EUR-Lex Access to European Union law

Back to EUR-Lex homepage

This document is an excerpt from the EUR-Lex website

Document 52017SC0330

COMMISSION STAFF WORKING DOCUMENT Accompanying the document REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS My region, My Europe, Our future: The seventh report on economic, social and territorial cohesion

SWD/2017/0330 final

Brussels, 9.10.2017

SWD(2017) 330 final

COMMISSION STAFF WORKING DOCUMENT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

My region, My Europe, Our future:
The seventh report on economic, social and territorial cohesion

{COM(2017) 583 final}


1.7.     REFERENCES    74

Figure 11: Coefficient of variation (index, 2000 = 100), GDP per head in PPS, employment rate, unemployment rate, EU-28 NUTS 2 regions, 2000-2016 Figure 1 1: Coefficient of variation (index, 2000 = 100), GDP per head in PPS, employment rate, unemployment rate, EU-28 NUTS 2 regions, 2000-2016

Figure 12: GDP per head growth rates of regions in less developed or moderately developed Member States, 2001-2008 Figure 1 2: GDP per head growth rates of regions in less developed or moderately developed Member States, 2001-2008

Figure 13: GDP per head growth rates of regions in less developed or moderately developed Member States, 2009-2015 Figure 1 3: GDP per head growth rates of regions in less developed or moderately developed Member States, 2009-2015

Figure 14: Growth of GDP per head in real terms, 2001-2018 Figure 1 4: Growth of GDP per head in real terms, 2001-2018

Figure 15 Employment shares in Industry (excluding construction), 1995-2014 Figure 1 5 Employment shares in Industry (excluding construction), 1995-2014

Figure 16 GVA shares in Industry (excluding construction, 1995-2014 Figure 1 6 GVA shares in Industry (excluding construction, 1995-2014

Figure 17 Employment shares in Agriculture, 1995-2014 Figure 1 7 Employment shares in Agriculture, 1995-2014

Figure 18 GVA shares in Agriculture, 1995-2014 Figure 1 8 GVA shares in Agriculture, 1995-2014

Figure 19: Evolution of total employment (number employed) in metro regions, 2000-2014 Figure 1 9: Evolution of total employment (number employed) in metro regions, 2000-2014

Figure 110 Employment shares in Industry (excluding construction), by club, 1995-2014 Figure 1 10 Employment shares in Industry (excluding construction), by club, 1995-2014

Figure 111 GVA shares in Industry (excluding construction), by club, 1995-2014 Figure 1 11 GVA shares in Industry (excluding construction), by club, 1995-2014

Figure 112: Firms density by metro region, 2014 Figure 1 12: Firms density by metro region, 2014

Figure 113: Employees per firm by metro region, 2014 Figure 1 13: Employees per firm by metro region, 2014

Figure 114: Enterprise birth rate per metro region, 2013 Figure 1 14: Enterprise birth rate per metro region, 2013

Figure 115: Death rate of enterprises, 2012 Figure 1 15: Death rate of enterprises, 2012

Figure 116: Number of high growth firms per 1000 inhabitants, in 2014 Figure 1 16: Number of high growth firms per 1000 inhabitants, in 2014

Figure 117: Patents by metro regions, average 2009-11 Figure 1 17: Patents by metro regions, average 2009-11 43

Figure 118: Total expenditure on R&D, 2014 Figure 1 18: Total expenditure on R&D, 2014 46

Figure 1 19: population (aged 25-64) with tertiary education, 2016 54

Figure 120: People’s levels of digital skills, by level of economic development, 2015 Figure 1 20: People’s levels of digital skills, by level of economic development, 2015 57

Figure 121: Households with access to Next generation Access (NGA) broadband, by type of area, 2012 and 2016. Figure 1 21: Households with access to Next generation Access (NGA) broadband, by type of area, 2012 and 2016. 64

Figure 122 - Regional competitiveness index, 2016 Figure 1 22 - Regional competitiveness index, 2016 68

Figure 123: Relationship between RCI and GDP per head (in PPS), by level of economic development Figure 1 23: Relationship between RCI and GDP per head (in PPS), by level of economic development 72

Figure 124: Relationship between RCI and the birth rate of firms (relative to population), by level of development Figure 1 24: Relationship between RCI and the birth rate of firms (relative to population), by level of development 73

Table 11 Change in employment and GVA by NACE sector per group of Member States, shares in 2016 and changes 2000-2016 Table 1 1 Change in employment and GVA by NACE sector per group of Member States, shares in 2016 and changes 2000-2016

Table 12: Decomposing average annual change in GVA per head per MS, 2001-2008 and 2009-2016 Table 1 2: Decomposing average annual change in GVA per head per MS, 2001-2008 and 2009-2016

Table 13 Change in GDP per head, productivity and employment per head by type of metropolitan region, 2001-2008 and 2009-2014 (average % per year) Table 1 3 Change in GDP per head, productivity and employment per head by type of metropolitan region, 2001-2008 and 2009-2014 (average % per year)

Table 14: Real GDP per head, productivity and employment per head growth by urban-rural typology, 2001-2008, and 2009-2014 Table 1 4: Real GDP per head, productivity and employment per head growth by urban-rural typology, 2001-2008, and 2009-2014

Table 15: European regions, by income club: some stylised facts Table 1 5: European regions, by income club: some stylised facts

Table 16: Total R&D expenditure and the distance to the EU2020 target, EU-28 regions, 2014 Table 1 6: Total R&D expenditure and the distance to the EU2020 target, EU-28 regions, 2014 45

Table 17: Population aged 30-34 with a tertiary education, by cohesion policy groups of regions, 2016 * Table 1 7: Population aged 30-34 with a tertiary education, by cohesion policy groups of regions, 2016 * 56

Map 11 GDP per head in real terms, 2015 Map 1 1 GDP per head in real terms, 2015

Map 12 Change in GDP per head index, 2000 vs 2015 Map 1 2 Change in GDP per head index, 2000 vs 2015

Map 13 Change in GDP per head index, 2000 vs 2008 Map 1 3

Map 14 Change in GDP per head index, 2008 vs 2015 Map 1 4

Map 15: Risks factors linked to globalisation and technological change Map 1 5: Risks factors linked to globalisation and technological change

Map 16: The economic development clubs of EU regions Map 1 6: The economic development clubs of EU regions

Map 17: Patent applications to the European Patents Office, 2010-2011 Map 1 7: Patent applications to the European Patents Office, 2010-2011 44

Map 18: Total expenditure on R&D, 2014 Map 1 8: Total expenditure on R&D, 2014 47

Map 19: Regional Innovation Scoreboard, 2017 Map 1 9: Regional Innovation Scoreboard, 2017 49

Map 110: Population aged 25-64 with tertiary education, 2016 Map 1 10: Population aged 25-64 with tertiary education, 2016 55

Map 111: Expected change in road accessibility due to TEN-T network composition, by NUTS3 region Map 1 11: Expected change in road accessibility due to TEN-T network composition, by NUTS3 region 60

Map 112: Average speed of direct rail connections, 2014 Map 1 12: Average speed of direct rail connections, 2014 60

Map 113: Rail accessibility during morning peak hours, Map 1 13: Rail accessibility during morning peak hours, 61

Map 114: Accessibility to passenger flights by NUTS3 region, 2013 Map 1 14: Accessibility to passenger flights by NUTS3 region, 2013 61

Map 115: Next generation access coverage by NUTS3 regions, 2016 Map 1 15: Next generation access coverage by NUTS3 regions, 2016 63

Map 116: Households with a broadband connection, by NUTS2 regions 2016 Map 1 16: Households with a broadband connection, by NUTS2 regions 2016 66

Map 117: Regional Competitiveness index, 2016 Map 1 17: Regional Competitiveness index, 2016 69

Map 118: Changes in RCI, 2016-2013; 2013-2010 and over the whole period, 2016-2010. Map 1 18: Changes in RCI, 2016-2013; 2013-2010 and over the whole period, 2016-2010. 71



Key messages

·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.

 

1.1.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 Union, 2017a).

Cohesion Policy has invested heavily in reducing economic disparities across EU regions. It has co-financed 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 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.

1.2.ECONOMIC TRENDS AMONG EU REGIONS AND MEMBER STATES

1.2.1 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 terms,  1 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, Portugal. 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 ). 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.

Figure 11: Coefficient of variation (index, 2000 = 100), GDP per head in PPS, employment rate, unemployment rate, EU-28 NUTS 2 regions, 2000-2016

Source: Eurostat and DG REGIO calculations.

Note: The coefficient of variation is weighted by the total regional population

Between 2000 and 2008, all the regions in the EU13 except Malta converged to the EU average ( Map 1 3 ), with big strides (more than 20 index points) in the capital regions of the Bulgaria, 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 EU13 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.

Overall, the biggest relative increases in GDP per head between 2000 and 2015 occurred in the EU13, while the biggest reductions were in Greece and Italy, in the latter both before and after the crisis ( Map 1 2 ). But also a few regions in Belgium, the Netherlands, France and the UK also experienced big drops.

Map 11 GDP per head in real terms, 2015

Map 12 Change in GDP per head index, 2000 vs 2015

Map 13 Change in GDP per head index, 2000 vs 2008

Map 14 Change in GDP per head index, 2008 vs 2015

Note: Annual average change on previous year, in %

Figure 12: GDP per head growth rates of regions in less developed and moderately developed Member States, 2001-2008

Figure 13: GDP per head growth rates of regions in less developed and moderately developed Member States, 2009-2015

Note: capital regions are indicated in red. Regions are ranked by growth rates for the period 2001-2008 in both figures

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).

Figure 14: Growth of GDP per head in real terms, 2001-2018

Source: Eurostat, DG ECFIN, DG REGIO calculations

EU outermost regions

The European Union includes 9 outermost regions, which are geographically remote from the continent and located in the Caribbean basin, in the Macaronesia area and in the Indian Ocean. They are European lands in the world governed by the provisions of the Treaties and they form an integral part of the Union.

Around 5 million people live in the outermost regions. Some of them experience a significant population growth due to inward migration. Population is for most of these regions much younger than their mainland.

The outermost regions have a level of GDP per inhabitant below the European average. Mayotte, with a population of around 213 000 inhabitants barely reaches one quarter of the EU average. They are facing a high unemployment rate, higher than in their mainland. This rate is particularly significant among young people (aged 15-24) with percentages of about 51 % for the Canary Islands, 55 % for Mayotte or 47% for Guadeloupe.

The outermost regions present many assets for the EU in the fields of biodiversity, climate change adaptation and mitigation, green growth and the circular economy and they are active in research fields such as energy, marine science and space. However due to their remoteness, their difficult topography and climate, their small markets related to a small number of products and the insularity of 8 of them, Article 349 of the Treaty on the Functioning of the European Union recognizes the particular situation of these regions and gives them a unique status. This status distinguishes the outermost regions from any other region in the EU and from the Overseas Countries and Territories that are associated to the Union.

In the autumn 2017 the Commission will adopt a new strategy for the outermost regions inspired by the work of the 4th Forum of the Outermost Regions held in March 2017 and by the proposals submitted by these regions, the concerned Member States and the European Parliament.

According to a European Commission reflection paper (European Union, 2017a), 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 2013 ( 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.

Map 15: Risks factors linked to globalisation and technological change

Determinants of GDP growth across NUTS 3 regions

Source: Lavalle et al. (2017)

According to mainstream economics, initial socio-economic 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 B1 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, in that most of them are in the bottom-left quadrant rather than the top left

Spatial spill-overs of regional growth rates in the EU, 2000-2014

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:

Determinants of GDP growth across NUTS 3 regions: continued

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. Specially, two regions are considered neighbours if they are within 150 minutes of travel time by road (based on the JRC-Trans Tools model).

 Estimation results ('+' is a positive impact;'–' is a negative impact)

The direct effect 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.

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.

1.2.2 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 5 and 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 ones 4 . 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 regions –implying an increase in productivity in industry relative to other sectors.

Figure 15 Employment shares in industry (excluding construction), 1995-2014

Source: Cambridge Econometrics, DG REGIO calculations

Figure 16 GVA shares in industry (excluding construction, 1995-2014

Source: Cambridge Econometrics, DG REGIO calculations

Figure 17 Employment shares in agriculture, 1995-2014

Source: Cambridge Econometrics, DG REGIO calculations

Figure 18 GVA shares in agriculture, 1995-2014

Source: Cambridge Econometrics, DG REGIO calculations

The reduction in the share of employment in agriculture in the EU over the 20 year-period has been 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 and Figure 1 8 ). 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.

The EU Common Agricultural Policy and the LEADER approach

The EU Common Agricultural Policy (CAP) is concerned with 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 5% of total employment, with 11 million farms providing work for roughly 22 million people. Together with food processing, food retail and food services, agriculture provides nearly 44 million jobs. The CAP contributes to smart, sustainable and inclusive growth in the EU through a range of policy measures which provide support to agriculture, food and forestry as well as to others 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 EUR 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 investment, knowledge-building and various forms of co-operation and innovation in the rural areas. Rural development also ensures payments to farmers who commit themselves to providing public goods through environment and climate-related activities 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 of enterprises and the creation of new ones as well as of jobs, improving access to ICT and fostering local development.

Rural development policy provides the means of stimulating employment both in agriculture and outside. Investment support helps to improve the economic viability and resilience of farming and rural businesses. For the programming period 2014-2020, some 365 000 farmers are due to receive investment aid to restructure and modernise their farms and a total of EUR 22.6 billion from the EAFRD is earmarked for productive investment in the rural economy, EUR 6.9 billion is planned to be spent to help over 173 000 young farmers to set up business

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 EUR 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.

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 (Sheperd, 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 Union, 2017, OECD, 2016 and Eurofound, 2016). Training may help workers losing their jobs to gain news ones as the structure of economic activity shifts, but there is a limit to what it can achieve.

1.3.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 1 ). 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% vs 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).

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 MS 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 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 employment 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 countries. 6  

The European Maritime and Fisheries Fund

The European Maritime and Fisheries Fund (EMFF), which has a budget of EUR 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:

·EFF support amounted to around 20% of EU fleet investment over the programming period and strengthened competiveness 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.

 

Table 11 Change in employment and GVA by NACE sector by group of Member States, shares in 2016 and changes 2000-2016

Note: blue bars indicate positive changes, red bars indicate negative changes. Annual average change on previous year, in %

Note: 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.

1.3.1 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 2 ). 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-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).

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) and changes in the share of working age population in the total population. Accordingly, the following identity holds:

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.

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 year) between 2009 and 2016. Both experienced a reduction in productivity and the employment rate fell as well in Italy.

Table 12: Decomposition of annual average change in GVA per head by Member State, 2001-2008 and 2009-2016

Source:    EUROSTAT, DG REGIO calculations; for Malta, real GDP was used instead of real GVA.

Note:    Blue bars indicate increases, red bars indicate reductions. Annual average change on previous year, in %.

Regions with expanding nontradable sectors were harder hit by the 2007-2008 crisis.

In the years following the 20072008 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 349 large OECD (territorial level 2, TL2) regions, 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, 2016b). 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 nontradable sectors expanded relative to tradable one over the years 2000-2007 than in others. This may seem surprising, but nontradable 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 nontradable services, while estimates for the United States suggest a local jobmultiplier of up to 1.6 (Moretti, 2010; Moretti and Thulin, 2013). Moreover, whereas nontradable 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.

Employment growth in the post2007-2008 period was lower in regions where nontradable sectors expanded more before the crisis

Annual average employment growth in 2008-13 and the change in the share of total employment in nontradable sectors 2000- 2007 in 19 OECD countries

Note: 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 (2017) and OECD Regional Statistics Database [Database]

1.3.2 Capital metropolitan regions more prone to boom and bust than in 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 3 ). 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.

Box: 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 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. It is not possible to use a fixed population threshold to distinguish these regions from smaller metro ones (i.e. the remaining metro regions), so 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  

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.

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.

Table 13 Changes in GDP per head, productivity and employment per head by type of metropolitan region, 2001-2008 and 2009-2014 (average % per year)

Source: EUROSTAT, DG REGIO calculations

Note: green bars indicate positive changes, red bars indicate negative changes. Annual average change on previous year, in %

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.

Figure 19: Evolution of total employment (number employed) in metro regions, 2000-2014

Source: Lavalle et al. (2017)

1.3.3 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 ( Table 1 4 ) in the EU-28 grew by 1.9% a year, slightly higher than in other types of region. 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.

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% less 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.

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 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:

These are then used to define three types of municipality (local administrative unit level 2) as follows:

These cells are also used to define NUTS 3 regions as follows:

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/index.php/Urban-rural_typology  

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;

1.cities: at least 50% of the population living in an urban centre

2.towns and suburbs: less than 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.

·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.

Table 14: Real GDP per head, productivity and employment per head growth by urban-rural typology, 2001-2008, and 2009-2014

 

Source: EUROSTAT, DG REGIO calculations

Note: green bars indicate positive changes, red bars indicate negative changes. Annual average change on previous year, in %

1.4.THE ECONOMIC DEVELOPMENT CLUBS OF EUROPEAN REGIONS AND THE MIDDLE-INCOME TRAP 7

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 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 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 below).

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.

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 urbanized or capital city-regions, and by a number of smaller but highly urbanized inter-connected ones (e.g. Rhine-Ruhr in Germany or Randstad in the Netherlands), specialized in the production of high-quality goods and services.

Map 16: The economic development clubs of EU regions

Source: EUROSTAT, REGIO-GIS elaborations.

The high-income regions share many characteristics with the very-high income ones but tend to be less city-centered. 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.

The structural characteristics of the four clubs differ markedly ( Table 1 5 ).

Table 15: European regions, by income club: some stylised facts

Income club

Growth of GDP per head, average annual rate, % (2001-15)

Population change, % (2001-15)

Employ-ment average annual change, % (2001-14)

Employ-

ment in Industry, % (2014)

Employ-

ment average annual change in Industry, % (2001-14)

Unempl-oyment rate, % (2016)

Patent applications per million inhabitants (2010-11)

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

EU28

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.

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. 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 between 2001 and 2014 while it increased in the other regions, especially in the very-high income ones. The share of employment in industry (excluding construction), as observed above, is largest in low-income regions. In all clubs, however, employment in industry declined over the period, the more so in the middle-income ones. Low income regions, as also seen above, 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 cheap, 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 Figure 1 11 ) and their costs are too high to compete with the former, their innovation systems not strong enough to compete with the latter.

Figure 110 Employment shares in industry (excluding construction), by club, 1995-2014

Source: Cambridge Econometrics, DG REGIO calculations

Figure 111 GVA shares in industry (excluding construction), by club, 1995-2014

Source: Cambridge Econometrics, DG REGIO calculations

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 under-performed 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 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 organization 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.

1.5.COMPETITIVENESS OF EU REGIONS

1.5.1 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 EUR 47.5 billion, 24% of the total ERDF, was allocated to support of SMEs. 8

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

In 2014, the largest number of firms with at least one employee 9 relative to population was in capital metro regions in most countries (the exceptions are France, Italy, Austria, and Spain ( Figure 1 12 ). 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.

Figure 112: Firms density by metro region, 2014

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 non-metro regions, apart from in Latvia and Hungary ( Figure 1 13 ) 10 .

Figure 113: Employees per firm by metro region, 2014

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 14 ).

Figure 114: Enterprise birth rate per metro region, 2013

High birth rates often go together with high death rates (Figure 115), 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.

Figure 115: Death rate of enterprises, 2012

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 116). 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 region. 12

Figure 116: Number of high growth firms per 1000 inhabitants, in 2014

Source: Eurostat, Business Demography, DG REGIO calculations

Note: France, Denmark, 2013

Entrepreneurship is crucial for regional development, but start-ups 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 final of 22.11.2016). This complements other financial instruments under the EU programme for the Competitiveness of Enterprises and SMEs (COSME) and Horizon 2020's Innovfin, to facilitate 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.

(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 and La Réunion, Mayotte, French Guiana and Martinique, Saint-Martin (France); Madeira and Azores (Portugal) and Canary Islands (Spain).

(3) See the Lexicon section for the list of less developed and moderately developed Member States.
(4) See Lexicon for a definition of ‘less developed’, ‘transition’ and ‘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.
(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.
(7) Prof. Simona Iammarino, Prof. Andrés Rodríguez-Pose, and Prof. Michael Storper substantially contributed to the content of this section.
(8) European Union (2016), 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, available at: http://ec.europa.eu/regional_policy/sources/docgener/evaluation/pdf/expost2013/wp2_final_en.pdf .
(9) The terminology employer firms will be used throughout the chapter to indicate firms employing at least one employee.
(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.
(11) A 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.
Top

Brussels, 9.10.2017

SWD(2017) 330 final

COMMISSION STAFF WORKING DOCUMENT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

My region, My Europe, Our future:
The seventh report on economic, social and territorial cohesion

{COM(2017) 583 final}


1.5.2 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 in maintaining the competitiveness of firms. This is particularly true for firms in Europe which have increasingly to compete with firms located in less developed parts of the world in emerging economies. 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, as well as 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 most commonly used one. Although it is imperfect because it covers only innovations which are patentable 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 – 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 731per million inhabitants in the period), followed by Heidenheim in Germany (1 049) and Rheintal-Bodenseegebiet in Austria (832). 1

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 institutes. 2

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 117), but there is less variation between them (as measured by the coefficient of variation), suggesting that they offer a more favourable environment. 3

Figure 117: Patents by metro regions, average 2009-11

Source: Eurostat, DG REGIO calculations.

Map 17: Patent applications to the European Patents Office, 2010-2011

Source: Eurostat, REGIO-GIS

One of the main indicators for assessing the capacity to innovate is the level of expenditure on R&D which tends to be essential for technical progress to take place. 4  

Expenditure on R&D in the EU-28 amounted to around 2% of GDP in 2014 (the latest data available) and only marginally increased 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 in 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 18 and Figure 118).

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 6 ). In the more developed regions, it was less than 1 percentage point below the target on average – though 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.

Table 16: 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

2.0

Distance to EU target (% point difference)

0.7

1.7

2.1

1.0

% of regions* that have reached the EU target

19

2

0

11

* BE, DE, EL, FR, AT, FI, SE: 2013

** includes only regions for which data are available

Source: Eurostat, DG REGIO calculations

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.

Figure 1-18: Total expenditure on R&D, 2014

Source: Eurostat, DG REGIO calculations.

Map 18: Total expenditure on R&D, 2014

Source: Eurostat, REGIO-GIS.

A 2017 European Commission report highlights the key role innovation plays in the development of regions, and not only the high-tech ones. 5 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 19).

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 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.

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

Map 19: Regional Innovation Scoreboard, 2017

Source: Regional Innovation Scoreboard 2017, REGIO-GIS.

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.

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. Therefore, it 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 EUR 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.

Regional breakdown of FP7 expenditure per inhabitant by NUTS 2 regions, 2007-2013

Tradable clusters in lowdensity and highdensity 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 B2).

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.

Figure B3. Average wage and share of total fulltime equivalent (FTE) employment in traded clusters located in mostly urban areas, 2014

Note: 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)

1.5.3 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 a tertiary education had an employment rate of 81%, compared to 74% for those with an upper secondary education and 54% for those without an upper secondary education. 6

The link between educational attainment and employment rates is also strong for the population aged 25-64. Only 54% of those without an upper secondary education are 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). 7

The share of people aged 25-64 with tertiary education, however, varies markedly across regions (Map 110 and Figure 119).

Metropolitan areas, especially larger ones, tend to have a more highly educated population than other areas. 8 Demand for highly skilled labour attracts those with such qualifications and makes it easier for them to find a job matching their skills. 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 (Map 110).

Figure 119: Population aged 25-64 with tertiary education, 2016

Map 110: Population aged 25-64 with tertiary education, 2016

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 none up to 2013), while 29% of less developed regions had done so (Table 17). 9

Table 17: Population aged 30-34 with a tertiary education, by regional groups, 2016 *

More developed

Transition

Less developed

EU-28

Population aged 30-34 with a tertiary education, 2016 (%)

43.2

32.7

33.0

39.1

% point change 2008-2016

6.8

1.0

12.3

8.0

% point change 2000-2008

9.7

9.3

8.0

8.8

Distance to EU-2020 target (% point difference)

0.0

7.3

7.0

0.9

% of regions that have reached the EU target

53

22

29

41

Note * includes only regions for which data are available

Sources: Eurostat, DG REGIO calculations

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 120). 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%.

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:

http://ec.europa.eu/eurostat/cache/metadata/en/tepsr_sp410_esmsip.htm  

Figure 120: People’s levels of digital skills, by level of economic development, 2015

Source: Eurostat, DG REGIO calculations.



1.5.4 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 under-used motorways, airports and high-speed railway lines (Flyvbjerg et. al., 2003, European Union, 2014b).

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 capture the market in the other city, leading to a reduction of 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 Union 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 ( Map 1 11). 10

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 makes it an unattractive alternative to travel by car (Map 112).

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 1-13). 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.

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 travel. 11 This will require substantial investment, especially in countries where the network is not very dense and the service tends to be slow and infrequent.

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 focussing support on cross-border connections (including maritime ones) and interoperability between national transport networks. Funding for the Facility amounts to EUR 24 billion for 2014-2020.

The CEF calls for proposals in 2014, 2015 and 2016 provided support to 604 projects with grants amounting to EUR 22 billion and with ca. EUR 41.6 billion of investment being mobilised. With the funding extended under the 2016 call, 96.3% of the budget for grants available from the Facility will have been allocated.

Financing for the TEN-T Comprehensive network mainly comes from the ESI Funds which also co-finance the TEN-T Core network, particularly non-cross-border parts and roads.

Current investment in the TEN-T amounts to around EUR 50 billion; though estimates suggest that EUR 607 billion is needed by the end of 2030 to complete the TEN-T Core Network Corridors alone.

For more details: https://ec.europa.eu/transport/themes/infrastructure/ten-t-guidelines/project-funding/cef_en  

Access to passenger flights is highly uneven across the EU, ranging from London and surrounding areas where people have access to over 3000 flights a day to regions in eastern Poland and Romania without any flights within 90 minutes driving time (Map 1-14).

Map 1-11: Expected change in road accessibility due to TEN-T network composition, by NUTS3 region

Map 1-12: Average speed of direct rail connections, 2014

(1)

   Note that the patent applications relate mainly to technological innovations in the manufacturing sector and do not a capture innovation in services, which are often intangible. It is therefore liable to be biased (Morrar, 2014).

(2) European Union and UN-HABITAT (2016).
(3) 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.
(4) One should note however that R&D expenditure is likely to underestimate innovation activities, particularly in sectors outside manufacturing where non-technological innovation is frequent.
(5)    Regional Innovation Scoreboard 2017, available at: : http://ec.europa.eu/growth/industry/innovation/facts-figures/regional_en .
(6) COM(2016), Education and Training Monitor 2016.
(7) COM(2016), Education and Training Monitor 2016, page 27.
(8) European Union and UN-HABITAT (2016).
(9) European Union (2014a).
(10) The map depicts expected changes relative to the situation in 2012.
(11)

   European Commission 'White Paper, Roadmap to a Single European Transport Area – Towards a competitive and resource efficient transport system.' COM(2011) 144 of 28.3.2011.

Top

Brussels, 9.10.2017

SWD(2017) 330 final

COMMISSION STAFF WORKING DOCUMENT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

My region, My Europe, Our future:
The seventh report on economic, social and territorial cohesion

{COM(2017) 583 final}


Map 113: Rail accessibility during morning peak hours,

by city, 2014

Map1-14: Accessibility to passenger flights by NUTS 3 region, 2013

1.5.5 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 achieved. 1

The coverage of Next Generation Access (NGA) 2 is expanding fast. In 2016, around 76% of households across the EU have 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 1-15 ).

Access to fast broadband services in rural areas remains a challenge. Even though 99% of rural households across the EU28 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 ( Figure 1-21 ). 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 urban areas), mostly in Greece (55%) and France (50%).

Map 1-15: Next generation access coverage in NUTS 3 regions, 2016 

Source: DG CONNECT: Europe's Digital Progress Report 2017, REGIO-GIS.

Figure 1-21: Households with access to Next generation Access (NGA) broadband, by type of area, 2012 and 2016.


Source: European Commission, 2016, Broadband Coverage in Europe 2016.

Data are for the end of 2012 and mid-2016.

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 1-16 ). 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.

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 (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 establishment of a network of "Digital Innovation Hubs" that make latest digital innovations available to any company in Europe, irrespective of their location, size and sector. The Hubs will create systems connecting users with suppliers of digital innovations and 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. including for agriculture and fisheries as well as manufacturing and services.

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, on the grounds that metropolitan and regional authorities can create the right environment for accelerating the transformation process. 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.

Map 1-16: Households with a broadband connection, 2016

Source: Eurostat, REGIO-GIS.

1.6.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, 2017a). 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 countries and within them, the latter, in many cases, due to the capital city region significantly out-performing others in the country ( Map 1-17 ).

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.

London and its commuting area, which includes seven NUTS 2 regions, 3 is ranked top in 2016, ahead of Utrecht in the Netherlands –for the first time is not the most competitive region – which is ranked joint second with Berkshire, Buckinghamshire and Oxfordshire in the UK. 4 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 country is highly ranked. For example, in Belgium and the UK, variations between regions are relatively wide, highlighting the limitations of a national-level analysis. Such variation raises questions over whether gaps in regional competitiveness are harmful or not for national competitiveness and how far t they can, and should, be reduced.

Figure 1-12 - Regional competitiveness index, 2016

Source: Annoni et al. (2017b).

Map 1-17: Regional Competitiveness index, 2016

Source: Annoni et al. (2017b), REGIO GIS.

The Regional Competiveness 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 its competitiveness increases, 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. (2017b).

The changes over time in the RCI scores, as opposed to the rankings, are informative. 5 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 1-18 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%).

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

Map 1-18: Changes in RCI, 2016-2013; 2013-2010 and over the whole period, 2016-2010. 

Note: Regions with an increase of over 5 % in the RCI range (z-scores) are categorised as improving in terms of competitiveness and with a reduction of over 5 % as deteriorating.

As might be expected, there appears to be a positive relationship between regional competitiveness and GDP per head, which is evident for both those both with high levels of the latter and those with low levels ( Figure 1-23 ).

Figure 1-23: Relationship between RCI and GDP per head (in PPS), by level of economic development

There is some evidence that regions which are more competitive have higher rates of start-ups, at least those which are most highly developed and those which are least developed ( Figure 1-24 ).

Figure 1-24: Relationship between RCI and the birth rate of firms (relative to population), by level of development

EU regions by development levels, as defined for the RCI

EU regions are divided into five development levels based on their average 4 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)

1.7.REFERENCES

Acs, Z. Parsons, W., and Tracy, S. (2008), High Impact Firms: Gazelles Revisited, Office of Advocacy, U.S. Small Business Administration.

Annoni, P. and Kozovska, K. (2010), EU Regional Competitiveness Index 2010, EUR 24346, Luxembourg: Publications Office of the European Union.

Annoni P., Dijkstra L. (2017a) 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., and Gargano, N. (2017b), EU Regional Competitiveness Index: RCI 2016, Working papers 02/2017, Directorate General for Regional And Urban Policy, European Commission.

Combes, P.P, Duranton, G., Gobillon, L., Puga, D., and Roux, S. (2012), The productivity advantages of large cities: distinguishing agglomeration from firm selection, Econometrica, 80 (6), 2543-94.

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., Garcilazo, E., and McCann, P. (2015), The effects of the global financial crisis on European regions and cities, Journal of Economic Geography, 15(5): 935-49.

Eurofound (2016), ERM annual report 2016: Globalisation slowdown? Recent evidence of offshoring and reshoring in Europe, Publications Office of the European Union: Luxembourg.

European Union (2014a), 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.

European Union (2014b), EU-funded airport infrastructures: poor value for money, Report of the European Court of Auditors, Publications Office of the European Union: Luxembourg.

European Union (2017a), Reflection Paper on Harnessing Globalisation, Publications Office of the European Union: Luxembourg.

European Union (2017b), Reflection Paper on the Future of EU Finances, Publications Office of the European Union: Luxembourg.

European Union 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.

Flyvbjerg, B. Bruzelius, N., and Rothengatter, W. (2003) Megaprojects and risks, Cambridge University Press: Cambridge.

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.

Ketels, C. and Protsiv, S. (2016) European Cluster Panorama 2016. European Cluster Observatory.

Lavalle, C., Pontarollo, N., Batista E Silva, F., Baranzelli, C., Jacobs, C., Kavalov, B., Kompil, M., Perpiña Castillo, C., Vizcaino, C., Ribeiro Barranco, R., Vandecasteele,I, Pinto Nunes Nogueira Diogo, V., Aurambout, J., Serpieri, C., Marín Herrera, M., Rosina, K., Ronchi, S., and Auteri, D. (2017) European Territorial Trends for Cities and Regions Ed. 2017, Publication Office of the European Union, Luxembourg, JRC107391.

Melitz, M.J and 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.

Moretti, E., and Thulin, P. (2013) Local Multipliers and Human Capital in the United States and Sweden, Industrial Corporate Change, 22 (1), 339-62.

Morrar, (2014), Innovation in Services: A Literature Review, Technology Innovation Management Review, 4(4): 6-14.

OECD (2014) Innovation and Modernising the Rural Economy, Paris: OECD Publishing.

OECD (2015) The Metropolitan century: Understanding Urbanisation and its Consequences. Paris: OECD Publishing.

OECD (2016) Automation and independent work in a digital economy, OECD Policy Brief on the Future of Work, May 2016.

OECD (2016b) Regional Outlook: Productive Regions for Inclusive Society. Paris: OECD Publishing.

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

Shepherd, B. (2013), Global Value Chains and Developing Country Employment: A Literature Review, OECD Trade Policy Papers, No. 156, OECD Publishing, Paris.

(1)

   Broadband Coverage in Europe 2016, available at:    
https://ec.europa.eu/digital-single-market/en/connectivityy .

(2)

   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.

(3)

   Table A.1.1 of the Appendix in Annoni et al. (2017b) lists the NUTS 2 regions comprising London and its commuting areas.

(4)

   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.

(5)

   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 et al. (2017b).

Top

Brussels, 9.10.2017

SWD(2017) 330 final

COMMISSION STAFF WORKING DOCUMENT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

My region, My Europe, Our future:
The seventh report on economic, social and territorial cohesion

{COM(2017) 583 final}


Chapter 2: Social cohesion

Contents

2.    Social cohesion    

2.1.    Population change is increasingly determined by migration    

2.1.1.    Two thirds of the EU-13 population live in a region of population decline    

2.2.    Employment rates are higher for those born in another EU country than for the native-born    

2.3.    Asylum seekers and refugees    

2.4.    The employment rate surpassed its pre-crisis level, but unemployment rates are still too high    

2.5.    Education and training    

2.6.    Adult proficiency in literacy and numeracy needs to be raised in several EU Member States    

2.7.    Poverty and social exclusion is declining in the EU-13 but growing in cities in the EU-15    

2.8.    Moving at different speeds to the Europe 2020 targets    

2.9.    More women are studying, working and being elected to regional assemblies    

2.10.    Life in the EU is among the longest in the world but regional disparities persist    

2.11.    Measuring social progress at the regional level    



Figure 2.1 Natural change and net-migration in EU-28, 1960-2015    

Figure 2.2: Population born outside the EU, 2011 and 2016    

Figure 2.3: Population born in another EU country, 2011 and 2016    

Figure 2.5: Employment rate gap between non-EU born men and women (aged 20–64,), 2016    

Figure 2.6 First-time asylum applications in the EU-28 by gender and age, 2016    

Figure 2.7: Literacy proficiency of adults (aged 16-64), 2016    

Figure 2.8 Numeracy proficiency of adults (aged 16-64), 2016    

Figure 2.9 The proportion of people at risk of poverty or social exclusion by degree of urbanisation, 2015    

Figure 2.10 Change in the proportion of people at risk of poverty or social exclusion by degree of urbanisation, 2008-2015    

Figure 2.11 The at-risk-of-poverty rate by degree of urbanisation, 2015    

Figure 2.12 Change in the at-risk-of-poverty rate by degree of urbanisation, 2008-2015    

Figure 2.13 Proportion of people living in Low work intensity households by degree of urbanisation, 2015    

Figure 2.14 Change in proportion of people living in low work intensity households by degree of urbanisation, 2008-2015    

Figure 2.15 Proportion of people with severe material deprivation by degree of urbanisation, 2015    

Figure 2.16 Change in the proportion of people with severe material deprivation by degree of urbanisation, 2008-2015    

Figure 2.18: Europe 2020 achievement index by degree of urbanisation, 2015    

Table 2.1 Population share in regions by determinants of population change, 2005-2015    

Table 2.2 Population change, natural change and net migration in capital metro, other metro and non-metro regions, 2005-2015    

Table 2.3 Population change, natural change and net migration in urban, intermediate and rural region, 2005-2015    

Table 2.4: Division of regions by median age in 2016 and direction of net migration, 2005-2015 (% of total)    

Table 2.5: Population age in capital metro, other metro and non-metro regions, 2016    

Table 2.6: Employment and unemployment by category of region    

Table 2.7 Youth unemployment, those not in employment, education or training (15-24) and participation in education and training (25-64), 2008-2016    

Table 2.8: EU 2020 regional achievement index, 2010-2015    

Map 2.1: Natural population change in NUTS 3 regions, 2005-2015    

Map 2.2: Net migration in NUTS 3 regions, 2005-2015    

Map 2.3: Total population change in NUTS3 regions, 2005-2015    

Map 2.4: Median age of the population in NUTS3 regions, 2016    

Map 2.5 Employment rate (20-64), 2016    

Map 2.6 Change in employment rate (20-64), 2008-2016    

Map 2.7 Unemployment rates, 2016    

Map 2.8 Change in unemployment, 2008-2016    

Map 2.9: Population aged 15-24 not in employment, education or training (NEET), 2016    

Map 2.10 Participation of adults aged 25-64 in education and training, 2016    

Map 2.11 Early school leavers aged 18-24, average 2014-2016    

Map 112 Low achievers in mathematics, reading and science    

Map 2.13 The EU-2020 achievement index, 2015    

Map 2.14 Change in the EU-2020 achievement index, 2010-2015    

Map 2.15 Difference between female and male employment rates (20-64), 2016    

Map 2.16 Difference between female and male employment rates, 2016    

Map 2.17 Gender balance of population 30-34 with a tertiary education, average 2014-2016    

Map 2.18 Gender gap for early school leavers, average 2014-2016    

Map 2.19 Women in regional assemblies, 2017    

Map 2.20 Change in the share of women in regional assemblies, 2007-2017    

Map 221 EU Life expectancy at birth, 2015    

Map 222 Infant mortality, 2015    

Map 2.23 Road fatalities, 2015    

Map 2.24: Road traffic fatalities in cities    

Map 2.25: The EU-SPI 2016 (0=lowest level of social development; 100=highest level of social development)    

Map 2.26 EU Social Progress index, three sub-indices    



Summary

·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, 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.


·

2.    Social cohesion

2.1    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.

Figure 2.1 Natural change and net-migration in EU-28, 1960-2015

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 concerned were born outside the EU, the number of whom rose from 6.3% of total population in 2011 to 6.9% in 2016 ( F igure 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).

Figure 2.2: Population born outside the EU, 2011 and 2016

Figure 2.3: Population born in another EU country, 2011 and 2016

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 reduction 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.

A smaller proportion of population in the EU, 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 ( Table 2.1 and Map 2.1 ). 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 population 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.

Because net inward migration in many cases fully offset a natural reduction in population, only 31% of the EU population live in a region that experienced a net loss of population over these ten years. The regions with a declining population are mostly located in EU-13 Member States. Reductions were particularly large in the Baltic States, Bulgaria, Hungary, Romania, Slovakia and Croatia, except in capital city or neighbouring regions or in those with major cities. Regions in Germany, especially in the eastern part, and Portugal also experienced substantial reductions in population.

Table 2.1 Population share in NUTS 3 regions by determinants of population change, 2005-2015, in %

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 2015 from population on 1 January in 2005 and dividing this by average population 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:

By country of birth

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.

Migration from outside the EU and mobility 1 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).

Table 2.2 Population change, natural change and net migration in capital metro, other metro and non-metro regions, 2005-2015

Capital metropolitan (metro) regions have experienced the highest population growth, especially in the EU15 Member States, where population increased 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.

Table 2.3 Population change, natural change and net migration in urban, intermediate and rural region, 2005-2015

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 remained unchanged, while it increased by 6%in urban regions and by 1% in rural ones.

Map 2.1: Natural population change in NUTS 3 regions, 2005-2015

Map 2.2: Net migration in NUTS 3 regions, 2005-2015

Map 2.3: Total population change in NUTS 3 regions, 2005-2015

 

Map 2.4: Median age of the population in NUTS 3 regions, 2016

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, 77% of regions with net inward migration over recent years are also those with the youngest populations, with a median age of less than 40 ( Table 2.4 ), while 68% of regions with a median age above 50 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.

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)

More emigration than immigration

Less emigration than immigration

[<40]

23%

77%

[40;50]

31%

69%

[>50]

68%

32%

Total

32%

68%

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.

Table 2.5: 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

2.2.    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, Hungary, Luxembourg, Latvia, Croatia and the UK, the employment rate of other EU-born was markedly higher than that of the native-born.

Figure 2.4: Employment rate by country of birth (15-64), 2016

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 tertiary 2 . 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.

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.

Figure 2.5: Employment rate gap between non-EU born men and women (aged 20–64,), 2016

2.3    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, namely unaccompanied minors, 4 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 assistance in order for the most viable and sustainable solutions to be found which are in their best interest.

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.

Figure 2.6 First-time asylum applications in the EU-28 by gender and age, 2016

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 (ESDE 2016). 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 (ESDE 2016, 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 (ESDE 2015, 2016).

2.4.    The employment rate 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.

The rate, however, varies markedly between types of region. The average employment rate in more developed regions 5 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 has not increased 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.

Map 2.5 Employment rate (20-64), 2016

Map 2.6 Change in employment rate (20-64), 2008-2016

Map 2.7 Unemployment rates, 2016

Map 2.8 Change in unemployment, 2008-2016

Table 2.6: Employment and unemployment by category of region

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 rise 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 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.

Table 2.7 Youth unemployment, those not in employment, education or training (15-24) and participation in education and training (25-64), 2008-2016

Box: 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’ 6 , 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 potential

Map 2.9: Population aged 15-24 not in employment, education or training (NEET), 2016

2.5.    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 low-qualified 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 low skilled or low qualified adults, of which 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.

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 qualification 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 from 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 away. 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 problematic 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.

Map 2.10 Participation of adults aged 25-64 in education and training, 2016

Map 2.11 Early school leavers aged 18-24, average 2014-2016

The New Skills Agenda for Europe

·A Graduate Tracking initiative to assemble information on their performance.

(1) Article 45 of the Treaty on European Union and the Treaty on the Functioning of the European Union
(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).
(3) Hungary has seen a large inflow (2.1% of its total population of 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.
(4) Unaccompanied minors are generally defined as those under the age of 18 who arrive without parents, other adult relatives or guardians (UNHCR).
(5) See Lexicon for the definition of less developed, transition and more developed regions.
(6) Council Resolution of 27 November 2009 on a renewed framework for European cooperation in the youth field (2010-2018), OJ C 311, 19.12.2009, p. 1.
(7) European Commission 'The New Skills Agenda for Europe', COM(2016) 381 of 10.6.2016
(8) OJ C 484, 24.12.2016, p.1.
Top

Brussels, 9.10.2017

SWD(2017) 330 final

COMMISSION STAFF WORKING DOCUMENT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

My region, My Europe, Our future:
The seventh report on economic, social and territorial cohesion

{COM(2017) 583 final}


Map 1.12 Low achievers in mathematics, reading and science

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 education 1 .

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) 2 was the average employment rate of those with VET qualifications similar or lower than those completing general upper-secondary programmes.

Despite this, for many young people and their parents, initial 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 2015 3 to further strengthen key competences in VET curricula and provide more effective opportunities to acquire or develop these skills.

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" 4 also announced that the Commission will propose a Council Recommendation for a Quality Framework for Apprenticeships.

2.6.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 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 ( Figure 2.1 and Figure   2.2 ). 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.

Figure 2.1: Literacy proficiency of adults (aged 16-64), 2016

Figure 2.2 Numeracy proficiency of adults (aged 16-64), 2016

2.7.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 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 2008 5 .

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 2.3 ). 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 2.4 ).

Figure 2.3 The proportion of people at risk of poverty or social exclusion by degree of urbanisation, 2015

Figure 2.4 Change in the proportion of people at risk of poverty or social exclusion by degree of urbanisation, 2008-2015

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 2.5 ). As in the case of the aggregate indicator, there was a somewhat larger proportion of households at risk in rural areas across 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 (26% as against 11%), 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 2.6 ).

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 indicators:

·At risk of poverty (or relative monetary poverty) measures the percentage of people living in a household with equivalised 6 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 survey 7 .

·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. 

Figure 2.5 The at-risk-of-poverty rate by degree of urbanisation, 2015

Figure 2.6 Change in the at-risk-of-poverty rate by degree of urbanisation, 2008-2015

Figure 2.7 Proportion of people living in Low work intensity households by degree of urbanisation, 2015

Figure 2.8 Change in proportion of people living in low work intensity households by degree of urbanisation, 2008-2015

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 2.7 ). 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, 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 2.8 ).

Figure 2.9 Proportion of people with severe material deprivation by degree of urbanisation, 2015

Figure 2.10 Change in the proportion of people with severe material deprivation by degree of urbanisation, 2008-2015

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 2.9 ), 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 2.10 ).

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.

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).

Figure 2.17 Income concentration in cities by income group, 2014 or latest available year (higher values indicate higher concentrations) 

Source: Adapted from OECD (2016), Making Cities Work for All, OECD Publishing, Paris.

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.

References: OECD (2016), Making Cities Work for All: Data and Actions for Inclusive Growth, OECD Publishing, Paris.

The European Pillar of Social Rights

2.8.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 targets. 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 targets ( Map 2.1 ). 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 2.2 ). 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 2.1 ).

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 degree of urbanisation (see Dijkstra and Athanasoglou, 2015).

A score of 100 means that a region or a degree of urbanisation has reached or surpassed all the EU targets, a score of zero means that it the region or degree of urbanisation 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 are weighted at 25%, while the two education indicators are weighted at 12.5%. For the index for regions grouped by degree of urbanisation, the employment and poverty indicator are both weighted at 33%, while the two education indicators are weighted at 16.6%.

Climate change indicators are not available below the national level and so could not be included in the two indices r. 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.

Table 2.1: EU 2020 regional achievement index, 2010-2015

Source: Dijkstra and Athanasoglou, 2015.

Map 2.1 The EU-2020 achievement index, 2015

Map 2.2 Change in the EU-2020 achievement index, 2010-2015

In general, cities are closer to achieving the targets ( Figure 2.11 ) 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; 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, if by not enough to meet them by 2020, in Greece and Cyprus, the situation deteriorated in all three types of area ( Figure 2.1 ). 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.

Figure 2.11: Europe 2020 achievement index by degree of urbanisation, 2015

Figure 2.19: Change in the Europe 2020 achievement index between 2010 and 2015

2.9.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 2.3 ). 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 2.4 ). 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 way 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 2.5 ). 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.



Figure 2.20 Difference between female and male graduates by field of tertiary education, 2015

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 2.6 ). 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.

Map 2.3 Difference between female and male employment rates (20-64), 2016

Map 2.4 Difference between female and male employment rates, 2016



Map 2.5 Gender balance of population 30-34 with a tertiary education, average 2014-2016

Map 2.6 Gender gap for early school leavers, average 2014-2016



Map 2.7 Women in regional assemblies, 2017

Map 2.8 Change in the share of women in regional assemblies, 2007-2017

(1)

   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.

(2)

   European Commission, 2016, Education and Training Monitor 2016, https://ec.europa.eu/education/sites/education/files/monitor2016_en.pdf .

(3)

   '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 .

(4)

   European Commission 'Investing in Europe's youth' COM(2016) 940 of 7.12.2016.

(5)

   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.

(6)

   ‘Equivalised’ means that income is adjusted for differences in the size and composition of households.

(7)

   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.

(8)

   The Pillar was published as a Commission Recommendation and as a proposal for an inter-institutional Proclamation with the European Parliament and the Council.

Top

Brussels, 9.10.2017

SWD(2017) 330 final

COMMISSION STAFF WORKING DOCUMENT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

My region, My Europe, Our future:
The seventh report on economic, social and territorial cohesion

{COM(2017) 583 final}


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 Acores and Madeira).

2.10.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 (WHO 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, Spain, 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. Further efforts and more investment are, therefore, needed in most regions to improve road safety.

Map 221 EU Life expectancy at birth, 2015

Map 222 Infant mortality, 2015

Map 2.23 Road fatalities, 2015

Map 2.24: Road traffic fatalities in cities



2.11.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 citizens 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 worldwide 1 . 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 environmental 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 regions 2 . 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 ( Figure 2.21 ).

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.

Figure 2.21: The framework of the EU-SPI index

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 progress 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 better than the rest of the country ( Figure 2.22 ).

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 ( Map 2.26 ). The ‘Foundations of well-being’ dimension shows greater variation with only the Nordic Member States, the Netherlands and Ireland scoring well in all regions ( Map 2.26 ). The largest differences relate to ‘Opportunity’, with low scores in many regions in the southern and central eastern countries ( Map 2.26 ).

Map 2.25: The EU-SPI 2016 (0=lowest level of social development; 100=highest level of social development)

Map 2.26 EU Social Progress index, three sub-indices

Figure 2.22: Degree of within-country variability of the EU-SPI

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 2.23 ). 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.

Figure 2.23: Relationship between EU-SPI and GDP per capita

References

ESPON (2017) The Geography of new Employment Dynamics in Europe. Delivery 2 – Interim Report

Dijkstra L. and Athanasoglou S. (2015) The Europe 2020 index: The progress of EU countries, regions and cities. Regional Focus Working Paper 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.

European Commission (2016). 'Labour Market Integration of Refugees' in Employment and Social Developments in Europe (ESDE) 2016 – available at: http://ec.europa.eu/social/main.jsp?catId=738&langId=en&pubId=7952&type=2&furtherPubs=yes

European Commission (2016). 'Labour Mobility and Migration in the EU' in Employment and Social Developments in Europe (ESDE) 2015 – available at: http://ec.europa.eu/social/main.jsp?catId=738&langId=en&pubId=7859&furtherPubs=yes  

Huber P and Bock-Schappelwein J (2014) The Effects of Liberalizing Migration on Permanent Migrants' Education Structure. Journal of Common Market Studies. 52: 268-284.

(1)

   For more information on the Global Social Progress Index: https://www.socialprogressindex.com .

(2)

   For more information on the regional EU-SPI:     http://ec.europa.eu/regional_policy/en/information/maps/social_progress .

Top

Brussels, 9.10.2017

SWD(2017) 330 final

COMMISSION STAFF WORKING DOCUMENT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

My region, My Europe, Our future:
The seventh report on economic, social and territorial cohesion

{COM(2017) 583 final}


3.4.6. River flooding    41

3.5. Cross-border cooperation and territorial dimension of cohesion policy    44

3.5.1. Border regions…………………………………………………………………..    44

3.5.2. Other types of cooperation: interregional, transnational and macro-regional    50

3.5.3. Local, urban and metropolitan development    53



Figure 1 Primary energy consumption, change 2005-2015 and distance to target    

Figure 2 Change in greenhouse gas emissions outside the Emissions Trading Scheme, 2005-2011 and Europe 2020 targets    

Figure 3 Share of renewable energy in gross final energy consumption, 2006, 2015, target 2020    

Figure 4 Share of waste landfilled in selected EU Member States, 2014    

Figure 5 Passenger travel by transport mode, 2014    

Figure 6 Freight transport by mode, 2014    

Figure 7 Distance to services by type of municipality in the EU    

Figure 8 Population living within 1km distance of different services, by city size and degree of urbanisation in the EU    

Figure 9 Access to public transport in large European cities, 2014-2016    

Figure 10 Access to public transport in mid-size European cities, 2014-2016    

Figure 11 People reporting that they live in an area with problems relating to pollution, grime or other environmental problems, by degree of urbanisation, 2015    

Figure 12 Proportion of people who are satisfied with the level of noise in their city, 2015    

Figure 13 Share of the Natura 2000 network which intersects with Functional Urban Areas    

Figure 14 Population of border areas having access to rail passenger services, 2014    

Table 1 Population of border areas having access to rail passenger services, 2014    

Map 1 Electricity generated from hard coal and lignite - Electricity generated from renewable energies    

Map 2 Negative climate change impacts under a 2°C warming scenario    

Map 3 Projected increase in multi-hazard exposure    

Map 4 Main water bodies with less than good ecological status or potential    

Map 5 Urban, wastewater not collected, 2012    

Map 6 Residential, industrial and commercial areas per inhabitant by city, 2012    

Map 7 Change in residential, industrial and commercial areas per inhabitant by city, 2006-2012    

Map 8 Concentrations of airborne particulate matter (PM10) in cities, 2014    

Map 9 Concentration of ground-level ozone (O3) in cities, 2014    

Map 10 Access to green urban areas in cities, 2012    

Map 11 Share of NO2 concentration removed by vegetation in cities, 2010    

Map 12 Population flooded in the case of the biggest 100 year flood in FUAs    

Map 13 Border regions, NUTS 3    

Map 14 Cross-border road network efficiency in border areas    



KEY MESSAGES

·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 explained by the slowdown in economic activity during the crisis and there is a risk that the current recovery will make it more difficult to maintain momentum towards meeting the targets.

·In transport, there needs to be a major shift towards using less energy, cleaner kind of modes and more efficient use of infrastructure to reach the EU objectives for greenhouse gas emissions.

·The impact of climate change is likely to be considerable for a number of EU regions, particularly the outermost regions, regions around the Mediterranean, along coastlines generally and mountainous ones.

·Adapting to changes caused by global warming is generally costly and further investment is needed to make EU regions and cities more resilient to the consequences of the changes concerned.

·Despite general progress in reducing environmental pressures (notably as regards waste water and waste treatment), more efforts are needed to meet key environmental objectives of the EU.

·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 also 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 policy objective of the EU, 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, training and other areas.

·National borders, however, 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 the remaining restrictions were removed.

3.1. 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 promotion of a flexible functional geographies approach to 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 helping Member States and regions to improve their capacity to mitigate the negative impact of climate change.

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 EUR 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 EUR 10 billion have been allocated to such cooperation for the 2014-2020 period.

3.2. 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 climate-friendly energy. The Energy Union is a European priority project in which five dimensions are closely interlinked: energy security, solidarity and trust; a 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 two 1 .

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 resilient infrastructure and risk prevention and management.

3.2.1. 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 2015 3 ( 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 Netherland and Bulgaria, substantial reductions in energy consumption 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 targets 4 .

Figure 3-1 Primary energy consumption, change 2005-2015 and distance to target

Source: Eurostat

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 2008 5 . 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 energy goals. This would require significant investment which can be supported to a major extent by cohesion policy in the majority of Member States.

3.2.2. 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 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.

Figure 3-2 Change in greenhouse gas emissions outside the Emissions Trading Scheme, 2005-2011 and Europe 2020 targets

In 18 Member States, the level of emissions in 2015 was lower than the target set under the ESD, most especially in Croatia, which 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.

3.2.3. 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 Directive 6 , 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 ).

Figure 3-3 Share of renewable energy in gross final energy consumption, 2006, 2015, target 2020

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. 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 ( Map 3-1 ). 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 sources 7 .

Map 3-1 Electricity generated from hard coal and lignite - Electricity generated from renewable energies

 

3.2.4. 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 more multiple climatic hazards 8 . Coastal areas face the risk of rising sea levels, increasing sea temperatures 9 and growing numbers of ‘marine dead’ zones 10 . 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 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 regions. 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 3-2).

Map 3-2 Adverse climate change effects under a 2°C warming scenario

 

Climate change is also expected to increase the occurrence of natural hazards throughout the EU in the coming decades. Recent studies 12 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 3-3 ).



Map 3-3 Projected increase in multi-hazard exposure

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 climate 13 . The PESETA II study estimates total damages in the EU of up to EUR 190 billion by the end of the 21st century under a high economic growth scenario 14 , 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 .

Outermost Regions and environmental challenges

The outermost regions are particularly vulnerable to climate change and natural disasters as was shown with the dramatic impact of the hurricane Irma on Saint Martin. Most of them are tropical or sub-tropical islands with a difficult topography and fragile economies and ecosystems. Climate change is also likely to impact on fauna and flora, with probable effects on some agricultural products on which their economies rely, notably sugar cane and bananas.

Being primarily concerned, the outermost regions have realised at an early stage the need to the fight against 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% renewables penetration in electricity production by 2030 mainly through combined use of solar, hydro energies, wind, geothermal and the use of smart grids. Guadeloupe develops building regulations specifically adapted to local conditions.

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 with pumping, installing additional wind power capacity, using only electric vehicles in the island and making further use of locally produced biomass.

3.3. State of environment

3.3.1. Water

One of the objectives of the Seventh Environment Action Programme (7th EAP) is to ensure the good status of transitional 16 , coastal and fresh water by 2020. Surface water 17 is a major component of fresh water and improving its ecological state is critical to achieving this objective.

The Water Framework Directive 18 (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 implemented 19 .

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 2020 20 .

Member States differ substantially in terms of the ecological status of their river basins ( Map 3-4 ). 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.

Map 3-4 Main water bodies with less than good ecological status or potential

 

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 changes 21 . Such investment can be supported by cohesion policy (in the 2007-2013 programming period around EUR 17.8 billion of the ERDF and Cohesion Fund was allocated to wastewater infrastructure in 22 Member States 22 ).

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 Directive 23 (UWWTD) sets minimum requirements in respect of urban wastewater treatment, making it mandatory for settlements with the equivalent to 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 the 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 generally 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 not compliant agglomeration 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 not taking place is even 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.

3.3.2. 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 waste-derived 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 economy 24 .

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.

Figure 3-4 Share of waste landfilled in selected EU Member States, 2014

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.

3.3.3. 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 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 year 25 . 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 raised 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 network 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 EUR 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 Union 26 , 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%.

Figure 3-5 Passenger travel by transport mode, 2014

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.

Figure 3-6 Freight transport by mode, 2014

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.

3.4. Sustainable cities

3.4.1. 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 resource and energy efficient than other areas where low-density 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 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 Netherland (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 ). 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.

Figure 3-7 Distance to services by type of municipality in the EU

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 )

Figure 3-8 Population living within 1km distance of different services, by city size and degree of urbanisation in the EU

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 central-eastern EU countries and the built-up area per inhabitant, 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 ).

Map 3-6 Residential, industrial and commercial areas per inhabitant by city, 2012


Map 3-7 Change in residential, industrial and commercial areas per inhabitant by city, 2006-2012


3.4.2. 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 study 27 , 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 built-up areas around towns and cities, creating widespread and relatively low density urban suburbs, often inefficient in terms of energy and land consumption 28 .

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 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, regions 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.

 

3.4.3. 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 ).

Figure 3-9 Access to public transport in large European cities, 2014-2016

Figure 3-10 Access to public transport in mid-size European cities, 2014-2016

3.4.4. 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 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 common 30 . 

Figure 3-11 People reporting that they live in an area with problems relating to pollution, grime or other environmental problems, by degree of urbanisation, 2015

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, has declined markedly in the EU over the years, partly as a result of EU legislation 31 . 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 standards 32 . 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 target 34 .

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 geographical 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 city 35 . 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 lives in areas in which the EU O3 target threshold for protecting human health was exceeded in 2013 36 .

(1) On 30 November 2016, the Commission proposed an update to the Energy Efficiency Directive including a new 30% energy efficiency target for 2030.
(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.
(5) See European Commission (2015), 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.
(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, OJ L 140, 5.6.2009, p. 16.
(7) Note that renewable energy is not necessarily environment friendly: 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.
(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 and 2015). 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 (EEA (2017), Climate change, impacts and vulnerability in Europe 2016, An indicator-based report, EEA Report N° 1/2017).
(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.
(11) See EEA (2017), Climate change, impacts and vulnerability in Europe 2016, An indicator-based report, EEA Report N° 1/2017 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), "Multi-hazard assessment in Europe under climate change", Climatic Change 137, 105–119 (doi: 10.1007/s10584-016-1661-x).
(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), Climate impacts in Europe: The JRC PESETA II Project, JRC Scientific and Policy Reports EUR 26586 EN, JRC87011, European Commission, Joint Research Centre, Seville.
(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 quick spread of new and efficient technologies, a convergence of world income and way of life and extensive social and cultural interactions worldwide.
(15) Navarra, A. and Tubiana, L. (2013), Regional assessment of climate change in the Mediterranean, Advances in Global Change Research, Springer Netherlands, Dordrecht.
(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, OJ L 327, 22.12.2000.
(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.
(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) COM(2016) 105 final of 4.3.2016.
(23) Council Directive 91/271/EEC 21 May 1991 concerning urban waste water treatment, OJ L 135, 30.5.1991, p. 40.
(24) European Commission 'Closing the loop - An EU action plan for the circular economy', COM(2015) 614 of 2.12.2015. 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.
(25) European Commission (2016), Statistical Pocket Book – EU Transport in Figures, Luxembourg: Publications Office of the European Union, 2016 (https://ec.europa.eu/transport/facts-fundings/statistics/pocketbook-2016_en).
(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.
(27) 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).
(28) See for instance Jaeger J., Bertiller R, Schwick C. and Kienast F., (2010) "Suitability criteria for measures of urban sprawl", Ecological Indicators 10(2): 427–441.
(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), "Measuring access to public transport in European cities", Regional Working Papers N° 01/2015.
(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.
(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 120μg/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 (2015), Air quality in Europe — 2015 report, Report No 5/2015. http://www.eea.europa.eu/publications/air-quality-in-europe-2015 .
(35) EUROSTAT (2016), Urban Europe, Statistics on cities, towns and suburbs, Edition 2016, Publications office of the European Union, Luxemburg.
(36) European Environment Agency (2015), Air quality in Europe — 2015 report, Report No 5/2015. http://www.eea.europa.eu/publications/air-quality-in-europe-2015 .
Top

Brussels, 9.10.2017

SWD(2017) 330 final

COMMISSION STAFF WORKING DOCUMENT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

My region, My Europe, Our future:
The seventh report on economic, social and territorial cohesion

{COM(2017) 583 final}


Map 3-8 Concentrations of airborne particulate matter (PM10) in cities, 2014

Map 3-9 Concentration of ground-level ozone (O3) in cities, 2014

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 2015 1 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.

Figure 3-12 Proportion of people who are satisfied with the level of noise in their city, 2015

3.4.5. 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 contributions 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 substantial 2 .

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 in designating of 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 funding 3 .

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 protection 4 .

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 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 ecosystems and GI

Cities have high concentrations of people who could profit from nature to improve 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 the economy and social security in cities. Improving biodiversity and the provision of multiple ecosystem services though GI would help to improve the quality of life, health and well-being, protect against the negative effects of climate change and natural disasters, regenerate cities and diversify local economies and create new businesses and innovative and sustainable jobs in a cost-effective way. Implementing GI and nature-based solutions in urban areas would also create a greater sense of community and help combat social exclusion and isolation.

Map 3-10 Access to green urban areas in cities, 2012

 

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., 2013). The latest (Vizcaino et al., 2017), which focuses on European functional urban areas (FUAs) 5 , 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.

Map 3-11 Share of NO2 concentration removed by vegetation in cities, 2010



3.4.6. 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 damage 6 .

Since the WFD, the Floods Directive 7 , 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 development 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) 8 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 its location and the hydrological characteristics of its 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.

Map 3-12 Population flooded in the case of the biggest 100 year flood in FUAs reoccurring

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 biodiversity 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 FUAs 9 in the EU on top of the Natura 2000 network 10 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.

Figure 3-13 Share of the Natura 2000 network which intersects with Functional Urban Areas

Source: JRC.


3.5. 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 co-operation.

3.5.1. 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.

Map 3-13 Border regions, NUTS 3

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, EUR 6.6 billion was allocated to 60 cross-border cooperation programmes 11 .

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 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 mobility even in places which are potentially functional regions.

A recent study 12 suggested 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 3-13 ).

(1)

   European Commission, Directorate-General for Regional and Urban Policy (2016), Quality of Life in European Cities 2015, Publications office of the European Union, Luxemburg.

(2)

   European Union (2013), The Economic benefits of the Natura 2000 Network, Publications Office, Luxembourg.

(3)

   Special Report No 1/2017: More efforts needed to implement the Natura 2000 network to its full potential, the European Court of Auditors.

(4)

   Vallecillo, S., Polce, C., Barbosa, A., Perpiña Castillo, C., Zulian, G., Vandecasteele, I., Rusch, G. and 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.

(5)

   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/European_cities_%E2%80%93_the_EU-OECD_functional_urban_area_definition .

(6)

   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 socioeconomic changes are projected to rise from EUR 6.9 billion a year to EUR 20.4 billion a year by the 2020s, EUR 45.9 billion a year by the 2050s and EUR 97.9 billion a year by the 2080s. See Rojas et al. (2013) Climate change and river floods in the European Union: Socio-economic consequences and the costs and benefits of adaptation, Global Environmental Change 23, 1737–1751:    
    http://www.sciencedirect.com/science/article/pii/S0959378013001416# .

(7)

   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, p. 27.

(8)

   Lung T., Lavalle C., Hiederer R., Dosio A. and 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.

(9)

    http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit#ua11-14 .

(10)

   Natura 2000 sites (2016)  ( https://www.eea.europa.eu/data-and-maps/data/natura-8#tab-metadata ).

(11)

   In the case of external border regions, the Instrument for Pre-Accession Assistance (IPA) supports cross-border co-operation 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.

(12)

   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.

Top

Brussels, 9.10.2017

SWD(2017) 330 final

COMMISSION STAFF WORKING DOCUMENT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

My region, My Europe, Our future:
The seventh report on economic, social and territorial cohesion

{COM(2017) 583 final}


Map 3-13 Percentage GDP loss in EU NUTS 3 land border regions due to cross border obstacles

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 3-14 ). In other places, the cross-border 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.

Map 3-14 Cross-border road network efficiency in border areas

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).

Figure 3-14 Population of border areas having access to rail passenger services, 2014

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.

Table 3-1 Change in population of land borders regions and other regions in the EU-15, EU-13 and EU-28, 2005-2015 (% change)

2005 - 2015

Land border region

Non border region

Total

EU-15

 

 

 

total change

4.0

4.3

4.2

natural change

0.7

1.4

1.2

net migration

3.4

2.9

3.0

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

3.5.2. Other types of cooperation: interregional, transnational and macro-regional

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 EUR 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 which together have been allocated EUR 2.1 billion for the present period ( Map 3-15 ). 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 territory, such as 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 in the implementation of reforms, by enabling the stakeholders to learn from experiences and good practices 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 co-operation. They are not, however, directly financed under cohesion policy but they focus on an optimal use of existing financial sources (e.g. the ESIF, Horizon 2020, COSME, LIFE), better implementation of existing legislation and better use of existing institutions.

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 3-15 ).

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 like 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.

Map 3-15 Transnational cooperation programmes 2014-20

3.5.3. Local, urban and metropolitan development

Cohesion policy promotes integrated and place-based 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 EUR 31 billion) is allocated to integrated territorial and urban development in the current period, the ERDF contributing the largest part (EUR 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 better 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 by 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 non-repayable 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 EUR 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 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 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.

Urban Agenda for the EU

The Urban Agenda for the EU which is designed to strengthen the urban dimension in EU policy-making 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 and regulation, 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 experiences).

A new website ( The EU Urban Agenda ) enables stakeholders to contribute to the Urban Agenda as a whole or to specific Partnerships.

Going beyond administrative boundaries: Integrated Territorial Investments

Cohesion policy pays particular attention to the specific socio-economic characteristics of functional area, 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 area such as remote and sparsely populated rural areas, islands and coastal areas, environmental protection sites and functional urban areas.

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 h 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 measures and different ways of cooperating as well as to mobilise interested parties and to reflect on urban polices.

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 and its territorial focus depends very much on the coordination with the EAFRD and EMFF. Complementary arrangements usually target 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.

Top

Brussels, 9.10.2017

SWD(2017) 330 final

COMMISSION STAFF WORKING DOCUMENT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

My region, My Europe, Our future:
The seventh report on economic, social and territorial cohesion

{COM(2017) 583 final}


Table 4 1 Summary of analysis of effects of quality of government and other factors on growth of GDP per head    

Table 4 2: e-Government benchmark: performance and progress    

Map 4 1 European Quality of Government index, 2017    

Map 4 2: Measuring meritocracy in the public sector in European regions    

Map 4 4: Share of single bidders in public procurement (levels and changes)    

Map 4 3: Public procurement using an open call for tender    



KEY MESSAGES

·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.

·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 counties which point to differences in the administrative capacity of regional and local governments.

·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.

·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.

4.1.GOOD GOVERNANCE AFFECTS ECONOMIC GROWTH AND THE QUALITY OF LIFE

According to the dominant economic theories 1 , 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 convergence 2 . 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 explaining 3 . 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 process. 4

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, 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 institutions 5 .

High-quality government has been found to be of outmost 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 head. 6

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 decided. 7  

In sum, there is a growing consensus that the quality of governance and institutions is a fundamental precondition for sustained increases in prosperity, well-being and territorial cohesion in the EU.

4.2. 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 them 8 . The institutional environment of a country depends on the efficiency and behaviour not only of public but also of private stakeholders. 9

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 Error! Reference source not found. and Error! Reference source not found. .

Figure 01: Government effectiveness, 1996 and 2015

Figure 02: Citizens' confidence in institutions, 1996 and 2015

Data source: World Bank Worldwide Government Indicators 2015

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 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 effective. 10

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 ( Error! Reference source not found. ). 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 ( Error! Reference source not found. ). 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, 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 03: Economic impact of government effectiveness

Figure 04: Social impact of government effectiveness

Competitiveness

Life satisfaction

Government effectiveness

Government effectiveness

Data source: World Bank Government Effectiveness 2015; World Economic Forum. Global Competitiveness 2016-2017.

Data source: World Bank Government Effectiveness 2015; Standard Eurobarometer 83, Spring 2015.

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 reginal 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.

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 rates. 12 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 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 2013 13 indicating that improvements in government may take time. Indeed, for them to occur is likely to require concerted efforts at all levels of the administration as well as the active involvement of the public at large.


Map 01 European Quality of Government index, 2017



4.2.1. 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 0 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 0 1 , right panel).

Table 01 Summary of the analysis of the effects of quality of government and other factors on growth of GDP per head

Key:

Source: own calculations on the basis of A. Rodriguez-Pose, T. Ketterer (2016).

Note: Panel data analysis for 249 NUTS 2 regions in the EEU using a standard Solow-Swan-type growth model. Investment is measured by gross fixed capital formation as a % of GDP. All independent variables are included with a 5-year lag. Variables are expressed in terms of natural logarithms apart from population growth. All regressions include a constant time trend.

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 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.

4.2.2. 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 then 14 .

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 05: Transparency International corruption perception index, 2012 – 2016

Panel 1: Results in 2016

Panel 2: Change 2012 – 2016

Source: Transperency international

While the general trend over the 5 years 2012-2016 is upwards, there were some significant downward movements in the last year. On average, there was a decline in EU countries of 0.75 of a point and three places in the ranking. Respondents in 12 EU Member States assessed corruption as being worse than a year earlier, with the biggest reduction in scores being in Cyprus (6 points.), the Netherlands (4), Hungary (3) and Greece, Croatia, Lithuania and Ireland (2 in each), which meant a fall of 10 or more places in the ranking for Cyprus and Greece (as well as for the Czech Republic and Malta because of increased scores for other countries). 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, 5 countries stand out as not following the general trend towards improvement. In Cyprus, Spain and Hungary, there was a significant increase in perceived corruption while in Bulgaria and Malta, it remained unchanged.

4.2.3. Trust in local authorities in line with perceptions of corruption

Corruption erodes trust in public services. According to various surveys carried out for the European Commission and information from the World Justice project 15 , 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 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 06: Trust in local authorities and perception of corruption in local government

Source: Cities report

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) 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.

4.3. 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 sustainability. 16  

4.3.1. Professional and impartial administrations provide better policy outcomes for citizens

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 successfully. 17

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.

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  

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 policy performance, while Cyprus and Greece score the lowest (Figure 4-7). Germany, Luxembourg and the UK are ranked only slightly below the three Nordic countries, though also 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.

Figure 07: Sustainable governance indictors – policy performance

Source: Own elaboration on the basis of Sustainable governance indicators http://www.sgi-network.org

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 accountable governments in the EU ( Figure 0 8 ), 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.



Figure 08: Sustainable governance indictors – capacity and accountability of government

Source: Own calculations on the basis of Sustainable governance indicators http://www.sgi-network.org

4.3.2. 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&D. 18

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 ( Figure 0 9 , which shows the effect on GDP in panel 1 and on employment in panel 2 assuming all Member States were to implement reforms). The effect on employment is smaller because of the boost to productivity but still significant.

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.



Figure 09: Macroeconomic effect of structural reforms

Panel 1

Panel 2

The figures show the results of the Quest model simulation in terms of the % difference in GDP and employment compared to a ‘no-reform’ scenario assuming all Member States implement reforms. Source: Varga J. and J. in’t Veld (2014) 'The potential growth impact of structural reforms in the EU. A benchmarking exercise", European Economy, Economic Paper no. 541'

Structural reforms can potentially have a big impact on lagging regions, accelerating the process of catching-up. 19

4.3.3. Meritocracy of the public sector varies greatly between and within EU countries

The Quality of Government Expert Survey 20 , 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 and salaries. The results are presented in three indices relating to professionalism, 'closedness' and impartiality. 21

They show that Western and Nordic EU counties tend to have more professional and impartial public administrations than the southern and eastern Member States, Poland, Lithuania and Estonia being the only ones of the EU-13 that are assessed as being 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 but are assessed as being relatively impartial and professional. On the other hand, France and Germany have a more closed and formalised system but have officials who are assessed as being professional and impartial.

Figure 010: Professionalism, impartiality and 'closedness' of the public sector

Note: Professionalism index: higher values indicate a more professional public administration. Closedness index: higher values indicate a more closed public administration. Source: own calculations on the basis of The QoG Expert Survey Dataset II.

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 0 2 ). 22 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.



Map 02: Measuring meritocracy in the public sector in European regions

The degree of local autonomy also varies across the EU ( Box 1 ).

Box 1: 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.

Figure 011: Local Autonomy Index per country, 1990, 2000 and 2014

Local authorities in the Nordic countries have a high degree of autonomy as do those in Germany, Switzerland and Poland, while those in Cyprus, Malta and Ireland have the lowest levels in the EU ( Figure 0 11 ). 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 0 12 ). Only in Belgium, Italy, Austria, Spain, Germany - countries with a strong regional or federal structure of government as well as in Ireland - is the degree of regional self-rule greater than at local level, though even in these countries, local authorities have significant discretion over policy.

Figure 012: Local and regional self-rule, 2014

4.3.4. Governments have advanced in making public services available online, but have focussed less on the quality of the delivery from 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 ( Box 2 ).

Box 2: e-Government Benchmark project 23

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 responses to a number of questions regarding the quality or quantity of e-Government services on a specific aspect.

Source: EU e-government benchmark project

Table 0 2 shows how EU Member States performed in 2016 compared to the average of 34 European countries. 24 The Nordic countries, the Baltic States, the Benelux countries, Germany, France and Austria performed best and show the most growth in e-Government.

Table 02: 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)

United Kingdom, 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

Notes: European average means average for: EU member states, Norway, Iceland, Switzerland, Serbia, Montenegro and Turkey. Average score: 61%. Average growth: 8%.

Source: Own calculations based on the EU e-government benchmark project.

In 2016, almost one in two of those in the EU (48%) used e-Government, and around four in every five or more in Denmark (88%), Finland (82%) and Sweden (78%) 25 . The share increased over the preceding 5 years in all Members States, except Slovakia, the Czech Republic and Bulgaria ( Figure 0 13 ), 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.

Figure 013: e-Government use by people, 2011-2016

Source: Eurostat

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 take-up of online services ( Figure 0 14 , right panel, which shows all EU countries as being below the diagonal). Accordingly, simply providing information and services online is not sufficient to create user-centric e-Government services.

Figure 014: Availability, usability, ease and speed of use of public online services

Source: Own calculation on the basis of EU E-Government Benchmark project data set.

There are marked differences between countries in terms of transparency as well as variations between the three indicators used to measure this 26 , which might indicate a lack of coordination between different parts of government ( Figure 0 15 ).

Malta, Estonia and Latvia score highest in terms of the publication of information and delivery of services, while Bulgaria, Hungary and Romania score lowest on the publication of information and Greece and Slovakia on the delivery of services. Malta also scores highest on transparency in relation to personal data followed by France with Slovakia, Hungary, Romania and the Czech Republic scoring lowest.



Figure 015: Transparency of e-Government

Source: EU E-Government Benchmark

Online public services are becoming increasingly accessible across the EU but growth is uneven and many Member States are lagging behind. For successful 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.

4.3.5. 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, distribute income and wealth more equitably and minimise social inequalities. 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 to frontier’).

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.

Figure 016: Ease of doing business, 2010-2017.

Source: Own calculations on the basis of World Bank Doing Business.

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 0 16 ). 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 indicators 27 , however, reveal substantial regional differences despite operating within the same legal and regulatory framework. So far, the indicators exist for only 6 EU countries: Italy (2012), Spain (2015), 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 below. 28

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. 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. To make online platforms 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-based. 29

In all countries, except Hungary, there is a large variation between different cities: in Romania, registration takes 12 days in Timisoara but 25 days in Craiova; in Spain, it takes 14 days in Gijon but 31 days in Ceuta.

Figure 017: Regional differences in starting a company

Source: Subnational doing business reports for: Poland, Spain and Bulgaria, Romania and Hungary.

Figure 018: Registration of companies online

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. In general, it is relatively easy to deal with construction permits 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).

Enforcing a contract shows the most variation in all 5 countries for which data are available 30 , 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 019: Regional differences in dealing with construction permits and enforcing contracts

Note: No subnational data for Spain for enforcing contracts. Source: Subnational doing business reports for: Poland, Spain and Bulgaria, Romania and Hungary.

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.

4.3.6. 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, 2016; OECD, 2015). It is a principal means through 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 Error! Reference source not found. ).

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, & King, 2016).

Definitions of public procurement governance indicators:

-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) database.

Source: Fazekas, M., Assessing the quality of government at the regional level using public procurement data (upcoming)

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 0 3 (left panel)). 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 sign of corruption. 31 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. 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.

Map 03: Share of single bidders in public procurement (levels and changes)

Source: Own elaboration on the basis of Fazekas, M., (upcoming)

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 suppliers 32 .

The use of open procedures is one of the indicators to measure transparency of procurement. The results (

Map 0 4 ) 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 overuse might indicate a lack of administrative capacity to run more complicated procedures (such as negotiated ones).


Map 04: Public procurement using an open call for tender

Source: Fazekas, M., Assessing the quality of government at the regional level using public procurement data (upcoming)

4.4.    Suitable institutions increases 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 (Rodriguez-Pose, Garcilazo, 2014).

According to a recent study by Diaz Ramirez, Kleine-Rueschkamp and Veneri 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 policy. 33

Figure 01: Discontinuity in the allocation of funds for entrepreneurship and SMEs

Source: 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

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. ( Figure 0 1 shows this 'discontinuity' in allocations from ESI funds for entrepreneurship and SMEs.) Overall, there was no relationship between the amount of funding and the total number of enterprises 34 . At the same time, the ‘right’ set of institutions seems to affect the relationship, in that the rate of business creation was significantly larger in regions where corruption is perceived as being relatively limited than in those where it considered to be relatively widespread. This was particularly the case for ‘employer’ firms (i.e. those with employees).

4.5.    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 government 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 of 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 authorities 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.



References

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.

Arrowsmith, S. (2009). EC Regime on Public Procurement. In K. V Thai (Ed.), International handbook of public procurement (pp. 254–290). New York: CRC Press.

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.

BertelsmannStiftung, Sustaiable Governance Indicators 2016,
http://www.sgi-network.org/2016/

Cappelen, A., Castellacci, F., Fagerberg, J., & Verspagen, B. (2003). The impact of EU regional support on growth and convergence in the European Union. JCMS: 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., 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., Dahlström, C. & Lapuente, V. Eur J Crim Policy Res (2016)

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., 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.

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.

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.

Crescenzi, R. & Rodríguez-Pose, A. (2012). Infrastructure and regional growth in the European Union , Papers in Regional Science , 91(3), 487-513.

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., Teorel J., Dahlberg S., Hartmann F., Lindberg A., and Nistotskaya M. (2015). The QoG Expert Survey Dataset II. University of Gothenburg: The Quality of Government Institute

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.

European Commission. (2014). Investment for jobs and growth. Promoting development and good governance in EU regions and cities. Sixth report on economic, social and territorial cohesion. (L. Dijkstra, Ed.). Brussels: Publications Office of the European Union.

European Commission. (2016). Public Procurement Indicators 2014. Brussels.

European Commission, Standard Eurobarometer 83, Spring 2015.

European Commission (2016); European semseter thematic factshhets: quality of public administration;
https://ec.europa.eu/info/strategy/european-semester/thematic-factsheets/public-administration_en

European Commission (2017); Single market scoreboard, http://ec.europa.eu/internal_market/scoreboard/

European Commission, eGovernment Benchmark 2016 A turning point for eGovernment development in Europe? (Insight report, background report and data set available at https://ec.europa.eu/digital-single-market/en/news/eu-egovernment-report-2016-shows-online-public-services-improved-unevenly)

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 elvel usijng the public procurement data (upcoming)

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. doi:10.1017/S0007123417000461

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.

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., ZurawickiI 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.

Henderson, J., Hulme, D., Jalilian, H., & Phillips, R. (2007). Bureaucratic Effects: ‘Weberian’ State Agenciesand Poverty Reduction”. Sociology, 41(3), 515–532.

Holmberg, S. & Rothstein, B. (eds.) (2012) Good Government: The Relevance of Political Science, Cheltenham: Edward Elgar Publishing.

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. (2002), Growth Without Governance, The World Bank.

Kaufmann, D., Kraay A, Zoido-Lobatón, (1999), Aggregating Governance Indicators, Policy Research Paper 2195, The World Bank

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, UK: Cambridge University Press.

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.

Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3-42.

Mungiu-Pippidi, A. (2015). The Quest for Good Governance. How Societies Develop Control of Corruption. Cambridge, UK: Cambridge University Press.

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, UK: Cambridge University Press.

Mankiw, N., Romer, P., & Weil, D. (1992). A contribution to the empirics of economic growth. Quarterly Journal of Economics 107, 407–437.

OECD. (2007). Integrity in Public Procurement. Good Practice from A to Z. Paris: OECD.

OECD (2013a), Investing Together: Working Effectively across Levels of Government, OECD Publishing, Paris. http://dx.doi.org/10.1787/9789264197022-en

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. (2015). Government at a glance. 2015. Paris: OECD.

OECD/Sigma. (2014). The Principles of Public Administration. Paris: OECD/Sigma.

GOVERNING THE COMMONS

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.r

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.

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 DG for Regional Policy).

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., & Teorell, J. (2008). What Is Quality of Government? A Theory of Impartial Government Institutions. Governance, 21(2), 165–190.

Rothstein, B. (2011). The Quality of Government: Corruption, Social Trust and Inequality in International Perspective. Chicago: University of Chicago Press.

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.

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.

Swan, T. (1956). Economic growth and capital accumulation. Economic Record, 32, 334-361.

Svallfors, S. (2013) 'Government Quality, Egalitarianism, and Attitudes to Taxes and Social Spending: A European Comparison', European Political Science Review, 5 (3): 363-380.

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 15May13. 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, 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.

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 (2016), Doing Business reports - Measuring Regulatory Quality and Efficiency

World Bank (2017), Doing Business in the European Union 2017: Bulgaria, Hungary and Romania; http://www.doingbusiness.org/reports/subnational-reports/eu-bulgaria-hungary-romania

World Bank (2015), Doing Business in Poland 2015; http://www.doingbusiness.org/Reports/Subnational-Reports/poland

World Bank (2015), Doing Business in Spain 2015; http://www.doingbusiness.org/Reports/Subnational-Reports/spain

World Bank, Worldwide Governance Indicators, http://data.worldbank.org/data-catalog/worldwide-governance-indicators

World Bank. (2009). Fraud and Corruption. Awareness Handbook. Washington DC: World Bank.

World Economic Forum, The Global Competitiveness Report 2016–2017, https://www.weforum.org/reports/the-global-competitiveness-report-2016-2017-1

(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).

(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); Holmberg and Rothstein, (2012); Rothstein, (2011); Uslaner, (2008); Tavits, (2008); Svallfors, (2013).

(7)

   Rothstein & Teorell, (2008).

(8)

    http://info.worldbank.org/governance/wgi/index.aspx#doc .

(9)

    http://www3.weforum.org/docs/GCR2016-2017/05FullReport/TheGlobalCompetitivenessReport2016-2017_FINAL.pdf .

(10)

    http://www.sgi-network.org/docs/2016/basics/SGI2016_Overview.pdf .

(11)

   EQI is based on an extensive survey covering the perceptions of people of h 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.

(12)

   Competitiveness in low-income and low-growth regions. The lagging regions report. European Commission, 2017.

(13)

   Due to slight changes in the methodology the two surveys are not fully comparable.

(14)

http://www.transparency.org/files/content/pressrelease/2012_CPIUpdatedMethodology_EMBARGO_EN.pdf.

(15)

   See the ‘Cities Report’ (European Commission, 2016) for details.

(16)

    https://ec.europa.eu/info/sites/info/files/european-semester_thematic-factsheet_quality-public-administration_en.pdf .

(17)

    http://www.sgi-network.org/docs/2016/basics/SGI2016_Overview.pdf .

(18)

   For more details see Varga J. and J. in’t Veld (2014), "The potential growth impact of structural reforms in the EU. A benchmarking exercise", European Economy, Economic Paper no. 541.

(19)

   See: European Commission 'Competitiveness in low growth and low income regions. The lagging regions report.' http://ec.europa.eu/regional_policy/sources/docgener/studies/pdf/lagging_regions%20report_en.pdf .

(20)

   Dahlström, Carl, Jan Teorell, Stefan Dahlberg, Felix Hartmann, Annika Lindberg, and Marina Nistotskaya. 2015. The QoG Expert Survey Dataset II. University of Gothenburg: The Quality of Government Institute.

(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, Carl, Jan Teorell, Stefan Dahlberg, Felix Hartmann, Annika Lindberg, and Marina Nistotskaya. 2015. The QoG Expert Survey Dataset II. University of Gothenburg: The Quality of Government Institute.

(22)

   Charron, N., Dahlström, C. & Lapuente, V. Eur J Crim Policy Res (2016) 22: 499. doi:10.1007/s10610-016-9307-0.

(23)

    https://ec.europa.eu/digital-single-market/en/news/eu-egovernment-report-2016-shows-online-public-services-improved-unevenl .

(24)

   EU member states, Norway, Iceland, Switzerland, Serbia, Montenegro and Turkey.

(25)

   People sending filled forms to public authorities, over the internet, last 12 months (Eurostat).

(26)

   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.

(27)

   The subnational indicators ones 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.

(28)

   Italian sub-national doing business indicators were examined in the 6th Cohesion Report.

(29)

   Subnational doing business for Poland.

(30)

   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.

(31)

   In the overall public procurement competition index (Fazekas 2017, upcoming) Sweden, UK, Ireland, Finland and Spain scored the best. The overall corruption risk index showed that North-West countries plus Latvia and Spain scored the best. The data and interactive maps are available at: https://public.tableau.com/profile/mihaly.fazekas#!/vizhome/regiopp/nuts2 .

(32)

   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 on contract notice publication, reporting completeness and voluntary reporting (see Fazekas, 2017, upcoming).

(33)

   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 self-employment and business start-ups. The analysis was based on comparing the growth of businesses between regions that had similar levels of GDP per head but differed significantly in the scale of funding received.

(34)

   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.

Top

Brussels, 9.10.2017

SWD(2017) 330 final

COMMISSION STAFF WORKING DOCUMENT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

My region, My Europe, Our future:
The seventh report on economic, social and territorial cohesion

{COM(2017) 583 final}


5.1. Public investment is still at very low levels despite the recent pick-up of the EU economy

5.1.1. 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 2011, only 5 were are still subject in 2016 (Spain, France, Greece, Croatia and the UK).

Figure 5-1 General government balance, EU-28 average, 2000-2016

Source: Eurostat



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 was a gradual decline in government expenditure relative to GDP, which was then further reduced by the automatic stabilising effect of the gradual economic recovery 1 (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.

Figure 5-2- General government balance, 2009, 2013 and 2016

Source: Eurostat

Figure 5-3- General government expenditure and revenue and general government balance, EU-28, 2001-2016

Source: Eurostat

5.1.2. 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) 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.

Figure 5-4- Selected categories of general government expenditure, 2000-2007 and 2016

Source: Eurostat

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 growth 2 . 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 by most (Figure 5-5).

Figure 5-5- Total public investment, 2016

Source: Eurostat

The recent Commission reflection paper on the completion of the Economic and Monetary Union 3 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 2007-2013 4 . Seven Member States reported a level of expenditure relevant for additionality lower than forecast ex-ante at the beginning of the programming period 2007-2013 before the economic downturn. Actual structural spending for 2007-2013 was 35% lower than the ex-ante forecast in Greece, over 25% lower in Italy and between 10% and 20% lower in Hungary, Lithuania and Portugal 5 .


5.1.3. 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 of 2.5%, spread across all categories) which have experienced a protracted economic downturn.

Figure 5-6- Change in growth-friendly categories of general government expenditure, 2008-2015

Source: Eurostat



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 and 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). In other areas, 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.

5.2. Sub-national governments play a key role in public expenditure and investment

5.2.1. 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 2001-2016 7 .

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.

Figure 5-7- Sub-national government expenditure, 2001 and 2016

Source: Eurostat

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.

Figure 5-8- Sub-national government investment, 2016

5.2.2. …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 government, 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.

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.

5.2.3. 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 going into small 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.

Figure 5-9- Sub-national government expenditure, revenue and budget balance, EU-28, 2001-2016

Source: Eurostat



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). Their budget was a 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 of the sub-national deficits results to some extent from the fact that public expenditure witnessed a trend over centralization more intense than revenues. That is, the trend in sub-national deficits is linked to the trend towards centralisation of public expenditure.

Figure 5-10- Sub-national governments budgetary balance, 2009 and 2016

Source: Eurostat

5.3. Reviewing how the ESI Funds are linked to new country-specific recommendations and to sound economic governance

5.3.1. 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 (hereafter the 'ESI Funds') for 2014-2020 introduced a number of new provisions which strengthened the linkages between the ESI Funds and sound economic governance, with the aim of improving the overall performance focus 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) other Council recommendations 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.

5.3.2. 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 programmes 8 . 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 administration 9 .



The relatively late adoption of the 2014-2020 programmes, combined with the ensuing delays in starting their implementation and the recent 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'. That is, the link refers only to CSRs relating to investment, so excluding those whose implementation depends on legislative and/or administrative legal changes or reforms. Therefore, the link between any reprogramming request and a relevant CSR must be indisputable, which is less likely with the new streamlined approach with fewer and more general CSRs. In addition, the nature and content of the CSRs since 2014 has been relatively stable, meaning 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 providing guidelines on the application of the measures 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 adequately 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.

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.

5.3.3. 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 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.

5.3.4. 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.

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.

5.4. Conclusions

Public investment remains at historically low levels (as a share of the GDP) in the EU. This is a result 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.

The share of public investment co-financed by EU cohesion policy increased considerably during the crisis period as national and regional government spending declined. In many countries, it played a major counter-cyclical role in stabilising public investment, accounting for half or more of the total that took place in many EU-13 countries. In that context, the total investment package under the European Cohesion policy shrank in some Member States as a result of the downward revision of the national co-financing rates. These decisions were adopted to take account of the temporary difficulties in national and sub-national budgets and pressure over public finances.

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.

(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).

(2)

   See Sixth Report on Economic, social and territorial cohesion for a summary of the literature.

(3)

   European Commission. 'Reflection paper on the deepening of the economic and monetary union'. COM(2017) 291 of 31.5.2017.

(4)

   European Commission 'Ex-post verification of additionality 2007-2013.' COM(2017) 138 of 23.3.2017.

(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.

(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.

(8)

   European Commission 'Investing in jobs and growth - maximising the contribution of European Structural and Investment Funds.' COM(2015) 639 of 14.12.2015.

(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.

(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) 494 of 30.7.2014.

Top

Brussels, 9.10.2017

SWD(2017) 330 final

COMMISSION STAFF WORKING DOCUMENT

Accompanying the document

REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS

My region, My Europe, Our future:
The seventh report on economic, social and territorial cohesion

{COM(2017) 583 final}


6.6. 6.6. Environment, transport and energy networks

6.6.1. 6.6.1. The environment

6.6.2. 6.6.2. Transport investment

6.6.3. 6.6.3. Energy efficiency in buildings

6.7. 6.7. Reinforced cooperation and addressing territorial challenges



Figure 6-1: Planned investment by key priority 2014-2020 (EUR bn) Figure 6-1: Planned investment by key priority 2014-2020 (EUR bn)

Table 6-1: EU and national contributions to cohesion policy, 2014-2020 Table 6-1: EU and national contributions to cohesion policy, 2014-2020

Figure 6-2: ERDF and Cohesion Fund allocations as a % of general government capital expenditure, 2015-2017 Figure 6-2: ERDF and Cohesion Fund allocations as a % of general government capital expenditure, 2015-2017

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 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

Figure 6-3 Funding committed to projects selected as % of total available, 2007-2013 and 2014-2020 Figure 6-3 Funding committed to projects selected as % of total available, 2007-2013 and 2014-2020

Figure 6-4 Cohesion policy funding over successive periods, 1986-2023 Figure 6-4 Cohesion policy funding over successive periods, 1986-2023

Table 6-3 The ex ante conditionalities for the 2014-2020 programming period Table 6-3 The ex ante conditionalities for the 2014-2020 programming period

Figure 6-5 Division of EU sources of funding for the 2014-2020 period Figure 6-5 Division of EU sources of funding for the 2014-2020 period

Table 6-4 Changes in Financial Instruments supported by cohesion policy between the 2007-2013 and 2014-2020 periods Table 6-4 Changes in Financial Instruments supported by cohesion policy between the 2007-2013 and 2014-2020 periods

Table 6-5 Cohesion lending in EIB figures (signatures) Table 6-5 Cohesion lending in EIB figures (signatures)

Figure 6-6 Operations signed under the Cohesion Objective in EU, (EUR bn) by country and programming period Figure 6-6 Operations signed under the Cohesion Objective in EU, (EUR bn) by country and programming period

Figure 6-7 Impact of Cohesion Policy on EU GDP, 2007-2023 Figure 6-7 Impact of Cohesion Policy on EU GDP, 2007-2023

Figure 6-8 Impact of 2014-2020 programmes on Member States GDP, 2023 Figure 6-8 Impact of 2014-2020 programmes on Member States GDP, 2023

Map 6-1 Cohesion policy, impact on NUTS 2 regions GDP, 2015 Map 6-1 Cohesion policy, impact on NUTS 2 regions GDP, 2015

Table 6-6 Cohesion policy impact on GDP, 2023 and 2030, % deviation from baseline Table 6-6 Cohesion policy impact on GDP, 2023 and 2030, % deviation from baseline

Figure 6-9 Impact of cohesion policy on non-cohesion countries, all programmes and programmes implemented in the cohesion countries, 2023 Figure 6-9 Impact of cohesion policy on non-cohesion countries, all programmes and programmes implemented in the cohesion countries, 2023

Figure 6-10 Main EU sources of funding for Research, innovation and ICT, 2014-2020 Figure 6-10 Main EU sources of funding for Research, innovation and ICT, 2014-2020

Figure 6-11 Main EU sources of funding for SMEs, 2014-2020 Figure 6-11 Main EU sources of funding for SMEs, 2014-2020

Table 6-7 Common indicators and targets for 2014-20 as regards innovation and competitiveness Table 6-7 Common indicators and targets for 2014-20 as regards innovation and competitiveness

Table 6-8 Incidence and volume of support to large enterprises 2007-2013 Table 6-8 Incidence and volume of support to large enterprises 2007-2013

Figure 6-12 Large Enterprise support 2007-2013 - Case study results Figure 6-12 Large Enterprise support 2007-2013 - Case study results

Figure 6-13 Main EU sources of funding for employment, training and social inclusion, 2014-2020 Figure 6-13 Main EU sources of funding for employment, training and social inclusion, 2014-2020

Table 6-9 Common indicators and targets for 2014-2020 as regards urban and social infrastructure Table 6-9 Common indicators and targets for 2014-2020 as regards urban and social infrastructure

Table 6-10 ERDF Urban and social projects in 2007-2013, % reporting a contribution to various goals Table 6-10 ERDF Urban and social projects in 2007-2013, % reporting a contribution to various goals

Figure 6-14 Main EU sources of funding for the environment 2014-2020 Figure 6-14 Main EU sources of funding for the environment 2014-2020

Table 6-11: Additional people served by water and wastewater projects co-financed by the ERDF and CF for 2007-2013 by end-2014 Table 6-11: Additional people served by water and wastewater projects co-financed by the ERDF and CF for 2007-2013 by end-2014

Table 6-12 Common indicators and targets for 2014-2020 as regards the environment Table 6-12 Common indicators and targets for 2014-2020 as regards the environment

Figure 6-15 Main EU sources of funding for transport and energy infrastructure in 2014-2020 Figure 6-15 Main EU sources of funding for transport and energy infrastructure in 2014-2020

Figure 6-16 ERDF and Cohesion Fund allocation to transport relative to total government capital expenditure on transport, 2007-2013 (%) Figure 6-16 ERDF and Cohesion Fund allocation to transport relative to total government capital expenditure on transport, 2007-2013 (%)

Table 6-13 Common indicators and targets for 2014-2020 as regards transport Table 6-13 Common indicators and targets for 2014-2020 as regards transport

Table 6-14 Common indicators and targets for 2014-2020 as regards energy efficiency and renewables Table 6-14 Common indicators and targets for 2014-2020 as regards energy efficiency and renewables

Figure 6-17 Evolution of Interreg 1990-2020 Figure 6-17 Evolution of Interreg 1990-2020

Table 6-15 Key common indicators and targets for Interreg programmes, 2014-2020 Table 6-15 Key common indicators and targets for Interreg programmes, 2014-2020



KEY MESSAGES

·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 2007-13 period is estimated to have increased GDP in the EU-13 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:

oex ante conditions, to stimulate structural reforms and to increase administrative capacity;

osmart specialisation strategies, where major players (including research centres, businesses and civil society) identify local potential and prioritise investment in key sectors;

oa stronger focus on results through programmes setting specific objectives, translated into clear result indicators with targets and benchmarks.

·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 the 2007-2013 period when spending was concentrated in the last 2-3 years. This suggests that issues such as simplification and capacity building still need to be addressed.

·Targets for the 2014-2020 period include:

o14.5 million additional households with broadband access

o17 million additional people in the EU connected to wastewater facilities

o4 600 km of renovated TEN-T railway line

o6.8 million children with access to new or modernised schools

o42 million people with access to improved healthcare services

o7.4 million unemployed helped into work

o8.9 million people provided with new qualifications

·Cohesion policy is also investing in the economy of the future, through:

osupport to 1.1 million SMEs

othe creation of 420 000 new jobs as a result

othe establishment of nearly 30 000 new research positions

ohelp to some 28 000 SMEs to bring new products to the market



6.1.The policy

Cohesion policy is the EU's main investment policy. Over the course of the 2014-2020 programming period, EUR 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 fostering social inclusion of the disadvantaged. It is also plays a major role in supporting innovation.

Figure 6-1: Planned investment by key priority 2014-2020 (EUR bn)

Source: ESIF Open Data Platform - https://cohesiondata.ec.europa.eu/ (September 2017)

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 EUR 480 billion of investment carried out under the policy, the rest coming from national co-funding (Table 6-1).

Table 6-1: EU and national contributions to cohesion policy, 2014-2020

EUR billion

EU contribution

National contribution

Total investment

Cohesion Fund

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).

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 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).

Figure 6-2: ERDF and Cohesion Fund allocations as a % of general government capital expenditure, 2015-2017

Note: Government capital expenditure is the sum of General Government gross fixed capital formation plus capital transfers, the latter being adjusted approximately for any abnormal transfers to banks and other companies to provide support.

Source: Open data platform, Eurostat - Government statistics

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 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).

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

 

Total planned investment
(EUR million)

Projects selected
(EUR million)

Expenditure by managing authorities
(EUR million)

Selection/ planned investment
%

AT

2,941.3

829.3

32.0

28.2%

BE

4,646.2

3,288.1

214.1

70.8%

BG

8,702.6

4,010.2

874.5

46.1%

CY

824.1

150.6

41.1

18.3%

CZ

28,703.0

8,760.2

1,376.6

30.5%

DE

30,326.7

13,594.9

3,572.7

44.8%

DK

798.5

352.7

56.0

44.2%

EE

4,891.7

2,385.5

500.1

48.8%

ES

39,339.3

7,352.6

131.9

18.7%

FI

2,608.9

1,260.4

450.6

48.3%

FR

28,915.9

11,827.5

2,877.0

40.9%

GR

19,123.4

7,738.3

2,064.8

40.5%

HR

9,945.1

2,363.1

326.3

23.8%

HU

25,420.9

18,220.1

2,141.3

71.7%

IE

1,971.4

1,687.2

13.8

85.6%

IT

51,771.6

18,865.2

1,724.6

36.4%

LT

7,887.8

2,823.8

906.5

35.8%

LU

88.3

57.2

8.2

64.8%

LV

5,192.8

2,310.8

401.7

44.5%

MT

865.2

416.4

39.9

48.1%

NL

2,389.0

1,096.1

299.8

45.9%

PL

90,576.3

33,951.2

6,810.0

37.5%

PT

27,462.5

15,002.8

3,545.4

54.6%

RO

27,664.8

2,838.4

396.3

10.3%

SE

3,509.7

2,067.8

428.5

58.9%

SI

3,756.2

1,032.5

134.1

27.5%

SK

17,958.2

4,925.3

1,059.4

27.4%

UK

19,655.9

10,621.1

913.0

54.0%

Interreg

12,464.6

5,888.8

247.1

47.2%

Grand Total

480,402.2

185,718.0

31,587.0

38.7%

Source: ESIF Open Data Platform - https://cohesiondata.ec.europa.eu/ .

The rate of project selection in the current programming period, while starting more slowly than in the 2007-13 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.



Figure 6-3 Funding committed to projects selected as % of total available, 2007-2013 and 2014-2020

Note: 2017 figure is a projection based on observations to July 2017

Source: DG Regional and Urban Policy, based on monitoring data provided by MS.

Moreover, programme periods cannot be seen in isolation. Periods overlap, with the closure and finalising of one period stretching into the next, 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.

Figure 6-4 Cohesion policy funding over successive periods, 1986-2023

Note: Time profile for 2014-20 based on up to July 2017 and is then projected to be the same as for the 2007-13 period.

Source: DG Regional and Urban Policy, historic data.

6.2.Improving the effectiveness of the policy

A number of measures have been taken to improve the delivery of results in the 2014-2020 period.

6.2.1.Ex ante conditionalities

The effectiveness of public investments and the durability of results depend on having in place suitable conditions. 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 period 1 .

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 :

Table 6-3 The ex ante conditionalities for the 2014-2020 programming period

Category

Examples

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.

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.

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.

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.

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 implementation of EU regulations in a systemic way.

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.

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 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.

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 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.

5. Improving administrative capacity and coordination

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 competitiveness.

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.

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 on-going 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.

Around 75% of all applicable ex ante conditionalities were fulfilled at the time of adoption of ESI Fund programmes. For those which remained, action plans were included in the programme. By mid-September 2017, 93% of action plans for cohesion policy were confirmed by the Commission as fulfilled. Had it not been for ex ante conditionalities, reforms might not have happened or they might have happened at a much slower pace.

6.2.2.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 (see below) 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.

6.2.3.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 2014-20 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 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 focussed.

·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 changes at the national or regional level and to learn lessons for the future 3 .

6.2.4.Smart specialisation

Smart Specialisation aims to boost national and regional innovation by enabling Member States and regions to focus on their strengths. It represents 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. EUR 65.8 billion are available to support these strategies from the ERDF and EAFRD, in addition to national and regional funding.

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" 4 . 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) 5  on energy, agri-food 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 bio-economy, 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 systems 6 .

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 EUR 1.8 billion over the present programming period to strengthening human capital in research, technological development and innovation.

6.2.5.Financial instruments (FIs)

The use of financial instruments in cohesion policy has increased significantly in recent years. In 2007-2013 around EUR 12 billion of Structural Funds was invested in this way, while plans for 2014-2020 suggest a figure of the order of EUR 21 billion 7 .

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 (Figure 6-5). 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.



Figure 6-5 Division of EU sources of funding for the 2014-2020 period

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) "Improving the take-up and effectiveness of Financial Instruments"

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 6-4).

Table 6-4 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 OR off-the-shelf OR MA loans/guarantees

Contribution to EU level instruments

Payments

Possibility of declaring 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 the 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.

The EIB Group: a key partner in promoting cohesion 8

Through a mixture of services, the EIB plays a key role in addressing regional economic imbalances and raising living standards across the EU.

EIB Cohesion Priority Regions cover all "less developed" and "transition" regions eligible under Cohesion Policy 2014-2020. In the last 10 years (2007 – 2016) more than EUR 200bn of lending has been provided to such regions (Table I and Figure 1). EIB operations support a broad range of areas such as: key infrastructure, including trans-European networks and sustainable energy, water, waste management, forestry and food security; small, medium-sized and innovative firms; education and training; information and communication technologies; and municipal lending for improved urban living environments.

Table 6-5 Cohesion lending in EIB figures (signatures)

2007-2013

2014-2016

Cohesion in EU Member States

EUR 147 bn

EUR 55 bn

of which Structural Programme Loans lOAlOANSLoans

EUR 20 bn

EUR 14 bn

6.3.Macroeconomic impact of the policy

Macroeconomic models suggest that Cohesion Policy interventions are likely to have a positive and significant impact on the EU economy (see Figure 6-6). The impact of Cohesion Policy 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 Policy investments (after taking account of their financing).



Figure 6-7 Impact of Cohesion Policy on EU GDP, 2007-2023 9

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 in 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).

The same types of result 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).



Figure 6-8 Impact of 2014-2020 programmes on Member States GDP, 2023

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 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 10 .

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 also have an impact in other regions, 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).



Map 6-1 Cohesion policy, impact on NUTS 2 regions GDP, 2015

   

Source: RHOMOLO.



Table 6-6 Cohesion policy impact on GDP, 2023 and 2030, % deviation from baseline

2023

2030

Top 10

Top 10

HU31

8.7

HU31

6.3

HU32

8.4

HU32

6.1

HU33

7.3

SK04

5.9

HU23

7.0

HU33

5.5

HU22

6.3

SK03

5.3

HU21

6.2

PT20

5.3

SK04

5.7

HU23

5.3

PT20

5.7

PL31

5.1

PL31

5.5

PL62

5.0

PL34

5.3

PL34

5.0

Source: RHOMOLO.

By the end of the programming period, GDP in Észak-Magyarország (HU31) and Észak-Alföld (HU32) in Hungary is estimated to be more than 8% higher than it would be without Cohesion policy (Table 6-5), while in the capital city region of Közép-Magyarország (HU 10), 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 (DK05), though it is still positive at 0.1% of GDP.

In most Member States, it is the least developed regions where investment relative to GDP is largest and where the impact is greatest. This is in line with the mandate enshrined in the Treaty which is to reduce disparities in regional GDP per head across the EU.

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 6-9 shows the impact of all cohesion policy programmes in 2007-13 on the non-cohesion 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 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 cohesion countries 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 counties 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 (0.03%) is due to spillovers 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 the impact of the policy is due to investment in the cohesion countries.

Figure 6-9 Impact of cohesion policy on non-cohesion countries, all programmes and programmes implemented in the cohesion countries, 2023

 

Note: CC programmes are programmes carried out in the cohesion countries.

Source: QUEST.

6.4.Innovation and Competitiveness

The ERDF is the largest single EU source of financing for innovation and competitiveness (Figure 6-10). For innovation (on which Horizon 2020 is concentrated), the ERDF is the second largest source, but, as noted above, it is the predominant source of support for SMEs.

Figure 6-10 Main EU sources of funding for Research, innovation and ICT, 2014-2020

Figure 6-11 Main EU sources of funding for SMEs, 2014-2020

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 target for gross jobs directly created being reduced from 1.2 million in the previous period to 420 000 – Table 6-6). In addition, support to large enterprises is now restricted to innovation.

6.4.1    Support to SMEs

Support to SMEs over the 2007-13 period was already focussed 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 in the sector (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 EUR 115 000, though there were wide variations between different measures of support, from several million euro (up to EUR 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 2014-20 as regards 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

EUR

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)

EUR

23.7 billion

Private investment matching public support to enterprises (non-grants)

EUR

8.6 billion

Start-ups supported

Enterprises

154,982

Source: ESIF Open Data Platform - https://cohesiondata.ec.europa.eu/

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.

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.

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% 11 . This may prove to be a key source of the added value of FIs in the longer term.

6.4.2.    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 EUR 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 Entrepreneurship.

6.4.3.    Support to large enterprises 12

Although SMEs are the main focus of enterprise support under cohesion policy, large enterprises are often key to regional development. An estimated EUR 6.1 billion from the ERDF was allocated to large enterprise support in the 2007-2013 period – roughly 20% of the total direct support to enterprises (Table 6-7).

Table 6-8 Incidence and volume of support to large enterprises 2007-2013

Direct enterprise support 13
(EUR million)

Large enterprise support
(EUR million)

Large enterprise / total support

Number of projects

Number of firms supported

Poland

6591

1153

17%

539

408

Portugal

4145

1134

27%

407

319

Germany

3200

704

22%

763

632

Czech Republic

1491

467

31%

520

339

Hungary

2581

453

18%

409

273

Spain

2543

311

12%

1269

398

Italy

2034

243

12%

416

270

Austria

283

133

47%

194

148

Total (EU-28)

31 233

6100 (est.)

20% (est.)

6000 (est.)

3700 (est.)

Source: Ex post evaluation of Cohesion Policy. 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.

This took the form of some 6 000 projects, with an average project size of EUR 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, 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 6-12). 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.

Figure 6-12 Large Enterprise support 2007-2013 - Case study results

Source: ex post evaluation of cohesion policy

Wherever it was possible to judge, it was found that the presence of the 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 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.



6.5.Employment, social inclusion and education

Figure 6-13 Main EU sources of funding for employment, training and social inclusion, 2014-2020

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 EUR 86 billion 14 , over EUR 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 schools, public employment services and other organisations.

6.5.1.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 ESF 15 .

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 EUR 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 EUR 4.2 billion 16 , matched by an equal amount from the ESF, i.e. EUR 8.4 billion in total, for Member States to invest directly in improving the employability of young people.

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, o 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) 17 , 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.

6.5.2.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 in the labour market. These include, in particular, migrants, ethnic minorities, such as Roma, and those with a different lifestyle, such as itinerant 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.

6.5.3.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 Europe 18 , 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.

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 programmes.

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 deinstitutionalisating 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.

6.5.4.Urban and social infrastructure

Table 6-9 Common indicators and targets for 2014-2020 as regards 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/

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

·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 of brownfield sites

·The installation of clean drinking water supply and wastewater treatment facilities

·Improvements in the energy efficiency of buildings.

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 different 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 6-9). 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 higher labour market participation.