GDP at regional level
- Data extracted in March 2017. Most recent data: Further Eurostat information, Main tables and Database. Planned article update: September 2018.
This article forms part of Eurostat’s annual flagship publication, the Eurostat regional yearbook. It uses regional economic accounts to analyse economic developments within the European Union (EU). The first section is based on gross domestic product (GDP), the principal aggregate for measuring the economic output of an economy. The second provides a brief analysis of labour productivity (defined here as gross value added per hour worked). The article closes with a regional analysis of structural differences in regional economies, according to economic activities as defined by the NACE classification.
- 1 Main statistical findings
- 2 Data sources and availability
- 3 Context
- 4 See also
- 5 Further Eurostat information
- 6 External links
Main statistical findings
- GDP per capita was higher in capital city regions, often considerably higher than in any other region; GDP per capita was also generally above average in other metropolitan regions.
- Many eastern regions of the EU were less adversely affected by the medium and long-term effects of the global financial and economic crisis and saw their relative living standards improve at a rapid pace; this was particularly the case for regions in Poland, Romania and Slovakia. By contrast, the impact of the crisis continues to be apparent across many southern regions of the EU.
- The crisis amplified economic inequalities in several EU Member States: while some regions continued to grow at a rapid pace, others — often former industrial heartlands or sparsely populated regions — were seemingly ‘left behind’, with their average GDP per capita stagnating.
- Territorial patterns of regional labour productivity closely resemble those recorded for GDP per capita. Those regions where these two ratios are relatively high are often characterised by specialisation in one or more of the following activities: scientific and high-technology manufacturing, financial and advanced business services. As such, their economic performance may reflect investment in education, knowledge, innovation and technology.
- Those regions with relatively high specialisation ratios for industrial and construction activities were also characterised by rapid growth for these activities, suggesting that their competitive advantage in these activities was being consolidated.
Gross domestic product
GDP is the central measure of national accounts, summarising the economic position of a country or region. It may be used to analyse economic performance and cycles (such as recessions, recoveries and booms). In order to compensate for price level differences between countries, GDP can be converted using conversion factors known as purchasing power parities (PPPs). The use of PPPs (rather than market exchange rates) results in the data being converted into an artificial common currency called a purchasing power standard (PPS). In broad terms, the use of PPS series rather than a euro-based series tends to have a levelling effect, as those regions with very high GDP per capita in euro terms also tend to have relatively high price levels (for example, the cost of living in central Paris is generally higher than the cost of living in rural regions of eastern Europe).
GDP at market prices in the EU-28 was valued at EUR 14.8 trillion in 2016; this equated to an average of EUR 29.0 thousand per capita. Developments over time can be analysed on the basis of a constant price GDP series, which removes the impact of price changes/inflationary effects. Figure 1 shows the considerable impact of the global financial and economic crisis on the EU-28’s economic output in 2009, as GDP fell by 4.4 % in real terms. Although there was a rebound in 2010 and continued growth in 2011, the EU-28 economy contracted again in 2012 (output falling by 0.5 %). Thereafter, there were four consecutive years (2013 to 2016) of growth in real GDP, with the latest rate of change in 2016 (1.9 %) slightly lower than that recorded in 2015 (2.2 %).
Measuring wealth and income by place of residence or place of work?
It is important to note that average GDP per capita does not provide any indication as to the distribution of wealth between different population groups within a region, nor does it measure the income ultimately available to private households of a region, as commuter flows may result in employees contributing to the GDP of one region (where they work) and to the household income of another region (where they live).
Areas that are characterised by a considerable number of inflowing commuters often display particularly high levels of regional GDP per capita. This pattern can be seen in many metropolitan regions of the EU, especially in/around capital cities. Because of this anomaly, it should be noted that high levels of GDP per capita do not necessarily translate into correspondingly high levels of income for (all of) the people living in the same region.
Almost two thirds of the EU’s GDP was generated in metropolitan regions
Metropolitan regions are defined in relation to NUTS level 3 regions; they may be composed of one or more regions and cover urban agglomerations with more than 250 thousand inhabitants. A time series for the period 2004 to 2014 (based on a PPS series) reveals that there was a gradual shift in the EU-28’s economic activity towards metropolitan regions, as their share of total GDP rose by 1.2 percentage points to reach almost two thirds (66.3 %).
A more detailed analysis for 2014 reveals that EU capital city metropolitan regions accounted for almost a quarter (23.0 %) of the EU-28’s GDP; this marked an increase of 1.5 percentage points compared with 2004. The share of capital city metropolitan regions in the economic activity of all metropolitan regions rose from 33.0 % in 2004 to 34.7 % by 2014. As such, there was a gradual shift in economic activity across the EU from rural regions and smaller towns towards metropolitan regions, and this pattern was particularly prevalent for capital city regions.
Figure 2 provides information on the GDP shares of metropolitan regions, identifying separately capital city metropolitan regions and other metropolitan regions. There were considerable differences in the structure of economic output between EU Member States, in part reflecting the size of each country; note that Cyprus and Luxembourg are both composed of single NUTS level 3 regions. Among the larger Member States (defined here as those with at least 10 million inhabitants), Germany, Italy, Poland, Spain and the Netherlands were characterised by a polycentric distribution of their economic activity, with each of their capital city metropolitan regions accounting for no more than one fifth of national GDP in 2014 and other several other metropolitan regions having relatively large (sometimes larger) shares; this pattern was particularly evident in Italy (where the relative weight of the capital city in total economic output was 9.2 %) and Germany (where an even lower share was recorded, at 5.4 %). By contrast, the distribution of economic activity in France, Belgium, the United Kingdom, the Czech Republic, Portugal and Greece was more monocentric in nature, as their capital city metropolitan regions accounted for more than 30 % of national GDP.
An analysis over time shows that the shift in economic activity towards capital city metropolitan regions was relatively rapid in France, Croatia, Slovakia, Denmark, Sweden and the United Kingdom, with the share of their capital city metropolitan regions in total GDP rising by 2.5–2.9 percentage points between 2004 and 2014. This pattern was even more pronounced in Lithuania (3.7 points), Ireland, Romania (both 5.5 points) and Bulgaria (8.4 points). Indeed, the redistribution of wealth creation towards rapidly expanding capital city metropolitan regions was particularly apparent in several of the eastern EU Member States, in contrast to agrarian-based lifestyles in many rural regions.
The European system of national and regional accounts (ESA 2010) is the latest internationally compatible accounting framework for a systematic and detailed description of the EU economy. ESA 2010 is consistent with worldwide guidelines on national accounting, as set out in the system of national accounts (2008 SNA) and has been implemented since September 2014.
ESA 2010 differs in scope as well as in concepts from its predecessor ESA 95 reflecting developments in measuring modern economies, advances in methodological research and the needs of users. ESA 2010 is not restricted to annual national accounting, as it also applies to quarterly and shorter or longer period accounts, as well as to regional accounts. It is harmonised with the concepts and classifications used in many other social and economic statistics (for example, statistics on employment, business or international trade) and as such serves as a central reference for socioeconomic statistics.
The ESA framework consists of two main sets of tables: institutional sector accounts and an input-output framework. The former provide a systematic description of the different stages of the economic process: production, generation of income, distribution of income, redistribution of income, use of income and financial and non-financial accumulation for each institutional sector, as well as balance sheets to describe stocks of assets, liabilities and net worth. The latter presents in more detail the production process (cost structures, income generated and employment) and the flows of goods and services (output, exports, imports, final consumption, intermediate consumption and capital formation by product group), whereby the sum of incomes generated in an activity is equal to the value added produced by that activity.
Regional GDP per capita
Map 1 shows GDP per capita in 2015 for NUTS level 2 regions: the values presented are based on GDP per capita in PPS, expressed as a percentage of the EU-28 average which is set equal to 100 %. Relatively ‘rich’ regions, where GDP per capita was above the EU-28 average, are shown in blue and relatively ‘poor’ regions, where GDP per capita was below the EU-28 average, are shown in purple. There are several aspects of note:
- a band of relatively ‘rich’ regions runs from northern Italy, up through Austria and Germany before splitting in one direction towards the Benelux countries, southern England and southern Ireland, and in the other direction towards the Nordic Member States;
- other pockets of relatively ‘rich’ regions’, for example, in the south of France, the north-east of Spain, or north-east of the United Kingdom;
- a relatively high concentration of wealth creation in capital city regions, which are often depicted as islands surrounded by ‘poorer’ regions;
- a band of relatively ‘poor’ regions running from the Baltic Member States down through eastern regions of the EU to Greece and southern Italy, before extending across the Mediterranean to the Iberian Peninsula.
The highest level of GDP per capita in the EU was recorded in Inner London - West
The distribution of wealth across the EU was somewhat skewed insofar as there were 101 NUTS level 2 regions where average GDP per capita was above the EU-28 average in 2015, compared with 175 regions where it was below; as such, wealth creation appears concentrated in regional pockets. Some 16 % of the 276 NUTS level 2 regions for which data are available (see Map 1 for coverage) reported that their GDP per capita was at least 25 % higher than the EU-28 average; these are shown in the two darkest shades of blue. Many of them were capital city regions or clusters of regions that neighboured capital city regions, while the vast majority of the others were grouped together in the centre of the map, covering western and southern Germany, western Austria and northern Italy (as well as Switzerland).
At the upper end of the ranking, there were four regions in the EU where GDP per capita was more than double the EU-28 average, namely: Inner London - West (one of two capital city regions in the United Kingdom), Luxembourg (a single region at this level of analysis), Hamburg (northern Germany) and Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest (the Belgian capital city region). Each of these is characterised by a high number of commuters, with large numbers of people travelling to work from neighbouring regions and sometimes further afield. Indeed, improvements in transport infrastructure have made longer commuting distances feasible and there has, in recent years, been a growing pattern of international commuting. One such example is Luxembourg, where a high proportion of the workforce travels each day across national borders from neighbouring Belgium, Germany or France.
A number of capital city regions followed in the ranking with the next highest levels of GDP per capita, around 75 % higher than the EU-28 average. These included regions covering the Slovakian and Czech capitals (Bratislavský kraj and Praha), the French capital (Île de France), the second of the two capital city regions in the United Kingdom (Inner London - East) and the Swedish capital (Stockholm); they were joined by Oberbayern (southern Germany), whose administrative centre is München.
Figure 3 confirms that capital city regions tended to record the highest levels of GDP per capita in each of the EU Member States. Indeed, the only exceptions to this rule (among the multi-regional Member States) were Germany and Italy. GDP per capita in Berlin was almost 20 % above the EU-28 average, but was below the German national average, while the same ratio in Lazio was approximately 10 % above the EU-28 average and was also higher than the Italian national average. As such, despite Germany having the highest number of regions with GDP per capita at least 25 % higher than the EU-28 average, the capital city region was not among them; indeed, there were 16 NUTS level 2 regions in Germany which posted GDP per capita above that recorded for Berlin. A similar comparison for the Italian capital city region reveals that there were five northern Italian regions which posted average GDP per capita above that recorded in Lazio.
Cohesion policy is targeted at regions where GDP per capita is less than 75 % of the EU-28 average
The distribution of EU regional development assistance in the form of cohesion policy funding is specifically targeted at those regions where GDP per capita is less than 75 % of the EU-28 average. Note that funding for the 2014 to 2020 programming period has already been fixed in relation to average GDP per capita for the three-year period covering 2007 to 2009.
Map 1 shows there were 82 NUTS level 2 regions where GDP per capita was less than 75 % of the EU-28 average in 2015; these are shown by the darkest shade of purple in Map 1. More than a quarter (22 out of the 82) of these regions registered GDP per capita that was less than half the EU-28 average, including: five out of the six NUTS level 2 regions from Bulgaria (the exception was Yugozapaden, the capital city region); five Polish regions; four out of seven Hungarian regions; four out of eight Romanian regions; three Greek regions; and Mayotte, a French overseas region. The lowest levels of average GDP per capita were recorded in three of the Bulgarian regions — Severozapaden, Severen tsentralen and Yuzhen tsentralen — and Mayotte, as economic output per inhabitant in each of these was less than one third of the EU-28 average.
A comparison between the NUTS level 2 regions recording the highest and lowest levels of economic activity reveals the wide disparities in wealth creation between regions. Average GDP per capita in Inner London - West (580 % of the EU-28 average) was 20 times as high — having taken account of differences in price levels — as in Severozapaden (Bulgaria) where the lowest level of GDP per capita was recorded (29 % of the EU-28 average). A similar analysis carried out for each of the multi-regional EU Member States reveals that the widest disparities in wealth creation were recorded in: the United Kingdom, where GDP per capita in Inner London - West was 8.6 times as high as in West Wales and The Valleys; France, where GDP per capita in Île de France was 5.6 times as high as in Mayotte; Romania, where GDP per capita in Bucuresti - Ilfov was 4.0 times as high as in Nord-Est. By contrast, wealth creation was relatively evenly spread across Croatia, Slovenia, the Nordic Member States, Portugal, Austria, the Netherlands, Greece, Ireland and Spain, as the region with the highest level of GDP per capita never recorded a value that was more than double that recorded for the region with the lowest value; this situation was also repeated in Norway and in Albania.
Analysis of regional economic developments over time
Figure 1 has already shown that there was a marked slowdown in the rate at which economic activity was expanding in the EU-28 in 2008, although the main impact from the global financial and economic crisis was not experienced until 2009. Given the crisis had already begun to affect some of the EU Member States in 2008, the analysis that follows is based on a comparison between the pre-crisis highs of 2007 and the latest information available for 2015.
Average GDP per capita in the EU-28 stood at 26.0 thousand PPS in 2007. It was almost unchanged in 2008 (rising by 100 PPS), but then fell considerably to 24.5 thousand PPS in 2009, after which it took two years before it had returned to the same level as in 2008. Thereafter, the EU-28’s economy expanded during four consecutive annual periods, as average GDP per capita reached 28.9 thousand PPS.
The most rapid growth in GDP per capita was recorded for one western and three eastern capital city regions
There were 124 NUTS level 2 regions that saw their relative wealth, as measured by GDP per capita, increase between 2007 and 2015, while a somewhat higher number (152) reported a decline. By far the biggest increase in wealth creation, in relation to the EU-28 average, was recorded for the region with the highest level of GDP per capita, namely, Inner London - West; it was followed by three capital city regions from eastern Europe, namely, Bucuresti - Ilfov (Romania), Bratislavský kraj (Slovakia) and Mazowieckie (Poland).
Despite wide variations in average levels of GDP per capita between the regions of some EU Member States, there was a relatively uniform pattern to changes in economic activity over the period from 2007 to 2015. Among the multi-regional EU Member States, GDP per capita grew at a faster pace than the EU-28 average in every region of Bulgaria, Hungary, Poland, Romania, Slovakia and all three of the Baltic Member States (each of which is a single region at this level of detail), as well as every region except for the capital city region in the Czech Republic and Austria, and every region except for the southern island region of Sjælland in Denmark. The vast majority of regions in Belgium and Germany — all but two in both cases — also recorded an increase in their relative living standards. By contrast, average GDP per capita in each region of Greece, Spain, Croatia, the Netherlands, Slovenia, Finland and Sweden grew at a slower pace than the EU-28 average, while all but one region in Italy and in Portugal — Provincia Autonoma di Bolzano/Bozen in the former and Norte in the latter — recorded a rate of change that was below the EU average.
Although there remains an east–west divide in terms of wealth creation in the EU-28 (as shown in Map 1), this pattern is less pronounced than before the accession of 13 Member States to the EU in 2004, 2007 and 2013, suggesting that EU membership and cohesion policy have been effective, at least in part, at addressing national and regional disparities. A closer examination reveals that there are a growing number of regions in western EU Member States with relatively low levels of average GDP per capita. These are often characterised as having previously been prominent industrial heartlands, and it would appear that they have, to some degree, been left behind by a move away from heavy industrial activities in much of the EU, as witnessed through their stagnating or falling living standards. Examples include several regions in southern Belgium (for example, Prov. Hainaut and Prov. Luxembourg), northern and eastern France (Picardie, Champagne-Ardenne and Lorraine), or the United Kingdom (West Wales and The Valleys, the Tees Valley and Durham and South Yorkshire).
National accounts ratios in relation to labour input are designed to provide insight concerning the competitiveness and productivity of a national/regional economy. Labour productivity may be defined as gross value added at basic prices expressed in relation to the number of persons employed or the total number of hours worked. Measures based on simple headcounts of labour input are, to some degree, a reflection of the structure of the employment market and may, for instance, be lowered by a shift from full-time to part-time working practices. As such, it is generally agreed that the number of hours worked provides a more reliable measure of labour input and this is the basis for the information presented in Map 3, which shows gross value added per hour worked for NUTS level 2 regions in 2014; note the results are expressed in relation to the EU-28 average (which is set equal to 100).
Relatively high levels of labour productivity may be linked to an efficient use of labour (without using more inputs), or may result from the mix of activities that make-up a particular economy, as some activities have higher levels of labour productivity than others. For example, business services and financial services play a particularly important role in most capital city regions, and this may explain (at least to some degree) the high levels of labour productivity recorded in these regions.
Across the EU-28, there was an average of EUR 33.92 of added value generated for each hour worked in 2014. The highest labour productivity ratio among NUTS level 2 regions was recorded in Inner London - West (the United Kingdom), where value added per hour worked was more than five times as high as the EU-28 average and also considerably higher than in any other region of the EU. Luxembourg (one region at this level of detail), Groningen (the Netherlands) and Île de France (the French capital city region) followed, with labour productivity ratios that were just over twice as high as the EU-28 average.
There were 17 regions in the EU where labour productivity was at least 50 % higher than the EU-28 average (as shown by the dark blue shade in Map 3). Aside from the four regions mentioned above, the remainder were all located in northern and western regions of the EU, principally in Denmark and Germany (four regions each), with two additional regions from the United Kingdom, an additional region from the Netherlands, and single regions from each of Ireland and Sweden.
Labour productivity lower in those EU Member States that joined the EU in 2004 or more recently
There were 62 NUTS level 2 regions where gross value added per hour worked was less than half the EU-28 average in 2014 (as shown by the darkest shade of purple in Map 3). These regions were principally from eastern regions of the EU and all three of the Baltic Member States (each one region at this level of detail), but also included a majority of the Greek regions and two mainland regions from Portugal, namely, Norte and Centro.
A closer examination reveals that there was not a single region from the Member States that joined the EU in 2004 or more recently that had a labour productivity ratio that was above the EU-28 average in 2014. Among these regions, the highest ratio was recorded in Bratislavský kraj (the Slovakian capital city region), where the added value generated by each hour worked was approximately three quarters of the level recorded across the EU-28.
The final three maps in this article (Maps 4 to 6) should be viewed in unison, insofar as they show structural changes during the period 2004 to 2014 for agriculture, forestry and fishing (NACE Section A), industry and construction (NACE Sections B–F), and services (NACE Sections G–U). Each map is based on developments for gross value added: on one hand the maps identify NUTS level 2 regions by degree of specialisation — the olive shading denotes a low degree of specialisation, while purple shading is a high degree of specialisation; on the other — the rate of change for the share of each activity grouping (in value added terms) is presented relative to the EU-28 average, with lighter shades denoting slower than average growth (or in fact a fall) in the share and darker shades representing higher than average growth in the share. Note also that the scales used in each map are different, reflecting the relative weight of each activity in the total economy.
The poorest region in the EU was characterised by its economic activity being concentrated within agriculture, forestry and fishing activities
Map 4 shows those regions in the EU that were relatively specialised in agriculture, forestry and fishing in 2014. As may be expected these tend to be relatively sparsely populated, rural regions. There were 17 NUTS level 2 regions where the share of agriculture, forestry and fishing in total value added was at least five times as high as the EU-28 average (1.6 %). They were predominantly located in eastern and southern regions of the EU: five regions from Greece, three regions from each of Bulgaria, Hungary and Romania, two regions from Portugal, and one from France. The highest degree of specialisation was recorded in Severozapaden in north-eastern Bulgaria — which was the ‘poorest’ region in the EU; in Severozapaden, agriculture, forestry and fishing accounted for a share of total value added (12.5 %) that was 7.9 times as high as the EU-28 average. There were two more regions where the share of agriculture, forestry and fishing in total value added was at least seven times as high as the EU-28 average, both of which were located in southern Hungary: Dél-Alföld (11.8 %) and Dél-Dunántúl (11.6 %).
Those regions with relatively high specialisation ratios for services tended to be either capital city regions or tourist destinations
Map 5 shows specialisation patterns for industry and construction and may be contrasted with the information shown in Map 6 for services; in many respects these two maps are complementary, insofar as agriculture, forestry and fishing generally accounts for a very small share of total value added and hence those regions which are relatively specialised in industry and construction tend to be unspecialised in services and vice-versa. Maps 5 and 6 may also be contrasted with the information shown in Maps 7.1 and 7.2, which provide an analysis of regional structural business statistics for employment (rather than value added) specialisation across industrial activities and non-financial services.
The distribution of regions according to their relative specialisation reveals that there were 113 NUTS level 2 regions where industry and construction accounted for a lower share of total value added than the EU-28 average (24.4 %) in 2014, while there were 163 regions where the share was equal to or above the average. A similar analysis reveals there were 171 regions across the EU where services accounted for a lower share of total value added than the EU-28 average (74.0 %), while there were 105 regions where the share was equal to or above the average. These differences are influenced by the relative (economic) size of each region, and suggest that relatively high specialisation in service activities was concentrated in the most economically dominant regions, often capital city regions.
In 2014, there were three NUTS level 2 regions where the share of industry and construction in total value added was more than twice as high as the EU-28 average: Groningen in the north of the Netherlands (particularly specialised in natural gas extraction and related activities), Nyugat-Dunántúl in western Hungary (motor vehicles), and Dytiki Makedonia in norther Greece (mining and power generation). Aside from these, most of the regions where the share of industry and construction in total value added was at least 20 % higher than the EU-28 average (as shown by the purple shades in Map 5) were located in Germany and Austria, as well as Poland, the Czech Republic, Slovakia and Romania. The high shares of industry and construction in these four eastern Member States reflects, in part, the relocation within the EU of manufacturing activities to lower cost centres. By contrast, there were 47 regions where the share of total value added accounted for by services was more than 10 % above the EU-28 average (as shown by the purple shades in Map 6). These were principally capital city regions or regions characterised as tourist destinations, for example, Ionia Nisia (a Greek island region including Corfu), Algarve (in southern Portugal), or Illes Balears (Spain).
Across the whole of the EU-28, the share of industry and construction in total value added fell from 26.2 % in 2004 to 24.4 % in 2014, while the share of services rose from 71.8 % to 74.0 % during the same period. The information shown in Maps 5 and 6 may be used to analyse structural shifts in regional economies, identified by their shading — those regions with rates of change that were above the EU-28 average have more intense (darker) shading.
There were 13 regions in the EU where the share of industry and construction in total value added rose by at least 5.0 percentage points during the period covering 2004 to 2014 (compared with an average reduction of 1.8 points for the EU-28). All of these regions were characterised by being relatively specialised in industrial and construction activities, thereby suggesting the distribution of these activities was becoming more specialised and concentrated within a relatively small number of regions. These 13 regions were primarily located in eastern EU Member States, with five from Bulgaria (all but the capital city region of Yugozapaden), two each from Hungary (Nyugat-Dunántúl and Dél-Alföld), Poland (Lubuskie and Dolnoslaskie) and Romania (Sud-Est and Sud - Muntenia), as well as single regions from each of Germany (Oberpfalz) and the Netherlands (Groningen).
By contrast, those regions where the share of industrial and construction activities in total value added declined at a faster pace than the EU-28 average were often those which already recorded a relatively low degree of relative specialisation in these activities. While the share of industry and construction in total EU-28 value added declined by 1.8 percentage points during the period 2004 to 2014, there were 39 regions where the share of total value added accounted for by industrial and construction activities fell by at least 5.0 percentage points. Among these, the largest contractions in activity were recorded in three Spanish regions (Principado de Asturias, Cataluña and Andalucía); two Greek (Attiki and Dytiki Ellada) and two Finnish regions (Etelä-Suomi and Pohjois- ja Itä-Suomi); the Irish capital city region (Southern and Eastern); and the two Mediterranean islands of Cyprus and Malta (both single regions at this level of detail).
Making a similar analysis for services the patterns of development were less clear. This may, at least in part, reflect the high share of services in total value added, with structural shifts more concentrated on movements between different services rather than between the broader aggregates of services and industry/construction. There were seven NUTS level 2 regions in the EU where value added share of services grew at least 10 % faster than the EU-28 average during the period 2004 to 2014. These seven regions were split: three of them were relatively unspecialised in services —Nord-Est and Sud-Vest Oltenia (in Romania) and Castilla-la Mancha (in Spain); two were highly specialised — the island regions of Cyprus and Malta (both single regions at this level of detail); and in two the weight of services in the regional economies was relatively close to the EU-28 average, although they too recorded rapid growth for the share of added value generated by services —Dytiki Ellada (in Greece) and Principado de Asturias (in Spain).
Data sources and availability
The European system of national and regional accounts (ESA) provides the methodology for national accounts in the EU. The current version, ESA 2010, ensures that economic statistics for EU Member State are compiled in a consistent, comparable, reliable and up-to-date way. The legal basis for these statistics is a Regulation of the European Parliament and of the Council on the European system of national and regional accounts in the European Union (Regulation (EU) No 549/2013).
Statistics from regional economic accounts are largely shown for NUTS level 2 regions. Data for Switzerland and Serbia are only available at a national level. The latest statistics available for Irish, Norwegian and Albanian regions refer to 2014.
The data presented in this article are based exclusively on the 2013 version of NUTS.
Glossary entries on Statistics Explained are available for a wide range of national accounts concepts/indicators, including: gross domestic product (GDP), gross value added, the European system of national and regional accounts (ESA 2010), purchasing power standards (PPS).
Economic development is commonly expressed in terms of GDP, which may be used to measure macroeconomic activity and growth. GDP per capita is often regarded as a proxy indicator for overall living standards. However, as a single source of information it should not be relied upon to inform policy debates, as it does not take account of externalities such as environmental sustainability or social inclusion, which are increasingly considered as important drivers for the quality of life and sustainable development.
The EU’s regional policy aims to support the broader Europe 2020 agenda. It is designed to foster solidarity and cohesion, such that each region may achieve its full potential by helping to alleviate inequalities such as social deprivation, poor-quality housing, healthcare or education, unemployment or inadequate infrastructure. Such inequalities may be due to a wide range of factors, including: geographic remoteness or sparse populations, social and economic change, or the legacy of former economic systems. Across the EU, regional policymakers seek to help every region achieve its full potential, through improving competitiveness and raising the living standards of the poorest regions towards the EU average (convergence) by stimulating investment in these regions, improving accessibility, providing quality services and preserving the environment.
In August 2009, the European Commission adopted a communication titled GDP and beyond: measuring progress in a changing world (COM(2009) 433 final), which outlined a range of actions to improve and complement GDP measures. This noted that there was a clear case for complementing GDP with statistics covering other economic, social and environmental issues, on which individuals’ well-being critically depends. A set of complementary indicators was detailed in a staff working paper called Progress on ‘GDP and beyond’ actions (SWD(2013) 303 final), including at regional and local levels.
International interest in sustainable development issues has been led by work conducted under the auspices of the United Nations (UN). Transforming our world: the 2030 agenda for sustainable development was adopted on 25 September 2015 and provides a commitment to eradicate poverty and achieve worldwide sustainable development by 2030, bringing social and environmental measures of development into the mainstream. In conjunction, the European Commission adopted a series of Communications titled, a decent life for all: ending poverty and giving the world a sustainable future (COM(2013) 92 final), a decent life for all: from vision to collective action (COM(2014) 335 final) and a global partnership for poverty eradication and sustainable development after 2015 (COM(2015) 44 final).
For more information:
2030 agenda for sustainable development
More than one third of the EU’s budget is devoted to cohesion policy, with the goal of removing economic, social and territorial disparities. GDP is an important indicator from this perspective, insofar as it is used to determine the extent to which each EU Member State should contribute to the EU’s budget. Regional accounts also serve as the basis for the allocation of expenditure under the EU’s cohesion policy. Every region of the EU is covered: however, most structural funds (the European regional development fund (ERDF) and the European social fund (ESF)) are directed to NUTS level 2 regions where GDP per capita in PPS — averaged over the period 2007 to 2009 — was less than 90 % of the EU average. The process for the allocation of cohesion funds was adapted during 2016 and is now based upon providing support to those EU Member States whose gross national income (GNI) per inhabitant — averaged over the period 2012 to 2014 — was less than 90 % of the EU average. More information on the EU’s structural and investment funds and cohesion policy is provided in an article on Regional policies and European Commission priorities.
For more information:
In 2014, the European Commission set its top priority as ‘boosting jobs, growth and investment’. This is a major new initiative that aims to unlock public and private investment by targeting infrastructure developments, such as broadband internet, energy networks and transport. In its Communication titled an investment plan for Europe (COM(2014) 903 final), the European Commission underlined the role that EU Member States and regional authorities should play to get the maximum impact from structural funds by capitalising on a variety of financial instruments in the form of loans, equity and guarantees. In January 2015, the European Commission adopted a Communication on making the best use of the flexibility within the existing rules of the stability and growth pact (COM(2015) 12 final); it aims to strengthen the link between investment, structural reforms and fiscal responsibility. This was followed in 2016 by two further Communications following a stock-taking exercise to analyse the progress made during the first two years of the investment plan: Europe investing again — taking stock of the investment plan for Europe (COM(2016) 359 final) and Strengthening European investments for jobs and growth: towards a second phase of the European Fund for strategic investments and a new European external investment plan (COM(2016) 581 final).
For more information:
EU investment plan
- Economy and finance statistics introduced
- European sector accounts - background (background article)
- European system of national and regional accounts - ESA 2010 (background article)
- GDP per capita, consumption per capita and price level indices
- National accounts and GDP
Further Eurostat information
- Regional economic accounts — ESA2010 (t_nama_reg)
- Regional economic accounts — ESA2010 (reg_eco10)
Methodology / Metadata
- Gross domestic product (GDP) at current market prices by NUTS 2 regions (ESMS metadata file — nama_r_e2gdp_esms)
- Household accounts - ESA95 (ESMS metadata file — reg_ecohh_esms)
Source data for figures and maps (MS Excel)