Statistics Explained

GDP at regional level

Data extracted in March 2021.

Planned article update: September 2022.


There were five regions across the EU where GDP (in PPS) per inhabitant in 2019 was more than twice as high as the EU average: Luxembourg, Eastern and Midland, Southern (both Ireland), Praha (Czechia), and Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest (Belgium).

In 2019, the four highest levels of regional primary income per inhabitant in the EU were all recorded in Germany: Oberbayern, Stuttgart, Darmstadt and Hamburg.

Source: Eurostat (nama_10r_2hhinc)

The European Union (EU’s) regional policy aims to support broader socioeconomic priorities such as the European Semester and the European Pillar of Social Rights. Regional accounts are important in this context, as they are used to determine the extent to which EU Member States should contribute towards the EU’s budget, while also serving as a key element when deciding upon the regional allocation of cohesion policy expenditure.

The EU’s regional expenditure has historically been allocated on the basis of gross domestic product (GDP) per capita and gross national income (GNI) per capita. From 2021 onwards, the rules for allocating funding will become simpler and will be tailored to locally-led development strategies that will continue to take account of GDP per inhabitant, alongside information on the socioeconomic and environmental situation (for example, youth unemployment, low levels of educational attainment, the reception and integration of migrants, or climate change).

This article starts with information on regional GDP, the principal aggregate for measuring economic output (presented in absolute values and per inhabitant ratios), and the related concept of gross value added. Having looked at GDP from an output approach, the focus of the second and third sections switches to the income of households: the article presents data for primary income (from paid work and self-employment, as well as from interest, dividends and rents) per inhabitant and the compensation of employees per hour worked. The final section looks at another indicator related to labour, namely labour productivity (or gross value added per person employed) in order to assess patterns of regional competitiveness.

Full article

Gross domestic product (GDP)

GDP at market prices in the EU was valued at EUR 14.0 trillion in 2019, equivalent to an average of EUR 31 200 per inhabitant. Behind these overall figures there are considerable differences between EU regions in terms of their economic performance. Among other factors, these might be explained by: the availability of natural and human resources; changes brought about by globalisation, such as the relocation and outsourcing of manufacturing and some service activities; the legacy of former economic systems; socioeconomic developments; geographic proximity or remoteness to markets.

The main focus of the EU’s cohesion policy is to help regions converge/catch-up. Many of the less-developed and transition regions in the EU may be characterised as having relatively low-growth, low-income (primarily in eastern, southern and Baltic Member States) or pockets of poverty, social exclusion and/or industrial decline (regions that have been ‘left-behind’); these are the regions that receive the bulk of EU regional funds.

The 10 regions with the highest GDP accounted for more than one fifth of the EU’s economic output

There are 240 NUTS level 2 regions across the EU for which GDP data are available. Map 1 is based on absolute values of regional GDP in euro terms and also the level of GDP per inhabitant (adjusted for purchasing power and then shown as a percentage of the EU average). Note that some of the differences between regions — particularly for the absolute level of GDP — reflect the (sometimes artificial) administrative boundaries that are used to delineate each region.

In 2019, the highest levels of regional GDP were recorded across major hubs of business activity (often within relatively large administrative areas). The French capital region of Île-de-France had by far the largest economy in GDP terms (EUR 739 billion), and was followed by the northern Italian region of Lombardia (EUR 399 billion) and the southern German region of Oberbayern (EUR 281 billion). There were seven more regions in the EU where GDP was EUR 200 billion or more (all shown by the largest circles in Map 1), all of which could also be characterised as major hubs of business activity: Lazio (in Italy), Düsseldorf, Stuttgart, Darmstadt (all in Germany), Comunidad de Madrid, Cataluña (both in Spain) and Rhône-Alpes (in France). These 10 regions with at least EUR 200 billion of GDP in 2019 collectively accounted for 21.5 % of the EU’s total economic activity. This is largely a result of these major hubs of economic activity also having much higher levels of regional population, although their economic output is typically boosted by commuters who live in surrounding regions. To give an idea of how concentrated economic activity was in these regions, at the other end of the range the smallest 85 regions — which each had a level of GDP that was less than EUR 25 billion — together provided 8.2 % of the EU’s economic output.

Map 1: GDP and GDP per inhabitant, 2019
(by NUTS 2 regions)
Source: Eurostat (nama_10r_2gdp)

Measuring the size of an economy

The central measure of national accounts, GDP, summarises the economic position of a country or a region. This well-known balance has traditionally been divided by the total number of inhabitants to create a proxy measure for analysing overall living standards, namely GDP per inhabitant.

While GDP continues to be used for monitoring economic developments, playing an important role in economic decision-making, it has been complemented by other indicators as a source of information for informing policy debates on social and environmental aspects of well-being. This is because GDP does not take account of externalities such as environmental sustainability or issues such as income distribution or social inclusion, which are increasingly seen as important drivers for sustainable development and the overall quality of life.

In order to compensate for price level differences across 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 data being denominated in an artificial common currency unit called a purchasing power standard (PPS). The use of PPS series, rather than euro-based series, tends to have a levelling effect, as countries and regions with very high GDP per inhabitant in euro terms also tend to have relatively high price levels (for example, the cost of living in Luxembourg is generally much higher than the cost of living in Bulgaria).

Map 1 also presents information for regional GDP per inhabitant in PPS terms; data are shown as an index relative to the EU average (EU = 100). Those regions considered as relatively ‘rich’ — where GDP per inhabitant was above the EU average — are shown in orange.In 2019, higher than average levels of GDP per inhabitant were primarily found in a band of regions that ran from the Nordic Member States, down through Germany and the Benelux countries into Austria and northern Italy. Otherwise, there were a few isolated pockets of relatively high regional values for GDP per inhabitant, for example, most of Ireland, specific regions in Spain and France, as well as many capital regions.

The regions in the EU where GDP per inhabitant was less than the EU average are shown in blue in Map 1. Regions where GDP per inhabitant was less than 70 % of the EU average (the two darkest shades of blue), were primarily located in a band running from Latvia and Lithuania in the north, down through eastern parts of the EU into Greece and southern Italy, before extending across the Mediterranean Sea to southern regions of Spain and parts of Portugal; most of the régions ultrapériphériques françaises also had GDP per inhabitant that was less than 70 % of the EU average.

GDP per inhabitant in Luxembourg was eight times as high as in Mayotte and Severozapaden

Luxembourg had the highest regional GDP per inhabitant in 2019; its level of economic output was 2.6 times as high as the EU average. There were four other NUTS level 2 regions in the EU where economic output per inhabitant was at least twice as high as the EU average. Two of these regions were in Ireland — Eastern and Midland (the capital region) and Southern — while the third was Praha (the Czech capital region) and the fourth was Région de Bruxelles-Capitale/Brussels Hoofdstedelijk Gewest (the Belgian capital region).

The lowest levels of regional GDP per inhabitant in 2019 were recorded in Mayotte (one of the régions ultrapériphériques in France) and Severozapaden (Bulgaria), both of which recorded a level just under one third of the EU average. GDP per inhabitant in Luxembourg was eight times as high as it was in these two regions.

Germany and Italy were characterised by a polycentric pattern of economic development

There are often large differences in the economic performance (measured by GDP per inhabitant) between regions within individual EU Member States and these are illustrated in Figure 1. In nearly all Member States composed of more than two NUTS level 2 regions, one region had by far the highest GDP per inhabitant. In several such cases — Bulgaria, Czechia, Denmark, Hungary, Slovakia and Sweden — the region with the highest value was the only region to record GDP per inhabitant that was above the national average. A completely different situation was observed in Austria and Spain, where there was less variation between the regions and a notably smaller gap between the highest and next highest region in terms of their GDP per inhabitant. Although it only had two regions, Croatia was like Austria and Spain, in that there was little difference in the values for the two regions; this can be contrasted with the other two Member States with just two regions, Lithuania and Slovenia, where the capital region had a notably higher value.

In particular, there is often a stark contrast between the economic performance of capital regions — which often act as hubs of business (and cultural) activity — and their surrounding regions. In 2019, this pattern was most apparent in eastern EU Member States: for example, Praha (Czechia), Bratislavský kraj (Slovakia), Warszawski stołeczny (Poland), Bucureşti-Ilfov (Romania) and Budapest (Hungary) all featured among the 20 regions in the EU with the highest levels of GDP per inhabitant, while each of the remaining regions within these Member States had levels of economic activity that were below the EU average. A similar pattern, although somewhat less pronounced, could be observed in Lithuania and Portugal.

Many of the EU Member States were characterised by this monocentric pattern of economic development, with regional GDP per inhabitant typically highest in capital regions. The only exceptions (among Member States composed of more than one NUTS level 2 region) were: Germany (where the highest level of GDP per inhabitant was recorded in Hamburg), Ireland (Southern), Italy (Provincia Autonoma di Bolzano/Bozen) and Austria (Salzburg). The situation in Germany and Italy was atypical insofar as they were both characterised by a more polycentric pattern of economic development. Indeed, GDP per inhabitant in the German capital region of Berlin was lower than in 11 of the 37 other German regions, while a similar analysis for Italy reveals that GDP per inhabitant in Lazio was lower than in 5 of the 20 other Italian regions.

Figure 1: GDP per inhabitant, 2019
(index based on GDP per inhabitant in purchasing power standards (PPS) in relation to the EU average = 100, by NUTS 2 regions)
Source: Eurostat (nama_10r_2gdp)

Across NUTS level 3 regions, approximately three quarters of the 81 regions with the highest levels of GDP per inhabitant were located in Germany

Map 2 provides a more detailed set of information, as it is based on NUTS level 3 regions; note that data for two level 3 regions in Ireland are not available and these have been substituted by making use of the higher aggregate for Southern (a NUTS level 2 region). More detailed data make it possible to have a regional analysis for several of the EU Member States that only have a single NUTS level 2 region: there are only two Member States — Cyprus and Luxembourg — that also remain a single region at NUTS level 3. Furthermore, this more detailed dataset offers a more enlightening regional analysis for those Member States with only two or three regions at NUTS level 2, namely Ireland, Croatia, Lithuania and Slovenia.

Map 2 shows the regional concentration of economic activity within the EU. For each of the 1 167 regions for which data are available in 2018, GDP per inhabitant in PPS was sorted in ascending order. The regions were subsequently divided into five groups (quintiles), each accounting for approximately one fifth (20 %) of the EU’s total GDP, some 2.7 trillion (million million) PPS. The regions with the lowest levels of GDP per inhabitant were generally located in Baltic, eastern and southern regions of the EU, although there were also many regions in southern Belgium, much of rural France and predominantly eastern regions of Germany. There were also two regions in Ireland, and single regions in each of the Netherlands and Austria with relatively low levels of GDP per inhabitant. By contrast, the highest levels of GDP per inhabitant were generally located in Germany, as well as more isolated pockets with relatively high living standards in a number of capital regions or other urban regions (such as Milano in Italy, Utrecht in the Netherlands, or Linz-Wels and Salzburg und Umgebung in Austria).

In total, there were 432 regions in the lowest quintile (at the bottom of the distribution), where GDP per inhabitant was in the range of 7 000 PPS (Silistra; Bulgaria) up to 23 100 PPS. The second quintile was composed of 322 regions that had GDP per inhabitant within the range of 23 200 PPS to 29 600 PPS. The third quintile was composed of 206 regions where GDP per inhabitant was close to the EU average of 30 200 PPS. Those regions with GDP per inhabitant considerably above the EU average are shown in the two darker shades of orange in Map 2. The fourth quintile was composed of 126 regions where GDP per inhabitant was within the range of 35 500PPS to 45 400 PPS. Finally, at the upper end of the distribution, there were just 81 regions that made up the highest (or fifth) quintile, with GDP per inhabitant in the range of 45 500 PPS to 166 500 PPS (Wolfsburg; Germany).

In this upper quintile there were 61 German regions (which together contributed 37 % of the total GDP for the fifth quintile), three Austrian regions, two regions each from Denmark, France, Ireland (note one of these was a NUTS level 2 region), Italy and the Netherlands, and one region each from Belgium, Czechia, Luxembourg, Poland, Romania, Slovakia and Sweden.

As noted above, for certain EU Member States and/or regions, commuting flows are very important. The economic activity taking place in region A may result from the work of people living in a neighbouring or nearby region B, which may even be in another Member State (for example, people crossing the border from neighbouring Belgium, Germany and France to work in Luxembourg). Two neighbouring NUTS level 3 regions in Germany — Wolfsburg and Helmstedt — illustrate this situation. Wolfsburg was the EU region with the highest level of GDP per inhabitant in 2018, at 166 500 PPS, while the neighbouring region of Helmstedt had a GDP per inhabitant that was considerably less than the EU average, at 18 300 PPS. This difference — with GDP per inhabitant some 9.1 times as high in Wolfsburg as in Helmstedt — can be principally attributed to commuting flows.

Map 2: Distribution of regional GDP in the EU, 2018
(based on GDP per inhabitant in PPS, by NUTS 3 regions)
Source: Eurostat (nama_10r_3gdp)

Value added developments

When calculated from the output side, the main component of GDP is value added. This is defined as output (at basic prices) minus intermediate consumption (at purchaser prices) and is the balancing item of the national accounts' production account. Value added can be analysed according to activity (for example, manufacturing or transport services) and by institutional sector (for example, government, households, financial corporations and non-financial corporations). The difference between value added and GDP is the treatment of some taxes and subsidies on products.

In the two years from 2017 to 2019, value added in the EU increased by an average of 1.8 % per year. Note that this is a real rate of change, in other words the effects of inflation have been removed from it. Data for this rate of change are shown in Map 3 and are available for 240 regions in the EU; note that for several regions data are presented for 2016-2018 rather than for 2017-2019.

In 14 NUTS level 2 regions, a negative rate of change was recorded for developments in gross value added over the period 2017-2019 (shown in Map 3 with the darkest shade of blue). Among these, 12 were in western and southern EU Member States, mainly in southern Italy, Germany and France, along with one region each in Greece (2016-2018) and the Netherlands. Negative rates of change were also recorded in one Bulgarian and one Finnish region. Åland in Finland recorded the largest decrease in value added, down on average by 2.4 % per year.

The two regions with the fastest growth rates for value added were both in Ireland; more generally, the fastest growth rates were in eastern and Baltic Member States

In the remaining regions, no change was recorded in three regions — Trier, Thüringen (both Germany) and Norra Mellansverige (Sweden) — and there was growth in 223 regions. The largest increases in value added, averaging 4.5 % per year or more, were recorded in 29 regions. These were principally located in Poland (12 regions; 2016-2018) and Hungary (6 regions; 2016-2018), but were also found in Ireland (three regions), Romania (two regions), and Belgium, Bulgaria, Estonia, France, Lithuania and Malta (one region each). The Irish capital region (Eastern and Midland) had the fastest value added growth, up 8.5 % per year on average. This was followed by Southern (also in Ireland) and the Polish capital region (Warszawski stołeczny; 2016-2018) with growth of 6.8 % and 6.7 % per year respectively.

Map 3: Average annual rate of change of gross value added, 2017-2019
(% per year, by NUTS 2 regions)
Source: Eurostat (nama_10r_2gvagr) and (nama_10_gdp)

Figure 2 also presents information concerning the real development of value added, but over a longer period of time. The figure shows developments for the EU as well as for the five regions with the highest and lowest rates of change between 2009 and 2019. Over this 10-year period, value added in the EU increased overall by 16.8 %, equivalent to 1.6 % per year.

The fastest value added growth during this period in any of the EU regions was 120.4 % (equivalent to 8.2 % per year), which was recorded in Southern (Ireland). The Romanian capital region (Bucureşti-Ilfov) also reported that value added more than doubled (up 103.6 %) in real terms during this period.

Among the 213 regions for which a time series from 2009 to 2019 is available (from 2009 to 2018 for regions of some EU Member States), 28 reported a lower value added at the end of this period than at the beginning. Nearly half of these (13) were in Greece (2009-2018), 10 were in Italy, three in Romania and one each in Finland and the Netherlands. The five largest falls — as shown in the bottom half of Figure 2 — were all in Greece. Between 2009 and 2018, value added decreased 28.9 % in Dytiki Makedonia, equivalent to a fall of 3.7 % per year. The Greek capital region (Attiki) recorded a 20.3 % fall (down 2.5 % per year).

Figure 2: Development of gross value added, 2009-2019
(index based on 2009 = 100, by NUTS 2 regions)
Source: Eurostat (nama_10r_2gvagr) and (nama_10_gdp)


The information presented above has already highlighted that wealth creation is often concentrated in capital and other major urban regions across the EU. However, it is likely that part of the income generated in these hubs of business activity may be attributed to commuters who live in surrounding regions (where the price of property and cost of living may be lower, among other possible advantages). As a result, GDP per inhabitant in capital and urban regions tends to be relatively high compared with income measures, whereas surrounding regions are often characterised by relatively high levels of income per inhabitant when contrasted with their economic output.

Primary income per inhabitant

Primary income covers income from paid work and self-employment, as well as from interest, dividends and rents. In 2018, EU primary income per inhabitant averaged 19 500 PPS; the use of data in PPS based on consumption (rather than in euro terms) takes account of price level differences between countries and also reflects the fact that household expenditure is predominantly related to consumption.

Oberbayern had the highest level of primary income per inhabitant

In 2018, there were 25 regions spread across seven different EU Member States where income per inhabitant was at least 26 500 PPS; these are shown by the darkest shade of orange in Map 4. A majority (17 regions) of these were located in Germany, with the highest income levels predominantly found in western (rather than eastern) regions. Five more regions were in Benelux Member States and the remaining three in France, Italy and Austria.

At the other end of the range, there were 14 regions (spread across six different EU Member States) where primary income per inhabitant was less than 10 000 PPS in 2018 (shown by the darkest shade of blue in Map 4). These regions were mainly concentrated in south-eastern Europe — all but one of the six regions that compose Bulgaria (the exception being the capital region of Yugozapaden), half of the eight regions that compose Romania and two Greek regions — but also included one region each from France, Hungary and Slovakia.

In 2018, primary income ranged from a high of 37 300 PPS per inhabitant in Oberbayern (Germany) down to 5 700 PPS in Severozapaden (Bulgaria). As such, the average level of income in Oberbayern was around seven times as high as the level recorded in Severozapaden. Three more German regions featured at the top of the ranking with the highest levels of income per inhabitant — Stuttgart, Darmstadt and Hamburg — and they were followed by Luxembourg. Note that Luxembourg had the second highest level of income in euro terms (EUR 39 500 per inhabitant) — behind Oberbayern (EUR 39 600 per inhabitant) — although Luxembourg’ relatively high cost of living meant that it ranked fifth when analysing the data in PPS terms.

Map 4: Primary income per inhabitant, 2018
(purchasing power standard (PPS), by NUTS 2 regions)
Source: Eurostat (nama_10r_2hhinc)

Compensation of employees

One of the principal areas of interest/concern for many employees is their level of remuneration. Employee compensation is defined (within national accounts) as remuneration, in cash or in kind (such as a company car or vouchers for meals), payable by an employer to an employee in return for work done; it also includes payments linked to social contributions (such as health or pension contributions). The data presented in Figure 3 refer to gross (in other words, before tax) hourly compensation in euro terms.

The highest level of employee compensation was recorded in Luxembourg

In 2018, employees working in the EU received an average of EUR 23.3 in gross compensation for each hour that they worked. The highest level of employee compensation was recorded in Luxembourg (EUR 46.9 per hour), while the lowest was in the Bulgarian region of Severen tsentralen (EUR 4.4 per hour). As such, the ratio between the highest and lowest levels of employee compensation was 11 : 1.

Capital regions often recorded the highest levels of employee compensation, which is perhaps unsurprising given the relatively high cost of living in these regions and the fact that they are often the location for company headquarters and national administrations. This pattern was repeated in a majority of multi-regional EU Member States in 2018: Figure 3 shows that the only exceptions were Oberbayern (that had the highest level of compensation per hour worked in Germany), Dytiki Makedonia (Greece), País Vasco (Spain) and Provincia Autonoma di Bolzano/Bozen (Italy).

There were six NUTS level 2 regions in the EU where the level of employee compensation was above EUR 40.0 per hour. Aside from Luxembourg, they were: the Belgian capital region (EUR 45.9 per hour); the Danish capital region (EUR 44.5); the French capital region (EUR 44.3); and two other Belgian regions that surround the Belgian capital — Prov. Vlaams-Brabant and Prov. Brabant Wallon (EUR 43.0 and EUR 42.9).

As for the analysis of GDP per inhabitant shown in Figure 1, it was not uncommon for one region — often the capital region — to have a notably higher level of average employee compensation per hour worked than all other regions in an EU Member State. In some of the Member States with more than two regions, the region with a particularly high value was the only one with a ratio that was above the national average; this occurred in Bulgaria, Czechia, Denmark, Ireland, Portugal, Romania, Slovakia and Sweden.

Among all EU Member States with at least two regions, the range in the regional values of the average employee compensation per hour worked within each Member State (calculated as the highest values as a percentage of the lowest value) was often narrower than the equivalent range for regional GDP per inhabitant; the only exception was Croatia (where it was marginally wider).

Figure 3: Compensation of employees, 2018
(EUR per hour worked, by NUTS 2 regions)
Source: Eurostat (nama_10r_2emhrw), (nama_10r_2coe), (nama_10_a10_e) and (nama_10_a10)

Labour productivity

Labour productivity may be defined as gross value added divided by a measure of labour input, typically either the number of persons employed or the number of hours worked. When based on a simple headcount of labour input, as in Map 5 and Figure 4, changes observed for this indicator can, at least to some degree, reflect changes in the structure of the employment market. For instance, the ratio falls if there is a shift from full-time to part-time work.

High regional levels of labour productivity may be linked to the efficient use of labour and/or reflect the skills and experience of the labour force. These in turn may result from the specific mix of activities present in each regional economy as some activities — for example, knowledge-intensive industrial activities, business or financial services — tend to be characterised by higher levels of labour productivity (as well as higher average employee compensation).

In 2018, an average of EUR 58 400 of value was added for each person employed in the EU. This figure can be used as the basis for deriving a set of labour productivity indices, which are presented relative to the EU average = 100 (see Map 5). There were six NUTS level 3 regions where labour productivity was more than twice as high as the EU average in 2018: three of these were situated in Germany and one each in Ireland, France and Luxembourg. The highest average labour productivity was EUR 161 700 per person employed in Dublin (Ireland), in other words 277.0 % (or nearly three times as high as) the EU average.

At the other end of the range, there were 25 NUTS level 3 regions in the EU where labour productivity was less than one fifth of the EU average in 2018, 21 of which were located in Bulgaria and the remaining four in Romania. The lowest level of labour productivity — EUR 7 600 per person employed — was recorded in Silistra in Bulgaria, equivalent to 13.0 % of the EU average.

As for GDP per inhabitant and for employee compensation, in a majority of the multi-regional EU Member States (only Cyprus and Luxembourg are mono-regional at NUTS level 3) the highest levels of labour productivity were often recorded in capital regions. Nevertheless, there were quite a few exceptions, where the highest labour productivity was recorded in a region other than the capital region: Københavns omegn (Denmark; note that this is the area around the centre of the capital), Wolfsburg, Kreisfreie Stadt (Germany), Tarragona (Spain), Hauts-de-Seine (France; note that this is an area around the western side of the capital), Milano (Italy), Kauno apskritis (Lithuania), Győr-Moson-Sopron (Hungary), Rheintal-Bodenseegebiet (Austria) and Alentejo Litoral (Portugal).

Map 5: Labour productivity, 2018
(index based on EUR per person employed in relation to the EU average = 100, by NUTS 3 regions)
Source: Eurostat (nama_10r_3gva), (nama_10r_3empers), (nama_10_a10) and (nama_10_a10_e)

The final analysis in this section shows the change between 2010 and 2019 in the labour productivity of NUTS level 2 regions compared with the EU average. In absolute terms, average labour productivity in the EU rose in current prices from EUR 50 300 per person employed in 2010 to EUR 59 700 per person employed in 2019, an overall increase of 18.7 %. There were 15 regions in the EU where labour productivity was actually lower in 2019 than it had been in 2010: all 13 Greek regions, Molise in Italy and Groningen in the Netherlands; note that regional data are not available for this indicator for France or Poland.

Figure 4 does not directly show the change in productivity (which would be influenced by inflation): instead it shows the percentage point change in the labour productivity of each region expressed as a percentage of the EU average. As such it indicates whether labour productivity has increased more or less than the EU average between 2010 and 2019. Clearly the 15 regions that experienced an actual fall in labour productivity had a lower productivity as a percentage of the EU average in 2019 than they had in 2010. A further 81 regions also recorded a fall in labour productivity relative to the EU average. By contrast, 100 regions recorded a rise in labour productivity relative to the EU average.

Relative to the EU average, all regions of Greece, Spain, Croatia, Italy and Sweden recorded a fall in relative labour productivity. A relative fall was also recorded in at least half of the regions in the Netherlands, Portugal and Finland. By contrast, an increase in relative labour productivity was observed in most regions of Belgium and Germany, all but one region of Ireland, Hungary, Austria and Slovakia, and all of the regions in Bulgaria, Czechia, Denmark, Lithuania, Romania and Slovenia. The remaining EU Member States either only have national data (France and Poland) or have only one region at NUTS level 2: the labour productivity of France and Cyprus fell relative to the EU average, while that of Estonia, Latvia, Luxembourg, Malta and Poland increased.

Looking at individual regions, the largest fall in labour productivity relative to the EU average was observed in Groningen, down 38.8 percentage points; 10 of the next 11 largest falls were in Greek regions, while there was also a considerable reduction in relative labour productivity in the Swedish region of Mellersta Norrland. By far the largest increase in labour productivity relative to the EU average was observed in the Southern region of Ireland, where productivity relative to the EU average increased by 170.4 points. The second largest increase was in the Irish capital region (up 65.8 points) and the third was in the Romanian capital region (up 25.4 points).

Figure 4: Change in relative labour productivity, 2010-2019
(percentage points, index in relation to the EU average = 100, by NUTS 2 regions)
Source: Eurostat (nama_10r_3gva), (nama_10r_3empers), (nama_10_a10) and (nama_10_a10_e)

Source data for figures and maps

Excel.jpg Economy at regional level

Data sources

European system of national and regional accounts

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 has been implemented since September 2014 and is consistent with worldwide guidelines on national accounting, as set out in the system of national accounts (2008 SNA).

ESA 2010 ensures that economic statistics for EU Member States are compiled in a consistent, comparable, reliable and up-to-date way. The legal basis for these statistics is Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013 on the European system of national and regional accounts in the European Union. 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.

Regional data

Statistics from regional economic accounts are largely shown for NUTS level 2 regions although in this article a more detailed regional analysis at NUTS level 3 is also used for GDP in one map in the first section as well as for the analysis of labour productivity in the final section. The data for statistical regions in the EFTA and candidate countries are often unavailable and have been replaced (where appropriate) by national aggregates. Note also that the data for these countries are sometimes less fresh than for EU regions; all discrepancies are footnoted under maps or figures.

Indicator definitions

Gross domestic product and value added

Gross domestic product (GDP) is a basic measure of the overall size of an economy. As an aggregate measure of production, GDP is equal to the sum of the gross value added of all resident institutional units engaged in production, plus any taxes on products and minus any subsidies on products.

Gross value added (GVA) is defined as output (at basic prices) minus intermediate consumption (at purchaser prices); it is the balancing item of the national accounts’ production account.

Primary income

Two types of income are recorded as primary income:

  • primary incomes receivable by virtue of direct participation in the production process, mainly operating surplus and mixed income and the compensation of employees;
  • property incomes receivable by the owner of a financial asset or a tangible non-produced asset in return for providing funds to, or putting the tangible non-produced asset at the disposal of, another institutional unit (interest, dividends, withdrawals from income of quasi-corporations, reinvested earnings of foreign direct investment, rents on land).

Compensation of employees and labour productivity

In national accounts, the compensation of employees is defined as the total remuneration, in cash or in kind, payable by an employer to an employee in return for work done by the latter during an accounting period. It consists of wages and salaries in cash or in kind and employer’s actual and imputed social contributions.

Labour productivity measures the (average) amount of goods and services produced by each member of the labour force or the output per input of labour. It can be measured in a variety of ways. For example, it may be measured by GDP (in terms of purchasing power standards) either relative to the number of employed people or to the number of hours worked. In both cases, it is then expressed as an index.


In August 2009, the European Commission adopted a communication 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 document Progress on ‘GDP and beyond’ actions (SWD(2013) 303 final), including regional and local indicators.

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. In conjunction, the European Commission adopted a series of communications including 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).

Following on from the global financial and economic crisis of 2008, the European Commission subsequently reset its priorities in 2014 as ‘boosting jobs, growth and investment’. This major initiative aimed to unlock public and private investment by targeting infrastructure developments, such as broadband internet, energy networks and transport. Subsequently, the priorities were reset for the period 2019-2024, including (alongside three other priorities) several directly concerning the economy: A European Green Deal, ‘A Europe fit for the digital age’ and ‘An economy that works for people’.

Among other actions for an economy that works for people, the European Commission has proposed a deepening of economic and monetary union, greater support for small and medium-sized enterprises (SMEs), the full implementation of the European Pillar of Social Rights, and fairer taxation.

In December 2020, the multiannual financial framework covering the period 2021-2027 was adopted. The recovery plan for Europe that was proposed by the European Commission in May 2020 explained how the budget set out to kick-start the European economy, boost the green and digital transitions, and make it fairer, more resilient and more sustainable for future generations. In the plan, the European Commission proposed not only to reinforce the multiannual financial framework for 2021-2027, but to support it with an emergency European Recovery Instrument (also known as Next Generation EU) to facilitate decisive responses to the most urgent challenges.

In total, EUR 243 billion (in 2018 prices) have been budgeted in the multiannual financial framework for regional development and cohesion between 2021 and 2027. In addition, a further EUR 47.5 billion has been allocated to regional development and cohesion as part of the European Recovery Instrument. The EU’s regional and cohesion policy will, in the coming years, support broader socioeconomic priorities by focusing on five investment priorities:

  • a smarter Europe (for example, through innovation and digitisation);
  • a greener, carbon free Europe (investing in energy transition, renewables and the fight against climate change);
  • a more connected Europe (highlighting strategic transport and digital networks);
  • a more social Europe (supporting quality employment, education, skills, social inclusion and equal access to healthcare);
  • a Europe closer to its citizens (promoting locally-led development strategies and sustainable urban development).

Alongside the budget for regional development and cohesion, EUR 672.5 billion from the European Recovery Instrument has been allocated to a Recovery and Resilience Facility, a small majority of which will be disbursed as loans and the rest as grants. This facility will support reforms and investments undertaken by EU Member States. The aim is to mitigate the economic and social impact of the COVID-19 pandemic and make EU economies and societies more sustainable, resilient and better prepared for the challenges and opportunities of the green and digital transitions.

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Regional economic accounts - ESA 2010 (t_reg_eco)
Regional economic accounts - ESA2010 (t_nama_10reg)
Regional economic accounts (reg_eco10)
Gross domestic product indicators (reg_eco10gdp)
Branch and household accounts (reg_eco10brch)
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Basic breakdowns of main GDP aggregates and employment (by industry and by assets) (nama_10_bbr)
Regional economic accounts (nama_10reg)
Gross domestic product indicators (nama_10r_gdp)
Branch and household accounts (nama_10r_brch)

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