial
improvement in compliance with collection
obligations compared to previous years. The
compliance rate is high, except for Cyprus (61%),
Slovenia (65%) and above all Bulgaria (26%)
and Romania (3%). Some Romanian regions as
well as several Bulgarian regions and Eastern
Slovenia show compliance rates below 40%, and ‑
even below 20% as in the case of Romania and
of south-western Bulgaria. This regional concentration
of non-compliant agglomerations has significant
implication for the water quality of the
affected river basins such as the Black Sea Basin.
The same applies to wastewater treatment. In the
majority of EU-12 Member States, secondary treatment
of wastewater shows high compliance rates
of above 85% for eight of the countries, the exceptions
being Romania, Bulgaria, Malta and Slovenia
which have much lower compliance rates. In some
regions, like Principado de Asturias (ES), Sicilia (IT),
Slovenia and most Bulgarian regions, the share
of agglomerations where secondary treatment is
taking place is below 40%. In these regions, human
and ecosystem health is critically threatened
due to the low degree of compliance.
Compliance rates are also high in most cases in
respect of stringent treatment, where applicable,
varying between 50% and 100%, except in
Romania, Bulgaria and Malta, where there are
substantial delays in implementing the necessary
measures.
3.2 Waste
Solid waste affects human health as well as the
environment since it generates emissions of polluting
substances into the air, soil, surface water
and groundwater. It also presents major challenges
for management as the quantity of waste produced
per person has increased steadily over time.
A transition to a more circular economy requires
action throughout a product’s life-cycle: from
production to the creation of markets for wastederived
materials. Waste management is one of
the main areas where further improvements are
needed and which are within reach. Accordingly,
reducing the generation of waste and promoting
its reuse and recycling are key objectives of the EU
action plan for the circular economy24
.
24	European Commission, Closing the loop — An EU action plan for
the circular economy, COM(2015) 614 of 2 December 2015. A
circular economy is one in which the value of products, materials
and resources is maintained for as long as possible, minimising
waste and resource use.
Seventh Report on economic, social and territorial cohesion
108
In 2014, an average of 4.9 tonnes of waste per
person were generated in the EU. Much of this was
produced by construction and demolition, mining,
quarrying and manufacturing. Households also
produced a substantial amount of waste, an average
of 411 kg per person. Marine litter, escaping
from waste management systems, is a growing
concern. The total amount of waste generated
(including mineral waste) in the EU increased by
around 2% between 2010 and 2014 though there
are wide variations between Member States.
Increasingly, waste is recycled or energy is recovered
from it. Between 2010 and 2014, the proportion
of treated waste (excluding mineral waste)
recycled increased only slightly from 53% to 55%,
while the proportion incinerated with energy recovery
rose from 11% to 14%. The increase in recycling
occurred against a background of measures
designed to stimulate it, including EU and national
legislation, support from the Structural Funds,
landfill taxes and pay-as-you-throw schemes.
In 2014, the proportion of waste (excluding mineral
waste) disposed of in landfill fell from 28% to 25%
in the EU (Figure 3.4). There are, however, marked
variations between Member States. Over 80% of
waste is still landfilled in Bulgaria and Greece and
over 50% in Estonia, Cyprus, Malta, Romania and
Slovakia. By contrast, less than 5% goes to landfill
in Belgium, Denmark and the Netherlands.
3.3 Sustainable transport
Besides making transport more competitive and
increasing the quality of the network, EU transport
policy has also sought to reduce dependence on
oil, greenhouse gas and other emissions (such as
0
20
40
60
80
100
0
20
40
60
80
100
MT BG EL EE CY IE HR SK RO LT HU ES PT UK LV CZ PL FR IT FI AT DE SI SE DK BE NL LU
% of total waste generated (excluding major mineral waste)
Share of waste landfilled in selected EU Member States, 2014Figure 3.4
IE, RO, DE: 2012
Source: Eurostat
EU-28 = 25%
Circular economy
The EU action plan for the circular economy
establishes a long-term approach to reducing
waste generation, increasing recycling and reuse
and reducing landfill and incineration. The
circular economy is aimed at ‘closing the loop’
of product lifecycles by keeping resources within
the economy so as to improve use of raw materials,
products and waste. It contributes to meeting
the EU’s environmental and climate objectives
and stimulates local and regional development.
Waste prevention, eco-design and similar measures
generate savings, increase turnover and
create jobs, particularly in re-manufacturing, repair
and product innovation. EU cohesion policy
is important in making the circular economy a
reality. In the 2014–2020 programmes, there is
substantial funding for waste management as
well as support for the circular economy through
investment in innovation, SMEs, resource efficiency
and renewables as well as green jobs.
Chapter 3: Territorial cohesion
109
SOx
, NOX
and fine dust), to limit congestion and to
improve safety.
Over the past 20 years, the volume of goods and
number of passengers transported within the EU
has grown steadily, apart from during the global
recession in 2008–2009. Between 1995 and 2014,
both passenger and freight transport increased on
average by just over 1% a year25
. Transport increasingly
faces serious social and environmental
challenges. It is second only to energy in greenhouse
gas emissions, accounting for 23% of the
total and, unlike energy, its emissions have risen
since 1990 (by around 20%). Transport may also
have significantly damaging effects on the quality
of the environment, such as by increasing fragmentation
of natural habitats.
The aim, therefore, is to establish a ‘sustainable
mobility’ model of transport, to develop an efficient
and competitive transport sector as a key element
of the EU internal market while at the same
time reducing costs from road accidents, respiratory
diseases, climate change, noise, environmental
damage and traffic congestion. The model entails
fostering environmentally-friendly modes of
transport as well as combined and inter-modal
transport.
In its 2011 White Paper on the future of transport
up to 2050, the Commission set the objective of
reducing greenhouse gas emissions from transport
by at least 60% in relation to 1990 levels by
2050. The interim aim is to reduce emissions by
20% in relation to 2008 levels by 2020–2030,
requiring a fundamental shift towards the use of
less and cleaner energy and more efficient utilisation
of transport infrastructure. To achieve these
objectives, the White Paper called for a shift of
30% of freight being transported over 300 km by
road to rail or water by 2030 and one of over 50%
by 2050, a tripling of the length of the existing
high-speed rail network by 2030 and a move of
the majority of medium-distance passenger travel
to rail by 2050. It also targets the establishment
of a fully functional multimodal TEN-T in the EU
by 2030 and a high-quality and high-capacity net-
25	European Commission (2016i).
work by 2050. In many places, achieving these
objectives implies improving markedly the quality
of transport infrastructure and new construction.
Transport is the main beneficiary of the Connecting
Europe Facility which has a budget of €24 billion
for the period up to 2020.)
Cars remain by far the predominant mode of passenger
transport in the EU. In 2014, they accounted
for over 83% of all inland passenger km travelled
in the Union26
, varying from 68% in Hungary to
almost 90% in Portugal and Lithuania (Figure 3.5).
Buses accounted for 9% of passenger km travelled,
the share varying from 3% in the Netherlands to
23% in Hungary. Trains accounted for 8%, though
the figure varies according to the size and state
of the rail network. In France, Austria and Sweden,
which have fast and frequent trains, around 10%
of travel was by rail, while in Greece, Estonia,
Lithuania, where the rail network is limited and of
low quality, the figure was less than 2%.
In the case of freight, around 75% of goods
were transported by road in 2014 (Figure 3.6). In
Cyprus, Malta, Ireland and Greece, all or almost all
were. Only 18% on average went by rail, though
in Austria, the proportion was 44% and in Latvia,
59%. In Romania, Belgium and the Netherlands,
there is an extensive network of inland waterways
and these carried around 20% of freight in the first
two and almost 40% in the last.
These figures have been remarkably stable over
time both for passenger and freight transport, except
in a few Member States, particularly Romania
and Estonia, where the share of freight going by
road increased by 10 and 18 percentage points,
respectively, between 2011 and 2014. Significant
effort is, therefore, needed to achieve a shift to
more environmentally-friendly modes of transport.
26	Passenger-kilometre represents one passenger travelling a distance
of one kilometre. The share is the percentage of transport
by passenger cars in total inland passenger transport, measured
in passenger-kilometres.
Seventh Report on economic, social and territorial cohesion
110
4. Sustainable cities
4.1 Cities can be environment-friendly
Cities are often considered to be inherently harmful
for the environment. In practice however, cities
are not just a source of pollution but also a
potential solution to current environmental challenges.
While urban areas in the EU generally
face more environmental challenges than other
places, they can often prove to be more resourceand
energy-efficient than other areas where lowdensity
settlements, energy-intensive buildings
(e.g. detached houses) and the level of dependency
on the car for transport are generally more
common. Housing in cities tends not only to occupy
less land but also more frequently takes the
form of apartments and townhouses which generally
require less energy to heat and cool.
Cities also offer more possibility adopting a low
carbon lifestyle. Living in cities tends to make it
possible to access a large number of services using
less energy-consuming modes of transport.
People generally prefer to be close to the services
and facilities they regularly have need of, such as
schools, healthcare services, childcare, cultural and
sports facilities and shops. The average distance to
0
20
40
60
80
100
0
20
40
60
80
100
EU-
28
PT LT NL SI UK DE FI FR HR SE LU ES MT CY RO EE EL LV IT BG DK PL IE AT SK BE CZ HU
Trains Motor coaches, buses and trolley buses Passenger carsPassenger-km as % total inland passenger transport
Passenger travel by transport mode, 2014Figure 3.5
Source: Eurostat
0
20
40
60
80
100
0
20
40
60
80
100
EU-
28
CY MT IE EL LU ES PT DK UK IT PL FR SI CZ HR BG SK HU FI SE LT EE DE BE RO NL AT LV
Railways Inland waterways RoadsTonne-km as % of total inland freight transport
Freight transport by mode, 2014Figure 3.6
Source: Eurostat
Chapter 3: Territorial cohesion
111
such services is usually much less for people living
in cities than in towns and suburbs or rural areas.
On average in the EU, the distance to access services
by road is 4.5 times greater in rural areas
(almost 9 km) than in cities (less than 2 km). In
countries which are more urbanised, the difference
is smaller, as in Malta (1.4 times greater in rural
areas), the Netherlands (2.3 times), Belgium (2.9
times) and the UK (2.8 times). In countries where
urban areas are more dispersed, the difference can
be much larger (at the extreme, in Finland, it is 13
times greater).
The difference between cities and other areas in
terms of accessing services varies according to the
service concerned. Local services (such as schools,
general health services, childcare, sports facilities
and shops) are generally available in all types of
municipality, even though they take longer to reach
in rural and suburban areas (Figure 3.7 Distance
to services by type of municipality in the EU). The
difference is greater for ‘sub-regional’ services,
such as high schools, hospitals, theatres, cultural
facilities and supermarkets, and greatest of all for
regional services, such as specialised education
and healthcare centres, large sports and cultural
facilities or government offices. The average distance
to reach such services in the EU is 48 km
in rural areas, 38 km in towns and suburbs and
less than 10 km in cities with a population of more
than 250 000.
Accordingly, large cities offer the possibility of accessing
services by walking or by bicycle while in
rural areas or in smaller towns, it is much more
difficult, or impossible, to do so. For instance, the
average share of population in the EU living within
1 km of local services increases rapidly with the
degree of urbanisation and the size of city, rising
from 12% in rural areas to over 80% in cities of
more than 5 million inhabitants (Figure 3.8)
Cities also tend to be more efficient in their use of
land. Built-up areas per person in cities are only
a quarter of those in rural areas. This reflects the
fact that the availability of land and its cost make
cities more attractive for less land-intensive activities,
such as services, company headquarters or
leisure facilities, than suburbs or rural areas. Land
scarcity also increases the incentive to economise
on land use for housing, which is generally smaller
in cities than in other areas where the average area
occupied per household tends to be much larger.
Although land use per inhabitant is usually greater
in large cities than in smaller ones, there are
wide variations across the EU. In particular, cities
in northern and western Europe are often more
densely populated than in southern and centraleastern
EU countries and the built-up area per in-
0
10
20
30
40
50
0
10
20
30
40
50
Rural Areas Towns and
Suburbs
<100 000 100 000 -
250 000
250 000 -
500 000
500 000 -
1 000 000
1 million -
5 million
> 5 million
Local service Sub-regional service Regional serviceAverage distance in km
Distance to services by type of municipality in the EUFigure 3.7
Source: JRC
City by population size
The location of services has been simulated taking into account population distribution and distances.
Seventh Report on economic, social and territorial cohesion
112
habitant, therefore, tends to be smaller (Map 3.6).
This difference tends to increase over time.
Between 2006 and 2012, the built-up area per inhabitant
increased most in cities in the southern
and central-eastern EU while it declined in a number
of large cities in northern and western Europe
(Map 3.7).
4.2 Changes in land use per person
The process of urbanisation is driven by a range
of factors that can be influenced by various types
of policy, including cohesion policy. According to a
recent study27
, land use per person in the EU increased
steadily from 0.94 of a hectare per 100
people in 1975 to 1.3 hectares in 2010. The overall
increase in land use per person is consistent
with an ‘urban sprawl’ phenomenon, or the rapid,
and sometimes uncontrolled, expansion of builtup
areas around towns and cities, creating widespread
and relatively low density urban suburbs,
often inefficient in terms of energy and land
consumption28
.
The observed increase in land use per person,
however, seems to be running out of steam as urban
areas in many EU regions have become more
27	Batista e Silva F, Alvarez M, Vizcaino P, Jacobs Crisioni C, Ghisetti
C, Pontarollo N, Lavalle C, D’Hombres B (2017).
28	See for instance Jaeger J., Bertiller R, Schwick C. and Kienast F.,
(2010).
densely populated over more recent years. The
main increase in land use per person occurred over
the period 1975–1990. In the period 2000–2010,
despite a continuing slight increase at EU level,
many regions experienced decreases.
The main developments in land use per person in
different types of EU region are as follows:
•• Metro and capital city regions: a NUTS 3 region
which is a metropolitan area or part of one is
more likely to experience increases in population
density, and even more so if it contains the
national capital city.
•• Rural regions: a rural NUTS 3 region is likely
to experience a decline in population density,
which means that built-up areas are expanding
at a faster pace than population.
•• Increases in population, GDP per head, employment
and accessibility are all positively
associated with growth of population density.
In general, socio-economic factors are major
determinants of a region’s attractiveness.
•• Regions with a high Percentage of Available
Land (PAL) have few or no physical constraints
on development which discourages growth of
population density. Pressure on land prices is
likely to be low and so extensive land development
is relatively inexpensive. Conversely, re-
0
10
20
30
40
50
60
70
80
90
0
10
20
30
40
50
60
70
80
90
Rural Areas Towns and
Suburbs
<100 000 100 000 -
250 000
250 000 -
500 000
500 000 -
1 000 000
1 million -
5 million
> 5 million
Local service Sub-regional service Regional service% of total population
Share of population living within 1km of different services, by city size and degree
of urbanisation in the EU
Figure 3.8
Source: JRC
City by population size
The location of services has been simulated taking into account population distribution and distances.
Chapter 3: Territorial cohesion
113
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.6 Residential, industrial and commercial areas per inhabitant by city, 2012
m²/inh.
< 140
140 – 175
175 – 200
200 – 250
250 – 300
>= 300
No data
Urban center population
< 100 000
100 000 – 250 000
250 000 – 500 000
500 000 – 1 000 000
1 000 000 – 5 000 000
>= 5 000 000
Residential, industrial, commercial, public and private built-up
areas.
Source: Copernicus Urban Atlas, Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Seventh Report on economic, social and territorial cohesion
114
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.7 Change in residential, industrial and commercial areas per inhabitant by city,
2006–2012
Total % change
< -2
-2 – 0
0 – 2
2 – 4
4 – 8
>= 8
No data
Urban centre population
< 100 000
100 000 – 250 000
250 000 – 500 000
500 000 – 1 000 000
1 000 000 – 5 000 000
>= 5 000 000
Residential, industrial, commercial, public and private built-up
areas.
Source: Copernicus Urban Atlas, Eurostat, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 3: Territorial cohesion
115
gions with limited space for development tend
to experience upward pressure on land prices,
leading to denser urban development.
•• Places with high initial levels of population
density generally experience lower growth
of density, suggesting that further densification
may be discouraged in such regions. This
could be because of two possible complementary
reasons: a concern to avoid or reduce the
diseconomies resulting from densification and
technical or legal constraints on population
growth.
4.3 Urban transport
Public transport is equally more accessible in large
cities. In the vast majority of large cities, the share
of the population with high or very high access to
public transport is above 60%, and up to 98% in
Madrid (Figure 3.9 which shows the situation in
a sample of large cities)29
. The only exception is
Dublin where the figure is only 38%. The figures
tend to be slightly lower for mid-size cities. The
proportion of inhabitants with high or very high
access to public transport is less than 50% in
Toulouse and Vilnius but close to 90% or more in
Bologna, Sevilla and Edinburgh (Figure 3.10).
4.4 People living in cities suffer more
from pollution
In 2015, the proportion of people in cities in the
EU reporting to live in an area with environmental
problems (19%) was larger than for those in
towns and suburbs (13%) and rural areas (8%)
(Figure 3.11). The proportion for those in cities
29	No access: it takes more than 5 minutes to walk to a bus or tram
stop and over 10 minutes to reach a metro or train station; Low
access: it takes less than this to walk to a public transport stop
— i.e. people can easily walk there — with less than four departures
an hour; Medium access: it people can easily walk to a public
transport stop with between 4 and ten departures an hour; High
access: people can easily walk to a bus or tram stop with more
than 10 departures an hour OR people can easily walk to a metro
or train station with more than 10 departures an hour (but not
both); Very high access: people can easily walk to a bus or tram
stop with more than 10 departures an hour AND a metro or train
station with more than 10 departures an hour. See Dijkstra, L. and
Poelman, H. (2015).
was particularly large in Malta (34%), Germany
(33%) and Greece (30%), while it was only around
10% or less in Ireland, Cyprus, Denmark, Croatia
and Finland, where environmental problems seem
less common30
.
Air pollution remains a major environmental concern
in the EU. Nine out of 10 people in urban areas
in the EU are exposed to pollution concentrations
above the levels recommended by the World
Health Organisation (WHO). Air pollution has a
major impact on human health, with an estimated
400 000 premature deaths each year due to high
levels of fine particles and ozone. It also has a significant
effect on ecosystems. Excessive nitrogen
deposits (eutrophication) and ozone concentrations
adversely affect biodiversity and crop yields
and cause other material damage in over half of
the EU.
At the same time, emission of air pollutants, notably
of carbon monoxide, sulphur oxides and lead,
30	Note that these figures relate to perceived problems which might
differ from actual problems as a result of differences in expectations
about the state of the environment.
Urban ecosystems and Green
Infrastructure (GI)
Cities have high concentrations of people who
could profit from nature to improve their health
and well-being. They have limited space which
needs to be better used in a multi-functional
way; they suffer from air, soil and water pollution
and from the effects of climate change such
as heat waves and flash floods — all of which
have effects on their economy and social security
system. Improving biodiversity and the
pro-vision of multiple ecosystem services
through GI would help to improve the quality
of life, health and well-being, protect against
the adverse ef-fects of climate change and
natural disasters, be a source of regeneration
and diversification and create new businesses
and innovative and sus-tainable jobs in a costeffective
way. Implement-ing GI and naturebased
solutions in urban areas could also create
a greater sense of community and help combat
social exclusion and isolation.
Seventh Report on economic, social and territorial cohesion
116
0
20
40
60
80
100
0
20
40
60
80
100
No access
Low
Medium
High
Very high
% of population in the urban centre
Access to public transport in large European cities, 2014-2016Figure 3.9
Source: Poelman and Dijkstra 2015
0
20
40
60
80
100
0
20
40
60
80
100
No access
Low
Medium
high
Very high
% of population in the urban centre
Access to public transport in mid-sized European cities, 2014-2016Figure 3.10
Source: Poelman and Dijkstra 2015
0
5
10
15
20
25
30
35
40
0
5
10
15
20
25
30
35
40
EU-
28
MT DE EL IT BE LV LT RO BG CZ LU SI FR NL HU PT AT PL SK ES EE SE UK HR FI DK CY IE
Cities Towns and suburbs Rural areas% of total population
People reporting they live in an area affected by pollution, grime or other
environmental problems, by degree of urbanisation, 2015
Figure 3.11
Source: Eurostat
Chapter 3: Territorial cohesion
117
has declined markedly in the EU over the years,
partly as a result of EU legislation31
. The application
of European standards has also been successful
in reducing vehicle emissions (such as after the
introduction of the diesel particle filter), and the
progressive renewal of the vehicle fleet means
that air quality in the EU is likely to improve over
the long-term. However, more needs to be done to
address the issue, such as introducing regional or
local incentives to favour very low pollutant emitting
vehicles or even zero emission ones.
Some areas are still far from complying with agreed
EU air quality standards32
. This is notably the case
in cities, where the majority of the EU population
lives and where levels of sulphur dioxide, nitrogen
oxides (NO2
), volatile organic compounds, ammonia,
fine particulate matter (PM2.5
and PM10
33
) and
ground level ozone (O3
) remain high.
Air pollution is severe in a number of cities in
southern and central Poland, the Czech Republic,
Romania and Bulgaria (Map 3.8) but also in
Southern Europe (Po Valley, Naples, Cyprus and
Greece). According to the EEA, in 2014 around
17% of the urban population in the EU was exposed
to PM10
levels above the daily limit and 9%
to PM2.5
levels above the EU target34
.
To a large extent, concentration of airborne particulate
matter is caused by emissions from diesel
engines or from coal mining and other heavy
industry. It is also affected by atmospheric conditions,
pollution levels rising with sunshine and
hot temperatures. These factors explain the geo-
31	Directive 2010/75/EU on industrial emissions, Directive (EU)
2015/2193 on medium combustion plants, Directive (EU)
2016/2284 on national emission ceilings and Directive 2008/50/
EC on ambient air quality.
32	Directive 2008/50/EC on ambient air quality and cleaner air for
Europe fixes air quality standards, with a limit of 40 μg/m³ for the
annual mean concentration of nitrogen dioxide. For fine particles,
the limit is not more than 35 days per year with a daily average
concentration exceeding 50 μg/m³ and a mean annual concentration
not exceeding 40 μg/m³. For ozone, the limit is a daily 8-hour
mean concentration not exceeding 120 μg/m³ on more than
25 days per year.
33	Particulate matter (PM) are microscopic solid or liquid matter suspended
in the atmosphere. Subtypes of atmospheric particulate
matter include respirable particles with a diameter between 2.5
and 10 micrometres (μm).
34	European Environment Agency (2015a).
graphical distribution of high PM concentrations.
In 2013, for example, the average concentration
rose above 40 μg per cubic metre in 9 cities in
Bulgaria (including Sofia), peaking at 62.2 per cubic
metre in Plovdiv, the second city35
. The Czech
cities of Havírov, Karviná and Ostrava in the coal
mining region of Moravia-Silesia also recorded
very high concentrations of PM. At the other end
of the spectrum, most cities with relatively low
levels of air pollution are located in the Nordic
and the Baltic Member States.
Concentration of ground-level ozone can cause
breathing and cardiovascular problems, asthma
and lung disease. High concentrations occur
mostly in cities in Northern Italy, Spain (e.g. Jaén
and Toledo), the East and South of France (e.g.
Sophia-Antipolis, Martigues, Mulhouse, Colmar
and Aix-en-Provence) and Southern Germany
(e.g. Freiburg im Breisgau, Karlsruhe, Hanau,
Friedrichshafen and Heidelberg) (Map 3.9). Around
15% of the urban population in the EU live in areas
in which the EU O3
target threshold for protecting
human health was exceeded in 201336
.
Other types of pollution are also important in
an urban environment, including noise pollution.
A perception survey on the quality of life in 79
European cities conducted in 201537
suggests
that in most cities, the level of noise is not a major
problem (Figure 3.12). In 62 cities, less than
half of respondents reported a problem with noise
levels, though the proportion was generally larger
in capital cities than in others. The proportion was
particularly small in the Nordic Member States
(Oulu, Finland, 12%; Aalborg, Denmark, 13%) and
the UK (Tyneside and Belfast, 14%). However, in
a number of other cities noise pollution seems
to cause discomfort and stress, particularly in
Bucharest, Palermo and Athens, in each of which
around two-thirds reported problems.
35	EUROSTAT (2016).
36	European Environment Agency (2015a).
37	European Commission, (2016h).
Seventh Report on economic, social and territorial cohesion
118
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.8 Concentration of airborne particulate matter (PM₁₀) in cities, 2014
Annual average concentration (µg/m³)
< 15
15 – 20
20 – 30
30 – 40
>= 40
No data
Urban center population
< 100 000
100 000 – 250 000
250 000 – 500 000
500 000 – 1 000 000
1 000 000 – 5 000 000
>= 5 000 000
Average recorded by measuring stations within city
boundaries.
WHO guideline: < 20 µg/m³
EU limit value: 40 µg/m³
Source: EEA, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 3: Territorial cohesion
119
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.9 Concentration of ground-level ozone (O₃) in cities, 2014
Number of days with more than 120 µg/m³
< 2
2 – 8
8 – 15
15 – 25
>= 25
No data
Urban center population
< 100 000
100 000 – 250 000
250 000 – 500 000
500 000 – 1 000 000
1 000 000 – 5 000 000
>= 5 000 000
Average recorded by measuring stations within
city boundaries.
EU target value of 120 µg/m³ should not be
exceeded more than 25 days per year (averaged
over 3 years).
Source: EEA, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Seventh Report on economic, social and territorial cohesion
120
4.5 Access to green spaces
Green Infrastructure (GI) is a strategically planned
network of natural and semi-natural areas with
other environmental features designed and managed
to deliver a wide range of ecosystem services.
Ecosystem services are the contribution of
nature to human well-being, such as the provision
of clean air and water, pollination of fruit and
vegetables by bees and the recreation provided
by natural areas.
The EU Habitats and Birds Directives have given
rise to Natura 2000 areas, the EU network of protected
areas, which is the backbone of EU Green
Infrastructure deployment, and is designated to
protect the most threatened habitats and species.
Natura 2000 also provides opportunities, for the
development of tourism, recreation, agriculture,
forestry, sustainable fisheries and aquaculture
as well as nature-based means of controlling
floods, mitigating and adapting to climate change
and producing other ecosystem services. Recent
studies have shown that the economic benefits
generated by the Natura 2000 network can be
substantial38
.
The establishment of Natura 2000 is to a large
extent complete on land (with more than 18% of
the EU’s landmass protected as a result). Progress
38	European Commission (2013).
30
40
50
60
70
80
90
30
40
50
60
70
80
90
IE FI LU AT LV SE LT UK SI DK NL HR EE PT CY DE SK CZ BE FR HU PL ES IT MT EL BG RO
Capital city
Other city
People reporting to be satisfied with the level of noise in their city, 2015Figure 3.12
% of city population
Figure 3.12
Source: Eurostat
Wien
Bruxelles
Antwerpen
Liège
Stockholm
Malmö
Graz
Sofia
Burgas
Lefkosia
Praha
Ostrava
Berlin
Hamburg
München
Essen
Leipzig
Dortmund
Rostock
København
Aalborg
Tallinn
Athina
(Greater
City)
Irakleio
Madrid
Barcelona
Málaga
OviedoHelsinki
Oulu / Uleåborg
Paris
(Greater
City)
Strasbourg
Bordeaux
Lille
Rennes
Marseille
Zagreb
Budapest
Miskolc
Dublin
Roma
Napoli
Torino
Palermo
Bologna
Verona
Vilnius
Luxembourg
Rīga
Valletta
Amsterdam
Rotterdam
Groningen
Warszawa
Kraków
Gdańsk
Białystok
Lisboa
(Greater
City)
Braga
Bucureşti
Cluj-Napoca
Piatra Neamţ
Ljubljana
Bratislava
Košice
London
Glasgow
Newcastle
Cardiff
Belfast
in designating marine areas for protection has
been slower, though 6% of EU seas and oceans
are now covered.
Improving the environment in less favoured regions
increases their attractiveness for external
investors and tourists and helps to strengthen
their regional identity, but there remain shortcomings
in the implementation of the Directives
concerned, partly as a result of a lack of adequate
funding39
.
Deploying Green Infrastructure in rural areas in
the EU can give rise to a wide range of ecosystem
services, but more investment is needed in
it in and around urban areas in order to increase
the beneficial effects of the services it produces,
even though the costs are likely to be higher for a
given level of nature protection40
.
Green urban spaces are a good example of this
general principle. Green urban spaces can mitigate
pollution problems and help to absorb carbon
from the atmosphere as well as rainwater.
They also offer shade and so help to limit temperature
increases, as well as being important
places for social interaction and for the quality of
life in general. Access to green urban areas varies
39	European Court of Auditors (2017).
40	Vallecillo, S., Polce, C., Barbosa, A., Perpiña Castillo, C., Zulian, G.,
Vandecasteele, I., Rusch, G. and Maes, J. (2016).
Chapter 3: Territorial cohesion
121
widely across EU cities (Map 3.10). In many cities
in western, central and northern Europe, people
have access to vast areas of green space. In
Chomutov-Jirkov in the Czech Republic, for example,
over 13 000 hectares of green space can be
accessed in less than 10 minutes walking. On the
other hand, such spaces are less present in many
eastern and southern EU cities, partly because of
the climate which often makes it costly to maintain
them, given the need for extensive watering
systems.
Urban green spaces also play an important role
in regulating air quality, as evidenced by many
studies (Escobedo and Nowak. 2009, Litschke and
Kuttler, 2008, Nowak et al. 2006, Nowak et al.,
0
20
40
60
80
100
0
20
40
60
80
100
FI RO SE PT EL ES LT IT LV IE UK EE SI HR PL BG HU SK BE FR AT CZ NL DK CY DE MT LU EU
Natura 2000 inside FUA Natura 2000 outside FUA% of FUAs
Share of the Natura 2000 network intersecting with Functional Urban AreasFigure 3.13
The entire territory of Luxembourg is classified as functional urban area
Source: JRC
A significant part of the Natura 2000 network lies within functional urban areas
Urban green infrastructure — trees, parks, green
roofs, gardens and urban forests — helps to improve
air quality, reduce noise and mitigate extreme summer
temperatures and the risk from floods. It also
provides a source of recreation. Significantly, people
who live in neighbourhoods with a high density of
trees on their streets or with large amounts of green
space report themselves as being healthier than
others. While the importance of urban green infrastructure
in this regard is increasingly recognised,
the potential role of protected areas to support biodiversity
in cities is often overlooked. But it can be
expected that in the near future cities will play an
increasingly important role in the management of
vulnerable ecosystems and biodiversity.
This is evidenced by linking spatial data on urban
areas with sites which are part of the Natura 2000
network, which is a key means of protecting biodi- 
	
versity in the EU. While some Natura 2000 sites are
located in remote areas, most of them are part of
the surrounding landscape, including in urban areas.
Overlaying spatial data for FUAs1
in the EU on top
of the Natura 2000 network2
shows that 11 041
Natura 2000 sites lie at least partly in FUAs, 15.2%
of the surface area, in practice. As would be expected,
more urbanised countries, like Malta or Belgium,
have a larger share of Natura 2000 sites inside
FUAs than countries like Finland or Sweden.
But the configuration of the network also matters
— for example, Germany has created a dense
network of relatively small protected sites which
often overlap with urban areas.
1 http://ec.europa.eu/eurostat/web/gisco/geodata/ref-
erence-data/administrative-units-statistical-units/
urban-audit#ua11-14
2	Natura 2000 sites (2016) (https://www.eea.europa.eu/
data-and-maps/data/natura-8#tab-metadata)
Seventh Report on economic, social and territorial cohesion
122
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.10 Access to green urban areas in cities, 2012
Hectares
< 10
10 – 15
15 – 20
20 – 25
25 – 30
>= 30
no data
Urban center population
< 100 000
100 000 – 250 000
250 000 – 500 000
500 000 – 1 000 000
1 000 000 – 5 000 000
>= 5 000 000
Population-weighted median area of green urban areas and forests
that can be reached within 10 minutes walking time.
Source: Poelman 2016
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 3: Territorial cohesion
123
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.11 Share of NO₂ concentration removed by vegetation in cities, 2010
Share
Low for NO₂
Low removal capacity and medium for NO₂
Medium removal capacity and medium for NO₂
High removal capacity and medium for NO₂
Low removal capacity and high for NO₂
Medium removal capacity and high for NO₂
High removal capacity and high for NO₂
No data
FUA population
< 100 000
100 000 – 250 000
250 000 – 500 000
500 000 – 1 000 000
1 000 000 – 5 000 000
>= 5 000 000
Source: JRC, DG REGIO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Seventh Report on economic, social and territorial cohesion
124
2013). The latest (Vizcaino et al., 2017), which focuses
on European functional urban areas (FUAs)41
,
finds that the contribution of green urban spaces
to reducing NO2
concentration varies widely across
the EU. In a number of Swedish cities (Gothenburg,
Uppsala, Umeå, Örebro and Jönköping), Târgovişte
in Romania, Vilnius in Lithuania and Ioannina in
Greece, more than 50% of NO2
concentration is
removed by green spaces (Map 3.11). By contrast,
in many cities in the southern UK, Belgium, the
Netherlands and northern Italy, because of low levels
of vegetation, only a small fraction is removed.
4.6 River flooding
There is a significant risk that large parts of Europe
will be confronted with an increase in the occurrence
and frequency of floods as a result of climate
change. Effective water management, as
required by the WFD, will help Member States to
prepare for extreme weather events which can
cause substantial damage42
.
Following the WFD, the Floods Directive43
, adopted
in 2007, is intended to create a pan-European
framework that can support Member States
to identify, assess and tackle flood risk. Since its
introduction, the management of flood risk has
been strengthened and new models and methods
for assessing and/or managing the risk have
been developed. A more systematic, coordinated
and holistic implementation of management plans
has been achieved with a better understanding of
priorities, along with a more focused discussion
and improved awareness of the risk and the devel-
41	 The functional urban area consists of a city plus its commuting area; ‑‑
see the EU-OECD FUA definition at http://ec.europa.eu/eurostat/
statistics-explained/index.php/Archive:European_cities_–_
the_EU-OECD_functional_urban_area_definition.
42	Under a no-adaptation scenario (i.e. assuming continuation of the
current protection against river floods up to a current 100-year
event), EU damages from the combined effect of climate and
socio-economic changes are projected to rise from €6.9 billion a
year to €20.4 billion a year by the 2020s, €45.9 billion a year
by the 2050s and €97.9 billion a year by the 2080s. See Rojas
Mujica, R.F., Feyen L., Watkiss, P. (2013).
43	Directive 2007/60/EC of the European Parliament and of the
Council of 23 October 2007 on the assessment and management
of flood risks, OJ L 288, 6/11/2007.
opment of partnerships, involving spatial and land
use planning and civil protection, to reduce it.
River flooding is a frequently occurring natural
hazard in Europe. It is of particular concern in urban
areas, where physical and human losses can
be high. The flood impact indicator developed by
Lund et al. (2013)44
enables the impact of flooding
at both regional and urban level to be assessed.
The methodology takes account of both the estimated
natural risk and the capacity of the region
or city to mitigate the event and recover from it.
When applied to Europe’s major FUAs, it shows
that, though the degree to which areas are affected
varies greatly depending on their location and
the hydrological characteristics of their surrounding
(upstream) area, the risk of flooding exists in
many cities right across the EU (Map 3.12). In a
large number of FUAs in the Netherlands, Italy
and Hungary, over 50% of the population is at
risk in the event of the biggest flood in the last
100 years reoccurring. There is also a high risk
in FUAs in Southern Germany, Poland, Romania,
Spain and France.
5. Cross-border cooperation and
territorial dimension of cohesion
policy
The EU is facing an increasing number of new
global challenges which have a significant impact
on the economic, social and territorial cohesion in
Europe. To respond to many of these challenges,
European territorial cooperation enables countries
and regions to identify solutions to common
problems in border regions and other functional
areas of cooperation.
44	Lung T., Lavalle C., Hiederer R., Dosio A. and Bouwer L. M. (2013).
Chapter 3: Territorial cohesion
125
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.12 Population flooded in case of the biggest 100-year flood in Functional Urban Areas
% of FUA population
0
< 5
5 – 10
10 – 20
20 – 30
>= 30
No data
FUA population
< 100 000
100 000 – 250 000
250 000 – 500 000
500 000 – 1 000 000
1 000 000 – 5 000 000
>= 5 000 000
Source: JRC
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Seventh Report on economic, social and territorial cohesion
126
5.1 Border regions
For analytical purposes,
border regions are defined
as NUTS 3 regions
located along or very
close to land and maritime
borders between
EU Member States and
other countries. There
are two types of border
region: internal ones,
i.e. regions located on
borders between EU
Member States and/or
European Free Trade
Area (EFTA) countries,
and external ones, i.e.
those located on borders
between an EU country and a non-EU or EFTA
one (Map 3.13).
As the severity of border effects is likely to diminish
with the distance from the border, the definition
of border regions is complemented by that of
border areas, which are those covering a 25 km
zone on both sides of the border. Indicators can be
defined for border regions or border areas or for
a combination of both. NUTS 3 regions not being
formally along land borders but which lie at least
partly inside the 25 km wide area along borders
are also considered to be border regions.
In the last few decades, integration among EU
Member States as well as with neighbouring countries
has been progressively extended. However,
despite the elimination of many institutional and
regulatory barriers, borders still continue to obstruct
the movement of goods, services, people,
capital and ideas, which prevents the benefits of
integration from being fully realised.
In this context, European Territorial Cooperation
has played an important role in mitigating the
adverse effects of internal borders and has realised
many concrete achievements with regard to
cross-border security, transport, education, energy,
health care, training and job creation. For the
2014–2020 period, €6.6 billion has been allocated
to 60 cross-border cooperation programmes45
.
In 2014, around a third of the EU population lived
in land border regions, the GDP of which was some
28% of the EU total, implying a GDP per head of
88% of the EU average. This average hides wide
variations, reflecting the differences in GDP per
head between different parts of the EU, with border
regions with a high GDP per head being located
in the north and west and those with a low level
being located in central and eastern Europe.
Recent research has identified some of the main
obstacles to the development of border regions.
There are often socioeconomic disparities between
regions on the two sides of the border which reduce
the opportunities to cooperate and hinder
integration. For some regions, physical obstacles
and poor transport infrastructure limit access to
markets and services on the other side of the border,
while cultural and language differences can
restrict interaction between people or businesses.
Legal and/or administrative difficulties can also
limit the scope for regional integration and labour
45	In the case of external border regions, the Instrument for PreAccession
Assistance (IPA) supports cross-border cooperation
between candidate countries, potential candidate countries and
EU Member States while the European Neighbourhood Instrument
(ENI) provides support to EU regions bordering Neighbourhood
countries to the East and the South.
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Domesticservices(%ofborderareapopulation)
Population of border areas having access to rail
passenger services, 2014
Figure 3.14
Cross-border services (% of border area population)
Source: UIC, rail operators, TomTom, OpenStreetMap, EuroGeographics, Eurostat, DG REGIO
Bubble size is relative to the border area population
Chapter 3: Territorial cohesion
127
mobility even in places which are potentially functional
regions.
A recent study46
suggests that if only 20% of the
existing legal and administrative obstacles were
removed, border regions could gain up to 2% in
GDP. Regions located along borders in central EU
and EFTA countries may have a lower GDP due to
these obstacles (Map 3.14).
The state of the cross-border road network varies
considerably across the EU. In some places, it
is good, even better than elsewhere in the region,
such as along the Belgian-French or Belgian-Dutch
borders (Map 3.15). In other places, the crossborder
road network is poor and limits the capacity
of the regions to develop. This can be due to
geophysical barriers, such as the mountain chain
which forms the border between France and Spain,
but it can also reflect the orientation of transport
policy.
Access to cross-border transport also varies across
the EU. While in some cases access to cross-border
rail services is as good as to services elsewhere
in the region (observations on the diagonal of
Figure 3.14), in many others, it is more limited (observations
above the diagonal).
46	Camagni et al. (2017).
A number of border regions face more serious demographic
challenges than other areas. Many located
in the EU‑13 have experienced substantial
loss of population over recent years as a result
of both a natural reduction (reflecting their older
population) and outward migration (Table 3.1).
Between 2005 and 2015, population in the EU‑13
land border regions fell by 3.5% as against 1.2%
in non-border regions, outward migration reducing
population by 1.9% combined with a natural
reduction of 1.5%.
The situation is different in the EU‑15, where population
increased in border regions as in non-border
ones, though at a slightly slower pace partly because
of less inward migration.
5.2 Interregional, transnational and
macro-regional cooperation
Interregional cooperation is needed to tackle in an
effective way common problems which affect most
regions to differing extents, to enable examples of
good practice and know-how to be shared, to build
networks and to support analysis of major territorial
development issues. Four interregional cooperation
programmes are currently in operation
(Interreg EUROPE, INTERACT, URBACT and ESPON)
which cover all EU Member States and a number
of third countries and which are allocated around
€1 billion for the 2014–2020 period.
There are, in addition, 15 transnational cooperation
programmes which group together regions in
different EU countries to tackle issues that are of
common concern to them and to which, together,
have been allocated €2.1 billion for the present period
(Map 3.16). They support a range of projects
relating to innovation, the environment, transport,
communication and sustainable urban development.
Transnational Cooperation can help to establish
functional links in a given area, such as
through sea basin strategies, the arctic framework
or macro-regional strategies. Under the ESF, transnational
cooperation helps improve the delivery of
employment and social policies and contributes to
the implementation of reforms by enabling stake-
Table 3.1 Demographic changes in border and
non-border regions, 2005–2015
2005–2015 Terrestrial
border
region
Non-
border
region
Total
EU‑15
Total change 4 4.3 4.2
Natural change 0.7 1.4 1.2
Net migration 3.4 2.9 3
EU‑13
Total change -3.5 -1.2 -2.5
Natural change -1.5 -0.9 -1.3
Net migration -1.9 -0.3 -1.2
EU‑28
Total change 1.3 3.6 2.8
Natural change -0.1 1.1 0.7
Net migration 1.4 2.5 2.1
Source: Eurostat and DG REGIO calculations
Seventh Report on economic, social and territorial cohesion
128
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.13 Border regions
Land border regions
Maritime border regions
Land and maritime border regions
NUTS 3 regions
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Chapter 3: Territorial cohesion
129
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.14 Loss of GDP in EU NUTS 3 land border regions due to cross-border obstacles
< 3
3.1 – 6
6.1 – 9
9.1 – 12
12.1 – 15
> 15
Total GDP loss as % of NUTS 3 GDP
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Source: Study “Quantification of the effects of legal and
administrative border obstacles in land border regions”,
Politecnico di Milano 2017
Seventh Report on economic, social and territorial cohesion
130
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 3.15 Cross-border road network efficiency in border areas, 2015
0.00 – 0.35
0.36 – 0.40
0.41 – 0.45
0.46 – 0.50
>= 0.50
No population / no data
Ratio between 0 and 1
Source: JRC
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 3: Territorial cohesion
131
holders to learn from experience and examples of
good practice in other countries.
Macro-regional strategies are a form of territorial
cooperation between countries which help to
improve the implementation of EU policies. They
are equally designed to tackle common problems,
such as relating to the environment or climate
change. Macro-regional strategies can also provide
an appropriate framework for cross-border
institutional cooperation. They are not, however,
directly financed under cohesion policy but are
intended to improve the use of existing financial
sources (e.g. the ESIF, Horizon 2020, COSME, LIFE)
and institutions and the implementation of existing
legislation.
Since the European Council endorsed the EU
Strategy for the Baltic Sea Region (EUSBSR) in
2009, three further macro-regional strategies have
been developed: the EU Strategy for the Danube
Region (EUSDR) in 2011, the EU Strategy for the
Adriatic and Ionian Region (EUSAIR) in 2014 and
the EU Strategy for the Alpine Region (EUSALP) in
2016 (Map 3.16).
At present 19 EU and 8 non-EU countries are involved
in macro-regional strategies which have
become an integral part of the EU policy framework.
They have increased interest in territorial
cooperation and cohesion and awareness of its
added-value. They have led to increased coordination
and strengthened cooperation in a number
of areas (such as navigability, energy and climate
change) and intensified cooperation with non-EU
countries, helping to mitigate possible adverse
effects on the EU’s external borders.
Each macro-regional strategy has achieved specific
results:
•• EUSBSR: the quality of the Baltic Sea water is
being improved and nutrient inflows reduced
through projects such as PRESTO or Interactive
water management (IWAMA), while the
SUBMARINER Network is further encouraging
the innovative and sustainable use of marine
resources;
•• EUSDR: the coordinated management of water
in the Danube river basin, though projects such
as SEERISK is reducing the risk of damage by
floods, while projects such as FAIRWAY and
DARIF are reducing bottlenecks to navigability
and improving the safety of navigation;
•• EUSAIR: cooperation with EU countries on issues
of common interest is helping Western
Balkan participating countries prepare for EU
accession; green/blue corridors linking land
and sea in the Adriatic and Ionian Sea have
been identified as areas where strategic projects
should be undertaken to achieve sustainable
economic growth respectful of the
environment;
•• EUSALP: projects such as ‘mountErasmus’ are
helping to establish a cross-border educational
space for dual vocational training in the Alpine
region, while ‘AlpinfoNet’ is being developed
into a cross-border information system to improve
passenger transport in the region.
5.3 Local, urban and metropolitan
development
Cohesion policy promotes integrated and placebased
approaches to foster economic, social and
territorial cohesion, while at the same time recognising
the role of sustainable urban development
in realising overall EU objectives. To allow more
flexibility in tailoring the provision of ESI Funds
to territorial needs, new and improved delivery
mechanisms were put in place for the 2014–2020
programming period, in particular Integrated
Territorial Investment (ITI) and Community-Led
Local Development (CLLD).
Almost 9% of the cohesion policy budget (around
€31 billion) is allocated to integrated territorial and
urban development in the current period, the ERDF
contributing the largest part (€25.5 billion) and the
rest coming from the other ESI Funds. Over half
the total is being provided using the new territorial
instruments. Overall, the new territorial provisions
are used in around 150 programmes, creating bet-
SeventhReportoneconomic,socialandterritorialcohesion
132
Balkan-Mediterranean Caribbean Area
Mediterranean AreaSouth West Europe
Adriatic-IonianCentral Europe
Amazonia Indian Ocean Area
Alpine Space Danube Area
North West EuropeBaltic Sea
Northern Periphery and ArcticNorth Sea
Atlantic AreaMap 3.16 ERDF Transnational Cooperation
Programmes 2014–2020
© EuroGeographics Association for the administrative boundaries
Non-EU areas are indicative only
Chapter 3: Territorial cohesion
133
ter links between the local strategies and the thematic
objectives set out in the programmes.
The rationale for applying integrated, place-based
approaches relates either to territorial integration,
to thematic integration, to the blending of different
financial resources or to institutional knowledge.
•• Territorial integration: around half of the integrated
strategies are using a functional approach,
under which horizontal coordination
arrangements help to improve the governance
of a functional area and promote urban-rural
or even cross-border links, though often it also
requires new coordination arrangements between
the administrative units involved.
•• Thematic integration: ITI was specifically designed
to combine investment under different
priority axes or from different programmes,
since a strategy supported through an integrated
multi-thematic priority axis can only be
financed through one programme. As a result,
strategies implemented through ITI include on
average more thematic objectives than those
implemented through a priority axis.
•• Blending different financial sources: the ERDF
provides in most cases the bulk of financing
together with the ESF, but the other ESI Funds,
other EU instruments and national or regional
public and private funding can also make
a significant contribution in some Member
States, especially for ITI strategies. In most
cases, the strategies will be funded by nonrepayable
grants, but financial instruments
are also important in several strategies or for
particular types of investment, such as for
improving energy efficiency.
•• Institutional knowledge: the strategic planning
process and, more especially, the delegation
requirements for sustainable urban
development and CLLD have led in a number
of Member States to the creation of new collaborative
arrangements and bodies responsible
for project selection and other tasks. In
other Member States, this delegation has also
resulted in capacity building and advisory
measures, such as the establishment of new
bodies or internal departments to support urban
authorities’ decision making.
Empowering cities: sustainable urban
development
The urban dimension is at the heart of cohesion
policy. for the 2014–2020 period, at least 50%
of the ERDF is invested in urban areas. Around
€14,5 billion (8 %) of the total ERDF budget has
been allocated directly to support over 900 integrated
sustainable urban development strategies,
with considerable additional financing from the
Urban Agenda for the EU
The Urban Agenda for the EU which is designed
to strengthen the urban dimension in EU policymaking
is a further development of the integrated
territorial approach.
The Urban Agenda is aimed at promoting cooperation
between Member States, cities, the European
Commission and other stakeholders in
order to maximise the growth potential of cities
and to tackle social problems and so to improve
the quality of life in urban areas. Partnerships
have been established around 12 priority
themes of EU and urban relevance, the intention
being to identify common problems and to recommend
action plans (to the EU, Member States
and cities) to tackle them. The action concerned
could, for example, be a proposal to amend an
EU Directive or for the new ESI Funds or a project
that worked well and could be scaled-up and
adopted more widely.
The Urban Agenda should lead to more effective
regulation and funding that is better adapted to
needs and is easier to access and better knowledge
(through more data, examples of good
practice or projects and exchange of experience).
A new website (The EU Urban Agenda) enables
stakeholders to contribute to the Urban Agenda
as a whole or to specific Partnerships.
Seventh Report on economic, social and territorial cohesion
134
ESF and from other EU or domestic sources in a
number of Member States.
Three options were provided for Member States to
implement sustainable urban development strategies
in the current period — through a dedicated
multi-thematic priority axis, a dedicated programme
or the use of the new ITI instrument. The
ITIs have been relatively slow to be taken up but
have been adopted in 13 Member States, where
well-functioning domestic programming and spatial
planning arrangements were already in place
or technical assistance was provided to help develop
the strategies concerned.
Going beyond administrative boundaries:
Integrated Territorial Investments (ITI)
Cohesion policy pays particular attention to the
specific socio-economic characteristics of functional
areas, making a wide range of investments
available and promoting the adoption of integrated
strategies targeted at specific needs.
Despite its novelty, ITI is being used flexibly for
multidimensional place-based interventions for
tackling complex territorial problems in 13 Member
States. It has been adopted by around 150 different
territorial strategies, which were developed not
only for administrative regions to replace regional
programmes but also for functional areas such as
remote and sparsely populated rural areas, islands
and coastal areas, environmental protection sites
and functional urban areas.
Strengthening local communities:
Community-led Local Development
Community-led Local Development (CLLD) has
been introduced under cohesion policy as a voluntary
instrument, extending the existing LEADER approach
for rural development and fisheries policies,
its territorial focus depending very much on coordination
with the EAFRD and EMFF. Complementary
arrangements are usually targeted at rural areas
with small or medium-sized towns or cities nearby,
while in some Member States, the ERDF and ESF
are used to support urban participatory measures
targeted at social inclusion and urban regeneration.
Joint Programming Initiative (JPI)
Urban Europe
The Urban Europe Joint Programming Initiative
(JPI) is a network of Member States and associated
countries of the EU intended to provide answers
to the major challenge of urbanisation in
Europe and beyond. It was set up in late-2011 as
one of 10 JPIs following a decision of the European
Council to address challenges which cannot
be effectively met by countries acting individually.
The idea is that it should foster a transnational
research and innovation programme between
European countries which is independent
from the research and innovation programmes
set up by the European Commission but complementary
to them and collaborating with them.
Apart from finding solutions to the challenges
concerned, the vision is to bring to life the European
Research Area through increased collaboration
between Member States.
Since 2012, the Urban Europe JPI has launched
annual joint calls for proposals that have resulted
in over 50 projects being undertaken with
around 200 participants, comprising researchers,
urban practitioners and civil society. The
initiative is also in the process of establishing a
Stakeholder Involvement Platform to facilitate
the implementation of its Strategic Research and
Innovation Agenda by reaching new countries
and cities. The platform is intended to support
experimentation with different kinds of measure
and different ways of cooperating as well as to
mobilise interested parties and to reflect on
ur-ban policies.
135
Improving institutions•••••••
• There is substantial evidence that the quality of government matters for social and
economic development across the EU and that it is an important determinant of
regional growth.
• The way that national regulations are implemented and their effect on development
varies within countries reflecting differences in the efficiency of regional
and local authorities. Institutional capacity affects the attainment of long-term
policy objectives and the ability to implement structural reforms which have the
potential to boost growth and employment.
• The perception of corruption remains widespread in a number of EU Member States
and this erodes trust in governments and their policies. In many regions across the
EU, public procurement is open to the risk of corruption and a lack of competition
for contracts as reflected in a number of instances where a contract was awarded
when only one bid had been submitted.
• Professional and impartial public authorities are of major importance in combating
corruption; however the degree to which meritocracy is a feature of the public
sector, rather than nepotism, varies greatly between and within EU countries.
• Doing business is easier in the north of Europe than elsewhere in the EU, but central
and eastern European countries are making significant efforts to catch up. There
are major variations in the ease of doing business between regions in a number
of countries which point to differences in the administrative capacity of regional
and local governments.
• Governments in many parts of the EU have made significant progress in providing
online access to services, but there has been insufficient focus on their quality and
ease of use, so limiting their take-up and growth.
• A suitable institutional framework is important to facilitate the creation of new
firms and to boost the effectiveness of cohesion policy support for entrepreneurship
and business start-ups.
chapter4
Seventh Report on economic, social and territorial cohesion
136
which should attract more FDI inflows. Secondly,
low-quality institutions that enable corruption to
occur add to the costs of investment and reduce
profits. Thirdly, the high sunk cost involved in FDI
makes investors highly sensitive to the political
uncertainty inherent in low-quality institutions5
.
High-quality government has been found to be of
utmost importance for the well-being of society,
and there is broad consensus that good governance
is a pre-requisite for long-term, sustainable
increases in living standards. It has equally been
found that the quality of governance strongly influences
people’s health, their access to basic services,
social trust and political legitimacy. It helps
to explain why living conditions vary between
countries and regions with much the same level of
GDP per head6
.
High quality institutions can be defined as those
which feature an “absence of corruption, a workable
approach to competition and procurement
policy, an effective legal environment, and an independent
and efficient judicial system. [...] strong
institutional and administrative capacity, reducing
the administrative burden and improving the quality
of legislation” (European Commission, 2014, p.
161). Such a broad definition is in line with academic
studies which view good governance as the
impartial exercise of public power, focusing on
policy implementation rather than the content of
policies or the democratic process through which
they are decided7
.
In sum, there is a growing consensus that the quality
of governance and institutions is a fundamental
5	 Kaufman et al. (1999), Wei (2000), Habib and Zurawicki (2002),
Kaufmann and Kraay (2002), Kinoshina and Campos (2003),
Levchenko (2004), Walsh and Yu (2010).
6	 Dahlström et al. (2015); Acemoglu and Robison (2012), North
(1990); Ostrom (1990); Rothstein (2011) and Holmberg and
Rothstein (2012), Dellepiane-Avellaneda (2010), Halleröd et al.
(2013); Uslaner (2008); Tavits (2008); Svallfors (2013).
7	 Rothstein & Teorell, (2008).
1. Good governance affects
economic growth and
the quality of life
According to the dominant economic theories1
,
economic growth is the result of a combination
of three factors — physical capital, human capital
(or labour) and innovation (or technical progress).
By and large, investment in these areas has borne
fruit in terms of greater convergence2
. However,
there has been an apparent decline in the return
on investment in all three areas and the variation
in economic growth across EU regions that they
are capable of explaining3
. This suggests that an
important factor underlying growth is missing.
According to a number of studies that factor is the
quality of governance.
Many studies in recent years have highlighted the
importance of this factor for economic performance
and the fact that poor government in lagging
areas in the EU represents a significant obstacle
to development. Indeed, it has been found not
only to adversely affect economic growth, but also
the returns to cohesion policy investment and regional
competitiveness, while corrupt or inefficient
government undermines the regional potential for
innovation and entrepreneurship. It has equally
been found that low quality of government affects
regional environmental performance and decisions
on public investment and threatens inclusiveness
and participation in the political process4
.
Institutional quality is a determinant of investment,
and foreign direct investment (FDI) in particular,
for a number of reasons. First, good governance
is associated with higher economic growth,
1	 Neoclassical growth: Solow (1956); Swan (1956); endogenous
growth approach: Romer (1986): Lucas (1988).
2	 Cappelen et al. (2003); Becker et al. (2010); Pellegrini et al. (2013).
3	 Rodriguez-Pose (2016a and 2016b).
4	 Rodríguez-Pose and Garcilazo, (2015), Rodríguez-Pose and Di
Cataldo, (2015), Annoni, (2013), Nistotskaya et al. (2015), Halkos
et al., (2015), Crescenzi et al. (2016), Sundström and Wängnerud,
(2014).
Chapter 4
Improving institutions
Chapter 4: Improving institutions
137
precondition for sustained increases in prosperity,
well-being and territorial cohesion in the EU.
2. Quality of governance varies
substantially in Europe
Governance encompasses the traditions and institutions
by which authority in a country is exercised.
This includes the process by which governments
are selected, monitored and replaced; the capacity
to formulate and implement sound policies
and the respect of citizens for the institutions that
govern economic and social interactions between
them8
. The institutional environment of a country
depends on the efficiency and behaviour not only
of public but also of private stakeholders9
.
Every year the World Bank produces the Worldwide
Governance Indicators (WGI), covering over 200
economies, to denote the quality of the institutions
responsible for governance. Governance itself
is defined according to dimensions related to
accountability, political stability, government effectiveness,
regulatory quality, confidence in institutions
and absence of violence and control of corruption.
The changes between 1996 and 2015 in
the indicators of the effectiveness of government
and citizens’ confidence in institutions are set out
in Figures 4.1 and 4.2.
The indicator for government effectiveness takes
account of government policies, the quality of public
services provided and the extent of independence
of the civil service from political pressure
as well as the credibility of the government. All
these aspects contribute to creating the stable political
environment needed for sustained economic
growth.
The EU countries assessed as having the most effective
governments in 2015 were Denmark, the
Netherlands, Finland and Sweden. Those with the
least effective were Romania, Bulgaria, Greece and
Italy, the difference between the two groups being
substantial. While Denmark, Netherlands, Finland
8	http://info.worldbank.org/governance/wgi/index.aspx#doc
9	 World Economic Forum (2017).
and Sweden were among the 10 best performing
countries in the world, Romania was ranked below
the global average.
Between 1996 and 2015, government effectiveness
diminished in 7 EU countries (Luxembourg,
Austria, Belgium, Spain, Hungary, Italy and Greece)
and increased in 8, all of them in the EU‑13, most
notably in Latvia, Lithuania and Estonia, which
climbed to the middle of the EU ranking. Among
the Member States with the least effective governments,
the situation improved in Romania,
Bulgaria and Croatia and worsened in Greece, Italy
and Hungary.
Guaranteeing opportunities for democratic participation
and respect for the rules of a society,
its institutions and civil rights help to generate the
confidence of people in the legitimacy of actions
taken by political leaders and to establish the support
for them which is necessary to make them
effective10
.
The indicator of citizens’ confidence in institutions
relates to the confidence people have in social rules
(like contract enforcement or property rights), social
institutions (the police and law courts) and their own
safety (measured by the likelihood of being affected
by crime and violence). It shows a similar pattern to
the government effectiveness indicator (Figure 4.2).
Finland, Sweden, Denmark and the Netherlands are
ranked highest, Romania and Bulgaria, lowest. The
three Baltic countries again show the biggest improvement,
once more climbing to the middle of the
EU ranking, and there is a similar improvement for
Croatia, though it remains at the lower end of the
ranking.
There is a close correlation between government
effectiveness and economic competitiveness
(Figure 4.3). Whereas, however, the most competitive
countries tend to have the most effective
governments, the fastest growing EU economies in
recent years (Bulgaria, Romania and Poland) tend
to have the least effective ones. This suggests
perhaps that in the early stages of development,
10	http://www.sgi-network.org/docs/2016/basics/SGI2016_Overview.
pdf
Seventh Report on economic, social and territorial cohesion
138
other factors play a dominant role, but to sustain
growth requires improvements in the quality of
government. The correlation between government
effectiveness and life satisfaction is equally close
and confirms the importance of the quality of government
for people’s lives (Figure 4.4).
There are significant variations across regions in
the quality of government which reflect the way in
which national regulations are implemented and
differences in the efficiency of regional and local
authorities in this respect. These differences are
important to take into account when assessing
the quality of governance in relation to economic
and social development. A regional European quality
of governance index (EQI)11
, constructed by the
Gothenburg Institute of Quality of Government,
which measures people’s perceptions of this in
different policy areas, enables this to be done
(Map 4.1).
11	EQI is based on an extensive survey covering the perceptions of
people of public sector services (education, healthcare law enforcement)
based on the experience they have of them. It specifically
measures the extent to which people feel that the services
concerned are not affected by corruption, are of a good quality and
are accessible in an impartial way.
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5
FI
SE
DK
NL
LU
AT
UK
IE
DE
BE
FR
EE
MT
PT
CZ
CY
SI
ES
PL
LT
LV
SK
HU
IT
EL
HR
RO
BG
201
5
199
6
Score in standard deviation
Citizen's confidence in
institutions, 1996 and 2015
Figure 4.2
1996 2015
Source: World Bank Rule of law 2015
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5
DK
NL
FI
SE
DE
UK
LU
IE
AT
FR
BE
PT
LT
ES
LV
EE
CZ
CY
SI
MT
SK
PL
HR
HU
IT
EL
BG
RO
201
5
199
6
Score in standard deviation
Government effectiveness,
1996 and 2015
Figure 4.1
1996 2015
Source: World Bank Government effectiveness indicators
Chapter 4: Improving institutions
139
The perceived quality
of government
varies markedly between
and within
EU Member States.
People in Sweden,
Finland, Denmark,
the Netherlands and
Germany are the most
positive about the quality
and impartiality of
education, healthcare
and law enforcement.
People living in regions
in Romania, Bulgaria
and Italy are the least
positive.
The index shows the
greatest variation
between regions in
Spain, Italy, Belgium,
Romania, Bulgaria,
Hungary and the Czech
Republic. This suggests
that the quality of services
provided locally
may vary substantially
in countries with
regions that are both
politically and administratively
relatively autonomous
(Spain, Italy
and Belgium) as well as
in countries which are
more centralised.
The quality of government
and institutions appears to be the main obstacle
to development in regions with persistently
low growth rates12
. Indeed, the 2017 EQI results
for Italy, Greece and Spain imply that some less
advantaged regions in these countries may be
stuck in a low-administrative quality, low-growth
trap. In regions in the east of the EU, especially
in those in Bulgaria and Romania, which have enjoyed
relatively high growth over the past decade
12	European Commission (2017a).
or so, the poor quality of government which is evident,
may eventually put a break on development
and the move to a higher value-added economy (A.
Rodriguez-Pose, T. Ketterer, 2016).
The results of the 2017 survey are much the same
as for 201313
indicating that improvements in government
may take time. Indeed, for them to occur
13	Due to slight changes in the methodology the two surveys are not
fully comparable.
AT
BE
BG
HR
CY
CZ
DK
EE
FI
FR
DE
EL
HU
IE
IT
LV
LT
LU
MT
NL
PL
PT
RO
SK
SI ES
SE
UK
40
50
60
70
80
90
100
40
50
60
70
80
90
100
-0.5 0.0 0.5 1.0 1.5 2.0
%ofpeoplesatisfiedwiththeir
life
Social impact of government effectiveness, 2015Figure 4.4
Government effectiveness indicator (-2.5 low to 2.5 high effectiveness)
Source: World Bank Government Effectiveness 2015; Standard Eurobarometer 83, Spring 2015.
Standard deviation (0 = global average)
AT
BE
BG
HR CY
CZ
DK
EE
FI
FR
DE
EL
HU
IE
IT LV
LT
LU
MT
NL
PL
PT
RO
SK
SI
ES
SEUK
3.5
4.0
4.5
5.0
5.5
6.0
3.5
4.0
4.5
5.0
5.5
6.0
-0.5 0.0 0.5 1.0 1.5 2.0
Competitivenessindex(2.7minto5.8max)
Economic impact of government effectiveness, 2015Figure 4.3
Government effectiveness indicator (-2.5 low to 2.5 high effectiveness)
Source: World Bank Government Effectiveness 2015; World Economic Forum.
Global Competitiveness 2016-2017
Standard deviation (0 = global average)
Seventh Report on economic, social and territorial cohesion
140
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 4.1 European Quality of Government index, 2017
< -1.75
-1.75 – -1.25
-1.25 – -0.75
-0.75 – -0.25
-0.25 – 0.25
0.25 – 0.75
0.75 – 1.25
> 1.25
no data
Standard deviation, range from poor quality (negative) to high quality (positive)
EU = 0
Source: World Bank data and a regional quality of government survey.
©
0 500 km
EuroGeographics Association for the administrative boundaries
REGIOgis
Chapter 4: Improving institutions
141
is likely to require concerted efforts at all levels of
the administration as well as the active involvement
of the public at large.
2.1 Quality of governance as a
determinant of regional growth
A recent study on the determinants of regional
growth between 1999 and 2013 (Rodriguez-Pose
& Ketterer, 2016) was aimed at differentiating
between the role of traditional aspects of investment
policy, such as infrastructure, human capital
and innovation, and that of various institutional
aspects.
The effect of the quality of regional government
and changes in this is included in the regression
analysis as both an aggregate measure (Table 4.1,
left panel) and separately in terms of the four
main constituent aspects distinguished: corruption,
confidence in police and regional law enforcement,
government effectiveness, and government accountability
(Table 4.1, right panel).
In line with the predictions of neoclassical growth
theory, there is a significant and negative relationship
between growth rates and initial GDP per head,
so implying a tendency towards convergence.
The three basic factors identified by growth theory
do not seem to have been important in determining
changes in GDP per head of regions over the
period of economic expansion followed by recession.
Following the abrupt change in economic conditions
in 2008, the determinants of growth during
the boom years no longer seem to work in the
same way. The initial level of regional investment,
accessibility, population growth and the quality of
regional institutions do not appear to be important
in explaining differences in the growth of GDP per
head between regions over the crisis years. The
same is true of human capital accumulation and
R&D expenditure relative to GDP (as a measure of
innovation efforts), though employment of those
Table 4.1 Summary of fixed effects analysis
Results of all growth determinants Results by institutional index components
Dependent variable:
GDP per capita growth (1999–2013)
All EU regions Dependent variable: GDP per capita
growth (1999–2013)
All EU regions
Initial GDP per capita Level of corruption index
Investment Change of corruption index
Population growth Level of rule of law index
Agglomeration Change of rule of law index
Level of accessibility index Level of government effectiveness
Change of accessibility index Change of government effectiveness
Level of human capital & innovation index Level of government accountability
Change of human capital
& innovation index
Change of government accountability
Level of institutional quality (QoG)
Change of institutional quality (QoG)
Key:
Positive and statistically significant impact
Negative and statistically significant impact
No statistically significant impact
Panel data analysis for 249 NUTS 2 regions in the European Union using a standard Solow-Swan-type growth framework. Investment is proxied
by regional gross fixed capital formation as a percentage of GDP. All independent variables are included with a five year lag. Natural logarithms
have been taken for most regressors apart from the population growth variable. All regressions include constant time dummies.
Source: DG REGIO calculations on the basis of A. Rodriguez-Pose, T. Ketterer (2016).
Seventh Report on economic, social and territorial cohesion
142
with tertiary education continues to have a significant
positive effect on growth. On the other hand,
changes in the quality of institutions show a continuously
positive and statistically significant effect
over the period.
Indeed, improvements in the quality of institutions
appear to have been among the most consistent
factors underlying economic growth and resilience
across the EU. Accordingly, the implication is that
bringing about such improvements, by either tackling
widespread corruption or introducing measures
aimed at making government decisions more
efficient and transparent, is important for regional
development — as important, indeed, as physical
investment.
2.2 Corruption remains widespread
in many EU countries and may erode
social capital
Corruption is a drag on economic growth. The true
social cost of corruption cannot be measured solely
by the amount of bribes paid or public funds
diverted. It also includes the loss of output due
to the misallocation of resources, distortion of
incentives and other inefficiencies that it causes.
Corruption can also have perverse effects on the
distribution of income and give rise to a disregard
for environmental protection. Most importantly,
corruption undermines trust in legitimate institutions,
diminishing their ability to provide adequate
public services and an environment conducive to
business development. In extreme cases, it may
lead to the state losing its legitimacy, giving rise to
political and economic instability, so reducing business
investment and making sustainable development
harder to achieve. (OECD, 2013b).
The Corruption Perceptions Index (CPI), first
launched in 1995 by Transparency International,
has been widely credited with putting the issue of
corruption on the international policy agenda. The
CPI each year ranks countries by their perceived
levels of corruption, as assessed by experts and
through opinion surveys. Corruption is defined as
the misuse of public power for private benefit and   
the index combines data from 13 sources to judge
this. As the methodology was updated in 2012, the
following focuses on the changes since then14
.
In 2016, the CPI ranked 176 countries on a scale
from 100 (very clean) to 0 (highly corrupt). The
global average score is 43, indicating endemic corruption
in many governments across the world. The
average score of EU countries is 65, with 6 countries
having a score below 50 and 7 in Northern and
Western Europe having one above 80 (Figure 4.5).
While the general trend over the 5 years 2012–
2016 is upwards, there were some
significant downward movements (Figure 4.6).
Between 2015 and 2016, the CPI score dropped
by more than a point in 7 Member States:
Cyprus (6 points), the Netherlands (4), Hungary
(3) and Greece, Croatia, Lithuania and Ireland (2
in each). It remains to be seen whether this is a
long-term reduction or the reaction to one-off
events (like a corruption scandal in the
Netherlands which happened shortly before the
survey). At the same time, there was increase in
the score in Italy (by 3 points) and Romania and
Latvia (by 2 points in each).
The ranking of the best performers among
EU Member States did not change much over
the 5 years. In particular, Denmark was
ranked first throughout the period with Finland
and Sweden close behind. There are more
changes in the middle-ranking countries with
Estonia, Latvia, Lithuania, the Czech Republic
and Poland having the biggest increases.
Over the 5 years, 3 countries stand out as not
fol-lowing the general trend towards
improvement. In Cyprus, Spain and Hungary, there
was a significant increase in perceived corruption.
2.3 Trust in local authorities in line with
perceptions of corruption
Corruption erodes trust in public services.
According to various surveys carried out for the
14	http://www.transparency.org/files/content/
pressrelease/2012_CPIUpdatedMethodology_EMBARGO_EN.pdf
Chapter 4: Improving institutions
143
European Commission and information from the
World Justice project15
, trust in local authorities
and people’s perception of corruption in them go
hand in hand.
Countries and cities in which people trust their
local government are also those in which people
believe the authorities concerned are not corrupt
(such as in the Nordic countries or Austria) while
in a large parts of central, eastern and southern
Europe, local authorities are perceived as being
prone to corruption. Hungary, Romania and
Belgium are somewhat different in that there is
15	See European Commission (2016h) for details..
a relatively high level of trust in local authorities
even though they are regarded as being relatively
corrupt. The three countries with the lowest level
of trust in local authorities (less than 35% of those
surveyed reporting having trust) were Bulgaria,
Poland and Italy, in all three of which perceptions
of corruption among local officials were the most
widespread (Figure 4.7).
National averages hide some marked differences
in how people perceive the situation in different
cities. For example, Marseille stands out from other
French cities with only 30% expressing trust in
the local government (as opposed to 55% in Lyon)
0
20
40
60
80
100
0
20
40
60
80
100
DK
(1)
FI
(3)
SE
(4)
NL
(8)
DE
(10)
LU
(10)
UK
(10)
BE
(15)
AT
(17)
IE
(19)
EE
(22)
FR
(23)
PL
(29)
PT
(29)
SI
(31)
LT
(38)
ES
(41)
LV
(44)
CY
(47)
CZ
(47)
MT
(47)
SK
(54)
HR
(55)
HU
(57)
RO
(57)
IT
(60)
EL
(69)
BG
(75)
Very clean (90+)
(80-89)
(70-79)
(60-69)
(50-59)
(40-49)
CPI index
Corruption Perception Index, 2016Figure 4.5
Source: Transparency International
Numbers in brackets indicate the country world ranking
35
40
45
50
55
60
65
70
75
80
85
90
95
35
40
45
50
55
60
65
70
75
80
85
90
95
DK FI SE NL DE LU UK BE AT IE EE FR PL PT SI LT ES LV CY CZ MT SK HR HU RO IT EL BG
2012 2016CPI index
Corruption Perception Index, 2012-2016Figure 4.6
Source: Transparency International
Score: 0 = very corrupt and 100= clean of corruption
Seventh Report on economic, social and territorial cohesion
144
and as many as 40% believing that local officials
are involved in corrupt practices (as against just
15% in Lyon). Equally, in Hungary, a much larger
proportion of people trust local officials in Miskolc
(80%) in the north-east of the country than in
Szeged (50%) in the south (Figure 4.8).
0
10
20
30
40
50
60
70
80
0
10
20
30
40
50
60
70
80
DK SE NL FI AT UK FR DE PT ES EE EL BE HU CZ RO IT PL SI BG HR
Capital city
Other city
Perception of corrupt practices in local government, 2012-2014
% of population that trust local government officials
Figure 4.7
Source: World Justice Project & JRC Calculations
tŝĞŶ
>ŝŶnj ŶƚǁĞƌƉ
ƌƵdžĞůůĞƐ
DĂůŵƂ
^ƚŽĐŬŚŽůŵ
'ƌĂnj ŚĂƌůĞƌŽŝ
'ŚĞŶƚ
EĂƌǀĂ
ĂŐƌĞď
ƌŶŽ
KƐƚƌĂǀĂ
WƌĂŚĂ
ƌŚƵƐ
<ƆďĞŶŚĂǀŶ
KĚĞŶƐĞ
ůƵũͲEĂƉŽĐĂ
dĂůůŝŶŶ
dĂƌƚƵ
ƐƉŽŽ
,ĞůƐŝŶŬŝ
dĂŵƉĞƌĞ
dƵƌŬƵ
>LJŽŶ
DĂƌƐĞŝůůĞ
WĂƌŝƐ
ĞƌůŝŶ
,ĂŵďƵƌŐ
DƺŶĐŚĞŶ
ƚŚŝŶĂ
WĂƚƌĂƐ
^ĂůŽŶŝĐĂ
dŚĞƐƐĂůŽŶŝŬŝ
ƵĚĂƉĞƐƚ
ĞďƌĞĐĞŶ
DŝƐŬŽůĐ
^njĞŐĞĚ
EĂƉŽůŝ
ZŽŵĂ
ŵƐƚĞƌĚĂŵ
ZŽƚƚĞƌĚĂŵ
ĞŶ ,ĂĂŐ
<ƌĂŬſǁ
tĂƌƐnjĂǁĂ
ƌĂŐĂ
>ŝƐďŽĂ
WŽƌƚŽ
sŝůĂ EŽǀĂ
ĚĞ 'ĂŝĂ
ƵĐŚĂƌĞƐƚ
/ĂƐŝ
dŝŵŝƐŽĂƌĂ
ĞůũĞ
DĂƌŝďŽƌ
>ũƵďůũĂŶĂ
DĂĚƌŝĚ
sĂůĞŶĐŝĂ
'ƂƚĞďŽƌŐ
ŝƌŵŝŶŐŚĂŵ
'ůĂƐŐŽǁ
>ŽŶĚŽŶ
ZŝũĞŬĂ
^Ɖůŝƚ
DŝůĂŶŽ
^ŽĨŝĂ
WůŽǀĚŝǀ
sĂƌŶĂ
ĂƌĐĞůŽŶĂ
BſĚǍ
10
20
30
40
50
60
70
80
10
20
30
40
50
60
70
80
HR IT BG EL ES PL DE CZ EE FR SI UK PT NL RO BE AT SE HU DK FI
Capital city
Other city
Trust in local government officials per city, 2012-2014Figure 4.8
% of population that think most or all people in local government are involved in corrupt practices
Figure 4.8
Source: World Justice Project & JRC Calculations
tŝĞŶ
>ŝŶnj
ŶƚǁĞƌƉĞŶ
ƌƵdžĞůůĞƐ
DĂůŵƂ
^ƚŽĐŬŚŽůŵ
'ƌĂnj
ŚĂƌůĞƌŽŝ
'ŚĞŶƚ
Narva
KƚŚĞƌ
ĂŐƌĞď
ƌŶŽ
KƐƚƌĂǀĂ
WƌĂŚĂ
ƌŚƵƐ
<ƆďĞŶŚĂǀŶKĚĞŶƐĞ
ůƵũͲEĂƉŽĐĂ
dĂůůŝŶŶ
dĂƌƚƵ
ƐƉŽŽ
,ĞůƐŝŶŬŝ
dĂŵƉĞƌĞ
dƵƌŬƵ
>LJŽŶ
DĂƌƐĞŝůůĞ
WĂƌŝƐ
ĞƌůŝŶ
,ĂŵďƵƌŐ
DƺŶĐŚĞŶ
ƚŚŝŶĂ
WĂƚƌĂ
^ĂůŽŶŝĐĂ
dŚĞƐƐĂůŽŶŝŬŝ
ƵĚĂƉĞƐƚ
ĞďƌĞĐĞŶ
DŝƐŬŽůĐ
^njĞŐĞĚ
EĂƉŽůŝ
ZŽŵĂ
ŵƐƚĞƌĚĂŵ
ZŽƚƚĞƌĚĂŵ
ĞŶ ,ĂĂŐ
<ƌĂŬſǁ
tĂƌƐnjĂǁĂ
ƌĂŐĂ
>ŝƐďŽĂ
WŽƌƚŽ
sŝůĂ EŽǀĂ
ĚĞ 'ĂŝĂ
ƵĐƵƌĞƔƚŝ
/ĂƔŝ
dŝŵŝƔŽĂƌĂ
ĞůũĞ
>ũƵďůũĂŶĂ
DĂƌŝďŽƌ
DĂĚƌŝĚ
sĂůĞŶĐŝĂ
'ƂƚĞďŽƌŐ
ŝƌŵŝŶŐŚĂŵ
'ůĂƐŐŽǁ
>ŽŶĚŽŶ
Rijeka
Split
Milano
Sofia
Plovdiv
Varna
Barcelona
Łódź
Chapter 4: Improving institutions
145
		
3. Institutional capacity affects
policy performance and capacity
to conduct reforms
Public administration reflects the institutional basis
on which countries are run and its quality determines
performance in all areas of public policy.
Public administration is responsible for responding
to the needs of society and as such it has significant
effect on the pace of economic and social development
and its sustainability16
.
3.1 Professional and impartial
administrations provide better policy
outcomes for people
In a context of a rapidly changing environment and
challenges such as globalisation, social inequality
and demographic change, any assessment of
sustainable governance needs to focus on policy
outcomes, the underlying democratic order and
people’s confidence in institutions as well as in
the capacity of government to implement policies
successfully17
.
The Sustainable Governance Indicators, developed
by Bertelsmann Stiftung, are intended to indicate
how well policies have performed in achieving
long-term objectives by examining outcomes in 16
areas. The indicators are built on three indices —
the Policy performance index, the Democracy index
and the Governance index — which together determine
the sustainability of governance (see Box).
As the confidence in institutions was discussed
above, the focus here is on policy performance and
governance.
The Sustainable Governance Indicators (SGI) show
major differences between EU Member States in
terms of both the design of economic and social
policies and the capacity of institutions to implement
them and achieve desired outcomes. Sweden,
Denmark and Finland score the highest on
16 https://ec.europa.eu/info/sites/info/files/european-semester-the-
matic-factsheet-quality-public-administration_en.pdf
17 http://www.sgi-network.org/docs/2016/basics/SGI2016_Overview.
pdf
policy performance, while Cyprus and Greece score
the lowest (Figures 4.9 and 4.11). Germany, Luxembourg
and the UK are ranked only slightly below
the three Nordic countries as well as Estonia and
Lithuania, while Hungary Romania, Croatia and
Bulgaria are ranked only a little above Greece and
Cyprus.
France, Slovenia, the Czech Republic and Austria
score better on the implementation of social policies
than the EU average but worse as regards
economic policies. On the other hand, Latvia and
Malta score well above the EU average on economic
policy but below average on social policy.
The Governance index of the SGI is intended to
capture the extent to which, on the one hand, a
country’s institutional arrangements increase the
government’s capacity to act (‘executive capacity’)
and, on the other, NGOs, other organisations
and the public in general have the ability to hold
government accountable for its actions (‘executive
accountability’).
Again the Nordic countries, followed by Germany,
Luxembourg and the UK, have the most capable and
Sustainable Governance Indicators
explained
The Policy Performance Index aggregates data
compiled on policy outcomes in 16 areas that
cover the three dimensions of sustainability
(economic development, environmental protection
and social policies).
The Democracy Index is based on an analysis
of each country’s democratic order and people’s
confidence in institutions on which it is founded.
It assesses the substantive and procedural features
of a system that enable long-term oriented
governance to be sustained.
The Governance Index assesses a government’s
capacity to steer and implement policies, its capacity
for institutional learning and reform and
the extent of executive accountability.
Source: http://www.sgi-network.org
Seventh Report on economic, social and territorial cohesion
146
accountable governments in the EU (Figures 4.10
and 4.12), while Greece, Cyprus, Croatia, Hungary,
Romania and Bulgaria have the least capable and
accountability. In Belgium and the Czech Republic,
stakeholders are relatively closely involved in policy
making, but governments are less capable than
the EU average. In Lithuania and Latvia, on the
other hand, the authorities are relatively capable,
but there is less involvement of stakeholders than
average.
3.2 Potential benefits of conducting
structural reforms is huge
Putting in place conditions conducive for investment,
growth and jobs is an important pre-condition
for sustainable economic development.
According to European Commission analysis, large
potential benefits in terms of GDP, productivity
and employment growth can be obtained through
structural reforms relating to market competition
and regulation, taxation, the labour market, unemployment
benefits and investment in human capital
and R&D18
.
18	For more details see Varga J. and J. in’t Veld (2014).
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9 10
SE
DK
FI
DE
EE
LU
UK
LT
NL
IE
LV
AT
EU
FR
CZ
BE
SI
PL
SK
ES
MT
PT
IT
HU
RO
BG
HR
CY
EL
Policy performance indicator,
2016
Figure 4.9
Source: DG REGIO calculations on the basis of Sustainable
governance indicators http://www.sgi-network.org
Scores: 0 = low performance; 10 = high performance
0 2 4 6 8 10
0 1 2 3 4 5 6 7 8 9 10
FI
DK
SE
LU
UK
DE
PL
IE
AT
LT
BE
ES
EE
NL
LV
EU
IT
CZ
FR
MT
PT
SI
SK
BG
HR
HU
EL
RO
CY
Governance performance
indicator, 2016
Figure 4.10
Source: DG REGIO calculations on the basis of Sustainable
governance indicators http://www.sgi-network.org
Scores: 0 = low performance; 10 = high performance
Chapter 4: Improving institutions
147
Simulations, using the
Quest model of structural
reforms that
would halve the gap
with the best performers,
show that they
could boost GDP by 3%
after 5 years over what
it otherwise would be,
almost 6% after 10
years and 10% after
20 years (assuming all
Member States were
to implement reforms,
Figure 4.13). The estimated
effect on employment
is similarly
high (Figure 4.14).
According to the model,
the reforms with the
largest impact relate to
increasing the participation
rates of women
and of people of 50
and over in the labour
force and increasing
the proportion of workers
in employment who
have tertiary-level education,
and correspondingly
reducing the proportion
with only basic
schooling. Improving
the business environment
also has a significant
effect.
Structural reforms can potentially have a big impact
on lagging regions, accelerating the process
of catching up19
.
19	European Commission (2017a).
3.3 Meritocracy of the public sector
varies greatly between and within EU
countries
The Quality of Government Expert Survey20
, which
is intended to assess the organisation of public
bureaucracies and their behaviour in different
countries worldwide, is based on the views of over
1 000 experts. It covers such issues as recruitment
procedures, internal promotion, career stability
20	Dahlström et al. (2015).
AT
BE
BG
HR
CY
CZ
DK
EE
EU
FI
FR
DE
EL
HU
IE
IT
LV
LT
LU
MT
NL
PL
PT
RO
SK
SI
ES
SE
UK
4.0
5.0
6.0
7.0
8.0
4.0
5.0
6.0
7.0
8.0
3.0 4.0 5.0 6.0 7.0 8.0
Socialpolicyperformance
Economic and social policy performance indicators, 2016Figure 4.11
Economic policy performance
Source: DG REGIO calculations on the basis of Sustainable governance indicators
http://www.sgi-network.org
Scores: 0 = low performance; 10 = high performance
ATBE
BG
HR
CY
CZ
DK
EE
EU
FI
FR
DE
EL
HU
IE
IT
LV
LT
LU
MT
NL
PL
PT
RO
SK
SI
ES
SE
UK
4.0
5.0
6.0
7.0
8.0
9.0
4.0
5.0
6.0
7.0
8.0
9.0
3.0 4.0 5.0 6.0 7.0 8.0 9.0
Executiveaccountability
Executive capacity and accountability indicators, 2016Figure 4.12
Executive capacity
Source: DG REGIO calculations on the basis of Sustainable governance indicators
http://www.sgi-network.org
Scores: 0 = low performance; 10 = high performance
Seventh Report on economic, social and territorial cohesion
148
‑
and salaries. The results are presented in terms of
three indices relating to professionalism, ‘closedness’
and impartiality21
.
They show that Western and Nordic EU countries
tend to have more professional and impartial
pub-lic administrations than the southern and
eastern Member States, Poland, Lithuania and
Estonia are the only ones of the EU-13 that are
assessed as
21	The index of impartiality measures the extent to which public sector
officials implement policies impartially. The index of professionalism
measures the extent to which public officials are professionals
rather than politicised. The index of ‘closedness’ measures
the extent to which public administration is more public-like than
private-like. Dahlström et al. (2015).
above the EU average in terms of both professionalism
and impartiality.
Whether the model is more ‘public-like’ (or
‘closed) or ‘private-like’ (or ‘open’) is not the decisive
factor in determining professionalism or
impartiality. Sweden, Finland, Denmark, Estonia
and Netherlands have ‘private-like’ rules of hiring
and career building and are assessed as being
relatively impartial and professional (Figures 4.15
and 4.16). On the other hand, France and Germany
have a more closed and formalised system but
have officials who are also assessed as being professional
and impartial.
0
5
10
15
20
0
5
10
15
20
EL IT MT RO BE HR PL SI LU FI FR AT PT HU EU-
28
IE LV ES NL SK BG CZ CY DE LT UK EE DK SE
5 years 10 years 20 years
% increase
Estimated effect of structural reforms on GDP after 5, 10 and 20 yearsFigure 4.13
Source: Varga J. and J. in 't Veld (2014)
The % difference in GDP between a 'reform' scenario and a 'no-reform' scenario according to simulations using the Quest model
0
5
10
15
20
0
5
10
15
20
MT EL BE PL IT LU HR HU SI RO FI IE FR AT CZ EU-
28
SK NL LV BG ES LT CY DE EE DK UK PT SE
5 years 10 years 20 years% increase
Estimated effect of structural reforms on employment after 5, 10 and 20
years
Figure 4.14
Source: Varga J. and J. in 't Veld (2014)
The % difference in employment between a 'reform' scenario and a 'no-reform' scenario according to simulations using Quest model
Chapter 4: Improving institutions
149
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 4.2 Public sector meritocracy, 2013
< 5.3
5.3 – 5.6
5.6 – 6.0
6.0 – 6.4
6.4 – 6.8
> 6.8
no data
Score (1 perfect meritocracy to 10 no role for merit)
Note:
Average score of a) public workers and b) citizens who do not work in the public sector to the following question:
Which statement comes closer to your own views ?
Pick 1 if you agree completely with ‘In the public sector most people can succeed if they are willing to work hard’
Pick 10 if you agree completely with ‘Hard work is no garantee of success in the public sector for most people —
it’s more a matter of luck and connections’.
If your views fall somewhere in between, you can choose any number between 1 and 10.
Source: Charron, N., Dahlström, C. & Lapuente, V. Eur J Crim Policy Res (2016)
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Seventh Report on economic, social and territorial cohesion
150
According to a recent
study carried out by
Charon, Dahlström
and Lapuente (2016),
based on the results of
the European Quality
of Government Survey,
regional and local governments
across the EU
vary markedly in terms
of the perceived level
of meritocracy, as opposed
to nepotism, in
appointments of public
officials and their
promotion (Map 4.2).
Whereas meritocratic
principles tend to predominate
in large parts
of the UK, Germany
and Finland (which
have scores of less
than 5 — low scores
signifying an absence
of nepotism), ‘luck and
connections’ are considered
the main determinants
in most parts
of the EU‑13, Italy and
Greece.
The degree of local
autonomy also varies
across the EU (see
Box), which may influence
trust in local
government.
AT
BE
BG
HR
CY
CZ
DK
EE
EU
FI
FRDE
ELHU
IE
IT
LV
LT
MT
NLPL
PT
RO
SK
SI
ES
SE
UK
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
2.5 3.5 4.5 5.5 6.5
Impartialityofpublicadministration
Professionalism and impartiality of the public sector
according to the Quality of Government Expert Survey,
2015
Figure 4.16
Professionalism of public administration
Source: DG REGIO calculations on the basis of The QoG Expert Survey Dataset II.
Professionality and impartiality indices: 1 = low impartiality and professionalism; 7 = high
impartiality and professionalism
1.0 2.0 3.0 4.0 5.0 6.0 7.0
1.0 2.0 3.0 4.0 5.0 6.0 7.0
CZ
SE
LV
FI
DK
UK
EE
SK
NL
HU
BG
EU
PL
MT
SI
PT
LT
RO
IE
EL
ES
DE
AT
HR
IT
BE
FR
Index
Closedness of the public sector according to the Quality of
Government Expert Survey, 2015
Figure 4.15
Source: DG REGIO calculations on the basis of The QoG Expert Survey Dataset II.
Closedness index: 1 = a non-closed, i.e. open public administration; 7 =a very closed public administration
Chapter 4: Improving institutions
151
Local autonomy and self-rule
The extent of autonomy of local governments in European countries has increased since 1990 according to
the Local Autonomy Index. There are, however, significant differences in autonomy across Europe. ‑‑ 
Local authorities in the Nordic countries have a high degree of autonomy as do those in Germany,
Swit-zerland and Poland, while those in Cyprus, Malta and Ireland have the lowest levels in the EU (Figure
4.17). There were increases in local autonomy in the EU-13 countries between 1990 and 2014, especially
in the early years of the transition, but it still remains less than in the EU-15 where there was only a small
increase over the period.
In most countries, local authorities have more autonomy than regional authorities (Figure 4.18). Only
in Belgium, Italy, Austria, Spain — countries with a strong regional or federal structure of government —
as well as Ireland, is the degree of regional self-rule greater than at local level, but even in these countries,
though less so in Ireland, local authorities have significant discretion over policy.
20
30
40
50
60
70
80
20
30
40
50
60
70
80
EU-
28
EU-
15
EU-
13
FI DK DE PL SE IT FR BG LT AT CZ EE SK PT BE NL RO HR LU LV ES HU SI EL UK CY MT IE CH IS NO
2014 2000 1990Index
Local Autonomy Index, 1990, 2000 and 2014Figure 4.17
Source: DG REGIO, Ladner, A., Keuffer, N. and Baldersheim, H. (2015).
Local autonomy index: 0 = low autonomy; 100 = high autonomy
0
3
6
9
12
15
18
0
3
6
9
12
15
18
MT IE CY UK EL LV ES LU SI RO HR FR HU SK PT IT LT BG BE NL EE AT CZ PL FI DK SE DE
Local RegionalIndex
Local and regional self-rule, 2014Figure 4.18
Source: DG REGIO, Hooghe, Liesbet, Gary Marks, Arjan H. Schakel, (2010) and Ladner, A., Keuffer, N. and Baldersheim, H. (2015).
Self-rule index: 0 = no self-rule; 18 = complete self-rule
Seventh Report on economic, social and territorial cohesion
152
cept Slovakia, the Czech Republic and Bulgaria
(Figure 4.19), the biggest increases being in Latvia
(28 percentage points) and Estonia (24 percentage
points). In the four countries with the smallest
usage, Poland, Italy, Romania and Bulgaria, there
was little change over the period in the first three
and a reduction in the last.
E-Government services potentially provide flexible
and personalised ways of interacting and performing
transactions with public authorities. However,
the ‘use of e-Government’ indicator reveals nothing
about the frequency of use or the completeness
of online services and their quality. Nor does
it indicate their transparency, which can help to
build trust between the government and the general
public, as well as making policy-makers more
accountable.
According to the e-Government benchmark project,
governments have advanced in making public
services digital but have tended to focus less
on quality. While the online availability of services
and their usability have increased, quality and
functionality, which are important for fast and
easy take-up, have barely increased at all which is
equally true of the transparency of procedures in
large parts of the EU.
Most countries score more highly on online availability
and usability than on indicators relating to
the ease and speed of online services (Figure 4.20,
which shows all EU countries as being below the  
		
3.4 Governments have advanced in
making public services available online,
but have focused less on the user’s
perspective
The use of ICT in the public sector, if implemented
correctly, is beneficial for both people and governments.
It can reduce administrative costs and the
burden of bureaucracy, lead to institutions being
re-organised in more citizen-friendly ways and increase
transparency. Accordingly, it can increase
the general efficiency of government and result in
the interaction of people and businesses with public
authorities being easier and less time-consuming.
The extent of e-Government, its quality and
the take-up of public e-services varies markedly
across the EU.
Table 4.2 shows how EU Member States performed
in 2016 compared to the average of 34 European
countries22
. The Nordic countries, the Baltic States,
the Benelux countries, Germany, France and
Austria performed best and show the most growth
in e-Government.
In 2016, almost one in two people in the EU (48%)
used e-Government, and around four in every
five or more in Denmark (88%), Finland (82%)
and Sweden (78%)23
. The share increased over
the preceding 5 years in all Member States, ex-
22 EU Member States, Norway, Iceland, Switzerland, Serbia,
Montenegro and Turkey.
23 People sending filled forms to public authorities over the
internet
in the last 12 months (Eurostat).
e-Government Benchmark project
The e-Government Benchmark assesses the priority
areas of the e-Government Action Plan 2011–2015.
Progress in each area is measured by one or more
indicators:
•• User-centric government assesses the availability
and usability of public e-Services and the
ease and speed of using them.
•• Transparent government assesses the transparency
of government operations, service provision
procedures and the level of control users
have over their personal data.
•• Cross-border mobility measures the availability
and usability of services for people and businesses
abroad.
•• Key enablers assess the availability of 5 functions,
such as e-ID cards.
The assessment in each area is based on survey
responses to a number of questions regarding the
quality or quantity of e-Government services on a
specific aspect.
Source: European Commission (2016c).
Chapter 4: Improving institutions
153
 
diagonal). Accordingly, simply providing information
and services online is not sufficient to ensure
that people will take them up, which requires them
also to be easy and fast to use.
There are marked differences between countries
in terms of transparency as well as variations between
the three indicators used to measure this24
,
which might indicate a lack of coordination between
different parts of government (Figure
4.21).
Malta, Estonia and Lithuania score highest in
terms of the delivery of services and
transparency in relation to personal data, while
24	Transparency is measured by three indicators: service delivery, the
publication of information and personal data. The first relates to
the extent to which public authorities inform users about administrative
procedures, the second the extent to which governments
publish information about themselves and about their activities;
the third, the extent to which governments proactively inform users
about their personal data and how, when, and by whom it is
being processed.
Bulgaria, Hungary and Romania score lowest on
the delivery of services, and Greece and
Slovakia on transparency as regards personal
data. Malta also scores highest on the
publication of information on public organisations
followed by France while Slovakia, Hungary,
Romania and the Czech Republic score lowest.
Online public services are becoming increasingly
accessible across the EU but growth is uneven
and many Member States are lagging behind. For
suc-cessful implementation of e-Government,
there is a need for demand-side measures as
well as supply-side ones, which means online
services being designed with the user in mind.
0
20
40
60
80
100
0
20
40
60
80
100
EU-
28
DK FI SE EE NL LU LV FR AT DE BE UK IE ES EL HU SK SI PT LT MT CY CZ HR PL IT BG RO
2011 2016% of population
E-Government use by citizens, 2011 and 2016Figure 4.19
Source: Eurostat.
Table 4.2 e-Government benchmark: performance and progress
Moderate performers
(both growth and absolute score below
European average)
Steady performers
(absolute score above and growth below
European average)
Accelerators
(both growth and absolute score above
European average)
UK, Ireland, Poland, Czech Republic,
Slovakia, Hungary, Italy, Slovenia, Croatia,
Romania, Bulgaria, Greece, Cyprus,
Finland, Spain, Portugal, Malta Sweden, Denmark, Estonia, Latvia,
Lithuania, Germany, Austria, Netherlands,
Belgium, Luxembourg, France
European average means average for: EU Member States, Norway, Iceland, Switzerland, Serbia, Montenegro and Turkey. Average score: 61%.
Average growth: 8%.
Source: DG REGIO calculations based on the EU e-government benchmark project.
Seventh Report on economic, social and territorial cohesion
154
3.5 Doing business is easier in the
North of Europe, but central European
countries are trying to catch up
Effective government policies are crucial to prevent
market failure and distribute income and
wealth more equitably. Simplicity, clarity and coherence
of business regulations can provide stable
and predictable rules for enterprises to function
effectively, so encouraging long-term growth and
sustainable economic development.
The World Bank ’Doing Business‘ indicators assess
10 regulatory areas which affect economic activity:
starting a business, dealing with construction
permits, getting electricity, registering property,
getting credit, protecting minority investors, paying
taxes, trading across borders, enforcing contracts
and resolving insolvency. The 2017 edition
compares the efficiency and quality of business
regulations for SMEs in 190 economies across the
world, the overall ranking being constructed on the
basis of how far they are from the best performing
economy (‘distance from frontier’).
0
20
40
60
80
100
0
20
40
60
80
100
MT EE LT PT AT FI ES DK FR NL BE LV SE DE EU IE IT LU HR UK SI PL CY CZ BG EL HU RO SK
Service delivery Public organisation Personal dataTransparency index (0 = low and 100 = high)
Transparency of e-Government, average 2014-2015Figure 4.21
Source: EU e-Government Benchmark project dataset
AT
BEBG
HR
CY
CZ
DK
EE
EU
FI
FR
DE
EL
HU
IE
IT
LV
LT
LU
MTNL
PL
PT
RO
SK
SI
ES
SE
UK
35
45
55
65
75
35
45
55
65
75
45 50 55 60 65 70 75 80 85 90 95 100
Online availability and usability
Availability, usability, ease and speed of use of public online
services
Figure 4.20
Easeandspeedofuse
Source: DG REGIO calculations on the basis of the EU e-Government Benchmark project data set
Values relate to the % of those surveyed that assessed online services or information as
being i) available and usable; ii) easy and fast to use
Chapter 4: Improving institutions
155
The Nordic countries (Denmark is ranked third in
the world) and Baltic States together with the UK,
Germany and Ireland are assessed as having the
most friendly business environments in the EU,
while Cyprus, Italy, Luxembourg and Malta (which
is ranked 76th
in the world) have the least friendly.
Many policy reforms have been introduced over the
past decade to make business environments more
‘enterprise-friendly’ and conducive to firm creation
and growth. Between 2010 and 2017, the distance
to the highest ranking economy shortened for all
EU countries, except the UK, Belgium and Ireland
(see Figure 4.22). The biggest improvements were
in Poland, the Czech Republic and Slovenia, each
of which jumped from the bottom of the EU ranking
to the middle. There were significant improvements
too in Croatia and Romania, but they remain
among the Members States furthest from
the frontier.
The sub-national doing business indicators25
, however,
reveal substantial regional differences despite
operating within the same legal and regula-
25	The subnational indicators cover a more limited number of dimensions
than the national ones, focusing on those most likely to be
affected at regional or local level: i.e. starting a business, dealing
with construction permits, getting electricity, registering property,
enforcing contracts.
60
65
70
75
80
85
90
60
65
70
75
80
85
90
DK UK SE EE FI LV DE IE AT LT PL PT CZ NL FR SI ES SK RO BG HU BE HR CY IT LU EL MT
2010 2014 2017Distance to frontier (0 = lowest performance, 100 = frontier, best performance)
Ease of doing business, 2010-2017Figure 4.22
Source: DG REGIO calculations on the basis of World Bank Doing Business.
0
10
20
30
40
50
0
10
20
30
40
50
Szczecin
Rzeszów
Kielce
GorzówWielkopolski
ZielonaGóra
Toruń
Ceuta
Bydgoszcz
Katowice
Łódź
Warsaw
Kraków
Lublin
Wrocław
Białystok
Opole
Craiova
Sofia
Gdańsk
Olsztyn
Palma
Melilla
Constanta
Pamplona
Vigo
Ruse
Iasi
Zaragoza
Valladolid
Bilbao
Burgas
Plovdiv
Santander
Logroño
Albacete
Brasov
ClujNapoca
Murcia
Badajoz
Pleven
Varna
Seville
LasPalmas
Barcelona
Madrid
Valencia
Gijón
Bucharest
Oradea
Ploiesti
Timisoara
Poznań
Budapest
Gyor
Szekesfehervar
Debrecen
Miskolc
Pecs
Szeged
Poland Spain Romania Bulgaria HungaryDays
Time and cost of starting a company in different cities in selected Member
States, 2016
Figure 4.23
Source: World Bank (2015a, 2015b and 2017b).
Time (bars) measured in number of days, cost (lines) as % of income per head
% of income per head
ES and PL: 2015
Seventh Report on economic, social and territorial cohesion
156
   
tory framework. So far, the indicators exist for
only 6 EU countries: Italy (2012), Spain and
Poland (2015) and Bulgaria, Hungary and
Romania (2017). (Indicators for Portugal, the
Czech Republic, Slovakia and Croatia will be
produced for 2018–2019.) The indicators for 5
of these countries, all apart from Italy, are
considered below26
.
Starting a company is easiest and quickest
in Hungary, while it takes longest to do so in
Polish cities (except in Poznan) — up to 42
days in Szczecin as compared with only 6 days
in Szeged in Hungary (Figure 4.23). The cost of
registration is also higher in Polish cities than in
Hungarian ones, due to the use of online
registration (which is why it is lower in Poznan).
However, online registration in itself does not
necessarily speed up the process — as, for
example, in Kielce (also in Poland), where 40% of
registrations were made online but it still took as
long (Figure 4.24). To make online plat-forms
work, they need to be accompanied by both
measures stimulating business take-up and
the possibility of completing the entire process
online (i.e. without the need for paper copies).
In some of the regions in Poland, the introduction
of online registrations did not remove the need
for paper copies of documents since
communication with the local court remained
paper-based27
.
In all countries, except Hungary, there is a
large variation between different cities: in
Romania, reg-istration takes 12 days in Timisoara
but 25 days in Craiova; in Spain, it takes 14 days
in Gijon but 31 days in Ceuta.
Similar differences between cities relate to
the time needed to deal with construction permits.
This is especially so in Spain, where in Logrono
(in La Rioja), the process takes 100 days but in
Vigo (in Galicia) almost 300 days (Figure 4.25). In
general, it is relatively easy to deal with
construction per-mits in Bulgaria — all 6 cities are
in the upper half of the ranking — and relatively
difficult in Romania (all cities being in the bottom
half of the ranking).
26	Italian sub-national doing business indicators were examined in
European Commission (2014).
27	World Bank (2015a).
Enforcing a contract shows the most variation in all
four countries for which data are available28
, ranging
in Bulgaria from 289 days in Pleven to 564 in
Sofia, while in Poland, it takes more than a year
longer in Gdansk than in Olsztyn (Figure 4.26).
The wide differences in time, procedures and costs
between different places within countries imply
that improving local and regional administrative
capacity can produce significant gains in the ease
of doing business.
28	No data for this dimension for Spain. Note that there are differences
in methodology of data gathering between Subnational doing
business and EU Justice Scoreboard.
0 20 40 60 80 100
0 20 40 60 80 100
All Hungarian cities
Sofia
Plovdiv
Burgas
Varna
Ruse
Poznań
Pleven
Kielce
Wrocław
Kraków
Lublin
Warsaw
Katowice
Constanta
Łódź and Szczecin
Opole and Toruń
Białystok
Bydgoszcz
Gdańsk and Olsztyn
Gorzów and Zielona Góra
Bucharest
Timisoara
Cluj-Napoca
Brasov
Oradea
Craiova
Ploiesti
Iasi
Hungary
Bulgaria
Poland
Romania
% of limited liability companies registered online
Registration of companies
online in different cities in
selected Member States,
2016
Figure 4.24
Source: World Bank (2015a, 2015b and 2017b).
PL: 2015
Chapter 4: Improving institutions
157
3.6 Public procurement is open to
the risk of corruption and lack of
competition in many EU regions
Public procurement, the process of purchasing of
goods and services by the public sector, plays a
crucial role in economic and social development
across the EU. It covers, on average, 29% of government
spending, equivalent to some 13% of
EU GDP (European Commission, 2016g; OECD,
2015a). It is a principal means by which governments
can influence the quality of investment and
public services and so affect economic growth. In
addition, the ESI Funds are largely spent through
public procurement. It is a genuinely cross-cutting
government function which concerns virtually
every public body from federal ministries to local
state-owned utilities, making it representative of
the quality of government in general.
Recent research has attempted to assess different
aspects of the quality of governance on the
basis of public procurement data (Fazekas, 2017,
upcoming; Fazekas and Kocsis, 2017). Indicators
relating to use of open procurement procedures,
the ratio of single bidders may provide an insight
into transparency, competition and corruption (see
Box.).
0 100 200 300 400
0 100 200 300 400
Timisoara
Constanta
Vigo
Cluj Napoca
Ploiesti
Iasi
Bucharest
Zaragoza
Brasov
Murcia
Warsaw
Kraków
Craiova
Poznań
Budapest
Madrid
Palma
Białystok
Gorzów Wielkopolski
Melilla
Gdańsk
Szczecin
Katowice
Wrocław
Bilbao
Ceuta
Debrecen
Kielce
Rzeszów
Zielona Góra
Ruse
Łódź
Plovdiv
Seville
Gyor
Santander
Miskolc
Toruń
Oradea
Szekesfehervar
Lublin
Olsztyn
Barcelona
Albacete
Pleven
Pamplona
Szeged
Badajoz
Pecs
Bydgoszcz
Las Palmas
Opole
Varna
Burgas
Valladolid
Valencia
Gijón
Logroño
Sofia
Poland
Spain
Romania
Bulgaria
Hungary
Number of days
Time needed to deal with
construction permits in
different cities in selected
Member States, 2016
Figure 4.25
Source: World Bank (2015a, 2015b and 2017b).ES and PL: 2015
0 200 400 600 800
0 200 400 600 800
Gdańsk
Brasov
Warsaw
Ploiesti
Budapest
Gyor
Łódź
Lublin
Sofia
Oradea
Wrocław
Szeged
Kielce
Cluj Napoca
Iasi
Katowice
Rzeszów
Bucharest
Pecs
Zielona Góra
Kraków
Constanta
Szczecin
Craiova
Poznań
Bydgoszcz
Timisoara
Plovdiv
Białystok
Gorzów Wielkopolski
Szekesfehervar
Opole
Toruń
Miskolc
Varna
Burgas
Debrecen
Olsztyn
Ruse
Pleven
Poland
Romania
Bulgaria
Hungary
Number of days
Time needed to enforce a
contract in different cities
in selected Member States,
2016
Figure 4.26
Source: World Bank (2015a, 2015b and 2017b).
ES and PL: 2015
SeventhReportoneconomic,socialandterritorialcohesion
158
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 4.3 Public procurement with a single bidder, average 2013-2015
< 10
EuroGeographics Association for the administrative boundaries
10 – 15
15 – 20
20 – 25
25 – 40
> 40
no data
% of contracts awarded by sub-national authorities
Excludes regions with less than 50 contracts awarded. Includes
only contracts above the threshold of the public procurement
directive.
In CY, EE, LT, LU, LV, and MT, contracts awarded by national
authorities in sectors used by regional and local authorities
were included.
Source: EU Tenders Electronic Daily and DIGIWHIST
©
0 500km
REGIOgis
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 4.4 Change in share of single bidders, between 2007–2009 and 2013–2015
< -12
-12 – -8
-8 – -4
-4 – 0
0 – 4
4 – 8
8 – 12
> 12
no data
Percentage points change
Change in share of contracts awarded by sub-national authorities.
Excludes regions with less than 50 contracts awarded. Includes
only contracts above the threshold of the public procurement
directive.
In CY, EE, LT, LU, LV, and MT, contracts awarded by national
authorities in sectors used by regional and local authorities
were included.
Source: EU Tenders Electronic Daily and DIGIWHIST
©
0 500km
REGIOgis
EuroGeographics Association for the administrative boundaries
Chapter 4: Improving institutions
159
The number of instances where there was only a
single bidder as a share of all contracts awarded
through public procurement might indicate potential
corruption or a lack of competition, including
collusion between companies in a given sector of
the economy. The single bidder-ratio varies significantly
across regions (Map 4.3). The cases where
there was only one bid exceeds 40% in many regions
in Greece, Poland, Slovakia and Italy. In regions
in Sweden, Ireland, UK and Denmark, the
ratio rarely exceeds 10%, pointing towards more
competitive markets and less risk of corruption29
.
The single bidder ratio shows wide regional differences
in Romania, Bulgaria, Poland, Hungary,
the Czech Republic and Spain, whereas in Sweden
and Greece, there is almost no variation. Between
2007 and 2015, the ratio declined markedly in
Lithuania, Latvia and in many regions in Poland,
the Czech Republic and Slovakia (Map 4.4). By
contrast, in Greece, Italy and Estonia — countries
with high levels of single bidding — the proportion
of contracts issued where there was only one bid
increased.
It is worth noting that, while in general public procurement
governance scores correlate with the
European quality of government index, regions in
Spain score considerably better than on the EQI.
On the other hand, Finland and Estonia scores are
lower (Fazekas 2017, upcoming), perhaps because
of a lack of transparency suggesting weaknesses
in national regulatory and information systems or
less competition from international suppliers30
.
The use of open procedures is one of the indicators
to measure transparency of procurement. The
results (Map 4.5) do not show the usual north-west
versus east-south divide like many indicators of
governance. Counter-intuitively, countries with a
high level of single bidding (Poland, Greece) are
among those with the most use of open procedures,
which may indicate a prevalence of informal
connections over formal requirements. Use of
open procedures is relatively infrequent in a number
of regions in Hungary, Austria, Estonia, France
and Bulgaria. There is a need for caution, however,
when interpreting the results, since while not using
open procedures hampers competition, their over-
29	In the overall public procurement competition index (Fazekas
2017) Sweden, UK, Ireland, Finland and Spain score highest. The
overall corruption risk index shows that north-west countries plus
Latvia and Spain score best. The data and interactive maps are
available at: https://public.tableau.com/profile/mihaly.fazekas#!/
vizhome/regiopp/nuts2
30	Transparency is the only dimension of the procurement good governance
score in which central and eastern Europe scores better
than north-west Europe. Apart from use of open procedures analysed
in this report, it takes into account contract notice publication,
reporting completeness and voluntary reporting (see Fazekas,
2017).
Principles and indicators used for
measuring the performance in public
procurement
The principle of transparency implies that information
on public procurement should be readily
available in a precise, reliable, and structured
form (Kovacic, Marshall, Marx, & Raiff, 2006). In a
narrower sense, it can be defined as compliance
with the information disclosure requirements in
EU Public Procurement Directives.
The principle of competition implies that the
beneficial effects of multiple bidders competing
against each other and having equal opportunity
to participate take the form of low prices, high
quality and on-time delivery of the goods, facilities
or services procured (Arrowsmith, 2009).
Corruption in public procurement is defined as
`the allocation and performance of government
contracts by bending rules and principles of open
and fair public procurement in order to benefit a
closed network while denying access to all others´
(Fazekas, Tóth and King, 2016).
Definitions of public procurement governance in-
dicators:
•• use of open procedures: contracts awarded
in an open or restricted procedure as a % of
all contracts awarded;
•• single bidding: contract awarded when only
one bid was submitted as a % of all contracts
awarded.
The indicators are based on information published
in the Tenders Electronic Daily (TED) da-
tabase.
Source: Fazekas (2017).
Seventh Report on economic, social and territorial cohesion
160
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 4.5 Public procurement using an open call for tender, average 2013–2015
< 65
65 – 75
75 – 85
85 – 95
> 95
no data
% of contracts awarded by sub-national authorities
Excludes regions with less than 50 contracts awarded. Includes only contracts above the threshold
of the public procurement directive. In CY, EE, LT, LU, LV, and MT, contracts awarded by national authorities
in sectors used by regional and local authorities were included.
Source: Fazekas (2017)
© EuroGeographics Association for the administrative boundaries
0 500 km
REGIOgis
Chapter 4: Improving institutions
161
use might indicate a lack of administrative capacity
to run more complicated procedures (such as
negotiated ones).
4. Suitable institutions increase
the effects of EU support on
entrepreneurship
As evidenced by the 6th
Cohesion Report, a lower
standard of governance can affect the impact of
cohesion policy and lead to funding losses. The report
also noted that quality of government may reduce
the returns from public investment, including
that financed under cohesion policy (RodriguezPose,
Garcilazo, 2014).
According to a recent study31
on the relationship
between the growth of businesses, institutions and
support of entrepreneurship by the ESI Funds, the
‘right’ set of institutions tends to increase the effects
of cohesion policy32
.
The amount of EU funding received in the 2007–
2013 period was found to significantly affect
business growth. Regions with GDP per head just
below 75% of the EU average, which accordingly
received relatively large amounts of funding, recorded
considerably more enterprise births as well
as deaths than regions that had GDP per head just
above the 75% threshold and so received much
less funding. Overall, there was no relationship between
the amount of funding and the total number
of enterprises33
. At the same time, the ‘right’ set
of institutions seems to affect the relationship, in
that the rate of business creation was significantly
higher in regions where corruption is perceived as
being relatively limited than in those where it is
31	Diaz Ramirez, Kleine-Rueschkamp and Veneri (2017).
32	The support considered included a wide range of measures, including:
support for R&D, and innovation support to SMEs for investment
in environmentally-friendly production processes;
and support for self-employment and business start-ups. The
analysis was based on comparing the growth of businesses
between re-gions that had similar levels of GDP per head but
differed signifi-cantly in the scale of funding received.
33	A 1% increase in the amount of funding received was associated
with an increase in the birth and death rate of firms by 0.06%. The
relationship between the amount of funding and the number of
enterprise births less the number of deaths was not significant.
considered to be relatively widespread. This was
particularly the case for ‘employer’ firms (i.e. those
with employees).
5. Conclusions
The way that national regulations are implemented
varies across regions, reflecting differences in the
efficiency of regional and local authorities, which
are important to take account of when assessing
the quality of government in relation to economic
and social development.
The quality of government matters for regional
development across the EU. The institutional dimension,
therefore, needs to become an integral
element in development strategies. Along with
strengthening infrastructure endowment and human
capital, it is important that there are improvements
in administrative capacity and the effectiveness
of government as well as reductions in the
incidence of corruption, which erodes trust in governments
and their policies.
While governments have advanced in making public
services digital and providing access to them
online, there has been insufficient focus on the
quality of online services from a user’s
perspective and their ease of use.
Institutional capacity affects the ability of government
to attain long-term policy objectives and to
make structural reforms which have significant potential
to boost growth and employment.
Independent and impartial administrations, in
which officials are appointed and promoted on
merit according to their ability, are of major importance
in combating corruption and in implementing
effective policies which benefit people.
Companies in different parts of the same Member
State can face substantial differences in the time,
number of procedures and costs needed to comply
with regulations and to do business. Improving local
and regional administrative capacity and making
appropriate changes in the way public authori-
Seventh Report on economic, social and territorial cohesion
162
ties are organised and managed can, therefore,
give rise to significant gains in business efficiency.
The evidence suggests that the ‘right’ set of institutions
can increase the rate of new business creation
as well as the effect of cohesion policy support
for enterprises.
163
National policies,
investment and growth•••••
• Government balances have improved over recent years, as a result of fiscal
consolidation and, since 2014, of economic growth. Public investment, which is
important to underpin growth, in many cases bore the brunt of expenditure cutbacks
to reduce budget deficits and remains low in Member States hit hardest by
the crisis.
• Other ‘growth-friendly’ public expenditure, such as on R&D and education, also
remains low in these countries, as the composition of spending has shifted towards
social benefits, so damaging their future growth prospects.
• There remain significant differences in the extent of decentralisation of both public
expenditure and, more especially, public investment across the EU and in the share
of investment managed at regional and local level. The overall tendency over the
past 15 years has been for the latter share to decline and for public investment
spending to become increasingly centralised, whereas there has been little change
in the sub-national share of total government expenditure.
• The tendency for the share of public investment managed at sub-national level to
decline over recent years has occurred in parallel with a tendency for the budget
balance of the authorities concerned to shift, on average, from being in deficit to a
surplus in 2016.
• The programmes financed by the ESI funds are in general very much aligned with
the country-specific recommendations made as part of the European Semester
process. The provisions linking these funds to sound economic governance and
to Member States responding to the recommendations have given an incentive
for national governments to comply with the budget targets. In consequence, the
Commission review of Article 23 suggests that there is no need for any further
legislation at this stage.
chapter5
Seventh Report on economic, social and territorial cohesion
164
2011, only 5 were are still subject in 2016 (Spain,
France, Greece, Croatia and the UK).
A similar pattern to the average is evident in almost
all Member States, though to differing extents. In
those hit hardest by the economic downturn, the
reduction in the fiscal deficit started from levels as
high as 13% of GDP in Ireland and 15% in Greece,
though in a number of other Member States, the
deficit never went above the 3% allowed under the
Stability and Growth Pact (Figure 5.2). The fiscal
consolidation effort has been impressive in Greece
and Ireland, in particular, with the government balance
being improved by more than 15 percentage
points of GDP between 2009 and 2016. It has
enabled public finances to return to a sustainable
path, which is a pre-condition for sustained and
sustainable economic recovery.
The widening of the deficit in 2009 and 2010 was
due mostly to stagnating revenues and a sharp
increase in government expenditure (Figure 5.3),
the combined result of automatic stabilisers and
one-off measures adopted as part of Economic
Recovery Packages. Most of the latter did not remain
in place beyond 2010 and, as a result, there
1. Public investment is still at very
low levels despite the recent pickup
of the EU economy
1.1 Government balances have
improved considerably over the recent
past
The Sixth Cohesion Report reported a significant
worsening of public finances as a result of the
sharp economic downturn which started in 2008.
This is reflected in a substantial general government
deficit of over 6% of GDP in both 2009 and
2010 on average across the EU as compared with
one of less than 1% of GDP two years earlier in
2007 (Figure 5.1).
From 2011 onwards, the deficit was reduced as a
result of increased fiscal consolidation and gradual
economic recovery from 2014 on. In 2014,
the deficit averaged 3% of GDP, the maximum allowed
under the Stability and Growth Pact, and it
then declined to 2.4% of GDP in 2015 and 1.7%
in 2016. Of the 20 Member States or more which
were subject to Excessive Deficit Procedures in
Chapter 5
National policies and cohesion
-7
-6
-5
-4
-3
-2
-1
0
-7
-6
-5
-4
-3
-2
-1
0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
% of GDP
General government balance, EU-28 average, 2000-2016Figure 5.1
Source: Eurostat
Chapter 5: National policies, investment and growth
165
was a gradual decline in government expenditure
relative to GDP, which was then further reduced by
the automatic stabilising effect of the gradual economic
recovery1
(lower expenditure and increased
revenue). Again, the same pattern is evident in
most Member States but with significant differences
in scale because of variations in the depth
of the economic downturn.
1	 Automatic stabilisers are usually defined as those elements of fiscal
policy which reduce tax burdens and increase public spending
without discretionary government action (i.e. without changes in
tax rates or allowances, benefit rates or expenditure programmes).
1.2 The composition of public
expenditure remains problematic,
with government investment
spending still low
After rising to an average of over 50% of GDP in
the EU in 2009, government expenditure by 2016
had returned almost to the average level of 2000–
2007 before the crisis (to 46.6% of GDP as against
45.5%).
However, the composition of public expenditure
was different in 2016 to what it had been. Public
investment (i.e. gross fixed capital formation)
-16
-14
-12
-10
-8
-6
-4
-2
0
2
4
-16
-14
-12
-10
-8
-6
-4
-2
0
2
4
ES FR RO UK BE IT PL PT FI HU SI SK AT DK HR IE BG LV EE LT CY NL CZ EL DE SE MT LU
2009 2012 2016% of GDP
General government balance, 2009, 2013 and 2016Figure 5.2
Source: Eurostat
0
10
20
30
40
50
60
-8
-6
-4
-2
0
2
4
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Deficit (left axis) Revenue (right axis) Expenditure (right axis)
% of GDP
General government expenditure and revenue and general government balance,
EU-28, 2001-2016
Figure 5.3
Source: Eurostat
% of GDP
Seventh Report on economic, social and territorial cohesion
166
amounted to 2.7% of GDP as compared with 3.2%
in the earlier pre-crisis period, half a percentage
point less despite total public expenditure being
higher (Figure 5.4). This contrasts with social expenditure
which was over 1% of GDP higher.
The reduction in public investment is more striking
in Member States hit hardest by the economic
downturn. In a number of Member States subject
to external financial assistance, public investment
was below 2% of GDP in 2016 (in Ireland, Portugal
and Spain) and though it was above 3% of GDP
in Greece, GDP was much lower in 2016 than before
the crisis. These low levels of public investment
are to some extent a reflection of high levels
of social expenditure, which were well above the
EU average in Greece (20% of GDP) and Portugal
(17%). The burden of servicing the debt is relevant
too. Despite historically low levels of interest rates
and the Quantitative Easing facilities provided
by the European Central Bank, debt interest payments
were still above 4% of GDP in Portugal and
over 3% in Greece, which is an indicator of their
vulnerability to changes in international financing
conditions.
According to the economic literature, government
investment has a positive effect on growth2
.
2	 See European Commission (2014).
Persistent low levels of
public investment are,
therefore, a cause for
concern, not least because
of their possible
effect on socio-economic
disparities between
Member States
and regions in the EU.
The Member States
with the lowest levels
of public investment
are those hit hardest
by the crisis and, accordingly,
where disparities
with the rest of
the EU widened most
(Figure 5.5).
The recent Commission reflection paper on the
completion of the Economic and Monetary Union3
emphasises that ‘Progress on economic convergence
is of particular relevance for the functioning
of the euro area but is equally important for
the EU as a whole’ and that ‘Moving towards high
living standards and similar income levels is key
to achieving the Union’s objectives, which include
economic and social cohesion alongside balanced
growth’.
The low levels of public investment are also evident
in the recent Commission Communication on
the principle of additionality 2007–20134
. Seven
Member States reported a level of expenditure relevant
for additionality lower than forecast at the
beginning of the programming period 2007–2013
before the economic downturn. Actual structural
spending for 2007–2013 was 35% lower than the
forecast in Greece, over 25% lower in Italy and between
10% and 20% lower in Hungary, Lithuania
and Portugal5
.
3	 European Commission, ‘Reflection paper on the deepening of the
economic and monetary union’. COM (2017)291 of 31 May 2017.
4	 European Commission, ‘Ex post verification of additionality 2007–
2013’. COM (2017)138 of 23 March 2017.
5	 It should be noted that since additionality was verified only in
Convergence Objective regions, these figures do not necessarily
depict the situation in the whole country except for Lithuania.
0
2
4
6
8
10
12
14
16
18
0
2
4
6
8
10
12
14
16
18
Public investment Compensation of
employees
Debt interest Social benefits
(other than in kind)
2000-2007 2016% of GDP
Selected categories of general government expenditure,
average 2000-2007 and 2016
Figure 5.4
Source: Eurostat
Chapter 5: National policies, investment and growth
167
1.3 And growth-friendly expenditure
has declined considerably in some
Member States
In addition to gross fixed capital formation, which
is the internationally recognised measure of public
investment, other categories of public expenditure
are also growth-friendly in that they help to create
the conditions for higher future economic growth.
These include, in particular, total expenditure on
transport, communication, energy, research and innovation,
environmental protection, education and
health.
While growth-friendly government spending in the
EU in 2015 was on average much the same as
in 2008 relative to GDP, in a number of Member
States it diminished considerably (Figure 5.6).
Most of these have a level of GDP per head below
the EU average, which raises questions over the
likelihood of the latter converging towards the EU
average.
The reduction is particularly large in Ireland (a decline
of close to 6% of GDP, spread across all categories),
but also in Croatia (a decline of 2% of
GDP, concentrated in transport) and Portugal (one
-6
-5
-4
-3
-2
-1
0
1
2
3
-6
-5
-4
-3
-2
-1
0
1
2
3
SK HU DK BG DE BE NL FR FI SI LU MT SE CZ EE AT IT CY UK ES PL EL RO LT LV HR PT IE EU-
28
Change as % of GDP
Change in growth-friendly categories of general government expenditure,
2008-2015
Figure 5.6
Source: Eurostat
0
1
2
3
4
5
0
1
2
3
4
5
EE SE FI LU DK RO LV CZ FR NL PL HR SK EL HU AT SI LT BG UK CY MT BE DE IT ES IE PT
% of GDP
Total public investment, 2016Figure 5.5
Source: Eurostat
Seventh Report on economic, social and territorial cohesion
168
of 2.5%, spread across all categories) which have
experienced a protracted economic downturn.
Over the EU as a whole, there was some shift over
the period in the composition of growth-friendly
government expenditure. A decline of 0.3% of GDP
on expenditure on transport was accompanied by
an increase of 0.5% of GDP on health, with all
other categories of growth-friendly expenditure
remaining much the same. The biggest decline in
spending on transport was in Croatia (by 2.6% of
GDP)6
, followed by Ireland (1.8%) and the Czech
Republic (1.0%). In Latvia, Lithuania, Bulgaria, the
Czech Republic and Netherlands, there was an
increase in health expenditure (of around 1% of
GDP), whereas in Greece and Ireland, it declined
(by 2% of GDP). There were significant cuts in
spending on education in Ireland and Romania (of
over 1% of GDP) and in the former, a reduction of
over two-thirds in expenditure on environmental
protection.
2. Sub-national governments play
a key role in public expenditure
and investment
2.1 Differences in the extent of
decentralisation of public expenditure
have widened in the EU…
Expenditure carried out by sub-national levels of
government accounts on average for around a
third of total public spending in the EU, and the
share has not changed much over the past two
decades despite the ups and downs in the total.
The average, however, conceals significant differences
across countries. In particular, the gap between
more centralised and more decentralised
Member States in the share of expenditure undertaken
at the sub-national level widened markedly
over the 15 years 2001–20167
.
6	 The Croatian authorities cut on public investment to restrain the
expenditure side. In the period before the crisis, this investment
largely consisted of road (motorway) construction.
7	 Note that the fact that public expenditure is implemented at the
regional or local level does not necessarily mean that decisions to
spend are taken at the same level.
The Nordic countries, where powers are very much
devolved to municipalities, and the Member States
with federal or regional structures of government,
which have the largest shares of public expenditure
carried out at sub-national levels, all experienced
further decentralisation of expenditure over this
period (Figure 5.7). In Denmark, the most decentralised
country in these terms, around two‑thirds
of public expenditure was managed at sub-national
level in 2016 and in Sweden, Belgium, Spain and
Germany, around half.
At the same time, there is a tendency towards even
further centralisation of expenditure in Member
States where responsibility for public sending has
traditionally been centralised. This is particularly
the case in the Baltic States and, most especially,
in Hungary, where the share of expenditure managed
at the local level was reduced by half between
2001 and 2016. A similar tendency, though
less marked, is also evident in Portugal, Greece
and Italy. On the other hand, in Bulgaria, Romania
and Slovakia, unlike in other EU‑13 countries, the
opposite tendency is evident.
Accordingly, in sum, differences in the extent of decentralisation
of public expenditure have tended to
widen across the EU in recent years, with spending
becoming more decentralised in the Nordic countries,
the federal States and a few EU‑13 Member
States and more centralised in most EU‑13 countries
and, to a lesser extent, in southern Member
States, apart from Spain.
2.2 …while public investment is now
slightly more centralised
Unlike total public expenditure, the management
of public investment is becoming increasingly
more centralised in the EU, the share managed
by sub-national governments declining from over
60% of the total in the mid-1990s to 56% in 2001
and 52% in 2016.
The difference in tendency compared to total public
expenditure is mostly a result of trends in Member
States with a federal or regional structure of gov-
Chapter 5: National policies, investment and growth
169
ernment, except Belgium (i.e. Germany, Austria
and Spain), in all of which the share of public investment
managed by sub-national governments
declined between 2001 and 2016 whereas their
share of total spending increased (Figure 5.8).
In the rest of the EU, changes in the share of public
investment managed at sub-national level are
very much in line with those for total public expenditure.
In the Baltic States and Hungary, therefore,
there was a significant decline in the share,
as there was in Poland. By contrast, the share of
sub-national governments more than doubled in
Bulgaria and Romania between 2001 and 2016.
2.3 The budget balance of sub-national
governments is now in surplus
Unlike in each of the previous 15 years, the budget
balance of sub-national governments in the EU
was, on average, in surplus in 2016, the culmination
of a steady reduction in deficits, which reached
a maximum of 0.9% of GDP in 2010 (Figure 5.9).
The gradual improvement in their budget balance
occurred in parallel with that of public finances
as a whole. In 2002, sub-national governments
were responsible for around a quarter of the general
government deficit and their share declined to
15% in 2011, before the balance went into small
0
10
20
30
40
50
60
70
80
90
100
0
10
20
30
40
50
60
70
80
90
100
BE DE ES FI FR SE IT NL RO PT SI DK CZ HR PL UK LU BG LT LV AT HU EE SK EL IE CY MT EU-
28
2001 2016% of total public investment
Sub-national government investment, 2001 and 2016Figure 5.8
Source: Eurostat
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
DK SE BE ES DE FI AT NL PL IT LV RO CZ UK HR EE LT FR BG SI SK HU PT LU IE EL CY MT EU-
28
2001 2016% of general government expenditure
Sub-national government expenditure, 2001 and 2016Figure 5.7
Source: Eurostat
Seventh Report on economic, social and territorial cohesion
170
surplus in 2016 (of 0.1% of GDP). The reduction in
the deficit of sub-national governments occurred
at the same time as their share of total public expenditure
remained unchanged at around a third.
Again, the average tendency conceals differences
between Member States, though there was a common
improvement in public finances at sub-national
level in all of them except Sweden (Figure 5.10).
The budget was in surplus in 19 Member States in
2016, in balance in four and in deficit, though by a
modest amount, in only 5.
The gradual reduction in sub-national deficits results
to some extent from more of a tendency towards
centralisation in the case of public expenditure
than in the case of revenue. In other words,
there was more of a shift of expenditure to central
government than revenue to finance it.
0
250
500
750
1 000
1 250
1 500
1 750
2 000
2 250
2 500
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Budget balance (left axis) Expenditure (right axis) Revenue (right axis)
% of GDP
Sub-national government expenditure, revenue and budget balance, EU-28,
2001-2016
Figure 5.9
Source: Eurostat
€ billion
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
FI AT UK SE ES NL HR CY MT LV BG FR IE BE PL RO EE SI IT LU EL DE PT DK HU LT SK CZ EU-
28
2009 2016% of GDP
Sub-national government budget balance, 2009 and 2016Figure 5.10
Source: Eurostat
Chapter 5: National policies, investment and growth
171
3. Reviewing how the ESI Funds
are linked to new country-specific
recommendations and to sound
economic governance
3.1 Introduction
Article 23(16) of Regulation (EU) N° 1303/2013
(the “Common Provisions Regulation” or “CPR”)
requires the Commission to carry out a review of
the application of Article 23 in 2017. This review
is to be in the form of a report to the European
Parliament and the Council, accompanied where
necessary by a legislative proposal modifying the
Article. The present report fulfils this requirement.
The legal framework of the European Structural
and Investment Funds (ESI) for 2014–2020 introduced
a number of new provisions which strengthened
the linkages between these Funds and sound
economic governance, with the aim of improving
the overall performance of ESI programmes.
Under paragraphs (1) to (8) of Article 23, the
Commission may request a Member State to review
its Partnership Agreement and relevant programmes
to (i) support the implementation of
relevant country-specific recommendations (CSRs)
adopted in the context of the general economic
policy or employment guidelines (Articles 121 (2)
and 148 (4) of the Treaty on the Functioning of
the European Union (TFEU)), (ii) to support other
Council recom-mendations adopted in the
context of Regulation (EU) N° 1176/2011 on
the prevention and correction of macroeconomic
imbalances or (iii) to maximise the
growth and competitiveness of Member
States under Union financial assistance. In the
event of non-effective action by the Member
State, the Commission may propose to the Council
to suspend all or part of the ESI payments to the
Member State concerned, after having set out the
grounds for concluding that the Member State
has failed to take effective action.
Under paragraphs (9) to (12) of Article 23, the
Commission will propose to the Council the suspension
of all or part of the commitments or
payments, if the Council decides that a Member
State has not taken effective action to correct its
excessive deficit in accordance with paragraphs 8
and 11 of Article 126 TFEU or in two successive
cases of not addressing excessive macroeconomic
imbalances in the same imbalance procedure in
accordance with Regulation (EU) N° 1176/2011.
The Commission will also propose such a suspension
in cases where a Member State has not taken
measures to implement an economic adjustment
programme.
3.2 New country-specific
recommendations linked to the ESI
Funds
Regarding the provisions under paragraphs (1) to
(8) relating to the power of the Commission to request
the Member State to review its Partnership
Agreement and relevant programmes, it is important
to recall that Article 15 of the CPR requires
Partnership Agreements to take account of the
relevant CSRs adopted in accordance with Articles
121 (2) and 148 (4) TFEU. That is, all relevant CSRs
adopted by the Council before the adoption of the
Partnership Agreements and programmes had
to be properly and sufficiently addressed by the
Partnership Agreements and programmes adopted
in all Member States.
Indeed, more than two-thirds of the CSRs adopted
in 2014 were considered relevant for the ESI Funds
and have been taken into account in Member
States’ Partnership Agreements and programmes8
.
They cover reforms in seven main areas: research
and innovation, energy and transport, health care,
labour market participation, education, social inclusion
and reform of the public administration9
.
The relatively late adoption of the 2014–2020
programmes, combined with the ensuing delays
in starting their implementation and the recent
8	 European Commission, ‘Investing in jobs and growth — maximising
the contribution of European Structural and Investment Funds’.
COM (2015)639 of 14 December 2015.
9	 European Commission, ‘European Structural and Investment Funds
2014–2020, 2016 Summary Report of the programme annual
implementation reports covering implementation in 2014–2015’.
COM (2016)812 of 20 December 2016.
Seventh Report on economic, social and territorial cohesion
172
streamlining of the CSRs has, to some extent,
curbed the possible launch of any reprogramming
request by the Commission. It is important
to recall that indent (a) of paragraph (1) of Article
23 refers to ‘relevant’ Council recommendations,
whose definition is provided in paragraph (35) of
Article 2. This legal provision provides that, for the
purposes of a possible reprogramming request by
the Commission, ‘relevant Council recommendation’
means a ‘recommendation relating to structural
challenges which it is appropriate to address
through multiannual investments that fall directly
within the scope of the ESI Funds as set out in
the Fund-specific Regulations’. T hat i s, t he l ink
refers only to CSRs relating to investment, so excluding
those whose implementation depends on
legislative changes or reforms and/or administrative
legal changes. Therefore, the link
between any re-programming request and a
relevant CSR must be indisputable, which is
less likely with the new streamlined approach
with fewer and more gener-al CSRs. In addition,
the nature and content of the CSRs since 2014
has been relatively stable, mean-ing that
Partnership Agreements and programmes are still
to a large extent aligned with the relevant CSRs
that were adopted as of 2015.
In this context, the Commission has not
found any reason to launch a request for a
review of Partnership Agreements or
programmes in any Member State. In its
Communication of 2014 pro-viding guidelines on
the application of the meas-ures of paragraphs
(1) to (6)10
, the Commission stated that ‘the
reprogramming powers granted to the
Commission would be used carefully [and that]
stability [would] be preferred over too frequent
reprogramming’. This Communication also
emphasised that ‘the priority in the
Partnership Agreements and programmes
[would] be to ad-equately address the
challenges identified in the CSRs and relevant
Council recommendations’ and that it would ‘limit
possible reprogramming under Article 23 in the
short term’. This has been the case.10	European Commission ‘Guidelines on the application of the
measures linking effectiveness of the European Structural and
Investment Funds to sound economic governance according to
Article 23 of Regulation (EU) 1303/2013’. COM (2014)0494 of 30
July 2014.
That Communication was following up on the commitment
given by the Commission. In particular, it
clarified the notion of ‘review’ and the types of
‘amendments’ to Partnership Agreements and programmes
and an indication of the circumstances
which may give rise to a suspension of payments.
3.3 Sound economic governance and the
ESI Funds
As regards the provisions of paragraphs (9) to
(12), the Commission will propose to the Council
the suspension of funding in case of non-effective
action by the Member States under one of the economic
governance surveillance procedures or under
an economic adjustment programme. The only
scenarios in which the conditions for the application
of these provisions could have been fulfilled
were the Council Decisions of July 2016 referring
to non-effective action by Spain and Portugal to
address their respective excessive deficits.
More specifically, on 12 July 2016, the Council concluded
that the response by Spain and Portugal to
the recommendations adopted according to Article
126(7) TFEU had been insufficient. The Council
therefore established that there had been no effective
action in response to its recommendations
within the period laid down according to Article
126(8) TFEU.
As required by paragraph 9, the Commission immediately
informed the Parliament by letter of
14 July 2016 from Vice-President Katainen, to
the President of the European Parliament. In the
letter, the Commission explained that the conditions
to make a proposal to suspend funding were
fulfilled and that the Commission remained at the
disposal of the European Parliament to participate
in a structured dialogue. This structured dialogue
is envisaged by paragraph 15 of Article 23, which
provides that ‘The European Parliament may invite
the Commission for a structured dialogue on the
application of this Article’.
On 25 July, the President of the European
Parliament replied through a letter addressed to
Chapter 5: National policies, investment and growth
173
President Juncker, which expressed his intention
to invite the Commission to a structured dialogue
‘at the earliest opportunity after the summer recess’.
Paragraph 9 provides that, when making its
proposal, the Commission ‘shall give due consideration
to any elements arising from and opinions
expressed through the structured dialogue under
paragraph 15’. That is, the Commission had to take
account of the results of the structured dialogue
with the Parliament.
On 26 September, the President of the Parliament
confirmed the invitation to a structured dialogue
in another letter addressed to the President of the
Commission. The structured dialogue started on 3
October 2016 in Strasbourg in a session involving
Vice-President Katainen and Commissioner Creţu,
with members of the committees of Regional
Development and of Monetary and Economic
Affairs of the Parliament.
After that session, the Parliament expressed some
days later its will to continue the structured dialogue
and to hear the views of the representatives
of the governments of the two Member States
concerned.
On the basis of the reports on action taken to address
their excessive deficits submitted by Spain
and Portugal, the Commission decided on 16
November 2016 that their respective Excessive
Deficit Procedures should be held in abeyance.
Paragraph 12 establishes that ‘the Commission
shall lift the suspension of commitments, without
delay, where the excessive deficit procedure is held
in abeyance in accordance with Article 9 of Council
Regulation (EC) No 1467/97’. That is, the conditions
to lift the suspension of funding were met
before the structured dialogue with the Parliament
was finalised.
3.4 At this stage legislative changes are
not required
Article 23 introduced a number of strengthened
linkages between the ESI Funds and sound economic
governance. This Article ensures consistency
between the implementation of the ESI Funds and
the economic policy agenda of the EU across the
whole programming period. The Commission considers
there has been no need to trigger the application
of this Article during the first half of the
current programming period.
The Partnership Agreements and programmes financed
by the ESI Funds are still aligned with the
latest relevant CSRs adopted by the Council. There
was no fundamental change since the adoption of
the Partnership Agreements and programmes to
justify any request for review. The Commission expressed
already in 2014, at the beginning of the
programming period, that such a request would
be launched only in cases where it could have a
better impact to address structural challenges and
that stability would be preferred over frequent reprogramming.
While the consistency between programmes
and economic policy recommendations
is essential, the Commission also attaches major
importance to the stability and predictability of the
programmes financed by the ESI Funds.
As regards the provisions linking the ESI Funds with
the economic governance surveillance procedures,
the Commission considers they have helped to
provide important incentives to the Member States
concerned to take effective action in a reasonable
time to correct and put an end to their excessive
deficits. This legal framework has also enabled
constructive and loyal cooperation between the institutions
of the EU in ensuring an efficient and balanced
implementation of these provisions. While
there is no specific deadline for the completion of
the structured dialogue, it is important that it is
concluded in a reasonable timeframe during which
the necessary incentives to take effective action
are provided to the Member State concerned.
While bearing in mind that stability and predictability
are important conditions for an effective
implementation of the ESI Funds, the Commission
will not hesitate to apply and implement the provisions
of this Article when deemed necessary or
when one of the milestones envisaged as triggering
points is reached.
Seventh Report on economic, social and territorial cohesion
174
On this basis, the Commission considers there is
no need to make any proposal to the Council and
the Parliament to modify this Article at this stage.
4. Conclusions
Public investment remains at historically low levels
(as a share of the GDP) in the EU. This is a result of
a decline in public expenditure since 2010 coupled
with the share of public investment in the total being
reduced, to some extent because of higher levels
of social spending and debt interest.
This is a cause for some concern because of the
importance of investment in fueling and underpinning
growth. Private investment is beginning
to recover after a number of years of substantial
decline and public investment has a major role to
play in helping to restore the conditions which encourage
enterprises to invest.
One of the consequences of public investment declining
is that the share co-financed by EU cohesion
policy increased considerably during the crisis
period, accounting for half or more of the total
in many EU‑13 countries. In these countries and
others, EU funding, accordingly, played a major
counter-cyclical role in preventing an even larger
reduction in public investment (see Chapter 6).
The management of public investment across the
EU has become more centralised over recent years.
The share managed by sub-national governments
is now close to 50% whereas it was over 60% two
decades ago. Since the composition of investment
did not change significantly, this seems to be a result
of political decisions to shift responsibility for
investment more to central government.
The budget balance of sub-national governments
has been transformed from a deficit of close to
1% of GDP in 2010 to a surplus, so that the overall
general government deficit in 2016, which averaged
just under 2% of GDP, was solely accounted
for by central government and the Social Security
funds.
175
The impact of cohesion policy
•• Cohesion policy is the EU’s main investment policy, providing funding equivalent
to 8½% of government capital investment in the EU and 41% in the EU‑13.
•• The impact of cohesion policy on the EU economies is significant. By the end
of their implementation, investment for the 2007–2013 period is estimated to
have increased GDP in the EU‑12 by nearly 3%, and by a similar amount for the
(now EU‑13) in the 2014–2020 period.
•• Several measures to improve the effectiveness of programmes were introduced
for the 2014–2020 period:
•• ex ante conditions, to stimulate structural reforms and to increase
administrative capacity;
•• smart specialisation strategies to identify local potential and prioritise
investment in key sectors;
•• a focus on results by programmes setting specific objectives and clear
indicators of achievement.
•• Projects selected as at July 2017 (halfway through the 2014–2020 period) will
invest just 39% of the total funding available for the period, similar to 2007–
2013 when spending was concentrated in the last 2–3 years. This suggests that
there is a continuing need for simplification and capacity building.
•• Targets for the 2014–2020 period include:
•• 14.5 million additional households with broadband access;
•• 17 million additional people connected to wastewater facilities;
•• 4 600 km of renovated TEN-T railway line;
•• 6.8 million children with access to new or modernised schools;
•• 7.4 million unemployed helped into work.
•• 	Cohesion policy is also investing in the economy of the future, through supporting
over 1 million SMEs, establishing around 30 000 new research positions and
helping substantial numbers of firms to bring new products to the market.
chapter6
Seventh Report on economic, social and territorial cohesion
176
In the wake of the crisis, the EU funds played a
stabilising role in ensuring a higher level of public
investment than there otherwise would have
been. In many countries, the funds became the
major source of finance for investment. In addition,
the reduction in national Government funding
as a result of the crisis led the EU to increase
co-financing rates — and so reduce the amount of
national co-financing required for cohesion policy
programmes in Member States where problems
were most severe. The increase helped the countries
concerned to maintain programmes as far
as possible, even if overall expenditure was reduced,
but also to mitigate the effects of the crisis.
For example, additional
resources from
the ESF were allocated
to short-term work arrangements
(e.g. in
Italy and the Czech
Republic) and instituting
general placement
services (as in Finland).
Support to investment
continues into the current
period and is especially
important for
Convergence regions.
For the EU‑13, EU funding
under cohesion policy,
or more specifically
1. The policy
Cohesion policy is the EU’s main investment policy.
Over the course of the 2014–2020 programming
period, €349 billion is being invested in a broad
range of areas, from enterprise support to infrastructure,
from urban regeneration to culture and
social infrastructure (Figure 6.1).
Cohesion policy is the EU’s principal means of support
for SMEs, the low carbon economy, transport
infrastructure, the integration of people into the
labour market and the social inclusion of the disadvantaged.
It is also plays a major role in supporting
innovation.
Cohesion policy consists of three main funds:
the Cohesion Fund, the European Regional
Development Fund (ERDF) and the European
Social Fund (ESF, which is coupled with the Youth
Employment Initiative, YEI). These in total provide
financing for nearly three-quarters of the €480 billion
of investment carried out under the policy, the
rest coming from national co-funding (Table 6.1).
Chapter 6
The impact of cohesion policy
Table 6.1 EU and national contributions to
cohesion policy, 2014–2020
€ billions
EU
contribution
National
contribution
Total
investment
CF 63.4 12.2 75.6
ERDF 196.4 80.5 276.8
ESF 83.1 37.3 120.5
YEI 6.5 1.2 7.7
Total 349.4 131.2 480.5
Source: ESIF Open Data Platform — https://cohesiondata.
ec.europa.eu/ (September 2017)
Research &
Innovation (41.1)
ICT (13.3)
SMEs (33.2)
Low-Carbon
Economy (39.6)
Climate Change
& risk (7.9)
Environment &
Resource
Efficiency (35.2)
Transport and
Energy (58.5)
Employment
(37.3)
Social Inclusion
(33.1)
Vocational
Training (33.4)
Other (16.9)EUR billion
Planned investment by key priority, 2014-2020Figure 6.1
Source: ESIF Open Data Platform - https://cohesiondata.ec.europa.eu/ (September 2017)
Chapter 6: The impact of cohesion policy
177
from the ERDF and Cohesion Fund, was equivalent
to 41% of total government spending on investment
over the three years 2015–2017 (and for
8.5% for the EU as a whole) and for Croatia, Latvia
and Lithuania, as well as Portugal, for over half
(Figure 6.2).
However, progress in implementation has been
slow, with only some 7% of expenditure disbursed
by July 2017, half way through the programming
period. To some extent this represents underreporting
(due to delays in the designation of managing
authorities and implementing bodies as well as
the setting up of control
systems), but it is also
due to programmes being
slow to get off the
ground.
The amount of funding
committed to projects
selected to be undertaken
gives a guide to
likely progress in the
near future, and this
is more positive, representing
at end-June
2017 some 39% of
total planned investment
in the EU‑28
(Table  6.2). However,
for some countries, even this is worryingly small
(notably Cyprus, Romania and Spain).
The rate of project selection in the current programming
period, while starting more slowly than
in the 2007–2013 period, has now caught up
(Figure 6.3), and it can reasonably be expected
that implementation rates from now on will be
broadly similar to those in the previous period.
Moreover, programme periods cannot be seen in
isolation. Periods overlap, with the closure and
finalising of one period stretching into the next,
0
10
20
30
40
50
60
0
10
20
30
40
50
60
PT HR LV LT PL SK EE BG CZ HU RO EL SI MT CY ES IT DE IE FR BE UK FI SE AT NL DK LU
% of general government capital expenditure
ERDF and Cohesion Fund allocations, 2015-2017Figure 6.2
Government capital expenditure is the sum of General Government gross fixed capital formation plus capital transfers, the latter being adjusted approximately
for abnormal transfers to banks and other companies during the crisis.
Source: Open data platform, Eurostat - Government statistics
0
20
40
60
80
100
0
20
40
60
80
100
2008/2015 2009/2016 2010/2017 2011/2018 2012/2019 2013/2020
2007-2013 2014-2020% of total available funding
Funding committed to projects selected, 2007-2013 and
2014-2020
Figure 6.3
2017 figure is a projection based on observations to July 2017.
Source: DG REGIO, based on monitoring data provided by Member States.
Seventh Report on economic, social and territorial cohesion
178
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
EU-12 EU-15 EU-25 EU-27 EU-28
Total
1989-1993
1994-1999
2000-2006
2007-2013
2014-2020
% of EU GDP
Cohesion policy funding, 1986-2023Figure 6.4
Source: DG REGIO, historical data.
The time profile of 2014-2023 expenditure has been established on the basis of the 2007-2013 outcome and an assumption of 100% absorption over the
period
Table 6.2 Projects selected and expenditure by managing authorities as at end-June 2017
compared to total planned investment for the 2014–2020 period
€ million
Total planned
investment
Projects
selected
Expenditure by
managing authorities
Selection as % of
planned investment
Austria 2 941.3 829.3 32.0 28.2%
Belgium 4 646.2 3 288.1 214.1 70.8%
Bulgaria 8 702.6 4 010.2 874.5 46.1%
Croatia 9 945.1 2 363.1 326.3 23.8%
Cyprus 824.1 150.6 41.1 18.3%
Czech Republic 28 703.0 8 760.2 1 376.6 30.5%
Denmark 798.5 352.7 56.0 44.2%
Estonia 4 891.7 2 385.5 500.1 48.8%
Finland 2 608.9 1 260.4 450.6 48.3%
France 28 915.9 11 827.5 2 877.0 40.9%
Germany 30 326.7 13 594.9 3 572.7 44.8%
Greece 19 123.4 7 738.3 2 064.8 40.5%
Hungary 25 420.9 18 220.1 2 141.3 71.7%
Ireland 1 971.4 1 687.2 13.8 85.6%
Italy 51 771.6 18 865.2 1 724.6 36.4%
Latvia 5 192.8 2 310.8 401.7 44.5%
Lithuania 7 887.8 2 823.8 906.5 35.8%
Luxembourg 88.3 57.2 8.2 64.8%
Malta 865.2 416.4 39.9 48.1%
Netherlands 2 389.0 1 096.1 299.8 45.9%
Poland 90 576.3 33 951.2 6 810.0 37.5%
Portugal 27 462.5 15 002.8 3 545.4 54.6%
Romania 27 664.8 2 838.4 396.3 10.3%
Slovakia 17 958.2 4 925.3 1 059.4 27.4%
Slovenia 3 756.2 1 032.5 134.1 27.5%
Spain 39 339.3 7 352.6 131.9 18.7%
Sweden 3 509.7 2 067.8 428.5 58.9%
UK 19 655.9 10 621.1 913.0 54.0%
Interreg 12 464.6 5 888.8 247.1 47.2%
Total 480 402.2 185 718.0 31 587.0 38.7%
Source: ESIF Open Data Platform — https://cohesiondata.ec.europa.eu/
Chapter 6: The impact of cohesion policy
179
alities, reforms might not have happened or they
might have happened at a much slower pace.
2.2 Closer link to EU economic
governance
A close relationship between the Cohesion policy
Funds and sound economic governance has been
incorporated in the legislation and in setting the
objectives of the programmes for 2014–2020.
Cohesion policy has in-built mechanisms to improve
fiscal and macroeconomic governance and
provides concrete support for fund-relevant structural
reforms through its link to Country-Specific
Recommendations (CSRs) under the European
Semester. Moreover, empirical evidence suggests
that the ex ante conditionalities introduced in the
current programming period have so far played a
significant role in improving the application of EU
legislation in Member States, as well as in fostering
structural reforms. Accordingly, they have improved
the overall investment climate in Member
States not only for investment funded under cohesion
policy but more generally.
2.3 A stronger ‘result orientation’
Experience of programme implementation and
evaluation evidence collected for the 2000–2006
programming period, which was confirmed by the
evaluation of the 2007–2013 period, made it
clear that cohesion policy needed a tighter focus
on results.
The 2014–2020 regulations, therefore, require the
following:
•• Programmes which set specific objectives at
the regional or national level, translated into
clear indicators of results with targets and
benchmarks to make it clear whether or not
the programmes are achieving their goals.
•• Project selection criteria which take account of
the objectives set at programme level to ensure
that projects are properly focused.
exerting a smoothing effect on expenditure flows
(Figure 6.4). The delay in starting spending under
the new programme period does not mean an interruption
in cohesion policy — actual investment
on the ground continues in a relatively seamless
way.
2. Improving the effectiveness of
the policy
A number of measures have been taken to improve
the delivery of results in the 2014–2020 period.
2.1 Ex ante conditionalities
The effectiveness of public investments and the
durability of results depend on suitable conditions
being in place. Unsound policy frameworks and
regulatory, administrative and institutional weaknesses
are major systemic obstacles hindering effective
and efficient public spending. It is therefore
of the utmost importance that such weaknesses
are identified and addressed at the beginning of
the programming period1
.
This is why a key reform of the ESI Funds for the
2014–2020 programming period was the introduction
of ex ante conditionalities (ExAC). These
are sector-specific or general preconditions that
needed to be met at an early stage of programme
implementation and by the end of 2016 at the
latest. They fall into five broad categories (see
Table 6.3)2
.
Around 75% of all applicable ex ante conditionalities
were fulfilled at the time of adoption of ESI
Fund programmes. For the non-fulfilled ones, over
800 distinct action plans were included in the pro-
grammes3
. Had it not been for ex ante condition-
1	 See for example OECD Recommendation on Effective Public
Investment Across Levels of Government adopted on March 12,
2014.
2	 European Commission (2017f).
3	 The final deadline for reporting by Member States was end-June
2017 in respect of the Annual Implementation Report for 2016
and end-August 2017 in respect of the Progress Report. The
Commission assesses completion of the ExAC action plans on the
basis of reporting by Member States.
Seventh Report on economic, social and territorial cohesion
180
Category
1. Improving the investment environment in the
EU
Many ExACs address horizontal and sector-specific
barriers that hinder investment in the EU. Through
their contribution to the creation of an investment-friendly
environment, they help to strengthen
the Single Market and to deliver the Investment
Plan for Europe, so fostering growth and jobs.
2. Supporting structural changes and implementation
of country-specific recommendations
Depending on the Member State context, many ExACs
can be catalysts for structural change and policy reform.
Preliminary results of the study on Country-Specific
Recommendations (CSRs) found that in several Member
States, ExACs speeded up execution of reforms and provided
the foundation for additional reforms and new
policy design.	
3. Accelerating the transposition and implementation
of the EU acquis
Several ExAC are linked to the transposition and implementation
of EU legislation and regulations. Such ExAC
also benefit projects that are not financially supported
by the ESI Funds.
ExACs for public procurement, State aid, environmental
legislation relating to Strategic Environmental
Assessment (SEA) and Environmental Impact
Assessment (EIA), non-discrimination, gender and disability
led several Member States to improve the
imple-mentation of EU regulations in a systemic way.
4. Better targeting of support from ESI funds
and other public funds
Many ExACs required that support from the ESI Funds
should form part of policy or strategic frameworks
which meet certain quality criteria. A number of ExACs
Examples
Malta, Portugal and Slovenia introduced the SME Test,
to ensure assessment and monitoring of the impact of
national legislation on SMEs.
In Slovenia, the Transport Development Strategy, set
out in the framework of the Transport ExAC, is the
first comprehensive national transport strategy covering
all modes of transport. It identifies the main bottlenecks
and sets out investment priorities for transport
at the national, regional and EU level.	
The 2014 & 2016 CSRs for Latvia recommended making
the research system more integrated, strengthening
links with the private sector and promoting internationally
competitive research institutions. As required by the
ExAC, a smart specialisation strategy was formulated,
which contributed to structural change in the R&D sector
through a reform of research institutions. It helped
to focus ESI Fund’s support on priority areas and to incentivise
private investment in innovation.
In Romania, the ExAC Access to employment and labour
market institutions supported structural reforms identified
in the 2014–2016 CSRs. The National Employment
Agency’s (NEA) services are being strengthened by tailoring
services to jobseeker profiles and better linking them
with social assistance. 90% of NEA beneficiaries have
already been profiled and a catalogue of services adopted.
Case management is being introduced to improve
cooperation between employment and social services.
In Italy, shortcomings in the transposition of the public
procurement acquis led in the past to several suspensions
of payments from the EU Funds. The public procurement
ExAC sped up the process of correcting the
relevant national legislation and of preparing regional
and national authorities to implement revised public
procurement rules.
In several Member States, including the Czech Republic,
Poland, Portugal, Slovenia, Spain and Italy, the need to
satisfy the energy efficiency ExAC gave a significant push
to the swift transposition of the Energy Performance of
Buildings Directives.
In Portugal, the adoption of a smart specialisation strategy
under the research and innovation ExAC helped to
focus public funding in R&D on a limited number of
smart specialisation areas. In Spain, as a result of the
same ExAC, regions previously lacking experience in this
Table 6.3 The ex ante conditionalities for the 2014–2020 programming period
Chapter 6: The impact of cohesion policy
181
•• Regular reporting of results and outputs and a
performance framework linked to the release
of a performance reserve.
•• An impact evaluation for each of the specific
objectives, to understand the contribution of
the programme to developments at the national
or regional level and to learn lessons for
the future4
.
2.4 Smart specialisation
Smart specialisation aims to boost national and
regional innovation by enabling Member States
and regions to focus on their strengths. It repre-
4	 For further details and explanation, see the European Commission
Guidance document on the monitoring and evaluation of the
Cohesion Fund and ERDF, http://ec.europa.eu/regional_policy/
sources/docoffic/2014/working/wd_2014_en.pdf
sents the most comprehensive industrial policy experiment
being implemented in Europe today.
The approach brings together the key players —
the research community, business, universities,
public authorities and civil society — to identify
strengths in their region and to direct support to
where local potential and market opportunities can
best be realised. This enables critical mass to be
achieved and accelerates the uptake of new ideas.
Since smart specialisation became one of the ex
ante conditionalities for the ESI Funds, over 120
smart specialisation strategies have been formulated
through partnership, multi-level governance
and a bottom-up approach. €65.8 billion are available
to support these strategies from the ERDF
and EAFRD, in addition to national and regional
funding.
required a needs analysis. Some required strategic policy
documents to ensure that funding is targeted to the people
most in need of support and to tackle identified challenges,
such as in the labour market. As a result, the selection
criteria and calls for projects to be co-financed by ESI Funds
are better tailored to the socio-economic context. This
should lead to increased effectiveness and efficiency — not
just of EU support, but also of national funding.
5. Improving administrative capacity and coordina-
tion
Insufficient capacity and efficiency of public administration in
some Member States and regions have an adverse effect on
the implementation of the ESI Funds as well as on their com-
petitiveness.
The institutional capacity and efficient public administration
ExAC requires the development and implementation of
a strategy to reinforce and reform administering authorities.
Several other ExACs establish requirements which reinforce
administrative capacity to implement EU regulations on public
procurement, state aid, environmental legislation relating to
EIA and SEA, or EU legislation and policy on anti-discrimination,
gender equality and disability.
area developed expertise and produced smart specialisation
strategies of high quality.
In Poland, adoption of national and regional transport plans
to meet the requirements of the Transport ExAC contributed
both to the identification of a mature project pipeline and to
better prioritisation of investments, from which the CEF has
also benefited.
As a result of the early school-leaving ExAC, Hungary and
Latvia implemented systemic improvements in the national
early school-leaving data collection and analysis system.
Estonia: Under the ExAC on Institutional capacity and efficient
public administration, the OECD Public governance review action
plan was revised and a quality management system introduced
to increase the administrative capacity of staff and
organisations (management systems, processes and structures).
The OECD action plan serves as a basis for the ongoing
State Reform.
Bulgaria: The action plan for the implementation, maintenance
and development of modern Quality Management Systems
(QMS), developed under the ExAC on Institutional capacity and
efficient public administration, accelerated the establishment
of a Common Assessment Framework (CAF) in 48 authorities
by the end of 2018. CAF is envisaged to be implemented in
at least 80 authorities by the end of 2020, while QMS will be
implemented in 350 authorities by the end of 2020. The ExAC
also gave a boost to the preparation of an analysis of the
existing needs of civil servants for training and of a methodology
for analysis of training needs in the public administration.
Seventh Report on economic, social and territorial cohesion
182
 
Since 2011, the European Commission has provided
advice to regional and national authorities on
how to develop, implement and review their
smart specialisation strategies via a mechanism
called “S3Platform”5
. The objective has been to
provide information, methodologies, expertise
and advice as well as to promote mutual
learning and trans-national cooperation. It has
around 200 members in total, including 18 EU
Member States and two non-EU countries, as
well as 170 EU and 9 non-EU regions.
In addition, in 2015–2016 the European
Commission responded to the increasing interest
by establishing three Thematic Smart
Specialisation Platforms (TSSP)6
on energy, agrifood
and industrial modernisation. These platforms
were created under the S3 Platform in order
to facilitate interregional cooperation and boost
private-public investment pipelines. More than
80 EU regions are currently involved in 18 different
partnerships covering different areas such as
advanced manufacturing for energy application,
efficient and sustainable manufacturing, the bioeconomy,
high performance production through 3D
printing, medical technology, innovative textiles,
production monitoring systems, industry 4.0, new
nano-enabled products, bio-energy, marine renewable
energy, smart grids, solar energy, sustainable
buildings, high-tech farming, traceability and big
data and smart electronic systems7
.
Placing investment in human capital and skills
at the heart of smart specialisation strategies is
key, as skilled human capital is a pre-condition for
the success of any innovation policy. This is why
the ESF will contribute €1.8 billion over the present
programming period to strengthening human
capital in research, technological development and
innovation.
5	http://s3platform.jrc.ec.europa.eu/
6	http://s3platform.jrc.ec.europa.eu/s3-thematic-platforms
7	 For further information see the European Commission’s smart
specialisation platform: http://s3platform.jrc.ec.europa.eu/
2.5 Financial instruments
The use of financial instruments (FIs) in cohesion
policy has increased significantly in recent
years. In 2007–2013 around €12  billion of the
Structural Funds was invested in this way, while
plans for 2014–2020 suggest a figure of the order
of €21 billion8
.
The FI landscape at EU level is complicated, with
various players, instruments and areas of intervention.
ESI Funds play a major role at the EU
level (Table 6.4). SMEs account for just over half of
planned spending from the ESI Funds supported by
FIs and, together with innovation and the low carbon
economy, they represent the bulk of planned
investment so supported. ESI Funds in the form of
FIs are the largest EU source of financing for SMEs
and the low carbon economy without considering
the substantial amount of ERDF support provided
to these areas through grants.
There are various changes in the extent of the use
of FIs and the arrangements for implementing and
reporting on them in the 2014–2020 period as
compared with the preceding one (Table 6.5).
3. Macroeconomic impact
of the policy
Macroeconomic models suggest that cohesion policy
investment is likely to have a positive and significant
impact on the EU economy (see Figure 6.6).
The impact builds up over time and continues long
after the programmes have come to an end. In the
short run, a substantial part of the impact stems
from the increase in demand generated by the additional
expenditure, which is partly crowded-out
through increases in wages and prices. In the medium
and long run, productivity-enhancing effects
of cohesion policy investment — the so-called
supply-side effects — materialise and increase
potential output, reducing inflationary pressure at
the same time. By 2023, EU GDP is expected to
be more than 1% higher as a result of cohesion
8	 Figure approximate: the means of financing used for each priority
axis is not fully pre-determined and may change.
Chapter 6: The impact of cohesion policy
183
Table 6.5 Changes in Financial Instruments supported by cohesion policy between the 2007–2013
and 2014–2020 periods
2007–2013 2014–2020
Scope Support for enterprises, urban development,
energy efficiency and renewable
energies in building sector
Support for all thematic objectives covered
under a programme
Set-up Voluntary gap analysis for enterprises
and at the level of Holding fund
Compulsory ex ante assessment
Implementation options Financial instruments at national or regional
level — tailor made only
Financial instruments at national, regional
level, transnational or cross-border
level: Tailor-made, off-the-shelf or MA
loans/guarantees
Contribution to EU level instruments
Payments Possibility to declare to the Commission
100% of the amount paid to fund — not
linked to disbursements to final recipients
Phased payments linked to disbursements
to final recipients.
National co-financing which is expected
to be paid can be included in the request
for the interim payment
Management costs and
fees, interest, resources
returned, legacy
Legal basis set out in successive amendments
of the regulations and recommendations/interpretations
set out in three
follow up notes.
Full provisions set out from outset in basic,
delegated and implementing acts
Reporting Compulsory reporting only from 2011
onwards, on a limited range of indicators
Compulsory reporting from the outset, on
a range of indicators linked to the financial
regulation.
Table 6.4 Division of EU sources of funding for the 2014–2020 period
EFSI ESIF
Total
(€ billion)
Infrastructure Connecting Europe Facility
22.4
SME
COSME (81%),
EASI (11%) and
Creative Europe (7%)
21.3
R&D&I Horizon 2020
11.7
Environment and
resource efficiency
NCFF (56%), PF4EE (44%)
6.1
ICT 3.3
Social cohesion Erasmus+ 2.7
Total (€ billion) 26.0 21.6 19.9 67.5
EU level instruments
Notes: ESI funds (“ESI”) are the “European Structural and Investment Funds”, i.e. cohesion policy funds plus EAFRD and EMFF
EFSI (“European Fund for Strategic Investments”) is an initiative launched jointly by the EIB Group and the European Commission
to help overcome the current investment gap in the EU by mobilising private financing for strategic investments.
The boxes representing budget commitments are broadly to scale. In the case of EFSI, the breakdown of commitments as at
November 2016 has been used as a proxy to disaggregate the commitment by objective.
Source: European Policies Research Centre (2017).
Seventh Report on economic, social and territorial cohesion
184
The EIB Group: a key partner in promoting cohesion
Through a mix of services, the EIB plays a major role in helping to reduce regional economic disparities and
to raise living standards across the EU.
EIB Cohesion Priority Regions cover all less developed and transition regions eligible for support under cohesion
policy for 2014–2020. Over the 10 years 2007–2016, over €200 billion was lent to these regions (Ta-
ble 6.6 and Figure 6.5), loans going to areas such as major infrastructure, including trans-European networks
and renewable energy, water supply, waste management, forestry and food security; SMEs; education and
training; ICT and to municipalities for improving urban living environments.
In the 2007–2013 programming period, the EIB lent nearly €20 billion through Structural Programme loans,
primarily to Member States in the east and south of the EU. This helped to co-finance programmes amounting
to over €200 billion. Such loans have become increasingly important since the beginning of the economic
and financial crisis. Fiscal consolidation has hampered the ability to find counterpart financing as regards EU
grants and these loans help in doing so.
In 2014, the EIB and DG REGIO set up ’fi-compass’ to provide advice on financial instruments to complement
the advisory services of JASPERS (created in 2005 and managed by the EIB in partnership with the European
Commission and the European Bank for Reconstruction and Development (EBRD), which helps cities and
regions to prepare major projects, as well as some smaller projects in smaller countries and strategic areas,
such as innovation and energy efficiency.
Table 6.6 EIB lending to Cohesion Priority Regions (€ billion)
2007–2013 2014–2016
Cohesion lending €147 bn €55 bn
of which Structural Programme Loans €20 bn €14 bn
0
5
10
15
20
25
30
35
0
5
10
15
20
25
30
35
EU-
28
DK LU NL SE MT FI AT IE BE FR HR EE LV LT CY UK BG SK SI RO CZ EL HU DE IT PT PL ES
2007-2013 2014-2016
EIB lending under the Cohesion Objective by country and programming
period (€ billion)
Figure 6.5
Source: EIB, 2017
Chapter 6: The impact of cohesion policy
185
policy investments (after taking account of their
financing).
Unsurprisingly, the impact is greatest in the main
beneficiary countries. For example, at the end of
the implementation period of the 2007–2013
programmes (i.e. in 2015), GDP in Latvia is estimated
to have been 3.9% higher thanks to the investments
supported by cohesion policy while in
Hungary, it was around 3.6% higher. On average,
GDP in the EU‑12 in 2015 is an estimated 2.8%
higher than it would have been without cohesion
policy investments.
In the EU‑15, the effects of the policy are smaller
during the implementation period but they
strengthen over time. The overall impact was
positive, though marginal in some cases, even in
Member States which are net contributors to the
policy. This is because the effect of higher taxes
to finance the investment concerned is more than
compensated by the boost to income and expenditure
in net recipient countries from the investment,
which leads to increased imports from net contributor
countries, so boosting the GDP of the latter
(see Box on Spatial spill-overs).
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2007-2013
2014-2020
Total
% addition to GDP
Impact of cohesion policy on EU GDP, 2007-2023Figure 6.6
Source: QUEST macroeconomic model
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
DK SE UK NL FI LU AT BE IE DE FR IT ES CY SI PT MT EL CZ EE BG LV HU LT RO SK PL HR EU-
13
EU-
15
EU-
28
% addition to GDP
Impact of 2014-2020 programmes on Member States' GDP, 2023Figure 6.7
Source: QUEST macroeconomic model
Seventh Report on economic, social and territorial cohesion
186
Similar results are expected for the 2014–2020
period (Figure 6.7). The largest impact is estimated
to be in Croatia where GDP is forecast to increase
by around 4% by the end of the implementation
period (2023) over and above what it would have
been in the absence of cohesion policy investment.
The impact is also large in Poland (+3.4%),
Slovakia (+3%) and Romania (+2.9%). In the long
run (in 2030), the increase in GDP is largest in
Croatia and Poland (more than 4% in each case).
As for 2007–2013, the expected impact in the
EU‑15 is smaller. However, in the long run the net
impact of the policy per euro spent is only slightly
Spatial spill-overs
Cohesion policy interventions not only positively affect the performance of the Member States and regions
in which they are implemented, but they also generate spill-overs elsewhere in the EU. These effects can
be modelled. Figure 6.8 shows the impact of all cohesion policy programmes in 2007–2013 on the noncohesion
countries. This is the sum of their contribution to the EU budget (negative), the impact of the programmes
implemented in the non-cohesion countries (positive) and the spill-over benefits from increased
exports to the cohesion countries (positive). It also shows the impact on this group of countries of only the
programmes implemented in the cohesion countries only.
Focusing on the latter, the negative effect of raising taxes dominates during the implementation of the
programmes, but once they are terminated, GDP in the non-cohesion countries is higher than what it would
have been without these programmes, due to the positive spill-over they generate on the economies of the
non-cohesion countries.
In the long-run, these spill-over benefits represent a substantial share of the total impact of the policy on
the non-cohesion country economies. By 2023, the impact of the 2007–2013 programmes is estimated to
be around 0.12% of GDP in non-cohesion countries, of which around a quarter is due to spill-overs from
spending in cohesion countries. This effect is particularly pronounced for Member States with strong trade
links with cohesion countries (Austria and Germany) or strong openness to trade in general (Ireland and
Luxembourg). In Austria and Luxembourg, more than half of the impact of the policy is due to investment
in the cohesion countries.
0.0
0.1
0.2
0.3
0.0
0.1
0.2
0.3
DK SE UK FI FR NL BE IT DE IE AT LU Non-
CC
All programmes
Programmes implemented in
the cohesion countries (CC)
% addition to GDP
Impact of cohesion policy on non-cohesion countries' GDP, all
programmes and programmes implemented in the cohesion
countries, 2023
Figure 6.8
Source: QUEST macroeconomic model
Chapter 6: The impact of cohesion policy
187
lower in the EU‑15. Indeed, as compared to the
EU‑13, investments in the EU‑15 tend to be relatively
more concentrated in R&D and human capital
which produce most of their effects long after
the spending involved has come to an end. Ten
years after the end of the programming period, in
2030, the impact is estimated to be around 2.7
times the money spent in the EU‑13 and 2.4 times
in the EU‑15. Over the 17-year period 2014–2030,
these figures correspond to an annual average return
of around 6% in the EU‑13 and 5% in the
EU‑15, good value for money from a policy which
generates social returns, in the form of non-quantified
environmental and other benefits which improve
the quality of life and the sustainability of
development, as well as purely financial ones.
3.1 Impact at regional level
The analysis conducted at the national level can
be complemented by simulations at the regional
level. This is important as the intensity of aid and
the policy mix, i.e. the investment priorities supported,
vary markedly from one region to another,
even within the same Member State. The impact
of the policy also depends on the economic and
social environment in which it is applied. The same
policy mix can potentially have quite different consequences
if implemented in a mostly rural region
where agriculture accounts for a substantial share
of GDP or in an urban region specialised in services.
In addition, some mechanisms which need to
be taken into account when assessing the impact
of cohesion policy are more likely to operate at a
regional than a national level. This is the case, for
example, with spatial spill-overs through which the
programmes implemented in one region also have
an impact in others, especially those that are geographical
neighbours.
The impact at NUTS 2 regional level shows wide
variations across the EU‑27 and even within the
same country (Map 6.1).
By the end of the programming period, GDP in
Észak-Magyarország and Észak-Alföld in Hungary
is estimated to be more than 8% higher than it
would be without Cohesion policy, while in the capital
city region of Közép-Magyarország, it is only
1.4% higher.
In regions in more developed Member States, the
impact is smaller but remains positive in spite of
the fact that these regions are net contributors to
the policy. This is particularly true in the long-run
because of the focus of investment as indicated
above. In 2030, the smallest impact is estimated
to be in Nordjylland in Denmark, though it is still
positive at 0.1% of GDP.
In most Member States, it is in the least developed
regions where investment relative to GDP is largest
and where the impact is greatest. This is in line
with the mandate for cohesion policy enshrined in
the Treaty which is to reduce disparities in regional
GDP per head across the EU.
4. Innovation and competitiveness
The ERDF is the largest single EU source of financing
for innovation and competitiveness (Figure 6.9).
For innovation (on which Horizon 2020 is concentrated),
the ERDF is the second largest source,
though, as noted above, it is the predominant
source of support for SMEs.
In line with the emphasis on smart specialisation,
cohesion policy is increasingly concentrated
on higher value-added support, with greater focus
on productivity and less on employment, the tarERDF
54.4
EAFRD, 3.6
H2020, 74.8
Main EU sources of funding for research,
innovation and ICT, 2014-2020 (€ bn)
Figure 6.9
Source: ESIF Open Data Platform and KPMG (2017)
ESI
funding
SeventhReportoneconomic,socialandterritorialcohesion
188
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 6.2 Impact of the 2014–2020 cohesion policy programmes on GDP, 2030
< 0.10
0.10 – 0.15
0.15 – 0.20
0.20 – 0.30
0.30 – 0.50
0.50 – 1
1– 2
2 – 4
> 4
Source: RHOMOLO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
% above baseline
Guadeloupe
Martinique
Canarias
Guyane
Açores
Mayotte Réunion
Madeira
Map 6.1 Impact of the 2014–2020 cohesion policy programmes on GDP, 2023
< 0.05
0.05 – 0.1
0.1 – 0.5
0.5 – 1
1 – 2
2 – 3
3 – 4
4 – 5
> 5
% above baseline
Source: RHOMOLO
© EuroGeographics Association for the administrative boundaries
0 500km
REGIOgis
Chapter 6: The impact of cohesion policy
189
get for gross jobs directly created being reduced
from 1.2 million in the previous period to 420 000
(Table 6.7). In addition, support to large enterprises
is now restricted to innovation.
4.1 Support to SMEs
Support to SMEs over the 2007–2013 period was
already focused on RTD and innovation. Some
400 000 SMEs across the EU received direct support
and 121 400 new businesses were helped to
start up. The firms directly supported represented
just under 2% of the 23 million or so SMEs in the
EU. This, however, greatly understates the potential
importance of the support since in many cases
it was targeted at the more strategic firms in a
region, such as those engaged in manufacturing or
tradable services and, accordingly, the main sources
of potential growth, rather than those in sectors
such as retailing or other basic services in which
most SMEs operate. Around 7% of manufacturing
SMEs in the EU were supported, including an estimated
15% of small firms (those with 10–49 persons
employed) and over a third of medium-sized
enterprises.
The average amount of funding going to each SME
is estimated at around €115  000, though there
were wide variations between different measures
of support, from several million euro (up to
€5 million in Poland for co-financing the purchase
of modern machinery, for example) to a few thousand
euro (such as in respect of short-term credit
for micro enterprises).
Table 6.7 Common indicators and targets for 2014–2020 in the fields of innovation and
competitiveness
Research, Innovation: number of enterprises
cooperating with research institutions
Enterprises 73 000
Research, Innovation: number of new
researchers in supported entities
Full time equivalents 29 500
Research, Innovation: number of enterprises supported
to introduce new-to-the-firm products
Enterprises 63 000
Research, Innovation: number of enterprises supported
to introduce new-to-the-market products
Enterprises 28 000
Research, Innovation: private investment matching
public support in innovation or R&D projects
€ 10.4 billion
Research, Innovation: number of researchers working
in improved research infrastructure facilities
Full time equivalents 72 000
Firms receiving non-financial support (advice) Enterprises 450 000
All firms receiving support Enterprises 1 100 000
Firms receiving grants Enterprises 370 000
Direct employment increase in supported enterprises Full time equivalents 420 000
Firms receiving financial instrument support (non-grants) Enterprises 200 000
Private investment matching public support to enterprises (grants) € 23.7 billion
Private investment matching public support
to enterprises (non-grants)
€ 8.6 billion
Startups supported Enterprises 155 000
Source: ESIF Open Data Platform — https://cohesiondata.ec.europa.eu/
ERDF
33.2
EAFRD, 27.9
EMFF, 2.6
SME initiative,
1.2
Creative
Europe, 1.5
COSME, 2.3
Main EU sources of funding for SMEs,
2014-2020 (€ bn)
Figure 6.10
Source: ESIF Open Data Platform and KPMG (2017)
ESI
funding
Seventh Report on economic, social and territorial cohesion
190
The evaluation found that a major result of support
was the help given to SMEs to withstand the
effects of the crisis by providing credit when other
sources of finance had dried up (see Box). There
was also support for innovation and the adoption
of more technologically advanced methods
of production as well as for the development of
new products. The evidence from the surveys and
case studies carried out as part of the evaluation
shows that support led to investment being maintained,
increased and/or accelerated, resulting in
increased turnover, profitability and exports.
It also led, in a number of cases, to observable
behavioural changes, such as SME owners and
managers being more willing to take risks and to
innovate. This was evident, for example, for R&D
grants in Castilla y León (Spain), which resulted in
SMEs being more capable of undertaking complex
projects, often in collaboration with other firms or
research centres. Overall, the ERDF provided support
for 35 500 projects for cooperation between
SMEs and research centres.
Support to the social economy
Social enterprises create new jobs, facilitate
labour market integration and are a source of
social innovation. Moreover, the development
of social enterprises and related social finance
markets can mobilise significant private investment
to address social issues, contributing to the
sustainability of welfare systems.
The ESF is actively supporting the establishment
of social enterprises as a source of jobs, in particular
for groups of people who find it difficult to
get work: young long-term unemployed, disabled
people and people in rural communities. Overall,
Member States have earmarked more than
€1 billion to this priority in 2014–2020 and several
Member States are using the ESF to boost
the social investment market, such as in Portugal
through the Social Innovation Fund and in Poland
through the National Fund for Social Entrepre-
neurship.
The contribution of financial
instruments (FIs)
Since FIs were particularly concentrated on supporting
SMEs in the 2007–2013 period, the ex
post evaluation was specifically focused on this.
It found that FIs played a crucial role in providing
funding to SMEs during the credit crunch in
the crisis and helped many firms to stay in business.
Indeed, the regulations were changed in
response to the crisis, allowing FIs to be used to
finance working capital as well as fixed capital,
so giving them a distinct advantage over grants.
In Lithuania, in particular, around 60% of loans
went to support of working capital, so keeping
many businesses afloat. FIs, however, also
helped to maintain investment in new technology
and in improving production processes more
generally.
It is equally evident that FIs have assisted in the
development of financial markets in a number of
regions. In North-East England, they led to the
creation of a revolving fund and helped to develop
a private investment sector in the region
as well as supporting investment in new technology
and innovation. In Bayern in Germany,
they helped to develop a business market and
in Hungary and Malopolskie in Poland, regional
financial intermediaries.
In addition, and perhaps unexpectedly, the evidence
from case studies suggests that SMEs
often prefer FIs to grants, since a loan covering
80% of an investment would mean them having
to find less additional financing than a grant covering
20%1
. This may prove to be a key source of
the added-value of FIs in the longer term2
.
1	 An 80% loan and a 20% grant are not atypical figures in
an ERDF context.
2	 For further information: Ex post evaluation of cohesion
policy 2007–2013
Chapter 6: The impact of cohesion policy
191
In some programmes, the ERDF was used to support
experimental and innovative policy measures
instead of replicating traditional national schemes.
This was the case, for example, in Denmark,
Sweden and Finland, where there was a focus on
research and innovation, in Puglia in Italy with the
‘Living Labs’ experiment and In Lithuania with the
Inno-voucher scheme.
4.2 Support to large enterprises9
Although SMEs are the main focus of enterprise
support under cohesion policy, large enterprises
are often key to regional development. An estimated
€6.1 billion from the ERDF was allocated to
large enterprise support in the 2007–2013 period
— roughly 20% of total direct support to enterprises
(Table 6.8).
This took the form of some 6 000 projects, with an
average project size of €1 million. In total, roughly
3 700 large firms were supported, with an average
of 1.6 projects in each of them (although some
firms received support for 4–5 projects). Poland,
Portugal and Germany accounted for half of total
ERDF support to large enterprises in 2007–2013.
Over 70% of the large enterprises concerned were
in manufacturing, in the automotive and aerospace
industries but also in packaging. For the most part,
9	 Large enterprises: 250 employees or more.
large firms were supported through non-refundable
grants, but in Italy, Spain, Portugal and Austria,
support was also provided in the form of loans
(usually combined with grants).
Support had a strong economic impact, with 90%
of projects achieving or more than achieving the
goals set (Figure 6.11). Both the production capacity
and the productivity of the enterprises concerned
was increased, often due to the adoption
of cutting-edge technologies that went beyond
simple replacement investment. Moreover, the
projects directly created at least 60 000 new jobs
in the 8 regions selected for in-depth case studies.
According to the case studies, 3 out of 4 of the
’wider benefits’ targeted were achieved, the most
common being knowledge spill-overs and the
building of local supply chains. Typically, however,
while ERDF support influenced the decision to invest,
it was only one factor among many. Since
large enterprises tend to have long-term strategies,
multiple grant options and easier access to finance
than SMEs, they are less influenced by grant
money.
Wherever it was possible to judge, it was found
that the presence of large enterprises in the region
concerned was more than temporary and, in
the case of the projects supported, the investment
concerned would be maintained for the mandatory
five-year period. Whether or not the enterprises
Table 6.8 Incidence and volume of support to large enterprises, 2007–2013
Direct enterprise
support*
(€ million)
Large enterprise
support
(€ million)
Large enterprise /
total support
Number of projects Number of firms
supported
Poland 6 591 1 153 17% 539 408
Portugal 4 145 1 134 27% 407 319
Germany 3 200 704 22% 763 632
Czech Republic 1 491 467 31% 520 339
Hungary 2 581 453 18% 409 273
Spain 2 543 311 12% 1 269 398
Italy 2 034 243 12% 416 270
Austria 283 133 47% 194 148
Total (EU‑28) 31 233 6 100 (est.) 20% (est.) 6 000 (est.) 3 700 (est.)
The countries listed are the 7 investing most in large enterprise, plus Austria, in which the proportion of funding for enterprise support going
to large enterprises was the largest in the EU.
Source: Ex post evaluation of cohesion policy 2007–2013 .
Seventh Report on economic, social and territorial cohesion
192
would sustain production in the region over the
longer term depended, in particular, on the lifecycle
of the plant or process in which investment had
been made and the technology involved as well as
corporate strategy.
5. Employment, social inclusion
and education
In the 2014–2020 period, the European Social
Fund (ESF) is providing support to four thematic
objectives: Employment, Social inclusion, Education
and skills, and Administrative capacity building. Of
the total ESF budget of €86 billion, over €75 billion
is going to support sustainable and quality
employment, social inclusion and investment in
education and training. The majority of funding is
allocated to employment and education objectives,
with 25% going to social inclusion. The funding is
expected to:
•• help more than 7.4 million of the unemployed
into work, together with another 2.2 million
people six months after they have completed
an ESF project;
•• help over 8.9 million people gain new
qualifications.
The ESF is also expected to help at least:
•• 9.9 million people with low education;
•• 7.5 million people who are disadvantaged;
•• 6.2 million young people;
•• 7.2 million people in employment, including the
self-employed and those working in public employment
services and related organisations.
Source: Ex post evaluation of cohesion policy 2007-2013
~ 30% of firms:
Little influence of support
90% of projects
achieved project targets
3 out of 4 planned
wider benefits emerged
~ 50% of firms:
some degree of
behavioural additionality
20% of firms:
strong behavioural
additionality
SUPPORT PROJECT
DIRECT 
EFFECT
INDIRECT
AND WIDER
BENEFITS


~ ~
Figure 6.11 Large Enterprise support 2007–2013 — Case study result
	.http://ec.europa.eu/regional_policy/en/policy/evaluations/ec/2007-2013
Source: Ex post evaluation of cohesion policy 2007–2013
Chapter 6: The impact of cohesion policy
193
5.1 Employment
Promoting high levels of employment and job
quality is the cornerstone of the ESF. It helps both
the unemployed and inactive to find a job through
training, counselling, job placement and other
means. It also helps those in employment to upgrade
their skills to remain competitive on the
labour market and adapt to change. The ex post
evaluation of the 2007–2013 ESF programmes
showed that, by the end of 2014, at least 9.4 million
people who found a job received support from
the ESF10
.
As part of its employment objective, the ESF is
helping tackle the major problem of youth unemployment.
Indeed, young people are among the
most important target groups for the ESF, representing
around 30% of all participations in ESF
programmes. Over the 2014–2020 period, the ESF
will directly invest at least €6.3 billion to support
the integration of young people into employment
across the EU. In addition, the Youth Employment
Initiative (YEI) was launched in 2013, with a budget
of €4.2 billion11
, matched by an equal amount
from the ESF, i.e. €8.4 billion in total, for Member
States to invest directly in improving the employability
of young people.
10	Ex post evaluation of the 2007–2013 ESF programmes:
Commission Staff Working Document (SWD) 2016 452.
11	The decision to increase the initial budget of €3.2 billion for the
Youth Employment Initiative by €1.2 billion was agreed upon by
the co-legislator in 2017.
The YEI helped to kick-start the implementation
of the Youth Guarantee — a guarantee that each
young person will be offered a job, further training
or education within 4 months of becoming unemployed.
By the end of 2016, over 1.6 million young
people had already been directly supported by the
Initiative. Alongside supporting investment, the
ESF is also being used to change the policy approach
to youth unemployment in Member States
by encouraging a more individual focus.
The preliminary assessment of the implementation
of the ESF and YEI up to 2016 shows positive
achievements, with over 6.8 million participations
in measures supported, 3.4 million of which
involved those unemployed, 1.8 million those
inactive, 2.6 million those below 25 and 2.6 million
those with only basic schooling (ISCED level
0-2)12
, confirming that the ESF is reaching its target
groups. Results are still limited and will take
time to materialise, since so far only 0.7 million
participants are reported to have gained a qualification
and only 0.6 million participants have found
employment, including self-employment, on leaving
programmes.
5.2 Social inclusion
One of the central purposes of the ESF is to support
people who are disadvantaged and at risk of
poverty, to help them into employment and to find
their place in society. For the 2007–2013 period,
10% of total ESF co-financed investment was allocated
to social inclusion measures, which, according
to evaluations, helped Member States to better
support those most severely hit by the crisis. In
the 2014–2020 period, at least 20% of the ESF
will go to such measures which should increase
the effects.
In addition, the ESF provides support to measures
to help groups who face discrimination and prejudice
on the labour market. These include, in particular,
migrants, ethnic minorities, such as Roma,
and those with a different lifestyle, such as itin-
12	The figures sum to more than the total because the groups are not
mutually exclusive.
ERDF, 21.5
EAFRD, 15.2
ESF, 75.8
EMFF, 0.6 YEI, 6.5
RECP, 0.4
3rd Health, 0.4
EaSI, 0.9
EGF, 1.1
AMIF, 3.1
FEAD, 3.8
Erasmus+,
14.8
Main EU sources of funding for employment,
training and social inclusion, 2014-2020 (€
bn)
Figure 6.12
Source: ESIF Open Data Platform and KPMG (2017)
ESI
funding
Seventh Report on economic, social and territorial cohesion
194
erant travellers. As well as co-financing education
and training for them, ESF-supported measures
are aimed at combating all forms of discrimination
and at breaking down the various barriers the
people concerned face in finding employment and
becoming integrated into society.
5.3 Education
The ESF is the main EU source of finance for investment
in human capital and the development of
skills which are crucial to achieving and maintaining
high levels of employment, As such, the Fund
helps Member States to improve the basic skills of
the low-qualified, as well as assisting workers to
increase their skill levels and the unemployed to
get back into work.
As highlighted by the New Skills Agenda for
Europe13
, it is of paramount importance for people
to have the right skills, both for their self-fulfilment
and for the competitiveness of the EU economy. To
this end, the ESF provides support across the entire
education cycle from early childhood schooling to
vocational training and life-long learning.
5.4 Urban and social infrastructure
The ex post evaluation of the 2007–2013 period
found that activities related to urban development
ranged from ‘investments in deprived areas’ and
‘support for economic growth’ to support of the
‘cultural heritage’ and ‘strategy development’. The
following kinds of project were undertaken with
the support provided:
•• the construction, repair and renovation of
schools, housing, social and cultural centres
and other buildings;
•• the creation of business space;
13	Communication from the Commission, The new skills agenda for
Europe — Working together to strengthen human capital, employability
and competitiveness, COM(2016) 381.
•• the renewal and revitalisation of town centres
and historic areas and the construction of flood
defences;
•• the construction of cycle paths;
•• the construction of public spaces and facilities;
•• the rehabilitation of wasteland and brownfield
sites;
•• the installation of clean drinking water supply
and wastewater treatment facilities;
•• improvements in the energy efficiency of
buildings.
Social innovation
The ESF has played an important role in changing
attitudes and systems of care and support
for people with disabilities in encouraging a shift
from care in institutions to care in the community,
following a human rights approach. In the
2014–2020 period, there is a more focused use
of the ESF on supporting a transition to such a
shift, with Member States being obliged to address
this transition in a more systemic way and
to make structural reforms rather than intervening
on an ad-hoc basis. Such reforms were
encouraged by allocating resources to their
implementation during the negotiation of pro-
grammes.
Bulgaria is an example of what has been achieved
so far. Through an ambitious programme of reform,
the Bulgarian Government, with support
from the EU and civil society, has made significant
progress in deinstitutionalising the care of
children with disabilities in a short space of time
the number in institutions being reduced by 82%
and all specialised institutions for such children
being closed down.
As part of ESF transnational cooperation, social
innovation is encouraged in most areas of support,
the objective being to stimulate new approaches
and the exchange of good examples of
innovative measures between Member States.
Chapter 6: The impact of cohesion policy
195
Achievements in the EU‑12 ranged from improvements
in infrastructure (water supply, sewerage
systems, schools, housing and cultural centres)
and the renovation of buildings to the execution
of urban integrated development plans and strategies.
In the Czech Republic, for example, Integrated
Plans for Urban Development for cities with more
than 50 000 inhabitants were formulated as the
basis for the construction of sports facilities, public
places and cultural and leisure facilities.
In the EU‑15, the focus in the UK was on the creation
of business centres and support of SMEs at
local level, while in other countries, the ERDF was
used to stimulate private investment in towns and
cities, such as in Rotterdam.
In the case of social infrastructure, the main
achievements included:
•• improvements in healthcare and social infrastructure
facilities through modernisation
of equipment and the increased efficiency of
ambulance, care and other services (e.g. in
Hungary and the Czech Republic), which helped
to close the gap between more and less developed
regions in the EU;
•• improvements of the education system in a
number of Member States (notably in Portugal
where a significant budget was spent on
schools, colleges and equipment);
•• improvements in training and employment services
(in, for example, Spain, Poland, the Czech
Republic and Lithuania) to better adapt the
work force to labour market needs;
•• improvements in the security of urban areas
and investment in the cultural heritage;
•• investment in cultural, sports and training facilities,
as part of urban development measures,
together with the establishment of support
centres for various disadvantaged groups.
Monitoring data show that three quarters of the
(small scale) projects examined in the evaluation
made a concrete contribution to growth and
jobs and a quarter of them a large contribution
(Table 6.10). The most common outcomes were
an improvement in skills and an expansion of local
businesses, but there were also beneficial effects
on a range of other factors from health to
business creation and increased labour market
participation.
6. Environment, transport
and energy networks
6.1 The environment
The environment has been a focus of cohesion policy
support since 1989. Along with transport, it is
one of the policy areas eligible for financing from
the Cohesion Fund, on the grounds that it is important
to have common environmental standards
across the EU for both the health of people and to
protect the eco-system.
The ex post evaluation for the 2007–2013 period
found a significant shift in EU‑12 countries in the
disposal of waste away from landfill to recycling.
A substantial number of landfill sites which did not
Table 6.9 Common indicators and targets for 2014–2020 in the fields of urban and social
infrastructure
Childcare and education: Capacity of supported
childcare or education infrastructure
Persons 6.8 million
Urban: Population living in areas with integrated
urban development strategies
Persons 41.2 million
Urban: Public or commercial buildings built or renovated in urban areas Square metres 2.2 million
Urban: Rehabilitated housing in urban areas Housing units 17 000
Urban: Open space created or rehabilitated in urban areas Square metres 29.2 million
Source: ESIF Open Data Platform — https://cohesiondata.ec.europa.eu/
Seventh Report on economic, social and territorial cohesion
196
comply with EU standards were closed down while
in the Czech Republic, Hungary, Lithuania, Poland
and Slovenia, as well as Croatia, the proportion of
waste which was recycled was increased by over
10 percentage points. Much of this shift was cofinanced
by the ERDF and Cohesion Fund.
EU cohesion policy is also key to making the circular
economy a reality, by ensuring funding for
waste management, innovation, SME competitiveness,
resource efficiency and low-carbon investments
and by promoting green public procurement.
The following achievements were identified in the
case studies on waste management:
•• In Bulgaria, the proportion of waste landfilled
was reduced from 80% to 70% between 2007
and 2013. A mechanical biological treatment
facility, co-financed by EU funding, was opened
in Varna in 2011 and a similar facility, but including
a composting plant, was opened in
Sofia in 2015.
•• In Estonia, 39 landfills and 11 industrial waste
sites were closed down between 2007 and
2013, the share of municipal solid waste composted
nearly doubled to 6% and the share of
biodegradable waste sent to landfill was reduced
significantly.
•• In Poland, the share of municipal waste going
to landfills was reduced from 90% to 53%,
while the share of waste going to recycling
increased from 6% to 16% and the share
composted rose from 6% to 13%. A number
of regional waste management centres were
constructed to replace smaller local and less
efficient ones. For example, a centre with a recovery
facility to handle various types of waste
and a composting facility was constructed in
Gdansk, with €48.2 million of the total cost of
€83.5 million coming from EU funds.
•• In Slovenia, EU funds co-financed some 200
waste collection centres and the construction
of a number of regional centres for waste
management as well as an incinerator and
the clean-up of old municipal waste landfills.
Between 2007 and 2013, recycling nearly
doubled to over 40% and composting was also
ERDF, 54.0
EAFRD, 50.5
EMFF, 2.3
CF, 28.6
EAGF, 312.3
NER300, 2.1
LIFE+, 3.5
Solidarity
Fund, 3.5
Main EU sources of funding for agriculture
and the environment, 2014-2020 (€ bn)
Figure 6.13
Source: ESIF Open Data Platform and KPMG (2017)
ESI
funding
Table 6.10 ERDF urban and social projects,
2007–2013 (% reporting a contribution to
various goals)
Improved skills/ educational
attainment/ qualifications
39%
Improved performance/
expansion of local businesses
32%
Improved health outcomes 25%
Entrepreneurship/new
business creation
24%
Higher rate of female and/or youth
participation in the labour market
17%
Other 26%
Total reporting some contribution 73% (and
24% a high
contribution)
Source: Ex post evaluation of cohesion policy 2007–2013
ERDF
25.8
CF, 32.6
CEF, 30.4
Main EU sources of funding for transport and
energy infrastructure, 2014-2020 (€ bn)
Figure 6.14
Source: ESIF Open Data Platform and KPMG (2017)
Chapter 6: The impact of cohesion policy
197
increased, though it remained relatively small
(only around 7% of the total in 2013).
Achievements as regards water supply and wastewater
treatment included:
•• an additional 5.9 million people connected to a
new or improved supply of clean drinking water,
1.6 million of whom were in the EU‑12 and
3.7 million in Convergence regions in the four
southern EU‑15 Member States, most of them
in Spain and Greece;
•• an additional 6.9 million people connected to
new or upgraded wastewater treatment facilities,
of whom 1.9 million were in the EU‑12
and 4.6 million in the four southern Member
States (Table 6.11).
A striking example is the construction of a new
sludge treatment facility at the Vilnius wastewater
treatment plant in Lithuania. Before the construction,
most of the sludge was landfilled while now
it is composted and used as fertiliser. The aim was
not only to comply with the EU Sludge Directive
(86/278/EEC) but also to reduce the smell from
untreated sludge, which affected half the population
of Vilnius.
Table 6.11 Additional people served by water
and wastewater projects co-financed by the
ERDF and Cohesion Fund, 2007–2013 (up to
end-2014)
Additional population (‘000) served by:
Water
projects
Wastewater
projects
Czech Republic 371.3 490.3
Estonia 13.7 15.8
Hungary 478.1
Lithuania 78.5
Latvia 672.2 90.1
Poland 262.2 537.3
Slovenia 291.6 194.2
Slovakia 33.0 44.2
Spain 1 929.0 2 172.3
Greece 1 455.5 370.8
Italy 825.0
Portugal 359.8 1 270.0
Germany 213.0
France 514.6 101.4
EU‑12 1 644.0 1 928.5
EU‑4 3 744.3 4 638.1
EU‑15 Other 514.6 314.4
EU 5 902.9 6 880.9
Note: EU‑4 = EL, ES, IT and PT
Source: DG REGIO, derived from Annual Implementation Reports
for 2014
Table 6.12 Common indicators and targets for 2014–2020 as regards the environment
Risk prevention and management: Population
benefiting from flood protection measures
Persons 13.2 million
Risk prevention and management: Population
benefiting from forest fire protection measures
Persons 11.8 million
Nature and biodiversity: Surface area of habitats
supported to attain a better conservation status
Hectares 6.4 million
Water supply: Additional population served
by improved water supply
Persons 12.4 million
Land rehabilitation: Total surface area of rehabilitated land Hectares 5 000
Solid waste: Additional waste recycling capacity Tonnes/year 5.8 million
Wastewater treatment: Additional population
served by improved wastewater treatment
Persons 16.9 million
Source: ESIF Open Data Platform — https://cohesiondata.ec.europa.eu/
Seventh Report on economic, social and territorial cohesion
198
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
70
CY RO SI CZ SK EE BG PL LV HU MT LT EU-
12
DE IT ES PT EL
% of total government capital expenditure on transport
ERDF and Cohesion Fund allocation to transport, 2007-2013Figure 6.15
Source: European Commission (2016k).
6.2 Transport
investment
Investment in transport
has always been
a major focus of support
from both the
ERDF and Cohesion
Fund, which represent
substantial sources of
funding for such investment,
accounting
for over 40% of total
Government capital expenditure
on transport
over the 2007–2013
period in the EU‑12.
(Figure 6.15).
Given the large number of projects, it is impossible
to describe them all, but the following examples
drawn from the ex post evaluation case studies14
give a flavour of the types of investment concerned
and the benefits they give rise to.
Cernavoda-Constanța motorway, Romania
The road is a 51 km long section of the A2 motorway
linking Bucharest and Constanta on the Black
Sea coast, the fifth largest city in Romania and
the largest port on the Black Sea as well as being
one of the largest in Europe. It also forms part of
the TEN-T priority axis number 7, which runs from
Patra in Greece, through Athens to Sofia and on to
Budapest and which is part, in turn, of the OrientEast-Med
Corridor. Accordingly, it is strategically
important for both the Romanian and the wider EU
economy. The section which completed the A2 motorway
opened to traffic towards the end of 2012.
Urban transport projects
A number of public transport projects were supported
over the period which had the effect of
reducing congestion in cities and improving the
	
14 For references see annex 3 of the ex post evaluation staff working
document, SWD(2016) 318 final, where further details can be found.
urban environment as well as reducing travel
times. Examples include the development of metro
systems in Budapest, Porto and Sofia (described
below), tramlines in Le Havre in France, Szeged
in the South of Hungary (also described below)
and Warsaw in Poland and the upgrading of urban
or suburban railways between Gdynia, Sopot
and Gdansk in Poland and between Nantes and
Châteaubriant in France (described below as well)
together with the city rail tunnel in Leipzig.
Sofia metro extension
Cohesion policy funding co-financed the construction
of metro line no.2 and the extension of line
no.1 in Sofia which increased the network from 18
km in 2009 to 39 km in 2015 and the number of
stations from 14 to 34. As a result, the Sofia metro
now serves the major residential areas situated
in the north and south of the city, as well as the
Sofia Business Park, and the airport. This has led to
changes in travel patterns, with an increased proportion
of journeys being made by public transport
and fewer by car, so resulting in significantly less
congestion in the city and reduced toxic emissions.
Chapter 6: The impact of cohesion policy
199
Development of Szeged electric public
transport
Cohesion policy funding was used to upgrade and
extend the tram system in Szeged in Hungary to
expand the capacity of routes linking residential
areas with the city centre and to give added incentive
to people to use public transport rather than
cars. Tramline 1 and sections of lines 3 and 4 were,
therefore, modernised (18.3 km in total) and a new
line 2 was constructed (of 4.8 km) along with an
extension of the trolleybus network (of 3.7 km).
Nine new low-floor trams and 10 new trolleybuses
were also purchased and a new passenger information
and traffic management system was installed
together with 8 bike-and-ride stations next
to tram and trolleybus stops. The result has been
a reduction in travel time between the main residential
areas and the city centre. Noise and air pollution
has also been reduced by expanding electric
public transport and favouring its use in the city.
Reopening of railway line Nantes —
Châteaubriant
The railway line, covering a distance of 64 km, was
re-opened in 2014 having been closed for passenger
traffic since 1980. The project was co-funded
by the ERDF and involved the replacement of existing
track, the electrification of the line, the installation
of safety systems at level crossings and of
signalling and telecommunication equipment and
the improvement of access to stations and services
at Nantes and other places along the route. The
line, which is now used by tram-trains, has made
commuting and other journeys to Nantes, a centre
of essential services in the area, much easier.
It has increased the attractiveness of using public
transport instead of cars and so has reduced both
congestion and pollution levels.
6.3 Energy efficiency in buildings15
As noted in Chapter 3, heating, cooling and lighting
buildings account for a substantial proportion of
the energy consumed across the EU. Accordingly,
improving the efficiency of energy use in buildings
can contribute considerably to reducing overall energy
consumption, so saving on the depletion of
fossil fuels, alleviating poverty, increasing energy
security and contributing to climate change mitigation
and adaptation.
Following the adaptation of the regulations in June
2009 as part of the European Economic Recovery
plan, improving energy efficiency in housing became
eligible for support in all parts of the EU, the
maximum funding for this being increased to 4% of
the total ERDF allocation at the same time. The
express intention was to boost economic activity
as well as to further social cohesion by helping to
reduce disparities in access to good quality housing
and to relieve energy poverty.
The ex post evaluation found that almost all of the
funding going to investment in increased energy
efficiency in buildings, overall around 90% of the
total, took the form of non-repayable grants. Only
a small amount of funding — around 9%, less
15	Cohesion policy investments in energy are broader, but this section
concentrates on energy efficiency in buildings, a significant area
of investment and a specific focus of investigation in the ex post
evaluation of the 2007–2013 period.
Table 6.13 Common indicators and targets for 2014–2020 as regards transport
Railway: total length of new railway line km 1 150
Railway: total length of reconstructed or upgraded railway line km 8 680
Railway: total length of new railway line, of which: TEN-T km 570
Railway: total length of reconstructed or upgraded railway line, of which: TEN-T km 4 640
Roads: total length of newly built roads km 3 430
Roads: total length of reconstructed or upgraded roads km 10 390
Roads: total length of newly built roads, of which: TEN-T km 2 020
Roads: total length of reconstructed or upgraded roads, of which: TEN-T km 800
Urban transport: total length of new or improved tram and metro lines km 750
Source: ESIF Open Data Platform — https://cohesiondata.ec.europa.eu/
Seventh Report on economic, social and territorial cohesion
200
than €1 billion — was in the form of loans, interest
subsidies, guarantees and other types of financial
instrument (FI), equities in particular. Many of the
FIs were organised through JESSICA funds managed
by financial intermediaries, the central purpose
of which was to provide funding for urban
regeneration16
.
The evaluation reviewed 27 OPs and found an
overall reduction of 2 904 GWh17
a year in electricity
consumption up to the end of 2013 from
energy efficiency measures, including 1 438 GWh
from measures for residential and public buildings.
To put this into perspective, the reduction in
respect of buildings amounts to an estimated cut
of some 0.2% in total yearly energy consumption
in the countries and regions concerned, not large
but significant given the relatively small amount
of funding involved. Moreover, by the end of 2013,
only around 55% of the total funding available for
energy efficiency had been spent, so a much larger
effect is expected when all projects had been
completed.
In addition, for 20 OPs, data were also collected on
the reduction in greenhouse gas emissions from
the projects supported. Up to the end of 2013,
this amounted to a cut of 826.4 kilo tonnes of
CO2
equivalent emissions a year from the projects
undertaken to increase energy efficiency in buildings
(and one of 1 454 kilo tonnes a year from
all the energy efficiency projects supported). This
16	JESSICA stands for Joint European Support for Sustainable
Investment in City Areas, which is an initiative of the European
Commission in cooperation with the European Investment Bank
(EIB) to support urban regeneration and development through financial
instruments.
17	Enough to light the city of Stuttgart for a year — or 1% of the UK’s
annual electricity consumption.
amounts to an estimated reduction of 0.1% a year
in annual emissions in the OP areas concerned.
In Lithuania, the result of the projects carried out
was much greater, in line with the larger share of
funding going to increasing energy efficiency in
buildings. By the end of 2014 (i.e. one year later
than the figures quoted above), energy use in the
864 public buildings which had been renovated
had been reduced by 236 GWh a year, which corresponds
to 3% of annual electricity consumption
in the country.
Other less quantifiable achievements came in the
form of technological advances as a result of innovative
projects undertaken, awareness raising
of the benefits of investing in energy saving and
policy learning, in the sense of acquiring a better
understanding of the policy measures available
and how they can best be implemented and
assessed.
7. Reinforced cooperation and
addressing territorial challenges
The current round of interregional cooperation
(Interreg) has a budget of €10.1  billion for
2014–2020 invested in over 100 cooperation programmes
between regions and territorial, social
and economic partners (Table 6.15). This amount
also includes the ERDF allocation for Member
States to participate in EU external border cooperation
programmes supported by other instruments
(Instrument for Pre-Accession and European
Neighbourhood Instrument). The breakdown of
programmes is as follows:
Table 6.14 Common indicators and targets for 2014–2020 as regards energy efficiency and
renewables
Energy efficiency: number of households with improved
energy consumption classification
Households 870 000
Renewables: additional capacity of renewable energy production MW 7 700
Energy efficiency: decrease of annual primary
energy consumption of public buildings
kWh/year 5.3 billion
Energy efficiency: number of additional energy users connected to smart grids Users 3.3 million
Source: ESIF Open Data Platform — https://cohesiondata.ec.europa.eu/
Chapter 6: The impact of cohesion policy
201
• ••• 
• 60 cross-border programmes — Interreg V-A,
along 38 internal EU borders: ERDF contribution:
€6.6 billion.
• 12 IPA cross-border programmes: Instrument
forPre-Accession
• 16 ENI European Neighbourhood Instrument
• 15 transnational programmes — Interreg V-B,
covering larger areas of cooperation such as
the Baltic Sea, Alpine and Mediterranean regions:
ERDF contribution: €2.1 billion.
The Interreg Europe regional cooperation programme
and three networking programmes
(Urbact III, Interact III and ESPON) cover all 28 EU
Member States and provide a means of exchanging
information and experience between regional
and local bodies in different countries. The ERDF
contribution amounts to €500 million.
In accordance with the new design of EU cohesion
policy for the 2014–2020 period and the focus
on concentration of funding, simplification of administration
and results as well as the pursuit of
the Europe 2020 targets, Interreg has been significantly
reshaped to achieve greater impact and
more effective use of funding.
Table 6.15 Evolution of Interreg 1990–2020
Interreg I
1990–1993
Interreg II
1994–1999
Interreg III
2000–2006
Interreg IV
2007–2013
Interreg V
2014–2020
Legal status Community
Initiative
Integrated into Structural
Funds Regulation
Own Regulation
Benefiting Member States
(internal borders)
11 11
- then -
15
15
- then -
25
27
- then -
28
28
Commitment budget
(in current prices)
ECU 1.1 bn ECU 3.8 bn €5.8 bn €8.7 bn €10.1 bn
Source: DG REGIO
Table 6.16 Key common indicators and targets for Interreg programmes, 2014–2020
Indicator Unit Target
Number of enterprises participating
in cross-border, transnational and
interregional research projects
Enterprises 6 900
Number of participants in projects
promoting gender equality,
equal opportunities and social
inclusion across borders
Persons 9 900
Number of participants in joint local
employment initiatives and joint training
Persons 53 000
Number of participants in crossborder
mobility initiatives
Persons 240 000
Number of participants in joint
education and training schemes to
support youth employment, educational
opportunities and higher and
vocational education across borders
Persons 53 000
Number of research institutions
participating in cross-border,
transnational and interregional
research projects
Organisations 1 400
Source: ESIF Open Data Platform — https://cohesiondata.ec.europa.eu/
Seventh Report on economic, social and territorial cohesion
202
A minimum of 80% of the budget for each cooperation
programme is concentrated on a maximum
of four thematic objectives of the 11 set out in the
ERDF regulations.
The amounts allocated to Interreg are relatively
small. The cross-border programmes, which account
for the bulk of funding, amounted to only
some €20 per head of population in the regions
covered in the 2007–2013 period. The programmes,
therefore, have to be highly strategic
and focused.
By the end of 2013, these programmes had
funded over 6 800 projects in policy areas at the
core of the Lisbon, and later, Europe 2020 strategy.
They included the creation and expansion of
economic clusters, the establishment of centres
of excellence, higher education and training, cooperation
networks between research centres and
cross-border advisory services for enterprises and
business start-ups. The 1 300 or so environmental
projects involved the joint management of natural
resources, including sea and river basins, cooperative
action to combat natural risks, to respond to
climate change and to preserve biodiversity and
pilot initiatives to develop renewable energy.
Specific examples as regards RTD include the joint
development of support for SMEs for image analysis
and optical measurement process control in
the mining industry and cross-border research and
business cooperation for the development of new
propulsion systems, liquefied natural gas technology
and a new generation of wind-assisted motor
boats.
Although the indicators available are limited and
incomplete, they show that around 3 500 jobs
were directly created as a result of the projects
undertaken, 487 km of roads were improved and
over 500 000 people participated in joint education
or training activities.
In the case of the transnational programmes, the
indicators show that 2 207 jobs were created
and 260 transnational projects in RTD and innovation,
accessibility, risk prevention and water
management were carried out. Most of the projects
involved tackling common problems through
collaboration, joint research or exchange of experience.
The most frequent outcomes were the establishment
of networks or partnerships between
SMEs and research centres, the joint management
of natural resources and joint action for environmental
protection. A major aspect was the creation
of critical mass, i.e. assembling funding on a sufficient
scale to tackle territorial and environmental
problems, to set up RTD networks and to create
common services (such as in the case of transport
in the North-West Region).
In the case of the interregional cooperation programme,
the aim of which was to improve the effectiveness
of regional policies through cooperation
and exchanges between regions, the programme
succeeded in setting up a framework in which local
and regional authorities from across the EU could
share experience and examples of good practice
in relation to the problems they faced. However,
the evaluation found little evidence of knowledge
or experience being disseminated outside of the
regions involved in the projects and outside of
Interreg more generally.
Beyond the outputs and results described above,
the programmes also had wider effects, notably in
terms of alleviating barriers to cooperation (mainly
cultural and physical barriers) and increasing
social integration.
Transnational cooperation under the ESF has
helped to make employment and social policies
more effective and has contributed to the implementation
of reforms, by facilitating the exchange
of experience and good practice. For 2014–2020,
Transnational Cooperation has been extended
through the establishment of Thematic Networks18
that bring together representatives from the bodies
managing the ESF Operational Programmes,
policy experts, academics, social partners and civil
society organisations in order to share examples
of good practice and innovation, as well as to coordinate
the launch of calls for projects.
18	Employment, Inclusion, Social economy, Youth employment,
Learning and skills, Migration, Governance, Partnership and
Simplification
203
References
Acs Z., Parsons, W., Tracy, S. (2008), High Impact Firms: Gazelles Revisited, Office of Advocacy, U.S. Small Business
Administration.
Agerberg, M. (2017), ‘Failed expectations: Quality of government and support for populist parties in Europe’,
European Journal of Political Research 56:3, 578-600.
Annoni, P., Dijkstra, L. (2016), EU regional competitiveness index (RCI 2013), Publications Office.
Annoni, P., Dijkstra, L. (2017) Measuring and monitoring regional competitiveness in the European Union. In
Huggins, R. and Thompson P. (Eds.): Handbook of Regions and Competitiveness - Contemporary Theories and
Perspectives on Economic Development, Edward Elgar Publishing.
Annoni, P., Dijkstra, L., Gargano, N. (2017), EU Regional Competitiveness Index: RCI 2016, Working papers
02/2017, Directorate General for Regional And Urban Policy, European Commission.
Annoni, P., Kozovska, K. (2010), EU Regional Competitiveness Index 2010, EUR 24346, Publications Office of the
European Union, Luxembourg.
Arrowsmith, S. (2009), EC Regime on Public Procurement, In K. V Thai (Ed.), International handbook of public procurement
(pp. 254–290). CRC Press, New York.
Batista e Silva, F., Alvarez, M., Vizcaino, P., Jacobs Crisioni, C., Ghisetti, C., Pontarollo, N., Lavalle, C., D’Hombres, B.
(2017) Determinants of urban land densification in Europe, forthcoming.
Becker, S., Egger, P., von Ehrlich, M. (2010), ‘Going NUTS: The effect of EU Structural Funds on regional performance’,
Journal of Public Economics, 94, 578-590.
Bertelsmann Stiftung (2016), Sustainable Governance Indicators 2016.
http://www.sgi-network.org/2016/
Camagni et al. (2017), Quantification of the effects of legal and administrative border obstacles in land border
regions, Final Report to the European Commission, Politecnico de Milano.
Cappelen, A., Castellacci, F., Fagerberg, J., Verspagen, B. (2003), ‘The impact of EU regional support on growth and
convergence in the European Union’, Journal of Common Market Studies, 41(4), 621-644.
Charron, N., Dahlström, C., Fazekas, M., Lapuente, V. (2017), ‘Careers, Connections, and Corruption Risks:
Investigating the impact of bureaucratic meritocracy on public procurement processes’, Journal of Politics, 79(1),
89–103.
Charron, N., Dijkstra, L., Lapuente, V. (2014), Regional Governance Matters: Quality of Government within European
Union Member States, Regional Studies, 48(1), 68–90.
Charron, N., Dijkstra, L., Lapuente, V. (2015), Mapping the Regional Divide in Europe: A Measure for Assessing
Quality of Government in 206 European Regions, Social Indicators Research, 122(2), 315–346.
Charron, N., Dahlström, C., Lapuente, V. (2016), ‘Measuring Meritocracy in the Public Sector in Europe: a New
National and Sub-National Indicator’, European Journal of Criminal Policy and Research2, 22(3), 499–523.
Charron, N., Lapuente, V. (2013), ‘Why do some regions in Europe have a higher quality of government?’ The
Journal of Politics, 75(3), 567-582.
Charron, N., Lapuente V., Rothstein, B. (2011), Measuring Quality of Government and Sub-national Variation,
Report for the EU Commission of Regional Development, European Commission Directorate-General Regional
Policy Directorate Policy Development.
Charron, N., Lapuente, V., Rothstein, B. (2013), Quality of Government and Corruption from a European
Perspective: A Comparative Study of Good Government in EU Regions, Cheltenham: Elgar.
S e v e n t h R e p o r t o n e c o n o m i c , s o c i a l a n d t e r r i t o r i a l c o h e s i o n
204
Cingolani, L., Fazekas, M. (2017), The administrative capacities behind competitive public procurement processes:
a comparative assessment of 32 European countries, Cambridge, UK.
Cingolani, L., Fazekas, M., Kukutschka, R. M. B., Tóth, B. (2015), Towards a comprehensive mapping of information
on public procurement tendering and its actors across Europe, Cambridge, UK.
Ciscar, J. C., Feyen, L., Soria, A., Lavalle, C., Raes, F., Perry, M., Nemry, F., Demirel, H., Rozsai, M., Dosio, A., Donatelli,
M., Srivastava, A., Fumagalli, D., Niemeyer, S., Shrestha, S., Ciaian, P., Himics, M., Van Doorslaer, B., Barrios, S.
(2014), Climate impacts in Europe: The JRC PESETA II Project, JRC Scientific and Policy Reports EUR 26586 EN,
JRC87011, European Commission, Joint Research Centre, Seville.
Combes, P.P, Duranton, G., Gobillon, L., Puga, D., Roux, S. (2012), The productivity advantages of large cities: distinguishing
agglomeration from firm selection, Econometrica 80 (6), 2543-94.
Crescenzi, R., Di Cataldo, M., Rodríguez-Pose, A. (2016), ‘Government quality and the economic returns of transport
infrastructure investment in European regions’, Journal of Regional Science (forthcoming).
Crescenzi, R., Rodríguez-Pose, A. (2012), ‘Infrastructure and regional growth in the European Union’, Papers in
Regional Science, 91(3), 487-513.
https://ideas.repec.org/a/bla/presci/v91y2012i3p487-513.html
Dahlström, C., Lapuente, V., Teorell, J. (2012), The Merit of Meritocratization Politics, Bureaucracy, and the
Institutional Deterrents of Corruption, Political Research Quarterly, 65(3), 656–668.
Dahlström, C., Teorell, J., Dahlberg, S., Hartmann, F., Lindberg, A., Nistotskaya, M. (2015), The QoG Expert Survey
Dataset II, University of Gothenburg: The Quality of Government Institute.
Dellepiane-Avellaneda, S. (2010), ‘Review Article: Good Governance, Institutions and Economic Development:
Beyond the Conventional Wisdom’, British Journal of Political Science, 40 195- 224.
Diaz Ramirez, M., Kleine-Rueschkamp, L., Veneri, P. (2017), Does quality of governance affect the returns of policy
for entrepreneurship?, Paper presented at the 57th Congress of the European Regional Science Association,
Groningen 29 August – 1 September 2017.
Dijkstra, L., Annoni P., Kozovska, K. (2011), A new European Regional Competitiveness Index: theory, methods and
findings, Working Papers 02/2011, Directorate General for Regional and Urban Policy, European Commission.
Dijkstra L., Athanasoglou S. (2015), The Europe 2020 index: The progress of EU countries, regions and cities,
Regional Focus Working Paper 01/2015.
Dijkstra, L., Garcilazo, E., McCann, P. (2015), The effects of the global financial crisis on European regions and cities,
Journal of Economic Geography, 15(5): 935-49.
Dijkstra, L., Poelman, H. (2015), Measuring access to public transport in European cities, Regional Working Papers
N° 01/2015.
Dumont J.-C., Liebig T., Peschner J., Tanay F., Xenogiani T. (2016), How are refugees faring on the labour market in
Europe?, OECD-EC Working Paper 1/2016.
Escobedo F.J., Nowak D.J. (2009), ‘Spatial heterogeneity and air pollution removal by an urban forest’, Landscape
and Urban Planning, 90: 102-110.
ESPON (2017), The Geography of new Employment Dynamics in Europe, Delivery 2 – Interim Report.
Eurofound (2016), ERM annual report 2016: Globalisation slowdown? Recent evidence of offshoring and reshoring
in Europe, Publications Office of the European Union: Luxembourg.
European Commission (2013), The Economic benefits of the Natura 2000 Network, Publications Office of the
European Union: Luxembourg.
European Commission (2014), Investments for jobs and growth, promoting development and good governance in
EU regions and cities. Sixth report on economic, social and territorial cohesion, Publications Office of the European
Union: Luxembourg.
R e fe r e n c e s
205
European Commission (2015a), Closing the loop – An EU action plan for the circular economy, COM(2015) 614 of
2 December 2015.
European Commission (2015b), Employment and Social Developments in Europe, 2015, Publications Office of the
European Union: Luxembourg.
European Commission (2015c), Securing Energy Efficiency to Secure the Energy Union - How Energy Efficiency
meets the EU Climate and Energy Goals, JRC Science and Policy Report EU 27450.
European Commission (2015d), Standard Eurobarometer 83, Spring 2015.
European Commission (2016a), Broadband access in the EU, Data as of January 2016, European Commission,
Directorate-General of Communications Networks, Content and Technology.
https://ec.europa.eu/digital-single-market/en/news/broadband-access-eu-data-january-2016
European Commission (2016b), Education and Training Monitor 2016, European Commission, Directorate-General
for Education and Culture.
https://ec.europa.eu/education/sites/education/files/monitor2016_en.pdf
European Commission (2016c), eGovernment Benchmark 2016, A turning point for eGovernment development in
Europe? (Insight report, background report and dataset.
https://ec.europa.eu/digital-single-market/en/news/eu-egovernment-report-2016-shows-online-public-services-
improved-unevenly).
European Commission (2016d), Employment and Social Developments in Europe, 2016, Publications Office of the
European Union: Luxembourg.
http://ec.europa.eu/social/main.jsp?catId=738&langId=en&pubId=7952&type=2&furtherPubs=yes
European Commission (2016e), European semester thematic factsheets: quality of public administration.
https://ec.europa.eu/info/strategy/european-semester/thematic-factsheets/public-administration_en
European Commission (2016f), Europe’s Digital Progress Report 2016.
https://ec.europa.eu/digital-single-market/en/news/europes-digital-progress-report-2016
European Commission (2016g), Public Procurement Indicators 2014, Brussels.
European Commission (2016h), Quality of Life in European Cities 2015, Directorate-General for Regional and
Urban Policy, Publications office of the European Union, Luxemburg.
European Commission (2016i), Statistical Pocket Book – EU Transport in Figures, Publications Office of the
European Union: Luxembourg.
https://ec.europa.eu/transport/facts-fundings/statistics/pocketbook-2016_en .
European Commission (2016j), Support to SMEs – Increasing Research and Innovation in SMEs and SME
Development, Ex post evaluation of Cohesion Policy programmes 2007-2013, focusing on the European Regional
Development Fund (ERDF) and the Cohesion Fund (CF), Executive Summary.
http://ec.europa.eu/regional_policy/sources/docgener/evaluation/pdf/expost2013/wp2_final_en.pdf
European Commission (2016k), Synthesis Report, Ex post evaluation of Cohesion Policy programmes 2007-2013,
focusing on the European Regional Development Fund (ERDF) and the Cohesion Fund (CF).
http://ec.europa.eu/regional_policy/sources/docgener/evaluation/pdf/expost2013/wp1_synth_report_en.pdf
European Commission (2017a), Competitiveness in low-income and low-growth regions — The lagging regions
report, Commission Staff Working Document (2017) 132 final of 10 April 2017.
European Commission (2017b), Europe’s Digital Progress Report 2017
European Commission (2017c), Reflection Paper on Harnessing Globalisation, Publications Office of the European
Union: Luxembourg.
European Commission (2017d), Regional Innovation Scoreboard 2017, Publications Office of the European Union:
Luxembourg.
http://ec.europa.eu/growth/industry/innovation/facts-figures/regional_en
206 
S e v e n t h R e p o r t o n e c o n o m i c , s o c i a l a n d t e r r i t o r i a l c o h e s i o n
European Commission (2017e), Single market scoreboard.
http://ec.europa.eu/internal_market/scoreboard/.
European Commission (2017f), The Value Added of Ex ante Conditionalities in the European Structural and
Investment Funds, Commission Staff Working Document (2017) 127 final of 31 March 2017.
European Commission and UN-HABITAT (2016), The State of European Cities 2016. Cities leading the way to a
better future, Publications Office of the European Union: Luxembourg.
European Court of Auditors (2014), EU-funded airport infrastructures: poor value for money, Report of the
European Court of Auditors, Publications Office of the European Union: Luxembourg.
European Court of Auditors (2017), More efforts needed to implement the Natura 2000 network to its full potential,
European Court of Auditors Special Report No 1/2017.
European Environment Agency (2015a), Air quality in Europe — 2015 report, Report No 5/2015.
http://www.eea.europa.eu/publications/air-quality-in-europe-2015
European Environment Agency (2015b), The European Environment: State and Outlook.
https://www.eea.europa.eu/soer
European Environment Agency (2017), Climate change, impacts and vulnerability in Europe 2016, An indicatorbased
report, EEA Report N° 1/2017.
https://www.eea.europa.eu/publications/climate-change-impacts-and-vulnerability-2016
European Policies Research Centre, Improving the take-up and effectiveness of Financial Instruments.
https://publications.europa.eu/en/publication-detail/-/publication/19847221-982d-11e7-b92d-01aa75ed71a1
EUROSTAT (2016), Urban Europe, Statistics on cities, towns and suburbs, Edition 2016, Publications office of the
European Union, Luxemburg.
Evans, P., Rauch, J. (1999), ‘Bureaucracy and Growth: A Cross-National Analysis of the Effects of ‘Weberian’ State
Structures on Economic Growth’, American Sociological Review, 64(5), 748–65.
Fazekas M. (2017), Assessing the quality of government at the regional level using the public procurement data
(forthcoming).
Fazekas, M. (2016), Options for benchmarking contracting authority performance using readily available databases,
Brussels.
Fazekas, M., Cingolani, L., Tóth, B. (2016), A comprehensive review of objective corruption proxies in public procurement:
risky actors, transactions, and vehicles of rent extraction, Government Transparency Institute Working
Paper Series No. GTI-WP/2016:03, Budapest.
Fazekas, M., Kocsis, G. (2017), ‘Uncovering High-Level Corruption: Cross-National Objective Corruption Risk
Indicators Using Public Procurement Data’, British Journal of Political Science, 1-10.
Fazekas, M., Tóth, I. J., King, L. P. (2016), ‘An Objective Corruption Risk Index Using Public Procurement Data’,
European Journal of Criminal Policy and Research, 22(3), 369–397.
Flyvbjerg B., Bruzelius N., Rothengatter W. (2003), Megaprojects and risks, Cambridge, Cambridge University Press.
Forzieri, G., Feyen, L., Russo, S., Vousdoukas, M., Alfieri, L., Outten, S., Migliavacca, M., Bianchi, A., Rojas, R., Cid, A.
(2016), Multi-hazard assessment in Europe under climate change, Climatic Change 137, 105–119.
Halleröd, B., Rothstein, B., Adel, D., Nandy, S. (2013), Bad Governance and Poor Children: A Comparative Analysis
of Government Efficiency and Severe Child Deprivation in 68 Low- and Middle-Income Countries, World
Development, 48 19-31.
Habib, M., Zurawicki L. (2002), ‘Corruption and Foreign Direct Investment’, Journal of International Business
Studies 33 (2).
Halkos, G. E., Sundström, A., Tzeremes, N. G. (2015), Regional environmental performance and governance quality:
a nonparametric analysis, Environmental Economics and Policy Studies, 17(4), 621-644.
R e fe r e n c e s
207 
Henderson, J., Hulme, D., Jalilian, H., Phillips, R. (2007), Bureaucratic Effects: ‘Weberian’ State Agencies and
Poverty Reduction, Sociology, 41(3), 515–532.
Holmberg, S., Rothstein, B. (eds.) (2012), Good Government: The Relevance of Political Science, Edward Elgar
Publishing: Cheltenham.
Huber P., Bock-Schappelwein J. (2014), ‘The Effects of Liberalizing Migration on Permanent Migrants’ Education
Structure’, Journal of Common Market Studies, 52: 268-284.
Iammarino, S., Rodriguez-Pose, A., Storper, M. (2017), Why Regional Development Matters for Europe’s Economic
Future, Working Papers 07/2017, Directorate General for Regional and Urban Policy, European Commission.
Ibáñez, J.N., and F. Rotoli, 2017, ‘Measuring the Impact of the Trans-European Road Transport Network on the
Accessibility of European Urban Agglomerations’, Transportation Research Record: Journal of the Transportation
Research Board, Volume 2605, pp. 72–82.
Jaeger J., Bertiller R, Schwick C., Kienast F., (2010), Suitability criteria for measures of urban sprawl, Ecological
Indicators 10(2): 427–441.
Kaufmann, D., Kraay, A. (2002), Growth Without Governance, The World Bank.
Kaufmann, D., Kraay, A., Mastruzzi, M. (2009), Governance Matters VIII: Aggregate and Individual Governance
Indicators, 1996-2008, World Bank Policy Research Working Paper No. 4978.
Kaufmann, D., Kraay, A., Zoido-Lobatón, P., (1999), Aggregating Governance Indicators, Policy Research Paper
2195, The World Bank
Ketels, C., Protsiv, S. (2016), European Cluster Panorama 2016, European Cluster Observatory.
Kinoshita, Y., Campos N. (2003), Why Does FDI Go Where it Goes ? New Evidence from the Transition Economies,
IMF, WP/03/228
Kovacic, W. E., Marshall, R. C., Marx, L. M., Raiff, M. E. (2006), Bidding rings and the design of anti-collusive measures
for auctions and procurements. In N. Dimitri, G. Piga, & G. Spagnolo (Eds.), Handbook of Procurement (pp.
381–411), Cambridge, Cambridge University Press.
KPMG (2017), The Co-ordination and Harmonisation of ESI funds and other policies.
Lavalle, C., Pontarollo, N., Batista E Silva, F., Baranzelli, C., Jacobs, C., Kavalov, B., Kompil, M., Perpiña Castillo, C.,
Vizcaino, M., Ribeiro Barranco, R., Vandecasteele, I., Pinto Nunes Nogueira Diogo, V., Aurambout, J., Serpieri, C.,
Marín Herrera, M., Rosina, K., Ronchi, S. and Auteri, D., European Territorial Trends - Facts and Prospects for Cities
and Regions Ed. 2017, Publications Office of the European Union, Luxembourg, 2017, JRC107391.
Levchenko, A. (2004), Institutional Quality and International Trade, IMF Working Paper, 04/231.
Lewis-Faupel, S., Neggers, Y., Olken, B. A., Pande, R. (2016), ‘Can Electronic Procurement Improve Infrastructure
Provision? Evidence from Public Works in India and Indonesia’, American Economic Journal: Economic Policy, 8(3),
258–283.
Litschke T., Kuttler W. (2008), ‘On the reduction of urban concentration by vegetation ; a review’, Meteorologische
Zeitschrift, 17: 229-240.
Lucas, R. E. (1988). ‘On the mechanics of economic development’, Journal of Monetary Economics, 22(1), 3-42.
Lung T., Lavalle C., Hiederer R., Dosio A., Bouwer L. M. (2013), ‘A multi-hazard regional level impact assessment
for Europe combining indicators of climatic and non-climatic change’, Global Environmental Change, 23, p.
522-536.
Mankiw, N., Romer, P., Weil, D. (1992), ‘A contribution to the empirics of economic growth’, Quarterly Journal of
Economics 107, 407–437.
Melitz, M.J., Ottaviano, G.I.P. (2008), ‘Market Size, Trade, and Productivity’, Review of Economic Studies, 75(1):
295-316.
Moretti, E. (2010), ‘Local Multipliers’, The American Economic Review, 100 (2), 373-77.
S e v e n t h R e p o r t o n e c o n o m i c , s o c i a l a n d t e r r i t o r i a l c o h e s i o n
208
Moretti, E., Thulin, P. (2013), ‘Local Multipliers and Human Capital in the United States and Sweden’, Industrial
Corporate Change, 22 (1), 339-62.
Morrar, R., (2014), ‘Innovation in Services: A Literature Review’, Technology Innovation Management Review, 4(4):
6-14.
Mungiu-Pippidi, A. (2015), The Quest for Good Governance. How Societies Develop Control of Corruption,
Cambridge, Cambridge University Press.
Navarra, A., Tubiana, L. (2013), Regional assessment of climate change in the Mediterranean, Advances in Global
Change Research, Springer Netherlands, Dordrecht.
Nistotskaya, M., Charron, N., Lapuente, V. (2015), ‘The wealth of regions: quality of government and SMEs in 172
European regions’, Environment and Planning C: Government and Policy, 33(5): 1125-1155.
North, D.C. (1990), Institutions, Institutional Change and Economic Performance, Cambridge, Cambridge University
Press.
North, D. C., Wallis, J. J., Weingast, B. R. (2009), Violence and Social Orders. A Conceptual Framework for
Interpreting Recorded Human History, Cambridge, Cambridge University Press.
Nowak, D.J., Crane, D.E., Stevens, J.C. (2006), ‘Air pollution removal by urban trees and shrubs in the United States
et al. 2006’, Urban forestry & urban greening, 4 : 115-123.
Nowak, D.J., Hirabayashi, S., Bodine, A. and Hoehn R. (2013), ‘Modeled PM2.5 removal by trees in ten U.S. cities
and associated health effects’, Environmental Pollution, 178: 395-402.
OECD (2007), Integrity in Public Procurement. Good Practice from A to Z. Paris, OECD.
OECD (2013a), Investing Together: Working Effectively across Levels of Government, Paris: OECD Publishing.
OECD (2013b), Issue paper on Corruption and economic growth.
http://www.oecd.org/g20/topics/anti-corruption/Issue-Paper-Corruption-and-Economic-Growth.pdf.
OECD (2014), Innovation and Modernising the Rural Economy, Paris: OECD Publishing.
OECD (2015a), Government at a glance 2015, Paris: OECD Publishing.
OECD (2015b), The Metropolitan century: Understanding Urbanisation and its Consequences. Paris: OECD
Publishing.
OECD (2016a), Automation and independent work in a digital economy, OECD Policy Brief on the Future of Work,
May 2016.
OECD (2016b), Making Cities Work for All: Data and Actions for Inclusive Growth, Paris, OECD Publishing.
OECD (2016c), Regional Outlook: Productive Regions for Inclusive Society, Paris, OECD Publishing.
OECD, Sigma (2014), The Principles of Public Administration. Paris, OECD/Sigma.
Ostrom, E. (1990), Governing the Commons: The Evolution of Institutions for Collective Action, New York,
Cambridge University Press.
Pellegrini, G., Terribile, F., Tarola, O., Muccigrosso, T., Busillo, F. (2013), ‘Measuring the effects of European Regional
Policy on economic growth: A regression discontinuity approach’, Papers in Regional Science, 92(1), 217-233.
Persson, A., Rothstein, B., Teorell, J. (2013), ‘Why anticorruption reforms fail—systemic corruption as a collective
action problem’, Governance, 26(3), 449-471.
Peters, B. G., Pierre, J. (Eds.). (2001), Politicians, bureaucrats and administrative reform, London, Routledge.
Peters, B. G., Pierre, J. (Eds.). (2004), Politicization of the Civil Service in Comparative Perspective, London,
Routledge.
Poelman, H. and Ackermans, L. (2016), From rail timetables to regional and urban indicators on rail passenger
services, Working papers 02/2016, Directorate General for Regional and Urban Policy, European Commission.
Puga, D. (2010), ‘The magnitude and causes of agglomeration economies’, Journal of Regional Science, 50:
203–219.
R e fe r e n c e s
209     
Rauch, J., Evans, P. (2000), ‘Bureaucratic structure and bureaucratic performance in less developed countries’,
Journal of Public Economics, 75(1), 49–71.
Rodríguez-Pose, A. (2013), Do institutions matter for regional development?, Regional Studies, 47(7), 1034-1047.
Rodríguez-Pose, A. and Di Cataldo, M. (2015), ‘Quality of government and innovative performance in the regions
of Europe’, Journal of Economic Geography 15, 4, 673-706.
Rodríguez-Pose, A., Garcilazo, E. (2015), Quality of government and the returns of investment: Examining the
impact of cohesion expenditure in European regions, Regional Studies, 49(8), 1274-1290.
Rodríguez-Pose A. and Ketterer T. (2016), Institutional change and the development of lagging regions in Europe
(a study for the European Commission, Directorate-General Regional Policy).
Rojas Mujica, R.F., Feyen, L., Watkiss, P. (2013) Climate change and river floods in the European Union: Socioeconomic
consequences and the costs and benefits of adaptation, Global Environmental Change 23, 1737–1751.
http://www.sciencedirect.com/science/article/pii/S0959378013001416#
Romer, P.M. (1986), ‘Increasing returns and long-run growth’, Journal of Political Economy, 94(5), 1002-37.
Rose-Ackerman, S. (1978), Corruption: a study in political economy, New York, Academic Press.
Rothstein, B. (2011), The Quality of Government: Corruption, Social Trust and Inequality in International
Perspective, Chicago, University of Chicago Press.
Rothstein, B., Teorell, J. (2008), What Is Quality of Government? A Theory of Impartial Government Institutions,
Governance, 21(2), 165–190.
Rothstein, B., Teorell, J. (2012), Defining and measuring quality of government. In S. Holmberg & B. Rothstein
(Eds.), Good Government: The Relevance Of Political Science, Cheltenham, Edward Elgar.
Shepherd, B. (2013), Global Value Chains and Developing Country Employment: A Literature Review, OECD Trade
Policy Papers, No. 156, OECD Publishing, Paris.
Solow, R. (1956), ‘A contribution to the theory of economic growth’, Quarterly Journal of Economics, 70(1), 65-94.
Soreide, T. (2002), Corruption in public procurement. Causes, consequences and cures, Bergen, Norway.
Sundström, A. (2013), Women’s local political representation within 30 European countries, University of
Gothenburg, QoG Working Paper Series, 2013:18.
Sundström, A., Wängnerud, L. (2014), ‘Corruption as an obstacle to women’s political representation Evidence
from local councils in 18 European countries’, Party Politics, 1354068814549339.
Svallfors, S. (2013), ‘Government Quality, Egalitarianism, and Attitudes to Taxes and Social Spending: A European
Comparison’, European Political Science Review, 5 (3): 363-380.
Swan, T. (1956), ‘Economic growth and capital accumulation’, Economic Record, 32, 334-361.
Tavits, M. (2008), ‘Representation, Corruption, and Subjective Well-Being’, Comparative Political Studies, 41 (12):
1607-1630.
Teorell, J, Charron, N,. Dahlberg, S,. Holmberg, S,. Rothstein, B,. Sundin, P., Svensson, R. (2013), The Quality of
Government Dataset, version 15 May 2013, University of Gothenburg: The Quality of Government Institute
http://www.qog.pol.gu.se.
Teorell, J., Dahlström, C., Dahlberg,.S. (2011), The QoG Expert Survey Dataset, University of Gothenburg: The
Quality of Government Institute.
Transparency International (2016), Corruption Perception Index 2016.
https://www.transparency.org/news/feature/corruption_perceptions_index_2016
Uslaner, E. M. (2008), ‘The Foundations of Trust: Macro and Micro’, Cambridge Journal of Economics, 32 (2):
289-294.
S e v e n t h R e p o r t o n e c o n o m i c , s o c i a l a n d t e r r i t o r i a l c o h e s i o n
210
Vallecillo, S., Polce, C., Barbosa, A., Perpiña Castillo, C., Zulian, G., Vandecasteele, I., Rusch, G., Maes, J. (2016),
Synergies and conflicts between the delivery of different ES and biodiversity conservation: Spatial planning for
investment in green infrastructure and ecosystem restoration across the EU, OpenNESS Deliverable D3.3/WP3.
Varga, J., in ‘t Veld, J. (2014), ‘The potential growth impact of structural reforms in the EU. A benchmarking exercise’,
European Economy, Economic Papers 541.
Wash, J.P., Yu, J. (2010), ‘Determinants of Foreign Direct Investment: A Sectoral and Institutional Approach’, IMF
Working Paper.
World Bank. (2009), Fraud and Corruption. Awareness Handbook. Washington DC, World Bank.
World Bank (2015a), Doing Business in Poland 2015.
http://www.doingbusiness.org/Reports/Subnational-Reports/poland
World Bank (2015b), Doing Business in Spain 2015.
http://www.doingbusiness.org/Reports/Subnational-Reports/spain
World Bank (2015c), Worldwide Governance Indicators.
http://data.worldbank.org/data-catalog/worldwide-governance-indicators
World Bank (2016), Doing Business reports - Measuring Regulatory Quality and Efficiency
World Bank (2017a), Doing Business 2017: Equal Opportunity for All.
http://www.doingbusiness.org/reports/global-reports/doing-business-2017
World Bank (2017b), Doing Business in the European Union 2017: Bulgaria, Hungary and Romania.
http://www.doingbusiness.org/reports/subnational-reports/eu-bulgaria-hungary-romania
World Economic Forum (2017), The Global Competitiveness Report 2016–2017.
https://www.weforum.org/reports/the-global-competitiveness-report-2016-2017-1
World Health Organisation (2017), Global Health Observatory: World Health Statistics
http://www.who.int/gho/database/en/
211
List of figures, maps and tables
List of figures
Figure 1	 Cohesion policy funding as an estimated share of public investment, 2015–2017...............xxiv
Figure 1.1	 Coefficient of variation of GDP per head, employment rate (20–64) and unemployment
rate in EU-28 NUTS 2 regions, 2000–2016 (indices, 2000=100)..............................................4
Figure 1.2	 Growth rates of GDP per head of regions in less developed and moderately developed
Member States, 2001–2008......................................................................................................7
Figure 1.3	 Growth rates of GDP per head of regions in less developed and moderately developed
Member States, 2009–2015......................................................................................................7
Figure 1.4	 Growth of GDP per head in real terms, EU-28, 2001–2018......................................................7
Figure 1.5	 Spatial spill-overs of economic growth between regions in the EU, 2000–2014......................8
Figure 1.6	 Employment and GVA shares in industry (excluding construction) 1995–2014...................... 11
Figure 1.7	 Employment and GVA shares in agriculture 1995–2014......................................................... 11
Figure 1.8	 Annual average employment growth in 2008–2014 and the change in the share of total
employment in non-tradable sectors 2000–2007 in 19 OECD countries................................17
Figure 1.9	 Evolution of total employment (number employed) in metro regions, 2000–2014..................19
Figure 1.10	 Employment and GVA shares in industry (excluding construction), by regional income
club, 1995–2014.....................................................................................................................24
Figure 1.11	 Firm density by metro region, 2014........................................................................................26
Figure 1.12	 Employees per firm by metro region, 2014.............................................................................26
Figure 1.13	 Enterprise birth rate by metro region, 2013............................................................................27
Figure 1.14	 Death rate of enterprises by metro region, 2012....................................................................27
Figure 1.15	 Number of high growth firms by metro region, 2014...............................................................27
Figure 1.16	 Patents by metro region, 2009–2011......................................................................................29
Figure 1.17	 Total expenditure on R&D, 2014.............................................................................................31
Figure 1.18	 Average wage and share of full-time equivalent (FTE) employment in tradable clusters
located in mostly urban areas, 2014.......................................................................................36
Figure 1.19	 Population aged 25–64 with tertiary education, 2016.............................................................37
Figure 1.20	 People’s levels of digital skills, by level of economic development, 2015...............................39
Figure 1.21	 Households with access to Next Generation Access (NGA) broadband by type of area,
2012 and 2016........................................................................................................................43
Figure 1.22	 Regional competitiveness index, 2016...................................................................................47
Figure 1.23	 Relationship between RCI and GDP per head (in PPS) in NUTS 2 regions by level of
economic development...........................................................................................................51
Figure 1.24	 Relationship between RCI and the birth rate of firms (relative to population) in NUTS 2
regions by level of economic development.............................................................................51
S e v e n t h R e p o r t o n e c o n o m i c , s o c i a l a n d t e r r i t o r i a l c o h e s i o n
212
Figure 2.1	 Natural change and net-migration in the EU-28, 1960–2015..................................................54
Figure 2.2	 Population born outside the EU-28, 2011 and 2016...............................................................55
Figure 2.3	 Population born in another EU-28 country, 2011 and 2016....................................................55
Figure 2.4	 Employment rate by country of birth (15–64), 2016................................................................61
Figure 2.5	 Employment rate gap between native-born men and women and non-EU born (20–64),
2016........................................................................................................................................62
Figure 2.6	 First-time asylum applications in the EU-28 by gender and age, 2016...................................63
Figure 2.7	 Literacy proficiency of adults (16–64), 2016...........................................................................73
Figure 2.8	 Numeracy proficiency of adults (16–64), 2016.......................................................................73
Figure 2.9	 Share of population at risk of poverty or social exclusion by degree of urbanisation, 2015...75
Figure 2.10	 Change in the proportion of people at risk of poverty or social exclusion by degree of
urbanisation, 2008–2015........................................................................................................75
Figure 2.11	 The at-risk-of-poverty rate by degree of urbanisation, 2015...................................................76
Figure 2.12	 Change in the at-risk-of-poverty rate by degree of urbanisation, 2008–2015.........................76
Figure 2.13	 Proportion of population living in very low-work intensity households by degree of
urbanisation, 2015..................................................................................................................76
Figure 2.14	 Change in the proportion of population living in very low-work intensity households by
degree of urbanisation, 2008–2015........................................................................................77
Figure 2.15	 Proportion of population living in severe material deprivation by degree of urbanisation,
2015........................................................................................................................................77
Figure 2.16	 Change in the proportion of population living in severe material deprivation by degree of
urbanisation, 2008–2015........................................................................................................77
Figure 2.17	 Income concentration in cities by income group, 2014 or latest available year......................78
Figure 2.18	 Europe 2020 achievement index, 2015..................................................................................82
Figure 2.19	 Change in the Europe 2020 achievement index between 2010 and 2015.............................82
Figure 2.20	 Difference between female and male graduates by field of tertiary education, 2015..............85
Figure 2.21	 Degree of within-country variability of the EU-SPI..................................................................91
Figure 2.22	 Relationship between EU-SPI and GDP per head..................................................................94
Figure 3.1	 Primary energy consumption, change 2005–2015 and distance to target..............................98
Figure 3.2	 Changes in greenhouse gas emissions outside the Emissions Trading Scheme,
2005–2015 and Europe 2020 targets.....................................................................................99
Figure 3.3	 Share of renewable energy in gross final energy consumption, 2006 and 2015 and target
in 2020..................................................................................................................................100
Figure 3.4	 Share of waste landfilled in selected EU Member States, 2014...........................................108
Figure 3.5	 Passenger travel by transport mode, 2014........................................................................... 110
Figure 3.6	 Freight transport by mode, 2014........................................................................................... 110
Figure 3.7	 Distance to services by type of municipality in the EU.......................................................... 111
Figure 3.8	 Share of population living within 1km of different services, by city size and degree of
urbanisation in the EU........................................................................................................... 112
Figure 3.9	 Access to public transport in large European cities, 2014–2016.......................................... 116
L i s t o f f i g u r e s , m a p s a n d t a b l e s
213
Figure 3.10	 Access to public transport in mid-sized European cities, 2014–2016................................... 116
Figure 3.11	 People reporting they live in an area affected by pollution, grime or other environmental
problems, by degree of urbanisation, 2015........................................................................... 116
Figure 3.12	 People reporting to be satisfied with the level of noise in their city, 2015.............................120
Figure 3.13	 Share of the Natura 2000 network intersecting with Functional Urban Areas.......................121
Figure 3.14	 Population of border areas having access to rail passenger services, 2014........................126
Figure 4.1	 Government effectiveness, 1996 and 2015..........................................................................138
Figure 4.2	 Citizen’s confidence in institutions, 1996 and 2015..............................................................138
Figure 4.3	 Economic impact of government effectiveness, 2015...........................................................139
Figure 4.4	 Social impact of government effectiveness, 2015.................................................................139
Figure 4.5	 Corruption Perception Index, 2016.......................................................................................143
Figure 4.6	 Corruption Perception Index, 2012–2016.............................................................................143
Figure 4.7	 Perception of corrupt practices in local government, 2012–2014.........................................144
Figure 4.8	 Trust in local government officials per city, 2012–2014.........................................................144
Figure 4.9	 Policy performance indicator, 2016.......................................................................................146
Figure 4.10	 Governance performance indicator, 2016.............................................................................146
Figure 4.11	 Economic and social policy performance indicators, 2016...................................................147
Figure 4.12	 Executive capacity and accountability indicators, 2016........................................................147
Figure 4.13	 Estimated effect of structural reforms on GDP after 5, 10 and 20 years...............................148
Figure 4.14	 Estimated effect of structural reforms on employment after 5, 10 and 20 years...................148
Figure 4.15	 Closedness of the public sector according to the Quality of Government Expert Survey,
2015......................................................................................................................................150
Figure 4.16	 Professionalism and impartiality of the public sector according to the Quality of
Government Expert Survey, 2015.........................................................................................150
Figure 4.17	 Local Autonomy Index, 1990, 2000 and 2014.......................................................................151
Figure 4.18	 Local and regional self-rule, 2014.........................................................................................151
Figure 4.19	 E-Government use by citizens, 2011 and 2016....................................................................153
Figure 4.20	 Availability, usability, ease and speed of use of public online services.................................154
Figure 4.21	 Transparency of e-Government, average 2014–2015..........................................................154
Figure 4.22	 Ease of doing business, 2010–2017.....................................................................................155
Figure 4.23	 Time and cost of starting a company in different cities in selected Members States,
2016......................................................................................................................................155
Figure 4.24	 Registration of companies online in different cities in Hungary, Bulgaria, Poland and
Romania, 2016......................................................................................................................156
Figure 4.25	 Time needed to deal with construction permits in different cities in selected Member
States, 2016..........................................................................................................................157
Figure 4.26	 Time needed to enforce a contract in different cities in selected Member States, 2016.......157
Figure 5.1	 General government balance, EU-28 average, 2000–2016.................................................164
Figure 5.2	 General government balance, 2009, 2013 and 2016............................................................165
S e v e n t h R e p o r t o n e c o n o m i c , s o c i a l a n d t e r r i t o r i a l c o h e s i o n
214
Figure 5.3	 General government expenditure and revenue and general government balance, EU-28,
2001–2016............................................................................................................................165
Figure 5.4	 Selected categories of general government expenditure, average 2000–2007 and 2016....166
Figure 5.5	 Total public investment, 2016................................................................................................167
Figure 5.6	 Change in growth-friendly categories of general government expenditure, 2008–2015.......167
Figure 5.7	 Sub-national government expenditure, 2001 and 2016........................................................169
Figure 5.8	 Sub-national government investment, 2001 and 2016.........................................................169
Figure 5.9	 Sub-national government expenditure, revenue and budget balance, EU-28, 2001–2016..170
Figure 5.10	 Sub-national government budget balance, 2009 and 2016..................................................170
Figure 6.1	 Planned investment by key priority, 2014–2020....................................................................176
Figure 6.2	 ERDF and Cohesion Fund allocations, 2015–2017..............................................................177
Figure 6.3	 Funding committed to projects selected, 2007–2013 and 2014–2020.................................177
Figure 6.4	 Cohesion policy funding, 1986–2023....................................................................................178
Figure 6.5	 EIB lending under the Cohesion Objective by country and programming period (€ billion)..184
Figure 6.6	 Impact of cohesion policy on EU GDP, 2007–2023..............................................................185
Figure 6.7	 Impact of 2014–2020 programmes on Member States’ GDP, 2023......................................185
Figure 6.8	 Impact of cohesion policy on non-cohesion countries’ GDP, all programmes and
programmes implemented in the cohesion countries, 2023.................................................186
Figure 6.9	 Main EU sources of funding for research, innovation and ICT, 2014–2020 (€ bn)...............187
Figure 6.10	 Main EU sources of funding for SMEs, 2014–2020 (€ bn)....................................................189
Figure 6.11	 Large Enterprise support 2007–2013 – Case study result....................................................192
Figure 6.12	 Main EU sources of funding for employment, training and social inclusion, 2014–2020
(€ bn).....................................................................................................................................193
Figure 6.13	 Main EU sources of funding for agriculture and the environment, 2014–2020 (€ bn)...........196
Figure 6.14	 Main EU sources of funding for transport and energy infrastructure, 2014–2020 (€ bn)......196
Figure 6.15	 ERDF and Cohesion Fund allocation to transport, 2007–2013............................................198
List of maps
Map 1	 Change in GDP per head index, 2000–2008...........................................................................................................................xv
Map 2	 Map 2 Change in GDP per head index, 2008–2015............................................................................................................xv
Map 3	 Young people (15–24) not in employment, education or training (NEET), 2016................................................xvi
Map 4	 Total population change in NUTS 3 regions, 2005–2015.............................................................................................xviii
Map 5	 Regional innovation performance, 2017.................................................................................................................................... xx
Map 6	 European Quality of Government index, 2017.....................................................................................................................xxii
Map 1.1	 GDP per head (PPS), 2015...................................................................................................................................................................3
Map 1.2	 Change in GDP per head, 2000–2015..........................................................................................................................................3
Map 1.3	 Change in GDP per head index, 2000–2008.............................................................................................................................5
Map 1.4	 Change in GDP per head index, 2008–2015.............................................................................................................................5
Map 1.5	 Risk factors linked to globalisation and technological change.....................................................................................10
Map 1.6	 The Economic Development Clubs of European Regions.................................................................................................22
L i s t o f f i g u r e s , m a p s a n d t a b l e s
215
Map 1.7	 Patent applications to the European Patent Office (EPO), average 2010–2011................................................30
Map 1.8	 Total expenditure on R&D, 2014...................................................................................................................................................32
Map 1.9	 Regional innovation performance, 2017...................................................................................................................................34
Map 1.10 	 Regional level of FP7 expenditure................................................................................................................................................35
Map 1.11	 Population aged 25–64 with tertiary education, 2016.....................................................................................................38
Map 1.12	 Expected change in road accessibility due to the TEN-T network completion, by NUTS 3 region............41
Map 1.13	 Average speed of direct rail connections, 2014...................................................................................................................41
Map 1.14	 Rail accessibility during morning peak hours, by city, 2014..........................................................................................42
Map 1.15	 Access to passenger flights by NUTS 3 region, 2015........................................................................................................42
Map 1.16	 Next generation access coverage by NUTS 3 region, 2016...........................................................................................44
Map 1.17	 Fixed broadband coverage by NUTS 3 region, 2016..........................................................................................................45
Map 1.18	 Regional Competitiveness Index — RCI, 2016.......................................................................................................................48
Map 1.19	 Changes in the Regional Competitiveness Index..................................................................................................................50
Map 2.1	 Natural population growth in NUTS 3 regions, 2005–2015..........................................................................................56
Map 2.2	 Net migration in NUTS 3 regions, 2005–2015......................................................................................................................56
Map 2.3	 Total population change in NUTS 3 regions, 2005–2015...............................................................................................59
Map 2.4	 Median age in NUTS 3 regions, 2016.........................................................................................................................................59
Map 2.5	 Employment rate (20–64), 2016..................................................................................................................................................64
Map 2.6	 Change in employment rate (20-64), 2008–2016.............................................................................................................64
Map 2.7	 Unemployment rate, 2016................................................................................................................................................................66
Map 2.8	 Change in unemployment rates, 2008–2016........................................................................................................................66
Map 2.9	 Young people (15–24) not in employment, education or training (NEET), 2016................................................67
Map 2.10	 Participation of adults aged 25–64 in education and training, 2016......................................................................70
Map 2.11	 Early school-leavers from education or training aged 18–24, average 2014–2016.....................................70
Map 2.12	 Proportion of 15-year-olds with low proficiency in mathematics, reading and science, 2016..................72
Map 2.13	 The Europe 2020 achievement index (EU target)s), 2015..............................................................................................81
Map 2.14	 Change in the Europe 2020 achievement index (EU targets), 2015-2010...........................................................81
Map 2.15	 Difference between female and male employment rates (20–64), 2016.............................................................84
Map 2.16	 Difference between female and male unemployment rates, 2016...........................................................................84
Map 2.17	 Gender balance of population aged 30–34 with tertiary education, average 2014–2016.........................86
Map 2.18	 Gender gap (females–males) for early school-leavers (18–24), 2014–2016.....................................................86
Map 2.19	 Women in regional assemblies, 2017........................................................................................................................................87
Map 2.20	 Change in the shares of women in regional assemblies, 2007–2017....................................................................87
Map 2.21	 Life expectancy, 2015..........................................................................................................................................................................89
Map 2.22	 Infant mortality rate, 2015...............................................................................................................................................................89
Map 2.23	 Road traffic fatalities, 2015.............................................................................................................................................................90
Map 2.24	 Road traffic fatalities in cities, 2013–2014............................................................................................................................90
Map 2.25	 EU Social Progress Index, 2016.....................................................................................................................................................92
Map 2.26	 EU Social Progress Index — sub-indices (2016)...................................................................................................................93
S e v e n t h R e p o r t o n e c o n o m i c , s o c i a l a n d t e r r i t o r i a l c o h e s i o n
216
Map 3.1	 Electricity generated from hard coal and lignite, 2015.................................................................................................101
Map 3.2	 Electricity generated from renewable sources, 2015.....................................................................................................101
Map 3.3	 Negative climate change impacts under a 2°C warming scenario..........................................................................103
Map 3.4	 Projected increase in multi-hazard climate-related exposure, 2050.....................................................................104
Map 3.5	 Rivers and lakes with less than good ecological status or potential.....................................................................106
Map 3.6	 Residential, industrial and commercial areas per inhabitant by city, 2012.......................................................113
Map 3.7	 Change in residential, industrial and commercial areas per inhabitant by city, 2006–2012..................114
Map 3.8	 Concentration of airborne particulate matter (PM10
) in cities, 2014......................................................................118
Map 3.9	 Concentration of ground-level ozone (O₃) in cities, 2014............................................................................................119
Map 3.10	 Access to green urban areas in cities, 2012........................................................................................................................122
Map 3.11	 Share of NO₂ concentration removed by vegetation in cities, 2010......................................................................123
Map 3.12	 Population flooded in case of the biggest 100-year flood in Functional Urban Areas.................................125
Map 3.13	 Border regions.......................................................................................................................................................................................128
Map 3.14	 Loss of GDP in EU NUTS 3 land border regions due to cross-border obstacles..............................................129
Map 3.15	 Cross-border road network efficiency in border areas, 2015.....................................................................................130
Map 3.16	 ERDF Transnational Cooperation Programmes 2014–2020.......................................................................................132
Map 4.1	 European Quality of Government index, 2017...................................................................................................................140
Map 4.2	 Public sector meritocracy, 2013.................................................................................................................................................149
Map 4.3	 Public procurement with a single bidder, average 2013-2015.................................................................................158
Map 4.4	 Change in share of single bidders, between 2007–2009 and 2013–2015......................................................158
Map 4.5	 Public procurement using an open call for tender, average 2013–2015............................................................160
Map 6.1	 Impact of the 2014–2020 cohesion policy programmes on GDP, 2023.............................................................188
Map 6.2	 Impact of the 2014–2020 cohesion policy programmes on GDP, 2030.............................................................188
List of tables
Table 1.1	 Estimation results (‘+’ is a positive impact;’–’ is a negative impact)........................................................................8
Table 1.2	 Employment and GVA by NACE sector and group of Member States, shares in 2016 and changes
2001–2008 and 2009–2016.......................................................................................................................................................13
Table 1.3	 Decomposition of annual average change in GVA per head, 2001–2008 and 2009–2016....................16
Table 1.4	 Changes in GDP per head, productivity and employment per head by metropolitan region,
2001–2008, and 2009–2014......................................................................................................................................................18
Table 1.5	 Real GDP per head, productivity and employment per head growth by urban-rural typology,
2001–2008, and 2009–2014......................................................................................................................................................20
Table 1.6	 European regions, by income club: some stylised facts................................................................................................23
Table 1.7	 Total R&D expenditure and the distance to the EU2020 target, EU‑28 regions, 2014..............................31
Table 1.8	 Population aged 30–34 with a tertiary education, by cohesion policy groups of regions, 2016...........37
Table 2.1	 Population share in NUTS 3 regions by determinants of population change, 2005–2015 (%)..............57
Table 2.2	 Population change, natural change and net migration in capital metro, other metro and
non-metro regions, 2005–2015.................................................................................................................................................58
L i s t o f f i g u r e s , m a p s a n d t a b l e s
217
Table 2.3	 Population change, natural change and net migration in urban, intermediate and rural region,
2005–2015............................................................................................................................................................................................58
Table 2.4	 Division of NUTS 3 regions by median age in 2016 and direction of net migration, 2005–2015
(% of total)..............................................................................................................................................................................................60
Table 2.5	 Division of population age in capital metro, other metro and non-metro regions, 2016..........................60
Table 2.6	 Employment and unemployment by category of region, 2016 and change 2008–2016.........................65
Table 2.7	 Youth unemployment, those not in employment, education or training (15–24) and participation
in education and training (25–64) by category of region, 2008–2016...............................................................65
Table 2.8	 Europe 2020 regional achievement index (EU targets), 2010–2015....................................................................82
Table 2.9	 The framework of the EU‑SPI index.........................................................................................................................................91
Table 3.1	 Demographic changes in border and non-border regions, 2005–2015............................................................127
Table 4.1	 Summary of fixed effects analysis.........................................................................................................................................141
Table 4.2	 e-Government benchmark: performance and progress..............................................................................................153
Table 6.1	 EU and national contributions to cohesion policy, 2014–2020.............................................................................176
Table 6.2	 Projects selected and expenditure by managing authorities as at end-June 2017 compared
to total planned investment for the 2014–2020 period...........................................................................................178
Table 6.3	 The ex ante conditionalities for the 2014–2020 programming period.............................................................180
Table 6.4	 Division of EU sources of funding for the 2014–2020 period...............................................................................183
Table 6.5	 Changes in Financial Instruments supported by cohesion policy between the 2007–2013 and
2014–2020 periods ......................................................................................................................................................................183
Table 6.6	 EIB lending to Cohesion Priority Regions (€ billion)......................................................................................................184
Table 6.7	 Common indicators and targets for 2014–2020 in the fields of innovation and competitiveness...189
Table 6.8	 Incidence and volume of support to large enterprises, 2007–2013..................................................................191
Table 6.9	 Common indicators and targets for 2014–2020 in the fields of urban and social infrastructure....195
Table 6.10	 ERDF urban and social projects, 2007–2013 (% reporting a contribution to various goals)................196
Table 6.11	 Additional people served by water and wastewater projects co-financed by the ERDF and
Cohesion Fund, 2007–2013 (up to end-2014)...............................................................................................................197
Table 6.12	 Common indicators and targets for 2014–2020 as regards the environment ...........................................197
Table 6.13	 Common indicators and targets for 2014–2020 as regards transport ...........................................................199
Table 6.14	 Common indicators and targets for 2014–2020 as regards energy efficiency and renewables.......200
Table 6.15	 Evolution of Interreg 1990–2020...........................................................................................................................................201
Table 6.16	 Key common indicators and targets for Interreg programmes, 2014–2020................................................201
S e v e n t h R e p o r t o n e c o n o m i c , s o c i a l a n d t e r r i t o r i a l c o h e s i o n
218
Getting in touch with the EU
In person
All over the European Union there are hundreds of Europe Direct information centres. You
can find the address of the centre nearest you at: http://europa.eu/contact
On the phone or by email
Europe Direct is a service that answers your questions about the European Union. You can
contact this service:
– by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls) or
– at the following standard number: +32 22999696
– by email via: http://europa.eu/contact
Finding information about the EU
Online
Information about the European Union in all the official languages of the EU is available on
the Europa website at: http://europa.eu
EU publications
You can download or order free and priced EU publications from EU Bookshop at:
http://publications.europa.eu/eubookshop. Multiple copies of free publications may
be obtained by contacting Europe Direct or your local information centre (see
http://europa.eu/contact).
EU law and related documents
For access to legal information from the EU, including all EU law since 1951 in all the official
language versions, go to EUR-Lex at: http://eur-lex.europa.eu
Open data from the EU
The EU Open Data Portal (http://data.europa.eu/euodp) provides access to datasets from the
EU. Data can be downloaded and reused for free, both for commercial and non-commercial
purposes.
KN-AM-17-001-EN-C
ISBN 978-92-79-71840-3
doi: 10.2776/5244
ISBN 978-92-79-71840-3
doi: 10.2776/5244
#CohesionPolicy
@EU_Regional
EUinmyRegion
ec.europa.eu/inforegio
STAY CONNECTED
Scan to open the
Cohesion Report website