Remove all filtering

75 Maps

Unemployment typology

The map shows a typology of European regions by combining information on pre-pandemic unemployment rates with unemployment rates in 2020, based on the annual Labour Force Survey (LFS) that is measured in November. On one axis, the typology considers the extent of the change in the unemployment rate between 2019 and 2020. On the other axis, it considers whether the unemployment rate in 2020 was above or below the EU average of 7.3%. Regions are divided into four types based on whether the unemployment rate decreased or increased and how it relates to the EU average. Regions falling into the first type, shown in red on the map, had an increase in the unemployment rate in 2020 as well as an above-average unemployment rate in general in 2020. These regions were most affected by the pandemic. They are mainly found in northern and central parts of Finland, southern and eastern Sweden, the capital area of Iceland, Latvia, Lithuania, Spain and central parts of France. Regions falling into the second type, shown in orange on the map, had an increase in the unemployment rate in 2020 but a below-average unemployment rate in general in 2020. These regions had low pre-pandemic unemployment rates and so were not as badly affected as the red regions, despite the rising unemployment rates. They are located in Denmark, Iceland, Norway, Åland, southern and western Finland, Sweden (Gotland, Jönköping, and Norrbotten), Estonia, Ireland, northern Portugal and central and eastern parts of Europe.

Change in the number of births in Europe

The map shows the number of births during the first nine months of 2021 (January to September) compared to the number of births during the same months in 2020. The babies born during the first nine months of 2021 were conceived between the spring and winter of 2020 when the first waves of the pandemic affected Europe. Babies born during the first nine months of 2020 were conceived in 2019 (i.e., before the pandemic). The map therefore compares the number of births conceived before and during the pandemic. At the time of writing, it seems as if both baby boom and baby bust predictions have been correct, with developments playing out differently across countries. In many Southern and Eastern European countries, such as Spain, Italy or Romania, the number of births declined by more than 1% during the first nine months of 2021. In Portugal and Poland, but also Greenland, drops in the number of births were particularly sharp with more than 5% fewer babies born in 2021. In several of these “baby bust” countries, these decreases in fertility came on top of already low fertility rates. Spain, Italy, Portugal and Poland, for instance, all already had a total fertility rate (TFR) of less than 1.5 children per woman before the crisis. These values are substantially below the so-called ‘replacement ratio’ of 2.1 children per woman, which is necessary to maintain population size. In these countries, existing demographic challenges have thus been aggravated during the pandemic.

Change in life expectancy 2019–2020 by country in Europe

The excess mortality has affected overall life expectancy at birth across Europe. In 2019, prior to the start of the pandemic, Spain, Switzerland, and Italy had the highest life expectancy in Europe, followed closely by Sweden, Iceland, France, and Norway. Finland and Denmark had slightly lower levels but were still at or above the EU average (Eurostat, 2021). Life expectancy across the EU as a whole and in nearly all other countries has been steadily increasing for decades. Declines in life expectancy are rare, but that is indeed what happened in many countries in Europe during the pandemic in 2020. One study of upper-middle and high-income countries showed that life expectancy declined in 31 of 37 countries in 2020. The only countries where life expectancy did not decline were New Zealand, Taiwan, Iceland, South Korea, Denmark and Norway. The largest falls were in Russia and the United States. The high excess mortality in Sweden in 2020 has had an impact on life expectancy. In Iceland, Norway, Finland, Denmark and the Faroe Islands, life expectancy went up for both sexes in 2020 (data not yet available for Greenland and Åland). In Sweden, life expectancy fell by 0.7 years for males from 81.3 years to 80.6 and for females by 0.4 years from 84.7 to 84.3 years. The steeper decline in life expectancy for males is consistent with the larger number of excess deaths among males.  Thus, compared to other Nordic countries, the adverse mortality impact of the pandemic has been greater in Sweden. However, when comparing Sweden to the rest of Europe, it is the Nordic countries, other than Sweden, which are exceptional. The trend among countries in Europe is for a fall in life expectancy in 2020. The largest declines were in countries in southern and eastern Europe. Italy and…

Household disposable income – gross regional product ratio 2016

This map shows the ratio between household disposable income (HDI) and gross regional product (GRP) at regional level across Europe in 2016. A ratio of around 1 indicates that HDI and GRP are at a similar level, in which case GRP could be an accurate measure of individual material wellbeing. A lower value indicates a greater difference between the two values, meaning that GRP cannot be used as a proxy for highlighting the economic situation of the population in a region. The ratio between HDI and GRP for European regions in 2016 ranges from 0.24 to 0.92, with an average of 0.60. Regions with the lowest ratio are mostly capital city regions, although regions of Ireland also have a low ratio. This is partly due to the presence of large companies transferring some of the generated income to foreign countries (e.g. the country where the company’s headquarter is located). Regions, where HDI is similar to GRP, are mostly peripheral regions in countries such as France, Hungary, Poland and the United Kingdom, for instance in Southern Scotland (0.91). In the Nordic regions (NUTS 2 level), the value of HDI is on average half the value of GRP, meaning that GRP cannot be used as a good proxy for highlighting the economic situation of the population in these regions. Many regions in Norway and Sweden, as well as Iceland, are close to this Nordic average value of 0.52, which is lower than the European average. Read the digital publication here.

Internal net migration by age group in the Northern sparsely populated areas 2018

This map shows the internal net migration rate in 2018 (left map) and net migration by age group (right map) in local labour market areas of the Northern sparsely populated areas in 2018 The map on left shows the internal net migration rate in the NSPAs in 2018. Although many regions experienced negative net migration, this was not the case for all segments of the population. The map on the right of shows the age groups in which more people moved in than moved out, despite these regions having negative net migration overall. The red colours on the map on the left indicates a net out-migration, while the blue colours indicate a positive net migration. In the map on the right, the red colours indicate net out-migration for all age groups and the grey positive net migration overall. The other colours indicate age groups with a positive net migration (while having negative net migration overall). Inward and outward migration are slightly different in the local labour market areas (LLMAs) compared to the independent labour markets. Among the LLMAs, Rovaniemi is the only one that has positive net migration in all age groups. This positive development may be due to the number of governmental offices, the strength of the tourism industry and the presence of two universities. On the contrary, 18 local labour market areas– four in Sweden, nine in Norway and five in Finland – have negative net migration in all age groups. Out-migration of young adults is seen in most of LLMAs, the exceptions being Umeå, Målsev, Joensuu and Oulu. The inflows of young adults in these regions reflect the presence of the universities in Umeå, Joensuu and Oulu and the army bases in Målselv. Despite this positive inflow, most of these LLMAs are unable to retain adults in…

Regional innovation scoreboard 2019

This map shows the regional innovation scoreboard (RIS) in the European regions in 2019. The small map shows the innovation scoreboard at national level. The index shows the performance of innovation systems, classified into four main performance groups (leader, strong, moderate and modest). The European innovation scoreboard provides a comparative assessment of the research and innovation performance in European countries. It assesses the relative strengths and weaknesses of national innovation systems and helps countries identify areas they need to address. The Regional innovation scoreboard (RIS), a regional extension of the European innovation scoreboard, assesses the innovation performance of European regions on a limited number of indicators. The RIS 2019 covers 238 regions across 23 EU countries, as well as Norway, Serbia and Switzerland. Cyprus, Estonia, Latvia, Luxembourg and Malta are also included at country level. The RIS 2019 is a comparative assessment of regional innovation based on the European innovation scoreboard methodology, using 18 of the latter’s 27 indicators. It provides a more detailed breakdown of the performance groups with contextual data that can be used to analyse and compare structural economic, business and socio-demographic differences between regions. The Nordic regions are doing well in an overall RIS comparison regarding innovation performance. There are, however, considerable differences in innovation performance between the Nordic regions. For example, the capital regions have higher levels of innovation performance than more rural and peripheral regions, according to RIS 2019. This is often due to the critical mass of companies and the spatial significance of the proximity of firms and entrepreneurs, enabling knowledge-sharing and spill-over effects. Read the digital publication here.

EU Regional Social Progress Index 2016

This map shows the regional social progress index in the regions of the European Union (EU) in 2016. Social progress is defined in this index as the capacity of a society to meet the basic human needs of its citizens, establish the building blocks that allow citizens and communities to enhance and sustain the quality of their lives and create the conditions for all individuals to reach their full potential. The EU Regional Social Progress Index is an aggregate index of 50 social and environmental indicators capturing three dimensions of social progress and its underlying components: basic human needs, foundation of well-being, and opportunity. This index is a complement to other indexes which are being developed currently to reflect similar dimensions of social situations or development. The blue tones indicate different levels of social progress index in the EU regions in 2016. The darker the tone the higher the social progress index. The grey colour indicates no data. All the Nordic regions perform well in terms of social progress. High scores are observed in all categories of the index. For some dimensions the Nordic regions are the top regions throughout Europe. At the regional scale the Nordic regions are among the top performers and only really challenged by some Dutch, UK, Austrian and German regions. The very top regions are found in Finland, northern and central Sweden, and in northern Denmark. Övre Norrland in Sweden is the top region in Europe, closely followed by the Danish capital region Hovedstaden, Helsinki-Uusimaa in Finland, Midtjylland in Denmark and Åland.

Employment rate 2016 related to the EU2020 goal and 2009-2016 change

This map shows the employment recovery from the financial crisis, with the employment rate (20-64 years) in European regions in 2016 related to the EU2020 goal of 75%, as well as the change in the employment rate between 2009 and 2016. The green colour indicates regions with employment rates above 75% and an increase in the employment rate between 2009 and 2016. The red colour indicates regions with employment rates below 75% and a decrease in the employment rate between 2009 and 2016. The yellow colour indicates regions with employment rates above 75% and a decrease in the employment rate between 2009 and 2016. The blue colour indicates regions with employment rates below 75% and a decrease in the employment rate between 2009 and 2016. The grey colour indicates regions with no data available. On a European scale, the effect of the financial crisis on employment became noticeable from 2009 onwards when average employment rates started to decline. This continued until 2013 when the average European employment rate reached its lowest level of 68.3% for the age group 20–64 years. After 2013 the employment rate started to rise again but it took until 2016 for the average European employment rate to reach and then surpass pre-crisis levels. In 2016 the average employment rate in the European union was 71%, edging closer to the EU2020 goal of 75%. In some regions, primarily in southern Europe, employment rates have still to recover to pre-crisis levels. This is particularly so for Greece, Spain, Italy and Portugal which were particularly hard hit by the debt crisis and thus had to undertake massive cuts across the public sector. On the other hand, some countries such as Germany, Austria and Switzerland saw rising employment rates even during the financial crisis. The differential nature of outcomes in…

Next Generation Access coverage 2016

This map shows the Next Generation Access (NGA) network coverage in European regions in 2016. The blue shading indicates the percentage of household covered by NGA broadband in European NUTS 3 regions. The darker the blue the larger the percentage of household covered by NGA broadband in the region, while the brightest colours represent regions with a low share. Regions with relatively small territories and important population densities stand out in terms of high NGA network coverage, e.g. urban regions in the Netherlands and Switzerland. Capital city regions also have high NGA network coverage scores, while the more rural regions continue to lag, e.g. in parts of France and Poland. The Nordic countries are characterised by having almost no differences within their territories, i.e. no large variation in terms of NGA network coverage, unlike the clear regional differences in countries such as France or Italy. All regions in the Nordic countries score in the range of 65% to 95% of households having NGA network coverage, except for Etelä-Pohjanmaa in Finland which has a coverage range of 35% to 65% and the Danish statistical region of Østjylland and the capital regions of Denmark and Iceland with scores between 95% and 100% respectively. The relatively high figures for the Nordic Region can in part be explained by the existence of national and regional digitalisation strategies over the last decade or so. In Denmark, as well as in the other Nordic countries, digitalisation has long been on the national agenda. One of the main goals of these strategies has been to increase the growth and productivity of the business community – and to make it easier and cheaper to establish digital infrastructure. The regional level has an important role to play in the development of digital infrastructure, hence the relevance of the elaboration…

Life expectancy at birth 2015 and change 2005-2015

This map shows the life expectancy at birth in the regions of Europe in 2015 and the change between 2005 and 2015. The green shading indicates the life expectancy at birth in years in the regions of Europe in 2015. The darker the green the longer the life expectancy at birth. The grey colour indicates regions with no data. The blue circles represent the changes in the life expectancy at birth between 2005 and 2015. The larger the circle, the greater the increase in the life expectancy at birth between 2005 and 2015. As one of the UN Sustainable Development indicators for good health and well-being, life expectancy at birth measures the general health status of a population. Life expectancy at the national level is highly correlated to national income indicators such as GDP per capita. Most of Europe has however reached a level where further increases in wealth no longer increase average life expectancy. The longest life expectancies are found in parts of Spain, France, Italy, Switzerland and Norway. In 2015 there is still a visible East-West divide, where countries in Eastern Europe, in the south-east of the Baltic Sea Region and Northwest Russia have a significantly lower life expectancy. The size of the blue bubbles indicate however that these regions have witnessed the largest increases over the last ten years. If their economies continue to grow it is likely that their life expectancy will continue to increase for some years to come. Regional differences are partly due to individual characteristics, but also to the local environment as well as the politics and institutions that influence the local economy, access to local services and care, general educational levels and the local norms that influence lifestyles.

Employment in the high-technology and knowledge-intensive sectors 2016

The map shows Europe’s regional disparities in the high-technology and knowledge-intensive sectors as a proportion of total employment in 2016. The blue tones indicate the total work force of employment in high-technology and knowledge-intensive sectors in the European regions in 2016. The darker the blue, the stronger the work force of employment in high-technology and knowledge-intensive sectors. The violet colour represents the regions with the highest work force of employment in high-technology and knowledge-intensive sectors. The grey colour indicates no data. Knowledge-intensive sectors play an essential role in facilitating innovation and economic growth across various sectors as they contribute to the renewal and growth of other businesses by creating fertile ground for innovations and the diffusion of novel practices. Apart from the Nordic countries, high levels of employment in the knowledge-intensive sectors are usually observed in European capital regions or regions close to capitals or other large cities. Examples here include Zürich (Switzerland) and Berlin (Germany) where the share of employment in technology and knowledge-intensive sectors accounts for 54.6% and 54.5% respectively. Other European regions with high shares of employment in the technology and knowledge-intensive sectors include several regions in Southern England and South-Eastern Scotland (the UK); several regions around Liège (Belgium); Groningen, Noord-Holland and Utrecht (the Netherlands); Île-de-France and Corse (France); Luxembourg; Prague (Czech Republic) and Vienna (Austria).

Eco-Innovation Scoreboard 2010 and 2016

These maps provide a comparative overview of the eco-innovation performance on the Eco-Innovation Index of the states of the European Union (EU) in 2010 (upper map) and in 2016 (lower map). Eco-innovation is currently at the core of EU policies as it is believed to be a key driver in the transition to a green economy, growth generation and new job creation in the years to come. The eco-innovation scoreboard is an index of eco-innovation performance in the EU-member states, based on 16 indicators in 5 thematic areas. It promotes a holistic view of economic, environmental and social performance and complement other measurement approaches in terms of assessing the innovativeness of the EU countries. The green tones indicate differences in eco-innovation performance in the EU-member states in 2010 and 2016. The darker the green, the more performant the state in eco-innovation. The grey colour indicates no data. The high scores of the three Nordic countries, along with Germany and Austria, can partly be explained by the fact that the Eco-Innovation Scoreboard is measured in relation to EU averages. The eco-innovation landscape has remained stable across the Nordic countries during the period 2010–2016, with no significant change in performance. Finland, Denmark and Sweden remain among the top-performing countries. In 2016, Finland scored highest in terms of the overall eco-innovation performance of all the Nordic countries, followed by Denmark and Sweden. In 2010, Denmark was the best Nordic performer on the eco-innovation index, closely followed by Finland and Sweden. Placing the Nordic countries in a European context, we see that other European countries are catching up but with different intensities. Lithuania and Latvia have significantly improved their positioning on the eco-innovation index in 2016 as have Slovakia, Greece and Portugal compared to their scores in 2010. In contrast, Austria, Belgium and the…

Regional Innovation Scoreboard 2017

This map shows the regional innovation scoreboard (RIS) in the regions of the European Union (EU), Norway, Iceland and Switzerland in 2017. The small map shows the innovation scoreboard at a national level. The index shows the performance of innovation systems, classified into four main performance groups (leader, strong, moderate and modest). The different colours stand for the four performance groups. The shading is a breakdown of performance for each group from the most innovative (dark shade) to the least innovative (light shade). The blue tones represent the leader group. The green tones represent the strong group. The orange tones represent the moderate group. The red tones represent the modest group. The grey colour indicates no data. With a long tradition of supporting innovation behind them, the Nordic countries have always been pacesetters when it comes to innovation-based development, topping the rankings of the most innovative economies in the EU. The Nordic countries (Iceland and Norway excepted) together with Switzerland, the Netherlands, the United Kingdom and Germany top the RIS ranking, demonstrating a high level of innovation performance. At the NUTS 2 level, all Nordic countries are represented by regions of two types i.e. innovation leader and strong innovator. As innovation leaders, Nordic regions perform well on all indicators, displaying a particularly high level of performance in the framework conditions group (e.g. population having completed tertiary education and lifelong learning), innovation activities (e.g. public-private co-publications) and investment (e.g. R&D expenditures in the business sector). As of 2017, Stockholm, Östra Mellansverige and Sydsverige (Sweden), Hovedstaden (Denmark) and Länsi Suomi (Finland) are rated ”innovation leader +”, the most innovative regions in the Nordics.

Population density 2015

This map shows the population density in the European continent and its surrounding territories in 2015. The shading represents the population in number of inhabitants per grid cell. Darker colours show high number of inhabitants per cell and lighter colours show low number of inhabitants per cell. North-eastern Europe is sparsely populated with people concentrated to large cities such as Mos-cow, St Petersburg, the Baltic capitals and around the coastal areas of the Nordic Region. From Denmark, Poland and the Ukraine south¬wards, more populated areas can be found along with a dispersed settlement pattern. There is a major concentration of population in an urban network corridor running from Northern England across the Benelux-countries through Germany towards Northern Italy. Central Spain and Southern Portugal display a more sparsely populated set¬tlement structure similar to that of the Northern parts of Europe. The Nordic population is to a large degree con¬centrated towards the coastal areas. There is a major settlement corridor from the area around the fjord of Oslo which contin¬ues into Sweden along the west coast towards the greater Copenhagen area. Another settlement corridor runs from the Greater Gothenburg region to the northeast, through Stockholm, to the Finnish triangle of Helsinki, Tampere and Turku. In Iceland, the population is to a large degree concentrated in the capital region of Reykjavík. There are also relatively significant settlements along Norway’s south-west coast and in urban settlements scattered around the Baltic and Bothnian Seas. Denmark is different, with a more distributed spatial settlement pattern, rather like that of Germany and other parts of continental Europe. The Faroe Islands have a rather evenly distributed spatial settlement pattern, while in Åland the population is more concentrated. The map is based on data from the ESA, Joint Research Centre, European Commission. The data from census or administrative units…

European eco-innovation parks in 2014

This map shows the location of the European eco-innovation parks in the states of the European Union (EU) and Iceland in 2014. The eco-innovation parks are eco-industrial parks and eco-innovative areas combining residential and industrial activities. The establishment of eco-industrial parks is one way to promote eco-innovation, resource efficiency and the circular economy. They create new and innovative business opportunities and improve ecosystems. The blue colour indicates the EU-member states having at least one identified eco-innovation park in 2014. The circles indicate the location and the type of the eco-innovation parks (green, in use; yellow, under construction; brown, planned; red, stopped). The grey colour indicates no data. A clear spatial concentration of eco-innovation parks can be observed in the Ruhr area and around Leipzig (Germany), in southern Belgium, south-western and northern Netherlands, western Switzerland, along the “arch” stretching from Turin via Pisa to Udine in North-East Italy, around Barcelona as well as around some national borders in the industrialised parts of north-West Europe namely Germany-Netherlands-Belgium-France and Switzerland. Eco-innovation parks differ in many respects, for instance regarding energy and material flows (e.g. waste heat, steam, power; wood chips, bark, ash, pulping chemicals), number and size of companies involved and jobs created, public sector involvement and finally, in terms of (the drivers behind) their evolution. The latter means that some parks evolved and expanded around a few economic activities and companies (e.g. saw mills) to include further activities (e.g. pulp mills, power plants etc.), while others were intentionally planned and put into operation.