Regional accounts - an analysis of the economy for NUTS level 3 regions

Data extracted in May 2025.

Planned article update: May 2029.

Highlights

20% of the EU’s GDP was generated in 69 NUTS level 3 regions, which collectively covered 1.4% of the EU surface.

Compared with the average for the EU, regional labour productivity increased markedly between 2012 and 2022 in South-West, Dublin, Mid-West, Mid-East (all Ireland) and Byen København (Denmark).

Source: Eurostat (nama_10r_3gdp) and (nama_10r_3popgdp)

This article provides an analysis of regional accounts data for the European Union’s (EU) NUTS level 3 regions. Regional data at level 3 provide more granular (detailed) information than the data at level 2; Eurostat publications commonly present the latter. Based on the 2024 version of NUTS, there are 1 165 level 3 regions in the EU compared with 244 level 2 regions.

The use of level 3 data makes it possible to have a regional analysis for several of the EU countries that only have a single level 2 region: there are only 2 EU countries – Cyprus and Luxembourg – that remain a single region at level 3. Furthermore, level 3 offers a more enlightening regional analysis for EU countries with only 2 or 3 regions at level 2, namely Ireland, Lithuania and Slovenia. For these EU countries, a level 2 analysis may well be particularly unbalanced due to the dominance of the region that contains the capital city; note that this can also happen at level 3 for EU countries with only 2 or 3 regions. Equally, the information available at level 3 is much more detailed than that at level 2 for some of the larger EU countries. Most notably, Germany has 400 level 3 regions compared with 38 level 2 regions.

While a regional analysis is more detailed at level 3 than at level 2, data are available for far fewer national accounts variables. For example, there are no regional accounts data on the compensation of employees, hours worked, investment or household accounts. The information available from regional accounts for level 3 concerns gross domestic product (GDP) and population, as well as a relatively aggregated activity analysis (depending on the EU country, either for 6 or for 10 activities according to NACE) for gross value added and employment.

Statistics on regions – the NUTS classification

At the heart of regional statistics is the NUTS classification – a classification of territorial units for statistics. This regional classification for EU countries is based on a hierarchy of regions and subdivides each EU country into regions that are classified according to 3 different levels, covering levels 1, 2 and 3 from larger to smaller areas. Some EU countries have a relatively small population and may therefore not be subdivided at some (or even all) of the different levels of the NUTS classification. For example, Estonia, Cyprus, Latvia, Luxembourg and Malta are each composed of a single level 2 region according to the 2024 version of the classification. Table 1 provides an overview of the number of NUTS regions in the EU as a whole and in individual EU countries.

Table 1: Number of NUTS 2024 regions
Source: Eurostat

Regional concentration of economic activity within the EU

Map 1 shows the regional concentration of economic activity within the EU. For each region, GDP per inhabitant (also referred to as GDP per capita) has been calculated based on GDP data presented in purchasing power standards (PPS).

GDP in PPS

GDP expressed in PPS is calculated by applying purchasing power parities, the latter being indicators of price level differences between countries. The use of purchasing power parities rather than market exchange rates to convert currencies makes it possible to compare the output of economies and the material welfare of their inhabitants in real terms, in other words, to control for differences in price levels. By convention, the GDP of the EU as a whole in PPS has the same value as in euro. By contrast, these values (in PPS and euro) are different for individual EU countries and regions, depending on the underlying price level differences.

GDP per inhabitant

By presenting indicators as ratios to the number of inhabitants in each region, the data are standardised to take account of the different size (in population terms) of each region.

The 1 165 regions in the EU for which data are available have been sorted based on the value of their GDP per inhabitant. They have then been grouped into 5 roughly equal parts, each accounting for approximately 3.2 trillion (thousand billion) PPS of GDP in 2022, in other words one fifth (20%) of the EU’s total GDP. Just 69 level 3 regions made up the top 5th of the EU economy in 2022. These included 46 German regions (which together contributed 31% of the total GDP for this group), 4 Dutch regions, 2 regions each from Belgium, Denmark, Ireland, France, Italy, Poland and Austria and 1 region each from Czechia, Hungary, Luxembourg, Romania and Sweden. These 69 regions covered an area of approximately 57 900 square kilometres, representing 1.4% of the total area of the EU. The other groups (2nd to 5th) are composed of 131, 212, 325 and 428 level 3 regions, respectively.

Map 1: Classification of regions based on their GDP per inhabitant in PPS, 2022
(NUTS level 3 regions)
Source: Eurostat (nama_10r_3gdp) and (nama_10r_3popgdp)

There are 2 issues to keep in mind when using GDP per inhabitant measured in PPS as an indicator of the economic situation in a region. First, commuting flows are very important for certain EU countries and/or regions. The economic activity taking place in region A may result from the work of many people living in a neighbouring or nearby region B, which may even be in another EU country. Dividing the GDP of region A by the population of region A, results in a GDP per inhabitant of region A which appears to be relatively high. Two neighbouring level 3 regions in Germany – Wolfsburg and Helmstedt – illustrate this situation. Wolfsburg was the level 3 region with the highest GDP per inhabitant in 2022 in Germany and the 3rd highest in the EU, at 136 500 PPS, while the neighbouring region of Helmstedt had a GDP per inhabitant of 22 500 PPS. This difference in GDP per inhabitant – 6.1 times as high in Wolfsburg as in Helmstedt – is mainly explained by commuting flows.

Secondly, the use of national PPS (as regional ones aren’t available) overstates the GDP per inhabitant in PPS in relatively expensive regions within any given EU country, while the values for relatively cheaper regions within an EU country are understated. The French regions of Paris and Mayotte are good examples. Applying the national PPS underestimates the price level in Paris and overestimates the price level in Mayotte. There are no official estimates of regional variations in price levels across the EU. However, estimates based on detailed data for consumer prices in Germany indicate that the regional variation may be as high as 26%, between level 3 regions, in particular, due to housing costs ^[1].

Bearing these issues in mind, Table 2 shows what percentage of the GDP for each EU country belonged to the 5 groups (classified according to the value of GDP per inhabitant in each region) for the year 2022. Looking at the bottom 20% group, it includes regions that represent 71.7% of the national economy in Slovakia and also more than 50% in Greece, Portugal, Hungary, Croatia, Poland, Bulgaria and Romania. By contrast, this group includes regions with less than 10% of the national economy in Belgium, Germany, Malta, Ireland and Austria, while no regions were in this group in Denmark, the Netherlands, Finland and Sweden, nor in Cyprus and Luxembourg (which are both mono-regional). The impact of commuting is clearly visible, as the capital regions of Hungary, Poland and Romania are part of the top 20% group while most of the remaining regions of these countries are included in the bottom group.

Table 2: Share of national GDP contributed by NUTS level 3 regions classified to each of the EU’s 5 GDP groups, 2022
Source: Eurostat (nama_10r_3gdp) and (nama_10r_3popgdp)

Another interesting aspect of the concentration of regional economic activity is the developments observed over time. Map 2 classifies the level 3 regions for which data are available according to 2 different criteria: whether their share of EU GDP decreased or increased between 2012 and 2022 and whether their share of national GDP decreased or increased during the same period. Only national data are available for Belgium, Germany, Italy, Poland and Portugal and, by definition, their share of national GDP remained the same whereas their share of the EU total could change.

Map 2: Change in the regional share of national and EU GDP in PPS, 2012–22
(NUTS level 3 regions)
Source: Eurostat (nama_10r_3gdp)

All 8 Slovakian level 3 regions saw their share of the EU’s GDP decrease between 2012 and 2022, while Germany’s and Italy’s shares (for national data) also fell. This share decreased in 96 of 101 regions in France, 49 of 52 regions in Greece, 17 of 19 regions in Finland, 18 of 21 regions in Sweden, 43 of 59 regions in Spain and 25 of 35 regions in Austria. Elsewhere, a majority of regions (national data for Belgium, Poland and Portugal) increased (or maintained) their share of EU GDP. For example, all 8 Irish level 3 regions increased their share of the EU’s GDP between 2012 and 2022, as did all 5 Estonian regions, both Maltese regions and the single regions of Cyprus and Luxembourg.

Looking at the share of national GDP (rather than of the EU total), 7 of the 8 Irish regions saw their share decrease, as was the case for 8 of the 10 Lithuanian regions, 33 of the 42 Romanian regions, 21 of the 28 Bulgarian regions and 9 of the 12 Slovenian regions. In most cases, the decreases in the shares were explained by increases in the more developed regions of these countries; for example, in the Lithuanian capital, Vilniaus apskritis, the share of national GDP increased from 38.6% to 43.7%. Decreases were also observed in at least half of the regions of Denmark, Czechia, the Netherlands, Sweden, France, Finland, Spain, Greece, Hungary and Malta. By definition, in the mono-regional countries (Cyprus and Luxembourg) and the 5 countries with national data, there was no change in the share of national GDP. Elsewhere, a majority of regions increased (or maintained) their share of national GDP. This was the case in 5 of the 8 Slovakian regions, 3 of the 5 Estonian regions, 3 of the 5 Latvian regions, 12 of the 21 Croatian regions and 18 or the 35 Austrian regions.

Many of the 69 level 3 regions that were in the top 20% of the EU economy (as shown in Map 1) were in countries for which only national data are available for the time period shown in Map 2. Of the 16 regions in the top 20% of the EU economy for which time series are available, increases in the share of national GDP were observed for 12 regions. These included the capital regions of Czechia, Denmark, France, the Netherlands, Romania and Sweden; the others were Københavns omegn in Denmark, South-West in Ireland, Utrecht and Zuidoost-Noord-Brabant in the Netherlands, and Linz-Wels and Salzburg und Umgebung in Austria. By contrast, the capital regions of Ireland and Hungary were in the top 20% of the EU economy but recorded falls in their national shares, as was the case for Hauts-de-Seine in France and Delfzijl en omgeving in the Netherlands.

Regional labour productivity

In the 1st part of this article, regions were initially categorised by their GDP per inhabitant, which is the result of dividing GDP by the population.

This section focuses on labour productivity. For this analysis, gross value added is used instead of GDP, so the labour productivity is calculated as gross value added per person employed. This is presented in euro rather than in PPS, as gross value-added data aren’t available in PPS; note that the PPS data for GDP are calculated from the expenditure approach for compiling GDP rather than the output approach which is used as the basis for data on gross value added. Another factor to consider before interpreting the results is that the average hours worked per person varies considerably across EU countries, regions and economic activities. People working in EU countries with higher productivity usually work fewer hours than people employed in EU countries that display lower levels of labour productivity. Furthermore, the productivity of an economy as a whole reflects the productivity of its different economic activities (implicitly weighted by the number of people employed in each activity). A region specialised in activities with a higher labour productivity will have a higher overall productivity than a region specialised in activities with a lower labour productivity, even if the productivity for each activity is the same in both regions.

Map 3: Labour productivity of NUTS level 3 regions, 2022
Source: Eurostat (nama_10r_3gva) and (nama_10r_3empers)

Map 3 shows a distribution of regional gross value added based on a region’s labour productivity. The 1st step was to calculate the labour productivity for each level 3 region. Five classes of labour productivity were defined, each containing approximately the same number of level 3 regions, starting from the highest of €79 000 or more of gross value added per person employed down to the lowest where labour productivity was less than €35 000. Once each region had been assigned to 1 of these 5 classes based on its labour productivity, the value added of the regions within each class were combined; from this value a distribution of national gross value added was calculated (see Table 3).

Map 3 clearly shows the differences in labour productivity measured in euros between western and Nordic regions on one hand and southern, eastern and Baltic regions on the other.

Table 3 shows what share of value added was contributed by regions classified to each of the labour productivity classes. The EU countries that recorded at least three quarters of their value added in the most productive regions were the Nordic EU countries (Denmark, Finland and Sweden), Luxembourg, Ireland, Belgium and Austria, while a majority of value added was also generated in regions with high productivity in the Netherlands and France. The only other EU countries with any regions that had an average labour productivity of at least €79 000 per person employed were Germany and Italy.

Table 3: Share of national gross value added contributed by NUTS level 3 regions classified to specified labour productivity classes, 2022
Source: Eurostat (nama_10r_3gva) and (nama_10r_3empers)

For each level 3 region, Figure 1 shows the change between 2012 and 2022 in a productivity index that has been calculated relative to the EU average; note that national data are shown for Belgium, Germany, Italy, Poland and Portugal. After deriving the level of labour productivity as described above, a ratio was calculated to show the labour productivity of each region relative to the average for the EU, with this ratio expressed as a percentage; by definition, the productivity index = 100 for the EU. The percentage point difference in this ratio between 2012 and 2022 was calculated and this is shown in the figure.

Between 2012 and 2022, all of the level 3 regions in Bulgaria, Czechia, Estonia, Latvia, Lithuania, Hungary, Malta, Romania and Slovenia reported an increase in labour productivity compared with the EU average. This was also the case in all but 1 region in Ireland. By contrast, none of the level 3 regions in Sweden reported an increase in labour productivity compared with the EU average, while only 1 region in each of Greece and Spain reported an increase. Concerning mono-regional countries and those for which only national data are shown, Belgium, Germany and Poland recorded increases, whereas Italy, Cyprus, Luxembourg and Portugal recorded decreases.

Figure 1: Change in the relative labour productivity of NUTS level 3 regions, 2012 to 2022
Source: Eurostat (nama_10r_3gva) and (nama_10r_3empers)

The regions with the largest increases in relative labour productivity were the Irish regions of South-West, Dublin, Mid-West and Mid-East, with increases of 378, 134, 67 and 59 percentage points, respectively. These were followed by the Danish capital region, Byen København, with an increase of 58 percentage points. All of these regions had a labour productivity in 2012 that was already above the EU average. The largest increase among regions that hadn’t had a labour productivity in 2012 already above the EU average was in the Bulgarian region of Stara Zagora, up 37 percentage points.

At the other end of the range, the largest decreases in relative labour productivity were recorded in the Dutch region of Delfzijl en omgeving (down 242 percentage points), the French region of Lozère (down 76 percentage points), the Greek region of Grevena, Kozani (down 51 percentage points) and the French region of Hauts-de-Seine (down 50 percentage points). With the exception of Lozère, all of these regions also had above average relative labour productivity in 2012.

Regional specialisation

The final section of this article looks at regional specialisation of level 3 regions. For this purpose, the Krugman specialisation index is used. In this implementation, the index measures the extent to which the structure of a region’s output (value added) differs from the average for the EU. If the index is close to 0, it implies that the economic structure of the region is very similar to that of the EU (as a whole). The structure has been analysed based on 6 economic activities which collectively cover the whole economy and for which data are available for all level 3 regions.

There are 32 level 3 regions in the EU for which the Krugman index was above 0.60. These relatively high levels can mainly be attributed to a high degree of specialisation in industry. The exceptions were Zakynthos and Pella in Greece; the 1st had a high specialisation in the category covering wholesale and retail trade, transport, accommodation and food service activities, and information and communication (reflecting the fact that it is a popular tourist destination) and the 2nd a high specialisation in the category covering agriculture, forestry and fishing (reflecting the rural nature of the region covered in arable land, grazing pastures, orchards and forests). An index above 0.60 was observed in 16 German regions, 4 regions in each of Greece and Poland, 3 Bulgarian regions and 1 region each in Estonia, Ireland, France, Croatia, and Romania.

Map 4: Krugman regional specialisation index for value added, 2022
(NUTS level 3 regions)
Source: Eurostat (nama_10r_3gva) and (nama_10_a10)

Source data for tables and graphs

Regional accounts – an analysis of the economy for NUTS level 3 regions: tables and figures

Data sources

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 provides the basis for the consistent, comparable, reliable and up-to-date compilation of economic statistics for EU countries. Regulation (EU) No 549/2013 of the European Parliament and the Council on the European system of national and regional accounts in the European Union provides the legal framework for these statistics. ESA 2010 isn’t restricted to annual national accounting, as it also applies to quarterly and shorter or longer period accounts, as well as to regional accounts. It’s 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.

2024 benchmark revision

Benchmark revisions are coordinated major revisions carried out at least once every 5 years to incorporate new data sources and major changes in international statistical methodology. In national accounts, they aim to ensure a maximum degree of consistency within national accounts. In other words, they should be implemented to obtain a long time series and ensure consistency across countries.

In line with the implementation of a harmonised European revision policy (HERP), 26 EU countries carried out coordinated benchmark revisions of national accounts in 2024; Luxembourg postponed the benchmark revision to 2027.

Due to an incomplete implementation of the 2024 benchmark revisions for regional data, only national data are shown in Map 2 and Figure 1 for Belgium, Germany, Italy, Poland and Portugal; the time series for these national data include a break in series. Equally, it should be noted that the revisions of French regional data haven’t yet been finalised at the time of writing; finalised revisions are expected towards the end of 2025. For further information, please refer to the data release metadata.

Context

In her political guidelines for the European Commission for 2024–29, President van der Leyen highlighted a new plan for Europe’s sustainable prosperity and competitiveness.

Notes

↑ Weinand, S. & L. von Auer (2019): Anatomy of regional price differentials: Evidence from micro price data, Discussion Paper 4/2019, Deutsche Bundesbank, Frankfurt am Main.

Explore further

Database

Regional statistics by NUTS classification (reg), see

Regional economic accounts (reg_eco10)

Annual national accounts (nama10), see

Regional economic accounts (nama_10reg)

Gross domestic product indicators (nama_10r_gdp)

Branch and Household accounts (nama_10r_brch)

Thematic section

Publications

Selected datasets

Regional statistics (t_reg), see

Regional economic accounts - ESA 2010 (t_reg_eco)

Annual national accounts (t_nama10), see

Regional economic accounts - ESA2010 (t_nama_10reg)

Methodology

Manuals and further methodological information

ESA 2010 – manuals and guidelines
Manual on regional accounts methods – 2013 edition
Methodological manual on territorial typologies – Eurostat – 2024 edition

Metadata

Regional economic accounts (ESMS metadata file – reg_eco10_esms)

External links

Visualisation

Maps can be explored interactively using Eurostat’s statistical atlas (see also the dedicated section)
Regional statistics illustrated
Regions in Europe

[1] Weinand, S. & L. von Auer (2019): Anatomy of regional price differentials: Evidence from micro price data, Discussion Paper 4/2019, Deutsche Bundesbank, Frankfurt am Main.

[1]