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research · subnational economics, european union, 2000-2024
How does economic activity differ across Europe's NUTS2 regions?
Aggregate EU statistics hide a regional economy that is more unequal within countries than between them. This page opens the NUTS2 lens, roughly 240 sub-national units ranging from Île-de-France to Bulgaria's Severozapaden, and asks the five questions any regional economist runs first: which regions produce the most output, which are richest and poorest, how does the EU's own Cohesion Policy typology sort them, has Europe converged or diverged since 2000, and where is within-country inequality concentrated. Everything is pulled live from Eurostat's regional accounts (NAMA_10R_2GDP for GDP, NAMA_10R_2GVAGR for gross value added by NACE sector), NUTS 2021 vintage.
dataEurostat NAMA_10R_2GDP · NAMA_10R_2GVAGR
geographiesEU27 + candidates, NUTS 2021
years2000-2024
unitsEUR · EUR per inhabitant · PPS (EU27=100)
1. The absolute-size superstars
At the top of the European regional hierarchy sits Île-de-France: Paris and its commuter belt produce more output than any other NUTS2 region in Europe, and more than the combined GDP of several smaller member states. Iammarino, Rodríguez-Pose & Storper (2019, Journal of Economic Geography) document that the European growth story of the last quarter-century has not been a uniform 'rising tide' but a re-concentration of activity into a handful of capital-city and metropolitan regions, the NUTS2 analogue of Moretti's (2012) divergent labour-market geography in the United States. The absolute-size ranking below is the first fingerprint of that pattern.
Figure 1
Top 20 NUTS2 regions by GDP, 2024 (EUR, current prices)
In 2024, FR10 · Île-de-France (France) produced €865.7B, about 1.7× the size of the second-ranked ITC4 · Lombardia (Italy) (€504.7B). The top 20 is dominated by capital regions (FR10 Île-de-France, ES30 Madrid, ITI4 Lazio) and the largest German and Italian industrial Länder/regioni (DE21 Oberbayern, DE71 Darmstadt, DEA1 Düsseldorf, ITC4 Lombardia). Ireland's IE06 (Eastern & Midland, i.e. Dublin) enters on the back of multinational profit booking, a reminder that NUTS2 output can diverge from local activity once corporate tax residence is factored in.
Source: Eurostat (2026) Gross domestic product at current market prices by NUTS 2 region, dataset NAMA_10R_2GDP, unit MIO_EUR, latest release retrieved April 2026. Values converted from millions of euros to euros for display. Methodology: ESA 2010 regional accounts. Türkiye (TR) is included here as a Eurostat-reporting candidate country; EU27 convergence, PPS-typology, and Gini calculations below exclude non-member states.
2. The per-capita spread: Luxembourg vs. Severozapaden
Switching from absolute levels to GDP per inhabitant reveals the stark European welfare gradient. Luxembourg and Ireland's two NUTS2 regions (both uplifted by cross-border commuting and multinational profit booking respectively) sit above €80,000 per head; at the other end, Bulgaria's Severozapaden and Romania's Nord-Est report per-capita output below €10,000. Gagliardi & Percoco (2017, Regional Studies) show in their impact evaluation of European Cohesion Policy that these lagging regions are precisely the ones where EU structural funds generate the largest growth effects, but only when absorptive capacity (local governance, human capital) is sufficient, a pattern that helps explain why the per-capita gap between top and bottom has closed only modestly over two decades.
Figure 2
GDP per inhabitant by NUTS2 region, richest 20 and poorest 20, 2024 (EUR per inhabitant, EU27 only)
3. The Cohesion Policy typology: PPS per inhabitant at EU27 = 100
The EU's own regional policy framework, set out in the Common Provisions Regulation 2021/1060 (Article 108), sorts NUTS2 regions into three eligibility tiers based on GDP per inhabitant in purchasing power standards (PPS) relative to the EU27 average. PPS strips out price-level differences across countries, so a given PPS index value is comparable in real-consumption terms across the Union, which is why the ESPON territorial-cohesion framework and the 9th Cohesion Report (European Commission 2024) both use this index rather than euro figures. The three tiers are: less developed (below 75% of the EU27 mean), transition(75% to below 100%), and more developed (at or above 100%).
Figure 3
NUTS2 regions by Cohesion Policy tier, PPS per inhabitant indexed to EU27 = 100, 2024
Of the 244 EU27 NUTS2 regions with PPS data in 2024, 83 (34%) fall below the 75% threshold and qualify as 'less developed' under Cohesion Policy 2021-2027, making them eligible for the highest co-financing rates (up to 85%) from the European Regional Development Fund and the Cohesion Fund. 71 are in the transition tier (75 to below 100%), and 90 are 'more developed' at or above the EU27 mean. The unweighted median NUTS2 region sits at 87% of EU27, materially below 100 because the distribution is right-skewed: a small number of high-PPS capitals pull the mean up. The less-developed tier is geographically concentrated in Bulgaria, Romania, Greece, southern Italy, Portugal, and eastern Poland, precisely the belt where Cohesion Policy has spent the most over the 2014-2020 programming period (European Commission 2024, Ninth report on economic, social and territorial cohesion).
Source: Eurostat NAMA_10R_2GDP, unit PPS_HAB_EU27_2020 (PPS per inhabitant, EU27 = 100), EU27 NUTS2 regions only. Tiers follow Article 108 of Regulation (EU) 2021/1060 (Common Provisions Regulation for the 2021-2027 period) and are applied here to the latest available year as an illustrative snapshot; actual programme-period eligibility is determined once per seven-year cycle using a three-year average.
4. Convergence or divergence? Two decades of the coefficient of variation
Regional convergence is measured most plainly as the dispersion of per-capita GDP across NUTS2 units over time, the classical σ-convergence statistic proposed by Sala-i-Martin (1996, European Economic Review). Iammarino, Rodríguez-Pose & Storper (2019) argue that European regional convergence is a 'twin-peaks' story: between-country catch-up of the 2004 accession states is real, but within-country divergence has widened almost everywhere, leaving overall dispersion broadly stable. The chart tracks the coefficient of variation (CV) of NUTS2 GDP per capita across all EU27 regions from 2000 to 2024; a falling CV means regions are becoming more alike, a rising CV means they are pulling apart.
Figure 4
Regional dispersion: coefficient of variation of NUTS2 GDP per capita, EU27, 2000-2024
The CV fell from 59.4% in 2000 to 47.6% by 2008, the clearest period of EU-wide regional convergence, coinciding with the 2004 and 2007 accession catch-up. The global financial crisis and the subsequent euro-area sovereign-debt crisis reversed that compression: the CV rose to 53.5% by 2014. Post-2015 the dispersion has drifted back down, reaching 49.3% in 2024. Net of two decades, Europe is modestly more equal across its regions than it was at the turn of the century, but the gains were made almost entirely in the pre-crisis decade and have been partly undone by the crisis years. This is exactly the pattern Iammarino, Rodríguez-Pose & Storper (2019) describe as 'convergence with development traps'.
Source: Eurostat NAMA_10R_2GDP, unit EUR_HAB, EU27 NUTS2 regions. CV computed as 100 × σ(value) / μ(value) across all reporting NUTS2 units per year, using population standard deviation (STDDEV_POP). No population weighting; this is the standard σ-convergence metric used in Sala-i-Martin (1996, European Economic Review 40, 1325-1352) and the Eurostat Regional Yearbook.
5. Within-country inequality: where is regional divergence worst?
National-average GDP per capita hides a within-country distribution that is wider in some EU members than in others. The figure below shows the Gini coefficient of NUTS2 GDP per capita within each EU27 country (restricted to members with at least three NUTS2 units). Iammarino, Rodríguez- Pose & Storper (2019) argue that within-country dispersion is the policy-relevant margin, the one that produces populist backlash, inter-regional fiscal conflict, and the 'places that don't matter' dynamic, and it is systematically higher in the newer member states and in the old Mediterranean members where a metropolitan capital vastly out-produces the rest of the country.
Figure 5
Within-country Gini of NUTS2 GDP per capita, 2024 (EU27 members with k ≥ 3 regions)
6. Sectoral composition: knowledge-intensive services and regional prosperity
What do the rich regions do? The NACE Rev. 2 breakdown in Eurostat's regional gross value added table (NAMA_10R_2GVAGR) lets us aggregate information & communication (section J), financial & insurance activities (K), and professional / scientific / technical / administrative services (M_N) into a single 'knowledge-intensive market services' bundle, then plot each NUTS2 region's share of GVA in this bundle against its GDP per inhabitant. The logic follows Gennaioli, La Porta, Lopez-de-Silanes & Shleifer (2013, Quarterly Journal of Economics), whose cross-regional human-capital decomposition shows that the share of GVA produced in high-skill services is one of the strongest predictors of regional income per head once initial-level effects are partialed out.
Figure 6
Knowledge-intensive services share of GVA vs. GDP per inhabitant, EU27 NUTS2, 2024
7. High-tech employment per inhabitant: where are the knowledge jobs?
Knowledge-intensive services share of GVA in Figure 6 is a value-added composition measure; high-tech employment per inhabitant is a labour- market complement, asking not how much output the high-skill sectors produce but how many people they actually employ relative to the local population. Eurostat's high-tech employment series at NUTS2 (HTEC_EMP_REG2) follows the OECD-Eurostat NACE Rev. 2 high-technology aggregation (manufacture of basic pharmaceuticals C21, computer and electronics C26, air and spacecraft C30, plus high-tech knowledge-intensive services). Population is implied as MIO_EUR / EUR_HAB for the same region and year, so the per-1,000-inhabitants metric is internally consistent with the rest of this page. The capital-region concentration of high-tech jobs is the mechanism behind the Iammarino, Rodríguez-Pose & Storper (2019) development trap: knowledge employment agglomerates in metropolitan cores faster than incomes spread out.
Figure 7
High-tech employment per 1,000 inhabitants by NUTS2 region, top 20, latest year
No data available for this chart.
Source: Eurostat HTEC_EMP_REG2 (high-tech employment by NUTS 2 region, sex; OECD-Eurostat NACE Rev. 2 high-tech aggregation), latest year with at least 200 EU27 NUTS2 regions reporting. Population denominator constructed as NAMA_10R_2GDP MIO_EUR divided by EUR_HAB for the same region and year (implied mid-year resident population). High-tech aggregation methodology: Eurostat (2024), High-tech industry and knowledge-intensive services classification.
What the seven figures say together
Europe's regional economy is a Paris-and-the-big-capitals story at the absolute-size level, a Luxembourg-and-Dublin-versus-Severozapaden story at the per-capita level, and a majority-of-the- map-still-below-the-EU27-average story once PPS adjustment is applied. It is a convergence- interrupted story over 2000-2024, a within-country-divergence story at the member-state level, and a knowledge-services-intensity story in the cross-section of NACE shares. The seven findings are jointly consistent with the Iammarino-Rodríguez-Pose-Storper (2019) 'development trap' framing: nominal catch-up of poorer regions is real but slow, capitals continue to pull ahead of their hinterlands, sectoral composition (not just capital deepening) drives most of the persistent income gap, and the policy-relevant inequality is within rather than between countries. Gagliardi & Percoco (2017) identify absorptive capacity as the binding constraint on Cohesion-Policy effectiveness; the stylised facts above are why that constraint matters. Every figure is reproducible from data/parquet/eu_nuts2_gdp.parquet and data/parquet/eu_nuts2_gva.parquet using the SQL blocks printed alongside each figure.
Method notes. NUTS2-level GDP is read from Eurostat's regional accounts dataset NAMA_10R_2GDP (retrieved April 2026), filtered to 4-character geo codes to isolate NUTS2 from NUTS0/NUTS1 aggregates. Absolute-size comparisons use MIO_EUR (current prices); market-exchange-rate per-capita comparisons use EUR_HAB; the Cohesion Policy typology uses PPS_HAB_EU27_2020, which is GDP per inhabitant expressed in purchasing power standards indexed so that the EU27 mean equals 100. PPS is the unit that Article 108 of Regulation (EU) 2021/1060 and the Ninth Cohesion Report (European Commission 2024) use to assign regions to the less-developed / transition / more- developed tiers. Sectoral composition (Figure 6) is from NAMA_10R_2GVAGR at basic prices, NACE Rev. 2 aggregates. For the CV and Gini calculations, the EU27 is restricted to the current member states via Eurostat's two-letter country prefixes (note EL, not GR, for Greece). Single-region member states are dropped from the within-country Gini. NUTS definitions follow the NUTS 2021 vintage throughout.
References
European Commission (2024). Ninth report on economic, social and territorial cohesion: cohesion in Europe towards 2050. Publications Office of the European Union. doi:10.2776/402204.
ESPON EGTC (2022). Territorial reference framework for Europe. ESPON Cooperation Programme, Luxembourg.
Eurostat (2024). Eurostat regional yearbook, 2024 edition. Publications Office of the European Union. doi:10.2785/108391.
Gagliardi, L., & Percoco, M. (2017). The impact of European Cohesion Policy in urban and rural regions. Regional Studies, 51(6), 857-868. doi:10.1080/00343404.2016.1179384.
Gennaioli, N., La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (2013). Human capital and regional development. Quarterly Journal of Economics, 128(1), 105-164. doi:10.1093/qje/qjs050.
Iammarino, S., Rodríguez-Pose, A., & Storper, M. (2019). Regional inequality in Europe: evidence, theory and policy implications. Journal of Economic Geography, 19(2), 273-298. doi:10.1093/jeg/lby021.
Regulation (EU) 2021/1060 of the European Parliament and of the Council of 24 June 2021 laying down common provisions on the European Regional Development Fund, the European Social Fund Plus, the Cohesion Fund, the Just Transition Fund and the European Maritime, Fisheries and Aquaculture Fund (Common Provisions Regulation), Article 108. Official Journal of the European Union, L 231, 30 June 2021, 159-706.
Sala-i-Martin, X. X. (1996). The classical approach to convergence analysis. Economic Journal, 106(437), 1019-1036. doi:10.2307/2235375.
The richest NUTS2 region, LU00 · Luxembourg (Luxembourg), recorded €127.0k per inhabitant in 2024; the poorest, BG31 · Severozapaden (Bulgaria), recorded €10.3k. The top-to-bottom ratio is 12.3×. Luxembourg's and Ireland's figures partly reflect cross-border commuting and the statistical treatment of foreign-owned intangible assets (IE06's post-2015 jump is the textbook example), so the 'real' welfare gap is narrower than the headline ratio suggests, but nowhere in Europe does it close to anything like national averages. The bottom of the distribution is a contiguous belt running from Bulgaria's north-west through Romania's Moldavia and Dobruja into eastern Hungary.
Source: Eurostat NAMA_10R_2GDP, unit EUR_HAB (euros per inhabitant), EU27 NUTS2 regions only. Values are in current euros at current market prices; candidate-country regions (Türkiye, Western Balkans) are excluded from the EU27 distribution. Latest year with at least 200 regions reporting. The figures are NOT purchasing-power adjusted, see Figure 3 for the PPS view.
Within-country inequality across NUTS2 GDP per capita is highest in Romania (Gini 0.226), Slovakia (0.226), and Hungary (0.219), post-2004 accession economies in which Bratislava, Bucharest, and Budapest pull sharply away from their respective rural peripheries. At the opposite end, Finland (0.079), Austria (0.084), and Sweden (0.087) are the most spatially even: small, densely- settled, high-redistribution Nordic and Central-European economies. Italy sits in the upper middle of the distribution, consistent with the well-documented Mezzogiorno gap; Spain, France, and Germany land lower than one might expect, because each country contains several large, high-income regions rather than a single dominant capital.
Source: Eurostat NAMA_10R_2GDP, unit EUR_HAB, latest common year. Gini computed unweighted over NUTS2 units within each country using the Lorenz-curve order-statistic formula: G = Σ(2i − k − 1)·x_i / (k · Σ x_i). Only EU27 member states with at least three NUTS2 regions are shown; single-region members (CY, EE, LV, LT, LU, MT) and two-region members are excluded because their within-country Gini is either zero by construction or too small to interpret.
Each dot is one EU27 NUTS2 region; the x-axis is the share of total GVA produced in NACE sections J (ICT), K (finance), and M_N (professional & administrative services), the y-axis is GDP per inhabitant in euros, and marker size scales with total GVA. The slope is strongly positive: a one-percentage-point increase in the knowledge-services share is associated with roughly €1.5k higher GDP per inhabitant in the cross-section (unweighted OLS, unit slope). The highest knowledge-services shares in 2024 are recorded by IE06 (53.7%), LU00 (46.5%), and BE10 (40.0%), all capital or global-city regions. Labeled points are the 19 EU27 capital NUTS2 regions; they cluster in the upper-right as expected, while peripheral and de-industrialised regions occupy the lower-left.
Source: Eurostat NAMA_10R_2GVAGR (gross value added at basic prices by NUTS 2 region, NACE Rev. 2), unit CP_MEUR, crossed with NAMA_10R_2GDP EUR_HAB for the same region and year. Knowledge-intensive market services bundle: NACE J (information and communication) + K (financial and insurance activities) + M_N (professional, scientific, technical, administrative and support service activities). Latest common year with broad coverage. Marker size is total regional GVA in current million euros.