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.

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.

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.

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.

High growth enterprises (those growing by 10% a year or more) play an important role in the economic growth of cities and regions through their contribution to productivity and innovation (Acs et al., 2008).

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.

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 ).

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.

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 ) .

In 2014, the largest number of firms with at least one employee 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.

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).

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.

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.

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.

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.

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.

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.

The main challenges for regions in each club can be summarised as follows:

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.

Patenting activity, which is an indicator of innovation, is highly concentrated in very-high and high income regions.

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.

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.

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 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.

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.

[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.

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).

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.

2.towns and suburbs: less than 50% of the population living an urban centre, but more than 50% in an urban cluster;

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;

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

This creates an especially close link between rural regions and rural areas which are defined in the exactly same way.

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

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

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:

Since the 5th Cohesion Report, the European Commission has developed two typologies of local areas which are linked to two typologies of regions.

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.

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.

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.

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.

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.

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.

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.

1.3.3 GDP growth in rural and intermediate regions proved to be more resilient during the crisis years

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.

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.

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.

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.

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.

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.

Accordingly, they are major centres of employment and business activity with higher productivity than elsewhere.

In 2014, metropolitan (metro) regions accounted for 58% of population in the EU, 61% of employment and 67% of GDP.

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.

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

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

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.

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 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.

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.

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.

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.

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.

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:

Over the 2009-2016 period, the less developed Member States had the highest growth in GVA per head (0.9% a year) mainly driven by an increase in productivity (1.2% a year) with only a slight increase in the employment rate (0.1% a year) but offset by a reduction in the share of working-age population (0.4% a year). The moderately and highly developed Member States followed a similar pattern, but with lower growth in GVA per head (0.4% and 0.2% a year, respectively) and productivity (0.7% and 0.4% a year).

Between 2009 and 2016, GVA per head 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.

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).

·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.

·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.

·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 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.

Due to the specific scope of the EMFF, support is concentrated in coastal areas and major freshwater sites.

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.

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.

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.

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.

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.

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.

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.

Less developed Member States tend to have a different economic structure than the others, with more employment in agriculture and industry ( Table 1 1 ). 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).

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.

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.

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

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 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.

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.

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 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 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.

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. 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.

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 ).

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.

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.

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.

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.

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.

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.

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).

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:

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

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).

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.

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.

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.

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.

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.

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.

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 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.

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).

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 ).

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 ).

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 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.

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

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.

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.

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.

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.

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.

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.

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 %).

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).

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 . 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.

In 2015, more than one in four EU residents (27%) lived in a NUTS 2 region with a GDP per head, in PPS terms, below 75% of the EU average ( Map 1 1 ).

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.

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.

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.

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 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.

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).

·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.

·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.

·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.

·GDP per head in the less developed regions is converging towards the EU average through both faster productivity growth and increased employment.

·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.

·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.

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

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).

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.

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.

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.

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.

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

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.

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).

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).

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.

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).

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.

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.

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%.

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).

Metropolitan areas, especially larger ones, tend to have a more highly educated population than other areas. 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).

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).

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.

Source: Calculations based on OECD Regional Statistics and data used in and provided by Ketels and Protsiv (2016)

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.

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.

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).

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).

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.

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

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.

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.

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 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.

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.

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.

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 most innovative region in the EU by this measure is Stockholm, followed by Hovedstaden in Denmark, and South East England (Map 19).

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

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.

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.

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 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).

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.

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.

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.

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).

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.

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.

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

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.

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

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.

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.

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 (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.

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.

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.

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:

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

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 ).

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.

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

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.

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

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.

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 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.

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.

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.

London and its commuting area, which includes seven NUTS 2 regions, 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. 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.

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.

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 ).

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 index is based on a definition of regional competitiveness from the perspective of both firms and residents (Dijkstra et al., 2011):

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.

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.

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.

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).

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.

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%).

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.

The coverage of Next Generation Access (NGA) 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 ).

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.

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.

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

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.

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.

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.

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%.

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.

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:

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).

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 ).

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.

Young people are one of Europe's greatest assets for the future. The economic crisis hit young people particularly hard. It has widened the gap between those with more opportunities and those with fewer. Some are increasingly excluded from social and civic life and, worse still, a number are at risk of disengagement, marginalisation and even radicalisation. This is why the Commission and Member States have increased their efforts since 2013 to improve their employability, their integration into the labour market, and their inclusion and participation.in society In the face of a growing socio-economic divide, policy must continue tackling the deep-seated social problems that many young people face. Sustainable solutions need to be found to reduce youth unemployment, strengthen social inclusion and prevent violent radicalisation. This requires more systematic cooperation across a range of policies at EU and Member State level in respect of employment, education, training, and social policy as well as culture, sport and health. The ‘cooperation framework for youth’, EU funding under the Erasmus+ programme, the European Social Fund (ESF) and the Youth Employment Initiative (YEI) are all targeted at young people to help them find quality jobs, participate in social life and develop their potential

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.

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 rate, however, varies markedly between types of region. The average employment rate in more developed regions 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.

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.

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.

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).

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.

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). The increase in asylum seekers brought with it an increased flow of the most vulnerable group seeking asylum, namely unaccompanied minors, 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.

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.

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 . 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.

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.

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.

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

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.

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.

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.

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.

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.

Migration from outside the EU and mobility 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).

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.

To capture the cumulative impact on population of international movements the following indicators are used:

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 dividi